Data-Driven Advances in Agriculture

Transcription

1 1 Data-Driven Advances in Agriculture Kenneth A. Sudduth Research Agricultural Engineer US Department of Agriculture, Agricultural Research Service Columbia, Missouri, USA Adjunct Professor Bioengineering Department, University of Missouri Past-President International Society of Precision Agriculture October 2-4, 2018 Curitiba, Brazil

2 2 Data-driven agriculture: on the farm and beyond

3 Historical perspective 3 Farmers have always used their knowledge of their fields, along with available information, to make decisions Example: expected date of last freezing temperature in the state of Illinois, USA

4 As time goes on, more focus on data, both temporal data... 4 Weather forecasts Variety test results

5 ... and spatial data. 5 In the 1920s in the US, a University of Illinois extension bulletin showed how to grid sample for ph, map variability, and create management zones. (Linsley and Bauer, 1929)

6 Where are we today? 6 Agriculture 4.0 Smart Farming Smart Agri-Food Systems Digital Farming/Agriculture Precision Farming/Agriculture All describe application of information technology to agriculture. Some are focused mainly on the farm (e.g., precision ag) while others encompass the entire agri-food value chain.

7 Applying IT in agriculture 7 Why? Make better decisions on the farm (precision ag) Profit for the farmer Environmental benefits for all Higher production; better food security Improve the value chain from farm to fork Knowledge-based input and output linkages Traceability and food safety Designer foods Make the supply chain more efficient

8 8 Smart Ag opportunities in the value chain

9 Know your food Farm to table traceability enabled by integrated data systems

10 10 Smart Ag opportunities in the value chain

11 11 Smart Ag opportunities in the supply chain

12 Precision Ag as a subset of Smart Ag 12 Precision agriculture is a management strategy that uses information technologies to bring data from multiple sources to bear on decisions associated with all types of agricultural production systems Focus on production IT is key How will IoT contribute to precision ag? Smart Ag Precision Agriculture

13 A changing paradigm in farming 13 Old New At some point, it will cease to be precision farming and just be farming

14 14 Improved insight through sensors and data mining

15 Opportunities in the precision agriculture cycle 15 Mapping of spatial variations in soil and crop based on sample collection is well-established. Sensor-based data collection provides advantages but universal calibrations to variables of interest are challenging. Can new sensing modalities provide improved and/ or new capabilities? Basic models have been evaluated in restricted environments. More general models that can respond appropriately under varying ambient conditions are still elusive. Can machine learning approaches help? Figure by Antonio Comparetti, University of Palermo

16 Opportunities in sensing and data collection Research Sensor evaluation in different situations Calibration of sensor data under local or regional conditions Development of spatially (3-d) and temporally dense datasets Development of usable methods to integrate data from multiple sensors - sensor fusion Depth (m) Sand & Clay (%) Wavelength (nm) EC (ms/m) Force (kpa) 4000 Depth (ft) -0.2

17 Opportunities in data collection and processing Research Converting sensor data into the variables needed for crop/soil models Applications Collecting spatial data to understand withinfield and year-to-year variability Multi-temporal mobile sensor data Repeated sampling campaigns Complete metadata is essential! 10 cm layer 20 cm 30 cm 40 cm 50 cm 60 cm 70 cm Clay Content (%) 3.0 to to to to to 41.0

18 Opportunities in data-driven decision making Decision support and analytics Today the pieces are coming together to be able to turn data into information into decisions: Data collection & machine interfaces telematics Data sharing standards projects such as SPADE Data analysis Big Data Multi-producer databases Models Weather and other public datasets Visualization and implementation

19 Telematics Visualization Processing Telematics More.

20 Data sharing, data transfer and standards SPADE = Standardized Precision Ag Data Exchange (AgGateway) Manufacturer Seed Company 1 API Management Templates (generic) Farmer FMIS 2 Wire Wireless Media Seed Company 2 Equipment Company 1 AgGateway API API Product XML Equip XML FMIS Standard XML FMIS XML ISO XML 4 Telematics ISO Standard XML Controller Core Data Consultant FMIS 1 AgFleet Service Provider FMIS 3 Local Data Implement Tractor XML AgGateway Standard XML FMIS XML Standard XML ISO Standard

21 Big Data analytics in agriculture Concept diagram illustrating just a few of the Agricultural Big Data sources and uses Public Data Sources Short term weather Longer term climate National soil database Digital elevation models Markets Regional and national inventories USDA projections Research Models and Decision Tools Integrated farm models Crop growth models Soil-water balance models Machinery selection algorithms Best Management Practice Evaluators Farm optimization tools Dr. Karen Plaut, Purdue University Private Data and Inputs Machinery and labor availability Crop rotations Marketing strategies Private inventories Remote sensing images Inputs prices Yield maps Crop inputs maps AG BIG DATA i Improving Farmer Decisions Advancing a Research Methods Enabling e Improved Policy

22 Data visualization and trusted advisors Previous ag big data projects failed due to a lack of personal contact and involvement Companies are realizing the need for a trusted link to the farmer The integrated nature of current decision support offerings, coupled with improved analytics and customer service, have created a strong opportunity for both agribusiness and farmers in this area.

23 Develop and deploy systems that help farmers turn data into information into decisions Complex task where partnering is required Public sector researchers Agronomy and equipment companies Other sectors, like data services Not an easy task Some successes already Much more to be done

24 Decision support case study: Systems to optimize in-season N application Empirical Models Crop Growth Models Canopy Sensing Which is the Most Reliable Corn Nitrogen Encirca N Service Maize-N Climate: Nitrogen Advisor Adapt-N Recommendation Tool? Soil Tests PPNT Pre-Plant Soil Nitrate Test SDNT Side-Dress Soil Nitrate Test

25 Empirical Models Crop Growth Models Encirca N Service Maize-N Climate: Nitrogen Advisor Adapt-N Side-by-Side Comparison Canopy Sensing Soil Tests PPNT Pre-Plant Soil Nitrate Test SDNT Side-Dress Soil Nitrate Test

26 Public-Private Partnership for Improving Performance of Corn Nitrogen Fertilization Tools David Franzen North Dakota St. University Fabián Fernández University of Minnesota John Sawyer Iowa State University Richard Ferguson University of Nebraska Newell Kitchen USDA-ARS University of Missouri Carrie Laboski University of Wisconsin Emerson Nafziger University of Illinois James Camberato Purdue University Paul R. Carter

27 Project Data Evaluate DuPont Pioneer proprietary products and decision aids University Evaluate public-domain decision aid tools, develop agronomic science for improved crop N management, train new scientists, and publish results

28 Research Locations 16 Locations/Year Total 49 Temperature Precipitation

29 . a wide array of weather and soil conditions.

30 N Treatments (kg/ha) TRT Planting N Side-dress N (V9 ± 1 Total N (kg/ha) # (kg/ha) stage) (kg/ha) Standardized Design Measurements Climate Precipitation Temperature Solar radiation Soil EC mapping (Veris TM ) Soil sampling (3x) Soil moisture ( TRT 3+16) Crop Plant N (VT & R6) Canopy reflectance (V9) Grain yield and moisture

31 What was the correct N rate? Best-fit model for Economic Optimal N Rate (EONR)

32 Variation in Economic Optimal N Rate

33 Economic Optimal Nitrogen Rate (EONR)

34 Tools Tested At Planting State-Specific Yield Goal Pre-plant Soil Nitrogen Test (PPNT) Maximum Return to Nitrogen (MRTN) Crop model (Maize-N) Split (40lbs/a at planting + top dress) State-Specific Yield Goal Side-dress Soil Nitrogen Test (PSNT) Maximum Return to Nitrogen (MRTN) Crop model (Maize-N) Canopy Reflectance - MU algorithm

35 Canopy Sensing

36 Algorithm Performance

37 Courtesy of Iowa State Extension

38 38 Adding covariates to crop sensor data

39

40 Encirca Nitrogen Management Service. Management Hybrid Seeding Rate Initial N Manure N applications ERU -Soil Yield Goal O.M. Inhibitor Residue Credit Tillage Tile Irrigation Decision Zone Soil Criteria Productivity Soil Water Cronus Crop Model Current and forecast (Soil N to date) Local Weather Multi-year historical (Potential N outcomes) N Recommendation Planned Application Date N Source/application method Current Soil N per DZ Target N Required Target N Level Cronus Crop Model 40

41 Sensor systems case study: 3-d soil variability mapping with EC sensors Geonics EM38 DUALEM-1hS Veris 3100 & 3150

42 Project goal 42 Develop layer-by-layer estimates of soil properties (especially texture) that could be used in crop models EC a is a useful approach because data is strongly affected by soil texture

43 Depth, m EC a responds to soil layering Because of variations in instrument response with depth, EC a is affected not only by soil variables (salinity, clay, water content, etc.) but also how they are layered in the profile Geonics EM38 vertical Veris 3100 shallow Veris 3100 deep DUALEM-2S shallow (PRP) DUALEM-2S deep (HCP) Layered soil property data are important for agronomic and environmental modeling Relative response

44 Diverse field sites in Missouri, USA 44 4 in MLRA 113 Central Claypan Areas 2 in MLRA 107B Deep Loess Hills 4 in MLRA 131A Southern Mississippi Valley Alluvium Analysis: inversion of EC results with commercial software

45 Data collection 45 Mobile EC a & m depths Veris 3100 Veris MSP Profile points 8-20 per field Profile EC Soil samples

46 Inversion results 46 Good representation of known field features Example: exposed clay (high EC) on eroded sideslope

47 47 Relating soil clay to P4000 and inversion EC

48 Example product: Inversion-based clay layer maps for Field cm layer 20 cm 30 cm 40 cm 50 cm 60 cm 70 cm Clay Content (%) 3.0 to to to to to 41.0

49 Access to information is becoming ubiquitous around the world Smart farming & precision agriculture approaches can be used in many different production situations. Who will attain the competitive advantage?

50 Precision agriculture is a win-win Improve production with the same level of physical resources Increase profitability Minimize/optimize the use of physical resources: land, water, fertilizer, seed, chemicals,... Less offsite movement of excess, especially fertilizer and chemicals Improved soil health Reduced impacts to environmental quality Resilience to changing climate Sustainability

51 Final thoughts Precision agriculture adoption is accelerating, particularly for those technology components and systems where an immediate value is perceived Improvements in data-driven decision making have potential to provide significant advances PA and the broader concept of information agriculture can provide value in any agricultural system Partnering is key Across geography developed and developing countries Across organizations public and private Across disciplines technologists and agriculturists

52 52 Thank you for your attention. Any questions?