Field Crops Research, Journal Year: 2025, Volume and Issue: 329, P. 109958 - 109958
Published: May 5, 2025
Language: Английский
Field Crops Research, Journal Year: 2025, Volume and Issue: 329, P. 109958 - 109958
Published: May 5, 2025
Language: Английский
Field Crops Research, Journal Year: 2025, Volume and Issue: 322, P. 109758 - 109758
Published: Jan. 21, 2025
Language: Английский
Citations
1Agronomy, Journal Year: 2025, Volume and Issue: 15(4), P. 950 - 950
Published: April 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
Language: Английский
Citations
0Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3580 - 3580
Published: March 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.
Language: Английский
Citations
0Field Crops Research, Journal Year: 2025, Volume and Issue: 329, P. 109958 - 109958
Published: May 5, 2025
Language: Английский
Citations
0