Table of contents
  1. Story
  2. Spotfire Dashboard
  3. Slides
    1. Slide 1 Data Driven Farming: Week 2 Data Understanding
    2. Slide 2 Sources of Farm Data and Precision Farming Success
    3. Slide 3 USDA and GODAN
    4. Slide 4 US Office of Management & Budget Memo 13-13
    5. Slide 5 Quotes on Data Access
    6. Slide 6 USDA Innovation Challenge Blog Post
    7. Slide 7 USDA Innovation Challenge Announcement
    8. Slide 8 USDA Innovation Challenge Required Datasets
    9. Slide 9 GODAN
    10. Slide 10 Key Data Sets in GODAN Report
    11. Slide 11 Week 2 Data Understanding Knowledge Base
    12. Slide 12 Week 2 Data Understanding Knowledge Base Attachments
    13. Slide 13 FarmDataDashboard Spreadsheet
    14. Slide 14 NASS Commodity Value in Spotfire
    15. Slide 15 AgGateway
    16. Slide 16 Ohio State University Precision Agriculture
    17. Slide 17 University of Minnesota Precision Agriculture Links
    18. Slide 18 CropWatch
    19. Slide 19 CropWatch: Management Topics
    20. Slide 20 CropWatch: Precision Agriculture
    21. Slide 21 The InfoAg 2015 Conference
    22. Slide 22 The InfoAg 2015 Conference Program
    23. Slide 23 Technology Evolution in Agriculture
    24. Slide 24 Three Pillars of Customer Value
    25. Slide 25 But Complexity Has Driven Slow Adoption
    26. Slide 26 Producer Challenges of Machine Management and Information and Relationships
    27. Slide 27 Wirelessly Connected Machines
    28. Slide 28 Machine Optimization
    29. Slide 29 MKC Maturity Progression
    30. Slide 30 Job Optimization
    31. Slide 31 Ag Decision Support
    32. Slide 32 John Deere Data Ecosystem
    33. Slide 33 Our Strategy is to LEAD and ENABLE Integrated Solutions
    34. Slide 34 Modern Cab
    35. Slide 35 SeedStar Mobile
    36. Slide 36 New Production System Apps
    37. Slide 37 MONITOR Your Operation with MyJohnDeere
    38. Slide 38 ANALYZE Your Operation with MyJohnDeere
    39. Slide 39 Implementing an Agronomic Decisions
    40. Slide 40 John Deere FarmSight 1
    41. Slide 41 John Deer WorkSight
    42. Slide 42 John Deere FarmSight 2
    43. Slide 43 Improving Performance and Uptime and Efficiencies of Machines Through the Use of Data: Video Notes
    44. Slide 44 The Climate Corporation
  4. Research Notes
    1. NuGIS
      1. Web
      2. Email
    2. AgriStats
      1. Glossary
        1. Crop Profiles - Resources
        2. Population (POP), total
        3. Crop Profiles - Crop Production
        4. Crop Profiles - Crop Productivity
        5. Crop Profiles - Yield Gaps
        6. Crop Profiles - Nutrient Use
        7. Crop Profiles - Current Fertilizer Use
        8. Crop Profiles - Attainable Fertilizer Use
        9. Crop Profiles - Fertilizer Gaps
        10. Charts
      2. Spotfire Dashboard
  5. International Plant Nutrition Institute
    1. About IPNI
  6. NuGIS: Nutrient Use Geographic Information System
    1. Visit the Interactive Map
      1. NuGIS Updated!
      2. About NuGIS
      3. More Information
    2. Applications of NuGIS and Conclusions
    3. Methods
      1. Estimating Nutrients from Commercial Fertilizers
        1. Census of Agriculture
        2. Estimating Farm Fertilizer Use
        3. Apportioning State Total or AAPFCO Unknown County Values
        4. Estimating Dollars Spent on Fertilizer
          1. Estimating Fertilized Acres
          2. Spatial Smoothing of County Fertilizer Sales data
      2. Nutrient Input from Recoverable Livestock Manure
      3. Nutrient Removal by Harvested Crops
      4. Conflation of County Data to Watersheds
      5. Calculating Partial Nutrient Balances
      6. Land Use Classification layers are used to identify areas of Agricultural Land Use
    4. Discussion
      1. Systematic Errors and Uncertainty
      2. Fertilizer Use
      3. Recoverable Manure
      4. Nitrogen Fixation by Legumes
      5. Nutrient Removal by Crops
      6. Partial Balances
    5. Tabular Data
      1. Regional Watershed Nutrient Balance Data
      2. HUC 8 Watershed Nutrient Balance Data
      3. County and State and 48 State Total Nutrient Balance Data
      4. Table Description for HUC8 Balance data
    6. References
  7. InfoAg Conference 2015
    1. InfoAg 2015 Program
      1. 07/28/2015 Tuesday
      2. 07/29/2015 Wednesday
      3. 07/30/2015 Thursday
    2. Keynotes
      1. Opening Plenary Keynote - David Zach - sponsored by IPNI
      2. PrecisionAg Plenary Keynote - Kevin Folta - sponsored by SST and Raven
      3. Closing Plenary - Robbie Schingler - sponsored by IPNI
    3. Speakers
      1. Jim Angel
      2. Joan Archer
      3. Randy Barker
      4. Wade Barnes
      5. Travis Bayer
      6. Don Bierman
      7. Terry Brase
      8. Max Bruner
      9. Christopher Budke
      10. Ben Chostner
      11. Ignacio Ciampitti
      12. Chad Colby
      13. Matthew Darr
      14. Jorge Delgado
      15. Greg Duhachek
      16. Jason Ellsworth
      17. Adrian Fay
      18. Paul Fixen
      19. Kevin Folta
      20. Eros Francisco
      21. Dan Frieberg
      22. John Fulton
      23. Clint Graumann
      24. Douglas Hackney
      25. Tim Harris
      26. Chad Hart
      27. Daniel Hedgecock
      28. Andy Hill
      29. Aaron Hunt
      30. Aaron Hutchinson
      31. Matthieu Hyrien
      32. Luke James
      33. Raj Khosla
      34. Greg Levow
      35. Devon Liss
      36. Ted Macy
      37. Nish Majarian
      38. Tim Marquis
      39. Larkin Martin
      40. Sara Masterson
      41. Tom McGraw
      42. Punch Moulton
      43. Scott Murrell
      44. Dave Nerpel
      45. Jason O'Flanagan
      46. Ron Olson
      47. Brenda Ortiz
      48. Phillip Owens
      49. Johnny Park
      50. Steve Phillips
      51. Patrick Pinkston
      52. Lisa Prassack
      53. Kevin Price
      54. Harold Reetz
      55. John Reifsteck
      56. Luis Sanchez
      57. Ryan Schacht
      58. Dave Scheiderer
      59. Robbie Schingler
      60. Paul Schrimpf
      61. Mark Shahinian
      62. Emily Short
      63. Phyl Speser
      64. Bob Stewart
      65. Jason Tatge
      66. Mary Kay Thatcher
      67. Nicolas Tremblay
      68. Harold van Es
      69. Marc Vanacht
      70. Jesse Vollmar
      71. Matt Waits
      72. Jason Warren
      73. Jason Webster
      74. Paul Welbig
      75. Justin Welch
      76. Robert Wilkinson
      77. David Zach
      78. Mark Zaller
      79. Lincoln Zotarelli
    4. Hands-on Workshops
      1. Modus Standard Workshop
      2. Ask the Futurist
      3. Managing Healthy Precision Agriculture Teams
      4. Decision Tools using Agronomic, Climatic, and Economic Information from U2U
      5. Innovation Forum
      6. Precision Education
    5. Tour
      1. Introduction
      2. Stop 1: Lange-Stegmann Fertilizer Import Terminal
      3. Stop 2: Sydenstricker John Deere Dealership
      4. Stop 3: UAV Demonstrations
    6. List of Exhibitors
      1. 360 Yield Center
      2. Advanced Reconnaissance Corporation
      3. Ag Leader Technology
      4. Ag Renaissance Software LLC
      5. Ag World
      6. Ag-Tester
      7. AGCO
      8. AgEagle
      9. AgGateway
      10. Agrian, Inc.
      11. Agribotix
      12. Agricultural Retailers Association
      13. AgriNews
      14. AgSource
      15. AgSync, Inc.
      16. AgWorks
      17. AutoProbe
      18. Ayrstone Productivity LLC
      19. BlackBridge
      20. Capstan Ag Systems Inc.
      21. Case IH
      22. CDMS
      23. Crop IMS
      24. CropMetrics, LLC
      25. Decisive Farming
      26. DTN/The Progressive Farmer
      27. EFC Systems
      28. Encirca services by DuPont Pioneer
      29. ESRI
      30. Falcon Automated Soil Sampling
      31. Farmers Edge
      32. FarmLink
      33. FarMobile
      34. Fialab Instruments, Inc.
      35. FirstWater Ag
      36. Geonics, Ltd.
      37. Geosys, Inc.
      38. GeoVantage
      39. GVM
      40. Hexagon Geospatial
      41. I.F.A.R.M.
      42. iCrop Trak
      43. Insero
      44. International Plant Nutrition Institute
      45. IRROMETER Company, Inc.
      46. Iteris, Inc.
      47. John Deere
      48. MapShots, Inc.
      49. Mavrx
      50. MicaSense Inc.
      51. Midwest Laboratories
      52. Mixmate
      53. My AgCentral
      54. MyWay RTK
      55. New Holland Agriculture
      56. NORAC
      57. NovAtel Inc.
      58. PAQ Interactive
      59. Planet Labs
      60. Precision Technologies
      61. PrecisionAg Institute
      62. Raven Industries
      63. Satshot
      64. ScoutPro
      65. senseFly
      66. SGS North America
      67. Sky Imaging Mapping Data
      68. Software Solutions Integrated, LLC
      69. SOILMAP
      70. SOYL
      71. Spectrum Technologies
      72. SST Software
      73. TapLogic, LLC
      74. TerraGo
      75. The Climate Corporation
      76. The Fertilizer Institute
      77. Topcon Precision Agriculture
      78. Trimble
      79. Valley Irrigation
      80. Veris Technologies
      81. Winfield
      82. XSInc.
      83. ZedX, Inc.
  8. How can we improve agriculture, food and nutrition with open data?
    1. What is open data?
    2. Table of contents
    3. 1. Executive summary
    4. 2. Introduction
    5. 3. How is open data solving problems in agriculture and nutrition?
      1. 1 Enabling more efficient and effective decision making
        1. Use cases
          1. Protecting crops from pest outbreaks with vegetation maps: GroenMonitor
          2. Helping farmers forecast with weather apps and SMS: AWhere
        2. Use cases
          1. Boosting crop yields with a best practice knowledge bank: Plantwise
        3. Saving $3.6m in drought damage with a climate-smart tool: CIAT Colombia
        4. Managing the California drought with data visualisations: California Department of Water Resources
      2. 2. Fostering innovation to benefit everyone
        1. Use case
          1. Saving crops and cash with weather simulation and smart insurance: Climate Corporation
        2. Use cases
          1. Improving crop varieties with open data on breeding trials: AgTrials
          2. Bringing agricultural research to the masses: FAO AGRIS portal
          3. Making agri-food data more discoverable: the CIARD RING
      3. 3. Driving organisational and sector change through transparency
        1. Use cases
          1. Tracking water, pesticide, water and fuel use with an open, collaborative platform: Syngenta
          2. Exposing misspent farm subsidies in Mexico: FUNDAR
        2. Use cases
          1. Empowering consumers to make smart food choices: US national nutrient database
          2. Helping consumers understand risks of the food they eat: EU Food alerts
          3. Highlighting restaurant inspection scores and improving food safety: LIVES
    6. 4. How ready are agriculture and nutrition for widespread open data innovation?
    7. 5. Realising the full potential of open data: next steps
    8. References
      1. 1
      2. 2
      3. 3
      4. 4
      5. 5
      6. 6
      7. 7
      8. 8
      9. 9
      10. 10
      11. 11
      12. 12
      13. 13
      14. 14
      15. 15
      16. 16
      17. 17
      18. 18
      19. 19
      20. 20
      21. 21
      22. 22
      23. 23
      24. 24
      25. 25
      26. 26
      27. 27
      28. 28
      29. 29 
      30. 30
      31. 31
      32. 32
      33. 33
      34. 34
      35. 35
      36. 36
      37. 37
      38. 38
      39. 39
      40. 40
      41. 41
      42. 42
      43. 43
      44. 44
      45. 45
      46. 46
      47. 47
      48. 48
      49. 49
      50. 50
      51. 51
      52. 52
      53. 53
      54. 54
      55. 55
      56. 56
      57. 57
      58. 58
      59. 59
      60. 60
      61. 61
      62. 62
      63. 63
      64. 64
      65. 65
      66. 66
      67. 67
      68. 68
      69. 69
      70. 70
      71. 71
      72. 72
      73. 73
      74. 74
      75. 75
      76. 76
      77. 77
      78. 78
      79. 79
    9. About GODAN
    10. About the ODI
    11. Glossary: Key data concepts
    12. Appendix 1: Useful references and tools
      1. Tools for publishing your data
      2. Tools for becoming an open data user and creating open data projects
      3. Examples of organisations with open access and data policies:
  9. Open Data and Smallholder Food and Nutritional Security
    1. Key data sets
      1. Government
        1. Space and meteorological agencies
      2. International organisations and partnerships
      3. NGOs
      4. Business sector
    2. Executive Summary
    3. Introduction
      1. The GODAN initiative invites all stakeholders to open up their agricultural and nutritionally relevant data to enforce global food and nutrition security
      2. In modern agriculture data are becoming more and more an important resource for food production, facilitation of the value chain and governance
      3. CTA wants to know the impact of the open data on the smallholder food and nutrition security
    4. Open data and their impact on developing countries
      1. Open data are data that can be freely used, reused and redistributed by anyone – subject only, at most, to the requirement to attribute and share alike
      2. Open data fuel the pyramid of wisdom enabling better decision-making
      3. Intermediaries between suppliers and consumers are key in the functioning of open data community
      4. Although the potential value of open data generally is estimated to be high, the actual measured impact of open data in (developing) countries is low
    5. The potential impact of open data on the smallholder ecosystem
      1. The availability of open data can catalyse the functioning of the smallholder ecosystem by providing each of the actors with relevant information about the ecosystem, its actors and its functioning
      2. Mobile operators and ICT service providers connecting smallholder farmers play a key role in achieving impact of open data
      3. The potential impact of open data on the smallholder food and nutrition security
        1. Open data and farmers’ rights
    6. Overview of the different sources of open data for food and nutrition security and their impact on the smallholder ecosystems
      1. Open government data
        1. Which data are being made available?
        2. Impact on the smallholder ecosystem
        3. Impact on governance
        4. Impact on the value chain and sustaining services
        5. Impact on agricultural production
      2. Space and meteorological agencies
        1. Which data are being made available?
        2. Impact on the smallholder ecosystem
          1. Impact on governance
          2. Impact on the value chain and sustaining services
          3. Impact on agricultural production
      3. International organisations and partnerships
        1. Which data are being made available?
        2. Impact on the smallholder ecosystem
          1. Impact on governance
          2. Impact on the value chain and sustaining services
          3. Impact on agricultural production
      4. Science
        1. Agricultural data
        2. Impact on the smallholder ecosystem
      5. NGOs
        1. Which data are being made available?
        2. Impact on the smallholder ecosystem
          1. Impact on governance
          2. Impact on the value chain, sustaining services and agricultural production
      6. Business sector
        1. Which data are being made available?
        2. Which data are being made available?
          1. Experiments based on telecom data
          2. Experiments based on e-mail data
          3. Experiments based on data from mobile agricultural advise services
          4. Other experiments on corporate data-sharing
        3. Impact on smallholder ecosystem
    7. Synthesis and outlook of the impact of the open development on smallholder food and nutrition security
      1. The current impact of open data on smallholder food and nutrition security is low
      2. Potentially there is a large impact of open data on smallholder food and nutrition security
      3. Other options to further improve the uptake and availability of open data for smallholder food and nutrition security
      4. There is a trade-off between the aggregation level of data, the amount of information it contains and farmers’ rights
    8. References
  10. NEXT

Week 2 Data Understanding

Last modified
Table of contents
  1. Story
  2. Spotfire Dashboard
  3. Slides
    1. Slide 1 Data Driven Farming: Week 2 Data Understanding
    2. Slide 2 Sources of Farm Data and Precision Farming Success
    3. Slide 3 USDA and GODAN
    4. Slide 4 US Office of Management & Budget Memo 13-13
    5. Slide 5 Quotes on Data Access
    6. Slide 6 USDA Innovation Challenge Blog Post
    7. Slide 7 USDA Innovation Challenge Announcement
    8. Slide 8 USDA Innovation Challenge Required Datasets
    9. Slide 9 GODAN
    10. Slide 10 Key Data Sets in GODAN Report
    11. Slide 11 Week 2 Data Understanding Knowledge Base
    12. Slide 12 Week 2 Data Understanding Knowledge Base Attachments
    13. Slide 13 FarmDataDashboard Spreadsheet
    14. Slide 14 NASS Commodity Value in Spotfire
    15. Slide 15 AgGateway
    16. Slide 16 Ohio State University Precision Agriculture
    17. Slide 17 University of Minnesota Precision Agriculture Links
    18. Slide 18 CropWatch
    19. Slide 19 CropWatch: Management Topics
    20. Slide 20 CropWatch: Precision Agriculture
    21. Slide 21 The InfoAg 2015 Conference
    22. Slide 22 The InfoAg 2015 Conference Program
    23. Slide 23 Technology Evolution in Agriculture
    24. Slide 24 Three Pillars of Customer Value
    25. Slide 25 But Complexity Has Driven Slow Adoption
    26. Slide 26 Producer Challenges of Machine Management and Information and Relationships
    27. Slide 27 Wirelessly Connected Machines
    28. Slide 28 Machine Optimization
    29. Slide 29 MKC Maturity Progression
    30. Slide 30 Job Optimization
    31. Slide 31 Ag Decision Support
    32. Slide 32 John Deere Data Ecosystem
    33. Slide 33 Our Strategy is to LEAD and ENABLE Integrated Solutions
    34. Slide 34 Modern Cab
    35. Slide 35 SeedStar Mobile
    36. Slide 36 New Production System Apps
    37. Slide 37 MONITOR Your Operation with MyJohnDeere
    38. Slide 38 ANALYZE Your Operation with MyJohnDeere
    39. Slide 39 Implementing an Agronomic Decisions
    40. Slide 40 John Deere FarmSight 1
    41. Slide 41 John Deer WorkSight
    42. Slide 42 John Deere FarmSight 2
    43. Slide 43 Improving Performance and Uptime and Efficiencies of Machines Through the Use of Data: Video Notes
    44. Slide 44 The Climate Corporation
  4. Research Notes
    1. NuGIS
      1. Web
      2. Email
    2. AgriStats
      1. Glossary
        1. Crop Profiles - Resources
        2. Population (POP), total
        3. Crop Profiles - Crop Production
        4. Crop Profiles - Crop Productivity
        5. Crop Profiles - Yield Gaps
        6. Crop Profiles - Nutrient Use
        7. Crop Profiles - Current Fertilizer Use
        8. Crop Profiles - Attainable Fertilizer Use
        9. Crop Profiles - Fertilizer Gaps
        10. Charts
      2. Spotfire Dashboard
  5. International Plant Nutrition Institute
    1. About IPNI
  6. NuGIS: Nutrient Use Geographic Information System
    1. Visit the Interactive Map
      1. NuGIS Updated!
      2. About NuGIS
      3. More Information
    2. Applications of NuGIS and Conclusions
    3. Methods
      1. Estimating Nutrients from Commercial Fertilizers
        1. Census of Agriculture
        2. Estimating Farm Fertilizer Use
        3. Apportioning State Total or AAPFCO Unknown County Values
        4. Estimating Dollars Spent on Fertilizer
          1. Estimating Fertilized Acres
          2. Spatial Smoothing of County Fertilizer Sales data
      2. Nutrient Input from Recoverable Livestock Manure
      3. Nutrient Removal by Harvested Crops
      4. Conflation of County Data to Watersheds
      5. Calculating Partial Nutrient Balances
      6. Land Use Classification layers are used to identify areas of Agricultural Land Use
    4. Discussion
      1. Systematic Errors and Uncertainty
      2. Fertilizer Use
      3. Recoverable Manure
      4. Nitrogen Fixation by Legumes
      5. Nutrient Removal by Crops
      6. Partial Balances
    5. Tabular Data
      1. Regional Watershed Nutrient Balance Data
      2. HUC 8 Watershed Nutrient Balance Data
      3. County and State and 48 State Total Nutrient Balance Data
      4. Table Description for HUC8 Balance data
    6. References
  7. InfoAg Conference 2015
    1. InfoAg 2015 Program
      1. 07/28/2015 Tuesday
      2. 07/29/2015 Wednesday
      3. 07/30/2015 Thursday
    2. Keynotes
      1. Opening Plenary Keynote - David Zach - sponsored by IPNI
      2. PrecisionAg Plenary Keynote - Kevin Folta - sponsored by SST and Raven
      3. Closing Plenary - Robbie Schingler - sponsored by IPNI
    3. Speakers
      1. Jim Angel
      2. Joan Archer
      3. Randy Barker
      4. Wade Barnes
      5. Travis Bayer
      6. Don Bierman
      7. Terry Brase
      8. Max Bruner
      9. Christopher Budke
      10. Ben Chostner
      11. Ignacio Ciampitti
      12. Chad Colby
      13. Matthew Darr
      14. Jorge Delgado
      15. Greg Duhachek
      16. Jason Ellsworth
      17. Adrian Fay
      18. Paul Fixen
      19. Kevin Folta
      20. Eros Francisco
      21. Dan Frieberg
      22. John Fulton
      23. Clint Graumann
      24. Douglas Hackney
      25. Tim Harris
      26. Chad Hart
      27. Daniel Hedgecock
      28. Andy Hill
      29. Aaron Hunt
      30. Aaron Hutchinson
      31. Matthieu Hyrien
      32. Luke James
      33. Raj Khosla
      34. Greg Levow
      35. Devon Liss
      36. Ted Macy
      37. Nish Majarian
      38. Tim Marquis
      39. Larkin Martin
      40. Sara Masterson
      41. Tom McGraw
      42. Punch Moulton
      43. Scott Murrell
      44. Dave Nerpel
      45. Jason O'Flanagan
      46. Ron Olson
      47. Brenda Ortiz
      48. Phillip Owens
      49. Johnny Park
      50. Steve Phillips
      51. Patrick Pinkston
      52. Lisa Prassack
      53. Kevin Price
      54. Harold Reetz
      55. John Reifsteck
      56. Luis Sanchez
      57. Ryan Schacht
      58. Dave Scheiderer
      59. Robbie Schingler
      60. Paul Schrimpf
      61. Mark Shahinian
      62. Emily Short
      63. Phyl Speser
      64. Bob Stewart
      65. Jason Tatge
      66. Mary Kay Thatcher
      67. Nicolas Tremblay
      68. Harold van Es
      69. Marc Vanacht
      70. Jesse Vollmar
      71. Matt Waits
      72. Jason Warren
      73. Jason Webster
      74. Paul Welbig
      75. Justin Welch
      76. Robert Wilkinson
      77. David Zach
      78. Mark Zaller
      79. Lincoln Zotarelli
    4. Hands-on Workshops
      1. Modus Standard Workshop
      2. Ask the Futurist
      3. Managing Healthy Precision Agriculture Teams
      4. Decision Tools using Agronomic, Climatic, and Economic Information from U2U
      5. Innovation Forum
      6. Precision Education
    5. Tour
      1. Introduction
      2. Stop 1: Lange-Stegmann Fertilizer Import Terminal
      3. Stop 2: Sydenstricker John Deere Dealership
      4. Stop 3: UAV Demonstrations
    6. List of Exhibitors
      1. 360 Yield Center
      2. Advanced Reconnaissance Corporation
      3. Ag Leader Technology
      4. Ag Renaissance Software LLC
      5. Ag World
      6. Ag-Tester
      7. AGCO
      8. AgEagle
      9. AgGateway
      10. Agrian, Inc.
      11. Agribotix
      12. Agricultural Retailers Association
      13. AgriNews
      14. AgSource
      15. AgSync, Inc.
      16. AgWorks
      17. AutoProbe
      18. Ayrstone Productivity LLC
      19. BlackBridge
      20. Capstan Ag Systems Inc.
      21. Case IH
      22. CDMS
      23. Crop IMS
      24. CropMetrics, LLC
      25. Decisive Farming
      26. DTN/The Progressive Farmer
      27. EFC Systems
      28. Encirca services by DuPont Pioneer
      29. ESRI
      30. Falcon Automated Soil Sampling
      31. Farmers Edge
      32. FarmLink
      33. FarMobile
      34. Fialab Instruments, Inc.
      35. FirstWater Ag
      36. Geonics, Ltd.
      37. Geosys, Inc.
      38. GeoVantage
      39. GVM
      40. Hexagon Geospatial
      41. I.F.A.R.M.
      42. iCrop Trak
      43. Insero
      44. International Plant Nutrition Institute
      45. IRROMETER Company, Inc.
      46. Iteris, Inc.
      47. John Deere
      48. MapShots, Inc.
      49. Mavrx
      50. MicaSense Inc.
      51. Midwest Laboratories
      52. Mixmate
      53. My AgCentral
      54. MyWay RTK
      55. New Holland Agriculture
      56. NORAC
      57. NovAtel Inc.
      58. PAQ Interactive
      59. Planet Labs
      60. Precision Technologies
      61. PrecisionAg Institute
      62. Raven Industries
      63. Satshot
      64. ScoutPro
      65. senseFly
      66. SGS North America
      67. Sky Imaging Mapping Data
      68. Software Solutions Integrated, LLC
      69. SOILMAP
      70. SOYL
      71. Spectrum Technologies
      72. SST Software
      73. TapLogic, LLC
      74. TerraGo
      75. The Climate Corporation
      76. The Fertilizer Institute
      77. Topcon Precision Agriculture
      78. Trimble
      79. Valley Irrigation
      80. Veris Technologies
      81. Winfield
      82. XSInc.
      83. ZedX, Inc.
  8. How can we improve agriculture, food and nutrition with open data?
    1. What is open data?
    2. Table of contents
    3. 1. Executive summary
    4. 2. Introduction
    5. 3. How is open data solving problems in agriculture and nutrition?
      1. 1 Enabling more efficient and effective decision making
        1. Use cases
          1. Protecting crops from pest outbreaks with vegetation maps: GroenMonitor
          2. Helping farmers forecast with weather apps and SMS: AWhere
        2. Use cases
          1. Boosting crop yields with a best practice knowledge bank: Plantwise
        3. Saving $3.6m in drought damage with a climate-smart tool: CIAT Colombia
        4. Managing the California drought with data visualisations: California Department of Water Resources
      2. 2. Fostering innovation to benefit everyone
        1. Use case
          1. Saving crops and cash with weather simulation and smart insurance: Climate Corporation
        2. Use cases
          1. Improving crop varieties with open data on breeding trials: AgTrials
          2. Bringing agricultural research to the masses: FAO AGRIS portal
          3. Making agri-food data more discoverable: the CIARD RING
      3. 3. Driving organisational and sector change through transparency
        1. Use cases
          1. Tracking water, pesticide, water and fuel use with an open, collaborative platform: Syngenta
          2. Exposing misspent farm subsidies in Mexico: FUNDAR
        2. Use cases
          1. Empowering consumers to make smart food choices: US national nutrient database
          2. Helping consumers understand risks of the food they eat: EU Food alerts
          3. Highlighting restaurant inspection scores and improving food safety: LIVES
    6. 4. How ready are agriculture and nutrition for widespread open data innovation?
    7. 5. Realising the full potential of open data: next steps
    8. References
      1. 1
      2. 2
      3. 3
      4. 4
      5. 5
      6. 6
      7. 7
      8. 8
      9. 9
      10. 10
      11. 11
      12. 12
      13. 13
      14. 14
      15. 15
      16. 16
      17. 17
      18. 18
      19. 19
      20. 20
      21. 21
      22. 22
      23. 23
      24. 24
      25. 25
      26. 26
      27. 27
      28. 28
      29. 29 
      30. 30
      31. 31
      32. 32
      33. 33
      34. 34
      35. 35
      36. 36
      37. 37
      38. 38
      39. 39
      40. 40
      41. 41
      42. 42
      43. 43
      44. 44
      45. 45
      46. 46
      47. 47
      48. 48
      49. 49
      50. 50
      51. 51
      52. 52
      53. 53
      54. 54
      55. 55
      56. 56
      57. 57
      58. 58
      59. 59
      60. 60
      61. 61
      62. 62
      63. 63
      64. 64
      65. 65
      66. 66
      67. 67
      68. 68
      69. 69
      70. 70
      71. 71
      72. 72
      73. 73
      74. 74
      75. 75
      76. 76
      77. 77
      78. 78
      79. 79
    9. About GODAN
    10. About the ODI
    11. Glossary: Key data concepts
    12. Appendix 1: Useful references and tools
      1. Tools for publishing your data
      2. Tools for becoming an open data user and creating open data projects
      3. Examples of organisations with open access and data policies:
  9. Open Data and Smallholder Food and Nutritional Security
    1. Key data sets
      1. Government
        1. Space and meteorological agencies
      2. International organisations and partnerships
      3. NGOs
      4. Business sector
    2. Executive Summary
    3. Introduction
      1. The GODAN initiative invites all stakeholders to open up their agricultural and nutritionally relevant data to enforce global food and nutrition security
      2. In modern agriculture data are becoming more and more an important resource for food production, facilitation of the value chain and governance
      3. CTA wants to know the impact of the open data on the smallholder food and nutrition security
    4. Open data and their impact on developing countries
      1. Open data are data that can be freely used, reused and redistributed by anyone – subject only, at most, to the requirement to attribute and share alike
      2. Open data fuel the pyramid of wisdom enabling better decision-making
      3. Intermediaries between suppliers and consumers are key in the functioning of open data community
      4. Although the potential value of open data generally is estimated to be high, the actual measured impact of open data in (developing) countries is low
    5. The potential impact of open data on the smallholder ecosystem
      1. The availability of open data can catalyse the functioning of the smallholder ecosystem by providing each of the actors with relevant information about the ecosystem, its actors and its functioning
      2. Mobile operators and ICT service providers connecting smallholder farmers play a key role in achieving impact of open data
      3. The potential impact of open data on the smallholder food and nutrition security
        1. Open data and farmers’ rights
    6. Overview of the different sources of open data for food and nutrition security and their impact on the smallholder ecosystems
      1. Open government data
        1. Which data are being made available?
        2. Impact on the smallholder ecosystem
        3. Impact on governance
        4. Impact on the value chain and sustaining services
        5. Impact on agricultural production
      2. Space and meteorological agencies
        1. Which data are being made available?
        2. Impact on the smallholder ecosystem
          1. Impact on governance
          2. Impact on the value chain and sustaining services
          3. Impact on agricultural production
      3. International organisations and partnerships
        1. Which data are being made available?
        2. Impact on the smallholder ecosystem
          1. Impact on governance
          2. Impact on the value chain and sustaining services
          3. Impact on agricultural production
      4. Science
        1. Agricultural data
        2. Impact on the smallholder ecosystem
      5. NGOs
        1. Which data are being made available?
        2. Impact on the smallholder ecosystem
          1. Impact on governance
          2. Impact on the value chain, sustaining services and agricultural production
      6. Business sector
        1. Which data are being made available?
        2. Which data are being made available?
          1. Experiments based on telecom data
          2. Experiments based on e-mail data
          3. Experiments based on data from mobile agricultural advise services
          4. Other experiments on corporate data-sharing
        3. Impact on smallholder ecosystem
    7. Synthesis and outlook of the impact of the open development on smallholder food and nutrition security
      1. The current impact of open data on smallholder food and nutrition security is low
      2. Potentially there is a large impact of open data on smallholder food and nutrition security
      3. Other options to further improve the uptake and availability of open data for smallholder food and nutrition security
      4. There is a trade-off between the aggregation level of data, the amount of information it contains and farmers’ rights
    8. References
  10. NEXT

  1. Story
  2. Spotfire Dashboard
  3. Slides
    1. Slide 1 Data Driven Farming: Week 2 Data Understanding
    2. Slide 2 Sources of Farm Data and Precision Farming Success
    3. Slide 3 USDA and GODAN
    4. Slide 4 US Office of Management & Budget Memo 13-13
    5. Slide 5 Quotes on Data Access
    6. Slide 6 USDA Innovation Challenge Blog Post
    7. Slide 7 USDA Innovation Challenge Announcement
    8. Slide 8 USDA Innovation Challenge Required Datasets
    9. Slide 9 GODAN
    10. Slide 10 Key Data Sets in GODAN Report
    11. Slide 11 Week 2 Data Understanding Knowledge Base
    12. Slide 12 Week 2 Data Understanding Knowledge Base Attachments
    13. Slide 13 FarmDataDashboard Spreadsheet
    14. Slide 14 NASS Commodity Value in Spotfire
    15. Slide 15 AgGateway
    16. Slide 16 Ohio State University Precision Agriculture
    17. Slide 17 University of Minnesota Precision Agriculture Links
    18. Slide 18 CropWatch
    19. Slide 19 CropWatch: Management Topics
    20. Slide 20 CropWatch: Precision Agriculture
    21. Slide 21 The InfoAg 2015 Conference
    22. Slide 22 The InfoAg 2015 Conference Program
    23. Slide 23 Technology Evolution in Agriculture
    24. Slide 24 Three Pillars of Customer Value
    25. Slide 25 But Complexity Has Driven Slow Adoption
    26. Slide 26 Producer Challenges of Machine Management and Information and Relationships
    27. Slide 27 Wirelessly Connected Machines
    28. Slide 28 Machine Optimization
    29. Slide 29 MKC Maturity Progression
    30. Slide 30 Job Optimization
    31. Slide 31 Ag Decision Support
    32. Slide 32 John Deere Data Ecosystem
    33. Slide 33 Our Strategy is to LEAD and ENABLE Integrated Solutions
    34. Slide 34 Modern Cab
    35. Slide 35 SeedStar Mobile
    36. Slide 36 New Production System Apps
    37. Slide 37 MONITOR Your Operation with MyJohnDeere
    38. Slide 38 ANALYZE Your Operation with MyJohnDeere
    39. Slide 39 Implementing an Agronomic Decisions
    40. Slide 40 John Deere FarmSight 1
    41. Slide 41 John Deer WorkSight
    42. Slide 42 John Deere FarmSight 2
    43. Slide 43 Improving Performance and Uptime and Efficiencies of Machines Through the Use of Data: Video Notes
    44. Slide 44 The Climate Corporation
  4. Research Notes
    1. NuGIS
      1. Web
      2. Email
    2. AgriStats
      1. Glossary
        1. Crop Profiles - Resources
        2. Population (POP), total
        3. Crop Profiles - Crop Production
        4. Crop Profiles - Crop Productivity
        5. Crop Profiles - Yield Gaps
        6. Crop Profiles - Nutrient Use
        7. Crop Profiles - Current Fertilizer Use
        8. Crop Profiles - Attainable Fertilizer Use
        9. Crop Profiles - Fertilizer Gaps
        10. Charts
      2. Spotfire Dashboard
  5. International Plant Nutrition Institute
    1. About IPNI
  6. NuGIS: Nutrient Use Geographic Information System
    1. Visit the Interactive Map
      1. NuGIS Updated!
      2. About NuGIS
      3. More Information
    2. Applications of NuGIS and Conclusions
    3. Methods
      1. Estimating Nutrients from Commercial Fertilizers
        1. Census of Agriculture
        2. Estimating Farm Fertilizer Use
        3. Apportioning State Total or AAPFCO Unknown County Values
        4. Estimating Dollars Spent on Fertilizer
          1. Estimating Fertilized Acres
          2. Spatial Smoothing of County Fertilizer Sales data
      2. Nutrient Input from Recoverable Livestock Manure
      3. Nutrient Removal by Harvested Crops
      4. Conflation of County Data to Watersheds
      5. Calculating Partial Nutrient Balances
      6. Land Use Classification layers are used to identify areas of Agricultural Land Use
    4. Discussion
      1. Systematic Errors and Uncertainty
      2. Fertilizer Use
      3. Recoverable Manure
      4. Nitrogen Fixation by Legumes
      5. Nutrient Removal by Crops
      6. Partial Balances
    5. Tabular Data
      1. Regional Watershed Nutrient Balance Data
      2. HUC 8 Watershed Nutrient Balance Data
      3. County and State and 48 State Total Nutrient Balance Data
      4. Table Description for HUC8 Balance data
    6. References
  7. InfoAg Conference 2015
    1. InfoAg 2015 Program
      1. 07/28/2015 Tuesday
      2. 07/29/2015 Wednesday
      3. 07/30/2015 Thursday
    2. Keynotes
      1. Opening Plenary Keynote - David Zach - sponsored by IPNI
      2. PrecisionAg Plenary Keynote - Kevin Folta - sponsored by SST and Raven
      3. Closing Plenary - Robbie Schingler - sponsored by IPNI
    3. Speakers
      1. Jim Angel
      2. Joan Archer
      3. Randy Barker
      4. Wade Barnes
      5. Travis Bayer
      6. Don Bierman
      7. Terry Brase
      8. Max Bruner
      9. Christopher Budke
      10. Ben Chostner
      11. Ignacio Ciampitti
      12. Chad Colby
      13. Matthew Darr
      14. Jorge Delgado
      15. Greg Duhachek
      16. Jason Ellsworth
      17. Adrian Fay
      18. Paul Fixen
      19. Kevin Folta
      20. Eros Francisco
      21. Dan Frieberg
      22. John Fulton
      23. Clint Graumann
      24. Douglas Hackney
      25. Tim Harris
      26. Chad Hart
      27. Daniel Hedgecock
      28. Andy Hill
      29. Aaron Hunt
      30. Aaron Hutchinson
      31. Matthieu Hyrien
      32. Luke James
      33. Raj Khosla
      34. Greg Levow
      35. Devon Liss
      36. Ted Macy
      37. Nish Majarian
      38. Tim Marquis
      39. Larkin Martin
      40. Sara Masterson
      41. Tom McGraw
      42. Punch Moulton
      43. Scott Murrell
      44. Dave Nerpel
      45. Jason O'Flanagan
      46. Ron Olson
      47. Brenda Ortiz
      48. Phillip Owens
      49. Johnny Park
      50. Steve Phillips
      51. Patrick Pinkston
      52. Lisa Prassack
      53. Kevin Price
      54. Harold Reetz
      55. John Reifsteck
      56. Luis Sanchez
      57. Ryan Schacht
      58. Dave Scheiderer
      59. Robbie Schingler
      60. Paul Schrimpf
      61. Mark Shahinian
      62. Emily Short
      63. Phyl Speser
      64. Bob Stewart
      65. Jason Tatge
      66. Mary Kay Thatcher
      67. Nicolas Tremblay
      68. Harold van Es
      69. Marc Vanacht
      70. Jesse Vollmar
      71. Matt Waits
      72. Jason Warren
      73. Jason Webster
      74. Paul Welbig
      75. Justin Welch
      76. Robert Wilkinson
      77. David Zach
      78. Mark Zaller
      79. Lincoln Zotarelli
    4. Hands-on Workshops
      1. Modus Standard Workshop
      2. Ask the Futurist
      3. Managing Healthy Precision Agriculture Teams
      4. Decision Tools using Agronomic, Climatic, and Economic Information from U2U
      5. Innovation Forum
      6. Precision Education
    5. Tour
      1. Introduction
      2. Stop 1: Lange-Stegmann Fertilizer Import Terminal
      3. Stop 2: Sydenstricker John Deere Dealership
      4. Stop 3: UAV Demonstrations
    6. List of Exhibitors
      1. 360 Yield Center
      2. Advanced Reconnaissance Corporation
      3. Ag Leader Technology
      4. Ag Renaissance Software LLC
      5. Ag World
      6. Ag-Tester
      7. AGCO
      8. AgEagle
      9. AgGateway
      10. Agrian, Inc.
      11. Agribotix
      12. Agricultural Retailers Association
      13. AgriNews
      14. AgSource
      15. AgSync, Inc.
      16. AgWorks
      17. AutoProbe
      18. Ayrstone Productivity LLC
      19. BlackBridge
      20. Capstan Ag Systems Inc.
      21. Case IH
      22. CDMS
      23. Crop IMS
      24. CropMetrics, LLC
      25. Decisive Farming
      26. DTN/The Progressive Farmer
      27. EFC Systems
      28. Encirca services by DuPont Pioneer
      29. ESRI
      30. Falcon Automated Soil Sampling
      31. Farmers Edge
      32. FarmLink
      33. FarMobile
      34. Fialab Instruments, Inc.
      35. FirstWater Ag
      36. Geonics, Ltd.
      37. Geosys, Inc.
      38. GeoVantage
      39. GVM
      40. Hexagon Geospatial
      41. I.F.A.R.M.
      42. iCrop Trak
      43. Insero
      44. International Plant Nutrition Institute
      45. IRROMETER Company, Inc.
      46. Iteris, Inc.
      47. John Deere
      48. MapShots, Inc.
      49. Mavrx
      50. MicaSense Inc.
      51. Midwest Laboratories
      52. Mixmate
      53. My AgCentral
      54. MyWay RTK
      55. New Holland Agriculture
      56. NORAC
      57. NovAtel Inc.
      58. PAQ Interactive
      59. Planet Labs
      60. Precision Technologies
      61. PrecisionAg Institute
      62. Raven Industries
      63. Satshot
      64. ScoutPro
      65. senseFly
      66. SGS North America
      67. Sky Imaging Mapping Data
      68. Software Solutions Integrated, LLC
      69. SOILMAP
      70. SOYL
      71. Spectrum Technologies
      72. SST Software
      73. TapLogic, LLC
      74. TerraGo
      75. The Climate Corporation
      76. The Fertilizer Institute
      77. Topcon Precision Agriculture
      78. Trimble
      79. Valley Irrigation
      80. Veris Technologies
      81. Winfield
      82. XSInc.
      83. ZedX, Inc.
  8. How can we improve agriculture, food and nutrition with open data?
    1. What is open data?
    2. Table of contents
    3. 1. Executive summary
    4. 2. Introduction
    5. 3. How is open data solving problems in agriculture and nutrition?
      1. 1 Enabling more efficient and effective decision making
        1. Use cases
          1. Protecting crops from pest outbreaks with vegetation maps: GroenMonitor
          2. Helping farmers forecast with weather apps and SMS: AWhere
        2. Use cases
          1. Boosting crop yields with a best practice knowledge bank: Plantwise
        3. Saving $3.6m in drought damage with a climate-smart tool: CIAT Colombia
        4. Managing the California drought with data visualisations: California Department of Water Resources
      2. 2. Fostering innovation to benefit everyone
        1. Use case
          1. Saving crops and cash with weather simulation and smart insurance: Climate Corporation
        2. Use cases
          1. Improving crop varieties with open data on breeding trials: AgTrials
          2. Bringing agricultural research to the masses: FAO AGRIS portal
          3. Making agri-food data more discoverable: the CIARD RING
      3. 3. Driving organisational and sector change through transparency
        1. Use cases
          1. Tracking water, pesticide, water and fuel use with an open, collaborative platform: Syngenta
          2. Exposing misspent farm subsidies in Mexico: FUNDAR
        2. Use cases
          1. Empowering consumers to make smart food choices: US national nutrient database
          2. Helping consumers understand risks of the food they eat: EU Food alerts
          3. Highlighting restaurant inspection scores and improving food safety: LIVES
    6. 4. How ready are agriculture and nutrition for widespread open data innovation?
    7. 5. Realising the full potential of open data: next steps
    8. References
      1. 1
      2. 2
      3. 3
      4. 4
      5. 5
      6. 6
      7. 7
      8. 8
      9. 9
      10. 10
      11. 11
      12. 12
      13. 13
      14. 14
      15. 15
      16. 16
      17. 17
      18. 18
      19. 19
      20. 20
      21. 21
      22. 22
      23. 23
      24. 24
      25. 25
      26. 26
      27. 27
      28. 28
      29. 29 
      30. 30
      31. 31
      32. 32
      33. 33
      34. 34
      35. 35
      36. 36
      37. 37
      38. 38
      39. 39
      40. 40
      41. 41
      42. 42
      43. 43
      44. 44
      45. 45
      46. 46
      47. 47
      48. 48
      49. 49
      50. 50
      51. 51
      52. 52
      53. 53
      54. 54
      55. 55
      56. 56
      57. 57
      58. 58
      59. 59
      60. 60
      61. 61
      62. 62
      63. 63
      64. 64
      65. 65
      66. 66
      67. 67
      68. 68
      69. 69
      70. 70
      71. 71
      72. 72
      73. 73
      74. 74
      75. 75
      76. 76
      77. 77
      78. 78
      79. 79
    9. About GODAN
    10. About the ODI
    11. Glossary: Key data concepts
    12. Appendix 1: Useful references and tools
      1. Tools for publishing your data
      2. Tools for becoming an open data user and creating open data projects
      3. Examples of organisations with open access and data policies:
  9. Open Data and Smallholder Food and Nutritional Security
    1. Key data sets
      1. Government
        1. Space and meteorological agencies
      2. International organisations and partnerships
      3. NGOs
      4. Business sector
    2. Executive Summary
    3. Introduction
      1. The GODAN initiative invites all stakeholders to open up their agricultural and nutritionally relevant data to enforce global food and nutrition security
      2. In modern agriculture data are becoming more and more an important resource for food production, facilitation of the value chain and governance
      3. CTA wants to know the impact of the open data on the smallholder food and nutrition security
    4. Open data and their impact on developing countries
      1. Open data are data that can be freely used, reused and redistributed by anyone – subject only, at most, to the requirement to attribute and share alike
      2. Open data fuel the pyramid of wisdom enabling better decision-making
      3. Intermediaries between suppliers and consumers are key in the functioning of open data community
      4. Although the potential value of open data generally is estimated to be high, the actual measured impact of open data in (developing) countries is low
    5. The potential impact of open data on the smallholder ecosystem
      1. The availability of open data can catalyse the functioning of the smallholder ecosystem by providing each of the actors with relevant information about the ecosystem, its actors and its functioning
      2. Mobile operators and ICT service providers connecting smallholder farmers play a key role in achieving impact of open data
      3. The potential impact of open data on the smallholder food and nutrition security
        1. Open data and farmers’ rights
    6. Overview of the different sources of open data for food and nutrition security and their impact on the smallholder ecosystems
      1. Open government data
        1. Which data are being made available?
        2. Impact on the smallholder ecosystem
        3. Impact on governance
        4. Impact on the value chain and sustaining services
        5. Impact on agricultural production
      2. Space and meteorological agencies
        1. Which data are being made available?
        2. Impact on the smallholder ecosystem
          1. Impact on governance
          2. Impact on the value chain and sustaining services
          3. Impact on agricultural production
      3. International organisations and partnerships
        1. Which data are being made available?
        2. Impact on the smallholder ecosystem
          1. Impact on governance
          2. Impact on the value chain and sustaining services
          3. Impact on agricultural production
      4. Science
        1. Agricultural data
        2. Impact on the smallholder ecosystem
      5. NGOs
        1. Which data are being made available?
        2. Impact on the smallholder ecosystem
          1. Impact on governance
          2. Impact on the value chain, sustaining services and agricultural production
      6. Business sector
        1. Which data are being made available?
        2. Which data are being made available?
          1. Experiments based on telecom data
          2. Experiments based on e-mail data
          3. Experiments based on data from mobile agricultural advise services
          4. Other experiments on corporate data-sharing
        3. Impact on smallholder ecosystem
    7. Synthesis and outlook of the impact of the open development on smallholder food and nutrition security
      1. The current impact of open data on smallholder food and nutrition security is low
      2. Potentially there is a large impact of open data on smallholder food and nutrition security
      3. Other options to further improve the uptake and availability of open data for smallholder food and nutrition security
      4. There is a trade-off between the aggregation level of data, the amount of information it contains and farmers’ rights
    8. References
  10. NEXT

Story

Data Driven Farming: Week 2 Data Understanding

To help increase data understanding, I selected the following content:

This content was repurposed from the Web and PDF files to MindTouch (Wiki) to have a searchable Knowledge Base (e.g. use Google Chrome Find). The Table of Contents gives each piece of content a well-defined Web address (URL) so it can be referenced and become a linked data index in a spreadsheet, that can be searched, and imported into Spotfire for content analytics and federated search with similar indices from other Knowledge Bases.

Finally an example Data Science on an excellent new data set (CSV) from the Open Data Enterprise was download and imported into Spotfire, a leading Data Science Tool for analytics and visualizations, for the live Spotfire Dashboard shown below.

Some screen captures explain the Data Science process for understanding these data which focus on the agriculture data available worldwide.

http://opendataenterprise.org/

OpenDataEnterpriseSlide1.png

http://opendataenterprise.org/convene.html

OpenDataEnterpriseSlide2.png

http://www.opendataenterprise.org/map/viz/index.html

OpenDataEnterpriseSlide3.png

http://www.opendataenterprise.org/map/viz/index.html

OpenDataEnterpriseSlide4.png

http://www.opendataenterprise.org/map/viz/index.html

Agriculture:
Data Type: 8 Pages
Industry Category: 3 Pages

OpenDataEnterpriseSlide5.png

CSV

OpenDataEnterpriseSlide6.png

Web Player

Mouse Over Location for Details and Links to Other Visualizations

OpenDataEnterpriseSlide7.png

Web Player

Filter by Agriculture and Mouse Over Location for Details and Links to Other Visualizations

OpenDataEnterpriseSlide8.png

Spotfire Dashboard

Source: http://www.opendataenterprise.org/map/viz/index.html CSV and Spotfire

For Internet Explorer Users and Those Wanting Full Screen Display Use: Web Player Get Spotfire for iPad App

Error: Embedded data could not be displayed. Use Google Chrome

Slides

Slides

Slide 2 Sources of Farm Data and Precision Farming Success

BrandNiemann08042015Slide2.PNG

Slide 3 USDA and GODAN

BrandNiemann08042015Slide3.PNG

Slide 4 US Office of Management & Budget Memo 13-13

BrandNiemann08042015Slide4.PNG

Slide 6 USDA Innovation Challenge Blog Post

http://blogs.usda.gov/2015/07/24/usd...re-developers/

BrandNiemann08042015Slide6.PNG

Slide 7 USDA Innovation Challenge Announcement

http://usdaapps.devpost.com/

BrandNiemann08042015Slide7.PNG

Slide 8 USDA Innovation Challenge Required Datasets

http://usdaapps.devpost.com/details/resources

BrandNiemann08042015Slide8.PNG

Slide 9 GODAN

http://www.godan.info/

BrandNiemann08042015Slide9.PNG

Slide 10 Key Data Sets in GODAN Report

Key data sets

BrandNiemann08042015Slide10.PNG

Slide 12 Week 2 Data Understanding Knowledge Base Attachments

BrandNiemann08042015Slide12.PNG

Slide 13 FarmDataDashboard Spreadsheet

FarmDataDashboard.xlsx

BrandNiemann08042015Slide13.PNG

Slide 14 NASS Commodity Value in Spotfire

BrandNiemann08042015Slide14.PNG

Slide 15 AgGateway

http://www.aggateway.org/

BrandNiemann08042015Slide15.PNG

Slide 16 Ohio State University Precision Agriculture

http://fabe.osu.edu/precisionag_contact

BrandNiemann08042015Slide16.PNG

Slide 17 University of Minnesota Precision Agriculture Links

http://www.precisionag.umn.edu/precision-links/

BrandNiemann08042015Slide17.PNG

Slide 18 CropWatch

http://cropwatch.unl.edu/home/

BrandNiemann08042015Slide18.PNG

Slide 19 CropWatch: Management Topics

http://cropwatch.unl.edu/management

BrandNiemann08042015Slide19.PNG

Slide 20 CropWatch: Precision Agriculture

http://cropwatch.unl.edu/ssm

BrandNiemann08042015Slide20.PNG

Slide 21 The InfoAg 2015 Conference

http://infoag.org/

BrandNiemann08042015Slide21.PNG

Slide 22 The InfoAg 2015 Conference Program

http://infoag.org/program/5/

BrandNiemann08042015Slide22.PNG

Slide 24 Three Pillars of Customer Value

BrandNiemann08042015Slide24.PNG

Slide 25 But Complexity Has Driven Slow Adoption

BrandNiemann08042015Slide25.PNG

Slide 26 Producer Challenges of Machine Management and Information and Relationships

BrandNiemann08042015Slide26.PNG

Slide 27 Wirelessly Connected Machines

BrandNiemann08042015Slide27.PNG

Slide 28 Machine Optimization

BrandNiemann08042015Slide28.PNG

Slide 29 MKC Maturity Progression

BrandNiemann08042015Slide29.PNG

Slide 30 Job Optimization

BrandNiemann08042015Slide30.PNG

Slide 31 Ag Decision Support

BrandNiemann08042015Slide31.PNG

Slide 32 John Deere Data Ecosystem

BrandNiemann08042015Slide32.PNG

Slide 33 Our Strategy is to LEAD and ENABLE Integrated Solutions

BrandNiemann08042015Slide33.PNG

Slide 34 Modern Cab

BrandNiemann08042015Slide34.PNG

Slide 35 SeedStar Mobile

BrandNiemann08042015Slide35.PNG

Slide 36 New Production System Apps

BrandNiemann08042015Slide36.PNG

Slide 37 MONITOR Your Operation with MyJohnDeere

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Slide 38 ANALYZE Your Operation with MyJohnDeere

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Slide 39 Implementing an Agronomic Decisions

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Slide 40 John Deere FarmSight 1

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Slide 41 John Deer WorkSight

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Slide 42 John Deere FarmSight 2

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Slide 43 Improving Performance and Uptime and Efficiencies of Machines Through the Use of Data: Video Notes

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Slide 44 The Climate Corporation

https://www.climate.com/

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Research Notes

NuGIS

Source: http://nugis.ipni.net/Home/thankyou/ 

Web

Thank you for registering. Please read the following disclaimer.
 
The NuGIS Interactive Mapping interface is a companion product to an IPNI bulletin entitled A preliminary Nutrient Use Geographic Information System (NuGIS) for the U.S. The Bulletin contains the methods used to generate the data behind this interface and users are encouraged to review its content. Pages 41 and 42 of the Bulletin are especially important for users of this web tool as they explain the issues associated with county or watershed views of the nutrient balance data.
 
Two of the most important issues are:
 
1) Fertilizer sales data are in most cases used to represent fertilizer use. Since fertilizer could be applied in a county other than the one in which it is purchased, care should be used in interpreting high resolution data, especially the county data. Since watersheds contain data from multiple counties, they will generally be more reliable than the county view. 
 
2) Crop removal is calculated at a county level for 21 crops. We estimate that these crops capture at least 90% of removal for 44 of the 48 states. The four states dropping below 90% are Arizona, California, Florida, and Georgia. Lettuce is the major missed crop for Arizona and wood for Georgia, while the nutrient removal and acreage of numerous crops are missed for California and Florida. Because of the spatial clustering of the numerous missed crops in California and Florida, county and watershed data are not currently available in this web tool. Adjustments are made at the state level such that the state contribution to national nutrient removal should be correct. 
 
Also, county and watershed data are not currently available for Oregon and Maine due to unresolved issues with the nutrient balance data in several counties.

Email

Thanks for registering for the NuGIS site.

The NuGIS Interactive Mapping interface is a companion product to an IPNI bulletin entitled A preliminary Nutrient Use Geographic Information System (NuGIS) for the U.S. The Bulletin contains the methods used to generate the data behind this interface and users are encouraged to review its content. Pages 41 and 42 of the Bulletin are especially important for users of this web tool as they explain the issues associated with county or watershed views of the nutrient balance data.

Two of the most important issues are:

1) Fertilizer sales data are in most cases used to represent fertilizer use. Since fertilizer could be applied in a county other than the one in which it is purchased, care should be used in interpreting high resolution data, especially the county data. Since watersheds contain data from multiple counties, they will generally be more reliable than the county view.

2) Crop removal is calculated at a county level for 21 crops. We estimate that these crops capture at least 90% of removal for 44 of the 48 states. The four states dropping below 90% are Arizona, California, Florida, and Georgia. Lettuce is the major missed crop for Arizona and wood for Georgia, while the nutrient removal and acreage of numerous crops are missed for California and Florida. Because of the spatial clustering of the numerous missed crops in California and Florida, county and watershed data are not currently available in this web tool. Adjustments are made at the state level such that the state contribution to national nutrient removal should be correct.

Also, county and watershed data are not currently available for Oregon and Maine due to unresolved issues with the nutrient balance data in several counties.

Please visit http://nugis.ipni.net to login

AgriStats

Source: http://www.ipni.net/article/IPNI-3157

My Note: Requested Access and Finally Received Access

Documentation: http://www.ipni.net/ipniweb/portal.n...%20Edition.pdf

Data Export
Table results can be exported directly to an Excel worksheet for the purposes of using AgriStats data externally. See IPNIAgriStats

Web

A unique web-based information system that is available to all IPNI Members and their Staff. The system is designed to collect yield and associated fertilizer use data from regions in which we conduct our Programs.
How to Register
If you are new to AgriStats we encourage you to register. Once registered, you will have full access to its available features.

Please note that your name and affiliation will be required for security reasons. After your request is processed, you will be provided with personal login details. Also note that requests for access are processed during the business hours of (≈14:00 to 23:00 GMT). 

Please consult the AgriStats Manual (see left links on this page). A glossary section is available within AgriStats for help on the available functions, the current dataset, and the terms and definitions used to describe the data.

Guidelines for Use of AgriStats Data
Access to AgriStats, its data tables, and reports represents a license to use these materials as a resource and does not grant permission for indiscriminate copying or unauthorized use. Although IPNI asks to be cited as a source when AgriStats data are used, the Institute cannot endorse external analyses resulting from its use or guarantee future nutrient use based on AgriStat predictions.

Brand, I have setup an account for you at agristats. 

Please let me know if you have any issues with access. 

Quentin

Quentin Rund, PAQ Interactive, Inc.
107 S State St, Ste. 300, Monticello, IL 61856
(217) 762-7955
qrund@paqinteractive.com
http://www.paqinteractive.com

I wanted to analyze that data for a paper to submit.

13th International Conference on Precision Agriculture

The 13th International Conference on Precision Agriculture (ICPA) will be held at the St. Louis Union Station Hotel, St. Louis, Missouri, USA from July 31st to August 3rd, 2016. The conference website is www.ispag.org/ICPA/.

Second Call for Abstracts for the 13th ICPA

Online abstract submission for the 13th ICPA is now open at http://www.ispag.org/abstracts.
We invite you to submit an abstract for the various conference topics. Abstracts will be reviewed for suitability based on scientific content and clarity. Abstracts meeting these criteria will be accepted for presentation as either oral or poster presentations at the conference. Abstract length must be in the range 300-500 words with no figures, references or tables. Authors of accepted abstracts will be entitled to present their research at the Conference after payment of registration fees. They will also be entitled to submit full papers (more details later) for the Conference Proceedings in February of 2016. Conference Proceedings will be published on the website and will be available after the conference.

The abstract submission process requires creating a secured account on the ISPA website. Establishing an account is free and easy. Please complete the online form at the following website http://www.ispag.org/abstracts. The email address you use will become your account login. All correspondence will be sent to this email, so please verify that it is entered correctly and that your email system will allow mail from the ispag.org domain. Check your junk mail folder if you do not receive your account information.

Shortly after you click Submit, the online-system will send you an email with a weblink. Clicking that link will activate your account. Once your account is activated you will be able to submit your abstract.
Deadline for submission of abstracts is November 30, 2015. Questions pertaining to abstract submissions should be addressed to abstracts@ispag.org.
 
Sincerely,
Dr. Ken Sudduth
Conference Chair and President International Society of Precision Agriculture
president@ispag.org

Glossary

Source: http://agristats.ipni.net/nodes/glos...p#cp_resources

Crop Profiles - Resources

Selected indicators of resource availability, demographics and the dependance of regional populations on agriculture. Data is provided from 1961 to the most recent year available. Sources: FAO and Government websites.

Population (POP), total

% Ag Population, total population that is economically active in agriculture.
Land Area (TLA), total ('000 ha)
Arable Land (ArA), total ('000 ha) primarily including annual crop lands, temporary meadows and pastures.
Arable Land per Person, Arable land/Total Population (ha)
Agricultural Area (AA), total ('000 ha) including arable land, permanent crop land (long-term multi-harvest crops) and permanent pastures (five years or more).
Irrigated Area (IA), total ('000 ha)
% Irrigated Area, total (%) Irrigated area/Agricultural Area
Forested Area (FA), total ('000 ha)

Crop Profiles - Crop Production

Crop yields, harvested areas and production data, as well as respective average annual growth rates for each. Data is provided from 1961 to the most recent year available. Sources: FAO and Government websites.

Area (A), harvested area (ha)
Production (P), total (t)
Yield (Y), Production/Area (kg/ha)

Crop Profiles - Crop Productivity

Actual, realistically attainable, and potential crop yields, as well as respective annual growth rate averages for each. Sources: IPNI expert knowledge, field study, crop models.

Attainable Yield (Ya), yield (kg/ha) of a modern variety that can be achieved in farmers' fields with good crop management and ample nutrient supply. Attainable yield is limited by the region's yield potential (see definition below) and other yield limiting factors in the growing environment that could currently not be easily overcome by management (e.g., water limitation to yield or low productivity on sandy soils). Investments in infrastructure (e.g., irrigation facilities) or soil improvement could substantially increase attainable yield. The maximum economic yield is often closely related to the attainable yield.
Potential Yield (Yp), yield (kg/ha) assumed to be only limited by variety (modern, high yielding) and climate (solar radiation, temperature). Note that by definition, potential yield is not water limited. Potential yield represents the upper yield boundary for the currently available genetic material.

Crop Profiles - Yield Gaps

Quantifies the portion of yield which is realistically exploitable and that which is not exploitable due to regional constraints related to climate, soil productivity, crop genetics or and common agronomic practices. Sources: IPNI expert knowledge, field study, crop models.

Exploitable Gap (Ya - Y), defined as the difference between Attainable Yield and Actual Yield, this portion of yield (kg/ha) is limited by advancements in knowledge and technologies, including adoption rate, that are expected to be surpassed.
Remaining Gap (Yp - Ya), defined as the difference between Potential Yield and Attainable Yield, this portion of yield (kg/ha) is limited by unfavorable economics (high inputs required following the law of diminishing return), high risks of crop failure (increasing pest and disease pressure, crop lodging, etc.), environmental limitations to high yield including water availability and poor soil productivity, or unforeseen advances in crop production technology.
Full Gap (Yp - Y), defined as the difference between Potential Yield and Actual Yield, this portion of yield (kg/ha) is the sum of the exploitable and remaining Yield Gaps.

Crop Profiles - Nutrient Use

Crop-wise fertilizer consumption and nutrient removal which considers both the harvested product and an estimate of crop residue removal from the field. Nutrient removal to use ratios are also provided. Sources: FAO and Government websites.

Fertilizer Use (FC), Crop-wise fertilizer (N, P2O5, K2O, S, MgO) consumption (t).
Nutrient Removal (NR), Crop nutrients (N, P2O5, K2O, S, MgO) removed within harvested products from crops receiving fertilizer and, where applicable, quantities of crop residues commonly removed from field (t).
Nutrient Removal-to-Use Ratio (NR/FC), Nutrient Removal/Fertilizer Use

Crop Profiles - Current Fertilizer Use

Estimates of the scope and intensity of current average fertilization practices and the resulting fertilizer consumption. Sources: IPNI expert knowledge.

Current Fertilizer Use (FX), Average N, P2O5, K2O, S, MgO application rates (kg/ha) which correspond to the crop area that receives fertilizer.
Current Fertilized Area (AF), Estimated crop area (%) currently receiving fertilizer.
Current Fertilizer Consumption (FC), Crop-wise fertilizer (N, P2O5, K2O, S, MgO) consumption (t).

Crop Profiles - Attainable Fertilizer Use

Estimates of the scope and intensity of crop fertilization required for attainable yield estimates and the resulting fertilizer consumption. Sources: IPNI expert knowledge.

Attainable Fertilizer Use (FXa), Estimates of N, P2O5, K2O, S, MgO application rates (kg/ha) which correspond to those suited for attainable yield levels.
Attainable Fertilized Area (AFa), Estimates of crop area (%) reflecting reasonable expectations for medium to long-term change in fertilized area of a specific crop.
Attainable Fertilizer Consumption (FCa), Crop-wise fertilizer (N, P2O5, K2O, S, MgO) consumption (t).

Crop Profiles - Fertilizer Gaps

An outline of the differences between the realistically attainable and actual fertilizer use scenarios. Sources: IPNI expert knowledge.

Fertilizer Use Gap (FXa - FX)
Fertilized Area Gap (AFa - AF)
Fertilizer Consumption Gap (FCa - FC)

Charts

Fertilizer Market Development Scenarios - Provides realistic agronomic boundaries for current and future fertilizer use by crop and region. Example chart:

Example Chart

Example Chart

Current: Actual Consumption, portrays estimates of consumption up to the most recent year based on current fertilizer application rates, harvested area, and percent fertilized area.
Current: Intensification Potential, portrays the currently attainable shift in crop fertilization, the "current market development potential", based on attainable fertilizer application rates, actual harvested area, and attainable percent fertilized area.
Future: Area Expansion Model, portrays future estimates of consumption based on current expectations for fertilization practices and realistic expectations for future harvested areas. Assumes current knowledge and application rates would not change and incentives for farmers to apply fertilizer remain constant.
Future: Intensification x Area Expansion Model, portrays future estimates of consumption based attainable improvements in fertilizer use and expected changes in harvested area, or the "future market development potential".

Note: Constraints to realizing the agronomic market development potential include among others unfavorable economics (e.g., commodity prices), resource availability (e.g., fertilizer), or lack of knowledge and technologies. The actual market development is expected to take place between the portrayed boundaries of intensification and area expansion.

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International Plant Nutrition Institute

Source: http://nap.ipni.net/

The mission of IPNI is to develop and promote scientific information about the responsible management of plant nutrition for the benefit of the human family.

IPNI is a global organization with initiatives addressing the world's growing need for food, fuel, fiber and feed. There is widespread concern for issues such as climate change and relationship of crop production to the environment and ecosystems, and IPNI programs are achieving positive results. Best management practices (BMPs) for nutrient stewardship encourage the concept of applying the right product (source), at the right rate, at the right time, and in the right place.

About IPNI

Source: http://nugis.ipni.net/AboutIPNI/

International Plant Nutrition Institute (IPNI)

The International Plant Nutrition Institute (IPNI) is a not-for-profit, science-based organization dedicated to the responsible management of plant nutrition for the benefit of the human family. IPNI began operating in January of 2007 and now has active programs in Africa, Australia/New Zealand, Brazil, China, Eastern Europe/Central Asia and Middle East, Latin America-Southern Cone, Mexico and Central America, Northern Latin America, North America (Canada and U.S.A.), South Asia, and Southeast Asia.

As a global organization, IPNI has initiatives addressing the world’s growing need for food, fuel, fiber, and feed. There is widespread concern for issues such as food security and the relationship of crop production to the environment and ecosystems. IPNI programs are achieving positive results in many areas. The program coordinators and IPNI regional directors are Ph.D. scientists. Through cooperation and partnering with respected institutions around the world, IPNI adds its strengths to agronomic research, education, demonstrations, training, and other endeavors. Best management practices for nutrient stewardship encourage the concept of applying the right product (source), at the right rate, at the right time, and in the right place.

Membership in IPNI is composed of companies that are basic producers of one or more of the major plant nutrients (nitrogen, phosphate, potash, and sulfur) for agricultural purposes. Large retail organizations that do not qualify as basic producers may qualify as associate members. Certain other organizations qualify as affiliate members.

NuGIS: Nutrient Use Geographic Information System

16 Jan 2015

The Nutrient Use Geographic Information System (NuGIS) integrates multiple tabular and spatial datasets to create county-level estimates of nutrients applied to the soil in fertilizer and livestock manure, and nutrients removed by harvested agricultural crops.

Visit the Interactive Map

Use the Interactive Map Viewer to explore the NuGIS data on a map. 
(requires that JavaScript is enabled on your browser.)

NuGIS Updated!

We've recently added NuGIS analyses for more recent years and updated some previous years using improved input data.

February 13, 2015:
  • 2012 has been added as the most recent year of analysis 
  • Manure nutrient contributions have been updated based on recently released 2012 Census of Ag data. This currently effects 2010, 2011 and 2012 data. 2008 and 2009 will be updated soon. 
  • Higher resolution, recently updated land use maps for 2011 and 2012 are now being used to help fine-tune fertilizer input data for 2011 and 2012
  • For years 2010, 2011, and 2012: estimates of nutrient removal by crops are based on annual data rather than 3-year averages

About NuGIS

The Nutrient Use Geographic Information System (NuGIS) integrates multiple tabular and spatial datasets to create county-level estimates of nutrients applied to the soil in fertilizer and livestock manure, and nutrients removed by harvested agricultural crops.  Nutrient balances and removal efficiencies were estimated for five years, coinciding with the USDA Census of Agriculture, from 1987 - 2007. Geospatial techniques were used to estimate balances and efficiencies for 8-digit hydrologic units using the county-level data. The results of this project are shown here with interactive thematic maps. 

The NuGIS project is sponsored and directed by the International Plant Nutrition Institute (IPNI). The two primary objectives of this project are to assess nutrient use efficiency and balance in crop production and identify weaknesses in the balance estimation processes and the datasets used for these estimations. 

The NuGIS Project is a cooperative effort among many people. The concept was first presented by the Potash & Phosphate Institute (PPI) in 2005, at one of the first meetings of the TFI-PPI Nutrient Use Task Force. The conceptual framework, interpretation, and reviewing were a joint effort of the IPNI staff: C.S. Snyder, T.W. Bruulsema, T.S. Murrell, H.R. Reetz, S.B. Phillips, R.L. Mikkelsen, W.M. Stewart, and T.L. Jensen. Quentin Rund and Ryan Williams of PAQ Interactive converted concepts and tactical approaches into numeric estimates and graphical representations. Final editing and production support were coordinated by  D.L. Armstrong and K.P. Griffin of IPNI's editorial group. The project is coordinated by P.E. Fixen of IPNI.

NuGIS is a work in progress and we welcome your comments at nugis@ipni.net

More Information

For more information about NuGIS, please visit http://www.ipni.net/NuGIS

Applications of NuGIS and Conclusions

Source: http://nugis.ipni.net/Applications/

Nutrient balances are important to farmers and to society as indicators of sustainability. Current status offers insights into both production and environmental consequences of existing practices and temporal trends in balances provide a vision of the future unless change occurs. Due to the interactive nature of nutrients in crops, soils, and ecosystems in general, evaluation of balances of multiple crop nutrients has advantages over a singular nutrient focus.


We see the final NuGIS as having several applications. These include: 
  • Offering guidance in nutrient management education.
  • A basis for science-based guidance in marketing of fertilizers and nutrient management related services.
  • A useful tool for integrating nutrient balances in water quality and nitrous oxide emission modeling.
  • Factual spatial and temporal input into environmental policy development involving plant nutrients.
 
As discussed earlier, weaknesses exist in our current capacity to accurately evaluate nutrient balances at appropriate resolution. These include incomplete information concerning crop nutrient removal coefficients, lack of Agricultural Census data for specific nutrient use expenditures, and missing county level fertilizer sales data in the AAPFCO database. We hope the transparency of NuGIS will motivate changes in nutrient data collection mechanisms that will lead to improved estimates of nutrient balances and cycling.

This spatial and temporal analysis of partial nutrient balances in the U.S. leads to the following general observations.
 
  • Crop nutrient removal in the U.S. is increasing faster than nutrient use.
  • Great variation exists across the country in major nutrient (N, P, K) balances.
  • The most positive P balances are found in New England and the South Atlantic Gulf. The most negative P balances are found in the Corn Belt.
  • Most of the Corn Belt has negative P balances and some of these same watersheds appear also to have negative K balances.
  • Removal to use ratios appear unsustainably high in some regions and unsustainably low in others, making intensive monitoring of soil fertility a critically important management practice.
  • Where trends for high partial balances of N and/or P are observed, and/or low removal to use ratios are noted, it may also be important to monitor quality of surface water and groundwater to identify opportunities for special management considerations to help remedy any unacceptable risks of potential water quality impairment.

Methods

Source: http://nugis.ipni.net/Methods/

In order to make consistent comparisons across space and time we selected years for our analysis where data were available from each source with some degree of consistency in reporting. Table 2.1 shows the availability of data by year of the U.S. Census of Agriculture. This is not to say that 1982 Census of Agriculture data do not exist, but it is not readily available in electronic format at the county level. Also, the lack of data from the Association of American Plant Food Control Officials (AAPFCO) would have changed the balancing procedure, so we chose to use the Ag Census data from 1987 to 2007 (which became available February 4, 2009).

Table2.1DataAvailabilityMatrix.png

Estimating Nutrients from Commercial Fertilizers

Source: http://nugis.ipni.net/Methods/Fertilizer/

Data for estimating the nutrients from commercial fertilizers were provided by the Association of American Plant Food Control Officials (AAPFCO). This group provides commercial fertilizer sales data each year for fertilizer products sold as tons of fertilizers, state and county sold in, year sold, season sold, container sold in,  fertilizer type code, formulation as percent N, P2O5, and K2O and the intended use of the fertilizer sold. We used these AAPFCO values as a basis for estimating the nutrients applied with farm use commercial fertilizers at the county level.

Table2.2Countiesinthelower48statesreportingfertilizersalesdatabyyear.png

 
 
This statement is from the appendix to the Commercial Fertilizers series (Slater and Kirby, 2008), “Commercial Fertilizers is based on fertilizer consumption information submitted by state fertilizer control offices. The consumption data include total fertilizer sales or shipments for farm and non-farm use. Liming materials, peat, potting soils, soil amendments, soil additives, and soil conditioners are excluded. Materials used for the manufacture or blending of reported fertilizer grades or for use in other fertilizers are excluded to avoid duplicate reporting. Some states do not report final grades; therefore, basic materials including both single-nutrient and multiple-nutrient, are reported. Significant effort was exerted to check the accuracy of and faithfully summarize each state’s data; however, AAPFCO is not responsible for the accuracy of the data.”

Commercial fertilizer data are available from AAPFCO dating back to 1985. The 1985-1994 data were compiled and originally published by the Tennessee Valley Authority (TVA). The structure of the data has remained fairly consistent over time. From the above statement, it is apparent that some safeguards are in place to help protect from counting sales of the same fertilizer more than once, but the conclusion is that the data are presented as is.

Generally, AAPFCO data are provided at the county level for most states in the U.S., but this varies by state and year. Some states have values reported for every county in the state, some states only have values reported for some of the counties in the state, and some states have only a state total value reported. Many states also reported ‘Unknown’ county values for fertilizer sales. These represent tons of fertilizer sold in-state that could not be attributed to a specific county.
Census of Agriculture
Estimations of Farm Production, Expenditures, Inventory, Size, Extent of Cultural Practices, and more are provided by the USDA-NASS Census of Agriculture, conducted every 5 years. Farms with more than $1,000 in annual sales are asked to provide information on their operation. This Census information is used by NASS to estimate statistical data about agriculture in the U.S. These data are made available to the public, but if data could be attributed to a specific producer, they are withheld to protect their privacy. When this occurs, the data for the specific commodity are listed as undisclosed for that county and no value is published. Often, although the value for a specific commodity is undisclosed at the county level, this value is included in the state total for that commodity. Fertilizer expenditure data were undisclosed for some counties and fertilized acres data were undisclosed for about half of those.
 
Abbreviations and notes: N = nitrogen; P = phosphorus; K = potassium.
Estimating Farm Fertilizer Use

Detailed ASCII data was obtained from AAPFCO for each year from 1987 thru 2012. This data includes detailed information on the county the fertilizer was sold in, the formulation of fertilizer sold as well as the intended use of the fertilizer, but this information is not always reported consistently throughout all states or over time. Not all states reported farm or nonfarm use for the fertilizer sold in that state. Some states reported all fertilizer sold as Farm, others reported it all as Unknown, and others reported it all as Non-Farm. This situation changed annually. Ruddy et. al. from the USGS encountered the same situation in a study they performed, and they developed a method to estimate farm / non-farm use for fertilizer types sold in a state based on national average ratios of farm / non-farm use for those fertilizer types.
We adapted methodology used by Ruddy et al. to estimate farm use fertilizer sales for products and locations that did not already provide reliable farm use sales reports. This involved:

  • selecting states that appeared to report reliable farm and non-farm use fertilizer sales
  • calculating a national average percent farm and non-farm use for each fertilizer formulation sold in those states
  • applying that same national average farm and non-farm use fertilizer percentage to state total fertilizer sales in states that did not appear to report reliable farm use fertilizer sales
  • apportioning State Total Farm use fertilizer sales to each county in a state based on each county’s reported Dollars Spent on Fertilizer and Lime products in the Census of Agriculture.

If fertilizer sales data for a state appeared to include reliable farm or non-farm use data then the county fertilizer sales reports for that state were used ‘as-is’ and were not recalculated from a state total. 

If fertilizer sales were only reported as a state total, then the estimated farm use fertilizer sales data was apportioned to each county in the state based on each county’s reported Dollars Spent on Fertilizer and Lime products in the Census of Agriculture.

If reliable farm use fertilizer sales were reported for some counties in a state but fertilizer sales data was also reported for an ‘Unknown’ location in that state, the estimated farm use fertilizer sales data from the Unknown location is apportioned to all counties in that state based on each county’s reported Dollars Spent on Fertilizer and Lime products in the Census of Agriculture. If a county in that state already had fertilizer sales data reported, the apportioned amount from the Unknown County is added to the existing fertilizer sales data for that county. 

A more detailed report of the development, testing, and implementation of the methods used to import and analyze AAPFCO ASCII data and produce annual county-level nutrient input estimates is available by contacting nugis@ipni.net

Apportioning State Total or AAPFCO Unknown County Values
When a state total of fertilizer sales was the only data reported, or data was reported for an unknown location in a state, that data was apportioned to all counties in the state based on each county's reported 'Dollars Spent on Fertilizer and Lime' in the Census if Agriculture. The amount of state total fertilizer sales or unknown location fertilizer sales that were apportioned to each county was calculated based on a ‘Fertilizer $ to Fertilizer tons coefficient’. This coefficient was calculated for N, P2O5, and K20 for each state by dividing the (State total tons X sold OR State Total Unknown Location tons X sold) by the (sum of $ spent on fertilizer and lime products in all counties in that state). The value for each county was then calculated using (County $ spent on fertilizer and lime products) X (Fertilizer $ to Fertilizer tons coefficient).
Estimating Dollars Spent on Fertilizer

Values for ‘Dollars Spent on Fertilizer and Lime Products’ and ‘Acres Fertilized’ are reported by county in the Census of Ag. Each year, the value of ‘Dollars Spent on Fertilizer and Lime Products’ is undisclosed for some counties. When “Dollars Spent on Fertilizer and Lime Products” was undisclosed for an individual county, the value from that county would still be included in the state total “Dollars Spent on Fertilizer and Lime Products”. The sum of “Dollars Spent on Fertilizer and Lime Products” was calculated for all counties disclosing this value in a state, and that sum was subtracted from the state total “Dollars Spent on Fertilizer and Lime Products” yielding a “State Remainder Dollars Spent on Fertilizer and Lime Products”.

This remainder was apportioned to all counties that were undisclosed using the value for ‘Acres Fertilized’. A coefficient was calculated that represented the ratio of ‘State Remainder Dollars Spent on Fertilizer’ to ‘Acres Fertilized in Counties Where Dollars Spent on Fertilizer Was Undisclosed’.

Estimating Fertilized Acres

In some rare situations, neither ‘Dollars Spent on Fertilizer and Lime’ nor ‘Acres Fertilized’ were disclosed in the Census of Ag. On these occasions, we estimated the acres fertilized for that county using data from the Census of Ag for ‘Number of Farms Spending Dollars on Fertilizer and Lime Products’ the ‘Average Farm Size’, and the ‘State Total Cropland Acres’. For counties that did not disclose dollars spent on fertilizer and lime products, we performed a rough estimate of fertilized acres using (Number of Farms Spending Dollars on Fertilizer and Lime Products) times (Average Farm Size). Because not every acre on every farm is fertilized each year, this estimate was refined using a coefficient representing the average percent of cropland acres fertilized for each state, calculated using (State Total Acres Fertilized) / (State Cropland). Our final Acres Fertilized estimate was calculated using:

((County ‘A’ Number of Farms Spending Dollars on Fertilizer and Lime) X (County ‘A’ Average Farm Size)) / ((State Total Acres Fertilized) / (State Cropland))

Spatial Smoothing of County Fertilizer Sales data

One common challenge when using AAPFCO fertilizer sales data to estimate fertilizer use is that, while fertilizer sales are attributed to a county, the fertilizers sold in that county are not necessarily applied in the same county. The presence of a large fertilizer distributor in one county that delivers fertilizer to several neighboring counties can cause artificially high and low rates of fertilizer nutrients sold per cropland acre in those counties. Also, fertilizer use is likely not constant across an entire county; this may be more apparent in the southwestern states. 

 
To account for these factors in modeling fertilizer use spatially we used a spatial interpolation method, similar to that used when creating soil test maps. A layer of points was created with each county represented by one point. The layer of points for each county on the map is primarily intended to serve as an average ‘sample point’ of all fertilizer sales reports in that county. To better model the location of fertilizer use within a county, analysis was performed to find the ‘mean center’ of crop land in each county. Assuming fertilizer is likely used near concentrations of cropland, it’s likely that this mean center location better represents the mean center of fertilizer use than the geographic center of the county. Each point was attributed with the fertilizer sales data for that county. 
 
County Cropland Centers were created that represent the center of all crop land in a county. At times, these differed greatly from the geometric center of a county. Fertilizer sales were attributed to these points and a Kriging method was used to interpolate a surface between them.
 
When reviewing the data for pounds of farm use fertilizer sold per total cropland acre, some counties stood out that appeared to be errors. Some rates exceeded 2,000 lbs/A and were caused in part by a low reported cropland acreage in counties that reported farm use fertilizer sales. Because of this, calculated (Pounds of Farm Use Fertilizer Nutrient Sold)/(Total Cropland Acre) were capped at a maximum of 2,000 lbs during the spatial interpolation. Adjustments are performed later to account for this and maintain the correct state fertilizer totals.

An Inverse Distance Weighted interpolation method was then used to create an interpolated raster map of fertilizer sales as Pounds of Farm Use Fertilizer Nutrient Sold per Total Cropland Acre across the entire lower 48-states. This routine was repeated for each nutrient and each year. This method allows for the use of fertilizer in counties other than where it is sold, but still nearby. 
 
Farm Use Fertilizer Nitrogen sold / Total Cropland was interpolated from cropland-weighted county centroids using a spatial interpolation method. This produced a raster layer that represented a smoothed representation of farm use fertilizer Nitrogen sold per total cropland acre.
 
Interpolated Farm Use Fertilizer Nitrogen sold / Total Cropland was overlaid with county boundaries and a land use raster to identify areas of cropland only.
 
The interpolated lbs sold/total cropland Acre raster was overlaid with the county boundary layer and a land use raster layer. Areas of Cropland land use were selected and a raster tabulation method was used to calculate the Average Pounds of Farm Use Fertilizer Nutrient sold per Cropland Acre, for agricultural areas only, for each county. This routine was repeated for each nutrient and each year. 

Generally, the county total farm use fertilizer nutrients sold as reported and as calculated using the interpolation were often very similar and did appear to smooth areas of very high or very low sales. However, the interpolation did cause some significant changes in some areas of the country. Because of this, the county sales data calculated using the interpolated raster were only used to reapportion the total fertilizer nutrients sold in each state. This results in a redistribution of fertilizer to all counties in the state based on the original county sales estimates and the results of the spatial interpolation but avoids adding or removing any fertilizer tonnage from our original, non-interpolated estimates.

Land Use Classification layers are used to identify areas of Agricultural Land Use

The balances and statistics generated by NuGIS are directly related to agriculture. The county maps display balances and statistics for a county, but do not effectively convey the extent of agriculture within that county. For example, compare a county in Illinois with a county in Nevada. Both counties may have similar values for nutrient balances, but the county in Illinois has much more land used for agriculture. However, on the map the county in Nevada will stand out more than the county in Illinois, due to the larger geographic size of the county in Nevada. To counter this effect, we used a land use map, placed on top of the county balance map, to mask out all areas not identified as having an agricultural land use. This method is effective and appropriate because the NuGIS balances and statistics relate directly to nutrient inputs to, or nutrient removal from, agricultural land.

Land use / land cover datasets were obtained from the USGS “Land Cover Institute” website (http://landcover.usgs.gov/). Two spatial datasets were immediately available: the 1992 enhanced National Land Cover Dataset (NLCD-e1992) and the 2001 National Land Cover Dataset (NLCD2001). Both of these datasets are made up of 30-meter by 30-meter blocks that cover the entire contiguous U.S. Each block is assigned a numeric code that corresponds to the dominant land use and land cover within that 30-meter block.

Agricultural land uses were defined as any areas defined in the land cover dataset as “Orchards, Vineyards, other”, “Pasture/Hay”, “Row Crops”, “Small Grains”, or “Fallow”. Blocks in the Land Cover Datasets that match these definitions have the codes: 61, 81, 82, 83, and 84, respectively. These definitions are the same as those used in other studies.

To create the masking layer, any blocks representing agricultural land use are set as transparent, while all other blocks are set as opaque (white). This mask was then placed over other layers. We used the 1992 mask for 1987, 1992, and 1997 data and the 2001 mask for the 2002 and 2007 data.

When this masking layer is placed over another layer, such as the county N balances, color from the N budget layer will only be visible in areas defined as having agricultural land uses. Our application of land cover data differs from that of some other studies. We did not apply any data to specific land use blocks, or calculate any statistics from the land use layer. We only used the land use layer as a visual mask to provide a more accurate visual representation of agricultural lands. All balances and statistics were calculated at the county level, and not for land use blocks.

Discussion

Systematic Errors and Uncertainty

Source: http://nugis.ipni.net/Discussion/

My Note: This was not listed in the drop-down menus and could be overlooked

One of the objectives of the NuGIS project was to develop a nutrient use assessment process with complete transparency that would reveal any weaknesses in input data or the process itself. Along the way, many conscious decisions were made concerning procedural steps that weighed potential error against data availability and cost. We hope that this exercise will illustrate where weaknesses exist, so that as time passes improved input data will become available and the process itself can be improved. Here we list the major issues adding uncertainty to the nutrient balance estimates.

Fertilizer Use

Border issues with AAPFCO data. The AAPFCO data, used as the primary source for county fertilizer nutrient use, are in reality sales data and uncertainty exists as to whether fertilizer is used in the same county as the point of sale. We employed the smoothing procedure to reduce the magnitude of this problem but the procedure did not likely eliminate the problem. Though this causes uncertainty for specific county estimates, clusters of counties should be affected less and the comingling of county data done to create the 8-digit HUC maps should help as well.

Lack of nutrient-specific Ag Census expenditures. AAPFCO data were available for only 70 to 75% of the counties. When they were not available, Ag Census data on fertilizer and lime sales were used to parcel the state AAPFCO data out to county levels. This procedure results in assuming the same N:P:K use ratio for an entire state. This is perhaps a reasonable assumption in some states, but it is problematic where gradients in soil supply of specific nutrients occur across a state or where regional differences exist within a state in crops grown. For example, in South Dakota where county level AAPFCO data are not available, from east to west across the state, soil K levels climb dramatically and cropping systems shift from row crops to small grains. In this case, apportioning nutrient use according to Ag Census sales data underestimates K use in the east and overestimates it in the west. The Ag Census would be much more useful for this purpose if it reported sales by nutrient.

Separating farm from non-farm use. The Ruddy et al. (2006) farm to non-farm coefficients we used were developed based on fertilizer mixture use relationships from 1987-2001. As new fertilizer materials enter the market and fertilizer practices change in homes, professional turf, and on farms, these coefficients could also change. A direct reporting system would be advantageous.

Recoverable Manure

Considerable variability exists in estimates of manure nutrient recoverability. We used the Kellogg et al. (2000) approach partly because it was well documented. A more direct systematic estimation technique driven by livestock animal units at a county level would likely result in improved accuracy. The nutrient content of manure may exhibit some temporal trends as feeding and livestock management systems change with time. Our approach did not account for such changes.

Nitrogen Fixation by Legumes

N Fixation

We assumed that N fixation was equal to the N removed in the harvested portion of the major leguminous crops: soybean, alfalfa, and peanut. Implicit in this assumption is that the partial N balance of these crops is zero (N fixed - N removed = 0). This appears well supported for soybeans as Salvagiotti et al. (2008) in an extensive review of the literature reported an average partial N balance for soybeans not receiving N fertilizer of -4 kg/ha. It also is likely a reasonable assumption for peanuts. However, Peterson and Russelle (1991) in a review of alfalfa production in the U.S. Corn Belt states estimated N fixation by alfalfa at 61 lb/ton of hay and our N removal coefficient is 51 lb N/ton or 84% of their figure. Thus, we may be underestimating N fixation by alfalfa in our procedure.

Nutrient Removal by Crops

Crop Removal

Spatial or temporal variation in crop coefficients. NuGIS uses a fixed set of nutrient concentrations for the harvested portion of crops except for P for corn where sufficient data exist to detect regional differences. The levels assumed are typical of those published by land grant universities with some cross checking to feed analysis data when it is available. These coefficients need updating for many crops and should probably not be treated as constants across the entire country. Also, it is possible that changes in cultural practices and genetics could alter the nutrient concentration in harvested crops. Unfortunately, at this time there is no systematic method for accounting for such differences. IPNI is currently working with the University of Missouri to develop a geospatial data base of nutrient concentrations for the harvested portions of major crops. The removal coefficients currently used in NuGIS for corn, soybeans and wheat incorporate the findings of this database project. As this database grows, it will be used for additional crops and possibly as a basis for regional differences in concentrations.

Estimates for specialty crops. In 2007, the planted area of the 21 crops for which NuGIS specifically calculates nutrient removal accounted for 78% of total cropland area in the 48 states. In states such as Iowa or Illinois it accounted for 92% and 96% respectively. However, in states with major areas of specialty crops such as California or Florida, the percentage drops to 48% and 51%. Since production data for specialty crops is not consistently available for the time periods needed, nutrient removal for them could not be explicitly calculated in NuGIS. To handle this, a specialty and 'Other' crop removal per unit area was estimated for each state based on review of specialty crop production and removal estimates previously published by the Potash and Phosphate Institute. Acreage growing specialty and 'Other' crops in each county was estimated by calculating the difference in acres between the sum of 21 Crop Harvested Acres and the acres reported in the Census of Agriculture as Harvested Cropland Acres, and adding an estimate of 21 Crop Acres that grew two crops in one season. The state estimated specialty and 'Other' crop removal rate was then multiplied by the estimated specialty and 'Other' crop acreage in each county to calculate a nutrient removal by specialty and 'Other' crops in each county. Total Nutrient Removal was estimated by adding the estimated specialty and 'Other' crop nutrient removal to the calculated 21 Crop nutrient removal.

Partial Balances

Our nutrient balance estimates are partial balances. They do not take into account atmospheric deposition, application of biosolids to ag lands, or nutrients contained in irrigation water (fertilizer nutrients used in fertigation are, however, included in our nutrient balance estimates). They do not take into account nutrient losses (soil erosion, leaching, gaseous losses) from agroecosystems other than crop removal. And, they do not directly account for soil nutrient content changes either from soil organic matter mineralization or immobilization or changes in inorganic levels from either surface soils or subsoils. Therefore, care should be exercised to avoid potential misinterpretation in view of these budget uncertainties.

 

Tabular Data

Source: http://nugis.ipni.net/TabularData/

NuGIS Balance inputs and calculated values are available for download below in tabular format. 

Data taken from NuGIS should be cited as:
IPNI. 2011. A Nutrient Use Information System (NuGIS) for the U.S. Norcross, GA. November 1, 2011. Available on line http://www.ipni.net/nugis

Regional Watershed Nutrient Balance Data

Hydrologic Region (HUC 2 Watersheds) balance data table created using latest methods as of January 12, 2012.

HUC 8 Watershed Nutrient Balance Data

HUC8 Watershed balance data table created using latest methods as of  January 12, 2012.

County and State and 48 State Total Nutrient Balance Data

County balance data table and State and Lower 48 State Summaries. Updated to include Census of Agriculture 'Total Harvested Cropland Acres' in addition to '21 Crop harvested acres' and Census of Agriculture 'Total Cropland Acres'. January 12, 2012, and November 7, 2013

Table Description for HUC8 Balance data

Descriptions of columns present in the HUC8 balance tabular data. 

References

Source: http://nugis.ipni.net/References/

Alexander, R.B., R.A. Smith, G.E. Schwarz, E.W.
Boyer, J.V. Nolan, and J.W. Brakebill.2008. Differences in phosphorus and nitrogen delivery to the Gulf of Mexico from the Mississippi River Basin. Environ. Sci. Technol. 42:822-830.

David, M.B., G.F. McIsaac, R.G. Darmody, and
R.A. Omonode. 2009. Long-term changes in Mollisol organic carbon and nitrogen. J. Environ. Qual. 38:200-211.
 
Grove, J.H., E.M. Pena-Yewtukhiw, M. Diaz-Zorita,
and R.L. Blevins. 2009. Does fertilizer N “burn-up” soil organic matter? Better Crops 93 (4): 6-8.
 
IPNI. 2010. A Preliminary Nutrient Use Information System        (NuGIS) for the U.S. IPNI Publication No. 30-3270.
Norcross, GA. Available on line http://www.ipni.net/nugis
 
Jaynes, D.B., T.S. Colvin, D.L. Karlen, C.A.
Cambardella, and D.W. Meek. 2001. Nitrate loss in subsurface drainage as affected by nitrogen fertilizer rate. J. Environ. Qual. 30: 1305–1314.
 
Kellogg, R.L., C.H. Lander, D.C. Moffitt, and N.
Gollehon. 2000. Manure nutrients relative to the capacity of cropland and pastureland to assimilate nutrients: Spatial and temporal trends for the United States. USDA-NRCS-ERS Publication No. nps00-0579.
 
Lander, C.H., D.C. Moffitt, and K. Alt. 1998.
Nutrients Available from Livestock Manure Relative to Crop Growth Requirements. Resource Assessment and Strategic Planning Working Paper 98-1. USDA-NRCS. On line at: http://www.nrcs.usda.gov/technical/N...ubs/nlweb.html.
 
Obreza, T.A. and K.T. Morgan (ed). 2008. SL 253
Nutrition of Florida Citrus Trees, 2nd Edition. Florida Coop. Extension Service, Univ. of Florida, Gainesville.
 
Peterson, T.A. and M.P. Russelle. 1991. Alfalfa and
the nitrogen cycle in the Corn Belt. J. Soil Water Conservation Soc. 46(3):229-235.
PPI/PPIC/FAR. 2002. Plant nutrient use in North
American agriculture. PPI/PPIC/FAR Technical Bul. 2002-1. Norcross, GA.

Ruddy, B.C., D.L. Lorenz, and D.K. Mueller.
2006. County-level estimates of nutrient inputs to the land surface of the conterminous United States, 1982–2001. Scientific Investigations Report 2006–5012. USGS, Reston, VA.

Salvagiotti, F., K.G. Cassman, J.E. Specht, D.T.
Walters, A. Weiss, and A. Dobermann. 2008. Nitrogen uptake, fixation and response to fertilizer N in soybeans: A review. Field Crops Res. 108: 1–13.
 
Sawyer, J., E. Nafziger, G. Randall, L. Bundy, G.
Rehm, and B. Joern. 2006. PM 2015 Concepts and Rationale for Regional Nitrogen Rate Guidelines for Corn. Iowa State University Extension, Ames.
 
Slater, J.V. and B.J. Kirby. 2008. Commercial
Fertilizers 2007. Association of American Plant Food Control Officials and The Fertilizer Institute, Washington, D.C.
 
Slaton, N.A., K.R. Brye, M.B. Daniels, T.C.
Daniel, R.J. Norman, and D.M. Miller. 2004. Nutrient input and removal trends for agricultural soils in nine geographic regions in Arkansas. J. Environ. Qual. 33:1606–1615.
 
Swink, S., Q.M. Ketterings, K. Czymmek, and L.
Chase. 2008. Proactive agricultural and environmental management by New York dairy farmers greatly reduces cropland P balances. What’s Cropping Up?
 
Cornell University Newsletter Vol 18, No 5,
September-October. USDA-NASS. 2007. The Census of Agriculture. On line at: http://www.agcensus.usda.gov/Publications/2007/ Getting_Started/index.asp.

InfoAg Conference 2015

Source: http://infoag.org/

Conference Index Spreadsheet

Highlights: 

Science of Precision AgPaul Fixen

ISPA and Precision Agriculture around the World  Nicolas Tremblay

ASA - Precision Ag Community Brenda Ortiz

InfoAg 2015 Program

Source: http://infoag.org/program/5/

Tentative program: topics, speakers, and times subject to change.

 

07/28/2015 Tuesday

  Grand Ballroom C Grand Ballroom AB Regency C Regency AB Midway Suites
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sponsored by the International Plant Nutrition Institute
 
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07/29/2015 Wednesday

  Grand Ballroom C Grand Ballroom AB Regency C Regency AB Midway Suites
7:00am
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sponsored by SST and Raven
 
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07/30/2015 Thursday

  Grand Ballroom C Grand Ballroom AB Regency C Regency AB Midway Suites
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Keynotes

Source: http://infoag.org/Program/Keynote/

Opening Plenary Keynote - David Zach - sponsored by IPNI

David Zach
David Zach is one of the few professionally trained futurists on this planet, having earned a masters degree in Studies of the Future from the University of Houston-Clear Lake. Of course, this was way back in the 1980s, so it's pretty much history by now. Since then, Dave has worked with over 1400 associations, corporations and colleges offering insights on the personal and professional impact of strategic trends. In other words, he gives funny and thought-provoking keynote speeches on the future of technology, economics, business, education, demographics and society.
 
He reads a lot – and he reads a lot more about the past than he does about the future. He knows that change is vital but it's also overrated and in this time of tumultuous change, it's far more important that we find the things that don't change and shouldn't change. Tradition and change are really choices, and we are only prepared to choose wisely when we look far and wide for causes, implications and lasting value.
 
Dave has only had two real jobs; one with Northwestern Mutual and the other with Johnson Controls in the roles of environmental scanning and strategic planning. Along the way, he taught Future Studies in the School of Education at the University of Wisconsin-Milwaukee. Since 1987, he mostly sits and read everything he can and then designs fascinating presentations that leave people either engaged in vivid conversations or quietly reflecting on what it all means. He gives talks about 50 times a year and really wishes he would write more. He is the author of two books, so far.
 
Dave is on the board of the American Institute of Architects and on the board of the American Chesterton Society. Past activities include: Wisconsin Small Business Development Center Advisory Council, AIA-WI Board, Future Milwaukee Advisory Board, Community Advisory Board for NPR station WUWM, board member of eInnovate, member of the downtown Rotary Club of Milwaukee, and chairman of the Goals for Greater Milwaukee 2000 Education Committee.

PrecisionAg Plenary Keynote - Kevin Folta - sponsored by SST and Raven

Kevin Folta Kevin M. Folta is a Professor and the Chairman of the Horticultural Sciences Department at the University of Florida.  His research uses state-of-the-art genomics approaches to identify novel genes that control important traits in small fruits, and led the strawberry genome sequencing project- producing the 12th plant genome sequenced. His laboratory also examines how plants interact with the light environment, how specific wavelengths of light may be applied to control development of high-value crop traits. He has been recognized with several prestigious awards such as the NSF CAREER Award, the University of Florida Research Professorship, and the HHMI Distinguished Mentor Award. A key part of his program is communicating science to non-scientific audiences, and training scientists how to effectively communicate scientific or controversial topics. BS/MS Northern Illinois University 1989/1992, Ph.D. University of Illinois at Chicago, 1998. 

Closing Plenary - Robbie Schingler - sponsored by IPNI

Will Marshall
Robbie Schingler is the Co-Founder and President of Planet Labs. Prior to Planet Labs, Robbie spent 9 years at NASA. He helped to formulate the Small Spacecraft Office at NASA Ames Research Center and was Capture Manager for the Transiting Exoplanet Survey Satellite (TESS). Robbie had two tours at NASA Headquarters. He served as NASAs Open Government Representative to the White House; and the White House recognized NASA as a model for openness in government. Robbie also served on the founding team as Chief of Staff for the Office of the Chief Technologist at NASA – where he incubated $650M Space Technology Program that focused on advanced technology. Robbie received a MBA from Georgetown University, a Masters of Science Space Studies from the International Space University, and a BS in Engineering Physics from Santa Clara University and was a 2005 Presidential Management Fellow. Learn more about Robbie and Planet Labs at www.planet.com and follow him and our Twitter for updates at @schingler and @PlanetLabs.

Hands-on Workshops

Source: http://infoag.org/Program/Workshops/

Modus Standard Workshop

TBD
Dr. Scott Murrell 
 
TBDJason Ellsworth
 
TBDDr. Ignacio Ciampitti
 
TBDDave Nerpel
 
TBD
Aaron Hunt 
 
Trends in precision farming, regulations and public oversight require new focus on ag data. Testing laboratories have long supplied the industry with data for managing crops and better input utilization. Multiple formats of data transfer have emerged as well as multiple terminologies for the same analysis. Soil, tissue and nematode components are now operational. Other features are being added.
 
Modus was developed by data users, testing labs and FMIS (farm management information system) originators to standardize data terminology and transfer. It supports greater accuracy in data tracking, analysis and in crop planning.  4R focus on precise recommendations is greatly enhanced.
 
The session is designed for CCAs, precision agronomists, data users and lab personnel. Current and planned Modus Standard practitioners will conduct this workshop and discussion. Perspective will be offered by industry users and researchers.

Ask the Futurist

David Zach
David Zach
A free form give and take. David Zach will present a few topics from his opening keynote and open it up to questions and discussion. 

Managing Healthy Precision Agriculture Teams

T.J. Stauffer
T. J. Stauffer
“Dollars & Sense” Tips to Investing in and Managing Healthy Precision Agriculture Teams. The emergence of precision agriculture technology has created the need for companies to invest in new technologies and to create specialized teams of precision agriculture professionals focused on leading these efforts.  This workshop is designed to address the ‘people’ part of precision agriculture by discussing ways to structure roles, build training programs, and manage teams.  Having worked as precision agriculture specialist for both an agronomy-based company and an equipment company, T.J. will be sharing his insights into the differences between these industries.

Decision Tools using Agronomic, Climatic, and Economic Information from U2U

TBD
Jim Angel
 
TBD
Chad Hart
A hands-on workshop exploring the agricultural decision tools from U2U. The featured tools outline historical weather patterns and normals, examine the likelihood of freezes and heat stress on the growing crops, explore the relationship among climate cycles and crop production, and investigate the costs/benefits of spring fertilizer applications.

Innovation Forum

 
 
 
 
TBD
Dr. Johnny Park
This is a new program innovation for InfoAg. We would like to allow 5-10 companies give a short 10-20 minute pitch of their project idea to potential investors. 
 
Spensa Technologies
Spensa Technologies designs, develops and delivers novel technologies for the agricultural industry that use both hardware and software tools to provide precision ag insights. Our data collection sensors such as our fully automated insect trapping system ZTraps or our unique software platforms MyTraps and OpenScout allow us to collect data, analyze the data and deliver decision making tools to the modern producer to reduce labor and enhance production efficiency.
 
Travis BayerTravis Bayer
Asilomar Bio
Asilomar Bio develops crop protection chemistry to help growers deal with environmental stress in the field, one of the biggest constraints on yield. We have discovered a compound that enhances the ability of crops to access water and tolerate drought stress, leading to healthier plants and increased yields. We are developing this technology into a product that will enable growers to maintain yields and margins in challenging conditions. There is an exciting potential for using this chemistry along with integrated analysis of real-time data and precision application to sense and respond to changing weather during the growing season.
Travis BayerBen Chostner
Weed Control: Robotics and Agriculture Automation
Blue River Technology is developing smart implements that provide an alternative to the expensive and environmentally damaging practices of chemical-intensive agriculture. With breakthrough computer vision techniques for identifying weeds and selective robotics for elimination of unwanted plants, Blue River Technology’s equipment is significantly more efficient than traditional methods of weed control. Our automated solution is particularly well-suited for fields with herbicide-resistant weeds and for organic production. 
Travis Bayer
Tim Harris
Swift Navigation
High Accuracy RTK GPS has long been a mainstay in precision agriculture. Until now, RTK has been limited to the high-end applications on the farm. Through a novel approach to RTK, Swift Navigation is providing high accuracy positioning to a range of new applications in precision agriculture, and brining down the cost of autosteer for an increasingly automated farm. With low cost, high accuracy GPS, Swift is putting more precision in precision agriculture. 
Travis Bayer
Sid Gorham
Granular
Granular CEO Sid Gorham will introduce Granular, a cloud software and analytics platform dedicated to helping industry leaders build stronger and smarter farms. He will discuss how it fits in the emerging farm management software (FMS) landscape, and outline the critical factors to consider when choosing a FMS solution. 
 
Travis Bayer
Jason Tatge
Farmobile 
Detailed description coming soon.

Precision Education

Terry Brase
Terry Brase
White Paper on Status of Precision Agriculture Education
The Precision Ag Institute is working with Terry Brase on the development of a White Paper describing the current status of education in precision agriculture.  A final draft of this white paper will be presented and then discussed in open forum during this workshop. The white paper will address issues such as collaboration between colleges, cooperation with industry, certifications, and defining skills and competencies needed by graduates.  Educators and industry representatives interested in precision ag education are welcome to attend.

Precision Ag Educators Group Meeting
Educators will have the opportunity to meet after the white paper presentation for updates on college programs, discuss additional issues related to the white paper, and general networking.  Goals for a consortium of interested colleges for Precision Ag Education will also be discussed. Though this session will be focused on college educators, industry representatives are also welcome to attend.  

Tour

Source: http://infoag.org/Tour/PreconferenceTour/

Introduction

The pre-conference tour will be Monday, July 27, 2015. The tour will leave Union Station at 9:00am and return by approximately 5:00pm. The tour will visit three locations featuring a wide range of technology. Please note: these stops and times are subject to change.
 
Bus transportation, lunch, and beverages will be provided. ATTENDEE NOTE: The end of July is a very hot and humid time of the year for St. Louis.  Please dress for the weather including comfortable shoes (No open toe, sandals or flip flops) as there are uneven walking surfaces with some possible slip/trip hazards.

Stop 1: Lange-Stegmann Fertilizer Import Terminal

Lange-Stegmann Company began as a progressive supplier of commercial fertilizer in 1926, and is still breaking new ground as a service provider to the fertilizer industry. Strategically located at the northernmost port on the Mississippi River open to barge access year round, the Lange-Stegmann fertilizer import terminal is positioned for efficiency. High volume storage capacity, enables LSCO to continuously feed fertilizer product to the country’s agricultural heartland in the most cost-effective manner possible. 
 
We will also have demonstrations from several vendors of automated soil sampling equipment on the grounds of Lange-Stegmann. The group will be divided into two and each group will get to see the terminal and demonstrations in turn. 
Lange-Stegmann Fertilizer Transload Facility
Lange-Stegmann
Tour of the Fertilizer Transload Facility including main office, Dome Storage, Covered Salt Storage Piles, St. Louis Urea Center, UAN Tank Farm, River Dock and Rail Loading Tower. The tour will feature the St. Louis Urea Center where the guests will get a close up view of storage and our high speed load rail and truck load out.
 
 
Automated Soil Sampler Demonstrations
There will be a number of automated soil sampling vendors at InfoAg this year and the first stop allows us to get a short demonstration of some of these machines.
AutoProbe AutoProbe
Falcon Automated Sampling System Falcon Automated Soil Sampling
GVM AgriProbe GVM AgriProbe
Precision Technologies Precision Technologies
Dakota Fluid Power Dakota Fluid Power
 

Stop 2: Sydenstricker John Deere Dealership

Sydenstricker John Deere Dealership
Sydenstricker John Deere Sydenstricker John Deere Dealership includes ten stores in north east Missouri. Last year they welcomed the InfoAg Tour with a wonderful lunch, informative presentations, and time to learn more about Deere precision equipment with a full range of tractors, combines, and implements on display. 
  Lunch provided by John Deere
John Deere presentations Presentations: we examine one of the most discussed topics around precision agriculture today; farm operation data. How do you analyze it? How do you share it? How do you mold it into profitable decisions that can safeguard your investments for future generations? There will be presentations on the service role of the dealer as as advisor for your business, your financial goals and your unique operation.
Sydenstricker John Deere equipment on display Equipment: Learn about the latest planting technology from John Deere: ExactEmerge™ Row Unit: 10mph planting that offers accurate singulation, population, spacing, and uniform depth, built to maximize productivity and yield potential. This patented technology delivers seed to the bottom of the trench at a rearward trajectory that truly matches the ground speed of the planter at any desired population. The technology provides precise seed placement in both corn and high-rate soybeans.

Stop 3: UAV Demonstrations

Drones will be featured at our final planned stop of the day. UAV vendors will demonstrate their products at the airfield. We'll get to see several UAVs in action and may have some additional tools to show. 

The Spirits of St. Louis R/C Flying Club will once again host the InfoAg tour for a demonstration of UAVs. We will also have additional demonstrations available. 

senseFly
senseFly Ltd is a Swiss company. We develop, assemble and market autonomous mini-drones and related software solutions for civil professional applications such as accurate mapping of mining sites, quarries, forests, construction sites, crops, etc.
AgEagle AgEagle
Agribotix Agribotix
Sky IMD
Sky IMD
Sky Imaging Mapping Data makes FAA certified (STC) pods for Cessnas to image 60,000 acres per day at 70% overlap with 5 inch GSD. NIR (Near Infrared) Canon 6D or 51 MegaPixel 5DS can acquire all three NDVI bands for as little as $.04/acre.

List of Exhibitors

Source: http://infoag.org/Exhibits/Exhibitor%20Descriptions/

360 Yield Center

 

Gregg Sauder and his team of farmers, agronomists and engineers have a mission: for every farmer to reach the yield potential of modern seed corn. 360 COMMANDER – a Web-based crop modeling tool – provides you with accurate, agronomic recommendations, so you can make informed management decisions. It generates optimized, actionable seed, nitrogen and irrigation recommendations based on real-time information, and pulls real-time data from outside sources and combines it with your data to create customized recommendations for your farm.

Advanced Reconnaissance Corporation

 

Advanced Reconnaissance Corp (ARC) has developed AgVu a superior never before seen crop analysis mapping tool that shows farmers areas of their field that need attention before they become untreatable. The information obtained can help identify crops issues related to irrigation, crop health, crop maturity and even variations in crop types. AgVu crop analysis maps are updated every two weeks in an easy to view personalized cost effective means to aid farmers in improving their yields and help reduce costs. Visit our booth and see how AgVu can help improve yields and help reduce costs. For more information call the Corporate Office at: (845) 896-0880 or visit our web site at: www.advrecon.com ARC provides solutions and services to agriculture, commercial and military customers through its offices in Fishkill, New York, and Annapolis, Maryland.

Ag Leader Technology

http://www.agleader.com

At the very beginning of Precision Farming, Ag Leader Technology was there. Starting in 1992, the Precision Farming innovations have continued year after year setting industry standards. The pioneering spirit to "find a better way" has kept Ag Leader in the forefront of the Precision Farming revolution. Stop by the Ag Leader booth to be introduced to the latest innovations in precision farming.

Ag Renaissance Software LLC

 
AgRenaissance Software LLC develops software solutions for the agricultural industry. Our newest offering is FieldX,™ which is a record-keeping tool for agricultural producers, agronomists, and applicators. The product includes an easy-to-use cloud-based and mobile system to record and synchronize data across your organization. FieldX is built upon FieldRecon® Technology.

Ag World

 
No vaporware. No empty promises. No gimmicks. Farming, Agronomy and Precision Ag are complicated enough, which is why Agworld has created farming software built for the field. Our main goal is to make farm management a whole lot easier to save you time. Our leading cloud-based platform provides a secure place for farmers, consultants, contractors, processors, labs, and industry groups to consolidate business and industry-wide information. Take notes and photos, write recommendations, create work orders, manage documents, manage maps and imagery, access more than just labels from the extensive built-in library and manage your workflow and precision. Visit our booth and check out what we?re doing for Precision Ag, plus our new Apple Watch app.

Ag-Tester

 
“Solutions for the Ag-Technology Specialist” Much of the frustration felt by field technicians working in agriculture is caused by the fact, there have never been tools to allow them to properly diagnose problems with all types of ag-electronics they encounter and are expected to fix. The industry calls it “swap-tronics”. Change the parts until it works! Allow your technical people the tools they need to get the job done in as little time as possible and at the lowest possible cost. Eliminate false parts replacement. Stop wasted time! We are building test equipment to address the needs of these field technicians. The designs are based on my 40 years plus as a field tech working in agriculture. Our Testers are complete in functionality yet easy to use. Visit agtester.com for all the details! Additionally for manufacturers; we build custom field test equipment to fit your requirements! Check out our web site agtester.com! Videos will show you the product line in action! http://www.agtester.com/#overviewvid is the link to the first video. Others in the “products” page! See you at Info-Ag 2015! John Dignan- Ag-Tester: http://agtester.com

AGCO

 
FuseTM Technologies: Connecting your farm enterprise like never before. Fuse is AGCO's next-generation approach to precision agriculture that connects the entire crop cycle from enterprise planning to planting, crop care, harvesting and grain storage – providing mixed-fleet farming operations improved access to their farm data to make more informed business decisions, resulting in enhanced productivity and profitability. · Connecting the mixed fleet · Respecting your data choices · Maximum productivity with mobile functionality · Pioneering the open approach to precision farming

AgEagle

 
AgEagle robotic aircraft helps growers increase profits by pinpointing areas where nutrients or chemicals need to be applied versus where they don?t need to be applied thus decreasing input costs and increasing yields. We build the robotic aircraft imaging systems and offer them for purchase to farmers, agronomists or other precision agricultural professionals. Users create georeferenced aerial images of fields to aid in quick, accurate and complete ground truthing. The AgEagle precision agriculture photography system is designed for agricultural professionals to provide a complete aerial view of their crops to help precisely identify crop health and field conditions much faster than any other method available. The AgEagle system is built from the ground up specifically for agricultural use. It is designed for daily rugged use. Stop by and learn how the AgEagle system can help increase yields on your farm.

AgGateway

 
AgGateway is a non-profit association with the mission to promote, enable and expand eBusiness in agriculture. We currently have more than 220 member companies working on projects in Precision Ag, Ag Retail, Crop Nutrition, Crop Protection, Feed and Grain, Seed, and Allied Providers (systems & software developers and service providers). Come by and find out more!

Agrian, Inc.

 
Founded in 2004, Agrian Inc. provides a comprehensive and widely adopted suite of Software-as-a-Service, mobile phone and tablet-based applications to all levels of the Agrifood supply chain. The Agrian platform provides the Agricultural industry with an easy-to-use and easy-to- access data capture, storing, and sharing service. Agrian currently provides agronomy, recordkeeping, and reporting services on over 18 million acres of production agriculture throughout the United States.

Agribotix

 
Agribotix is a drone-enabled software company that provides advanced imaging and analysis for precision agriculture. Agribotix provides a turn-key solution: ultra-reliable drone platforms coupled with cloud-based data analysis that results in affordable and timely images and maps of your fields. Customers rely on Agribotix to provide actionable intelligence that results in reduced inputs, improved yield and increased profits.

Agricultural Retailers Association

 
The Agricultural Retailers Association (ARA) offers programs and services designed to keep agricultural retailers on top of critical issues that impact profitability. ARA is a 501(c)(6) non-profit trade association that represents the interests of agricultural retailers and distributors across the United States on legislative and regulatory issues. As the political voice for agricultural retailers and distributors, ARA advocates on critical issues, educates legislators and collaborates with regulatory officials on important issues affecting the industry.

AgriNews

 
The Illinois AgriNews and Indiana AgriNews editorial staff is in the field each week, covering topics that affect local farm families and their businesses. Some of the topics the staff reports on include crop and livestock management, agribusiness and new products, market information and national and state political issues. Their work earned the prestigious Meritorious Service in Communication Award from the American Society of Farm Managers and Rural Appraisers in 2007. With field editors working throughout Illinois and Indiana, AgriNews readers receive news tailored to their farm areas every week. Local producers also share their stories each week on the newspaper?s From the Fields and From the Barns pages. Rotating each week, weekly special sections include PorkNews, BeefNews, Farm Family Life and MoneyNews. The newspaper also publishes more than 20 special sections during the year. Since its inception, AgriNews? goal has remained the same. It still is focused on reporting events — both big and small — that impact the lives of farm families.

AgSource

 
AgSource Laboratories is an industry leader in broad-based agronomic testing and management information services. With five locations throughout the Midwest and the Northwest, our laboratories serve a diverse client base across all 50 states and in nearly 40 different countries. For over 50 years, AgSource has worked to meet the needs of our clients. We offer comprehensive services, including soil sampling, plant tissue analysis, VRT files and GPS mapping. Visit AgSource at booth #15 to discuss how we can meet your unique needs.

AgSync, Inc.

 
AgSync, Inc. is a web-based business management system that accurately and efficiently enables applicators to break through the barriers in communication, organization, operations, and limited resources. The company?s mission is to design products that utilize the latest technology and integrate with strategic partners to streamline the crop protection and fertilizer application process. AgSync Ground is an extension of the web-based aerial application solution. Growers, Retail, and 3rd party applicators can utilize AgSync Ground to seamlessly manage work-orders from recommendations to application to record keeping internally and between multiple partners or outlets.

AgWorks

 
AgWorks new AgOS® is a unified set of tools that allow you to effectively manage data in one place and drives efficiency for retailers and their customers.

AutoProbe

 
In Agriculture all things begin with one thing, the soil. That is still true; however, there is a caveat that changes everything: knowledge about the soil. The ability to analyze soil composition is nothing new – what isnew is the AutoProbe. Every other method of gathering soil, in order to have the best and most complete understanding of the soil, is obsolete. Simply put, there is nothing like the AutoProbe but the AutoProbe. After nine years of testing and proof in the field, there is no reason that the AutoProbe cannot pulling fifty cores per minute – that?s pulling a soil core every 8.5 feet while cruising at five miles per hour. The AutoProbe is capable of sampling a 2.5 acre grid on the go, delivering 40 cores in 45 seconds into the tractor cab without stopping. That is 3X faster than manual core sampling and 10X more accurate; AND IT?S REPEATABLE YEAR TO YEAR! In the coming years soil is not only the Alpha; it is the Omega. Knowing how to be a good steward of the soil has always been in the interest of growers. But the future of good stewardship hinges on precision and accuracy. There is not another machine on the market that can compete with the PATENTED AutoProbe: the most intensive, accurate, and repeatable soil-sampling machine on earth. It is capable of pulling consistent 6” cores every 8.5 feet inside grids, zones or any other sampling scheme imaginable. FOR DATA INTEGRITRY, RESULTS AND RESPONSIBLE STEWARDSHIP, THERE IS ONLY ONE MACHINE: AUTOPROBE. You can see the AutoProbe in action at http://www.autoprobe.ag

Ayrstone Productivity LLC

 
Ayrstone Productivity is the company behind the AyrMesh line of Wireless Farm Networking products. AyrMesh products allow any grower to quickly, easily, and inexpensively build a private wireless network that stretches for miles and works better and cheaper than cellular connections. The network uses standard WiFi, so it can be used for Internet access, automatic data collection, or remote control of farm machinery. See us at http://ayrstone.com

BlackBridge

 
 
BlackBridge is focused on providing end to end solutions across the geospatial value chain. This includes satellite operations, ground station services, data center and geocloud solutions, and worldwide satellite imagery distribution through over 100 BlackBridge partners, combined with the creation of value added products and services.

Capstan Ag Systems Inc.

 
Capstan Ag Systems, a technology based company, specializes in creating new proprietary systems for the agricultural industry, particularly with chemical and fertilizer applications. Headquartered in Topeka, KS, Capstan Ag?s inventive process involves research, engineering design, and lab and field testing often working with other industry experts. Capstan Ag maintains strong ties to university agricultural departments across North America.

Case IH

 
Crop Data Management Systems (CDMS) provides crop consultants, retailers, applicators and producers with a complete agronomic solution for managing all of their crop production data. Using a web platform, ADVISOR is the industry?s only complete solution providing mapping, sampling, fertility, planting, scouting, crop protection, irrigation and harvesting data layers for analysis and decision making. Robust reporting, complete precision ag connectivity and data management options enable information sharing between producers, their service providers and vertically to commodity and end use purchasers.

Crop IMS

 
Crop IMS was formed by a group of five investors with a vision to create an independent, third-party resource that growers could turn to for accurate, unbiased crop production information. As the company grew, we also gained purchasing power and a voice in the precision ag industry. Not only can we secure more attractive equipment prices, but manufacturers now integrate our field observations into the products they develop. Because we?re not connected to a retail organization, we don?t approach our customers with an agenda. Our goal is to objectively analyze your situation and make recommendations that best fit your needs. We will evaluate your existing technology, discuss your production goals, and recommend any additional components needed to maximize the effectiveness of your current system, if necessary. Though we aren?t affiliated with any single manufacturer, we do sell the finest technology and equipment from industry leaders such as Ag Leader®, Topcon Precision Ag, Juniper Systems, SST, RAM Mounting Systems, and Ag Cam. We?re also very comfortable with systems from John Deere, Case IH, and Agco. With support staff located from Wisconsin to Kentucky, Crop IMS is ready to serve producers throughout the Corn Belt.

CropMetrics, LLC

 
CropMetrics is a precision agriculture company focused on advanced agronomic solutions while specializing in precision irrigation management. Our mission is to develop and supply precision management technology solutions that increase water, nutrient and energy use efficiency while fostering natural resource conservation. CropMetrics is a leader in Precision Irrigation Management with the introduction of the first commercially available full-service Variable Rate Irrigation program. CropMetrics continues it's pursuit in innovation and strives to deliver ultimate value to the grower customer. The seamless integration of these unique offerings provides the framework for large-scale, data-driven, precision agronomic services that are readily marketed and supported by professional trained Precision Data Specialists.

Decisive Farming

 

DTN/The Progressive Farmer

 
 
EFC Precision Agronomy solution is a web-based, flexible, data management system designed to make precision agriculture as simple and profitable as possible. The easy to use platform allows you to take control of your data and farm operation by supplying you with advanced data analysis and decision making, while providing the user access critical information from anywhere with an internet connection. EFC Precision Agronomy works in conjunction with Merchant Ag. Merchant Ag and Precision Agronomy provides users a complete agronomy solution handling both Data Analysis, Management, Point of Sale, and Accounting software solutions for Agribusinesses.

Encirca services by DuPont Pioneer

 
DuPont Pioneer is the world's leading developer and supplier of advanced plant genetics, providing high-quality seeds to farmers in more than 90 countries. Pioneer provides agronomic support and services to help increase farmer productivity and profitability and strives to develop sustainable agricultural systems for people everywhere. Science with Service Delivering SuccessTM.

ESRI

 
Before InfoAg 2015, attend the Esri Mid-West Agriculture User Group Meeting to learn about the latest precision agriculture solutions on the ArcGIS platform. We'll discuss how to increase yields and revenue with advanced modeling tools for ArcGIS and how you can leverage location to improve crop health, farm management, and benchmarking. The meeting is free to attend and is open to all with registration. Register at http://www.esri.com/events/midwestaguser-2015 Visit us at booth numbers 71 and 72 at the 2015 InfoAg Conference, or email the Esri Agriculture Team to schedule a meeting during the Conference.

Falcon Automated Soil Sampling

 
With precision farming technology advancing fast, detailed field information is vital to placing fertilizer, seed and other inputs precisely where they're needed. That's why Monroe, N.C., farmer Allan Baucom custom-engineered the Falcon automated soil sampling system: To help commercial soil sampling operators and other farm input providers quickly collect high-quality samples with unsurpassed precision, consistency and efficiency. Baucom developed the Falcon because soil sampling is still "shovel work" in a fast-paced world of highly automated, satellite-guided precision farming equipment. The Falcon ends the days of time-consuming, labor-intensive, inconsistent and tedious sample collection. Low-maintenance and easy to use, the Falcon delivers high-quality, homogenous soil samples with unparalleled efficiency. Coupled with GPS tracking and barcode sample identification, Falcon technology delivers better samples, better analysis, smarter input decisions, bigger harvests, and increased customer satisfaction. It can support virtually any cropping system throughout the United States. It?s built "farm tough” in the U.S.A. to commercial-grade standards, for maximum reliability and minimum maintenance. http://www.FalconSoil.com

Farmers Edge

 
Farmers Edge™ is a global leader in precision agriculture and independent data management solutions. Leading the development and application of new technologies on the farm, Farmers Edge™ is defining the future of agriculture through innovation.

FarmLink

 
As an independent company headquartered in Kansas City, Missouri, FarmLink offers unique services to help farmers maximize their resources through targeted investment. Through its TrueHarvest yield benchmarking service, FarmLink brings to farming the science of benchmarking to measure the impact of inputs and decisions, helping to increase productivity and profit. Through MachineryLink's fleet of rental combines, farmers access the latest technology without the expense, maintenance and time costs of combine ownership. And that's just today. FarmLink's innovation pipeline identifies and creates ongoing opportunities for farmers to use technology and data to better assess and invest in their operations, for today's needs and tomorrow's potential.

FarMobile

 
Farmobile was founded in the fall of 2013 with the strong spirit of Midwest pragmatism and a huge vision: To simplify data collection from machines to decisions. Delivering the simplest way for farmers to get their data in one place was crucial. We knew most farmers already worked with other parties to analyze their data. So, data analysis could be left to others. Farmobile put together the business model, technology, and team to make our vision a reality.

Fialab Instruments, Inc.

 
FIAlab Instruments Inc. was founded in 1987 and is a leading manufacturer and distributor of Flow and Sequential Injection Analysis systems and components. We offer a variety of automated systems for use in process/industrial, laboratory, field, and educational environments. Our systems are utilized for diverse applications such as on-line process monitoring, sensor development, routine agricultural and environmental assays.

FirstWater Ag

 
At FirstWater Ag, we believe the world will continue to seek new solutions and technology for our water resources. Customized irrigation systems to manage water that not only give us greater control and precision in the application of water but also guidance on how and when to time those applications. Our goal in this process is to ultimately provide products and services to waters users that brings value to their operations. Today, FirstWater Ag has a primary focus in agricultural water use and crop production environments. We are always looking for new and innovative answers to the challenges growers face as well as ways to enhance and build upon current solutions. In our approach we seek to place a high value on relationships with customers and partners in finding ways to work together in managing water more efficiently. FirstWater Ag was incorporated in 2013 and is based in Atwood, KS. In forming the company, we brought together a small group of founders, including Rick Heard. Rick has been working in agricultural electronics his entire life and been involved with variable rate irrigation systems for over ten years. Together we are continuing on with that same work and are looking forward to incorporating additional solutions in working with water.

Geonics, Ltd.

 
Geophysical instrumentation for exploration and the environment. Leaders in electromagnetics.

Geosys, Inc.

 
The GEOSYS mission is to help agribusinesses, agricultural professionals and farmers make better decisions using the latest research in agronomic, information and geographic technologies. With over 25 years in business, 50 full time employees and with projects in more than 50 countries, GEOSYS is the world leader for agricultural information and decision support tools based on remote sensing.

GeoVantage

 
GeoVantage applies today's advanced technology to a revolutionary aerial imaging approach. This approach provides on-demand land asset information more quickly and cost-effectively than ever before. State-of-the-art sensors composed of integrated GPS, inertial measurement units and 3 or 4-band digital cameras, enable rapid response and worldwide geographic coverage – we measure turn-around times in days, not months.

GVM

 
GVM Inc. is proud to part of an industry that is vital to the sustainability of our nation. We hold a great amount of respect for our suppliers and even more for the many hard-working Americans that provide our food, shelter and fuels. We value our suppliers and customers as partners and we are committed to their success. We believe in honesty and transparency; we want to be a company you can trust. We believe that what we say matters, and we stand behind the promises that we make. We work together, we work as a team, and we respect each other. We believe that every member of the team plays a vital role in our success. Our biggest asset and competitive advantage is our people, and our success is because of our people. Our people care; they care about our business and your business and about personal excellence in everything they do. We find our success not in managers, but in leaders. We rely on everyone within our organization to be a leader. We believe that leaders initiate action, motivation and confidence, while building morale. Leaders understand the importance of our other core values, including mutual respect, honesty and people/teamwork.

Hexagon Geospatial

 
Hexagon Geospatial helps you make sense of the dynamically changing world. Look to our Agricultural solutions for complete end to end handling and processing of UAV-collected imagery. We offer photogrammetric processing of the raw imagery, as well as Remote Sensing tools for getting valuable crop information from the pixels themselves. Once the data is collected, our data cataloging solution helps you manage your data and get it to the users that need it. Come take a look at Erdas Imagine, Imagine-Photogrammetry, Image Station, ECW and Erdas Apollo. Hexagon Geospatial is a division of Intergraph Corporation.

I.F.A.R.M.

 
United Soils Inc. (est. 1993) is a soil testing lab that specializes in analysis of soils, plant tissue, site specific precision agriculture and variable rate technology via the use of i-F.A.R.M (our proprietary web based software program that utilizes soil test results in conjunction with GPS Yield Data to prepare variable rate application maps/files to spread plant nutrients in the proper amounts, in the proper field locations to efficiently and economically maximize crop yields). Users of i-FARM can access all their data, run reports, even create prescription files right from their smart phone cell phone, tablet, or any internet connected computer.

iCrop Trak

 
iCropTrak brings iPads/iPhones and the Amazon cloud to Farm and Field Management. iCropTrak allows for collaboration and coordination of distributed teams; each user unique application configuration based on roles; central data and user management; and user defined scouting, sampling, and collection forms.

Insero

 
Insero builds simple controllers that can connect directly to your iPhone or iPad. You can log data, monitor speed, acres and volume, or even control your flow rate - everything directed by the user from an Apple device. No data charges or subscription fees necessary. Insero's Controllers are driven by a combination of GPS/GNSS, inertial systems and ground speed sensors. All information is recorded on the controller and can be moved to an Apple device or to the cloud - on demand. No Apple device is necessary for the controller to function. It can be setup and then left to operate as a stand-alone device. Insero is the creator of AgOtter, a flow monitor, rate control and data recording system designed for Orchard Spraying. A thick canopy of trees can block GPS satellites but it will not stop the AgOtter from functioning fully. Logging, monitoring and rate control will remain uninterrupted regardless of GPS status.

International Plant Nutrition Institute

 
The International Plant Nutrition Institute (IPNI) is a new, not-for-profit, scientific organization dedicated to responsible management of plant nutrients — N, P, K, secondary nutrients, and micronutrients — for the benefit of the human family. With established programs in Latin America, North America, China, India, Southeast Asia, and planned expansion in other areas of the world, IPNI is a global organization ready to respond to the worldâ?™s demand for food, fuel, feed, and fiber.

IRROMETER Company, Inc.

 
From the early 1950s, when Shel Pooley cruised the dusty farm roads of California and Arizona in a Nash Rambler stocked with IRROMETERs, to the computer equipped irrigation manager of today, both IRROMETER and WATERMARK sensors are standard management tools being used to Optimize Irrigation, Worldwide. Ask us today how we can help you join this ever growing irrigation management team and help make irrigation a more sustainable and profitable practice.

Iteris, Inc.

 
Iteris is a leader in providing weather information solutions. Our innovative precision ag decision-support system, ClearAg(tm), leverages a data delivery platform to enable agribusinesses to ingest specific, actionable, high-resolution weather, crop and soil information for data-driven farming. Iteris' ClearAg(tm) API platform takes field-specific weather, combines it with the science of soil and agronomy to deliver the right information, for the right field, at the right time for the right decisions.

John Deere

 
John Deere FarmSight™ offers growers technology products and services that bring even greater power to their equipment, backed by local dealer support. This includes the MyJohnDeere Operations Center—an online resource where growers can manage and share information about their operations, improving their ability to farm. The Operations Center becomes even stronger as third-party developers contribute to the platform, creating more possibilities for customers to analyze and draw insights from their data.

MapShots, Inc.

 
MapShots is a leading developer of the renowned AgStudio product line used by growers and service providers across North America. Growers take advantage of the power and flexibility of AgStudio Farm which provides data they want in the formats they need to manage agronomic data effectively and efficiently to make the right business decisions. Crop Service Providers, such as consultants and crop input retailers, rely on the customization capabilities of AgStudio Pro to provide crop planning and nutrient management services to their farm customers. Stop by our booth and ask about our new products AgStudio Select, AgStudio Notes and AgStudio MAP and learn how our suite of products we can make your business more productive.

Mavrx

 
Mavrx is an imagery-based analytics platform offering a complete smart farming solution. With our network of UAVs, aircraft, and satellites, Mavrx provides total field awareness by capturing images at the right time and resolution, processing and layering image data, and delivering automated alerts and in-field tools to growers and agronomists for seamless crop management. We enable growers to maximize yields and reduce costs by detecting and predicting changes in crop and soil conditions—before they become problems. Headquartered in San Francisco, Mavrx currently operates in the U.S. and internationally. http://www.mavrx.co

MicaSense Inc.

 
MicaSense is a developer of precision sensors and data solutions designed specifically for agricultural remote sensing applications on unmanned aircraft (drones). Our scientific-grade hardware and advanced analytics provide new tools for crop managers and agronomists to make critical decisions to maximize the health and yield of their crops. MicaSense offers RedEdge™, a rugged yet lightweight multispectral camera that offers agricultural professionals the highest quality data on their crops. MicaSense has launched ATLAS™, an imagery processing, analytics, and management platform to work with RedEdge, providing a complete solution from image capture to data outputs and online tools for deeper insights and analysis of multispectral imagery.

Midwest Laboratories

 
Midwest Laboratories is a leader in the area of information technology. Midwest Laboratories works with companies across different industries with respect to sending and receiving laboratory information. From smart phones to networks, the Midwest Laboratories staff are always working with the latest technology vendors to insure that data can be communicated and sent in a number of different formats to insure proper data quality. Please stop by our booth and learn more about the capabilities that can assist you and your clients.

Mixmate

 
Mixmate is a mobile chemical mixing system designed for automated batch blending to increase the accuracy and efficiency of chemical application in the field. This innovative system bridges the gap between accounting and spacial records with the automated record keeping of each ingredient in every batch. With the ability to record the exact amount of every product used in a batch with superior accuracy, as well as mix location with time and environmental conditions during application, Mixmate's technology will take precision agriculture operations to another level.

My AgCentral

 
MyAgCentral delivers web-based tools to enhance and simplify the delivery of precision ag services supporting data from multiple vendors in real-time from a single dashboard. Our platform provides a revolutionary online bridge that connects service providers, consultants, and agri-retailers to their producer customers and the cutting edge precision services they demand. MyAgCentral is the center of a modern producer?s agricultural universe where growers control their own data and can share information seamlessly with their chosen precision ag team.

MyWay RTK

 
MyWay RTK LLC was established by a group of agriculture industry partners with a mission to engineer an open-technology RTK network for the specific guidance needs of growers using precision farming technologies in their operations. “MyWay RTK was developed by a group of farmers, for farmers. We saw the need to grow an RTK network that empowers farmers and their service providers who want to help build out this RTK system at multiple levels,” explained Andy Hill, manager for MyWay RTK. "The very reason this service exists is to provide open access to RTK for all brands of equipment and hardware, as well as provide a dedicated service team that understands agriculture and the needs of growers using precision technology today?and in the future.” – Andy Hill, Manager, MyWay RTK.

New Holland Agriculture

 
New Holland Agriculture and New Holland Construction sell and service an innovative line of agricultural and construction equipment, including a full line of tractors, hay and forage equipment, harvesting, crop production, skid steer and compact track loaders, compact wheel loaders, tractor loader backhoes and mini excavators. Sales, parts and service are provided by more than 1,000 New Holland dealers throughout North America. More information on New Holland can be found at http://www.newholland.com/na.

NORAC

 
With over fifteen years of experience, NORAC is a pioneer with ultrasonic technology in agriculture. NORAC offers three Spray Height Control systems that can be installed on most sprayer models; the UC4+™ and UC4.5™ come complete with a stand-alone control panel and the UC5™ is an ISOBUS system that can be operated through a variety of existing control panels. NORAC?s Spray Height Control systems use ultrasonic sensors mounted on the left, right and center sections of the boom to monitor field terrain and make responsive boom height adjustments. With NORAC Spray Height Control systems, the boom automatically follows the contours of the land and maintains a preset height above the ground or the top of the crop allowing more effective chemical use and stress free spraying for the operator. Hybrid Mode™, the new advanced crop sensing feature for in-crop spraying, eliminates the need for the operator to take manual control of the boom while spraying in row crops or adverse situations such as lodged, thin and uneven crop. Together with Soil Mode™ and Crop Mode™, NORAC provides the boom stability and spray height accuracy you can trust in all field conditions. Guaranteed.

NovAtel Inc.

 
NovAtel's complete product line is developed to meet a wide range of accuracy and cost requirements. Providing exceptional positioning performance with leading edge technology, NovAtel receivers are known for their low power consumption and comprehensive message suites for configuration and data logging. Our product line also extends to sophisticated reference receivers which we supply to the national aviation ground networks of USA, Japan, Europe, China and India. By searching for true innovation in RF (Radio Frequency) and digital design, signal processing, and embedded software, NovAtel continues to build its technology and product portfolio thereby strengthening its position as a true innovator and technology leader in precise positioning.

PAQ Interactive

 
PAQ Interactive is a technology services company specializing in Web and GIS solutions. Our web design and development work focuses on the web as a communication tool. More than static pages, we assist our clients through the design of interactive, content rich, fully customizable web sites that allow our clients to use their web technology investment for internal and external communications. In GIS services, PAQ consults with clients in creating and maintaining a business-level GIS. We provide training, custom programming, data analysis, and GIS project management solutions to create or expand on our clients initial investment in GIS. PAQ helps leverage your technology investment to increase productivity and maximize the benefits technology can bring.

Planet Labs

 

Precision Technologies

 
 
The Wintex1000 is the optimum solution for collecting soil samples. Very fast and effective you will be amazed at how much your productivity, soil core quality, consistency and operator fatigue will be improved!

PrecisionAg Institute

 
The PrecisionAg Institute was developed in 2006 as an independent global forum dedicated to the sharing of precision agriculture practices, ideas, research, products, services and success stories.

Through advocacy, education and research, the Institute intends to advance precision technology and its efficiency, stewardship and profitability on farms around the world through a variety of integrated media offerings:

· Grower research 
· www.precisionag.com/institute 
· PrecisionAg eNews 
· PrecisionAg Award of Excellence 
· PrecisionAg Live Forums and Events 
· Educational promotion

The Institute is managed by the Agribusiness Group of Meister Media Worldwide. The staff has been instrumental in knowledge transfer to the benefit of precision agriculture even as the practice began to be formalized in the early 1990s. First through Farm Chemicals and Dealer PROGRESS magazines, then through PrecisionAg.com, the PrecisionAg Buyer's Guide, PrecisionAg Special Reports, CropLife and other outlets, the group has been a steady advocate of the technology and its benefits to growers and practitioners.

Raven Industries

 
Since 1978, Raven has helped define the concept of precision agriculture—and continues to create ground-breaking ideas and products that improve the position of growers around the world. Raven is publicly traded on NASDAQ (RAVN) and has earned an international reputation for agricultural innovation, product reliability and unmatched service and support. From field computers to boom controls, from GPS guidance to steering systems, Raven is leading the way for precision ag technology that delivers in the field. In partnership with our network of Raven dealers and distributors, we?re helping growers of all sizes and all types work smarter, reduce input costs and increase yields and profitability. Success in farming has always been about using less and getting more. With Raven, you can simply do it better.

Satshot

 
Since 1996, Satshot Technologies has been a leader in bringing remote sensing to the ag industry. Satshot.com, its dynamic internet based imagery delivery and analysis system allows agriculture users to access satellite and aerial imagery of farm fields across North America and Australia.

ScoutPro

 
ScoutPro brings you the latest in accurate, efficient, identification of weeds, insects, diseases, and disorders. Use our drill down identification, or quickly sort favorites and searches within our applications for corn, soybeans, and wheat. Record your path through a field via GPS pins while adding stand counts, performing yield checks, and even recording irrigation activities. Our applications collect the data, you manage the data through the ScoutPro webservice for a complete scouting program. Flexible for every type of scout and every scouting program, our software is designed to provide you and your clients consistent, field specific, scouting data.

senseFly

 
SenseFly is a Swiss company that designs ultra-light and fully autonomous flying devices equipped with a camera (also called UAVs) and related software solution for flight planning and flight monitoring. The RGB or NIR (Near Infra Red) pictures taken during a mission are then processed with the included processing software in order to create geo-referenced 2D maps or 3D elevation models (precision up to 2/4 inches). These maps can be used as an input to display information and even calculate relevant indicators, like the NDVI. Because of it?s simplicity of use and capability to fly up to 2 500 acres in one single flight, this technology could change the way farmers work!

SGS North America

 
SGS is the world?s leading inspection, verification, testing and certification company. We are recognized as the global benchmark for quality and integrity. With more than 80,000 employees, we operate a network of more than 1,650 offices and laboratories around the world.

Our core services can be divided into four categories:

Inspection: our comprehensive range of world-leading inspection and verification services, such as checking the condition and weight of traded goods at transshipment, help you to control quantity and quality, and meet all relevant regulatory requirements across different regions and markets 
Testing: our global network of testing facilities, staffed by knowledgeable and experienced personnel, enable you to reduce risks, shorten time to market and test the quality, safety and performance of your products against relevant health, safety and regulatory standards 
Certification: we enable you to demonstrate that your products, processes, systems or services are compliant with either national or international standards and regulations or customer defined standards, through certification 
Verification: we ensure that products and services comply with global standards and local regulations. Combining global coverage with local knowledge, unrivalled experience and expertise in virtually every industry, SGS covers the entire supply chain from raw materials to final consumption.

We are constantly looking beyond customers? and society?s expectations in order to deliver market leading services wherever they are needed. As the leader in providing specialized business solutions that improve quality, safety and productivity and reduce risk, we help customers navigate an increasingly regulated world. Our independent services add significant value to our customers? operations and ensure business sustainability.

Sky Imaging Mapping Data

 
SkyFusion Pak is the most cost-effective and advanced turnkey solution for aerial intelligence in a broad range of applications. Combining ease of use with sophisticated features, SkyFusion Pak is a breakthrough in value and functionality. The SkyFusion Pak system includes state-of-the-art equipment to collect and view surveillance information, communication to the ground, flight-management software to control and process the data, and responsive customer service and support. For the first time, government agencies and companies can afford an inclusive system solution for their airborne surveillance needs. The SkyFusion Pak system is approved for installation on over 30 aircraft in over 30 countries. SkyIMD will tailor SkyFusion Pak to your specific needs, including custom solutions such as integrating with your communication system or existing airborne cameras, or unique installation mounts.

Software Solutions Integrated, LLC

 
Agvance is an all-encompassing, completely open, agribusiness platform that enables today?s ag professional to connect with customers, employees, trading partners and the next generation of devices and apps. Designed for the diversified needs of an agricultural cooperative, Agvance provides seamless integration throughout all facets of the agronomy, grain and energy operations within a single financial package.

SOILMAP

 
SOILMAP Desktop is a web application designed to process the data and information gathered through soil sampling of your fields. The software is then used by the SOILMAP team and your agronomists to determine fertilizer recommendations. Additionally, SOILMAP software allows you to plan (chemicals, fertilizers, seed), create cost comparisons, acquire history reports and determine blendsheets. Other capabilities include soil-type books, seed population maps and farm maps. We are currently integrated with a number of accounting programs to facilitate greater accuracy and efficiency with the accounting program you utilize. We are also integrated with Murray and Kahler automated blenders. With SOILMAP software, you?re able to gain valuable insight into the needs and capabilities of your fields that will, in turn, assist you in making the best possible management decisions in the effort to maximize your return on investment.

SOYL

 
SOYL have been providing PrecisionAg services to growers since 1993. Our latest development is iSOYL – a variable rate application controller. This iOS app, free to download uses your cloud stored application files to control your fertiliser spreader, planter, or sprayer. iSOYL works with a wide variety of hardware devices, allows in field observations, and faster, easier, more accurate record keeping with the as applied file write back.

Spectrum Technologies

 
Spectrum Technologies manufactures and distributes affordable, leading-edge measurement information technology to the agricultural market throughout the world. Founded in 1987 by Mike Thurow, Spectrum is headquartered in Aurora, IL.
Spensa Technologies
Spensa Technologies designs, develops and delivers novel technologies for the agricultural industry that use both hardware and software tools to provide precision ag insights. Our data collection sensors such as our fully automated insect trapping system ZTraps or our unique software platforms MyTraps and OpenScout allow us to collect data, analyze the data and deliver decision making tools to the modern producer to reduce labor and enhance production efficiency.

SST Software

 
SST has developed an agricultural information management system that helps growers maximize farm management, efficiency, and yield. SST's system equips agriculturalists at all levels with software and services suited to their businesses, whether they are a thousand-acre soybean farmer in Iowa, a seed company doing corn trials in Hawaii, a fertilizer dealer in Brazil, a crop consultant in Mississippi, or one of the world's largest cotton processors, headquartered in Australia.

TapLogic, LLC

 
Soil Test Pro SP is a Management Tool Designed for the Agricultural Service Provider and Retailer Manage your soil sampling operation in the office on your Web Headquarters and in the field on your Mobile App. Pull GPS referenced soil samples, choose a lab from our recommended list, then ship your samples. Get Lab results fast (5-7 days). Share results with your clients and build unlimited recommendations & controller files. It's also Raven Slingshot ready. Other key features of Soil Test Pro SP include: Create work orders for your employees and the Navigate to Field function which gets you to the right location every time. 1-866-761-8001

TerraGo

 
From sharing feature-rich maps and imagery to deploying on-demand apps for a mobile workforce, we build products that enable collaboration from any place on the planet.TerraGo invented the industry's most widely adopted geospatial collaboration technology with its GeoPDF products and is revolutionizing field data collection with TerraGo Edge mobile platform.

The Climate Corporation

 
The Climate Corporation's mission is to help all the world's people and businesses manage and adapt to climate change. The Climate Corporation aims to protect the $3 trillion global agriculture industry from the financial impact of adverse weather—the cause of over 90% of crop loss—with automated, full-season weather insurance.

The Fertilizer Institute

 
The Fertilizer Institute (TFI) represents the nation?s fertilizer industry including producers, wholesalers, retailers and trading firms. 4R Nutrient Stewardship (right source at the right rate, the right time, and in the right place) is a key initiative for TFI and our industry partners. Visit the TFI booth to interact with growers and retailers implementing site specific nutrient management on the farm using precision technologies, learn about available information resources for your employees and clients, and learn about an industry effort to increase our understanding how precision application reduces the impact of nutrients on the environment.

Topcon Precision Agriculture

 
Topcon Precision Agriculture's goal is to serve agribusinesses, professional farmers, and agricultural contractors with easy-to-use precision farming solutions. Topcon precision farming products employ innovative technology that creates superior value through improved operational efficiency and input cost reduction. The Topcon product portfolio includes advanced technology products for guidance, autosteering, GNSS positioning, variable rate control, auto section control, electric steering, on-the-go crop canopy sensing, water management, and farm data management. Topcon offers powerful precision made easy.

Trimble

 
Trimble's Agriculture Division is a leader in precision agriculture, GPS and guidance solutions that help customers operate farm vehicles and implements more efficiently, save on input costs and increase yield and productivity. To ensure better decision making, Trimble offers an integrated operations management solution called Connected Farm that provides information exchange across the entire farm using industry-leading software and hardware. Additional Trimble solutions include vehicle and implement guidance and steering; application control for seed, liquid and granular products; laser- and GPS-based water management technology; and a harvest solution. For more information on Trimble Agriculture, visit: www.trimble.com/agriculture.

Valley Irrigation

 
Valley® Irrigation is the industry leader in mechanized irrigation equipment – center pivots and linears. Valley irrigation equipment irrigates approximately 25 million acres globally, while conserving water, saving time, reducing costs and increasing yields.

Veris Technologies

 
Veris Technologies produces innovative soil sensors that measure soil texture, organic matter, and pH on-the-go. At InfoAg 2014, Veris will showcase its new data fusion technology which combines Soil EC, OM, and pH data—generating maps for variable rate seeding, irrigation, nitrogen, and other inputs.

Winfield

 
Winfield Solutions, a Land O?Lakes company, offers top-performing seed and crop protection products. With unmatched agricultural expertise and performance as core company values, WinField brands match leading seed and crop protection products to help farmers, retailers and other industry partners achieve high levels of success. WinField brands include CROPLAN® seed, crop protection products, plant nutrients and other proprietary products. Backed with unrivaled technical services and knowledge, WinField™ products deliver outstanding solutions and value for the agricultural industry.

XSInc.

 
We are an ag software company that analyzes big data. Instead of spending hours staring at yield maps and field records to see whether your seed, fertilizer, or drainage really made a difference, we can turn this guess work into an easy-to-use, easy-to-read analysis that tells you what mattered on your field. We compare every yield point in a field with its neighbors, resulting in literally thousands of head-to-head comparisons in a single field. This means that yield data from on-farm comparisons can now be as statistically accurate as replicated, small block trials.

ZedX, Inc.

 
For twenty years, ZedX® has developed and applied state-of-the-art technologies and decision support systems to help public and private organizations throughout the world do their work more efficiently. ZedX®'s success is built on reliable information, innovative technologies, and a strong commitment to customer service.

How can we improve agriculture, food and nutrition with open data?

Source: http://www.godan.info/wp-content/upl...7-05-20152.pdf (PDF and Word)

Open Data Institute 2015

What is open data?

Data anyone can access, use and share. It must:

  • be accessible, which usually means published on the web
  • be available in a machine-readable format
  • have a licence that permits anyone to access, use and share it

Closed data

Data that only data owners or people within an organisation can access, for reasons like privacy, commercial sensitivity and security.

Shared data

Data that is shared with specific people and organisations for a specific purpose: to provide services, connect information and contribute to research.

Find out more at http://theodi.org/what-is-open-data

Acknowledgements
Authors: Liz Carolan (The ODI), Fiona Smith (The ODI), Vassilis Protonotarios (Agro-Know), Ben Schaap (Wageningen UR), Ellen Broad (The ODI), Jack Hardinges (The ODI), William Gerry (The ODI).

With thanks to: Jeni Tennison (The ODI), Tim Davies (Practical Participation), Sander Janssen (Wageningen University), Martin Parr (CABI), Ana Brandusescu (CABI), Johannes Keizer (FAO), Fabrizio Celli (FAO), Nikos Manouselis (Agro-Know), Daniel Jimenez (CIAT), Shaun Hobbs (CABI), Christopher Brewster (Aston University), Gerbert Roerink (Wageningen UR), Medha Devare (CGIAR).

Editing: Anna Scott (ODI) Production: Phil Lang (ODI) Design: Adrian Philpott

Table of contents

1. Executive summary

4

2. Introduction

6

3. How open data is solving problems in agriculture and nutrition? 14 use cases

9

Enabling more efficient and effective decision making

  • Protecting crops from pest outbreaks with vegetation maps: GroenMonitor
  • Helping farmers forecast with weather apps and SMS: AWhere
  • Boosting crop yields with a best practice knowledge bank: Plantwise
  • Saving $3.1m in drought damage with a climate-smart tool: CIAT Colombia
  • Managing the California drought with data visualisations: California Department of Water Resources

 

9

Fostering innovation to benefit everyone

  • Saving crops and cash with weather simulation and smart insurance: Climate Corporation
  • Improving crop varieties with open data on breeding trials: AgTrials
  • Bringing agricultural research to the masses: FAO AGRIS portal
  • Making agri-food data more discoverable: the CIARD RING

 

14

Driving organisational and sector change through transparency

  • Tracking water, pesticide, water and fuel use with an open, collaborative platform: Syngenta
  • Exposing misspent farm subsidies in Mexico: FUNDAR
  • Empowering consumers to make smart food choices: US national nutrient database
  • Helping consumers understand the risks of the food they eat: US Food alerts
  • Highlighting restaurant inspection scores and improving food safety: LIVES

 

19

4. How ready are agriculture and nutrition for widespread open data innovation?

 

24

5. Realising the full potential of open data: next steps

 

26

About GODAN and the ODI

 

29

Glossary: Key data concepts

 

30

Appendix 1: Useful references and tools

 

31

 

1. Executive summary

In today’s evolving global landscape we face complex challenges. Populations are growing, climates are changing and markets are often volatile.

As the world’s population grows to around 9 billion by 2050, 1 global demand for food, feed and fibre is predicted to nearly double. The number of people at risk of hunger is likely to increase from 881 million in 2005 to more than one billion. 2

Alongside these challenges come huge opportunities: a global data infrastructure is emerging and, with innovative business models and international political will, we believe now is the time to invest in open data-driven solutions in agriculture and nutrition.

In this discussion paper we highlight three specific ways open data can help solve practical problems in the agriculture and nutrition sectors:

  1. Enabling more efficient and effective decision making
  2. Fostering innovation that everyone can benefit from
  3. Driving organisational and sector change through transparency

We present a series of 14 use cases showing how open data can be useful in different stages of agriculture, food production and consumption. From managing scarce water resources during the California drought or helping farmers in Africa estimate the outbreak of animal diseases, to helping consumers avoid harmful allergens in their food – open data is becoming a valuable tool for policy-makers, industry, small-scale farmers and consumers alike.

These are some examples of the range of stakeholders involved in agriculture and nutrition. We hope they stimulate discussion about the potential uses, needs and challenges of people, organisations and governments interested in exploring open data within this field.

There are still obstacles to realising open data’s full potential in agriculture and nutrition. 3 In a recent stakeholder survey we conducted for the Global Open Data for Agriculture and

Nutrition (GODAN) initiative, 4 we found that although the amount of data openly available is constantly increasing, there are still challenges related to data management, licensing, interoperability and exploitation. There is a need to evolve policies, practices and ethics around closed, shared, and open data.

The best outcomes are realised when open data is used to enable, enhance and unlock the wider potential through focused collaboration: these use cases were developed as solutions to specific problems, such as drought, pest infection or food safety  concerns.

We propose establishing projects and initiatives that start with real-world problems, and engage with problem owners to identify how data can provide actionable information that helps to address needs. To achieve this, we set out five steps for pursuing solution-focused open data initiatives for agriculture and nutrition:

  1. Engage with the growing open data community, including key problem owners and experts at GODAN, to identify the challenges that open data can help solve.
  2. Build open data strategies and projects with a focus on finding solutions to these agriculture and nutrition problems.
  3. Develop the infrastructure, assets and capacities for open data in relevant organisations and networks.
  4. Use open data and support users of relevant data.
  5. Learn through ongoing evaluation, reflection and sharing to ensure we can all continue to improve our practice. GODAN provides a forum for shared learning, through creating case studies, mapping partner activity, and bringing partners together. Advocate for the data you use from others, and that you produce, to be provided as part of the commons of open data in order to help stimulate network learning.

Do you have an idea for a solution-focused open data initiative for agriculture and nutrition? Submit it at http://bit.ly/godan-ideas.

2. Introduction

Agriculture and food are major contributors to the global economy, underpinning livelihoods and economic growth in the developed and developing world alike. Overall, the agriculture and food industries account for 6% of GDP in the EU, comprising 15 million businesses and 46 million jobs. 5 Meanwhile, agriculture accounts for 65% of Africa’s workforce and 32% of the continent’s GDP. 6 In some of Africa’s poorest countries, including Chad and Sierra Leone, it accounts for more than 50% of GDP. 7

Yet the global food system is struggling under the combined pressures of a growing population, climate uncertainty and volatile market forces. As the world’s population grows to around 9 billion by 2050, 8 global demand for food, feed and fibre is predicted to nearly double, with the number of people at risk of hunger increasing from 881 million in 2005 to more than a billion.  9 Fundamental concerns around inequality of access to nutritious food also persist. The Food and Agriculture Organisation of the United Nations (UN FAO) estimates that one in nine people in the world suffered from chronic undernourishment between 2012-2014. 10

Alongside these shifts, new and traditional demand for agricultural produce will put growing pressure on scarce resources. While agriculture competes with sprawling urban settlements for land and water, it will also be required to serve on other major fronts: adapting to (and helping to mitigate) climate change, preserving natural habitats, protecting endangered species and boosting biodiversity. Climate change is expected to make it more difficult to continue to grow crops and raise livestock in the ways and places we have been. 11 The increased unpredictability of weather patterns will also make it more difficult to make sound decisions.

Poor access to market information and credit has made it hard for smallholder farmers to respond to extremely volatile food prices. 12 Farmers struggle to plan their economic activities and make decisions regarding when and how much to plant. Providing farmers with more accurate, accessible, timely market information – from large agriculture groups to the individual smallholders – will help to ensure food commodity markets function well in  future.

The ‘Sustainable Development Goals’ (SDGs) – a framework for the next global development agenda, led by the United Nations – have highlighted “achieving food security and improved nutrition and promoting sustainable agriculture” as a global priority for the next 15  years. 13

As the World Bank has stated, increasing smallholder farmers’ productivity and access to markets can have “a profound impact on the livelihoods and general prosperity of literally millions of the world’s poor.” 14

This is no simple task. It will mean sustainably increasing agricultural productivity, while shaping more efficient and equitable markets. Progress will be driven largely by providing better access to accurate, timely information for individual smallholder farmers, businesses and policy-makers alike.  Open data can and should be part of the solution.

Fortunately, information and social structures exist in different parts of the world that can support the uptake and implementation of open data. Open data builds upon and extends a rich tradition of knowledge and information sharing in the sector. Since the 1990s, ‘agricultural knowledge systems’ have been used to promote knowledge exchanges among farmers (farmer field schools, for example), and with industry, public and private research and education institutions. 15 Data from cultivar trials, as we discuss below in the AgTrials use case, has been shared in other formats even before the internet era.

In this paper we outline how open data is boosting innovation in agricultural and nutritional business and service models. We explain how open data promotes transparency across the sector to accelerate progress, identify areas for improvement and help create new insights. Finally, we suggest how this important progress can continue to scale.

We do this through presenting a series of use cases from around the world. These demonstrate some of the ways open data can help solve problems for a range of stakeholders, from smallholder farmers to agribusinesses. These use cases were identified through a  process of rapid desk research and key informant interviews with a sample of GODAN members. It therefore does not represent the voices of all 125 GODAN members. We hope that this paper will lead to more conversations among the GODAN network and wider global development community about the potential for open data in agriculture and nutrition.

3. How is open data solving problems in agriculture and nutrition?

Because open data is data that anyone can access, use and share, it is shaping solutions to problems that would otherwise be expensive, time intensive or impossible to solve using closed data sources. Through speeding up innovation, open data fosters collaboration between governments, businesses, NGOs and individuals to make new discoveries and help sustainably feed a growing population.

We have identified three key ways in which open data can play a critical role in addressing challenges in agriculture and nutrition:

  1. Enabling more efficient and effective decision making
  2. Fostering innovation that everyone can benefit from
  3. Driving organisational and sector change through transparency

1 Enabling more efficient and effective decision making

The World Wide Web has fundamentally transformed the way we access and use information. We have reached a point where information can be gained from a variety of online sources as and when we need it. As more information is made available online, this helps us make faster, more informed decisions.

Open data works in the same way as the web, except it enables computers to pull data from various sources and process it for us, and does not rely on humans to interpret and integrate information contained in web pages. Open data underpins new products and services by presenting information from a wide range of sources that helps everyone from policy-makers to smallholders find gaps in markets or fine-tune their products or services.

Open data

  • lowers direct costs associated with accessing data – i.e. licensing fees and subscriptions that can put off new providers and users
  • lowers indirect costs of data access – the time and resources it takes to locate, request and negotiate access to data
  • drives better decision making and improved products and services based on lower costs and the wider availability of data

One key way in which open data is transforming the agricultural sector is by underpinning a variety of tools for farmers themselves. A range of applications and services have been built using open data to help keep farmers informed of various issues that affect their work: from pest prevalence and weather predictions to crop growth and market  prices.

Use cases
Protecting crops from pest outbreaks with vegetation maps: GroenMonitor

arm productivity is often hit by crop damage caused by pests. Mice and other pests are difficult to detect on large farms through manual inspection alone. The GroenMonitor (GreenMonitor) is a tool that shows a current vegetation map of the Netherlands, based on satellite images and maps made publicly available through the European Space Agency (ESA). 16  This makes pest outbreaks easy to identify and mitigate relatively quickly. In 2014, the GroenMonitor helped to identify 12,000 hectares (29,652 acres) of fields affected by mice. 17 The tool is now being exploited for various other applications, including plant phenology, crop identification and yield, identification of agricultural activities (e.g. mowing, ploughing and harvesting), nature and water management.

Helping farmers forecast with weather apps and SMS: AWhere

It is often difficult for farmers to access essential information that affects their cultivation practices – such as temperature highs and lows, humidity and precipitation, especially in areas with low internet access. A number of data providers now provide individual farmers with much needed weather information, including commercial companies like AWhere. Via its global database, Weather Terrain, AWhere combines weather observations, forecast models and historical records from all over the world and downscales them to field level, helping farmers better forecast and plan activities.

Many farmers, especially in the developing world, use mobile phones (rather than computers) as their main communication tool. With this in mind, one group in Ghana worked with AWhere to develop an app on top of Weather Terrain’s open API to make their  rich  data  accessible  through  mobile  phones.  The  weather  information  was transformed into a simple SMS messaging service, which uses basic keywords (e.g. partly sunny, partly cloudy, windy) and images. Farmers can now access weather information at a low cost to help them make decisions regarding cultivation. 18

In other cases, openly licensed data may be combined with other private (restricted) data sources to gain new insights. This can provide a more accurate picture of issues affecting the sector as a whole, where information is otherwise dispersed or inaccessible.

Use cases
Boosting crop yields with a best practice knowledge bank: Plantwise

Plant  pests  and  disease  are  currently  responsible  for  about  40%  of  global  crop production losses. 19 Plantwisehelps smallholder farmers in developing countries deal with plant health issues. It aims to increase food security and improve rural livelihoods by reducing crop losses from pests and diseases. It does so by combining global and local open access data from sources such as CABI’s databases, research publications and governmental data. It makes the data available and easy to search for via an online platform. Reports of disease from plant clinic operations on the ground are also used to supplement the knowledge bank and notify local partners of pest issues.

In two years the Plantwise knowledge bank has become a vital tool to support plant clinic operations in 33 countries. Over 600,000 farmers from 198 countries have visited the knowledge bank including over 9,000 factsheets to access critical agricultural data on crop pest prevalence and best practices to help manage and prevent potential crop loss from pests and diseases. 20

Saving $3.6m in drought damage with a climate-smart tool: CIAT Colombia

A recent collaboration between public (open) and private data sources is helping farmers take precautions to avoid drought damage in Colombia. Between 2007-2013 an association of farmers (National Federation of Rice Growers; Fedearroz), an international research centre (Centro Internacional de Agricultura Tropical; CIAT) and Colombia’s Ministry of Agriculture joined forces to identify the issues behind yearly reductions in rice crop yields, one of the most important food crops for the  country. 21

Making use of both open and private data (the latter obtained from companies through special agreements), CIAT analysed large datasets from annual rice surveys, harvesting records, field experiments and weather data and identified the complex and region- specific issues behind the decreasing rice crop yields. This led to the development of a climate-smart agriculture decision-making tool for the Colombian rice growers, which is openly available to anyone. 22

The impact on the agricultural sector as well as the Colombian economy was significant. Actions informed by this data helped farmers avoid extreme damage from the drought saving an estimated $3.6m of potential economic losses. 23

END OF USE CASE

Open data can also provide a tool for policy-makers, researchers and organisations to plan and distribute resources around critical information. Making basic geospatial data available for anyone to use and share, for example, has been particularly valuable when responding to natural disasters such as the 2010 Haiti earthquake. By offering more detailed and accurate insights into local needs, and the extent of damage, open data can help to improve coordination in disaster response, as demonstrated by the government response to the California drought, explained in the following use case.

Managing the California drought with data visualisations: California Department of Water Resources

California is experiencing one of its most severe droughts on record. Water shortages pose a critical threat to the agriculture sector, which accounts for 80% of water consumption across the state. Economic estimates in 2014 predicted direct costs of

$1.5bn to the agricultural sector, and the loss of 17,000 jobs on top of reduced food production. 24 To ensure a safe and sustainable water supply, the California Department of Water Resources announced a water rationing plan, reducing water allocation to farmlands and cutting consumer water usage by 25%.

Open data is being used to inform how the state allocates its scarce water resources under these conditions. 25 The drought has been visualised by the US Geological Survey (USGS) with publicly accessible open data collected by the USDA’s network of research facilities, on long-term physical, chemical, and biological data on agricultural sustainability, climate change, and natural resource conservation at the watershed or landscape scale. 26 This allows researchers and policy-makers to monitor conditions and plan water management. 27 Models based on the data, which is constantly updated, estimate actual water volume levels, water consumption and other factors, allowing for timely forecasting and decision making regarding how much water to use in agriculture.

The US Department of Agriculture (USDA) is also making research data related to the California drought open. 28 Dr Catherine Woteki, Under Secretary for the United States Department of Agriculture’s (USDA) Research, Education and Economics division, hopes this will “stimulate the use of open data by both the private and public sectors to give decision support for farmers on water use, crop choice [and so on].” 29

2. Fostering innovation to benefit everyone

As a raw material for creating tools, services, insights and applications, open data makes it inexpensive and easy to create new innovations. When data is open for all to experiment with, there is no need to invest large amounts in repeating already completed trials. When data is openly licensed, it also allows for novel combinations with other data to gain new insights.

Open data provides SMEs, startups and other organisations with a level playing-field, exposing gaps in markets and helping them compete against established market players to deliver new products and services. It also benefits established companies, who learn from and react to innovation in their sector – they might invest in these new products and services being delivered, acquire new talent and adjust their own business practices. In the agricultural sector, large open datasets have stimulated business creation and provided farmers with advisory services that boost their  productivity.

Entities and people sometimes might release data as open data, without the tools or capacity to make use of it themselves, especially in the context of smallholder farmers. It is important to explore ways to overcome this issue:

“Open data is an opportunity that we simply cannot miss for creating new businesses and jobs. Creating new products or enhancing existing ones using open data requires a wide, diverse and often domain-specific skill-set that can boost our knowledge economy. In the agri-food sector, I would like to see new data-powered innovation ecosystems to emerge, grow and flourish in the years to come. And I would like to see new business models that will bring value, and possibly revenue, to the public and private stakeholders that release open data.” Nikos Manouselis, Agro-Know

Use case
Saving crops and cash with weather simulation and smart insurance: Climate Corporation

In the past, farmers struggled with predictive climate models that failed to take into consideration local conditions, leading to inefficient risk calculations. Climate Corporation is an open data business that offers more accurate insurance 30 and a commercial advisory service to help farmers manage and adapt to climate change. 31

They do this through analysing huge volumes of data points from open and other data sources, to simulate weather events and assess risk to the yield of specific crops. The company utilises open data from sources including the National Oceanic and Atmospheric Administration (NOOA), the Next Generation Radar (a network of 159 Doppler radar stations operated by the National Weather Service), as well as maps of terrain and soil types from the U.S. Geological Survey. 32

Farmers can use the detailed weather forecasting data to enhance their cultivation practices and activities like spraying, fertilising and seeding. For example, using moisture and precipitation maps provided by the company, farmers can tell if a specific part of their fields is too wet to be ploughed. On an industry level, the impact of the company’s open data-powered service could be significant. In 2013 Climate Corporation’s customers farmed more than 10 million acres. 33

END OF USE CASE

Open data helps governments, established companies and other organisations to outsource and/or crowdsource their research and development. By releasing the data for anyone to use they can benefit from a large group of people – outside the original data holders – who are solving problems with the data. For example, the online atlas RTBMaps aggregates individual maps provided by researchers and scientists through ‘scientific crowdsourcing’ to produce a unique map showing variables affecting crops, such as susceptibility to pests and diseases or vulnerability to failed harvests. 34 Its 32 map layers are made available online for anyone to download and use for advanced analysis.

In many cases agricultural scientists focus on answering very specific questions. They may not consider other potential uses for their raw data and other research data; but even when they do, they need a centralised platform or repository to share their results. In this context, opening data up can help promote better data management and workflows.

Meanwhile, accessing research findings in the form of closed publications can be prohibitively expensive. 35 But thanks to the open access mandates of an increasing number of major research funders, 36 more of such findings (including data) will be made open and free to anyone. In particular, the opening of scientific research and global datasets for application to local conditions by smallholder farmers has the potential to transform the structure of agricultural research and innovation systems. 37

“Open data makes all research accessible to target users and helps in the efforts of open access initiatives to strengthen agricultural research for development, support decision makers, improve livelihoods, and support food security.” Ahlam Musa, Agricultural Research Corporation (ARC) Central Library, Sudan AGRIS Resource Center

Use cases
Improving crop varieties with open data on breeding trials: AgTrials

Cultivar testing is an important means of improving crop varieties. A wide range of trials are taking place on sites all over the world, addressing issues such as drought tolerance, heat stress, and soil management. However, almost all of the data generated has been inaccessible to other researchers – filed away on laboratory hard drives, or sometimes lost completely due to bad data management.

By compiling data from agronomic and plant breeding trials and making it open, the Global Agricultural Trial Repository (AgTrials38 hosted by a CGIAR Research Programme on Climate Change, Agriculture and Food Security (CCAFS) offers a rich knowledge base to inform ongoing, collaborative research, while eliminating unnecessary and costly duplication of efforts.

Scientists used 250 open AgTrials datasets to build crop models specific to the West Africa region. The models are used to project the local impacts of climate change, and define breeding programmes for adaptation. 39

Bringing agricultural research to the masses: FAO AGRIS portal

AGRIS 40  is an international network of research institutions and information nodes making agricultural research information globally available. It collects and disseminates bibliographic information on diverse food and agricultural publications, from over 150 data providers in 65 different countries.

AGRIS uses bibliographic data as an aggregator for locating related content online and organises it via an open data repository (of over 8 million records). An application combines records with other open data repositories and links to other quality sources of data such as the World Bank, Nature, and the Chinese Germplasm Database.

The AGRIS portal has received more than 7.5 million visitors from 204 countries. For many undergraduate and graduate students – AGRIS is the most important access point to scientific and technical information.

Making agri-food data more discoverable: the CIARD RING

Despite the fact that there is already a wealth of information related to open data (like datasets, portals and standards), the retrieval of relevant information is still an important issue that needs to be addressed. In this context, the CIARD Routemap to Information Nodes and Gateways (RING) 41 acts as a global registry of web-based information services and datasets for agricultural research for development (ARD).

The registry allows data providers to register and categorise their services. It also promotes and leverages standards by ensuring all datasets are accompanied by metadata about which standards are adopted (such as vocabularies, dimensions and protocols). This promotes data reuse and discovery, and allows for greater automation. It currently features over 1000 information services of which about a third are agri-food datasets. More than 500 registered data providers have published about 30 million records through the registered datasets. The RING is used by AGRIS, AgriFeeds, 42 the agINFRA harvester API 43 and Drupal modules, the International Livestock Research Institute (ILRI) 44 and other global initiatives in agri-food research.

3. Driving organisational and sector change through transparency

Open data helps shape best practice within a sector, market or organisation. Transparency around targets, subsidy distribution and pricing, for example, creates incentives which affect the behaviours of producers, regulators and consumers.

Where an organisation has targets, open data about its work makes progress transparent and accessible across the sector chain. When an outcome or target is measured, and those measures made openly available, organisations and individuals adjust their behaviour so that they perform better against those measures. As a basic example, to reduce the number of single-use carrier bags provided by supermarkets, the UK Government requires sellers to keep records and report the numbers of carrier bags that are used. 45 Here, transparency motivates sellers to reduce their use.

By requiring companies, government departments and other organisations to publish key datasets – performance data, spend data or supply-chain data, for example – governments, regulators and companies can monitor, analyse and respond to trends in that sector. More importantly, publishing this data across a sector can ultimately transform how products and services are delivered.

Many resources can be overused or misspent in agriculture systems, resulting in wasted money and environmental damage.

The unnecessary use of agrochemicals (such as fertilisers and pesticides) can damage the environment and raise production costs for farmers. In the United States alone, $10bn are annually spent on pesticides. While pesticide use saves around $40bn on crops that otherwise would be lost to pest destruction, this does not take into account any of the negative effects that result from the use of these pesticides. The issues related to the use of pesticides lead to a social cost of more than $12bn in addition to the $10bn spent for these pesticides. 46 If standards and best practices are openly available, these outputs can be monitored by farmers and their unnecessary costs can be prevented.

Farm subsidies generally represent a huge cost to governments; for example the European Union spends approximately €59bn on subsidies each year. However, subsidies do not always reach those who need them most. By publishing data about the distribution of agricultural payments, such as subsidies, government agencies can adjust targeting so it reaches those who really need it, thereby helping to strengthen the sector as a whole. 47

Use cases
Tracking water, pesticide, water and fuel use with an open, collaborative platform: Syngenta

In 2013, Syngenta announced its Good Growth Plan: six commitments to improve crop productivity, protect soil and biodiversity, train smallholders and ensure labour standards, with targets to be achieved by 2020. The initiative aims to allow farmers to increase crop yields in a sustainable way, through monitoring activities like nutrient and pesticide application and water and fuel usage.

A data management system has been established to track these agricultural outputs and inputs using farm and public data, which is collected, validated and analysed by independent companies. Baseline data for 2014 on all commitments are available in a machine-readable format (as CSV files) and published using different Creative Commons licenses 48 (such as NonCommercial-NoDerivatives and Attribution-ShareAlike, an open data license). 49 Through these actions, Syngenta in collaboration with ODI, aims to build an open, collaborative platform to co-create solutions that minimise the use of resources required to feed a growing population and preserve habitats for biodiversity.

“Making this data public  will  allow  people  to  make  their  own  assessments of the progress of our Good Growth Plan. It is also blurring the traditional roles of business, government and NGOs by highlighting our collective responsibility to address acute global challenges. Above all, the data will be of value to farmers, enabling them to increase productivity sustainably and to enhance their livelihoods.” MikeMack,Syngenta

Exposing misspent farm subsidies in Mexico: FUNDAR

PROCAMPO is the largest federal farm subsidy programme in Mexico supporting the poorest farmers. There have been concerns since 2007 that its subsidies were not received by those meeting the requirements, who were in dire need of support.

To better understand the situation, a Mexican NGO named FUNDAR Center of Analysis and Research 50 called for information related to  the distribution of subsidies  from the Mexican Ministry of Agriculture. After initial requests resulted in incomplete  data in non-machine readable formats, the agency in charge finally published the data. Analysis showed that 57% of the benefits were distributed among the wealthiest 10% of recipients, 51 confirming initial fears.

An important outcome of the data release was the development of a database (Subsidios al Campo en México) 52 by FUNDAR and other NGOs, which publishes ongoing information about the farm subsidies to ensure more transparency over the process. 53 A series of resignations followed the revelations and the government imposed limits on the eligibility of subsidies. 54

END OF USE CASE

Open data also enables consumers to demand better standards, through promoting transparency around elements of sustainable food production and safety. This has a dual effect of influencing consumer behaviour, while creating incentives for food producers to improve their performance.

In the food sector, requiring the publication of certain information – such as ingredient lists, greenhouse gas emissions, allergen information and the origin of produce – serves three purposes:

  1. It helps regulators identify areas where food standards are not being complied with or where targets are not being met
  2. It informs consumers so they can make better choices
  3. It drives sector change in response to this increased consumer choice

An initiative recently developed to meet these aims in the UK is FoodTrade Menu by Food Trade, 55 which combines data provided by the food business, government open data, and data from food producers to provide restaurants with tailored food menus flagging these allergens in their food to their customers. 56

Use cases
Empowering consumers to make smart food choices: US national nutrient database

Consumers have clearly indicated that they want to be more well-informed on the quality and ingredients of the food that they are consuming. Although basic information already exists on food packaging, more detailed information on food nutrients could allow people to make better decisions regarding food selection based on their individual needs (e.g. following the advice of a dietitian).

The USDA National Nutrient Database for Standard Reference (SR25) 57 is the major source of food composition data in the United States, and provides data sources for most public and private sector databases. SR25 contains nutrient data for more than 8,500 food items and about 150 food components, such as vitamins, minerals, amino acids, and fatty acids. The use of this data is not limited to commercial applications (e.g. smartphones apps). It provides the basis for new services like ChooseMyPlate. gov, an initiative launched by US First Lady Michelle Obama and USDA Secretary Tom Vilsack to provide “practical information to individuals, health professionals, nutrition educators, and the food industry to help consumers build healthier diets with resources and tools for dietary assessment, nutrition education, and other user-friendly nutrition information.” 58

Helping consumers understand risks of the food they eat: EU Food alerts

Food safety is another important issue affecting consumers. The European RASFF (Rapid Alert System for Food and Feed) portal 59 provides access to a database of publicly available information about recently transmitted food safety alerts and notifications.

Consumers can access data on food safety issues such as the presence of allergens, pathogens, toxins or other harmful substances in food products, 60 and share preventative information. 61 Following the March 2011 Fukushima nuclear power plant incident, the RASFF was used to monitor fishery and other marine products caught in the Pacific region for the presence of radioactive substances which would be harmful to consumers. 62

Highlighting restaurant inspection scores and improving food safety: LIVES

pen data is also being used to help consumers choose where to dine, while incentivising improvements in food safety. Local Inspector Value-Entry Specification (LIVES) aims to “normalise restaurant inspection scores across jurisdictions, allowing consumers to get a sense for restaurant food safety compliance across municipalities and within their home town.” 63 LIVES was launched in 2013 as a project between San Francisco, Socrata, Code for America, and Yelp, and is providing the standard for publishing open data on restaurant inspections 64. By allowing citizens to make better use of inspection results, LIVES facilitates food transparency and decision making on approved restaurants. When the City of Los Angeles began to require that restaurants displayed hygiene grade cards on their entrances, studies found it was associated with a 13% decrease in hospitalisations due to foodborne illness. 65

4. How ready are agriculture and nutrition for widespread open data innovation?

“I look forward to a time when stakeholders will be able to have easy, unbridled access to data on agricultural practice, market realities and attendant issues, thus making agriculture the sector of choice.” Richard-Mark Mbaram, Agro-Nigeria

As we have shown, there are a number of emerging examples of open data making a difference in the agriculture and food sectors. However, there is still a lot of untapped potential. There are a number reasons to be optimistic that agriculture and nutrition are well-positioned to use more open data.

First, a rich data infrastructure is emerging. Data collection tools (such as sensors, satellites and storage) cover multiple parts of the global food chain – from production through to markets and consumption. Sophisticated analytical tools, algorithms and increasingly accessible and affordable technology put information in the hands of people who can make decisions. This includes smallholder farmers with access to mobile phones and consumers having access to nutritional information before the food reaches their table.

Second, this is an area where innovation is needed by public and private sectors working together to develop new solutions. In most geographic regions of the world fewer people will be living in rural areas and even fewer will be farmers. They will need new technologies to grow more from less land, with fewer hands. 66

In this environment, there is wide scope for locally contextualised solutions to find a market. For example, there is increasing demand from smallholder farmers to purchase tailored market information services built on open data to inform their business decisions. iShamba 67 is one of a growing number of mobile phone services providing farmers in the developing world with agricultural advice on various topics such as how to increase maize yield. It focuses on localised weather forecasts and market prices; both of them aggregated through open data sources.

Lastly,there is a growing sense of political will to address the sustainability of agriculture and nutrition through open data and open access within the international policy, research and development and private sector communities. At the 2012 G8 summit, world leaders committed to “share relevant agricultural data available from G8 countries with African partners” 68 and to convene the April 2013 G8 International Conference on Open Data for Agriculture 69 which led to the establishment of GODAN. 70

Meanwhile major funding bodies of agri-food and nutrition research are making open access mandatory, requiring research outcomes and research data produced through their funding to be made publicly available. These include the European Commission, 71 the US Department of Agriculture (USDA), 72 the CGIAR Fund, 73 the UK Department for International Development (DFID), 74 the Bill and Melinda Gates Foundation 75  and the Ford Foundation. 76

Individual businesses are also recognising potential efficiency and productivity gains and voluntarily embarking on their own open data initiatives. In 2015, Syngenta, one of the world’s largest agribusinesses, published agricultural sustainability data as part of its Good Growth Plan. 77

Nonetheless, there are still challenges to realising open data’s full potential78 A recent stakeholder survey for GODAN indicated that although the amount of data openly available is constantly increasing, there are still issues related to data management, licensing, interoperability and exploitation, as well as capacity to implement open data initiatives. More than this, there remains vast, untapped potential for innovation in the sector using open data. There are practical steps everyone can take – as individuals and as communities – to realise this.

5. Realising the full potential of open data: next steps

“Simply making data available is only one half of the open data equation; the other is interoperability – making our data machine readable so it can be mixed with others’ datasets to produce new information and insights.” Krysta Harden, U.S. Department of Agriculture

“Our challenge is the opening of access to this data not just through coding but through awareness and use.” Chris Addison, CTA

Open data is helping, and can continue to help, the agriculture and nutrition sector meet the challenge of sustainably feeding the world in the context of population growth, climate change and volatile markets. Open data is still a relatively new concept, and there remains a vast, untapped potential for using it to innovate. Many open data initiatives have been established to help unlock this potential, with many taking the publication of data and development of data standards and capacity as their starting points.

However, as the use cases in this report show, the best outcomes are realised when an open data approach is used to enable, enhance and unlock the wider potential through focused collaboration. These use cases were mostly developed as solutions to specific problems, such as drought, pest infection or food safety concerns. When it comes to realising the full potential for open data, it is important to start with real world problems, and to engage with problem owners, and not simply focus on the data.

Open data strategies should be developed with both the common good, and specific goals in mind: building projects that bring together a range of actors to work on problems, using and driving the improved publication of open data. Effective strategies will enable communities to work through some of the technical and social steps to realising the potential of open data at a manageable scale. These challenges are best addressed at the level of a particular problem, where standards can be identified or developed, and data released as part of solving a problem This is especially true when advocates can point to a clear theory of change.

To help achieve this, we have set out five steps for pursuing solution-focused open data initiatives for agriculture and nutrition:

  1. Engage with the growing open data community, including key problem owners and experts at GODAN, to identify the challenges that open data can help solve, and commit to work together on open data projects that address them.
  2. Build open data strategies and projects with a focus on finding solutions to agriculture and nutrition problems. At an organisational level, identify the datasets most likely to be used by others to address pressing issues. At a collaborative level, build projects with partners in order to demonstrate impact. An open data approach enables you to build diverse partnerships, including data owners, problem owners, innovators, intermediaries, open data experts and crucially, end-users, from smallholder farmers to consumers, who stand to benefit from change.
  3. Develop the infrastructure, assets and capacity for open data in your organisation and networks. At the organisational level, publish your own data, prioritising datasets identified through engagement, and build capacity of your team to publish, use open standards and apply open licenses. Collaborating on projects with partners will highlight gaps in datasets, standards and capacity. It will also present opportunities to take action to fill these gaps, by advocating for data from others to be published and standards developed and implemented, for example. The GODAN secretariat is mandated to advocate at the global level and can support this work with partners.
  4. Use open data and support users of relevant data. Advocate for the data you use from others to be provided as part of the commons of open data. Increased use contributes to increased data quality – as feedback loops lead to data being made more relevant, or links being made between datasets that enhance it. Think also about how you can support use of your own data, and listen to feedback from users. Supporting use may involve thinking about the creation of intermediary tools, or investing in support for capacity building of users at different stages of the open data value chain. Consider where you have a role to play building the capacity for effective use of open data.
  5. Learn through ongoing evaluation, reflection and sharing to ensure we can all continue to improve our practice. GODAN provides a forum for shared learning, through creating case studies, mapping partner activity, and bringing partners together.

This paper has outlined the growing use of open data to meet agriculture and nutrition challenges, and the potential that remains. This potential will be realised more quickly if we focus the starting point of our efforts away from the data and instead towards the problems faced in the sector. There are practical steps that can help us realise the potential of open data, as individuals as well as part of collaborative action – as part of the GODAN  community.

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About GODAN

The Global Open Data for Agriculture and Nutrition (GODAN) initiative supports global efforts to make agricultural and nutritionally relevant data available, accessible, and usable for unrestricted use worldwide. The initiative focuses on building high-level policy and public and private institutional support for open data. The initiative encourages collaboration and cooperation among existing agriculture and open data activities, without duplication, and brings together all stakeholders to solve long-standing global problems. 79 It aims to achieve that by facilitating access to agri-food & nutrition data that may unlock the agricultural productivity; and by increasing agricultural productivity and promoting good nutrition the global issues of hunger and poverty will be addressed.

The GODAN initiative is a growing network of 125 organisations that advocate for global open data in agriculture and nutrition. GODAN encourages:

  • new and existing open data initiatives with a core focus on agriculture and nutrition
  • agreement on and release of a common set of agricultural and nutrition data
  • collaborative efforts on future agriculture and nutrition open data programmes, good practices, and lessons learned that enable the use of open data, particularly by and for the rural and urban poor

Any organisation or individual is invited to participate in GODAN and can take part in its activities and events. More information can be found at http://www.godan.info.

About the ODI

The Open Data Institute (ODI) is an independent, non-profit and non-partisan company based in London, UK. The ODI convenes world-class experts from industry, government and academia to collaborate, incubate, nurture and explore new ideas to promote innovation with open data. It was founded by Sir Tim Berners-Lee and Professor Sir Nigel Shadbolt, and offers training, membership, research and strategic advice for organisations around the world looking to explore the possibilities of open data.

Glossary: Key data concepts

  • Open data - data anyone can access, use and share. It must: be accessible, which usually means published on the web; be available in a machine-readable format; and have a licence that permits anyone to access, use and share it.
  • Closed data - data that only the data owners or people within an organisation can access. It might be personal or commercially sensitive information, or information for national security.
  • Shared data - data that is shared with specific people or organisations for a specific purpose: to provide services, connect information and contribute to research. Data about consumer shopping habits, or electoral registries, are two examples of data that is shared with specific groups of people. Publishing information about how data is shared and with whom as open data can be an important transparency mechanism.
  • Personal data - personally identifiable information. Some personal data can be open data: for example, MP salaries, information about company directors and personal insolvency records. Open data can help people understand how their personal data is being accessed and used.
  • Big data - a vendor-driven term used to describe large quantities of rapidly changing data being collected from various sources that is used for (statistical) analysis. Big data can contain open data, and can be released as open data if licensed for anyone to access, use and share.
  • Linked data - a way of publishing data so that it can be easily linked with other data and automatically read by computers.
  • Open government - requiring the publication of open government data enables greater civic participation in government affairs through transparency, accountability and providing a platform for engagement.
  • Open access - within the research community, outputs such as raw research data can be released as open data as part of an organisation’s ‘open access’ policy.
  • Open source - within the software community, “open source” denotes software made available under a licence that enables anyone to access, modify and use its underlying code.

Appendix 1: Useful references and tools

Links available at http://bit.ly/godan-resources My Note: This link is broken

Tools for publishing your data

  • ODI Certificates
  • ODI Pathway
  • ODI Guide:
    • Engaging with re-users
    • Publisher’s guide to open data licensing How to make a business case for open data
    • How to prioritise open data to drive global development
  • Software tools for collecting, cleaning, scraping and publishing data
    • Dataverse data repository software
    • CKAN open-source data portal platform
    • DKAN Drupal-based open data platform
    • Zenodo open digital repository for publications & data
    • Fraunhofer Open Data Platform (ODP)
    • Socrata Open Data Portal
    • Open Data Kit
    • Biomart data software and services portfolio
    • CSV Lint
    • OpenRefine
    • DM2E Tools
    • Metadata Interoperability Services (MINT)
    • OpenDataSoft Open Data Platform opendatasoft.com
    • DataPress Open Data Platform
    • Scraperwiki PDF Tables
    • Import.io
    • Zizo
    • Sibdo Desktop 5.0
    • Datactics FlowDesigner DQM
    • DigIn
    • ArcGIS Online esri
    • Redlink PublishMyData Swirrl
    • Epimorphics
    • Elda
  • EUDAT Research Collaborative Data Infrastructure (CDI)
  • re3data.org global registry of research data repositories
  • DataDryad digital data repository
  • agINFRA open access recommendations for the agri-food research community
  • the CIARD RING for registering open data sets and make them accessible

Tools for becoming an open data user and creating open data projects

  • ODI Guide: 5 ways to be a better open data re-user
  • School of Data
  • Open Development Toolkit
  • Open Knowledge Global Network
  • Open Knowledge Working Groups
  • Open Knowledge Network Projects

Examples of organisations with open access and data policies:

  • The European Commission
  • The US Department of Agriculture (USDA)
  • CGIAR
  • The World Bank
  • The World Health Organisation
  • The UK Department for International Development (DFID)
  • The Bill and Melinda Gates Foundation
  • The Ford Foundation
  • CABI
  • Overseas Development Institute (ODI)
  • Association of European Research Libraries (LIBER)

Open Data and Smallholder Food and Nutritional Security

Source: http://www.cta.int/images/Opendatafo...rs-report_.pdf (PDF and Word)

CTA Working Paper 15/01 | February  2015

Andre Jellema,1 Wouter Meijninger1 and Chris Addison2
1. Alterra, P.O. Box 47, 6700 AA Wageningen, The Netherlands

2. Technical Centre for Agricultural and Rural Cooperation (CTA), PO Box 380, 6700 AJ Wageningen, The Netherlands

About CTA

The Technical Centre for Agricultural and Rural Cooperation (CTA) is a joint international institution of the African, Caribbean and Pacific (ACP) Group of States and the European Union (EU). Its mission is to advance food and nutritional security, increase prosperity and encourage sound natural resource management in ACP countries. It provides access to information and knowledge, facilitates policy dialogue and strengthens the capacity of agricultural and rural development institutions and communities.

CTA operates under the framework of the Cotonou Agreement and is funded by the EU. For more information on CTA visit, http://www.cta.int

About Alterra

Alterra is the research institute for our green living environment. We offer a combination of practical and scientific research in a multitude of disciplines related to the green world around us and the sustainable use of our living environment: Flora and fauna, soil, water, the environment, geo-information and remote sensing, landscape and spatial planning, man and society. These are just a few of the numerous aspects of our green environment that Alterra focuses on.

Alterra is part of the Wageningen University and Research Centre (Wageningen UR). In research and education we closely co-operate with the school of Environmental Sciences from Wageningen University. With this partner we contribute to a high quality and sustainable green living environment. The exchange of expertise and capacity and the match between fundamental and practical research in various projects give us a scientific advantage.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. This license applies only to the text portion of this publication.

Key data sets

Government

Space and meteorological agencies

International organisations and partnerships

Science:

NGOs

Executive Summary

This report was commissioned Technical Centre for Agricultural and Rural Cooperation (CTA) as a member of the Global Open Data for Agriculture and Nutrition (GODAN) initiative. It aims to provide a better understanding of the actual impact of the open data movement on the food and nutrition security of smallholders and highlight the areas of potential unfilled opportunity. This study was carried out by Alterra as a rapid desk-based study to identify possible relevant sources of open data and determine the current and potential impact of these sources by assessing the content of the sources and possible applications. The sources identified were governments, meteorological and space agencies, international, science-based and non-governmental organisations and businesses. The impact was determined by looking at the applicability of the data to facilitate the smallholder ecosystem, specifically looking at the potential to enhance smallholder production, the value chain, support services and governance.

The study found that, despite the potential value of open data to smallholder farmers in developing countries being high, there are few readily available examples of direct impact on food and nutrition security of smallholders. However, there is a clear indirect benefit of open data usage for smallholders, including the contribution to better governance.

Most impact in other domains is from open space and meteorological data. However, even in this area open data access is still in its infancy and there are remaining issues, such as reliability of data at smallholder resolution (i.e. the spatial resolution of the satellite data with respect to the size of most agricultural fields) and lack of connectivity to provide services to the smallholder ecosystem.

Most mature open data sources are international organisations such as the World Bank and FAO. These data sources are very much oriented towards global governance and have limited impact in a local smallholder context.

Potentially, businesses, such as mobile service providers, are a promising source of information. With the rapidly increasing use of mobile phone technology there are potentially large information sources about the smallholder ecosystem within these mobile networks. Corporate data-sharing is indicated as the best way to obtain fine-grained information about the smallholder ecosystem and to provide better production advice, facilitate the value chain, provide better services and governance. However issues around privacy, farmers’ rights and business confidentially remain to be solved.

Other potential areas to develop open data sets relevant to enhancing the food and nutrition security of smallholders are:

  1. Developing dedicated scientific open data sets targeted at specific stakeholders needs in the smallholder ecosystem.
  2. Opening NGO project monitoring and evaluation data and encouraging NGOs to collect specific data during project implementation.

Figure 1: The impact and potential impact of open data from different sources on smallholder production, the value chain, support services and governance rated between 0 and 4 (0: no/little impact, 4: high impact).

 

Impacts 1 2 3 4 5 6 7 8
Government Data 0 0 0 2 0 1 2 3
Meteo and Space Data 1 0 2 2 2 0 3 3
Data from Int.Organisations 0 0 0 2 0 0 0 3
Science Data 0 0 1 2 3 3 3 3
NGO Data 0 0 0 2 3 3 3 3
Business Data 0 0 0 0 2 4 4 4

1 Impact on Production

2 Impact on Value Chain

3 Impact on Support Services

4 Impact on Governance

5 Potential Impact on Production

6 Potential Impact on Value Chain

7 Potential Impact on Support Services

8 Potential Impact on Governance

Introduction

The GODAN initiative invites all stakeholders to open up their agricultural and nutritionally relevant data to enforce global food and nutrition security

At the G8 Conference in London in October 2013, the Global Open Data in Agriculture and Nutrition initiative (GODAN) was launched (The Guardian, 2013). The objective of GODAN is to build high-level policy and institutional support for open data relevant to agriculture and nutrition across the public and private sector to enforce global food and nutrition security (GODAN, 2014). Open data are: “data that can be freely used, reused and redistributed by anyone – subject only, at most, to the requirement to attribute and share alike” (The Open Foundation, 2012). Open sharing of data is considered important because data collected for a specific task may have value to other people or organisations in different contexts and/or for different reasons. The value of reusing the data may be even larger than the original value of the data, considering the original purpose it has been collected for. The potential global value is estimated US$3 trillion a year (McKinsey, 2014). By  making  data available, the alternative values can be harvested, although it will be impossible to predict precisely how, where, and by whom this value will be created in the future. Open data can contribute to (Opendatahandbook, 2014):

  • participation and self-empowerment;
  • improved or new products and services;
  • new knowledge from combined data sources and patterns in large data volumes;
  • improved effectiveness of government services and impact measurement;
  • transparency and democratic control on public processes.

In line with other global movements for open data and open access, GODAN advocates for:

  • open data and open-access policies by default, in both public and private sectors, while respecting and working to balance openness with legitimate concerns in relation to privacy, security, community rights and commercial interests;
  • the release and re-usability of data in support of innovation and economic growth, improved service delivery and effective governance, and improved environmental and social outcomes.

Open access to agricultural and nutritionally relevant data is vital for innovation in agriculture and value chain development driven by farmers, farmer organisations, researchers, extension experts, policy-makers, governments and other private sector and civil society stakeholders (GODAN, 2014).

In modern agriculture data are becoming more and more an important resource for food production, facilitation of the value chain and governance

A strong example of data use is precision agriculture. Precision agriculture, also known as precision farming, satellite farming or site-specific crop management (SSCM) uses GPS (global positioning system), soil testing, yield monitors, remote sensing and variable-rate technologies, information technology and geographic information systems (GIS) and the like to observe, measure and respond to spatial variations in crops (within one field or between different fields). Precision data together with computer-based decision support systems help optimise production (yield), conserve resources (e.g. water and nutrients) and reduce costs (Venkatalakshmi and Devi, 2014). Examples of (satellite-based) crop monitoring services are Cropio, FarmSat, FieldLook and ClimatePro (Precision Agriculture, 2014).

In dairy farming, automatic milking machines are collecting data down to an individual level; each cow can be tracked and examined, and the farmer will be alerted when there are unusual changes in the animal that might indicate illness or injury. Farmers can use these data to analyse the effect of various animal feeds on milk yield (Automatic milking, 2014).

Data are also collected at all stages of food value chains, from the farm to the consumer. Each partner is challenged to be more efficient, more sustainable and more effective. Information is added to the produce along the chain and producers and customers are increasingly looking beyond one chain-partner back or ahead. Allergy information, fair production and trade, footprints and many other quality characteristics are not only relevant to consumers but in the end, all chain partners need the information (Lundqvist et al., 2012).

Governments are implementing all kinds of e-governance data services, including facilitating agricultural accountability, obtaining subsidies and participatory governance. In the Netherlands, examples can be found in the ‘National Single Window for Trade and Transport’ to avoid repeated data entry for business to with different government and in the national agricultural statistical survey used for monitoring, policy-making, research and agricultural subsidies (Wassenaar, 2000; RVO, 2014).

CTA wants to know the impact of the open data on the smallholder food and nutrition security

The Technical Centre for Agricultural and Rural Cooperation (CTA) is a joint international institution of the African, Caribbean and Pacific (ACP) Group of States and the European Union (EU). The mission of CTA is to advance food and nutritional security, increase prosperity and encourage sound natural resource management in the ACP countries by strengthening the agricultural policy processes; improving smallholder agricultural value chains and enhancing information, communication and knowledge management capacities for rural development in ACP countries. As a member of the GODAN initiative, CTA wants to know what the actual impact is of the open data movement on the food and nutrition security of smallholders and which opportunities remain unfulfilled. In this report we attempt to answer this question based on a quick desk study and describe the results. We do this in both a narrative form and by ranking each data source and impact field. This rank is subjectively based on our current best knowledge and not based on an analytical methodology. The rank varies from 0 to 4, where 0 means no impact on the food and nutrition security of the smallholders found, 1 means initial impact, 2 means substantial impact, 3 means a large impact and 4 means a very large impact.

This ranking is not a final judgement, but a means of communicating a quick scan overview. It needs to be discussed within the wider open data community for further confirmation.

  • In the first section of the report, we provide a brief explanation of open data, the open data community and the current impact of open data in developing countries.
  • In the second section, we describe the smallholder ecosystem and how the actors within the smallholder ecosystem may benefit from open data development.
  • In the third section, we provide an overview of the status of the different sources of open data relevant to food and nutrition security and their (potential) impact, including the ranking.
  • In the last section, we provide the conclusions and outlooks for the further development of open data to enhance food and nutrition security of smallholder communities.

Open data and their impact on developing countries

Open data are data that can be freely used, reused and redistributed by anyone – subject only, at most, to the requirement to attribute and share alike

The full Open Definition (Opendefinition.org) gives details about what this means. To summarise the most important are:

  • availability and access: The data must be available as a whole and at no more than a reasonable reproduction cost, preferably by downloading over the internet. The data must also be available in a convenient and modifiable form;
  • reuse and redistribution: The data must be provided under terms that permit reuse and redistribution including the intermixing with other data sets;
  • universal participation: Everyone must be able to use, reuse and redistribute - there should be no discrimination against fields of endeavour or against persons or groups. For example, ‘non-commercial’ restrictions that would prevent ‘commercial’ use, or restrictions of use for certain purposes (e.g. only in education), are not allowed (Open Knowledge Foundation, 2012).

Data can be ‘open’ at different levels. Tim Berners-Lee, founder of the World Wide Web, proposed a five-star model of ‘openness’ (Wikipedia Open Data, 2014):

* Data are online available in any format.

** Data are online available in a structured file format which is appropriate for automatic reuse (a table in Excel format rather than a JPG picture).

*** Data are online available in an open file format (CSV rather than Excel).

**** All of the above-mentioned and data formats are used like Resource Description Framework (RDF) and SPARQL, which allow others to specifically point at data objects.

***** All of the above-mentioned and links are made to other related data sets providing more contexts about the data set.

The spirit of open data development is of the same nature as other openness developments focusing on different aspects, such as open access (CIARD, 2014a; Peters, 2014) focusing on the access of scientific and other information and knowledge, and open source focusing in the open access to software codes (Open Source, 2014).

Open data fuel the pyramid of wisdom enabling better decision-making

Data and access to data is not directly useful for most actors in society. The data needs to be contextualised and combined with other data in order to produce relevant, comprehensive information and new knowledge ultimately leading to wisdom. This framework for adding value to data by combining data and adding information is captured in the knowledge pyramid in Figure 2 of Lokers and Janssen (2014). The basic concern at the bottom of the pyramid is to have enough data available from different sources in order to be able explore and combine and understand the world better. Governments, international organisations and others who make their data available in open format are contributing to the pool of data, fuelling the basic layer of  the pyramid of knowledge. However intermediaries, scientists, data analysts, modellers and IT experts are needed to take the intermediary steps from data to information and from information to knowledge. Ideally, the data at the bottom of the pyramid is annotated and linked to other data (linked open data). These links provide information about the data and their quality. Linked open data help experts to find the right data and to make the right interpretation of the data. The next step is to bring the data into a domain (e.g. food security, climate change, biodiversity loss) and to analyse the data from the domain perspective in combination with other data sources.

Figure 2. The pyramid of knowledge, demonstrating what is needed to get from a pool of data to knowledge and ultimately to wisdom for evidence-based decision-making.

Source: Lokers and Janssen (2014)

OpenDataandSmallholderFoodandNutritionalSecurityFigure2.png

Intermediaries between suppliers and consumers are key in the functioning of open data community

Deloitte (2012) describes the open data community, as simultaneously simple and complex. On the one hand, the chain between the suppliers of open data and those who demand their services is short; on the other hand, almost every entity in the open data community can link to every other entity (Figure 3). The same organisations can be found in different roles, suppliers, users or intermediary in the open data chain. The intermediaries play a key role in the open data community, enabling the wider use of the open data sources.

Figure 3. The open data community is simultaneously simple and complex. The chain between suppliers of open data and demanders of services is short, but almost every entity in the open data community can link to every other entity.

Source: Deloitte (2012)

OpenDataandSmallholderFoodandNutritionalSecurityFigure3.png

Deloitte distinguish five archetypes of actors:

  • Suppliers: organisations that publish their data via an open interface to allow others to use and reuse it
  • Aggregators: organisations that collect aggregate open data and sometimes, other proprietary data, typically on a particular theme, find correlations, identify efficiencies or visualise complex relationships
  • Developers: organisations and software entrepreneurs that design, build and sell web-based, tablet or smartphone applications for individual consumption
  • Enrichers: organisations (typically larger, established businesses) that use open data to enhance their existing products and services through better insights
  • Enablers: organisations that facilitate the supply or use of open data, such as the competition initiatives

Although the potential value of open data generally is estimated to be high, the actual measured impact of open data in (developing) countries is low

In literature, the estimated value of open data for society varies from large to enormous:

  • Jamaica benefitted by US$21 million in 2013 (CTA/AgriHack, 2015a)
  • Ireland could benefit by EUR 126.4 million in the for geospatial information sector alone (Lee et al., 2014)
  • A report produced by accountancy firm Deloitte (2012) estimates the economic value of the data held by the public sector in the UK and released for use and reuse to be around £5 billion per year (UN, 2014)
  • The European Commission (EC) estimates the aggregated direct and indirect economic impact from applications based on open data across the EU27 economy to be €140 billion annually (EC, 2011)
  • A report from McKinsey Global Institute (McKinsey, 2013) puts the global value of better and more open data at US$3 trillion per year with most of this benefit accruing to the USA and Europe.

Whether this value actually materialises depends on the functioning of the open data community in a country. In the Open Data Barometer, Davies (2013) assesses a country’s ability to secure and sustain the benefits of open data. Focusing on government data, these components are:

  • the government’s capacity and commitment to open data, addressing the political will and organisational ability of governments to both make open data available, and to secure benefits from open data, such as increased operational efficiency;
  • citizen and civil society freedoms and engagement with the open data agenda, including the presence of strong Right to Information and Data Protection regimes, which are important for empowering citizens to hold government to account, and protecting citizens from potential abuses of open data (Davies, 2013);
  • resources available to entrepreneurs and businesses to support economic reuse of open data and to catalyse intermediary actions, including internet penetration, the availability of training for businesses and government support for open-data-led innovation.

Figure 4 shows the deviation of different regions in the world from the global average in open data readiness. The African continent has the lowest open data readiness (Davies, 2013). This is caused by limited internet penetration and a scarcity of entrepreneurs and civic technologists who often act as key intermediaries between open data and wider use of that data. To achieve impact, a substantial focus on capacity building and sustainability of intermediaries is required, as well as an exploration of different approaches to making data accessible that do not rely on internet penetration, such as through print media, community radio and mobile phones (Davies, 2013). In the Barometer, the Caribbean and Pacific regions are included in larger regions, Americas and Asia, respectively, and therefore no specific conclusions can be drawn for these regions. Recently CTA has carried out an open-data readiness assessment for open data in the Caribbean (CTA/AgriHack, 2015b). This research indicates a rising Caribbean tech ecosystem and interest in open data. However, few of the developers interviewed were using open data and developing agricultural apps was seen as challenging. Lack of domain knowledge was given as main reason. Also, the need for capacity building and sustainability of intermediaries and data availability was mentioned.

Overall, there is a large gap in terms of access and uptake of ICT between the ‘advanced economies’ and the rest of the world as also demonstrated in the UN report A World that Counts (UN, 2014). As a result, open data development will currently mainly impact ‘advanced economies’.

Figure 4. The difference in open data readiness in the regions of the world compared to the global average. The index for open data readiness ranks from 0 to 100. The global average scores for government, civil society and business are 50, 50 and 40.

Source: Davies (2014)

OpenDataandSmallholderFoodandNutritionalSecurityFigure4.png

The potential impact of open data on the smallholder ecosystem

The availability of open data can catalyse the functioning of the smallholder ecosystem by providing each of the actors with relevant information about the ecosystem, its actors and its functioning

The smallholder ecosystem consists of many value chain actors including smallholders, cooperatives, input providers, traders, processors, exporters and wholesalers and global businesses (Figure 5). Around the value chain there are different service providers: financial services (credit, insurance), logistic services (transport, storage, grading, certification) and the extension services (farm management advice, business advice) facilitating the value chain. There are ‘governance’ actors such as the local, regional and national governments, donors, NGOs and researchers. Each of these actors is interested in information about the production, the functioning of the value chain and the availability of services and governance. The more information that is available about the ecosystem and its functioning, the better the different actors can fulfil their role, ultimately strengthening the food and nutrition security of the smallholder farmers. Not all actors are interested in the same information or look at the same information in the same way. A smallholder wants to have the information needed for decision-making in the context of his or her farm: What crops should I grow? How do I grow these crops? Where do I store my harvest? Which inputs do I use and where can I get them? Where do I sell my crops and at which price? A government may be more interested in the general picture of an area so it can adapt its policies accordingly. Ease of access to information in the ecosystem will ensure smooth functioning of the smallholder ecosystem. Sharing open data makes the information available in a transparent and efficient way.

Figure 5. The smallholder ecosystem and its actors, including the upcoming mAgri services.

Source: Freely adapted from Fonzi and Chau (2012)

OpenDataandSmallholderFoodandNutritionalSecurityFigure5.png 

Mobile operators and ICT service providers connecting smallholder farmers play a key role in achieving impact of open data

Mobile operators and ICT service providers are the most recent emergent actors in the farmer ecosystem. Sharing of data or information exchange between smallholders or between smallholders and other actors is traditionally very difficult in rural areas of developing countries because of large distances, bad roads and sparse, weak landlines for telecommunication. However, this picture has been changing in the last 10 years. Mobile operators are penetrating the rural areas with their networks and the prices of mobile devices are falling, resulting in more smallholder farmers who are connected to the outside world (World Bank, 2011). The mobile operators and ICT service operators are developing and hosting agricultural advice services on these mobile platforms, providing information based on text messages, structured menus, voice messages etc. In some cases, these services are developed as part of the rural marketing strategy of mobile providers. The GSM association has current identified 122 deployed services worldwide (GSMA, 2014). Mobile agricultural advice services vary from: services enhancing productivity on the farm; services facilitating farmers’ access to microcredit and insurance; services helping cooperatives organise their stock and trade, services allowing inclusion of farmers and cooperatives in agribusiness supply chains or providing access to the global market (World Bank, 2011). Smallholders are getting connected to the global economy and agricultural knowledge base via the mobile network. On top of this, development information is not only flowing from the global community to the smallholders, but the farmers are also providing information about themselves and their environment through these mAgri services. Opening up these data streams will provide the opportunity to better understand the smallholder world and therefore the world at large, enabling better progress in terms of development and governance and achievement of the Millennium Development Goals (UN, 2014).

The potential impact of open data on the smallholder food and nutrition security

The potential impact of open data on the food and nutrition security of smallholder farmers and the smallholder ecosystem is manifold. Table 1 provides an overview.

Table 1: Overview of potential impact of open data on smallholder food and nutritional security.

 

Topic General Smallholder perspective
Impact on governance
  • Improved effectiveness of government services and impact measurement
  • Better targeted development programmes
  • Transparency and democratic control
  • Better contextualised science
  • Participation and self- empowerment
Impact on services
  • Improved or new products and services
  • More clients
  • Better access to logistic, extension, financial, input, trade services
Impact on the value chain
  • Improved traceability and quality standards for buyers
  • More efficient value chain
  • Better access to the (global) markets
  • Better price negotiations
  • Better functioning cooperatives
Impact on production
  • More stable supply
  • Continuous market
  • Higher yields
  • Less perishing yields
  • Higher availability of inputs
  • Better pest control

 

Currently the main sources of open data are:

  • governments
  • government agencies such as space and meteorological agencies
  • science
  • NGOs
  • business

In the next chapter, each of these sources will be explored and the current and potential impact for the smallholder food and nutrition security will be discussed. Relevant applications or potential applications are presented. Each of the data sources will be ranked for its current and potential impact on smallholder food and nutrition security. The rank is subjectively based on our current best knowledge and not based on an analytical methodology. The rank varies from 0 to 4, where 0 means no impact on the food and nutrition security of smallholders, 1 means initial impact, 2 means substantial impact, 3 means large impact and 4 means very large impact. This ranking is not a final judgement, but a means of communicating a quick scan overview. It needs to be discussed with the wider open data community for further confirmation.

Open data and farmers’ rights

Although open data has a large potential for positive impact on smallholder food and nutrition security, this does not mean that all data should be automatically open. A number of issues must be considered (Maru, 2014):

  • Smallholders should benefit from the data they provide. Open data about the smallholder ecosystem should be made accessible to the smallholders in a timely, fair and equitable manner; they should be affordable, relevant, useful and trustworthy for famers to effectively use them. To realise this, smallholders should be included in processes related to the decision on which data and information they want to generate, share and exchange, according to their needs and preferences.
  • Open data about smallholders may create or increase the inequality between smallholders and other actors in the smallholder ecosystem. Smallholders may lack the capacity or the technical means to benefit from the information provided. Therefore, open data development may also imply capacity building, technical enablement and the implementation of legislation.
  • Open data should not violate the privacy of smallholders. Data may contain elements that are sensitive from a business, political, social, religious or traditional perspective and should not be spread automatically or only in such a way that these issues are dealt with.

Overview of the different sources of open data for food and nutrition security and their impact on the smallholder ecosystems

Open government data

The number of countries with open data programmes has grown rapidly over the last few years. As at mid- 2014, there are at least 50 national governments running open data portals and initiating OGD initiatives (Davies, 2014b). One of the organisations catalysing this development is the Open Government Partnership (OGP, 2014) providing an international platform for domestic reformers committed to making their governments more open, accountable, and responsive to citizens. Although the OGP declaration does not explicitly mention open data, many governments made commitments to open data development as a result (Davies, 2014b). As can be seen in Figure 6, participation in the OGP is not evenly distributed across the globe. The number of APC countries participating in the OGP is limited when compared to the Americas, Europe and Australia. African countries who are participating are: Ghana, Kenya, Liberia, Malawi, Sierra Leone and Tanzania. In the Caribbean, the Dominican Republic and Trinidad and Tobago are participating. In the Pacific no partner countries are participating (OGP, 2014).

Of the 16 member-countries of the Caribbean Community (CARICOM), seven had enacted freedom-of- information laws, four had drafted such legislation and two had guaranteed freedom of information as a constitutional right in 2011 (CTA/AgriHack, 2015).

Figure 6. Participating countries in the Open Government Partnership.

Source: OGP (2014)

OpenDataandSmallholderFoodandNutritionalSecurityFigure6.png

Which data are being made available?

A description of different data sources generally provided by national governments as identified by the Open Data Barometer study (Davies, 2013) is presented in Table 2.

Table 2: A description of different data sources generally provided by national governments.

 

Innovation cluster Social policy cluster Accountability cluster

Data commonly used in open data applications by entrepreneurs or with significant value to business.

Data useful in planning, delivering and critiquing social policies and with the potential to support greater inclusion and empowerment.

Data central to holding governments and corporations to account.
  • Map data
  • Public transport timetables
  • Crime statistics
  • International trade data
  • Health sector performance
  • Primary or secondary education Performance data
  • National environment statistics
  • Detailed census data
  • Land ownership data
  • Legislation
  • National election results
  • Detailed government budget
  • Detailed government spend
  • Company register

 

According to Davies (2013) categories of data managed by statistical authorities are most often accessible online (Figure 7), but are often only released in aggregated forms with unclear or restrictive licences. National budgets are available more often than the spending data and when available, spending data are often published in very aggregated forms. Land and company registration data are least likely to be openly available, reflecting both the absence of coherent land and company registry data sets in a number of countries and a low priority placed by many OGD initiatives on making these data sets available.

In developing countries, much government information is still managed on paper at local offices and is not digitised. Data sets are seldom clearly open licensed and there is poor understanding of what open licences entail. There is a frequent mismatch between open data supply and demand in developing countries; politically sensitive data sets are among the least likely to be published; key data sets such as company registers, digital maps and land registration databases are not held in digital format (Davies, 2014a).

 

Many data sets are of low quality, which hinders their usage and limits their value. Data may be aggregated, outdated data sets or poorly structured data. Also, the navigation through data sets and limited information about the data sets may hinder uptake and usability (Mutuku and Mahihu, 2014).

Less than 7% of the data sets surveyed in the Open Data Barometer study were published both in bulk machine-readable forms and under open licences. This makes it unnecessarily difficult for users to access, process and work with government data, and potential entrepreneurs face significant legal uncertainty over their rights to build businesses on top of government data sets. (Davies, 2013). In the second edition of the Barometer (Opendatabarometer, 2015), the general outlook on development of government open data has changed very limited. The total amount of government open data has grown only 3%. In the survey of 2014, 31 countries have at least one open data set, but only 50% of the data sets surveyed among the 11 top-ranked countries qualified as fully open.

Figure 7. Average openness score of the data sets.

Source: Davies (2013)

OpenDataandSmallholderFoodandNutritionalSecurityFigure7.png

Impact on the smallholder ecosystem

Evidence on the impact of open government data is almost universally lacking. Few open government data programmes have yet been evaluated and the majority of discussions of impacts are based on anecdote (Davies, 2013). The Open Data Barometer study asked about six kinds of open government data impact (government efficiency, transparency and accountability, environmental sustainability, inclusion of marginalised groups, economic growth, and supporting entrepreneurs). In countries with some form of open government data policy no examples of impact could be found in 45% of the impact questions and on average evidence of impact was scored at just 1.7 out of 10 (Figure 8, Davies, 2013).

Figure 8. Average impact score across all countries based on an expert survey.

Source: Davies (2013)

OpenDataandSmallholderFoodandNutritionalSecurityFigure8.png

Impact on governance

In general, the smallholder will benefit, like all citizens, from a transparent government. Data about government budgets, government spending and the performance public institutes such as schools and health clinics will contribute to this transparency.

An example can be found in the UN report on the data revolution for development:

In Mexico, a budget research and advocacy group called Fundar developed an online database of government farm subsidies. One of the problems brought to light was the way in which billions of dollars of the funds were distributed. Though many farm subsidy programs claim to target the neediest farmers, the database revealed that a small group of wealthy farmers had captured the vast majority of subsidy funds over time (the top 10% of recipients had received over 50% of the funds). The studies contributed to the government decision to review and change the distribution of the subsidies. — (UN, 2014)

The impact of open government data on better governance starts emerging, but considering all improvements that still can be made we ranked the current impact 1, with a potential impact of 3 (on a scale of 0 to 4).

Impact on the value chain and sustaining services

Potentially, the value chain and sustaining services can also benefit from government data as presented above (Davies, 2013). In particular, the following data would be relevant at a regional or local scale. This is also one of the recommendations in the second edition of the Open Data Barometer (Opendatabarometer, 2015):

  • ownership and legal status of companies; input suppliers, traders, financial advisers;
  • land ownership;
  • (regional) trade statistics and prices;
  • infrastructure, transport.

The impact of open government data on better sustaining service and the value chain is nil, but relevant information could be made available; therefore, we rank the current impact as 0 with a potential impact of 2 (on a scale 0 to 4). We expect the impact to be less than on the governance itself.

Impact on agricultural production

The data as presented by Davies (2013) will have limited impact on agricultural production. The open data portal of the US Government gives some examples of data that can impact agricultural production.

  • The plant Hardiness Zone Map. This is the standard by which gardeners and growers can determine which plants are most likely to thrive at a particular location (USAID, 2014).
  • SoilWeb: An online soil survey browser, providing access to soil survey data (CSRL, 2014).

The impact of open government data related to governance on actual production in developing countries is ranked as 0. A government can decide to generate specific data sets like the American examples above, but to us this could also be seen as a scientific output hosted by the government. There is a boundary issue here.

Therefore, we rank the current impact as 0, with a potential impact of 1. Governments may subsidise specific data sets dedicated to production.

Space and meteorological agencies

In 1991, NASA adopted the Earth Science Data Policy to promote full and open sharing of all data with the research and applications communities, private industry, academia and the general public. NASA was the first agency in the United States and the first space agency in the world to provide full and open access in a timely manner and at no cost. Gradually, other US agencies and international space agencies have adopted similar open-access policies and practices.

  • 2008: The USGS decided to change their data policy of the Landsat programme, meaning that all Landsat data (since 1972) is freely available to any user. As a result, the distribution of Landsat satellite images increased dramatically (~25,000 in 2001 to more than 2.5 million in 2010) and they are used in a wide range of disciplines, including agriculture and nutrition (e.g. to monitor crop water use, crop growth and crop yield) (Wulder et al., 2012).
  • 2010: This enabled Google, in partnership with NASA, USGS and others to launch Google Earth Engine (Google, 2014a). Google Engine is a cloud-computing platform for processing satellite imagery and other observation data and facilitates the usage of satellite imagery by non-expert scientists. Currently, the platform contains over 40 years of Landsat data, taken from the USGS Landsat archive and MODIS data from NASA. Not only does it provide easier access to a large warehouse of ‘ready-to-use’ satellite imagery (compared to the Landsat and MODIS archives of the USGS and NASA) but it also provides unprecedented computational power for the individual scientist. The latter is a major relief in big-data research and processing. Researchers can log on (after registration), access all the data and run their own algorithms.
  • 2013: The ESA (2013) announced the European Delegated Act on Copernicus on data and information policy (together with EUMETSAT). This act provides free, full and open access to users of environmental data from the Copernicus programme, including data from the Sentinel satellites (the first of a total of six satellites was launched in April 2014).

 

Open-access policy also takes place, though slowly, in the national meteorological and hydrological services worldwide. In 1995, the WMO (World Meteorological Organisation) committed itself to broadening and enhancing the free and unrestricted exchange of meteorological and related data and products (Resolution 40). The WMO has issued many requests to Member States to provide their data to international data centres so that the data may be freely available for research and operational use. However, in practice, there are still many obstacles. For example, in Europe, the databases are primarily a national matter. And there is still a lack of data in international repositories and for some of these, data restrictions are imposed by the data providers, which may limit accessibility (Klein Tank et al., 2010). Nevertheless, a number of weather services follow an open data policy.

  • National Weather Service (NWS, part of the National Oceanic and Atmospheric Administration [NOAA]) in the United States. As the NWS is a government agency, most of its products are in the public domain and are available free of charge, ranging from satellite observations and station data to radio soundings and oceanic buoy data.
  • Norwegian Meteorological Institute (MET Norway): Official data and products are regarded as public- sector information and are freely available to the public for use, distribution and processing.
  • The Royal Netherlands Meteorological Institute (KNMI): Since 1999 the KNMI gradually released their weather station observation data via the internet.
Which data are being made available?

Initially most space agencies provided only raw satellite data, which require expert knowledge for processing and interpretation. Nowadays, many suppliers realise that in order to improve the applicability of their data, so-called higher-order-level products (or end products) need to be provided. For example, with the launch of MODIS in 1999, a wide range of higher-order-level products were developed, such as vegetation indices (NDVI), leaf area index (LAI), land surface temperature (LST), anomalies and fires. These products are all archived and available (most of them at no charge), and have been developed by MODIS – science teams in four discipline groups: atmosphere, calibration, land and ocean. This has been further elaborated by Google with the Earth Engine and other space agencies also provide ‘ready-to-use’ products.

The range of products is diverse and extensive (based on operational meteorological satellites (EUMETSAT and NOAA) and ‘research type’ satellites (NASA and ESA)): soil moisture data and anomalies (SMOS, ASCAT, SMAP); rainfall data (TRMM, FEWS and GPCP), 10-daily global vegetation index data (SPOT); LAI; downward radiation (LandSAF); digital elevation maps (SRTM); flood maps; land use/cover maps; lake level data; and many more.

The openness of space agencies has triggered free access of other data sources. For example, a number of global data sets on surface soil moisture have become available in the last decade. These products are based on different satellite sensors. Ground validation is required in order to demonstrate their applicability and further improve such products. This resulted in the International Soil Moisture Network initiated by GEWEX and ESA (ISMN, 2014) to establish and maintain a global in situ soil moisture database (available after registration), which is essential for validating and improving global satellite observations and land surface models.

More and more meteorological services release most of their ‘standard’ weather station observation data (e.g. air temperature, relative humidity, wind speed and rainfall). This is not the case for weather forecasts, with the exception of the NWS in the United States and MET in Norway. The spatial resolution for most of the weather forecasts is limited to national and regional levels.

The weather services in the United States (NOAA) and in Europe (European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)) have a number of operational satellites. NOAA also follows an open data policy. The role of EUMETSAT is different (but changing, e.g. the Copernicus project), although their data and services are provided free to all African countries (EUMETSAT, 2014).

Impact on the smallholder ecosystem
Impact on governance

In general, the smallholder will benefit, like all citizens, from a government that performs well and can act swiftly to sudden events. The open science data policy has led to a number of global food security applications, most of them specifically intended for local governments, NGOs and decision-makers, which in turn also benefits smallholder farmers:

  • Crop explorer: The global Crop Explorer provided by USDA’s Foreign Agricultural Service enables global food supply monitoring, and allows users to explore by crop or region. The explorer combines weather data and coarse satellite observations (e.g. MODIS) with soil moisture and crop models to monitor agro-meteorological variables and crop conditions throughout the world.
  • FEWS NET (http:fews.net): Famine Early Warning System Network created by USAID. The goal of FEWS NET is to lower the incidence of drought- or flood-induced famine by providing to decision-makers, timely and accurate information regarding potential food-insecure conditions. With early warning, appropriate decisions regarding interventions can be made. The agricultural and vegetation conditions are monitored in Africa, based on information, satellite (MODIS - NDVI) and weather data (rainfall estimates) provided by the USGS, NASA and NOAA. Beside satellite information, FEWS contains information from other sources

e.g. commodity prices.

  • SERVIR Global: This is a regional visualisation and monitoring system (a joint effort of NASA, USAID, World Bank and CCAD) specifically intended for decision-makers. The system provides earth observation (EO) and predictive models (to monitor and forecast ecological changes and respond to natural disasters (e.g. droughts, floods, frost, and fire events)) based on data from orbiting satellites.
  • Global Forest Watch (GFW): The launch of the Google Earth Engine has led to an improved (higher spatial resolution) forest-mapping tool. GFW is an interesting example how open satellite data can be used to empower the tribal of people in the forest. GFW is free and follows an open data approach in putting decision-relevant information in the hands of governments, companies, NGOs and the public.

There is considerable experience in the application of space and meteorological data to the governance of food and nutrition security. Data from this effort are now more and more ‘openly’ available. The impact of open space and meteorological data on better governance is clearly there, but will develop and improve further over time. Therefore, we ranked the current impact as 2, with a potential impact of 3.

Impact on the value chain and sustaining services

Potentially, many of the above-mentioned services facilitate the value chain and sustaining services based on EO data. Crop monitoring and harvest prediction services enable farmers, traders, storage providers, processers and other actors in the value chain to anticipate events in the coming harvest season. Satellite monitoring can be used to detect excessive rainfall or flooding of infrastructure. The satellite monitoring of crops may increase the confidence of microfinance companies to provide loans to smallholder farmers or to serve as an index for micro-insurance companies.

EARS – Earth Environment Monitoring (http://www.ears.nl) – is an example of a small innovative business that acts in this domain. They provide a crop monitoring service for Africa, which is based on hourly data from Meteosat (EUMETSAT). The visual and thermal infrared images are used to determine the crop water use and rainfall.

These are then applied in drought monitoring, crop yield forecasting and river flow forecasting systems. Since 2009, EARS has developed a satellite-based drought and excessive rainfall index for insurance companies (based on 30 years of data).

Other index insurance pilots exist and the G4AW programme of the Dutch Government will result in more services (http://g4aw.spaceoffice.nl/en/). As a result, impact on services is emerging and we rank the current impact as 1, with a potential impact of 3. The impact on the value chain for smallholders has not been found (ranked 0), but potential impacts of flooding on the road network can be measured (ranked 1). For crop and yield estimates from space there is currently an issue of resolution as will be explained in the section below.

Impact on agricultural production

Direct monitoring and agricultural advice on the management practice in the field based on satellite information is difficult. There is a tension between the scale of observation (25–250 m) and the size of the cropping areas (which generally containing mixed crops). Direct advice based on satellite information on the production on the ground is in many cases difficult.

Despite this discrepancy, the first applications directly impacting smallholder agricultural production are emerging. For example eLEAFis an advisory firm that operates in the transition area between  RS science and operational applications. eLEAF is specialised in using satellite data (including open satellite data from Landsat and MODIS, Meteosat, etc.) for estimating crop water use and crop growth (biomass and yield) and has developed a satellite-based crop monitoring service, which is operational in a number of countries. In 2012, eLEAF was involved in a pilot project called SMART-ICT, funded by IFAD. The project included developing and using tools for smallholder farmers that can monitor plot specific information from satellite measurements and was tested in Egypt, Ethiopia and Sudan. Detailed and field-/crop-specific information (e.g. crop water use, irrigation requirement and biomass) were provided on demand through web platforms and SMS services.

Although more and more space and meteorological data are becoming openly available, their uptake and usage to enhance smallholder food and nutrition security is limited. This is partly because it takes specialist skills and knowledge to apply this data in a relevant and reliable manner, partly because the resolution of current generation of open satellite and meteorological data are too coarse in space and time. There are two programmes that aim to further stimulate the usage of open satellite data for smallholder food and nutrition security:

  1. NASA and AGRA (Alliance for a Green Revolution in Africa) joined forces to get the data in the hands (and mobile phones) of people who could use it most. Currently, they are investigating ways to get satellite data to farmers and distributors through the mFarms platform (an ICT platform designed to help stakeholder in agricultural value chains communicate with each other efficiently, establish and maintain business relationships and manage the flow of goods and services among them). mFarms provides agricultural information via cell phones to their network – 80,000 farmers and thousands of other distributors, warehouses and more in 17 African countries (NASA, 2014b).
  2. In 2013, the Dutch Government launched the Geodata for Agriculture and Water (G4AW) programme (http://g4aw.spaceoffice.nl/en/). Its objective is to improve food security in developing countries by providing food producers with relevant information, advice or products facilitated by satellite information. Netherlands Space Office (NSO) is executing this programme, commissioned by the Dutch Ministry of Foreign Affairs.

As a result, we ranked the current impact of open space and meteorological data on smallholder production as 1, because some experiments have been carried out, and the potential impact as 2 with the current available sources, because of the spatial and temporal resolution of the available data. This may change if very-high- resolution sensors become available in openly accessible systems.

International organisations and partnerships

International organisations and partnerships are key drivers for the availability of open data in developing countries in two ways (Boyera and Iglesias, 2014). On the one hand, they encourage and facilitate open government movements by sponsoring or setting up projects and programmes and on the other hand, they make their own data resources available in open data format. The biggest player is the World Bank, active in all developing regions, leading a wide variety of sectorial initiatives such as open transport, open finance, open aid, open climate etc. World Bank was also the first large international organisation to open their data resources in 2010, starting with 2000 data sets. Currently all main international organisations make their global data sets and country reports available in open data format. Examples are: World Bank (http://data.worldbank.org/), http://www.opendataforafrica.org, FAO (http://faostat3.fao.org/faostat-gateway/go/to/home/E), UNEP (http://geodata.grid.unep.ch/), UN (http://data.un.org) and WTO (http://www.wto.org/english/res_e/sta..._e.htm#summary)

Which data are being made available?

In general, data such as global- and country-level indicators, derived from governments, economical models and census are being made available. This type of data is very useful to monitor the state of the earth over time or to make a first characterisation of a country or compare between countries. A visualisation of such data is presented in the Figure 9. Different application programming interfaces (API) and other tools are being developed to make the data easily reusable by others (Figure 10).

Impact on the smallholder ecosystem
Impact on governance

Open data presented by international organisations has primarily an impact on governance. The data allows us to make a quick assessment of a country’s statistics; to study changes over time; and to compare countries, including general agricultural indicators such as the amount of harvested or processed crops. The data allows governments, international organisations and NGOs to make policy decisions, especially at a higher level. If a country does not have reliable statistical or census agencies, these portals may contain the only available data sets about that country. They may also include data which is generally not collected by national governments but may be important to help solve the major global challenges such as the Millennium Development Goals, how to feed the world, address climate change, generate sustainable energy and address loss of biodiversity. These data are suitable to generate infographics that are used to inform decision-makers and the public.

Figure 9. Visualisation of the total yearly cereal production of the countries in the world as typical example of an open data set from of the international organisations.

Source: http://www.foodsecurityportal.org/

OpenDataandSmallholderFoodandNutritionalSecurityFigure9.png

Figure 10: The IFPRI Food Security Portal as an example of the reuse of open data from the World Bank and other sources.

OpenDataandSmallholderFoodandNutritionalSecurityFigure10.png

However, international organisations generate more impact when they open their raw data rather only the aggregated numbers. Many of these country-specific data sets will be based on data aggregated from regional, national and subnational levels. This is efficient in times when computational and storage capacity is low and may be the only way to analyse and comprehend large global data sets. But with each aggregation, information is lost. In recent times, computational and storage capacity have increase dramatically, and new ways of visualising and analysing large data sets are being discovered. Data from international organisations will become much more informative and generically applicable to a smallholder context as the raw, fine-grained data that form the basis for the global statistics become available.

Therefore we ranked the current impact of data from international organisations on governance as 2, with a potential to grow to 3 if more detailed data become available.

Impact on the value chain and sustaining services

No direct evidence for impact of open data presented by the international organisations has been found. The potential impact on the value chain and sustaining services is low. Most of the data have a high (regional, national, subnational) aggregation level and is not specific enough for direct application in the value chain or sustaining services. Exceptions to this are world and national commodities price information which is relevant for trade.

Therefore, we rank the current and potential impact of open data from international organisations on the value chain and sustaining services as 0.

Impact on agricultural production

No evidence for impact or potential impact on agricultural production could be found.

As a result, and by nature of the current data available, we do not see current or future impact on the food and nutrition security of the smallholder communities (ranked 0).

Science

The concept of open data first emerged in the scientific community with the establishment of the World Data Centres (WDC) in 1957. The objective of the WDCs was to minimise the risk of data loss and to maximise data accessibility (Wikipedia Open Data, 2014). Other advantages of open access to research data are that:

  • research results based on data can be verified and critically examined;
  • unnecessary duplication of research work can be avoided;
  • data can be analysed comprehensively and made use of, e.g. in follow-up projects;
  • the research process can be accelerated through data-sharing;
  • new findings can be achieved by merging data from different sources;
  • the merging of data brings an informal added value and yields higher-quality data products, e.g. indices and databases;
  • data sets which are collaboratively assembled and jointly used are more cost-efficient;
  • open access promotes reuse of data by the public and by industry (Open Access, 2014).

The first WDC system was founded in the United States, USSR and several other countries by the International Council of Science (ICSU) to archive and distribute data collected from the observational programmes of 1957– 58 (The International Geophysical Year). Most of the data was exchanged on a barter system; non-members could acquire data at the basis of copying cost. However, the usage and up take of this ‘open’ data was limited and was only available to a selected number of individuals and organisations (Landers, 1979). Later, the data became machine-readable and with the foundation of the internet in 1989, the potential for exchange of documents and information between individuals and organisations increased dramatically.

In 2004, the science ministers of all nations of the OECD (Organisation for Economic Co-operation and Development), which includes most developed countries, signed a declaration which essentially states that all publicly funded archive data should be made publicly available. Following a request and an intense discussion with data-producing institutions in Member States, the OECD published in 2007 the OECD Principles and Guidelines for Access to Research Data from Public Funding as a soft-law recommendation (OECD, 2007).

From the social sciences community, the Dataverse Network was created in 2006 at Harvard University (http://thedata.harvard.edu/dvn/). The Dataverse Network is an open-source application for sharing, citing, analysing and preserving research data. Its main goal is to solve the problems of data-sharing through building technologies that enable institutions to reduce the burden for researchers and data publishers, and incentivise them to share their data. By installing Dataverse Network software, an institution can host multiple individual virtual archives, called ‘Dataverses’ for scholars, research groups, or journals, providing a data publication framework that supports author recognition, persistent citation, data discovery and preservation. Examples of Dataverse Networks installed at universities and organisations, where data can be downloaded for reproducible research, are:

  • Harvard Dataverse Network (http://thedata.harvard.edu/dvn/): which contains scientific data from all disciplines worldwide, including the world's largest collection of social science research data;
  • Dutch Dataverse Network (https://dataverse.nl/dvn/): which contains the data from a number of Dutch universities;
  • European Union Democracy Observatory (EUDO).

In 2008, the WDCs were reformed (together with the ICSU Federation of Astronomical and Geophysical data- analysis services) in the new World Data System (ICSU-WDS) to bring all scientific sources together under a single umbrella with same quality standard (ICSU-WDS, 2014). Accreditation criteria are set in terms of: scientific relevance, governance, data management, technical infrastructure and security, and open and equitable access to quality-assured scientific data, data services, products and information. All shared data will be made available with minimum time delay and at minimum cost. ICSU-WDS data portal gives access to data from members (currently more than 80 members), primarily active in the field of geophysical science: the WDC for Meteorology (USA, maintained by NOAA), the Ocean Data Portal (oceanographic data held by the IODE global network of 80 National Oceanographic Data Centres), the Global Change Master Directory (GCMD), or the Global Earth Observations System of Systems (GEOSS), etc.

There are many other data archives and repositories, some of which are specific to certain research communities. Databib and re3data contain a large searchable collection of online research data repositories.

This year, the next step in open science data development has been taken by launching the Open Data Journal for Agricultural Research (ODjAR) (http://library.wur.nl/ojs/index.php/odjar/). ODjAR aims to make open data publication more attractive for researchers while enabling authors to get scientific credit for their work through citations and digital object identifiers for future reference.

In the remainder of this section, we will discuss the open research data from a smallholder perspective, focusing on agricultural research data, using the CGIAR Consortium as an example.

Agricultural data

As a consortium of 15 international agricultural research centres, CGIAR developed its open data policy in 2013, which will be fully implemented in 2018. Many of the member organisations – AfricaRice, ICRAF, IFPRI, Bioversity International, CIFOR, and CGIAR Research Program on Forests, Trees and Agroforestry – are also using Dataverse.

In 2014, CGIAR launched the CGIAR Consortium Data Management System (CGDMS) (http://www.cgiar.org/resources/open/data-management-system/).

Some randomly chosen examples of open data sets from members of the CGIAR Consortium are:

  • ILRI Data portal: ADA Nicaragua - Baseline Survey. The database of 169 households containing monitoring productivity of animals in DGEA1 (Germplasm for Dairy Development in East Africa) dairy cattle keeping households: calving information, milk production, etc. (http://data.ilri.org/portal/)
  • IRRI Dataverse: SOCIO-ECONOMIC EVALUATION OF HYBRID RICE CULTIVATION IN THE PHILIPPINES Basic household and farm characteristics and input and output data in rice production such as; yield, fertiliser, insecticide, weedicide and other pesticides, and labour use. Data are also available on adoption of modern rice technologies, prices of input and output and farm wage rates. 1970 – 1971(http://irri.org/tools-and-databases/irri-dataverse).
  • IRRI Dataverse: CENTRAL LUZON LOOP SURVEY: Basic household and farm characteristics and input and output data in rice production such as; yield, fertiliser, insecticide, weedicide and other pesticides, and labour use. Data are also available on adoption of modern rice technologies, prices of input and output and farm wage rates (http://irri.org/tools-and-databases/irri-dataverse).
  • IWMI Water Data portal: District-wise crop area, production and yield for all crops growing in the region of 52 districts in Andra Pradesh, Maharashtra and Karnataka. Data were downloaded and processed by ACIAR project (http://waterdata.iwmi.org/DataArchive.php).

The ‘standard’ open science data portals are focused on sharing data within the research community to enable better science. Open agricultural research data is diverse in nature and generally very specific and focused on answering a certain research question. They tend to be collected in a limited time span. Only a few data sets are opened (151 in IRRI Dataverse and 30 in the ILRI data sets portal).

Some data sets are aimed at a more general application and are developed for the wider public.

  • NEXTGEN Cassava project: ‘Cassavabase’ provides a ‘one-stop shop’ for cassava researchers and breeders worldwide, including genomic selection analysis tools and phenotyping tools.
  • The 3000 Rice Genome project: The giga-data set contains the genome sequences (averaging 14× depth of coverage) derived from 3,000 accessions of rice with global representation of genetic and functional diversity. The challenge now is to comprehensively and systematically mine this data set to link genotypic variation to functional variation.
  • RTB Atlas is an online mapping resource for the community of people working to improve roots, tubers and banana (RTB) crops. The platform helps scientists set priorities for interventions to improve production of cassava, yam, potato, sweet potato, banana and plantain and allows users to overlay 25 sets of variables onto a world map (including harvested area, potential yield, and yield gap).
  • Global Yield Gap and Water Productivity Atlas aims to inform policy-makers about the difference in current average farm yields and the potential rain-fed and irrigated yield. Water productivity is used as indicator to express the efficiency in converting water to food.
  • IWMI World Water and Climate Atlas gives irrigation and agricultural planners rapid access to accurate data on climate and moisture availability for agriculture. The atlas includes monthly and annual summaries for precipitation, temperature, humidity, hours of sunshine, evaporation estimates, wind speed, total number of days with and without rainfall, days without frost and Penman-Montieth reference evapotranspiration rates.
  • The Integrated Database Information System (IDIS) is an online data-sharing platform that provides access to water, agriculture and environment scientific data to help researchers and their research partners improve the water productivity. IDIS contains over 1 billion records, with a focus on IWMI and CPWF river basins.
Impact on the smallholder ecosystem

The direct applicability, and therefore impact, of the open science data sets in the smallholder ecosystem is limited. However in the long run, better scientific research on smallholder production and the smallholder ecosystem will have a large impact on the smallholders, but only if the new knowledge and insights generated in the science world find its way to other actors in the smallholder ecosystem, for example via extension workers. We ranked this indirect impact as 0, because it is not the result of open data development.

Another way is to develop specific data sets dedicated to actors outside the science community. Information systems such as Harvest Choices, Global Yield Gap and Water Productivity Atlas, IWMI World Water and Climate and IDIS aim for better governance, allowing planners to make better decisions on governance, investments or to optimise irrigation. Cassavabase, the Rice Genome project and the RTB Atlas are aimed at actors in the value chain facilitating breeders in crop improvement; these improved crops will impact the smallholder production directly by enabling to plant crops with specific characteristics. So far, no open data sets dedicated to agricultural production have been found, but IRRI provides the Rice Crop Manager app as a way to let smallholders benefit from scientific insights.

To increase the impact of scientific research, more dedicated open data sets need to be developed to facilitate knowledge- driven decision-making.

The current impact of dedicated data sets is ranked as 1 for the support services (breeders) and as 2 for governance. However, all four impact groups will benefit if more dedicated open data sets emerge (ranked as 3), although each of the actors within these impact groups will have specific questions and needs.

NGOs

Currently, there is a transition going on in the development sector towards ‘open development’. This development is driven by the idea that transparency in development (Broek et al., 2012):

  • increases accountability of the development process in the South as well as in the North;
  • improves allocation of scarce development resources in developing countries;
  • increases impact of development in reducing poverty;
  • improves lives in developing countries; and
  • maintains domestic support for development at times of financial stringency.

At four High Level Forums for Aid Effectiveness (2002–2011), the international community committed itself to transparency and accountability as two cornerstones for effective development cooperation.

In March 2005, the Paris Declaration on Aid Effectiveness was signed. Donor governments, multilateral organisations, NGOs and partners agreed to work together to make developing countries more in charge of their own development processes, and to hold all stakeholders in the development process accountable for achieving concrete development results. Transparency is needed to achieve this goal. To demonstrate the commitment of NGOs to transparency and accountability the International NGO Accountability Charter was launched (INGO Accountability Charter, 2014). It aims to speed up progress in transparency among international NGOs by helping its members to establish a high quality accountability framework that helps them to report on transparency and effectiveness, among others. Increasingly, NGOs are making their project descriptions, development goals, activities and spending available in IATI Standard (IATI Standard, 2014).

Different governmental and private donors organisations (IDRC, DFID, Hewlett Foundation, Open Society Foundation) now also require open data publication from their beneficiaries (Boyera and Iglesias, 2014).

Which data are being made available?

As a result, most of the open data in the NGO world is focused on the project accountability data set. An XML structure describes the project in detail including the summaries, activities, the budget and the time schedule etc. These data are quarterly updated with the results of activities. Currently these types of data sets are used to:

The NGO world is also experimenting with crowdsourcing techniques in order to monitor and evaluate these projects or to collect data. Examples are GoThree60 and Open RBF:

  • In their pilot project, GoThree60 collects opinion about the function of a maternity health care clinic in Uganda to collect an independent opinion directly from the users (GoThree60, 2014).
  • Open RBF take transparent financial monitoring a step further. For a health care centre in Burundi, Open RBF collects the improvement goals; monthly or quarterly health care providers enter their performance data in the system and the local community verifies service delivery and quality by providing independent feedback through interviews and mobile devices. The RBF system measures all these performance data against the agreed criteria and determines the amount of money to be allocated. This will then be transferred to the clinic or hospital. A fundamental aspect of Open RBF is that, once validated, all data are displayed on the internet for everyone to see and accessible through easy-to-read dashboards. This allows funders to see how resources are allocated, but more importantly, it encourages citizen engagement with a vital development issue. Through the Open RBF system, patients can follow the budgets, targets and performances of any clinic or hospital in real time. This information enables them to hold their governments to account for their efforts to provide better health care for all. The transparency offered by Open RBF can help to restore trust between citizens, local and national governments and private sector service providers (Cordaid, 2014).

Similar developments can be found in agriculture-focused projects. AgriTerra, an NGO that supports farmers’ organisations in developing countries, currently works on different methodologies (on paper, via SMS, using special software on tablets) to enable individual farmers of households to record data at farm level, experimenting in Peru, the Philippines and some African countries. The data collected by the farmers can be used by farmers during the project to compare and to learn, by the farmer organisations to do a meta-analysis on their members and by AgriTerra for monitoring and evaluation of their project. The collected data at farm level is also relevant for third parties, e.g. labels such as UTZ may use these data to sustain their quality claim (AgriTerra, personal communication).

CABI’s Plantwise project demonstrates how project data can be used to build up a detailed large-scale database of the spread of plant diseases in the world. Plantwise works with national governments to set up plant clinics where trained plant doctors provide farmers with practical, science-based diagnosis and advice to prevent and manage crop loss. Supporting this network of clinics, the Plantwise knowledge bank ensures an online and offline gateway to diagnostic services, pest tracking, and best-practice farmer recommendations specific to every country. The plant doctor reports back to the system the occurrence of diseases in the crops and within time a detailed map of global plant disease spread emerges (http://www.plantwise.org/).

Impact on the smallholder ecosystem
Impact on governance

NGOs become more transparent with the development and implementation of their open project data policies, explaining why, where, when and for what activities project money is being spent and what the result was.

Smallholders, like other beneficiaries of NGOs, will benefit from this development as a result of better governance and money better spent.

An interesting development would be if NGOs were to open their data collected at grassroots level for monitoring and to evaluation and steer their projects. Currently, only a few structured large-scale data sets with a longer time span are available about smallholders at household level. Opening up this kind of data will reveal the detailed patterns behind large-scale census data collected by governments and other institutes. The monitoring and evaluation data may enable the understanding of the bigger picture supported by quantitative data, resulting in better policies.

However opening up farm household data is not an obvious development. It is the responsibility of the data owners – farmers, farmers’ unions and/or NGOs – to do so. While doing this, farmers’ rights need to be considered and maybe intermediate steps are needed such as anonymising or aggregating the data into relevant groups.

We ranked the current impact of open data on governance as 2, because the principle of open development is clearly emerging. Soon, this development will mature and have an impact rank of at least 3.

Impact on the value chain, sustaining services and agricultural production

A lot can be learned about the smallholder ecosystem from projects targeting agricultural production or the value chain; this can be used to further educate farmers, farmers’ cooperatives or to design better projects. Currently, little of this information is available as open data. If data and results are shared more, the impact of these projects can become much larger and cross-fertilisation of projects, even across continents, can take place.

We ranked the current impact of NGO monitoring and evaluation data on the value chain, sustaining services and agricultural production as 0. However, potentially these data can provide a lot of information about the functioning of the smallholder ecosystem, and thus would be ranked 3.

Business sector

The United Nations Global Pulse states that open access to corporate data is the next frontier in the development of open data (Verhulst, 2014). Private sector companies accumulate a tremendous amount of data in their day-to-day operations. Market research, communications tracking, client relationship management and market activities generate a wealth of information, which tends to stay in the private domain (Responsible Data Forum, 2014). The corporate world in effect ‘owns’ terabytes of data and metadata, e.g. almost 7 billion telephone subscriptions are producing communication data every day, more than 1.82 billion people communicate on some form of social network and almost 14 billion sensor-laden everyday objects (trucks, health monitors, GPS devices, refrigerators, etc.) are connected and communicating over the internet, creating a steady stream of real-time, machine-generated data (Verhulst and Sangokoya, 2014). If this corporate data were made available in a de-identified and aggregated manner, researchers, public interest organisations and third parties would gain greater insights on patterns and trends that could help inform better policies and lead to greater public good. However, access to corporate data is sensitive and extremely limited. This is related to privacy issues, security and proprietary interests. The United Nations Global Pulse has the vision that the corporate world should not keep this data to themselves, but that it should be harnessed safely and responsibly as a public good. The United Nations Global Pulse is trying to persuade the corporate world to start making data sets more openly available (Verhulst, 2014).

Also the private sector itself has a lot to gain from data philanthropy or corporate social responsibility addressing data. Sharing data may spark innovation, can be used to scout talent and can help to safeguard the client base. As an imaginary sample of the latter case, the data from a mobile operator operating in a developing country may contain the signals of misfortune of its clients discussing floods, crops failures or unaffordable price raises. By sharing and analysing this data with governments or NGOs, the misfortunes of the clients may be prevented (UN Global Pulse, 2013).

Which data are being made available?

A quick survey of the Responsible Data Forum (2014) identified six categories in which business are experimenting in sharing their data for the public good:

  • academic research partnerships, in which corporations share data with specific universities and other research organisations;
  • prizes and challenges, in which companies make data available to qualified applicants who compete to develop new apps or discover innovative uses for the data;
  • trusted intermediaries, where companies share data with a limited number of known (often commercial) partners;
  • APIs, which allow developers and others to access data for testing, product development, and data analytics;
  • intelligence products, where companies share (often aggregated) data that provides general insight into market conditions, customer demographic information, or other broad trends;
  • corporate data cooperatives or pooling, in which corporations group together to create ‘collaborative databases’ with shared data resources.
Which data are being made available?

In this document we will further focus on telecom and IT data, but global companies in agricultural inputs and food processing are interacting more and more with smallholders both as a market and as a resource.

Experiments based on telecom data

Analysing the spread of malaria

In Kenya, researchers from the Harvard School of Public Health have deducted the influence of human mobility on malaria spread based on all calls and text messages sent by the Kenyan mobile phone subscribers of SafariCom during a period 1 year and combined with detailed diseases data. The researchers could estimate the probability for each person in the data set to carry malaria parasites and build a map of parasite movements between ‘source’ areas (areas that act as reservoirs of disease) and ‘sink’ areas (areas that mostly receive disease) obtaining a better understanding of how a disease is spreading (Harvard, 2012).

Enforcing smallholder food security

In 2014 Orange launched the ‘Data for Development Senegal’, an innovation challenge on ICT big data for the purposes of societal development. For this challenge, Sonatel and the Orange Group are making anonymous data, extracted from the mobile network in Senegal, available to international research laboratories, as well as data on hours of sunshine. Suggestions for applications in the agricultural domain can be found in Box 1 (Orange, 2014).

Box 1: Possible applications of telecom data in the agricultural domain as suggested by Orange in the ‘Data for Development Senegal’ Challenge

  • analyses based on hours of sunshine:
    • exploration of explanatory factors and modelling of soil productivity and basic foodstuffs according to climatic conditions, as well as development of predictive models
    • anticipatory analyses of soil evaporation and the depth of wells and watercourses, modelling and anticipation of drought
    • exploration of factors explaining low production of rain-fed crops according to meteorological conditions
    • evaluation of the evaporative demand in bodies of water (holding tanks, agricultural reservoirs…)
    • correlation between hours of sunshine, temperature and conditions of conservation and quality of seeds
    • analyses of correlation between measurements of hours of sunshine and temperature and variation in basic foodstuff prices
  • analyses based on mobile network use statistics:
    • optimisation of irrigation infrastructure in order to maximise the safe access to water and the productivity of irrigated land
    • optimisation of the efficiency of the harvesting infrastructure: especially for peanuts
    • optimisation of the location of warehouses for the various foodstuffs
    • impact analysis about the access to a local food supply
      • on the health of workers' families (see Health theme)
      • on population movements and insecurity
    • analyses of livestock migrations and method for minimising mortality
      • locating markets...
    • analysis of the impact of the seasons on rural populations
      • emigration of temporary workers
      • activity during the rainy season
      • migration in the case of drought
    • measurement of the impacts of fluctuation of prices of farming products in productive areas
    • modelling of possible impacts for roll-out of measuring methods
      • water level, crowdsourcing sensors…
    • impact analysis about a locust invasion
    • comparison measures with other emerging countries
    • farming yield forecasts (e.g. changes in call volume during periods of drought)
    • performance of the groundnut marketing campaign (e.g. telephone top-up amounts purchased during the marketing period in November–December in the groundnut growing region)
    • movements of herdsmen in northern and eastern Senegal (e.g. definition of transhumance routes from SIM cards).

Source: Orange (2014)

Measuring actual rainfall patterns

In the Netherlands, it has been demonstrated how rainfall pattern can be derived from commercial cellular communication networks (Figure 11, Overeem et al., 2013). The potential of such networks is high, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors is virtually absent and telecommunication networks are expanding, e.g. in most African countries.

Figure 11: Space–time dynamics of 15-min rainfall depths (two panels per time step) from links (left) and radars plus gauges (right) for 10 September 2011, 2030–2045 and 2230–2245 hours UTC (validation).

Source: Overeem et al. (2013)

OpenDataandSmallholderFoodandNutritionalSecurityFigure11.png

Experiments based on e-mail data

Analysing migration patterns

Researchers from the Max Plank Institute and Yahoo! Research have studied age and gender specific international migration patterns based on a large sample of Yahoo! e-mail messages, mapping anonymised e- mail users to the geographic locations using IP addresses. The findings were in line with existing administrative data sources, but provide new and rich information on mobility patterns and social networks of migrants (Zagheni and Weber, 2012).

Experiments based on data from mobile agricultural advise services

Early warnings for disease and prices spikes

Currently, more and more agricultural advisory services are being developed that can be accessed by mobile telephones (GSMA, 2014). Many of these applications ask the farmer or extension worker to enter data into the system in order to provide the right advice. Recently Palantir, a data mining company, analysed a data set of 1,000,000 requests from the Community Knowledge Worker programme of the Grameen Foundation. They were able to locate early outbreaks of animal diseases in Uganda and link these outbreaks to spikes in food prices (Palantir, 2012).

Large-scale agricultural and nutrition patterns

Similarly, at the Africa Open Data Jam (August 2014) IBM® used a cloud platform for cognitive computing to demonstrate an app that analyses SMS data to collect agricultural and nutrition information from farmers with survey questions and shared back aggregate information as open data (IBM, 2014).

Other experiments on corporate data-sharing

Early locust warning

The Disaster Charter is a cooperation of space companies and agencies that coordinated the of space facilities in the event of natural or technological disasters. An example is the usage of DMC data to predict locust breeding grounds and swarms in North Africa (DCMII, 2013).

Early disease warning

Google offers a service to visualise the search intensity for certain key words. Using this feature, Google is capable of following the outbreak of flu almost in real time. They discovered that there is close relationship between how many people search for flu-related topics and how many people actually have flu symptoms. Historical data demonstrate that the estimates based on Google search queries about flu closely match traditional flu activity indicators. Therefore the influenza can be used to detect outbreaks much faster than using traditional methods, enabling health professionals to better respond to seasonal epidemics and pandemics (Google, 2014b).

Impact on smallholder ecosystem

It can be concluded from the experiments described above that the potential impact of open business data coming from telecom and IT sources is very large.

  • Mobile operators, in particular, have the opportunity to collect information on individuals at the grassroots level, almost in real time and in large volumes, even from remote areas. The mobile network has the potential to grow into a community sensor and, if governed with care, be used as a valuable monitor of rural life. The examples above do not only include the telecom data themselves, but also the weather sensors on the antennae. Even the microwave signals themselves can function as a sensor for rainfall.
  • Related to the mobile operators are the operators of mobile services who provide (paid) information services to smallholders and other rural actors, e.g. price information or farm advice. In many case this is two-way communication, often related to agriculture and value chains. In the experiments mentioned above, Palantir demonstrates how this kind of data can be used to discover relevant patterns that are very relevant to smallholder communities.
  • Data from these sources can have impact on an all four domains described. Data from mobile operators or mobile service providers can be used to:
    • Collect or validate national or regional statistics and census data, improving government data. In this way, baseline data or the effectiveness of a policy measure can be assessed in more reliable way, probably obtaining the results cheaper and in a more-timely manner then using traditional methods.
    • Validate global data sets. Many global data sets are now derived from satellite imagery with a coarse resolution of 1 km or more. Mobile data can be used to ground-truth and update these data sets or contextualise the data when applied a specific situation, especially in a smallholder context.
  • Mobile data can be used to detect patterns and relations in smallholder practice that are now unknown because of lack of observations. Mobile data allow access to relatively cheap large-scale data sets over a long time span, which is currently unaffordable.
  • Patterns detected in mobile data can be used to make agricultural advice services more specific for the context of a farm or region and using multiple sources the advice can be validated or improved over time when more data enters the system.
  • New insights and information that can be derived from mobile data can be fed back into the farmer ecosystem.

However, the application of these data to the search for patterns that might benefit smallholder food and nutrition security is not common practice. There are limitations from the perspective of the private sector and from the point of view of the users of the networks and the services. Therefore, we ranked the current impact as 0, but the potential impact as 4. We gave business data the highest rank in this survey, because there are no other means to collect so much individual information at the grassroots level in remote areas. The experiments above demonstrate the potential of the data to provide impact. The question is if and how these data can be shared to the benefit of rural communities.

The UN is advocating the use of data from mobiles and mobile service operators to monitor the Millennium Development Goals (UN, 2014).

Synthesis and outlook of the impact of the open development on smallholder food and nutrition security

The current impact of open data on smallholder food and nutrition security is low

In general, the impact of open data in developing countries is low. There are various reasons for this, but this study shows that the data needed to have local impact is not there or not openly available.

  • Government data are limited and are often outdated, too aggregated or unreliable.
  • International organisations generally provide coarse data sets about country statistics, with limited direct relevance to smallholder communities.
  • Agricultural research provides a limited number of research-oriented data sets which are difficult to apply.
  • NGOs focus on open data for project accountability which is important for development but have limited application for food and nutrition security.
  • Telecom and ICT businesses hold a wealth of data but do not regularly share this with other stakeholders.

As a result, only a few examples can be found of open data applications targeting food security issues directly  in the smallholder ecosystem. In Figure 12 the applications mentioned in this report are overlaid on their application domain in the smallholder ecosystem. Most applications are targeting food security at a higher level and in the governance domain, a hand full applications target small holder food security and nutrition issues at the local level: in the service domain the value chain or to the farmers directly.

Figure 12. Examples of applications using open data impacting the smallholder ecosystem and its actors, overlaying the examples mentioned in this paper and their application domain.

Source: Freely adapted from Fonzi and Chau (2012).

OpenDataandSmallholderFoodandNutritionalSecurityFigure12.png

Potentially there is a large impact of open data on smallholder food and nutrition security

Traditionally, the exchange of data and information between smallholder farmers and the outside world was difficult. This picture has changed with the emergence of mobile operators and ICT service providers in the rural areas of developing countries. Information exchange is now possible between all actors the smallholder ecosystem. The more information is exchanged between the different actors of the smallholder ecosystem, the more smoothly it can function. Mobile and mobile service are the key for open data to become useful to enhance food and nutrition security of smallholders by bringing information to the local level. However, in order to be relevant, in most cases this data needs to be locally relevant. Interestingly, the key to obtaining local relevant information is in the hands of the telecom and mobile service providers.

Business, in particular telecom and mobile service providers, should share their data about the smallholder ecosystem in an appropriate and effective way in order to make ‘the big leap forward’.

If the data from the emergent mobile operators and mobile service providers become available in an appropriate way there is a lot to be learned about the functioning of smallholder ecosystems. This information must be used to further improve the services provided. Fine-grained data about the smallholder ecosystem can then be used in a variety of ways:

  • National or regional statistics and census data can be collected or validated against mobile data at the lowest level. In this way, baseline data or the effectiveness of a policy measure can be assessed in a more reliable way, probably obtaining the results cheaper and in a more-timely manner than using traditional methods.
  • Mobile data can be used to make agricultural advisory services more specific for the context of a farm or region and using multiple sources, the advice can be validated or improved over time when more data enters the system.
  • Mobile data can be used to validate global data sets. Many global data sets are now derived from satellite imagery with a coarse resolution of 1 km or more. Mobile data can be used to ground-truth and update these data sets or contextualise the data when applied to a specific situation.
  • Mobile data can be used to detect patterns and relations in smallholder practice which are now unknown due to lack of observations. Mobile data allows us to have relatively cheap, large-scale data sets over a long time span, which are currently unaffordable.
  • New knowledge and information that can be derived from ICT data can be beneficial to actors in the farmer ecosystem.

Beside telecom providers, there are other businesses that have relevant data on smallholders. The international food processing industry and supply chain is increasingly doing business with smallholders, both as a source of commodities and as a market.

The question is, how should this data be shared and who is the ‘owner’ of this data: business, individual farmers, or farmer cooperatives? Before this ICT data can be shared, these issues need to be cleared up. The solution probably differs from case to case.

Other options to further improve the uptake and availability of open data for smallholder food and nutrition security

Possible improvements:

  1. More, and more reliable, regional and local government data. Specifically smallholders can benefit from data about:
  • ownership and legal status of companies; input suppliers, traders, financial advisers
  • land ownership
  • (regional) trade statistics and prices
  • infrastructure and transport.
  1. Data from international organisations would become much more informative and applicable to the smallholder context if the raw fine-grained data could become available rather than the aggregated data at country level.
  2. A translation is needed for open research data towards open data that is applicable for other stakeholders.
  3. By opening up the NGO monitoring and evaluation data, the fine-grained details behind the large-scale census data collected by governments and institutes becomes as visible as looking through a magnifying glass. These detailed patterns may lead to a better understanding of the bigger picture, supported by quantities data, especially when many NGOs are operating in the same country.

There is a trade-off between the aggregation level of data, the amount of information it contains and farmers’ rights

Fine-grained data contain more information, and are more useful for different applications. However, there is a delicate balance between the benefits of open data and the risk of their potential misuse (Figure 13). Telecom data and monitoring data of NGO projects or data collected by farmers or farmers’ organisations may contain data at the individual level. These data are valuable to better understand the farmer ecosystem, but also contain an inherent risk of misuse. Therefore, rules are needed to prevent misuse; ultimately it should be the ‘data subjects’ – the actors in the smallholder ecosystem – who should determine what and in what way data are shared or opened, taking into account the balance between information content and privacy. The UN (2014) is proposing a set of ‘Basic Principles for the Data Revolution for Sustainable Development’. These rules can be the starting point for a more rigorous discussion.

Figure 13. The more fine-grained data are, the more information they contain, the more useful they are for different applications. However there is a delicate balance between the benefits of open data and the risk of potential misuse.

OpenDataandSmallholderFoodandNutritionalSecurityFigure13.png

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