Soil and terrain properties that predict differences in local ideal seeding rate for soybean

Abstract

The agronomic optimum seeding rate (AOSR) of soybean [Glycine max (L.) Merr.] varies based on environment. Understanding where AOSR varies within a field is useful for farmers utilizing variable rate seeding technology. An AOSR representing an area smaller than a whole field is referred to as local ideal seeding rate (LISR). The objective of this study was to identify soil and terrain properties that were most predictive of differences in LISR. Seeding rate trials were established at four fields in 2017 and three fields in 2018. Yield data were used to estimate LISR 33-68 times per field.Soil properties were estimated at the same scale as LISR using 0.2-ha grid samples and terrain properties were calculated from 0.76-m digital elevation model developed using light detection and ranging data. Random forest analysis was performed to identify which soil and terrain properties were predictive of LISR within each site-year. At all site-years, terrain properties were generally more predictive of LISR compared to soil properties. Valley depth and general curvature were in the top-five most predictive properties at four of seven site-years. Moving from the lowest valley to the highest ridge was associated with an increase in LISR of 76,000 seeds ha −1 . Moving from the lowest relative slope position to the highest relative slope position was associated with a 38,000 seeds ha −1 increase in LISR. Terrain properties may be appealing to farmers because they relate to LISR, are publicly available data, and stable over time.

Comparison of variable‐rate prescriptions and optimum seeding rate in soybean

“…As an alternative to delineating management zones based on soil map unit, soil characteristics, yield history, or a combination of these factors, terrain properties may be a more useful option. Matcham et al (2020) found terrain properties, including aspect, hillshade, and elevation, to be better predictors of optimum seeding rate than soil properties. Terrain properties strongly influence water availability and movement and temperature (Hanna et al, 1982), which subsequently impact soybean growth and yield.…”

“…In the early adoption of VRS for soybean production, field characteristics such as soil map unit, soil P, and soil organic matter were identified as important factors when creating management zones (Smidt et al, 2016). Landscape properties, such as valley depth and general curvature, could also be used to create seeding rate management zones within fields (Matcham et al, 2020). In practice, soybean farmers use a variety of methods to make VRS management zones, including soil properties and yield history, and generally plant fewer seeds in highly productive zones and more seeds in less productive zones, which is opposite of VRS in corn (Carciochi et al, 2019;Devlin et al, 1995;Gaspar et al, 2020;Grisso et al, 2009).…”

Implications of choosing different interpolation methods: A case study for soil test phosphorus

“…ppm, parts per million index. Terrain attributes were used as covariates in the GWR and RF models.Geographically weighted regressions were run in SAGA GIS according to the procedures detailed inMatcham et al (2020) with a Guassian kernel and bandwidth = 1. Random forest models were built by the package randomForest within R (3.4.2) with the parameters ntree = 500 and mtry = 2, which are the values recommended by the package for a dataset of our size…”