SWAT-IRR: A new irrigation algorithm for soil and water Assessment tool to facilitate water management and Conservation in irrigated regions

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 232, P. 110142 - 110142

Published: Feb. 20, 2025

Language: Английский

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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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The Application of Machine Learning to Model the Impacts of Extreme Climatic Events on the Productivity of Dwarf Green Coconut Trees in the Eastern Amazon

AgriEngineering, Journal Year: 2025, Volume and Issue: 7(2), P. 33 - 33

Published: Jan. 30, 2025

The coconut crop (Cocos nucifera L.) is essential in humid tropical regions, contributing to the economy and food security. However, its perennial nature makes it sensitive climate variability, particularly extreme events that affect productivity. This study evaluated impacts of climatic on productivity dwarf green northeastern Pará, analyzing rainy (PC—December July) less (PMC—August November) periods between 2015 2023. Meteorological experimental data were used, including variables such as maximum temperature (HT) precipitation (HEP), defined by 90th percentiles, low (LP, 10th percentile). Predictive models, Multiple Linear Regression (MLR) Random Forest (RF), developed. RF showed better performance, with an RMSE equivalent 20% average productivity, while MLR exceeded 50%. struggled generalization test set, likely due overfitting. inclusion lagged (productivity t-1) highlighted significant influence. During PC, high (HEP) excessive water surplus (HE) occurring after fifth month inflorescence development contributed increased whereas during PMC, low-precipitation (LP) led reductions. Notably, under certain circumstances, elevated can mitigate negative availability. These findings underscore need for adaptive management strategies promote stability production.

Language: Английский

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Modelling of pome fruit pollen performance using machine learning

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 26, 2025

Agriculture, particularly fruit production, is considered a crucial industry with significant economic impact in many countries. Extreme fluctuations air temperature can negatively affect the flowering periods of species. Therefore, it important to conduct studies on pollen performance analysis determine these effects. Pollen has seen advancements agricultural research, emergence new germination methods driven by advances technology and equipment. In analyses, addition traditional approaches, Artificial Neural Networks deep learning have gained importance recently. The main objective this study develop model that predicts rate based given set input variables. Firstly, tube length were determined vitro. grains from four cultivars pome sown three different media incubated for durations at seven temperatures an vitro test. Three deep-learning models two hidden layers developed optimizers development. best was selected through validation test.This aimed machine predicting rates fruits. subjected tests under varying temperatures, media, durations. Using artificial neural networks, achieved R² value 0.89 Adam optimizer, demonstrating high accuracy rates. These findings highlight potential advancing research.

Language: Английский

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Trend analysis and performance of machine learning models for agroclimatology parameters in Bosso, Nigeria

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(4)

Published: April 1, 2025

Language: Английский

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An interpretable machine learning technique to identify the key meteorological factors influencing the incidence of wheat Fusarium head blight in Korea

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 235, P. 110355 - 110355

Published: April 8, 2025

Language: Английский

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A continuous learning framework based on physics-guided deep learning for crop phenology simulation

Agricultural and Forest Meteorology, Journal Year: 2025, Volume and Issue: 368, P. 110562 - 110562

Published: April 16, 2025

Language: Английский

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Mapping and Analyzing Winter Wheat Yields in the Huang-Huai-Hai Plain: A Climate-Independent Perspective

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(8), P. 1409 - 1409

Published: April 16, 2025

Accurate diagnostics of crop yields are essential for climate-resilient agricultural planning; however, conventional datasets often conflate environmental covariates during model training. Here, we present HHHWheatYield1km, a 1 km resolution winter wheat yield dataset China’s Huang-Huai-Hai Plain spanning 2000–2019. By integrating climate-independent multi-source remote sensing metrics with Random Forest model, calibrated against municipal statistical yearbooks, the exhibits strong agreement county-level records (R = 0.90, RMSE 542.47 kg/ha, MRE 9.09%), ensuring independence from climatic influences robust driver analysis. Using Geodetector, reveal pronounced spatial heterogeneity in climate–yield interactions, highlighting distinct regional disparities: precipitation variability exerts strongest constraints on Henan and Anhui, whereas Shandong Jiangsu exhibit weaker dependencies. In Beijing–Tianjin–Hebei, March temperature emerges as critical determinant variability. These findings underscore need tailored adaptation strategies, such enhancing water-use efficiency inland provinces optimizing agronomic practices coastal regions. With its dual ability to resolve pixel-scale dynamics disentangle drivers, HHHWheatYield1km represents resource precision agriculture evidence-based policymaking face changing climate.

Language: Английский

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Harnessing the power of machine learning for crop improvement and sustainable production

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: Aug. 12, 2024

Crop improvement and production domains encounter large amounts of expanding data with multi-layer complexity that forces researchers to use machine-learning approaches establish predictive informative models understand the sophisticated mechanisms underlying these processes. All aim fit target data; nevertheless, it should be noted a wide range specialized methods might initially appear confusing. The principal objective this study is offer an explicit introduction some essential their applications, comprising most modern utilized have gained widespread adoption in crop or similar domains. This article explicitly explains how different could applied for given agricultural data, highlights newly emerging techniques users, lays out technical strategies agri/crop research practitioners researchers.

Language: Английский

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⁠Climate Change Prediction in Sustainable Healthcare Systems for Biodiverse Ecosystem Based on Satellite Data Modelling

Remote Sensing in Earth Systems Sciences, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 31, 2024

Language: Английский

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