Predicting spatial patterns of within-field crop yield variability

Abstract Over the last two decades, there has been significant advancements in the application of geospatial technologies in agriculture. Improved resolutions (spectral, spatial and temporal) of remotely sensed images, coupled with more precise on-the-ground systems (yield monitors, geophysical sensors) have allowed farmers to become more sensitive about the spatial and temporal variations of crop yields occurring in their fields. Previous research has extensively looked at spatial variability of crop yields at field scale, but studies designed to predict within-field spatial patterns of yield over a large number of fields as yet been reported. In this paper, we analyzed 571 fields with multiple years of yield maps at high spatial resolution to understand and predict within-field spatial patterns across eight states in the Midwest US and over corn, soybean, wheat and cotton fields. We examined the correlation between yield and 4 covariates, three derived from remote sensing imagery (red band spectral reflectance, NDVI and plant surface temperature) and the fourth from yield maps from previous years. The results showed that for spatial patterns that are stable over time the best predictor of the spatial variability is the historical yield map (previous years’ yield maps), while for zones within the field that are more sensitive to weather and thus fluctuate from one year to the next the best predictor of the spatial patterns are the within-season images. The results of this research help quantify the role of historical yield maps and within-season remote sensing images to predict spatial patterns. The knowledge of spatial patterns within a field is critical not only to farmers for potential variable rate applications, but also to select homogenous zones within the field to run crop models with site-specific input to better understand and predict the impact of weather, soil and landscape characteristics on spatial and temporal patterns of crop yields to enhance resource use efficiency at field level.

[1]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[2]  Atul K. Jain,et al.  Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation , 2013 .

[3]  S. Blackmore.  The interpretation of trends from multiple yield maps , 2000 .

[4]  Robin M. Reich,et al.  Spatial Variation and Site-Specific Management Zones , 2010 .

[5]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[6]  David M. Johnson.  An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States , 2014 .

[7]  James S. Schepers,et al.  Appropriateness of Management Zones for Characterizing Spatial Variability of Soil Properties and Irrigated Corn Yields across Years , 2004, Agronomy Journal.

[8]  Bruno Basso,et al.  Analyzing the effects of climate variability on spatial pattern of yield in a maize-wheat-soybean rotation , 2007 .

[9]  N. Sugiura.  Further analysts of the data by akaike' s information criterion and the finite corrections , 1978 .

[10]  Robert J. Hijmans,et al.  Geographic Data Analysis and Modeling , 2015 .

[11]  Q. Ge,et al.  Influences of agricultural phenology dynamic on land surface biophysical process and climate feedback , 2017, Journal of Geographical Sciences.

[12]  Bjarne Joernsgaard,et al.  Intra-field yield variation over crops and years , 2003 .

[13]  J. Ritchie,et al.  A strategic and tactical management approach to select optimal N fertilizer rates for wheat in a spatially variable field , 2011 .

[14]  William J. Sacks,et al.  Crop management and phenology trends in the U.S. Corn Belt: Impacts on yields, evapotranspiration and energy balance , 2011 .

[15]  M. Peruggia.  Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed.) , 2003 .

[16]  Bruno Basso,et al.  Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions , 2014, Precision Agriculture.

[17]  D. Bates,et al.  Linear Mixed-Effects Models using 'Eigen' and S4 , 2015 .