Table of contents
  1. Story
  2. Slides
    1. Slide 1 Data Driven Farming Week 4: Modeling
    2. Slide 2 Outline
    3. Slide 3 The InfoAg Conference 2015
    4. Slide 4 The InfoAg Conference 2015 Program
    5. Slide 5 FarmData Spreadsheet
    6. Slide 6 Science of Precision Ag
    7. Slide 7 ISPA and Precision Agriculture Around the World 
    8. Slide 8 ASA - Precision Ag Community
    9. Slide 9 Convergence of Data Revolution
    10. Slide 10 International Plant Nutrition Institute
    11. Slide 11 Nutrient Balance Assessments in Crop Production
    12. Slide 12 Nu GIS-U.S.
    13. Slide 13 Nutrient Removal In a Crop Harvest and Nutrient Removal to Use Ratios
    14. Slide 14 N and P Budgets For Four States and the United States and the U.S. in 2007
    15. Slide 15 Nu GIS
    16. Slide 16 NuGIS Interactive Map
    17. Slide 17 Nu GIS Tabular Data
    18. Slide 18 NuGIS Knowledge Base
    19. Slide 19 Applications of NuGIS and Conclusions 1
    20. Slide 20 Applications of NuGIS and Conclusions 2
    21. Slide 21 Applications of NuGIS and Conclusions 3
    22. Slide 22 Visualization and Modeling in Spotfire: Data
    23. Slide 23 IPNI NuGIS: Regional Watershed Nutrient Balance Data-Spotfire
    24. Slide 24 IPNI NuGIS: HUC 8 Watershed Nutrient Balance Data-Spotfire
    25. Slide 25 IPNI NuGIS: County and State and 48 State Total Nutrient Balance Data-Spotfire
    26. Slide 26 Broad Impact
    27. Slide 27 AgriStats 1
    28. Slide 28 AgriStats 2
    29. Slide 29 AgriStats 3
    30. Slide 30 Precision Agriculture Journal
    31. Slide 31 Highlights of Slides from InfoAg 2015
    32. Slide 32 InfoAg2015 Paper 359 1
    33. Slide 33 InfoAg2015 Paper 359 2
    34. Slide 34 InfoAg2015 Paper 336
    35. Slide 35 InfoAg2015 Paper323
    36. Slide 36 InfoAg2015 Paper 357
    37. Slide 37 InfoAg2015 Paper 358
    38. Slide 38 InfoAg2015 Paper 329
    39. Slide 39 InfoAg2015 Paper 350
    40. Slide 40 InfoAg2015 Paper 302
    41. Slide 41 InfoAg2015 Paper 332
    42. Slide 42 InfoAg2015 Paper 333
    43. Slide 43 InfoAg2015 Paper 300
    44. Slide 44 InfoAg2015 Paper 305
    45. Slide 45 InfoAg2015 Paper 326
    46. Slide 46 InfoAg2015 Paper 334
    47. Slide 47 InfoAg2015 Paper 298
    48. Slide 48 InfoAg2015 Paper 345
  3. Spotfire Dashboard
  4. Research Notes
  5. InfoAg 2015 Session: Science of Precision Ag
    1. ISPA and Precision Agriculture around the World
    2. ASA - Precision Ag Community
  6. ISPA and Precision Agriculture around the World
    1. Slide 1 ISPA and Precision Agriculture Around the World
    2. Slide 2 Mandate
    3. Slide 3 Purposes of ISPA
    4. Slide 4 ISPA Today
    5. Slide 5 Leadership
    6. Slide 6 A Brief History of ICPSs
    7. Slide 7 A Poster of ICPA 2014
    8. Slide 8 Sir Ronald Aylmer Fisher (1890-1962)
    9. Slide 9 Consequence: Scientists and Farmers Are Not Alike...
    10. Slide 10 Scientists Handling Uncertainties
    11. Slide 11 Precision Agriculture Can Help
    12. Slide 12 Managing Nitrogen in Crop Production
    13. Slide 13 PA: Who Can Help?
    14. Slide 14 The International Future of PA
    15. Slide 15 A New Paradigm
    16. Slide 16 What Role for ISPA?
    17. Slide 17 Special Acknowledgements
    18. Slide 18 13th International Conference on Precision Agriculture (ICPA)
  7. ASA - Precision Ag Community
    1. Slide 1 Towards the 21st Century Farming: From Precision Ag to Decision Ag
    2. Slide 2 Challenges Facing Farmers
    3. Slide 3 Sustainable Development Agriculture Systems
    4. Slide 4 Outline
    5. Slide 5 Main Research Topics in Precision Ag 1
    6. Slide 6 Main Research Topics in Precision Ag 2
    7. Slide 7 Main Research Topics in Precision Ag 3
    8. Slide 8 Main Research Topics in Precision Ag 4
    9. Slide 9 Main Research Topics in Precision Ag 5
    10. Slide 10 Main Research Topics in Precision Ag 6
    11. Slide 11 Main Research Topics in Precision Ag 7
    12. Slide 12 Main Research Topics in Precision Ag 8
    13. Slide 13 Top Four Precision Agriculture Services
    14. Slide 14 Gartner Hype Cycle for Technologies
    15. Slide 15 The Emerging Technologies Hype Cycle 2014 1
    16. Slide 16 Data Science
    17. Slide 17 Big Data
    18. Slide 18 Data Analytics Solutions
    19. Slide 19 The Emerging Technologies Hype Cycle 2014 2
    20. Slide 20 Internet of Things
    21. Slide 21 The Emerging Technologies Hype Cycle 2014 3
    22. Slide 22 From Drones to Autonomous Tractors
    23. Slide 23 Questions
  8. NUGIS: A Nutrient Use Geographic Information System For the U.S.
    1. Abstract
    2. Introduction
    3. Methods and Their Challenges
      1. The Basic NuGIS Model
      2. Farm Fertilizer Nutrient Use
      3. Recoverable Manure Nutrient Use
      4. Biological N Fixation
      5. Nutrients in Harvested Crops
    4. Results and Discussion
      1. Figure 1. Nutrient removal in crop harvest and nutrient removal to use ratios for the U.S. over a 20-year period (NuGIS, January 2012).
      2. Table 1. N and P budgets for four states and the U.S. in 2007 (NuGIS, January 2012).
    5. References
  9. Slides
    1. Slide 1 Data Warehouses/Exchanges
    2. Slide 2 Data Generated for Individual Corn Plants
    3. Slide 3 Digital Agriculture
    4. Slide 4 Future Data Exchange for Growers
    5. Slide 5 Additional Data Scenarios
    6. Slide 6 Data Service Scenario for a Grower
    7. Slide 7 Major Ag Data Hurdles
    8. Slide 8 Ag Data Warehouse
    9. Slide 9 Precision Service Offerings
    10. Slide 10 Choice of Data Collection Method
    11. Slide 11 Lack of Interoperability Between Software Companies Is a Problem
    12. Slide 12 Choices for Storing Data
    13. Slide 13 Grower Expectations for Their Data
    14. Slide 14 What Are Farmers Responsible for Archiving Their Own Data?
    15. Slide 15 Data Exchange/Warehouse Attributes
    16. Slide 16 Digital Agriculture
  10. NEXT

Week 4 Modeling

Last modified
Table of contents
  1. Story
  2. Slides
    1. Slide 1 Data Driven Farming Week 4: Modeling
    2. Slide 2 Outline
    3. Slide 3 The InfoAg Conference 2015
    4. Slide 4 The InfoAg Conference 2015 Program
    5. Slide 5 FarmData Spreadsheet
    6. Slide 6 Science of Precision Ag
    7. Slide 7 ISPA and Precision Agriculture Around the World 
    8. Slide 8 ASA - Precision Ag Community
    9. Slide 9 Convergence of Data Revolution
    10. Slide 10 International Plant Nutrition Institute
    11. Slide 11 Nutrient Balance Assessments in Crop Production
    12. Slide 12 Nu GIS-U.S.
    13. Slide 13 Nutrient Removal In a Crop Harvest and Nutrient Removal to Use Ratios
    14. Slide 14 N and P Budgets For Four States and the United States and the U.S. in 2007
    15. Slide 15 Nu GIS
    16. Slide 16 NuGIS Interactive Map
    17. Slide 17 Nu GIS Tabular Data
    18. Slide 18 NuGIS Knowledge Base
    19. Slide 19 Applications of NuGIS and Conclusions 1
    20. Slide 20 Applications of NuGIS and Conclusions 2
    21. Slide 21 Applications of NuGIS and Conclusions 3
    22. Slide 22 Visualization and Modeling in Spotfire: Data
    23. Slide 23 IPNI NuGIS: Regional Watershed Nutrient Balance Data-Spotfire
    24. Slide 24 IPNI NuGIS: HUC 8 Watershed Nutrient Balance Data-Spotfire
    25. Slide 25 IPNI NuGIS: County and State and 48 State Total Nutrient Balance Data-Spotfire
    26. Slide 26 Broad Impact
    27. Slide 27 AgriStats 1
    28. Slide 28 AgriStats 2
    29. Slide 29 AgriStats 3
    30. Slide 30 Precision Agriculture Journal
    31. Slide 31 Highlights of Slides from InfoAg 2015
    32. Slide 32 InfoAg2015 Paper 359 1
    33. Slide 33 InfoAg2015 Paper 359 2
    34. Slide 34 InfoAg2015 Paper 336
    35. Slide 35 InfoAg2015 Paper323
    36. Slide 36 InfoAg2015 Paper 357
    37. Slide 37 InfoAg2015 Paper 358
    38. Slide 38 InfoAg2015 Paper 329
    39. Slide 39 InfoAg2015 Paper 350
    40. Slide 40 InfoAg2015 Paper 302
    41. Slide 41 InfoAg2015 Paper 332
    42. Slide 42 InfoAg2015 Paper 333
    43. Slide 43 InfoAg2015 Paper 300
    44. Slide 44 InfoAg2015 Paper 305
    45. Slide 45 InfoAg2015 Paper 326
    46. Slide 46 InfoAg2015 Paper 334
    47. Slide 47 InfoAg2015 Paper 298
    48. Slide 48 InfoAg2015 Paper 345
  3. Spotfire Dashboard
  4. Research Notes
  5. InfoAg 2015 Session: Science of Precision Ag
    1. ISPA and Precision Agriculture around the World
    2. ASA - Precision Ag Community
  6. ISPA and Precision Agriculture around the World
    1. Slide 1 ISPA and Precision Agriculture Around the World
    2. Slide 2 Mandate
    3. Slide 3 Purposes of ISPA
    4. Slide 4 ISPA Today
    5. Slide 5 Leadership
    6. Slide 6 A Brief History of ICPSs
    7. Slide 7 A Poster of ICPA 2014
    8. Slide 8 Sir Ronald Aylmer Fisher (1890-1962)
    9. Slide 9 Consequence: Scientists and Farmers Are Not Alike...
    10. Slide 10 Scientists Handling Uncertainties
    11. Slide 11 Precision Agriculture Can Help
    12. Slide 12 Managing Nitrogen in Crop Production
    13. Slide 13 PA: Who Can Help?
    14. Slide 14 The International Future of PA
    15. Slide 15 A New Paradigm
    16. Slide 16 What Role for ISPA?
    17. Slide 17 Special Acknowledgements
    18. Slide 18 13th International Conference on Precision Agriculture (ICPA)
  7. ASA - Precision Ag Community
    1. Slide 1 Towards the 21st Century Farming: From Precision Ag to Decision Ag
    2. Slide 2 Challenges Facing Farmers
    3. Slide 3 Sustainable Development Agriculture Systems
    4. Slide 4 Outline
    5. Slide 5 Main Research Topics in Precision Ag 1
    6. Slide 6 Main Research Topics in Precision Ag 2
    7. Slide 7 Main Research Topics in Precision Ag 3
    8. Slide 8 Main Research Topics in Precision Ag 4
    9. Slide 9 Main Research Topics in Precision Ag 5
    10. Slide 10 Main Research Topics in Precision Ag 6
    11. Slide 11 Main Research Topics in Precision Ag 7
    12. Slide 12 Main Research Topics in Precision Ag 8
    13. Slide 13 Top Four Precision Agriculture Services
    14. Slide 14 Gartner Hype Cycle for Technologies
    15. Slide 15 The Emerging Technologies Hype Cycle 2014 1
    16. Slide 16 Data Science
    17. Slide 17 Big Data
    18. Slide 18 Data Analytics Solutions
    19. Slide 19 The Emerging Technologies Hype Cycle 2014 2
    20. Slide 20 Internet of Things
    21. Slide 21 The Emerging Technologies Hype Cycle 2014 3
    22. Slide 22 From Drones to Autonomous Tractors
    23. Slide 23 Questions
  8. NUGIS: A Nutrient Use Geographic Information System For the U.S.
    1. Abstract
    2. Introduction
    3. Methods and Their Challenges
      1. The Basic NuGIS Model
      2. Farm Fertilizer Nutrient Use
      3. Recoverable Manure Nutrient Use
      4. Biological N Fixation
      5. Nutrients in Harvested Crops
    4. Results and Discussion
      1. Figure 1. Nutrient removal in crop harvest and nutrient removal to use ratios for the U.S. over a 20-year period (NuGIS, January 2012).
      2. Table 1. N and P budgets for four states and the U.S. in 2007 (NuGIS, January 2012).
    5. References
  9. Slides
    1. Slide 1 Data Warehouses/Exchanges
    2. Slide 2 Data Generated for Individual Corn Plants
    3. Slide 3 Digital Agriculture
    4. Slide 4 Future Data Exchange for Growers
    5. Slide 5 Additional Data Scenarios
    6. Slide 6 Data Service Scenario for a Grower
    7. Slide 7 Major Ag Data Hurdles
    8. Slide 8 Ag Data Warehouse
    9. Slide 9 Precision Service Offerings
    10. Slide 10 Choice of Data Collection Method
    11. Slide 11 Lack of Interoperability Between Software Companies Is a Problem
    12. Slide 12 Choices for Storing Data
    13. Slide 13 Grower Expectations for Their Data
    14. Slide 14 What Are Farmers Responsible for Archiving Their Own Data?
    15. Slide 15 Data Exchange/Warehouse Attributes
    16. Slide 16 Digital Agriculture
  10. NEXT

  1. Story
  2. Slides
    1. Slide 1 Data Driven Farming Week 4: Modeling
    2. Slide 2 Outline
    3. Slide 3 The InfoAg Conference 2015
    4. Slide 4 The InfoAg Conference 2015 Program
    5. Slide 5 FarmData Spreadsheet
    6. Slide 6 Science of Precision Ag
    7. Slide 7 ISPA and Precision Agriculture Around the World 
    8. Slide 8 ASA - Precision Ag Community
    9. Slide 9 Convergence of Data Revolution
    10. Slide 10 International Plant Nutrition Institute
    11. Slide 11 Nutrient Balance Assessments in Crop Production
    12. Slide 12 Nu GIS-U.S.
    13. Slide 13 Nutrient Removal In a Crop Harvest and Nutrient Removal to Use Ratios
    14. Slide 14 N and P Budgets For Four States and the United States and the U.S. in 2007
    15. Slide 15 Nu GIS
    16. Slide 16 NuGIS Interactive Map
    17. Slide 17 Nu GIS Tabular Data
    18. Slide 18 NuGIS Knowledge Base
    19. Slide 19 Applications of NuGIS and Conclusions 1
    20. Slide 20 Applications of NuGIS and Conclusions 2
    21. Slide 21 Applications of NuGIS and Conclusions 3
    22. Slide 22 Visualization and Modeling in Spotfire: Data
    23. Slide 23 IPNI NuGIS: Regional Watershed Nutrient Balance Data-Spotfire
    24. Slide 24 IPNI NuGIS: HUC 8 Watershed Nutrient Balance Data-Spotfire
    25. Slide 25 IPNI NuGIS: County and State and 48 State Total Nutrient Balance Data-Spotfire
    26. Slide 26 Broad Impact
    27. Slide 27 AgriStats 1
    28. Slide 28 AgriStats 2
    29. Slide 29 AgriStats 3
    30. Slide 30 Precision Agriculture Journal
    31. Slide 31 Highlights of Slides from InfoAg 2015
    32. Slide 32 InfoAg2015 Paper 359 1
    33. Slide 33 InfoAg2015 Paper 359 2
    34. Slide 34 InfoAg2015 Paper 336
    35. Slide 35 InfoAg2015 Paper323
    36. Slide 36 InfoAg2015 Paper 357
    37. Slide 37 InfoAg2015 Paper 358
    38. Slide 38 InfoAg2015 Paper 329
    39. Slide 39 InfoAg2015 Paper 350
    40. Slide 40 InfoAg2015 Paper 302
    41. Slide 41 InfoAg2015 Paper 332
    42. Slide 42 InfoAg2015 Paper 333
    43. Slide 43 InfoAg2015 Paper 300
    44. Slide 44 InfoAg2015 Paper 305
    45. Slide 45 InfoAg2015 Paper 326
    46. Slide 46 InfoAg2015 Paper 334
    47. Slide 47 InfoAg2015 Paper 298
    48. Slide 48 InfoAg2015 Paper 345
  3. Spotfire Dashboard
  4. Research Notes
  5. InfoAg 2015 Session: Science of Precision Ag
    1. ISPA and Precision Agriculture around the World
    2. ASA - Precision Ag Community
  6. ISPA and Precision Agriculture around the World
    1. Slide 1 ISPA and Precision Agriculture Around the World
    2. Slide 2 Mandate
    3. Slide 3 Purposes of ISPA
    4. Slide 4 ISPA Today
    5. Slide 5 Leadership
    6. Slide 6 A Brief History of ICPSs
    7. Slide 7 A Poster of ICPA 2014
    8. Slide 8 Sir Ronald Aylmer Fisher (1890-1962)
    9. Slide 9 Consequence: Scientists and Farmers Are Not Alike...
    10. Slide 10 Scientists Handling Uncertainties
    11. Slide 11 Precision Agriculture Can Help
    12. Slide 12 Managing Nitrogen in Crop Production
    13. Slide 13 PA: Who Can Help?
    14. Slide 14 The International Future of PA
    15. Slide 15 A New Paradigm
    16. Slide 16 What Role for ISPA?
    17. Slide 17 Special Acknowledgements
    18. Slide 18 13th International Conference on Precision Agriculture (ICPA)
  7. ASA - Precision Ag Community
    1. Slide 1 Towards the 21st Century Farming: From Precision Ag to Decision Ag
    2. Slide 2 Challenges Facing Farmers
    3. Slide 3 Sustainable Development Agriculture Systems
    4. Slide 4 Outline
    5. Slide 5 Main Research Topics in Precision Ag 1
    6. Slide 6 Main Research Topics in Precision Ag 2
    7. Slide 7 Main Research Topics in Precision Ag 3
    8. Slide 8 Main Research Topics in Precision Ag 4
    9. Slide 9 Main Research Topics in Precision Ag 5
    10. Slide 10 Main Research Topics in Precision Ag 6
    11. Slide 11 Main Research Topics in Precision Ag 7
    12. Slide 12 Main Research Topics in Precision Ag 8
    13. Slide 13 Top Four Precision Agriculture Services
    14. Slide 14 Gartner Hype Cycle for Technologies
    15. Slide 15 The Emerging Technologies Hype Cycle 2014 1
    16. Slide 16 Data Science
    17. Slide 17 Big Data
    18. Slide 18 Data Analytics Solutions
    19. Slide 19 The Emerging Technologies Hype Cycle 2014 2
    20. Slide 20 Internet of Things
    21. Slide 21 The Emerging Technologies Hype Cycle 2014 3
    22. Slide 22 From Drones to Autonomous Tractors
    23. Slide 23 Questions
  8. NUGIS: A Nutrient Use Geographic Information System For the U.S.
    1. Abstract
    2. Introduction
    3. Methods and Their Challenges
      1. The Basic NuGIS Model
      2. Farm Fertilizer Nutrient Use
      3. Recoverable Manure Nutrient Use
      4. Biological N Fixation
      5. Nutrients in Harvested Crops
    4. Results and Discussion
      1. Figure 1. Nutrient removal in crop harvest and nutrient removal to use ratios for the U.S. over a 20-year period (NuGIS, January 2012).
      2. Table 1. N and P budgets for four states and the U.S. in 2007 (NuGIS, January 2012).
    5. References
  9. Slides
    1. Slide 1 Data Warehouses/Exchanges
    2. Slide 2 Data Generated for Individual Corn Plants
    3. Slide 3 Digital Agriculture
    4. Slide 4 Future Data Exchange for Growers
    5. Slide 5 Additional Data Scenarios
    6. Slide 6 Data Service Scenario for a Grower
    7. Slide 7 Major Ag Data Hurdles
    8. Slide 8 Ag Data Warehouse
    9. Slide 9 Precision Service Offerings
    10. Slide 10 Choice of Data Collection Method
    11. Slide 11 Lack of Interoperability Between Software Companies Is a Problem
    12. Slide 12 Choices for Storing Data
    13. Slide 13 Grower Expectations for Their Data
    14. Slide 14 What Are Farmers Responsible for Archiving Their Own Data?
    15. Slide 15 Data Exchange/Warehouse Attributes
    16. Slide 16 Digital Agriculture
  10. NEXT

Story

Data Driven Farming: Week 4: Modeling

Outline (ADD LINKS)

The objective was to data mine a recent precision agriculture conference (InfoAg 2015 - billed as the Premier Event in Precision Agriculture) into a spreadsheet. That led to the International Society of Precision Agriculture (ISPA)  and American Society of Agronomy (ASA),  and the International Plant Nutrition Institute IPNI).

The IPNI has two data sets: NuGIS: Nutrient Use Geographic Information System, which is publicly available, and AgriStats, which one has to register for and be approved to access.

The NuGIS 4 data sets were visualized and modeled in Spotfire to provide useful results by time, county, watershed, and state.

MORE TO FOLLOW

Include Spotfire Dashboard of Farm Data Modeling Results for National Soil Survey?

Include AgriStats?

Include UNL Cropwatch data in PDF files that cannot be copied? (Find original data sources)

Include the Journal of Precision Agriculture?

The Springer journal, Precision Agriculture, is now available to ISPA members through this website. When you login to the website you will see a Members tab at the far right of the navigation tabs. Click on the Members tab to see the menu and select Precision Agriculture journal to be redirected to the Springer site. Once at the site, click on the blue "Browse Volumes & Issues" button to access journal articles. Journal Impact Factor for 2013 is 2.01.

I paid $50 and registered for the Newsletters and Journal access

The InfoAg 2015 Awards:

2015 Crop Adviser/Entrepreneur Award
David Scheiderer
Integrated Ag Services

2015 Farmer Award
Rod Weimer
Fagerberg Farm

2015 Legacy Award
Randy Taylor
Oklahoma State University

2015 Educator/Researcher Award
Raj Khosla
Colorado State University

Mine their presentations in more detail?

The Power of an Idea is best measured by the resistance given that idea!

Slides

Slides

Slide 2 Outline

BrandNiemann08122015Slide2.PNG

Slide 3 The InfoAg Conference 2015

http://infoag.org/

BrandNiemann08122015Slide3.PNG

Slide 4 The InfoAg Conference 2015 Program

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

BrandNiemann08122015Slide4.PNG

Slide 5 FarmData Spreadsheet

FarmData.xlsx

BrandNiemann08122015Slide5.PNG

Slide 6 Science of Precision Ag

http://infoag.org/session/5/363/

BrandNiemann08122015Slide6.PNG

Slide 7 ISPA and Precision Agriculture Around the World 

BrandNiemann08122015Slide7.PNG

Slide 8 ASA - Precision Ag Community

BrandNiemann08122015Slide8.PNG

Slide 9 Convergence of Data Revolution

BrandNiemann08122015Slide9.PNG

Slide 10 International Plant Nutrition Institute

http://www.ipni.net/

BrandNiemann08122015Slide10.PNG

Slide 11 Nutrient Balance Assessments in Crop Production

http://www.ipni.net/nugis

BrandNiemann08122015Slide11.PNG

Slide 12 Nu GIS-U.S.

http://www.ipni.net/nugis

BrandNiemann08122015Slide12.PNG

Slide 13 Nutrient Removal In a Crop Harvest and Nutrient Removal to Use Ratios

PDF

BrandNiemann08122015Slide13.PNG

Slide 14 N and P Budgets For Four States and the United States and the U.S. in 2007

Link

BrandNiemann08122015Slide14.PNG

Slide 16 NuGIS Interactive Map

http://nugis.ipni.net/map/

BrandNiemann08122015Slide16.PNG

Slide 17 Nu GIS Tabular Data

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

BrandNiemann08122015Slide17.PNG

Slide 18 NuGIS Knowledge Base

Research Notes

BrandNiemann08122015Slide18.PNG

Slide 19 Applications of NuGIS and Conclusions 1

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

BrandNiemann08122015Slide19.PNG

Slide 20 Applications of NuGIS and Conclusions 2

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

BrandNiemann08122015Slide20.PNG

Slide 21 Applications of NuGIS and Conclusions 3

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

BrandNiemann08122015Slide21.PNG

Slide 22 Visualization and Modeling in Spotfire: Data

http://water.epa.gov/lawsregs/lawsgu...viewoftmdl.cfm

BrandNiemann08122015Slide22.PNG

Slide 23 IPNI NuGIS: Regional Watershed Nutrient Balance Data-Spotfire

Web Player

BrandNiemann08122015Slide23.PNG

Slide 24 IPNI NuGIS: HUC 8 Watershed Nutrient Balance Data-Spotfire

Web Player

BrandNiemann08122015Slide24.PNG

Slide 25 IPNI NuGIS: County and State and 48 State Total Nutrient Balance Data-Spotfire

Web Player

BrandNiemann08122015Slide25.PNG

Slide 28 AgriStats 2

BrandNiemann08122015Slide28.PNG

Slide 29 AgriStats 3

http://agristats.ipni.net/

BrandNiemann08122015Slide29.PNG

Slide 31 Highlights of Slides from InfoAg 2015

BrandNiemann08122015Slide31.PNG

Slide 32 InfoAg2015 Paper 359 1

BrandNiemann08122015Slide32.PNG

Slide 33 InfoAg2015 Paper 359 2

BrandNiemann08122015Slide33.PNG

Slide 34 InfoAg2015 Paper 336

BrandNiemann08122015Slide34.PNG

Slide 35 InfoAg2015 Paper323

BrandNiemann08122015Slide35.PNG

Slide 36 InfoAg2015 Paper 357

BrandNiemann08122015Slide36.PNG

Slide 37 InfoAg2015 Paper 358

BrandNiemann08122015Slide37.PNG

Slide 38 InfoAg2015 Paper 329

BrandNiemann08122015Slide38.PNG

Slide 39 InfoAg2015 Paper 350

BrandNiemann08122015Slide39.PNG

Slide 40 InfoAg2015 Paper 302

BrandNiemann08122015Slide40.PNG

Slide 41 InfoAg2015 Paper 332

BrandNiemann08122015Slide41.PNG

Slide 42 InfoAg2015 Paper 333

BrandNiemann08122015Slide42.PNG

Slide 43 InfoAg2015 Paper 300

BrandNiemann08122015Slide43.PNG

Slide 44 InfoAg2015 Paper 305

BrandNiemann08122015Slide44.PNG

Slide 45 InfoAg2015 Paper 326

BrandNiemann08122015Slide45.PNG

Slide 46 InfoAg2015 Paper 334

BrandNiemann08122015Slide46.PNG

Slide 47 InfoAg2015 Paper 298

BrandNiemann08122015Slide47.PNG

Slide 48 InfoAg2015 Paper 345

BrandNiemann08122015Slide48.PNG

Spotfire Dashboard

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

Research Notes

John Fulton, Ohio State University, Precision Agriculture, InfoAg 2015, Data Warehouse/Exchanges, Slides

My Note: Downloaded and surveyed 60 PDFs and made a selection of data analysis and modeling results

InfoAg 2015 Session: Science of Precision Ag

Source: http://infoag.org/session/5/363/

Moderator Paul Fixen
Date/Time Tuesday, July 28, 2015 | 3:30 pm - 4:20 pm
Room Regency C

ISPA and Precision Agriculture around the World

As a “management strategy that uses information technology to bring data from multiple sources to bear on decisions associated with crop production”, precision agriculture has a tremendous potential to change the face of agriculture for the best all around the world. The International Society for Precision Agriculture is well positioned to federate the scientific expertise available on the planet and generate consensus on critical issues leading to powerful decision rules based on the wealth of information generated by the technologies at our disposal.

Nicolas Tremblay

Agriculture and Agri-Food Canada, St-Jean-sur-Richelieu, J3B 3E6

450 515-2102

Nicolas.Tremblay@agr.gc.ca

Biography:
Dr. Nicolas Tremblay is President-Elect of the International Society for Precision Agriculture (ISPA). He leads an important research program for Agriculture and Agri-Food Canada and is known for his ability to generate new knowledge for the benefit of the agricultural sector. He is currently involved in the management of nitrogen fertilizer applications using remote sensing, geomatics, geostatistics and meta-analyses. He has also conducted research on fluorescence techniques for the detection of stresses affecting crops. Dr. Tremblay is the author of more than a hundred scientific peer-reviewed publications. Dr. Tremblay is adjunct-professor at Laval University in Quebec City, University of Ottawa and the Université de Montréal.

ASA - Precision Ag Community

An update on the science of precision ag and the latest efforts of the U.S. research community.

Brenda Ortiz

Agronomy and Soils Department, Auburn University, 204 Extension Hall, Auburn, 36849

334-844-5534

bvo0001@auburn.edu

Biography :
Brenda V. Ortiz with a Ph.D. is in Agricultural Engineering from The University of Georgia is currently an Assistant Professor in the Department of Agronomy and Soils Department at Auburn University (USA). Dr. Ortiz is responsible for research and extension in Grain Crops, Precision Agriculture and Agroclimatology. She has particular expertise in crop growth modeling, spatial statistics and remote sensing. During the last years she has been focusing on studying the impact of weather and climate on row and forage crops, use of crop growth modeling to evaluate different management strategies for improving row crops production, evaluation of different management practices to reduce aflatoxin contamination in corn, and the use of remote sensing technologies for variable rate application of nitrogen. She is currently leading the Climate Extension program of the Alabama Cooperative Extension System.

ISPA and Precision Agriculture around the World

Slides

Slide 1 ISPA and Precision Agriculture Around the World

https://www.ispag.org/

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Slide 2 Mandate

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Slide 3 Purposes of ISPA

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Slide 4 ISPA Today

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Slide 5 Leadership

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Slide 6 A Brief History of ICPSs

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Slide 7 A Poster of ICPA 2014

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Slide 8 Sir Ronald Aylmer Fisher (1890-1962)

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Slide 9 Consequence: Scientists and Farmers Are Not Alike...

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Slide 10 Scientists Handling Uncertainties

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Slide 11 Precision Agriculture Can Help

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Slide 12 Managing Nitrogen in Crop Production

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Slide 13 PA: Who Can Help?

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Slide 14 The International Future of PA

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Slide 15 A New Paradigm

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Slide 16 What Role for ISPA?

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Slide 17 Special Acknowledgements

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Slide 18 13th International Conference on Precision Agriculture (ICPA)

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ASA - Precision Ag Community

Slides

Slide 1 Towards the 21st Century Farming: From Precision Ag to Decision Ag

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Slide 2 Challenges Facing Farmers

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Slide 3 Sustainable Development Agriculture Systems

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Slide 4 Outline

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Slide 5 Main Research Topics in Precision Ag 1

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Slide 6 Main Research Topics in Precision Ag 2

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Slide 7 Main Research Topics in Precision Ag 3

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Slide 8 Main Research Topics in Precision Ag 4

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Slide 9 Main Research Topics in Precision Ag 5

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Slide 10 Main Research Topics in Precision Ag 6

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Slide 11 Main Research Topics in Precision Ag 7

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Slide 12 Main Research Topics in Precision Ag 8

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Slide 13 Top Four Precision Agriculture Services

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Slide 14 Gartner Hype Cycle for Technologies

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Slide 15 The Emerging Technologies Hype Cycle 2014 1

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Slide 16 Data Science

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Slide 17 Big Data

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Slide 18 Data Analytics Solutions

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Slide 19 The Emerging Technologies Hype Cycle 2014 2

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Slide 20 Internet of Things

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Slide 21 The Emerging Technologies Hype Cycle 2014 3

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Slide 22 From Drones to Autonomous Tractors

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NUGIS: A Nutrient Use Geographic Information System For the U.S.

Source: http://www.ipni.net/ipniweb/portal.n...IS%20FINAL.pdf PDF and Word

Paul E. Fixen, Ryan Williams and Quentin B. Rund International Plant Nutrition Institute, Brookings, SD pfixen@ipni.net (605-692-6280)

Abstract

Several critical contemporary agricultural issues involve the nutrient balance of U.S. cropland. Knowledge of the current status and temporal trends of nutrient balance can offer guidance in nutrient management education, serve as a basis for science-based guidance in marketing of fertilizers and nutrient management related services, and provide useful input to water quality and nitrous oxide emission modeling and to environmental policy development involving plant nutrients. The Nutrient Use Geographic Information System (NuGIS) creates county-level estimates of N, P and K applied to the soil in fertilizer and livestock manure, and removed by harvested agricultural crops. Geospatial techniques are used to estimate balances for 8-digit hydrologic units using the county-level data. The current version makes estimates for five years, coinciding with the USDA Census of Agriculture, from 1987 - 2007. A version that can be updated annually for non-Census years is under development. Model output and detailed methodology are available on line (http://www.ipni.net/nugis) through interactive thematic maps or as exportable tabular data. The analysis reveals areas of both highly positive and highly negative nutrient balances, and several weaknesses in data sets essential to the estimation process. The NuGIS model is being developed by the International Plant Nutrition Institute (IPNI) and PAQ Interactive.

Introduction

Agriculture and the context within which agriculture operates are experiencing remarkable changes. Many of those changes have the potential to impact nutrient balances for cropland. Global population growth and economic development make increasing productivity a high priority for all agricultural systems and suggest that nutrient removal in crop harvest is likely to continue to increase. Production of bioenergy can alter nutrient removal due to changes in crop species and plant parts harvested, and can alter nutrient additions due to production of bioash and changes in manure composition induced by feeding distillers grain. Climate change may cause changes in crop yields, cropping patterns, and soil processes. Accelerated genetic changes have been promised that could alter crop yields, crop nutrient concentrations and nutrient use efficiency. Increased volatility in fertilizer and crop prices has altered farm fertilizer use decisions. And, government policy can cause shifts in many of these factors.

Our belief is that wise nutrient stewardship decisions in the future, ranging in scale from the field to the farm to the watershed to the region and to the policy arena, will be facilitated by a fact-based understanding of the current status and temporal trends of cropland nutrient balances. Such an understanding establishes the baseline from which the impact of future changes can be measured. The interactive nature of crop nutrition and associated nutrient use efficiencies suggests that a singular nutrient focus, whether the objective is production or environmentally driven, could be counterproductive. Therefore, NuGIS evaluates balances of all three primary nutrients. An additional motivation for the development of NuGIS was an observation that the increases in nutrient removal in crop harvest as crop yields have increased is often underappreciated.

Methods and Their Challenges

The Basic NuGIS Model

The basic NuGIS model is a very simple field based partial nutrient balance algorithm as follows: Balance = Farm fertilizer nutrient used + Recoverable manure nutrient use + Biological fixation – Nutrient in harvested crops (IPNI, 2010). It is a partial balance because it does not take into account atmospheric deposition, nutrients in irrigation water, land application of biosolids, or several nutrient losses such as eroded soil, gaseous N emissions, or leaching. It also does 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. Future NuGIS versions will likely add additional components but current developmental focus has been only on the four factors mentioned.

NuGIS currently covers a 20-year period at 5-year intervals set by the years of the Census of Agriculture (COA). We felt a 20-year period was adequate to establish trends without taking us back to years when needed data were not accessible in electronic form. The spatial objective of NuGIS was USGS 8-digit hydrologic units (HUC) of which there are 2,150 in the U.S. This compares to 3,117 counties. The 8-digit HUC was chosen to accommodate watershed-based models and because we felt it was the highest level of spatial resolution possible with available aggregate data. NuGIS will output county level estimates but these are an intermediate step to the watershed unit.

Development of NuGIS has been more challenging than the basic algorithm at its core might suggest. The merging of disparate incomplete data sets having temporal structural changes and the use of data sets not intended for the specific purpose of estimating nutrient balance, contributed to the challenge. However, one of our objectives was to call attention to weaknesses in the databases essential to determining nutrient balances, whether at the farm level or in aggregate.

This summary of methods will not be detailed, as detailed methods are available online (http://www.ipni.net/nugis). The emphasis here will be on the methodology choices made and the reasons behind those choices.

Farm Fertilizer Nutrient Use

We use the commercial fertilizer sales data provided annually by the American Association of Plant Food Control Officials and The Fertilizer Institute (AAPFCO; Slater and Kirby, 2008) as the starting point in estimating fertilizer use. AAPFCO provides county-level data for approximately 72% of the counties in the 48 states. When county data were unavailable from AAPFCO, COA “Dollars spent on Fertilizer and Lime Products” was used to apportion state AAPFCO nutrients to individual counties. Not all fertilizer sold is used for farm purposes. We adapted USGS methodology used by Ruddy et al.(2006) to estimate farm use fertilizer sales for locations that did not already provide reliable farm use sales reports.

A problem when using AAPFCO fertilizer sales data to estimate fertilizer use is that the fertilizer may not be used in the same county in which it is sold. Also, fertilizer use is likely not constant across an entire county. 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. The ‘mean center’ of cropland in each county was attributed with the fertilizer sales data for that county. An inverse distance weighted interpolation method was then used to create an interpolated raster map of farm fertilizer nutrient use per total cropland acre across the lower 48- states.

Apparent aberrations do appear in AAPFCO sales data over time and space and have led to criticism of their application as surrogates for fertilizer use. These are most apparent where land use diversity is high such as in counties where cropland is intermingled with urban centers, extensive forests, mountains, or grasslands. We were aware of these issues so considered alternatives to using AAPFCO data. However, alternatives appeared to all have greater limitations than the procedure outlined above. One alternative was to rely completely on COA dollars spent on fertilizer and lime for county-level estimates. Problems with that approach include an assumption that fertilizer costs are the same across a state and that the N-P2O5-K2O ratio is constant within each state. Both assumptions do not hold in many states. Another approach is to use USDA-ERS survey data on nutrient use for specific crops. The major limitation with this approach is the limited number of crops for which county-level data are available in any given year.

Recoverable Manure Nutrient Use

A combination of livestock inventory and sales data from the COA, and findings from previously published studies was used to estimate the annual volume of manure, nutrients excreted, and nutrients recoverable, by several different species of livestock, by county (Lander et al., 1998; Kellogg et al., 2000). Non-recoverable manure nutrients are those in manure that are not collected for land application (e.g. that which is deposited while grazing in pastures) and the nutrients considered unavailable owing to losses during collection, transfer, storage, and treatment. Potassium estimates were not reported by Kellogg et al., but were obtained at the state level from Chuck Lander (personal communication) and published in a bulletin by the Potash & Phosphate Institute (Appendix 6.3 in PPI/PPIC/FAR, 2002). Because data were available only at the state-level, all counties in a state received the same K recoverability coefficient.

A limitation of the current NuGIS model is that it does not reflect temporal changes in the P concentration of excreted manure due to changes in livestock feeding. As a consequence of the adoption of more rigorous nutrient management plans, producers have adopted practices (precision feeding for ruminants, phytase for monogastrics) that reduce the amount of nutrients excreted by their livestock. As an example, Swink et al. (2008) estimated that the amount of P excreted per dairy cow per production period has been reduced from 62 to 40 pounds. Figures from The Fertilizer Institute indicate that total domestic feedgrade phosphate sales peaked  around 1996, declined by 30% by 2006 and for the last two years (2008-2009) have been down to only 44% of the 1996 peak level. A considerable portion of this decline may have been offset by increases in use of dried distillers grains with soluble (DDGS) from the ethanol industry. Such reductions in manure P content with time are not captured by NuGIS.

Biological 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.

Nutrients in Harvested Crops

The National Agricultural Statistics Service (NASS), COA, and the USDA-ERS were sources of data for planted acres, harvested acres, average yield, and production of crops at the county level. Data were analyzed for alfalfa, apples, barley, dry beans, canola, corn for grain, corn for silage, cotton, other hay, oranges, peanuts, potatoes, rice, sorghum, soybeans, sugar beets, sugarcane, sunflower, sweet corn, tobacco, and wheat. The majority of the crop production, harvested acres, and planted acres data comes from the “NASS Annual Ag Statistics Summary” datasets. When production data were not available from NASS summaries, other sources were investigated, including the COA, State NASS office publications and ERS Publications. County crop production data were used in conjunction with crop nutrient removal coefficients for N, P2O5, and K2O, to estimate the nutrient removal by crops. Crop production, harvested acres and planted acres data were averaged over a three year period, centered on the COA years of 1987, 1992, 1997, 2002, and 2007.

The 21 crops mentioned above account for an average of 95% of the COA harvested cropland acres for the U.S. When adjusted for double cropping, the value drops to 90%.

However, for some states and counties the 21 crops account for much lower portions of harvested cropland. For example, we estimate that in California the 21 crops account for only about 55% of harvested cropland with some important agricultural counties dropping below 25%. To account for the nutrient removal represented by these missing crop acres, we calculated an “other crop” acreage at a county level by first subtracting the 21 crop acreage from the COA total harvested crop acreage, then adjusting for double cropping using state level estimates.

Nutrient removal per acre for these other crop acres was an expert judgment based on the average removal per acre for the 21 crops and consideration of the likely crops making up the other crops category with the provision that the removal per acre could not exceed the state average removal for the 21 crops. These methods create considerable uncertainty in the predictions made for states such as California, Florida, Maine, Massachusetts, Oregon, Rhode Island, and Vermont, where the 21 crops represent less than 70% of the COA harvested cropland acreage.

One of the challenges we encountered in estimating nutrient removal was assembling reliable crop removal coefficients. For major crops like corn, soybeans and wheat, measured concentrations from research plots, quality surveys, field samples, and feed analysis were frequently lower than those reported in published fact sheets. This process resulted in the establishment of a research project at the University of Missouri to build and fill a national database of measured crop nutrient concentration data that will explore the potential for spatially dependent concentration data. Findings thus far have led to regionalized estimates of P in corn grain and for N, P and K in wheat. NuGIS utilizes removal coefficients based on data summaries whenever possible.

Results and Discussion

The methodology of NuGIS will likely continue to undergo changes in the years ahead as its output is scrutinized at a localized scale, its methods are more thoroughly vetted, and improvements are made in the input datasets. IPNI plans to maintain NuGIS with periodic updates and improvements as new data become available. The output of NuGIS is perhaps best evaluated via the online interactive thematic maps where input layers and calculated output can be viewed with panning capability at user selected resolution and using either county or watershed boundaries. In this article, we will share only national trends and offer a few examples of the differences among states.

Nutrient removal in crop harvest for the U.S. has increased dramatically from 1987 to 2007 for all three nutrients with N and P climbing about 35% and K about 26% (Figure 1). This occurred while total cropland acres declined from 443 million acres in 1987 to 410 million acres in 2007. Since farm fertilizer use experienced a smaller increase, nutrient removal to use ratios also increased during this same period with K showing the largest increase and N the smallest (Figure 1).

Figure 1. Nutrient removal in crop harvest and nutrient removal to use ratios for the U.S. over a 20-year period (NuGIS, January 2012).

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Care needs to be used in interpreting national figures on nutrient balance due to the great variability existing among regions within the U.S. Table 1 illustrates the diversity in nutrient budgets and the resulting balances among states.

Table 1. N and P budgets for four states and the U.S. in 2007 (NuGIS, January 2012).

State

Nutrient (Thousand tons)

Fertilizer (Thousand tons)

Recoverable manure (Thousand tons)

N fixation (Thousand tons)

Harvest removal (Thousand tons)

Balance* R/U (Thousand tons)

Balance* Cropland A (lb/A)

Florida

N

167

13

4.5

102

0.55

56

Florida

P2O5

56

13

 

33

0.47

25

Illinois

N

1,018

21

727

1,531

0.87

19

Illinois

P2O5

332

37

 

567

1.54

-16

N. Carolina

N

187

94

75

197

0.55

61

N. Carolina

P2O5

101

148

 

69

0.28

70

S. Dakota

N

450

17

333

679

0.85

13

S. Dakota

P2O5

212

29

 

219

0.91

2

U.S.

N

12,594

1,405

6,643

15,847

0.77

23

U.S.

P2O5

4,337

1,809

 

5,484

0.89

3

*Balance = Farm fertilizer + Recoverable manure + N fixation – Harvest removal; R/U = ratio of harvest removal to nutrient use; Cropland A = net balance on a per acre of cropland basis.

Summary: 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 the South Atlantic Gulf, New England and California watershed regions.
  • Much of the Corn Belt has negative P balances and the entire western half of the country has highly negative K balances.
  • Removal to use ratios appear unsustainably high in some regions and unsustainably low in others calling for intensive monitoring of soil fertility and more intensive nutrient management with greater adoption of 4R Nutrient Stewardship.
  • Substantial uncertainty exists in such aggregate data and points to a need for farm level measurement of nutrient balance and removal to use ratios as a basis for indicating progress in nutrient management.

References

IPNI. 2010. A preliminary nutrient use geographic information system (NuGIS) for the U.S. IPNI Publication No. 30-3270. Norcross, GA.

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. Ault. 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/NRI/pubs/nlweb.html.

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.

Slater, J.V. and B.J. Kirby. 2008. Commercial Fertilizers 2007. American Association of 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.

Slides

Slides

Slide 1 Data Warehouses/Exchanges

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Slide 2 Data Generated for Individual Corn Plants

http://www.sensefly.com

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Slide 3 Digital Agriculture

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Slide 4 Future Data Exchange for Growers

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Slide 5 Additional Data Scenarios

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Slide 6 Data Service Scenario for a Grower

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Slide 7 Major Ag Data Hurdles

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Slide 8 Ag Data Warehouse

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Slide 9 Precision Service Offerings

Source: PurdueCropLife Precision Dealer Survey

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Slide 10 Choice of Data Collection Method

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Slide 11 Lack of Interoperability Between Software Companies Is a Problem

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Slide 12 Choices for Storing Data

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Slide 13 Grower Expectations for Their Data

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Slide 14 What Are Farmers Responsible for Archiving Their Own Data?

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Slide 15 Data Exchange/Warehouse Attributes

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Slide 16 Digital Agriculture

http://fabe.osu.edu/precisionag

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