利用降雨不足影响参数(DRIP)分析降雨多变性对主要农作物的影响:印度卡纳塔克邦案例研究

IF 2.3 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Matadadoddi Nanjundegowda Thimmegowda, Melekote Hanumanthaiah Manjunatha, Lingaraj Huggi, Santanu Kumar Bal, Malamal Alickal Sarath Chandran, Dadireddihalli Venkatappa Soumya, Rangaswamanna Jayaramaiah
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引用次数: 0

摘要

了解异常天气和农民在异常天气下的行为对于实现气候适应能力至关重要。在各种天气参数中,降雨量对作物生产,从播种前的决策到收获,都有重大影响。然而,现有的指数往往忽视了农民的决策。为了弥补这一不足,我们采用了一种新的降雨不足影响参数(DRIP)指数来评估降雨变化对印度第二大旱地农业邦卡纳塔克邦主要雨养作物的影响。分析了 2011 年至 2022 年卡纳塔克邦主要农作物的面积、产量和生产率数据集。值得注意的是,该邦 2016 年和 2019 年的旱季高粱 DRIP 得分最高(分别为 12.8 分和 8.6 分),表明降雨不足对其产量造成了影响。同样,在 DRIP 得分较高的 2016 年,rabi 高粱种植面积减少较多(10.9)。相反,2018 年(1.6)和 2016 年(1.2)虽然降雨不足,但雨养水稻的面积却减少不多。相比之下,玉米在 2015-18 年期间躲过了干旱的影响,DRIP 分数为负,表明作物发生了转移。然而,指粟在 2016 年和 2018 年遭受了水分胁迫。由于降雨不足,2016 年、2017 年和 2018 年拉比小麦的 DRIP 分数较高(12.2、2.2 和 19.0)。同样,2016-2018 年鸽子豆的 DRIP 分数为正,表明降雨不足导致种植面积减少。鹰嘴豆主要种植在蛭石土壤中,除 2016 年和 2021 年外,降雨不足对其影响甚微。由于降雨不足,落花生在 2017-2018 年(1.1 和 0.5)和 2020-2021 年(1.7 和 0.5)的 DRIP 分数为正,原因是用洋葱替代了作物。另一方面,除 2019 年、2020 年和 2022 年外,蓖麻在大多数年份的 DRIP 分数均为正值。这项研究强调了了解降雨变异性及其对农业实践影响的重要性,从而有助于做出知情决策和战略规划,确保地区和国家粮食安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Impact of rainfall variability on major crops using the deficient rainfall impact parameter (DRIP): A case study over Karnataka, India

Impact of rainfall variability on major crops using the deficient rainfall impact parameter (DRIP): A case study over Karnataka, India

Understanding the aberrant weather and farmers' behavior under those is crucial for achieving climate resilience. Among weather parameters, rainfall significantly affects crop production, from pre-sowing decisions to harvesting. However, the existing indices often overlook farmers' decision-making. To address this gap, a new deficient rainfall impact parameter (DRIP) index was utilized to evaluate rainfall variability's effects on principal rainfed crops in Karnataka, India's second-largest dryland agriculture state. Datasets from 2011 to 2022 on area, production, and productivity of major crops of Karnataka were analyzed. Notably, the state's highest DRIP score was recorded in Kharif sorghum during 2016 and 2019 (12.8 and 8.6), indicating an impact of deficient rainfall on its production. Similarly, a higher reduction in the area under rabi sorghum was observed in 2016 with higher DRIP scores (10.9). Conversely, a meager decrease in the area under rainfed rice was observed in 2018 (1.6) and 2016 (1.2) even though there was a deficit of rainfall. In contrast, maize evaded drought impact during 2015–18 with negative DRIP scores, indicating crop shifts. However, finger millet suffered moisture stress in 2016 and 2018. Rabi wheat showed higher DRIP scores in 2016, 2017, and 2018 (12.2, 2.2, and 19.0) due to rainfall deficits. Similarly, the positive DRIP scores for pigeonpea in 2016–2018 signified decreased cultivation due to rainfall deficits. Chickpea, mainly cultivated in vertisols, showed marginal impact from rainfall deficits, except in 2016 and 2021. Groundnut had positive DRIP scores in 2017–2018 (1.1 and 0.5) due to deficit rainfall and in 2020–2021 (1.7 and 0.5) due to crop replacement with onion. Castor, on the other hand, exhibited positive DRIP scores in most years, except 2019, 2020, and 2022. This study underscores the importance of understanding rainfall variability and its implications for agricultural practices, thereby contributing to informed decision-making and strategic planning to ensure regional and national food security.

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来源期刊
Meteorological Applications
Meteorological Applications 地学-气象与大气科学
CiteScore
5.70
自引率
3.70%
发文量
62
审稿时长
>12 weeks
期刊介绍: The aim of Meteorological Applications is to serve the needs of applied meteorologists, forecasters and users of meteorological services by publishing papers on all aspects of meteorological science, including: applications of meteorological, climatological, analytical and forecasting data, and their socio-economic benefits; forecasting, warning and service delivery techniques and methods; weather hazards, their analysis and prediction; performance, verification and value of numerical models and forecasting services; practical applications of ocean and climate models; education and training.
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