Abstract
Insect pest infestations can vary due to spatial differences in microclimates and food availability within agroecosystems. Covariates can reflect these environmental conditions. This study tested whether using environmental covariates in two-phase sample optimization improved the spatial predictions for soybean insect pests. During the 2021–2022 crop season, insect pest samples were collected at 50 georeferenced points in a commercial soybean field in Brazil, alongside data on environmental covariates such as vegetation indices, soil properties, terrain topography, and distances from riparian areas. Three covariates were selected using correlation and principal component analysis (PCA). In the 2022–2023 crop season, sample designs were optimized using the iterative algorithm optimization of sample configurations using spatial simulated annealing (SPSANN) using the selected covariates, resulting in two optimized designs that were compared to a regular grid. Data from the three sampling designs comprising 50 points were evaluated using geostatistical methods, regression analysis (pest abundance), and classification (pest presence or absence) via the random forest algorithm. The data showed no spatial dependence, making using geostatistical interpolators inappropriate. However, a multi-objective optimized sampling design, tailored to refine configurations for identifying and estimating variograms and spatial trends essential for spatial interpolation, produced the most accurate predictions. Therefore, a two-phase sample optimization with prior in situ selection of environmental covariates improves pest predictions in agricultural systems, contributing to more efficient and sustainable agricultural management.
Published Version
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