Developing a composite weighted indicator-based index for monitoring and evaluating climate-smart agriculture in India

IF 2.5 3区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Shiv Narayan Singh, Jigyasa Bisaria, Bhaskar Sinha, Maneesh Kumar Patasaraiya, P. P. Sreerag
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Abstract

Climate change is a serious concern that threatens global food security in several ways and exerts pressure on the already stressed agriculture system. The future prediction of a decline in the yield of major food grains like rice, wheat, and maize due to adverse impacts of increased warming and other climatic variabilities paves the way to shift the existing agriculture practices to more resource-efficient agriculture. This has entailed the government promoting climate-smart agriculture with its triple objectives, i.e. adaptation, mitigation, and food security. The current study developed a composite weighted indicator-based index to compute climate smartness score (CSS) at the farm level in India and tested its effectiveness in measuring the climate resilience of the farmers in Sehore, Satna, and Rajgarh districts of Madhya Pradesh, India, who adopted climate-smart practices in a pilot project. Thirty-four indicators grouped in five dimensions were selected from relevant peer-reviewed articles and various technical documents through an intensive literature review. These indicators were validated through online and offline expert consultation with ninety-two experts and farmers, and weights were assigned using AHP-express. The study inferred that the final scores and weightage across dimensions and the indicators did not differ significantly, implying that each dimension and indicator is important. A strong positive linear relationship between the climate smartness score and the crop yield further suggested that the wider adoption of these interventions would reduce the climate risk in agriculture for farming communities. This framework would help monitor the effectiveness of various climate-smart agriculture programmes and improve the implementation and upscaling of such programmes.

Abstract Image

为监测和评估印度气候智能型农业制定基于加权指标的综合指数
气候变化是一个令人严重关切的问题,它以多种方式威胁着全球粮食安全,并对本已不堪重负的农业系统造成压力。根据未来预测,由于气候变暖和其他气候变异的不利影响,水稻、小麦和玉米等主要粮食作物的产量将下降,这就为将现有农业生产方式转变为资源效率更高的农业生产方式铺平了道路。这就要求政府促进气候智能型农业,实现其三重目标,即适应、减缓和粮食安全。本研究开发了一种基于指标的综合加权指数,用于计算印度农场层面的气候智能得分(CSS),并测试了该指数在衡量印度中央邦 Sehore、Satna 和 Rajgarh 地区农民气候适应能力方面的有效性,这些农民在一个试点项目中采用了气候智能做法。通过深入的文献审查,从相关的同行评议文章和各种技术文件中筛选出了五个维度的 34 个指标。通过与 92 位专家和农民进行在线和离线专家咨询,对这些指标进行了验证,并使用 AHP-express 分配了权重。研究推断,各维度和指标的最终得分和权重差异不大,这意味着每个维度和指标都很重要。气候智能得分与作物产量之间的强正向线性关系进一步表明,更广泛地采用这些干预措施将降低农业社区的农业气候风险。这一框架将有助于监测各种气候智能型农业计划的有效性,并改进这些计划的实施和推广。
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来源期刊
CiteScore
6.60
自引率
0.00%
发文量
50
审稿时长
3 months
期刊介绍: The Earth''s biosphere is being transformed by various anthropogenic activities. Mitigation and Adaptation Strategies for Global Change addresses a wide range of environment, economic and energy topics and timely issues including global climate change, stratospheric ozone depletion, acid deposition, eutrophication of terrestrial and aquatic ecosystems, species extinction and loss of biological diversity, deforestation and forest degradation, desertification, soil resource degradation, land-use change, sea level rise, destruction of coastal zones, depletion of fresh water and marine fisheries, loss of wetlands and riparian zones and hazardous waste management. Response options to mitigate these threats or to adapt to changing environs are needed to ensure a sustainable biosphere for all forms of life. To that end, Mitigation and Adaptation Strategies for Global Change provides a forum to encourage the conceptualization, critical examination and debate regarding response options. The aim of this journal is to provide a forum to review, analyze and stimulate the development, testing and implementation of mitigation and adaptation strategies at regional, national and global scales. One of the primary goals of this journal is to contribute to real-time policy analysis and development as national and international policies and agreements are discussed and promulgated.
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