Manuscript Library

Fine-resolution maps of agronomic soil properties are essential for capturing within-field variability, supporting precision agriculture, improving understanding of soil–crop interactions, and providing reliable inputs for agroecosystem models. This study evaluated the use of digital soil mapping (DSM) with machine learning to predict 18 properties to a depth of 200 cm. Prediction performance peaked at shallow…

The objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, viz., multi-layer perceptron (MLP), random forest (RF), extra trees regressor (ETR), CatBoost, and gradient boost (GB), considering the impact of variation in sample sizes for the prediction of soil nutrients. In this context, this study evaluates the…

Soil drainage is an essential factor that influences plant growth and various biophysical processes, such as nutrient cycling and greenhouse gas fluxes. Therefore, soil drainage maps are fundamental tools for managing crops, forests, and the environment. This study compared two approaches to mapping soil drainage classes in the state of São Paulo, Brazil, using geographic…

Soil map updates can be expensive due to soil sampling and analysis costs. This study introduces the Optimal Sample Size Index (OSSI), a flexible framework for digital soil mapping that balances model accuracy and sampling costs to update soil spatial models. OSSI determines optimal sample size from subsets of initially available training sets, accounting for…

Essential soil nutrients are dynamic in nature and require timely management in farmers’ fields. Accurate prediction of the spatial distribution of soil nutrients using a suitable sampling density is a prerequisite for improving the practical utility of spatial soil fertility maps. However, practical research is required to address the challenge of selecting an optimal sampling…

Spatial adjustments are used to improve the estimate of plot seed yield across crops and geographies. Moving means (MM) and P-Spline are examples of spatial adjustment methods used in plant breeding trials to deal with field heterogeneity. Within the trial, spatial variability primarily comes from soil feature gradients, such as nutrients, but a study of…

This study presents a regional digital soil mapping (DSM) product that used a locally enhanced method in support of a bottom-up approach to create spatial soil predictions that were more accurate than one of the most accurate and detailed conventional soil mapping (CSM) products in the world, the Soil Survey Geographic (SSURGO) map from the…

The restoration of soil organic matter (SOM, as measured by soil organic carbon (SOC)) within the world’s agricultural soils is imperative to sustaining crop production and restoring other ecosystem services. We compiled long-term studies on the effect of management practices on SOC from Iowa, USA—an agricultural region with relatively high-quality soil data—to highlight constraints on…

Prairie strips, or plantings of diverse perennial vegetation integrated into cropland, can have disproportionate ecological benefits compared to the amount of land they occupy. These benefits include improved water quality, reduced soil loss, reduced nutrient loss, and increased abundance and diversity of wildlife. However, the impacts of prairie strips on the adjacent cropland soil and…

Many distinct glacial episodes in the past ~1 million years in the Central Lowlands of North America left behind a patchwork of glaciated landscapes of different ages and formed through different glacial, paraglacial, and proglacial processes. Herein, we synthesize and reconcile diverse data sources across nine states in the Central Lowlands to create a generalized…