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Integrating Weather and Soil Information With Sensor Data

This research focuses on optimizing nitrogen fertilizer recommendations for corn crops by integrating weather and soil information with sensor data. The study assesses various factors such as crop stage, soil characteristics, and weather conditions in generating N fertilizer suggestions. Utilizing different tools and algorithms, the project aims to enhance in-season N fertilization strategies for improved crop yield, profitability, and nutrient use efficiency. By evaluating datasets across diverse soil and weather scenarios, the study seeks to refine decision-making tools for N fertilizer applications, considering factors like soil EC and soil water holding capacity. Standardized protocols are applied at multiple sites to collect data and evaluate the performance of the tools. The research emphasizes the importance of regional investigations to provide a comprehensive understanding of N fertilizer management practices, considering varying soil types, weather patterns, and agricultural norms. Through standardized procedures and side-by-side testing of different tools, the study aims to identify the most effective approaches for optimizing N fertilization in corn production systems.

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Integrating Weather and Soil Information With Sensor Data

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  1. Integrating Weather and Soil Information With Sensor Data Newell Kitchen USDA ARS Cropping Systems and Water Quality Research Unit Columbia, Missouri

  2. What factors should an algorithm account for when generating an N fertilizer recommendation?

  3. Calculation for N fertilizer Rate 4 2 3 1 Missouri NRCS Agronomy Technical Note MO-35: Corn Variable-Rate Nitrogen Fertilizer Application for Corn Using In-field Sensing of Leaves or Canopy

  4. Optimal N Rate as a Function of Canopy Reflectance 1 N Rate for Max. Econ. Yield (kg N ha-1) 3 2

  5. The SoilFactor

  6. Precipitation

  7. Abundant and Well-DistributedRainfall

  8. What Factors Should Be Considered? • Crop • Stage of crop • Sensor specific • Soil • Soil water holding capacity • Mineralizable N • N Loss vulnerabilities • Weather • Poor health, poor stand, no stand • Hybrid • Farmer intuition (Max and Min) • Economics Robustness Ease of Use

  9. What Tool(s) and Supporting Algorithm(s) Captures the Important Factors and Performs Best? Universal Farm/Field Specific

  10. Regional NUE Project • Results confounded by • Varied methods of sensing • Varied N management practices • Varied other cultural practices

  11. Needed: Datasets for evaluation and validation, over a wide range of soil and weather scenarios, the yield and economic performance of model and plant sensing decision tools for determining the amount of N fertilizer to be applied to corn.

  12. Performance and Refinement of In-season Corn Nitrogen Fertilization Tools

  13. Data from Project Performance and Refinement of In-season Corn Nitrogen Fertilization Tools University Evaluate DuPont Pioneer proprietary products and decision aids Evaluate public-domain decision aid tools, develop agronomic science for improved crop N management, train new scientists, and publish results

  14. Tools Assessment • Yield and soil measurements from these plot studies will provide N response functions that will be used to reference each of the decision tool methods to be evaluated. • The N rate that would have been recommended by a tool will be matched with the optimal N-rate. Performance of the tool can be for yield, profitability, NUE, N loss, etc.

  15. Standardized Protocols • Site Selection • Site characterization • Treatment implementation • Weather data collection • Equipment • Soil and plant sampling • Management notes • Data management

  16. 16 Sites in 2014

  17. Integrating Weather and Soil Information With Sensor Data Newell Kitchen USDA ARS Cropping Systems and Water Quality Research Unit Columbia, Missouri

  18. How might soil EC help characterize in-season corn N fertilizer rate both within field and across the cornbelt?

  19. Infiltration good PAWC good Infiltration poor PAWC poor Infiltration good PAWC poor Relative Productivity Sand Loam Clay 0 10 20 30 40 50 60 70 Soil Electrical Conductivity (mS/m)

  20. Site Soil EC Maps

  21. IL BRT IL URB NE BRD NE SCAL IN SAND IN LOAM IA AMES IA MC MN ST CH MN New Rich MO BAY Relative Productivity MO TRT ND AMEN ND DUR (+110) WI STU WI WAU Sand Loam Clay 0 10 20 30 40 50 60 70 Soil Electrical Conductivity (mS/m)

  22. Infiltration good PAWC good Infiltration poor PAWC poor Infiltration good PAWC poor Relative Productivity Sand Loam Clay 0 10 20 30 40 50 60 70 Soil Electrical Conductivity (mS/m)

  23. Why Regional Investigation of this kind? • Breadth. More comprehensive story when a wider range of soil, weather, and cultural norms are included using standardized procedures • Balance. Build on the unique perspectives and strengths each investigator brings (both with critical and creative thinking), and perhaps also it helps neutralize individual’s biases • Strengthens and Weaknesses. Side-by-side testing of the tools will allow for better understanding of where and when they work best

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