Validating a Digital Soil Map with Corn Yield Data for Precision Agriculture Decision Support

Abstract

Capturing the variability in soil-landscape properties is a challenge for grain producers attempting to integrate spatial information into the decision process of precision agriculture (PA). Digital soil maps (DSMs) use traditional soil survey information and can be the basis for PA subfi eld delineation (e.g., management zones). However, public soil survey maps provide only general descriptions of soil-landscape features. Th erefore, improved DSMs are needed that use high-resolution data that more precisely model soil-landscape characteristics. Additionally, reliable methods are needed to validate DSM products for PA. Th e objective of this study was to validate with corn (Zea mays L.) yield data the performance of a new DSM product, termed Environmental Response Unit (ERU), compared with the USDA Soil Survey Geographic (SSURGO) soil map. Th e ERU was developed by integrating SSURGO information with highresolution elevation data. For validation, corn yield maps were collected and corrected for common data collection errors from 409 fi elds across Indiana, Iowa, Minnesota, and Nebraska in 2010 to 2012. Reductions in the area-weighted variance (R v ) of corn yield for ERU and SSURGO were calculated relative to the whole-fi eld variance. Th e average R v across all site-years for SSURGO and ERU was 16 and 25%, respectively, which equated to a 57% higher median yield variance reduction with ERU over SSURGO. Th is variance reduction technique showed the potential of ERU as an improved model better representing soillandscape properties that impact corn yield. Th is research also has application potential for determining the success of a DSM for identifying management zones in PA.

Satellite detection of cover crops and their effects on crop yield in the Midwestern United States

“…While NCCPI serves as the best currently available nationwide soil quality index, it cannot account for soil properties such as compaction or nutrient depletion which vary at a scale far below what is mapped in SSURGO [49]. The index also cannot account for farmer practices which are likely to vary at the field or subfield level.…”

Using a Crop Modeling Framework for Precision Cost-Benefit Analysis of Variable Seeding and Nitrogen Application Rates

“…Additionally, studies have also attempted to quantify optimum site-specific seed densities (Licht et al, 2017), which may represent a more economically impactful management change in many cropping systems compared to changes in nutrient applications. Variable rate zones defining different application rates have been generated using precision agriculture data sources including yield monitor maps (Adamchuk et al, 2004;Basso et al, 2016;Maestrini and Basso, 2018), remotely sensed data (Hong et al, 2006;Basso et al, 2016;Gao et al, 2018;Jin et al, 2019), gridded soil sampling (Fleming et al, 2000), digital soil maps (Bobryk et al, 2016), topography (Long et al, 2015;Walters et al, 2017), and real-time optical sensors (Raun et al, 2002;Tremblay et al, 2009;Kitchen et al, 2010;Stefanini et al, 2018).…”

Importance of terrain attributes in relation to the spatial distribution of soil properties at the micro scale: a case study

“…At 0-15 cm, these three variables accounted for 82 % of the variability observed in sand (Table 7), 70 % of the variability observed in silt and 68 % of Analyses based on the spatial relationship between terrain attributes and soil properties can be applied to develop sample grids which reduce resources required for soil sampling and laboratory analysis (Wu et al 2009a, b). Furthermore, the spatial variability of soil properties and terrain attributes, integrated from high resolution elevation data and soil survey, could be used in development of management zones for crop production (Bobryk et al 2016). …”