A market-based insurance approach aligns environmental and economic outcomes in maize nitrogen management

Communications Earth & Environment · 2025-12-24

Abstract Meeting the growing demand for maize while reducing nitrogen losses remains a key challenge for sustainable agriculture. Here, we present a market-based nitrogen insurance concept that allows farmers who apply excess fertilizer to reduce nitrogen use without financial risk. Using a large-scale process-based crop model dataset covering 4270 maize fields in Illinois, over 30 years of weather data, and 33 fertilizer rates, we found that the insurance is profitable for farmers applying at least 40 kilograms of nitrogen per hectare above recommended rates, yielding average annual gains of 14 dollars per hectare for farmers and 10 dollars per hectare for insurers. Applied across the eligible Illinois maize area, the insurance annually reduces nitrate leaching by 31% and lowers greenhouse gas emissions by 682,000 metric tonnes of carbon dioxide equivalent. This self-sustaining approach could be extended to other nitrogen-intensive crops and regions, providing a globally relevant pathway to reconcile farm profitability with environmental stewardship.

Genomic analysis and predictive modeling in the Northern Uniform Soybean Tests

Crop Science · 2025-08-29 · 1 citations

Abstract The Northern Uniform Soybean Tests (NUST) are a regional field trial network coordinated by the United States Department of Agriculture to evaluate experimental soybean ( Glycine max L.) strains developed by public institutions. Historical data from the NUST compiled, curated, and reported herein comprise a valuable multi‐environment trial dataset including relevant elite soybean germplasm from maturity groups 00 to IV evaluated over 28 years in 199 locations, totaling 1652 environments. Our aim was to characterize the genetic structure of the NUST experimental strains, perform genome‐wide association studies using historical phenotypic data, and assess the usefulness of these historical data for genomic prediction model training. Molecular marker information was collected on 2544 unique NUST experimental strains using the BARCSoySNP6K assay. High fixation index values between early and later maturity groups were observed in a region on chromosome 10 near the known soybean maturity gene E2 . We failed to find strong genetic divergence between strains from different breeding programs, reflecting the germplasm sharing common among public programs. Genome‐wide association analyses on important agronomic traits identified marker‐trait associations, many of which overlap with quantitative trait loci previously reported in the literature. Genomic prediction models trained using the historical NUST data produced moderate to high predictive abilities in most cases, suggesting these data could make useful contributions to training sets. We have made these data publicly available as a data resource for others to study genotype–phenotype relationships within elite public soybean germplasm and develop predictive models for advancement and implementation of genomics‐assisted breeding.

Associations among weed communities, management practices, and environmental factors in U.S. snap bean (Phaseolus vulgaris) production

PLoS ONE · 2025-09-23

Weed species that escape control (hereafter called residual weeds) coupled with changing weather patterns are emerging challenges for snap bean processors and growers. Field surveys were conducted to identify associations among crop/weed management practices and environmental factors on snap bean yield and residual weed density. From 2019-2023, a total of 358 snap bean production fields throughout the major U.S. production regions (Northwest, Midwest and Northeast) were surveyed for residual weeds. Field-level information on crop/weed management, soils, and weather also were obtained. To determine associations among management and environmental variables on crop yield and residual weed density, the machine learning algorithm random forest was utilized. The models had 24 and 22 predictor variables for crop yield and residual weed density, respectively, and both were trained on 80% of the data with the remainder used as a test set to determine model accuracy. Both models had pseudo-R2 values of over 0.50 and accuracy over 80%. The models showed that crop yield was higher in the Northwest compared to the Midwest region, while higher average temperatures during early season growth and planting midseason (June-July) predicted greater crop yield compared to other time periods. The use of row cultivation was associated with lower snap bean yield and weed density, suggesting row cultivation had less-than-ideal selectivity between the crop and weed. Moreover, multiple spring tillage operations prior to planting were linked with an increase in weed density, implying that excessive tillage may favor the emergence of residual weeds in snap bean. Over the coming decades, climate change-driven weather variability is likely to influence snap bean production, both directly through crop growth and indirectly through weeds that escape control practices that also are influenced by the weather.

The Seasonal Characterization Engine, an Application for Describing Environment from the Perspective of Crop Development

SSRN Electronic Journal · 2025-01-01

Maturity Prediction in Soybean Breeding Using Aerial Images and the Random Forest Machine Learning Algorithm

Remote Sensing · 2024-11-21 · 15 citations

Several studies have used aerial images to predict physiological maturity (R8 stage) in soybeans (Glycine max (L.) Merr.). However, information for making predictions in the current growing season using models fitted in previous years is still necessary. Using the Random Forest machine learning algorithm and time series of RGB (red, green, blue) and multispectral images taken from a drone, this work aimed to study, in three breeding experiments of plant rows, how maturity predictions are impacted by a number of factors. These include the type of camera used, the number and time between flights, and whether models fitted with data obtained in one or more environments can be used to make accurate predictions in an independent environment. Applying principal component analysis (PCA), it was found that compared to the full set of 8–10 flights (R2 = 0.91–0.94; RMSE = 1.8–1.3 days), using data from three to five fights before harvest had almost no effect on the prediction error (RMSE increase ~0.1 days). Similar prediction accuracy was achieved using either a multispectral or an affordable RGB camera, and the excess green index (ExG) was found to be the important feature in making predictions. Using a model trained with data from two previous years and using fielding notes from check cultivars planted in the test season, the R8 stage was predicted, in 2020, with an error of 2.1 days. Periodically adjusted models could help soybean breeding programs save time when characterizing the cycle length of thousands of plant rows each season.

Yield environment changes the ranking of soybean genotypes

Field Crops Research · 2024-11-28 · 8 citations

Using agro-ecological zones to improve the representation of a multi-environment trial of soybean varieties

Frontiers in Plant Science · 2024-03-25 · 9 citations

This research introduces a novel framework for enhancing soybean cultivation in North America by categorizing growing environments into distinct ecological and maturity-based zones. Using an integrated analysis of long-term climatic data and records of soybean varietal trials, this research generates a zonal environmental characterization which captures major components of the growing environment which affect the range of adaptation of soybean varieties. These findings have immediate applications for optimizing multi-environment soybean trials. This characterization allows breeders to assess the environmental representation of a multi-environmental trial of soybean varieties, and to strategize the distribution of testing and the placement of test sites accordingly. This application is demonstrated with a historical scenario of a soybean multi-environment trial, using two resource allocation models: one targeted towards improving the general adaptation of soybean varieties, which focuses on widely cultivated areas, and one targeted towards specific adaptation, which captures diverse environmental conditions. Ultimately, the study aims to improve the efficiency and impact of soybean breeding programs, leading to the development of cultivars resilient to variable and changing climates.

Diseños experimentales para detectar variabilidad espacial del efecto de tratamientos a escala de lote

SADIO Electronic Journal of Informatics and Operations Research · 2024-05-31

La agricultura de precisión supone la existencia de variabilidad espacial de la respuesta de los cultivos a la aplicación de insumos. Los experimentos a escala de lote permiten explorar dicha variabilidad. No obstante, la interacción entre la variabilidad espacial de los factores que controlan la respuesta del cultivo y el diseño experimental aplicado condicionan los resultados. Es necesario identificar diseños experimentales que optimicen la obtención de información fiable de la respuesta de los cultivos intra-lote. Se evaluaron diseños experimentales a escala de lote con distinta resolución espacial para estimar la variabilidad espacial de la respuesta de un cultivo a la aplicación de insumos. Se simularon patrones espaciales de respuesta como proceso subyacente para generar mapas de rendimiento. Mediante regresión ponderada geográficamente (GWR) se estimaron los patrones de respuesta del cultivo que se compararon con el campo estocástico subyacente. Los diseños con alta resolución espacial permiten capturar mejor los patrones de variabilidad espacial subyacente en un amplio rango de estructuras espaciales consideradas. A su vez, diseños en parcelas tipo tablero de ajedrez superan a los diseños en franjas ya que permiten detectar variabilidad espacial en ambas direcciones. No obstante, la concordancia entre los mapas de respuesta estimados por GWR y los de referencia son sensibles a la selección de kernel y ancho de banda.

The nitrogen value of cover crops: How much N can cover crops replace?

Agricultural & Environmental Letters · 2024-12-01 · 8 citations

Abstract Achieving high corn yields while reducing fertilizer losses seems attainable through nitrogen (N) management decisions that include the use of cover crops (CCs). To determine whether CCs result in a net positive balance between N fertilization and crop utilization, we used US field trial data comparing corn systems with and without CCs, and estimated the amount of N fertilizer that CCs would replace and lead to equivalent grain yields under both systems. Overall, applying lower nitrogen rates to corn without cover crops resulted in similar or higher yields when legumes were used as cover crops (indicating positive nitrogen replacement in the amount of 62 kg ha −1 ), but lower yields when grasses were used as cover crops (indicating negative nitrogen replacement in the amount of 24 kg ha −1 ). Our results illustrate the benefits and trade‐offs of integrating single CC species into a corn system, that is, reducing N inputs with legume CCs or supplementing N fertilizer to avoid possible grain yield penalties in the case of grass CCs. Quantifying the N replacement value of CCs would facilitate field‐level recommendations and policy regulations aimed at promoting sustainable corn production in the United States. Core Ideas Managing N fertilizer rates is essential for maximizing the benefits of legume and grass cover crops in corn‐based systems. N fertilizer replacement of grass and legume cover crops was assessed based on data from field experiments in the United States. Legume cover crops were found to positively replace N fertilizer; potentially reducing corn N inputs. Grass cover crops were found to negatively replace N fertilizer; potentially requiring supplementary fertilizer to reduce corn yield penalties.

Corrigendum: Exploring trade-offs between profit, yield, and the environmental footprint of potential nitrogen fertilizer regulations in the US Midwest

Frontiers in Plant Science · 2023-01-10 · 1 citations

[This corrects the article DOI: 10.3389/fpls.2022.852116.].