Water and heat resource utilization influence cotton yield through sowing date optimization under varied climate

Drought threatens to destroy almost 70 % of the world's cotton supply. Optimizing sowing dates is an agricultural strategy that may help synchronize ecology and productivity. Field data on the coupling impact of various environmental resources on cotton and its response to climate change under sowing date control is still lacking, though. This study examined how resource use efficiencies like water use efficiency (WUE), water consumption, water productivity and heat production efficiency (PEsoil) changed during six sowing dates (S1-S6) over two years (2023 and 2024), characterized by distinct temperature and rainfall. Results revealed that in 2023, optimal climatic conditions and well-timed rainfall events led to a maximum seed cotton yield under S4 (+178 % increase), whereas late sowing (S6) led to a −10 % decrease compared to S1. However, in 2024, delayed sowing had a more adverse impact, with yield declined up to −39 %, likely due to irregular rainfall and suboptimal temperature distribution during critical reproductive stages. The highest water use amounted to the flowering and boll development stages, exceeding 700 mm in late sowing treatments. However, WUE and WPc in delayed sowing were substantially lower than in early sowing, indicating inefficient resource conversion. Furthermore, statistical analysis of year-to-year specific positive correlations with resource use metrics were found to be significant with seed cotton yield. In 2023, WUE (R² = 0.8350), WPc (R² = 0.7189), and PEsoil (R² = 0.8586) were correlated (strongly) with early sowing dates (S1 and S2) due to optimal timing of growth stages with respect to temperature and rainfall regimes. Though the overall R² values were slightly reduced with changed rainfall pattern and cooler peak temperatures, early sowing still had a positive correlation with WUE (R² = 0.81), WPc (R² = 0.69), and PEsoil (R² = 0.78) during 2024, implying stable performance under variable climatic conditions. Similarly, these early sowing treatments also had more stable aboveground biomass, had higher LAI and demonstrated the ability to synchronize phenological state with hydrothermal availability. Principal component analysis (PCA) also confirmed that early sowing increased resource use coupling and yield resilience under the two climatic years. This study introduces a novel integration of temporal sowing optimization, multi-sensor environmental monitoring, and resource coupling analysis. Future studies should focus on integrating climate forecasting models with sowing date recommendations to enable dynamic, site-specific cotton management.