NCERA-180 Jan 5, 2007 Spatial Variability & Crop Simulation Modeling James W. Jones Yield Soil type Images Pests Elevation Drainage Fertility Diagnose Causes of Yield Variability Economics, Develop Prescriptions Prediction, Risk Assessment Help Guide Model Improvement GeneticsWeather Crop Models & Precision Farming

NCERA-180 Jan 5, 2007 Research Teams & Crop Model Applications in PA W. D. Batchelor, J. Paz, … J. T. Ritchie, B. Basso, … J. W. Jones, A. Irmak, R. Nijbroek, R. Braga, A. Ferreyra J. Sadler, … K. Sudduth, Fraisse, Kitchen, … Others

NCERA-180 Jan 5, 2007 Crop Models & Precision Agriculture Crop Models: Tools that do some things very well, but do not address all important factors Main limitations: –Accurate inputs are needed by models –Incompleteness of models Important to understand this for all applications, not just PA

NCERA-180 Jan 5, 2007 Purpose Overview of Crop Models Basis for use in precision agriculture (PA) What they are used for Examples Discussion

NCERA-180 Jan 5, 2007

Gainesville, FL 1978 Yield Crop Models Simulate Growth and Development Daily to Predict Final Yield

NCERA-180 Jan 5, 2007 Crop Models Can Predict Crop Yield over Time and Space Observed vs. simulated yields, Georgia yield trials ( )

NCERA-180 Jan 5, 2007 Using Crop Models in Agronomic Research Question, Problem Hypotheses Experiments Analysis Conclusions, Decisions Recommendations Crop Model Experiments

NCERA-180 Jan 5, 2007 Crop Model Applications Applications –Field Management –Risk management, marketing –Watershed management –Regional policy analyses –Climate change impacts on agriculture –Impact Assessment –Tradeoffs among economic & environmental quality –Natural resource management –Site-Specific Crop Management Used by those in: –Research, Education, Policy, Extension, Private Sector

NCERA-180 Jan 5, 2007 Basis for Crop Model Applications in Precision Agriculture Based on understanding of physical and physiological processes Integrate many factors that are important determinants in yield variability over space & time Providing inputs that vary across a field and weather on a yearly basis allows the models to simulate spatial yield variability from year to year With models that respond to factors that limit yield in a particular field, one only needs accurate inputs over space to simulate crop yield variations

NCERA-180 Jan 5, 2007 Practical Limitations -1 Factors included in most crop models and those that usually are not included Weather (temperature, solar radiation) Plant Population Soil Water Soil Nutrients – Nitrogen Other Soil Nutrients Lateral flow of water, nutrients,… Diseases Insects Nematodes Weeds

NCERA-180 Jan 5, 2007 Practical Limitations – 2 Obtaining accurate model inputs over space Soil physical properties Plant population density Rainfall Soil chemical properties Nutrient availability Initial conditions (soil water, nutrients, …) Weeds and damage from diseases, insects, insects (or densities)

NCERA-180 Jan 5, 2007 Crop Model Applications in PA Diagnose causes of yield variability over space and time; quantify associated yield gaps Explore options for site-specific management to increase yield, profit; develop recommendations Prediction of yield under anticipated climate conditions Help guide model improvements via identification of limitations for specific environments

NCERA-180 Jan 5, 2007 Obtaining Spatially Variable Inputs for Crop Models Measurement of inputs at many points in field, then interpolate, geostatistical analysis Use of pedo-transfer functions to estimate soil properties from texture –Using available soil maps (NRCS) –Measurement of texture across space –Electrical conductivity measurements –Remote sensing (NDVI) Inverse modeling, various approaches

NCERA-180 Jan 5, 2007 Suggs Field 4 (Kentucky) Yield maps –MZ, SB, WH Water content Weather Soil survey Management From Andres Ferreyra, 2004 NDVI Soil EC Elevation

NCERA-180 Jan 5, 2007 Weather From Andres Ferreyra, 2004

NCERA-180 Jan 5, 2007 Elevation From Andres Ferreyra, 2004

NCERA-180 Jan 5, 2007 Derived Indices from Elevation Data, e.g.Wetness Index High-WI areas correspond to waterways or frequent ponding. From Andres Ferreyra, 2004

NCERA-180 Jan 5, 2007 Soil water content (TDR) Approximately 50 dates from 7/2000 to 12/2002 Started with 8 points, increased to 26 From Andres Ferreyra, 2004

NCERA-180 Jan 5, 2007 Electroconductivity (EC) From Andres Ferreyra, 2004

NCERA-180 Jan 5, 2007 Yield maps From Andres Ferreyra, 2004

NCERA-180 Jan 5, 2007 Examples Early results (Batchelor, Paz, et al. – Iowa) Direct measurement of soil water holding input properties over space (Braga, Basso et al. – Michigan) Inverse modeling to quantify yield losses associated with different factors over space –Batchelor, Paz et al. (Iowa) –Irmak et al. (Iowa, Florida) Combine crop model and statistical model (Irmak et al. – Iowa & Florida) Help guide research by identifying model limitations (Sudduth, Fraisse et al. – Missouri)

NCERA-180 Jan 5, 2007 Examples Early results (Batchelor, Paz, et al. – Iowa) Direct measurement of soil water holding input properties over space (Braga, Basso et al. – Michigan) Inverse modeling to quantify yield losses associated with different factors over space –Batchelor, Paz et al. (Iowa) –Irmak et al. (Iowa, Florida) Combine crop model and statistical model (Irmak et al. – Iowa & Florida) Help guide research by identifying model limitations (Sudduth, Fraisse et al. – Missouri)

NCERA-180 Jan 5, 2007 Baker Farm (1994) Transect 1 Soybean W. D. Batchelor et al., 1999 Iowa State University

NCERA-180 Jan 5, 2007 S oybean Y ield C omparison B aker F arm (1994) W. D. Batchelor et al., 1999 Iowa State University

NCERA-180 Jan 5, 2007 Examples Early results (Batchelor, Paz, et al. – Iowa) Direct measurement of soil water holding input properties over space (Braga, Basso et al. – Michigan) Inverse modeling to quantify yield losses associated with different factors over space –Batchelor, Paz et al. (Iowa) –Irmak et al. (Iowa, Florida) Combine crop model and statistical model (Irmak et al. – Iowa & Florida) Help guide research by identifying model limitations (Sudduth, Fraisse et al. – Missouri)

NCERA-180 Jan 5, 2007 Predicting the spatial pattern of grain yield under water limiting conditions R.P.Braga Braga et al., Michigan (MSU and UF) Michigan Maize Field Soil Map (polygons) and measurement sites (points) Soil Map Measurements at points: Texture Soil depth Soil water over time, TDR

NCERA-180 Jan 5, 2007 Simulated vs. observed corn grain yield over two years using field-measured, spatially varying soil parameters in Michigan. R. Braga (2000). Braga et al., Michigan (MSU and UF)

NCERA-180 Jan 5, 2007 Inverse modeling or parameter estimation A crop model is run repeatedly for different combinations of “possible” input parameters. There is some “goodness of fit criterion” that can rank different parameter combinations. In PA applications, the criterion typically involves comparisons of predicted and observed yields. Some algorithm is used to choose the best set of parameters. These input parameters are then used for each year to allow the model to simulate spatial yield variability. Annual variability in yield, in each cell, is due to weather differences

NCERA-180 Jan 5, 2007 Examples Early results (Batchelor, Paz, et al. – Iowa) Direct measurement of soil water holding input properties over space (Braga, Basso et al. – Michigan) Inverse modeling to quantify yield losses associated with different factors over space –Batchelor, Paz et al. (Iowa) –Irmak et al. (Iowa, Florida) Combine crop model and statistical model (Irmak et al. – Iowa & Florida) Help guide research by identifying model limitations (Sudduth, Fraisse et al. – Missouri)

NCERA-180 Jan 5, 2007 Diagnosing Causes for Yield Variability in an Iowa Soybean Field W. D. Batchelor et al., 2000 Iowa State University

NCERA-180 Jan 5, Measured Yield 1997 Predicted Yield W. D. Batchelor et al., 2000 Iowa State University

NCERA-180 Jan 5, 2007 SCN Effect Water Stress Weed Effect Estimated Yield Losses Attributed to Each Factor

NCERA-180 Jan 5, 2007 Examples Early results (Batchelor, Paz, et al. – Iowa) Direct measurement of soil water holding input properties over space (Braga, Basso et al. – Michigan) Inverse modeling to quantify yield losses associated with different factors over space –Batchelor, Paz et al. (Iowa) –Irmak et al. (Iowa, Florida) Combine crop model and statistical model (Irmak et al. – Iowa & Florida) Help guide research by identifying model limitations (Sudduth, Fraisse et al. – Missouri)

NCERA-180 Jan 5, 2007 Irmak et al., Using Rules with Inverse Modeling Rules used by Irmak et al. (2001) to select appropriate parameters to fit, depending on characteristics of each cell

NCERA-180 Jan 5, 2007 A. Irmak et al., 2001 University of Florida Irmak et al. (2001) results after Inverse Model Estimation of Soil Model Inputs

NCERA-180 Jan 5, 2007 A. Irmak et al., 2000 University of Florida Irmak et al. (2001) results after Inverse Model Estimation of Inputs

NCERA-180 Jan 5, 2007 Examples Early results (Batchelor, Paz, et al. – Iowa) Direct measurement of soil water holding input properties over space (Braga, Basso et al. – Michigan) Inverse modeling to quantify yield losses associated with different factors over space –Batchelor, Paz et al. (Iowa) –Irmak et al. (Iowa, Florida) Combine crop model and statistical model (Irmak et al. – Iowa & Florida) Help guide research by identifying model limitations (Sudduth, Fraisse et al. – Missouri)

NCERA-180 Jan 5, 2007 Combining crop models and statistical techniques Where: f(CN i ), CROPGRO model yield prediction with optimum curve number (CN i ) for grid i;pH, soil pH; Wd, weed density (1 to 6); Pi, the SCN eggs/100 cc soil at planting; β 1, β 2, and β 3 are coefficients for pH, weed, and P i, respectively a is a coefficient used to adjust simulated yields when no yield-limiting factors occurred. ε is the residual error Field data are used to estimate a, C ni, β 1, β 2, and β 3

NCERA-180 Jan 5, 2007 Mean RMSE = kg/ha A. Irmak et al., 2000 University of Florida 1996 Season Rain = 418 mm 1998 Season Rain = 700 mm

NCERA-180 Jan 5, 2007 Limits to Inverse Modeling Overfitting (# samples per cell = # years of data) Ignoring spatial water movement Biased calibration weather Unknown initial conditions –In all of the above, unknown phenomena are “explained” away using existing parameters Years of yield data needed Computational tractability and user friendly issues

NCERA-180 Jan 5, 2007 Examples Early results (Batchelor, Paz, et al. – Iowa) Direct measurement of soil water holding input properties over space (Braga, Basso et al. – Michigan) Inverse modeling to quantify yield losses associated with different factors over space –Batchelor, Paz et al. (Iowa) –Irmak et al. (Iowa, Florida) Combine crop model and statistical model (Irmak et al. – Iowa & Florida) Help guide research by identifying model limitations (Sudduth, Fraisse et al. – Missouri)

NCERA-180 Jan 5, 2007 Site-Specific Crop Modeling Two fields located near Centralia, Missouri Claypan soils, poorly drained Minimum till corn- soybean rotation Monitoring sites selected based on yield history and field characteristics N Low elevation point Fraisse, Wang, Sudduth and Kitchen,

NCERA-180 Jan 5, 2007 Simulated Yield (kg ha -1 ) CROPGRO-Soybean Site in low area of field: standing water in early spring Fraisse, Wang, Sudduth and Kitchen,

NCERA-180 Jan 5, 2007 Grain yield in relation to topsoil depth Low elevation, deep soil point, outlier in the previous slide. This area is the best during dry years! Dry year Fraisse, Wang, Sudduth and Kitchen,

NCERA-180 Jan 5, 2007 Discussion Crop models can assist in spatial variability analysis and management. New approaches are needed to face the complex task of spatial model parameterization –Remote Sensing –Other sensors that measure soil, other limiting factor inputs –inverse modeling combined with AI –Geostatistical analysis –Data assimilation (Kalman Filter etc.) –Combine model with other statistical approaches (Principal Component or Canonical Correlation Analyses)