Agrosystems, Geosciences & Environment最新文献

Soil health is critical to sustain crop productivity and ecosystem functions. However, assessing soil health remains challenging due to variability in soil types, management practices, and climatic conditions. The objective of this study is to compare selected soil health indicators between croplands and undisturbed reference sites across a gradient of soil and climatic conditions in Nebraska. Four paired sites were selected from Cropland Reference Ecological Units representing distinct soil textures and precipitation regimes in Major Land Resource Areas (MLRAs) 106 and 67A. Soil samples were analyzed for organic matter (OM), β-glucosidase (BG) and phosphomonoesterase (PME), inorganic phosphorus (P), pH, bulk density (BD), and microbial community components (fungal-to-bacterial ratio and total fungal biomass). Reference sites with higher precipitation and finer soil textures had greater OM, BG, and PME and lower pH than those with low precipitation and coarse soils. Croplands at sites with manuring and no-till recorded lower OM depletion from reference site levels (28%) than sites with continued conventional tillage (31%–54%), underscoring the benefits of low-intensity land preparation and organic amendments. Croplands had lower PME activity than reference sites, suggesting that crop production entirely depends on P input to meet crop P requirements. A manured cropland had a higher BD than the reference site, illustrating the overall impact of management on soil health, which would have been otherwise overlooked had it not been compared against reference sites. Such comparisons between croplands and reference sites with regional consideration also help establish site-specific soil health targets.

The Mississippi River Basin has been extensively altered with levees, channelization, and agricultural land conversion. Much of the disturbed land was historically bottomland hardwood forests (BHF) and wetlands. As knowledge about sediment and nutrient nonpoint pollution has expanded, several wetland restoration and water quality monitoring programs have been developed through the United States Department of Agriculture Natural Resources and Conservation (e.g., the Agricultural Conservation Easement Program, Environmental Quality Incentive Program, and Edge-of-Field (EOF) Water Quality Monitoring Activity). Traditional water quality monitoring includes the use of pressure transducers, piezometers, and grab samples. The EOF water quality monitoring system generally focuses on agricultural inputs and uses non-contact measurement sensors. EOF monitoring stations include a flume, water sampler, and an ultrasonic level sensor. This system can monitor water quality in most flow conditions; however, BHF wetlands have more complex flow requiring a modification of the standard EOF station. By using a laser Doppler velocimeter (e.g., LaserFlow, Teledyne ISCO) in place of the flume and ultrasonic sensor, stage and velocity can be calculated in bidirectional flow at various speeds and levels. Nonetheless, more research is required into alternative instrumentation in complex heterogeneous hydrology found in BHFs.

Digital soil mapping (DSM) provides a low-cost approach for characterizing the spatial variation in soil properties, which contributes to inconsistent productivity. This study utilized random forest (RF) models to facilitate the DSM of apparent soil electrical conductivity (EC_a), cation exchange capacity (CEC), and soil organic matter (SOM) in agricultural fields across the Lower Mississippi Alluvial Valley based on a bare soil composite of Landsat 9 multispectral imagery and digital elevation models. Model data were collected during nongrowing seasons from 2019 through 2024. Field data included EC_a of the upper 0.5 m of soil from agricultural fields (n = 347) and soil-test-estimated values for CEC and SOM collected on a ∼0.4-ha (1-acre) grid from the upper 0- to 15-cm depth (n = 14,349). The RF model utilized a stratified K-fold cross-validation with fivefolds. Stratification by farm was used to ensure each fold in the cross-validation process contained a representative distribution of data drawn from all farm locations. Data were divided into an 80/20 split for training and testing purposes. Models had moderate accuracy (R² = 0.45, 0.74, and 0.72 for EC_a, CEC, and SOM) with moderate predictability (ratio of performance to deviation = 1.35, 1.95, and 1.91 for EC_a, CEC, and SOM). The contrasting performance between the CEC and SOM models with the EC_a model is likely due to the dynamic nature of soil properties, which is more pronounced in EC_a. Accordingly, models could have benefitted from covariates such as soil moisture and climatic factors or higher spectral resolution imagery, such as hyperspectral.

Cover crops play a significant role in improving and maintaining good soil quality. However, there are often some agronomic and cost challenges associated with successfully establishing cover crops. In Northeastern regions of the United States, abiotic stressors such as cold affect and high costs limit uptake of the practice. Using two field trials, a high tunnel study, and laboratory methods, we investigated the possibility of growing improved winter peas (Pisum sativum L.) as a cash cover crop in Northeastern regions of the United States. Results of the field trial showed no significant variance in winter survival between the winter pea genotypes tested. The genotypes tested include cold-hardy cultivars traditionally cultivated for forage and improved winter pea breeding lines selected for edible traits. In two field trial seasons of 2021/2022 and 2022/2023, all genotypes reached their reproductive stage in the first week of June when seeded the previous year around the end of September in Vermont. Our results show that although peas are a viable overwinter crop allowing potential double cropping. However, the mid-June maturity date for dry or fresh pea harvest conflicts with spring planting of cash crops on many Vermont and Northeastern farms, greatly limiting the potential of double cropping to increase winter cover cropping uptake. Consequently, some reported barriers to winter cover crop adoption in the far Northeast, such as high seed cost and time constraints, cannot easily be solved by double cropping.

Pastoral grasslands in New Zealand's mountainous landscapes contribute substantially to a primarily agricultural national economy. This landscape supports mosaics of regenerating indigenous biodiversity among more productive naturalized exotic herbage, each raising fundamental ecological, agronomic, and environmental concerns. Our objective was to investigate whether increased or lesser attention to native plant assemblages in these vegetation mosaics significantly influences soil carbon (C) stocks. At mid-altitudes below the original tree line, we compared paired plots of regenerating successional endemic myrtaceous woody shrub communities (Kunzea ericoides, kānuka) with exotic pasture at comparable slopes and aspects. We also investigated native snow tussock grass communities, the dominant vegetation at higher altitudes bordering the original tree line. Soils beneath kānuka had significantly higher C concentrations, C stocks, and carbon:nitrogen (N) ratios than adjacent areas of pasture. Soil C stocks were 15.43% higher under kānuka than under adjacent pasture. The bases of snow tussocks were frequently raised 10–20 cm above the surrounding inter-tussock land surface with a sparse vegetation cover that provides pathways for stock, making them more susceptible to soil erosion. Soil directly beneath the snow tussocks had significantly higher C, nitrogen, and phosphorus (P) in comparison with adjacent inter-tussock spaces. Soil C stocks were 38.7% higher under snow tussock than in the adjacent inter-tussock spaces. Our findings indicate that maintaining and enhancing endemic woody shrub and snow tussock assemblages is beneficial to productive and sustainable pasturage, while also playing a significant role in biodiversity conservation and climate change mitigation.

Islam, M. R., Alam, M. A., Rahman, M. M., Shahin-Uz-Zaman, M., Iqbal, M. S., El-Sabagh, A., Ismaan, H. N., Islam, M. A., Sultana, N., & Islam, M. S. (2025). Optimizing water-stressed mungbean for climate-smart sustainable intensification: Potassium's role in improving soil moisture, physio-biochemical traits, and yield sustainability. Agrosystems, Geosciences & Environment, 8, e70209. https://doi.org/10.1002/agg2.70209

The last name of co-author Hassan Nuur Ismaan has been corrected from “Issman” to “Ismaan” in the byline, the Author Contributions and How to Cite This Article sections.

The sixth byline has been updated from “Somali Agricultural Research and Technology Centre (SARTEC), Somali, Somalia” to “Faculty of Agriculture, Jazeera University, Mogadishu, Somalia.”

We apologize for this error.

Maize (Zea mays L.) production in Ethiopia spans across various agro-ecologies, encompassing humid highland, humid midland, dry lowland, and humid lowland areas. Identifying well-adapted and productive genotypes for target production environments could be achieved by evaluating new experimental hybrids across various representative test environments. This study aimed to examine the mean grain yield performance, grain yield stability, and genotype-by-environment interaction of quality protein maize (QPM) hybrids evaluated across environments in Ethiopia. Forty-eight QPM experimental hybrids, along with two commercial check hybrids, were evaluated across six environments. Analysis of variance for grain yield exhibited highly significant (p ≤ 0.001) differences due to genotype, environment, and genotype by environment interaction (GEI). Additive main effect and multiplicative interaction (AMMI) analysis revealed that genotype, environment, and GEI effects contributed to 4.57%, 78.59%, and 16.84% of the total variation, respectively. The first two interaction principal component axes (IPCAs) explained 66.29% of the total variations attributed to GEI sum of squares, indicating that these IPCAs captured most of the interaction effects. The AMMI stability value identified G5, G19, G22, and G42 as stable and high-yielding QPM hybrids, while G5 was the most stable genotype identified by yield stability index analysis. Genotype main effects plus GEI (GGE) biplot analysis identified G13, G14, and G25 as the most desirable hybrids. Among the test environments, Holeta was identified as an ideal test environment, exhibiting the highest discriminating power among the tested hybrids and the most representative of the test environments. The polygon view of GGE biplot subdivided the testing environments into different groups, mainly represented by Holeta, and Haramayaand Kulumsa. Among the various analytical models, the GGE biplot proved to be the most effective and precise tool for identifying high-yielding and stable hybrids. Results of this study indicated the possibility of developing stable and high-yielding QPM hybrids suited to representative maize production environments.

Aromatic rice (Oryza sativa) cultivation is economically essential, but its successful production depends on genotype adaptability and stability across different environments. In Texas, where environmental conditions can vary substantially between regions, it is essential to develop aromatic rice varieties that deliver high yields and maintain stability in diverse growing conditions. This study aimed to assess the performance and adaptability of 120 aromatic rice genotypes across two distinct environments, Beaumont and Eagle Lake, and to identify superior genotypes for each location. The study was conducted at the Texas A&M AgriLife Research Center in Beaumont and Eagle Lake, TX, and was motivated by the need to develop rice varieties with improved yield and stability under diverse environmental conditions. We assessed diverse morphological and agronomic traits and used genotype main effect plus genotype-by-environment interaction (GGE) biplot analysis to elucidate genotype–environment interactions. Our results revealed significant variations in several characteristics, including days to heading, plant height, and grain yield, among genotypes and across locations. GGE biplot analysis allowed us to identify the best-performing genotypes for each environment, with G85 and G98 excelling in Beaumont and G73 and G90 performing well in Eagle Lake. Furthermore, the analysis provided insights into genotype stability, revealing G27 as a highly stable genotype with above-average grain yield. Cluster analysis categorized the genotypes into four distinct groups based on their overall trait performance. This study highlights the importance of multi-environment trials in aromatic rice breeding programs. It demonstrates the utility of GGE biplot and cluster analysis for identifying superior genotypes with high yield potential and adaptability to specific environments. The findings can be valuable for developing region-specific cultivars and enhancing rice production in diverse agro-ecological zones.