Optimization of Nitrogen Fertilization Strategies for Drip Irrigation of Cotton in Large Fields by DSSAT Combined with a Genetic Algorithm DOI Creative Commons
Zhuo Yu, Weiguo Fu

Applied Sciences, Год журнала: 2025, Номер 15(7), С. 3580 - 3580

Опубликована: Март 25, 2025

This study presents a hybrid modeling framework synergizing process-based crop with evolutionary optimization to reconcile yield sustainability nitrogen management in arid cotton systems. Building upon the DSSAT-CROPGRO model’s demonstrated superiority over pure machine learning approaches simulating nitrogen–crop interactions (calibrated multi-year phenological datasets), we develop genetic algorithm-embedded decision system that simultaneously optimizes use efficiency (NUE) and economic returns. Field validations across contrasting growing seasons demonstrate framework’s capacity reduce inputs by 15–20% while increasing profitability 8–12% compared conventional practices, without compromising stability. The tight coupling of mechanistic understanding multi-objective advances precision agriculture through two key innovations: (1) dynamic adaptation fertilization strategies both biophysical processes constraints (2) closed-loop integration physiology simulations computation. paradigm-shifting methodology establishes new template for developing environmentally intelligent decision-support systems water-limited agroecosystems.

Язык: Английский

Efficient root nitrogen transport is a key factor in improving nitrogen utilization and yield of semi-dwarf rapeseed DOI
Bowen Zhao, Lou Hong-xiang, Yueyao Wang

и другие.

Field Crops Research, Год журнала: 2025, Номер 322, С. 109758 - 109758

Опубликована: Янв. 21, 2025

Язык: Английский

Процитировано

2
Path Analysis on the Meteorological Factors Impacting Yield of Tartary Buckwheat at Different Sowing Dates DOI Creative Commons
Jin Zhang,

Jing Sun,

Hong Chen

и другие.

Agronomy, Год журнала: 2025, Номер 15(4), С. 950 - 950

Опубликована: Апрель 14, 2025

Tartary buckwheat is an important characteristic multigrain crop, mainly planted in Sichuan, Guizhou, Yunnan and Tibet, other alpine remote ethnic mountainous areas. In order to clarify the effect of sowing date on yield quality its relationship with meteorological factors The variety Jinqiao No. 2 was used for a two-year trial at Dingxiang Test Base Shanxi Province four dates (15 June, 26 6 July 17 2022 19 30 10 21 2023) starting from bud stage. Responses were investigated by examining growth period structure, yield, component, quality, their climatic factors. results showed that during grain grain-filling stage different when different. Compared times, treatment early mid-July had less than 13.5~27.9 h sunshine, 28.8~48.5 mm rainfall, more 10.5~19 days ≤15 °C days, but most serious low-temperature stress (≤15 up 27 days). 69.8~77.0% 69.9~79.1% lower June 2023 respectively, later yield. Delayed beneficial accumulation flavonoids protein grains, average value 11.55% 14.64% higher first sowing, content fat starch significantly reduced. result path analysis low temperature days) solar radiation duration key points attaining high due mean daily flowering maturity negative 1000-seed weight, seed setting rate, crude lipid buckwheat, direct sunshine flavonoid greatest. sown treatments, because avoiding long rainy sunless weather filling stage, which enabled blossoming normally finally attained

Язык: Английский

Процитировано

0
Optimizing root morphology is a key to improving maize yield under nitrogen reduction and densification cultivation DOI
Bowen Zhao, Liang Tong, Huiling Liu

и другие.

Field Crops Research, Год журнала: 2025, Номер 329, С. 109958 - 109958

Опубликована: Май 5, 2025

Язык: Английский

Процитировано

0
Optimization of Nitrogen Fertilization Strategies for Drip Irrigation of Cotton in Large Fields by DSSAT Combined with a Genetic Algorithm DOI Creative Commons
Zhuo Yu, Weiguo Fu

Applied Sciences, Год журнала: 2025, Номер 15(7), С. 3580 - 3580

Опубликована: Март 25, 2025

This study presents a hybrid modeling framework synergizing process-based crop with evolutionary optimization to reconcile yield sustainability nitrogen management in arid cotton systems. Building upon the DSSAT-CROPGRO model’s demonstrated superiority over pure machine learning approaches simulating nitrogen–crop interactions (calibrated multi-year phenological datasets), we develop genetic algorithm-embedded decision system that simultaneously optimizes use efficiency (NUE) and economic returns. Field validations across contrasting growing seasons demonstrate framework’s capacity reduce inputs by 15–20% while increasing profitability 8–12% compared conventional practices, without compromising stability. The tight coupling of mechanistic understanding multi-objective advances precision agriculture through two key innovations: (1) dynamic adaptation fertilization strategies both biophysical processes constraints (2) closed-loop integration physiology simulations computation. paradigm-shifting methodology establishes new template for developing environmentally intelligent decision-support systems water-limited agroecosystems.

Язык: Английский

Процитировано

0