Climatic impacts across agricultural crop yield distributions: An application of quantile regression on rice crops in Andhra Pradesh, India

Barnwal, Prabhat; Kotani, Koji

doi:10.1016/j.ecolecon.2012.11.024

Cited by 90 publications

(90 citation statements)

References 41 publications

(47 reference statements)

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“…Using these methods, several studies have assessed the effect of weather change on crop output across the world (for instance see Sarker et al, ; Agba et al, ; Zhang, Zhang, & Chen, ; Sbaouelgi, ; Attiaoui & Boufateh, ). Some of the studies employing the econometric models to examine effects of weather variation on crop productivity in India include Guiteras (), Moorthy, Buermann, and Rajagopal (), Barnwal and Kotani (), Gupta et al (), Birthal et al (), Farook and Kannan (), Nath and Mandal (), and Pal and Mitra (). Lastly, the overview of the literature reported that changes in climate have an adverse effect on food and non‐food production.…”

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

120

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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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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

120

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“…The Ricardian model measures the impact of climate factors through their contribution to the prices of agricultural land. Nevertheless, a Ricardian type model does not take into account time-independent, location-specific factors such as unobserved farming skills and soil quality (Barnwal and Kotani, 2013). In addition, the Ricardian approach does not account for the effect of unchanging variables on the region (such as carbon dioxide concentration, the effects of annual changes in the weather, changes in climate change or extreme events, and future climates) (Salvo et al, 2013).…”

Section: Ricardian (Hedonic) Approachmentioning

confidence: 99%

Economic Impacts of Climate Change on Agriculture: Empirical Evidence From The ARDL Approach for Turkey

Dumrul¹,

Kilicaslan²

2017

Pressacademia

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Purpose-The agricultural sector is one of the sectors most sensitive to climate change. This sector is directly affected by temperature and precipitation, which is an input in agricultural production. The main objective of this study is to evaluate the effects of climate change in agricultural production in Turkey. Methodology-The data cover the period 1961-2013. In this study, economic effects of climate change on agriculture were analyzed for Turkey using a time series approach. Findings-The increase in precipitation affects agricultural GDP positively, while the increase in temperature has a negative effect on agricultural GDP. Conclusion-In order to minimize the adverse effects of climate change in Turkey, which is one of the largest countries in the world in terms of agricultural land, it is important to establish policies, strategies, plans and programs to combat climate change.

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“…This methodology allows for examination of the entire distribution of the variable of interest rather than a single measure of the central tendency of its distribution (Koenker, 2005). QR models have been used in a broad range of applications: economics and financial market analysis (Kudryavtsev, 2009;Taylor, 2007), agriculture (Barnwal and Kotani, 2013), meteorology (Bremnes, 2004;Friederichs and Hense, 2007;Cannon, 2011), wind forecasting (Nielsen et al, 2006;Møller et al, 2008), the prediction of ozone concentrations (Baur et al, 2004;Munir et al, 2012), etc. In hydrological modelling the QR method has been applied as an uncertainty post-processing technique in previous research studies with different configurations.…”

Section: Introductionmentioning

confidence: 99%

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Dogulu

López

Solomatine

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. In operational hydrology, estimation of the predictive uncertainty of hydrological models used for flood modelling is essential for risk-based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analysing and predicting uncertainty. However, studies devoted to comparing the performance of the methods in predicting uncertainty are limited. This paper focuses on the methods predicting model residual uncertainty that differ in methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC). The comparison of the methods is aimed at investigating how well a simpler method using fewer input data performs over a more complex method with more predictors. We test these two methods on several catchments from the UK that vary in hydrological characteristics and the models used. Special attention is given to the methods' performance under different hydrological conditions. Furthermore, normality of model residuals in data clusters (identified by UNEEC) is analysed. It is found that basin lag time and forecast lead time have a large impact on the quantification of uncertainty and the presence of normality in model residuals' distribution. In general, it can be said that both methods give similar results. At the same time, it is also shown that the UNEEC method provides better performance than QR for small catchments with the changing hydrological dynamics, i.e. rapid response catchments. It is recommended that more case studies of catchments of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features, be considered.

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Climatic impacts across agricultural crop yield distributions: An application of quantile regression on rice crops in Andhra Pradesh, India

Cited by 90 publications

References 41 publications

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

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

Economic Impacts of Climate Change on Agriculture: Empirical Evidence From The ARDL Approach for Turkey

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

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