Estimation of the Extent of the Vulnerability of Agriculture to Climate Change Using Analytical and Deep-Learning Methods: A Case Study in Jammu, Kashmir, and Ladakh

Irtiqa Malik, Muneeb Ahmed, Yonis Gulzar (), Sajad Hassan Baba (), Mohammad Shuaib Mir, Arjumand Bano Soomro, Abid Sultan and Osman Elwasila
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Irtiqa Malik: School of Agricultural Economics and Horti-Business Management, SKUAST-K, Shalimar 190025, India
Muneeb Ahmed: Bharti School of Telecom Technology, Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India
Yonis Gulzar: Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Sajad Hassan Baba: School of Agricultural Economics and Horti-Business Management, SKUAST-K, Shalimar 190025, India
Mohammad Shuaib Mir: Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Arjumand Bano Soomro: Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Abid Sultan: School of Agricultural Economics and Horti-Business Management, SKUAST-K, Shalimar 190025, India
Osman Elwasila: Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia

Sustainability, 2023, vol. 15, issue 14, 1-25

Abstract: Climate stress poses a threat to the agricultural sector, which is vital for both the economy and livelihoods in general. Quantifying its risk to food security, livelihoods, and sustainability is crucial. This study proposes a framework to estimate the impact climate stress on agriculture in terms of three objectives: assessing the regional vulnerability (exposure, sensitivity, and adaptive capacity), analysing the climate variability, and measuring agricultural performance under climatic stress. The vulnerability of twenty-two sub-regions in Jammu, Kashmir, and Ladakh is assessed using indicators to determine the collective susceptibility of the agricultural framework to climate change. An index-based approach with min–max normalization is employed, ranking the districts based on their relative performances across vulnerability indicators. This work assesses the impact of socio-economic and climatic indicators on the performance of agricultural growth using the benchmark Ricardian approach. The parameters of the agricultural growth function are estimated using a linear combination of socio-economic and exposure variables. Lastly, the forecasted trends of climatic variables are examined using a long short-term memory (LSTM)-based recurrent neural network, providing an annual estimate of climate variability. The results indicate a negative impact of annual minimum temperature and decreasing land holdings on agricultural GDP, while cropping intensity, rural literacy, and credit facilities have positive effects. Budgam, Ganderbal, and Bandipora districts exhibit higher vulnerability due to factors such as low literacy rates, high population density, and extensive rice cultivation. Conversely, Kargil, Rajouri, and Poonch districts show lower vulnerability due to the low population density and lower level of institutional development. We observe an increasing trend of minimum temperature across the region. The proposed LSTM synthesizes a predictive estimate across five essential climate variables with an average overall root mean squared error (RMSE) of 0.91, outperforming the benchmark ARIMA and exponential-smoothing models by 32–48%. These findings can guide policymakers and stakeholders in developing strategies to mitigate climate stress on agriculture and enhance resilience.

Keywords: agricultural vulnerability; agricultural growth; climate variability; Ricardian approach; AI; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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