Sustainability of Maize–Soybean Rotation for Future Climate Change Scenarios in Northeast China

Journal of Agronomy and Crop Science, Journal Year: 2025, Volume and Issue: 211(2)

Published: March 1, 2025

ABSTRACT Climate change poses a global challenge to agricultural production and food security, especially in developing countries. In Northeast China, major grain‐producing region, the Maize–Soybean rotation is crucial for sustainable development. However, previous studies have mainly focused on single crops lacked attention soil health regional scale analysis. This study utilises APSIM model predict crop yields organic carbon (SOC) under two Representative Concentration Pathways 4.5 8.5 (RCP4.5 RCP8.5) future climate scenarios different latitude regions of China. The result shows that has significant spatial temporal variations yield storage system. Compared baseline (1980–2010), maize from −11.6 42.8 kg 10a −1 (RCP4.5) 7.1 39.8 (RCP8.5), soybean vary −13.1 3.9 −16.2 −5.6 (RCP8.5). SOC increases slowly 0 20 cm decreases 40 cm, resulting decrease 21–334 ha 26–280 (RCP8.5) predicted storage. PLS‐PM results show precipitation negative impact accumulation, temperature rise RCP8.5 scenario positively correlated with yields, correlation stronger RCP8.5, which higher explanation changes. significantly affects stocks system Northeastern during extreme weather. Therefore, adaptation strategies should fit local needs, early‐maturing opt drought‐resistant, early varieties employ conservation tillage water‐saving methods, while medium late‐maturing areas select late varieties, adjust sowing enhance fertiliser efficiency.

Language: Английский

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Challenges, Opportunities, and Future Research Directions of Aggregate Production Planning: A State-of-the-Art Analysis of Sustainability, Uncertainty, and Case Studies

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: 489, P. 144686 - 144686

Published: Jan. 1, 2025

Language: Английский

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A Regional Water and Soil Resource Spatial Coordination and Optimization Method Considering Crop Suitability and Drought-Induced Yield Reduction Effects

Published: Jan. 1, 2025

This study proposes a new spatial multi-objective collaborative optimization method for water and soil resources, coupling regional crop suitability drought effects. The aims to optimize the grid of resources balance trade-off between economic benefits blue resource utilization, while mitigating adverse impacts on agricultural production. constructs multi-scale evaluation index (MSDEI) quantitatively characterize interrelationship yield reduction, integrates with cellular automaton model. It also develops grid-based planning model that balances utilization efficiency, enabling fine-tuned resources. Application results in Sanjiang Plain, major grain-producing region China, demonstrate proposed collaboratively optimized adjusted distribution across approximately 4.5 million 100m × cells. cropping structure improved suitability, productivity rice, maize, soybeans increasing by 18.3%, 16.9%, 8.8%, respectively. significantly enhanced coordination efficiency yield. (2) Fully considering heterogeneity growth characteristics requirements, irrigation strategy demonstrates trend "low early stage, high mid-stage, reduced late stage." altered use surface groundwater, proportion groundwater shifting from 63% 37% 72% 28%, current situation over-extraction Plain; (3) In years, considers drought-induced reduction effects reduces risk caused drought-related loss 14% compared traditional models through refined optimization. validate applicability under conditions, providing scientific basis fine management sustainable development

Language: Английский

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Techno-economic and environmental optimization of agrivoltaics: A case study of Cornell University

Applied Energy, Journal Year: 2025, Volume and Issue: 384, P. 125436 - 125436

Published: Feb. 4, 2025

Language: Английский

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Quantifying basin water balance considering water-economic society-ecology nexus

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133067 - 133067

Published: March 1, 2025

Language: Английский

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Multi-Source Feature Fusion Network for LAI Estimation from UAV Multispectral Imagery

Agronomy, Journal Year: 2025, Volume and Issue: 15(4), P. 988 - 988

Published: April 20, 2025

The leaf area index (LAI) is a critical biophysical parameter that reflects crop growth conditions and the canopy photosynthetic potential, serving as cornerstone in precision agriculture dynamic monitoring. However, traditional LAI estimation methods relying on single-source remote sensing data often suffer from insufficient accuracy high-density vegetation scenarios, limiting their capacity to reflect variability comprehensively. To overcome these limitations, this study introduces an innovative multi-source feature fusion framework utilizing unmanned aerial vehicle (UAV) multispectral imagery for precise winter wheat. RGB datasets were collected across seven different stages (from regreening grain filling) 2024. Through extraction of color attributes, spatial structural information, eight representative indices (VIs), robust dataset was developed integrate diverse types. A convolutional neural network (CNN)-based backbone, paired with (MSF-FusionNet), designed effectively combine spectral information both imagery. experimental results revealed proposed method achieved superior performance compared models, R2 0.8745 RMSE 0.5461, improving by 36.67% 5.54% over VI respectively. Notably, enhanced during phases, such jointing stages. Compared machine learning techniques, exceeded XGBoost model, rising 4.51% dropping 12.24%. Furthermore, our facilitated creation distribution maps key stages, accurately depicting heterogeneity temporal dynamics field. These highlight efficacy potential integrating UAV deep wheat, offering significant insights evaluation agricultural management.

Language: Английский

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Sustainable Agriculture and Climate Change: A Deep Learning Approach to Remote Sensing for Food Security Monitoring

Remote Sensing in Earth Systems Sciences, Journal Year: 2024, Volume and Issue: 7(4), P. 709 - 721

Published: Oct. 31, 2024

Language: Английский

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