Climate Change and Management Impacts on Soybean N Fixation, Soil N Mineralization, N2O Emissions, and Seed Yield

Quantifying the impact of climate and management strategies on groundwater conservation in the High Plains Aquifer

Groundwater depletion in semi-arid, irrigated regions is accelerating due to intensive agricultural water use. This study uses a linked hydro-agronomic model (DSSAT-MODFLOW) to evaluate crop yield and groundwater elevation under several scenarios of future climate, irrigation system, and planting decision in Finney County, southwest Kansas, a region that has experienced significant groundwater decline over the past 50 years as a result of irrigation within the U.S. High Plains Aquifer region. Model calibration was conducted using the Generalized Likelihood Uncertainty Estimation (GLUE) based on Monte Carlo simulation. The calibrated model was applied to quantitatively assess the impacts of projected climate conditions (2021-2050), using downscaled data from seven General Circulation Models (GCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) under SSP245 and SSP585 scenarios, in combination with various irrigation systems and land-crop-water allocation strategies on crop yield and water table elevation. Results indicate that, under climate change alone, groundwater saturated thickness is projected to decline by 20 %-55 % by 2050. When combined with different management practices, groundwater levels continue to decline regardless of irrigation type and allocation level, indicating that groundwater resources can only be conserved but not fully sustained. Maize production becomes increasingly vulnerable without the adoption of heat- and drought-tolerant cultivars, while soybean, winter wheat, and sorghum remain more resilient across scenarios. A drier future climate condition further constrains management options that simultaneously support yield and groundwater conservation goals. These findings provide critical insights into developing adaptive irrigation and cropping strategies in the High Plains Aquifer and other groundwater-dependent agricultural regions worldwide.

Statistical approaches are inadequate for accurate estimation of yield potential and gaps at regional level

Accurate spatial information on yield potential and gaps is key to determine crop production potential. Although statistical methods are widely used to estimate these parameters at regional to global levels, a rigorous evaluation of their performance is lacking. Here we compared outcomes derived from four published statistical approaches based on highest average farmer yields over time and space against those derived from a 'bottom-up' approach based on crop modelling and local weather and soil data for major rain-fed crops in the United States. Statistical methods failed to capture spatial variation in water-limited yield potential, consistently under- or overestimating yield gaps across regions. Statistical methods led to conflicting results, with production potential almost doubling from one method to another. We emphasize the need for well-validated crop models coupled with local data, robust spatial frameworks and extrapolation methods to provide more reliable assessments of production potential from local to regional scales.

Nitrate-N trends in Mississippi and Atchafalaya River Basin Watersheds: Exploring correlations of watershed features with nutrient trends components 2000-2020

Nutrient reduction strategies in the Mississippi and Atchafalaya River Basin (MARB) have been implemented to attenuate drinking water concerns and hypoxia in the northern Gulf of Mexico. Of all nutrients, nitrate has been identified as the principal cause of Gulf hypoxia, with loads coming disproportionately from the Upper Mississippi River Basin and the Corn Belt region. Identifying long-term changes of riverine nitrate would provide valuable information to evaluate the performance of reduction strategies. The objective of this study was to estimate the flow-normalized (FN) nitrate-N concentration and yield trends for the 2000-2020 period across the MARB at monitoring sites with adequate data. A harmonization and in-depth screening of paired nitrate-N and streamflow datasets resulted in a robust water quality monitoring network of 217 sites. Trends magnitude and likelihood were computed using the Weighted Regression on Time, Discharge, and Season (WRTDS) coupled to a bootstrap test, and trends results were correlated with basin features and initial values of concentrations and yields. The impact of streamflow long-term variations on trends was separated from all other factors through stationary and non-stationary flow normalizations. Results indicated that 59.4 % of the 217 sites had likely decreasing concentration trends, while 27.7 % likely increased, and the remaining 12.9 % had no likely change detected. Reductions in riverine FN nitrate-N were predominant at watersheds dominated by cultivated cropland areas having relatively high FN concentrations and yields in 2000 followed by likely downward trends. For the vast majority of sites, the non-streamflow component was more dominant, but the streamflow component was nonetheless influential.

Mapping 1-km soybean yield across China from 2001 to 2020 based on ensemble learning

Soybean is a critical agricultural product in China, with domestic production unable to satisfy the substantial demand, leading to a huge reliance on imports. To support the scientific formulation of agricultural policies and the optimization of domestic planting structures, we developed a high-resolution annual soybean yield dataset for China (2001-2020), ChinaSoyYield1km. This dataset was generated by applying ensemble learning algorithms and spatial decomposition to a comprehensive set of multi-source data, including climate variables, remote sensing imagery, soil properties, agricultural management practices, and official yield records. The integration of these diverse datasets allows for a nuanced understanding of the factors influencing soybean yield at a 1-km resolution. The resulting dataset captures over 50% of the yield variability at the county scale, demonstrating superior accuracy compared to publicly available datasets with reductions in Root Mean Square Error (RMSE) ranging from 0.18 to 0.60 t/ha. It is anticipated that our dataset will enhance agricultural studies, planning, and policy-making related to soybean cultivation, providing a valuable resource for both the scientific community and government.

Crop production and water quality under 1.5 °C and 2 °C warming: Plant responses and management options in the mid-Atlantic region

The 2015 "Paris Agreement" aims to limit the global average temperature rise to significantly less than 2 °C, preferably within 1.5 °C above pre-industrial levels. A multitude of studies have focused on evaluating how different sectors respond to such levels of warming. Nonetheless, most of these studies fail to provide a clear roadmap to mitigate these impacts. A case in point is the anticipated decline in corn and soybean yields and increased phosphorus (P) and nitrogen (N) discharge into water bodies, a trend linked to past agricultural practices and climate change. In this research, we employ a novel assessment of how existing management practices under 1.5 °C and 2 °C global warming (GW) scenarios can affect nutrient availability in time and space as well as crop yield in a typical agricultural watershed in the Mid-Atlantic Region, specifically the Upper Maurice River Watershed (UMRW) in New Jersey. Using the Soil and Water Assessment Tool (SWAT) with multiple Global Climate Model (GCM) projections, we found that compared to 1.5 °C, a 2 °C GW scenario would exacerbate runoff, leading to amplified nutrient leaching. These losses decrease nutrient availability during the crop growing season. Moreover, a mismatch between the timing of fertilizer application and crop nutrient absorption caused nutrient-related stress. This nutrient and anticipated temperature stress resulted in a more significant decrease in crop yields under the 2 °C GW scenario than the 1.5 °C scenario. We have designed a management scenario to reduce future nutrient losses while increasing crop yields. The strategy involves altering the timing of planting/harvesting and the fertilizer application rate in response to a warming climate. This approach is projected to increase corn and soybean yields by +39 % (+21 %) and +2 % (+17 %), respectively, under the 1.5 °C (2.0 °C) GW scenario for the RCP-4.5 pathway. Simultaneously, it is expected to decrease the N and P loads at 1.5 °C (2.0 °C) GW. Comparable projections are also observed under the RCP-8.5 pathway.

Leguminous plants significantly increase soil nitrogen cycling across global climates and ecosystem types

Leguminous plants are an important component of terrestrial ecosystems and significantly increase soil nitrogen (N) cycling and availability, which affects productivity in most ecosystems. Clarifying whether the effects of legumes on N cycling vary with contrasting ecosystem types and climatic regions is crucial for understanding and predicting ecosystem processes, but these effects are currently unknown. By conducting a global meta-analysis, we revealed that legumes increased the soil net N mineralization rate (Rmin ) by 67%, which was greater than the recently reported increase associated with N deposition (25%). This effect was similar for tropical (53%) and temperate regions (81%) but was significantly greater in grasslands (151%) and forests (74%) than in croplands (-3%) and was greater in in situ incubation (101%) or short-term experiments (112%) than in laboratory incubation (55%) or long-term experiments (37%). Legumes significantly influenced the dependence of Rmin on N fertilization and experimental factors. The Rmin was significantly increased by N fertilization in the nonlegume soils, but not in the legume soils. In addition, the effects of mean annual temperature, soil nutrients and experimental duration on Rmin were smaller in the legume soils than in the nonlegume soils. Collectively, our results highlighted the significant positive effects of legumes on soil N cycling, and indicated that the effects of legumes should be elucidated when addressing the response of soils to plants.