Scientific reports · 2026
Associations of meteorological variability with sesame yield in semi-arid Bundelkhand region of India.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Singh R N, Sah Sonam, Rathod Santosha, Das B, Singh Yogeshwar, Singh A K, Kumar Anil, Reddy K S
Аннотация
Understanding long-term changes in weather variables and their impact on crop productivity is essential for climate resilient agriculture in semi-arid regions of India. This study assessed the district wise long-term trends in temperature and rainfall and examined their linkages with sesame (Sesamum indicum L.) yield across Bundelkhand region of India. Long-term monthly temperature and seasonal rainfall trends were analyzed using simple linear regression, Mann-Kendall, modified Mann-Kendall and Sen's slope methods. District-wise crop weather relations between sesame yield and weather variables were evaluated using correlation analysis and stepwise linear regression models. The long-term trends revealed a consistent warming across the region (0.9-2.2 °C/100 years), particularly during the late monsoon and post-monsoon months, whereas monsoon as well as annual rainfall showed a declining trend at the rate of -1.03 to -1.51 mm/year. Correlation results indicated that sesame yield was positively associated with temperature and heat accumulation indices (r = 0.28 to 0.48) especially during August-September, reflecting favourable thermal conditions for crop growth. In contrast, rainfall-related variables, including rainfall amount, number of rainy days, heavy rainfall events, and prolonged wet spells showed negative correlations with sesame yield (r = -0.26 to -0.47), with August rainfall exerting the strongest adverse influence. Regression models (R2 = 0.29 to 0.78) identified monthly temperature indices (Tmin, Tmax, Tavg, DTR, and GDD) and rainfall distribution and intensity indices (RainyDays, R10_days, R20_days, CWD, SDII, and CDD) as more important predictors than seasonal averages. The findings revealed that moderate warming may benefit sesame production, whereas excess and poorly distributed rainfall remains an important climatic constraint. Overall, this biometeorological assessment offers a strong scientific basis for formulating climate-resilient sesame production strategies that effectively address long-term climate trends as well as short-term weather variability.