在印度干旱易发地区通过气候智能型农业提高农业收入复原力

J. Samuel, C. A. Rama Rao, Pushpanjali, C. N. Anshida Beevi, B. Raju, A. Amarender Reddy, R. Nagarjuna Kumar, A. G. K. Reddy, V. K. Singh, M. Prabhakar, G. S. Siva, Raju G. Teggelli
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摘要

气候的多变性影响着农业生产,尤其是旱地的农业生产。有必要了解和量化抗灾技术的影响以及极端事件的影响。有鉴于此,我们从该地区的国家气候适应性农业创新(NICRA)计划采用村和可比对照村各抽取了 60 名农民,收集了有关家庭特征和农业收入的原始数据。样本还被分为小农、中农和大农,以反映不同土地等级的经济影响。由于数据允许我们有效使用跨时间和跨地区的数据,因此我们采用了差异模型(DiD)来估算影响。结果表明,NICRA 村农户的平均收入比非 NICRA 村高出 40%以上,在干旱期间,NICRA 村农民的收入比非 NICRA 村高出 19.5%。被采用村庄的农作物和牲畜生产收入明显高于对照村庄。DiD 模型输出显示,被采纳村庄的农业收入高出 40%,这表明更好的气候智能干预措施提高了农业收入。估算结果显示,在干旱年份,接受干预的农户收入比对照组高 54,717 卢比。更好地了解和量化技术采用对农业收入的影响,特别是在干旱期间的影响,将有助于有效地设计技术和政策干预措施,以改善干旱地区的干旱管理。
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Enhancing farm income resilience through climate smart agriculture in drought-prone regions of India
The variability in climate affects the agricultural production especially in drylands. It is necessary to understand and quantify the impacts of resilient technologies as well as effects of extreme events. Keeping these in view, the primary data on household characteristics and the farm income was collected from a sample of 60 farmers each from National Innovations in Climate Resilient Agriculture (NICRA) program adopted village and a comparable control village in the district. The sample was also post classified into small, medium and large farmer to bring out the economic impact across land classes. The impact was estimated following the difference-in-differences (DiD) model as the data allows us to use effectively the data across time and regions. The results show us that the average income of a farm household in the NICRA village is more than 40 percent than non-adopted village and during a drought situation the farmers under NICRA intervention where better off by 19.5 percent. The income from crops and livestock production in adopted village was significantly higher than the control village. The DiD model output showed the farm income of adopted village was 40 percent higher showing that better climate smart interventions improved the farm incomes. The estimate showed that the treated farm household had higher income of Rs. 54,717 than the control during a drought year. Better knowledge and quantification of impact of technology adoption on farm income specially during drought will help to effectively design technological and policy interventions for better drought management in drylands.
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