Climate Change, Agricultural Production, and Poverty in India

Kar, Saibal; Das, Nimai

doi:10.1007/978-981-287-420-7_4

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Our study is in line with the study by Japneet Kaur et al [31] that used the state-level dataset of the Indian major crop paddy, wheat, sugarcane, and cotton from 2004 to 2013. We considered various climatic conditions from the seven states and analyzed the impact of climate change on food security and Indian agriculture.…”

Section: Discussionsupporting

confidence: 86%

Improved Optimization Algorithm in LSTM to Predict Crop Yield

2023

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Agriculture is the main occupation across the world with a dependency on rainfall. Weather changes play a crucial role in crop yield and were used to predict the yield rate by considering precipitation, wind, temperature, and solar radiation. Accurate early crop yield prediction helps market pricing, planning labor, transport, and harvest organization. The main aim of this study is to predict crop yield accurately. The incorporation of deep learning models along with crop statistics can predict yield rates accurately. We proposed an improved optimizer function (IOF) to get an accurate prediction and implemented the proposed IOF with the long short-term memory (LSTM) model. Manual data was collected between 1901 and 2000 from local agricultural departments for training, and from 2001 to 2020 from government websites of Andhra Pradesh (India) for testing purposes. The proposed model is compared with eight standard methods of learning, and outcomes revealed that the training error is small with the proposed IOF as it handles the underfitting and overfitting issues. The performance metrics used to compare the loss after implementing the proposed IOF were r, RMSE, and MAE, and the achieved results are r of 0.48, RMSE of 2.19, and MAE of 25.4. The evaluation was performed between the predicted crop yield and the actual yield and was measured in RMSE (kg/ha). The results show that the proposed IOF in LSTM has the advantage of crop yield prediction with accurate prediction. The reduction of RMSE for the proposed model indicates that the proposed IOFLSTM can outperform the CNN, RNN, and LSTM in crop yield prediction.

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Section: Discussionsupporting

confidence: 86%

Improved Optimization Algorithm in LSTM to Predict Crop Yield

2023

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“…However, a limited number of studies applied the Ricardian framework in the context of Indian agriculture. Some of the studies using cross-sectional models include Dinar et al (1998), Sanghi and Mendelsohn (2008), Kumar (2011), Kar and Das (2015), and Mishra, Sahu, and Sahoo (2016). Furthermore, a systematic study has done by Kumar and Parikh (2001) to evaluate the impact of climate sensitivity on crop productivity in Indian agriculture.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the impact of climate change on Indian agriculture: Evidence from major crop yields

Guntukula

2019

Journal of Public Affairs

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This study empirically examines the effect of climate change on the yields of primary food as well as non‐food crops in India. The present study uses annual time‐series data of seven major crops such as rice, wheat, pulses, rapeseeds and mustard, cotton, sugarcane, and groundnut for 58 years (1961–20 17) to assess the influence climatic variables namely rainfall, maximum, and minimum temperatures on crop yields. The empirical findings of the study indicate that a significant effect on major crop yields from rainfall, maximum and minimum temperatures, but the level of impact varies across the crops studied. A rise in rainfall has an adverse effect on food crops except for pulses, however it has a positive relationship with non‐food crops throughout the study period. Further, the average maximum temperature has a positive influence on food and non‐food crops excluding rice. The average minimum temperature has an adverse impact on non‐food crops, but it has a positive association with food crops. The adverse effects of climatic factors on crop yields may be likely to pose severe implications for food and nutritional security. Conclusively, this study recommends taking adaptation activities to cope with the adverse impacts of climate change.

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“…In line with the global trends, there has been increasing use of statistical models in the context of Indian agriculture too, both for impact assessments based on physical outcomes, such as yield (see, for example, Auffhammer et al 2012;Birthal et al 2014;Gupta, Sen, and Srinivasan 2014;Krishnamurthy 2012;Lobell, Schlenker, and Costa-Roberts 2011;Pattanayak and Kumar 2014;and Saravanakumar 2015), and for impact assessments based on economic outcomes, such as net revenue (see, for example, Das 2015 andKumar 2011). While these studies may be accurately assessing the impact of weather shocks, attributing the results to climate change could be misleading for several reasons, as highlighted by Dell, Jones, and Olken (2014).…”

Section: Methodological Issuesmentioning

confidence: 99%

“…Pattanayak and Kumar (2017), for example, show in the context of rice crop that regional impacts are overestimated when simulated using an all-India yield response function, as against those based on region-specific yield response function that incorporates the features of regional crop calendar and regional crop management practices. Also, for effective policymaking, there is urgent need to improve our understanding about the impact distribution across geographic regions as well as various socio-economic groups (see Gupta, Ramaswami, and Somanathan 2017;Jacoby, Rabassa, and Skoufias 2014;Kar and Das 2015).…”

Section: Methodological Issuesmentioning

confidence: 99%

Mainstreaming Climate Change Adaptation

Kumar¹,

Viswanathan²

2019

India in a Warming World

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This chapter provides an overview of issues surrounding the mainstreaming of climate change adaptation in the agriculture sector with focus on India. The status of adaptation research focusing on triggers of adaptation and adaptation strategies, such as innovation, adoption of technologies, risk management, and migration, are discussed. The chapter then deliberates on approaches for mainstreaming climate change adaptation policies, namely, climate-proofing, climate-first, and development-first. The wide-ranging budgetary requirements made by the State Action Plans on Climate Change for the agricultural sector highlight the need for a coherent approach for assessing adaptation budgets, along with the establishment of climate and disaster cells in the line departments of the state governments to integrate the climate risks with the developmental plans.

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Climate Change, Agricultural Production, and Poverty in India

Cited by 7 publications

References 16 publications

Improved Optimization Algorithm in LSTM to Predict Crop Yield

Improved Optimization Algorithm in LSTM to Predict Crop Yield

Assessing the impact of climate change on Indian agriculture: Evidence from major crop yields

Mainstreaming Climate Change Adaptation

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