Climate change effects on Chickpea yield and its variability in Andhra Pradesh, India
Article Main
K. Nirmal Ravi Kumar
https://orcid.org/0000-0002-0041-572X
M. Jagan Mohan Reddy
Suresh Chandra Babu
https://orcid.org/0000-0002-0041-572X
M. Jagan Mohan Reddy
Suresh Chandra Babu
Abstract
Farmers usually do not know the precise output that is affected by climatic factors such as temperature and rainfall and are characterized by inter-annual variability, part of which is caused by global climate change. No study covers the influences of climate factors on yield and yield risk in the context of chickpea farming in Andhra Pradesh, India. In this context, this study aimed to investigate the trends in climate change variables during Rabi season (October to January, 1996-2020) and evaluated their variability on chickpea yields across different agro-climatic zones in Andhra Pradesh by employing Just and Pope production function. Four non-parametric methods-Alexandersson’s Standard Normal Homogeneity Test, Buishand’s Range Test, Pettitt’s Test and Von Neumann’s Ratio Test are applied to detect homogeneity in the data. Mann–Kendall (MK) test and Sen’s slope (SS) method were employed to analyze monthly rainfall trends and minimum and maximum temperature trends. Results of Just and Pope (panel data) quadratic and Cobb-Douglas methods revealed that monthly minimum temperature positively influenced the mean yield of chickpea (0.22% and 0.16%, respectively). However, rainfall (-0.41% and -0.31%) and maximum temperature (-0.08% and -0.04%) negatively influenced the mean yield of chickpea under quadratic and Cobb-Douglas models, respectively. Accordingly, rainfall (0.08% and 0.06%) and maximum temperature (0.83% and 0.72%) positively influenced the yield variability and minimum temperature (-0.77% and -0.67%) reduced yield variability of chickpea under quadratic and Cobb-Douglas models respectively. In view of these findings, it is imperative to advocate the farmers about the importance of cultivating drought-tolerant chickpea varieties, drought-proofing and mitigation strategies, micro-irrigation practices and improving their access to agro-meteorological information towards sustainable chickpea cultivation in Andhra Pradesh.
Article Details
Article Details
Keywords
Chickpea, Climate, Just and Pope production function, Mann–Kendall test, Panel data, Sen’s slope estimator, Trend analysis
References
Agossou Gadedjisso-Tossou, Komlavi Adjegan & Armand Ketcha Malan Kablan (2021). Rainfall and temperature trend analysis by Mann–Kendall test and significance for rainfed cereal yields in Northern Togo. Sci, 3, 17. https://doi.org/10.3390/sci3010017
Agricultural Statistics at a Glance (1951-2020). Various issues; Ministry of Agriculture and Farmers’ Welfare; Government of India
Ahmad, I., Tang, D. Wang, T., Wang, M. & Wagan, B. (2015). Precipitation trends over time using Mann-Kendall and Spearman’s Rho tests in Swat river basin, Pakistan. Adv. Meteorol. 431860. Article ID 431860 | https://doi.org/10.1155/2015/431860
Ahmad, N.H. & Deni, S.M. (2013). Homogeneity Test on daily rainfall series for Malaysia. Matematika, 29, 141–150. .https://doi.org/10.11113/matematika.v29.n.586
Barnwal, P. & Kotani, K. (2010). Impact of variation in climate factors on crop yield: A case of rice crop in Andhra Pradesh, India. Economics & Management Series, https://www.iuj.ac.jp/workingpapers/index.cfm?File=EMS_2010_17.pdf
Boo, K.O., Kwon, W.T., Oh, J.H. & Baek, H.J. (2004). Response of global warming on regional climate change over Korea: An experiment with the MM5 model.” Geophysical Research Letters, 31(21), L21206. https://doi.org/10.3741/JKWRA.2012.45.10.1069
Boubacar, Inoussa (2012). The effects of drought on crop yields and yield variability: An economic assessment. International Journal of Economics and Finance, 4(12): 51-60. https://doi.org/10.5539/ijef.v4n12p51
Cabas, J., Weersink, A. & Olale, E. (2010). Crop yield response to economic, site and climatic variables. Climatic Change, 101, 599-616. https://doi.org/10.1007/s10584-009-9754-4
Cameron, A.C. & Trivedi, P.K. (2009). Microeconometrics using stata. Stata Corp LP, Texas
Carew, R., Meng, T., Florkowski, W.J., Smith, R. & Blair, D. (2018). Climate change impacts on hard red spring wheat yield and production risk: Evidence from Manitoba, Canada, Can. J. Plant Sci, 98: 782–795 dx.doi.org/10.1139/cjps-2017-0135
Chaturvedi Rajiv Kumar, Jaideep Joshi, Mathangi Jayaraman, Bala, G. & N. H. Ravindranath. (2012). Multi-model climate change projections for India under representative concentration pathways. Current Science, 103, 7, https://www.researchgate.net/publication/279896993
Chen, C.C., & Chang, C.C. (2005). The impact of weather on crop yield distribution in Taiwan: Some new evidence from panel data models and implications for crop
insurance. Agricultural Economics, 33, 503 11. https://doi.org/10.1111/j.1574-0864.2005.00097.x
Chen, C.C., McCarl, B.A. & Schimmelpfennig, D.E. (2004). Yield variability as influenced by climate: A statistical investigation. Climatic Change, 66, 239-61. https://doi.org/10.1023/B:CLIM.0000043159.33816.e5
Dagar, J.C., Sharma, P.C., Chaudhari, S.K., Jat, H.S. & Sharif Ahamad. (2016). Climate change vis-a-vis saline agriculture: Impact and adaptation strategies. Springer India. Innovative Saline Agriculture, https://doi.org/10.1007/978-81-322-2770-0_2
Damodar, N. (2004). Basic Econometrics; Mc-Graw Hill publishing, Co.: New York, NY, USA.
Di Falco, S., Chavas, J.P. & Smale, M. (2006). Farmer management of production risk on degraded lands: The role of wheat genetic diversity in Tigray region, Ethiopia. Environmental and Production Technology Division. Discussion Paper 153 IFPRI, Washington DC, USA. http://dx.doi.org/10.1111/j.1574-0862.2007.00194.x
FAO. International Seminar on Drought and Agriculture. (2017), https://www.fao.org/land-water/water/drought/droughtandag/en/
Feroze, S.M., Singh, R., Aheibam, M. & Singh, K.L. (2020). Climate change effects on crop yields are evident in North Eastern hills states of India. Indian Journal of Hill Farming, 33 (2), 209-215
Granger, C.W.J., & Newbold, P. (1974). Spurious regressions in Econometrics. Journal of Econometrics, 2, 111-120. http://dx.doi.org/10.1016/0304-4076(74)90034-7
Guttormsen, A. G. & Roll, K.H. (2013). Production risk in a subsistence agriculture. Journal of Agricultural Education and Extension, 20(1), 133-145. https://doi.org/ 10.1080/1389224x.2013.775953
Haile, Mekbib, G., Tesfamicheal Wossen, Kindie Tesfaye. & Joachim von Braun. (2017). Impact of climate change, weather extremes, and price risk on global food supply. Economics of Disasters and Climate Change, 1 (1), 55-75. https://doi.org/10.1007/s41885-017-0005-2
Hand Book of Statistics, Ananthapuramu. (2020). Government of Andhra Pradesh
Harris, R.D.F., & Tzavalis, E. (1999). Inference for unit roots in dynamic panels where the time dimension is fixed. Journal of Econometrics,91 (2), 201–26. https://doi.org/10.1016/S0304-4076(98)00076-1
Humayun, K.M.D. (2015). Impacts of climate change on rice yield and variability - An analysis of disaggregate level in the southwestern part of Bangladesh especially
Jessore and Sathkhira districts. J Geogr Nat Disast, 5,3. https://doi.org/10.4172/2167-0587.1000148
Isik, M., & Devadoss, S. (2006). An analysis of the impact of climate change on crop yields and yield variability. Applied Economics, 38, 835-844. https://doi.org/10.1080/00036840500193682
Judge, G. G., Griffiths, W.E., Hill, R.C., Lutkepohl, H. & Lee T.C. (1985). The theory and practice of Econometrics. 2nd ed. New York: Wiley. https://doi.org/10.2307/1240726
Just, R.E. & Pope, R.D. (1978). Stochastic specification of production functions and economic implications. Journal of Econometrics, 7, (1)67–86. https://doi.org/10.1016/0304-4076(78)90006-4
Just, R.E. & Pope, R.D. (1979). Production function estimation and related risk considerations. American Journal of Agricultural Economics, 61, (2),276–84. https://doi.org/10.2307/1239732
Kendall, M.G. (1975). Rank Correlation Methods; Griffin: London, UK, ISBN 9780852641996.
Kim, M.K. & Pang, A. (2009). Climate change impact on rice yield and production risk. Journal of Rural Development, 32: 17-29. https://doi.org/10.22004/ag.econ.90682
Komali Kantamaneni, Louis Rice, Komali Yenneti. & Luiza, C. Campos. (2020). Assessing the vulnerability of agriculture systems to climate change in coastal areas: A Novel Index. Sustainability, 12, 4771; https://doi.org/10.3390/su12114771
Koudahe, K., Djaman, K.,, Kayode, J.A.,, Awokola, S.O. & Adebola, A.A. (2018). Impact of climate variability on crop yields in southern Togo. Environ. Pollut. Clim. Chang, 2, 148. [CrossRef]. https://doi.org/10.4172/2573-458X.1000148
Koundouri, P. & Nauges, C. (2005). On production function estimation with selectivity and risk considerations. Journal of Agricultural and Resource Economics, 30(3), 597-608. https://doi.org/10.22004/ag.econ.30977
Krishnan, R., Sanjay, J., Chellappan Gnanaseelan, Milind Mujumdar, Ashwini Kulkarni. & Supriyo Chakraborty. (2020). Assessment of climate change over the Indian region - A report of the Ministry of Earth Sciences (MoES), Government of India, ISBN 978-981-15-4327-2 (eBook) https://doi.org/10.1007/978-981-15-4327-2
Kumar, A., Sharma, P. & Ambrammal, S.K. (2015). Effects of climatic factors on productivity of cash crops in India: Evidence from state-wise panel data. Glob. J. Res. Soc. Sci., 1, 9–18.
Kumbhakar, S. C. & Tveteras, R. (2003). Risk preferences, production risk, and firm heterogeneity. The Scandinavian Journal of Economics, 105(2), 275-293. https://doi.org/10.1111/1467-9442.t01-1-00009
Maddala, G.S. & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61, 631-652. https://doi.org/10.1111/1468-0084.0610s1631
Mahdiyeh Saei, Hamid Mohammadi, Saman Ziaee. & Sajjad Barkhordari Dourbash. (2019). The impact of climate change on grain yield and yield variability in Iran. Iranian Economic Review, 23, No.2, /511. DOI: http://dx.doi.org/10.15666/aeer/1605_66916707
Man-Keun Kim. & Arwin Pang. (2009). Climate change impact on rice yield and production risk. Journal of Rural Development, 32(2), 17～29. https://doi.org/ 10.22004/ag.econ.90682
Mann, H.B. (1945). Nonparametric tests against trend. Econometrica, 13, 245. http://dx.doi.org/10.2307/1907187 [CrossRef]
McCarl, B. A., Villavicencio, X. & Wu, X. (2008). Climate change and future analysis: Is stationary dying? American Journal of Agricultural Economics, 90(5), 1241-1247. https://doi.org/10.1111/j.1467-8276.2008.01211.x
Min, S.K., Legutke, S., Hense, A. & Kwon, W.T.. (2005). Internal variability in a 1000-yr control simulation with the coupled climate model ECHO-G. Part I: Near surface temperature, precipitation, and mean sea level pressure. Tellus A: Dynamic Meteorology and Oceanography, 57 (4), 605-621 https://doi.org/ 10.3402/tellusa.v57i4.14712
Mulungu, K., Tembo, G., Bett, H. & Ngoma, H. (2021). Climate change and crop yields in Zambia: Historical effects and future projections. Environ Dev Sustain, 23, 11859–11880. https://doi.org/10.1007/s10668-020-01146-6
Patakamuri, S.K., Muthiah, K. & Sridhar, V. (2020). Long-term homogeneity, trend, and change-point analysis of rainfall in the arid district of Ananthapuramu, Andhra Pradesh State, India. Water, 12, 211. https://doi.org/10.3390/w12010211 [CrossRef]
Poudel, S. & Kotani, K. (2013). Climatic impacts on crop yield and its variability in Nepal: Do they vary across seasons and altitudes? Clim. Change, 116, 327–355. DOI: 10.1007/s10584-012-0491-8 [CrossRef]
Raju Mandal. & Pratiti Singha. (2020). Impact of climate change on average yields and their variability of the principal crops in Assam. Indian Journal of Agricultural Economics, Vol.75, No.3, July-September
Ramaswami, Bharat. (1992). Production risk and optimal input decisions. American Journal of Agricultural Economics, 74(4): 860-869. https://doi.org/10.2307/1243183
Resop, J.P., Fleisher, D.H., Timlin, D.J. & Reddy, V. (2014). Biophysical constraints to potential production capacity of potato across the US eastern seaboard region. Agron. J., 106, 43–56. doi:10.2134/agronj2013.0277 [CrossRef]
Rosegrant Mark, W. & James A. Roumasset. (1985). The effect of fertiliser on risk: A heteroscedastic production function with measurable stochastic inputs. Australian Journal of Agricultural Economics, 29(2), 107-121. https://doi.org/10.1111/j.1467-8489.1985.tb00651.x
Roumasset, J. A. 1989. Fertilizer and crop yield variability: A review. Variability in Grain Yields: 223-233.
Saha, A., Havenner, A. & Talpaz, H. (1997). Stochastic production function: Small sample properties of ML versus FGLS. Applied Economics, 29(4), 459-469. https://doi.org/ 10.1080/000368497326958
Samira Shayanmehr, Shida Rastegari Henneberry, Mahmood Sabouhi Sabouni. & Naser Shahnoushi Foroushani. (2020). Drought, climate change, and dryland wheat yield response: An Econometric Approach. Int. J. Environ. Res. Public Health, 17, 5264; https://doi.org/10.3390/ijerph17145264
Sanjay, J., Krishnan, R., Ramarao, M.V.S., Mahesh, R., Bhupendra Singh, Jayashri Patel, Sandip Ingle, Preethi Bhaskar, Revadekar, J.V., Sabin, T.P. & Mujumdar M. (2017). Future climate change projections over the Indian Region, https://doi.org/10.48550/arXiv.2012.10386
Sarker, M.A.R, Alam, K. & Gow, J. (2014). Assessing the effects of climate change on rice yields: An econometric investigation using Bangladeshi panel data. Econ. Anal. Policy, 44, 405–416. http://dx.doi.org/10.1016/j.eap.201 4.11.004 [CrossRef]
Sarker, M.A.R., Alam, K. & Gow, J. (2019). Performance of rain-fed Aman rice yield in Bangladesh in the presence of climate change. Renew. Agric. Food Syst., 34, 304–312. https://doi.org/10.1017/S1742170517000473[Cross Ref]
Sen, P.K. (1968). Estimates of the regression coefficient based on Kendall’s Tau. J. Am. Stat. Assoc., 63, 1379–1389. http://dx.doi.org/10.1080/01621459.1968.10480934 [CrossRef]
Sinnarong, N., Chen, C.C., McCarl, B. & Tran, B.L. (2019). Estimating the potential effects of climate change on rice production in Thailand. Paddy Water Environ., 4, 1–9. https://doi.org/10.1007/s10333-019-00755- [CrossRef]
Srivastava, R., Talla, A., Swain, D. & Panda, R. (2019). Quantitative approaches in adaptation strategies to cope with increased temperatures following climate change in potato crop. Potato Res., 62, 175–191, https://doi.org/10.1007/s11540-018-9406-z [CrossRef]
Statistical Abstract (2020). Government of Andhra Pradesh
Surendra Singh, Alka Singh. & Sanatan Nayak. (2020). Future climate change impacts on crop productivity in coastal regions of India: A panel estimation. Climate Change, 6(21), 100-108. http://www.discoveryjournals.org/climate_change/current_issue/v6/n21/TOC21.pdf
Tveteras, R. (1999). Production risk and productivity growth: Some findings for Norwegian Salmon Aquaculture. Journal of Productivity Analysis, 12(2), 161-179. https://www.jstor.org/stable/41770884
Tveteras, R., & Wan, G. H. (2000). Flexible panel data models for risky production technologies with an application to Salmon Aquaculture. Econometric Reviews, 19(3), 367-389.
Vashisht, B., Nigon, T., Mulla, D., Rosen, C., Xu, H., Twine, T. & Jalota, S. (2015). Adaptation of water and nitrogen management to future climates for sustaining potato yield in Minnesota: Field and simulation study. Agric. Water Manag, 152, 198–206. https://doi.org/10.1016/j.agwat.2015.01.011 [CrossRef]
Wijngaard, J.B., Klein Tank, A.M.G., & Können, G.P. (2003). Homogeneity of 20th century European daily temperature and precipitation series. Int. J. Clim, 23, 679–692. https://doi.org/10.1002/joc.906 [CrossRef]
www.fao.org
Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002). The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol. Process, 16, 1807–1829. https://doi.org/10.1002/hyp.1095 [CrossRef]
Agricultural Statistics at a Glance (1951-2020). Various issues; Ministry of Agriculture and Farmers’ Welfare; Government of India
Ahmad, I., Tang, D. Wang, T., Wang, M. & Wagan, B. (2015). Precipitation trends over time using Mann-Kendall and Spearman’s Rho tests in Swat river basin, Pakistan. Adv. Meteorol. 431860. Article ID 431860 | https://doi.org/10.1155/2015/431860
Ahmad, N.H. & Deni, S.M. (2013). Homogeneity Test on daily rainfall series for Malaysia. Matematika, 29, 141–150. .https://doi.org/10.11113/matematika.v29.n.586
Barnwal, P. & Kotani, K. (2010). Impact of variation in climate factors on crop yield: A case of rice crop in Andhra Pradesh, India. Economics & Management Series, https://www.iuj.ac.jp/workingpapers/index.cfm?File=EMS_2010_17.pdf
Boo, K.O., Kwon, W.T., Oh, J.H. & Baek, H.J. (2004). Response of global warming on regional climate change over Korea: An experiment with the MM5 model.” Geophysical Research Letters, 31(21), L21206. https://doi.org/10.3741/JKWRA.2012.45.10.1069
Boubacar, Inoussa (2012). The effects of drought on crop yields and yield variability: An economic assessment. International Journal of Economics and Finance, 4(12): 51-60. https://doi.org/10.5539/ijef.v4n12p51
Cabas, J., Weersink, A. & Olale, E. (2010). Crop yield response to economic, site and climatic variables. Climatic Change, 101, 599-616. https://doi.org/10.1007/s10584-009-9754-4
Cameron, A.C. & Trivedi, P.K. (2009). Microeconometrics using stata. Stata Corp LP, Texas
Carew, R., Meng, T., Florkowski, W.J., Smith, R. & Blair, D. (2018). Climate change impacts on hard red spring wheat yield and production risk: Evidence from Manitoba, Canada, Can. J. Plant Sci, 98: 782–795 dx.doi.org/10.1139/cjps-2017-0135
Chaturvedi Rajiv Kumar, Jaideep Joshi, Mathangi Jayaraman, Bala, G. & N. H. Ravindranath. (2012). Multi-model climate change projections for India under representative concentration pathways. Current Science, 103, 7, https://www.researchgate.net/publication/279896993
Chen, C.C., & Chang, C.C. (2005). The impact of weather on crop yield distribution in Taiwan: Some new evidence from panel data models and implications for crop
insurance. Agricultural Economics, 33, 503 11. https://doi.org/10.1111/j.1574-0864.2005.00097.x
Chen, C.C., McCarl, B.A. & Schimmelpfennig, D.E. (2004). Yield variability as influenced by climate: A statistical investigation. Climatic Change, 66, 239-61. https://doi.org/10.1023/B:CLIM.0000043159.33816.e5
Dagar, J.C., Sharma, P.C., Chaudhari, S.K., Jat, H.S. & Sharif Ahamad. (2016). Climate change vis-a-vis saline agriculture: Impact and adaptation strategies. Springer India. Innovative Saline Agriculture, https://doi.org/10.1007/978-81-322-2770-0_2
Damodar, N. (2004). Basic Econometrics; Mc-Graw Hill publishing, Co.: New York, NY, USA.
Di Falco, S., Chavas, J.P. & Smale, M. (2006). Farmer management of production risk on degraded lands: The role of wheat genetic diversity in Tigray region, Ethiopia. Environmental and Production Technology Division. Discussion Paper 153 IFPRI, Washington DC, USA. http://dx.doi.org/10.1111/j.1574-0862.2007.00194.x
FAO. International Seminar on Drought and Agriculture. (2017), https://www.fao.org/land-water/water/drought/droughtandag/en/
Feroze, S.M., Singh, R., Aheibam, M. & Singh, K.L. (2020). Climate change effects on crop yields are evident in North Eastern hills states of India. Indian Journal of Hill Farming, 33 (2), 209-215
Granger, C.W.J., & Newbold, P. (1974). Spurious regressions in Econometrics. Journal of Econometrics, 2, 111-120. http://dx.doi.org/10.1016/0304-4076(74)90034-7
Guttormsen, A. G. & Roll, K.H. (2013). Production risk in a subsistence agriculture. Journal of Agricultural Education and Extension, 20(1), 133-145. https://doi.org/ 10.1080/1389224x.2013.775953
Haile, Mekbib, G., Tesfamicheal Wossen, Kindie Tesfaye. & Joachim von Braun. (2017). Impact of climate change, weather extremes, and price risk on global food supply. Economics of Disasters and Climate Change, 1 (1), 55-75. https://doi.org/10.1007/s41885-017-0005-2
Hand Book of Statistics, Ananthapuramu. (2020). Government of Andhra Pradesh
Harris, R.D.F., & Tzavalis, E. (1999). Inference for unit roots in dynamic panels where the time dimension is fixed. Journal of Econometrics,91 (2), 201–26. https://doi.org/10.1016/S0304-4076(98)00076-1
Humayun, K.M.D. (2015). Impacts of climate change on rice yield and variability - An analysis of disaggregate level in the southwestern part of Bangladesh especially
Jessore and Sathkhira districts. J Geogr Nat Disast, 5,3. https://doi.org/10.4172/2167-0587.1000148
Isik, M., & Devadoss, S. (2006). An analysis of the impact of climate change on crop yields and yield variability. Applied Economics, 38, 835-844. https://doi.org/10.1080/00036840500193682
Judge, G. G., Griffiths, W.E., Hill, R.C., Lutkepohl, H. & Lee T.C. (1985). The theory and practice of Econometrics. 2nd ed. New York: Wiley. https://doi.org/10.2307/1240726
Just, R.E. & Pope, R.D. (1978). Stochastic specification of production functions and economic implications. Journal of Econometrics, 7, (1)67–86. https://doi.org/10.1016/0304-4076(78)90006-4
Just, R.E. & Pope, R.D. (1979). Production function estimation and related risk considerations. American Journal of Agricultural Economics, 61, (2),276–84. https://doi.org/10.2307/1239732
Kendall, M.G. (1975). Rank Correlation Methods; Griffin: London, UK, ISBN 9780852641996.
Kim, M.K. & Pang, A. (2009). Climate change impact on rice yield and production risk. Journal of Rural Development, 32: 17-29. https://doi.org/10.22004/ag.econ.90682
Komali Kantamaneni, Louis Rice, Komali Yenneti. & Luiza, C. Campos. (2020). Assessing the vulnerability of agriculture systems to climate change in coastal areas: A Novel Index. Sustainability, 12, 4771; https://doi.org/10.3390/su12114771
Koudahe, K., Djaman, K.,, Kayode, J.A.,, Awokola, S.O. & Adebola, A.A. (2018). Impact of climate variability on crop yields in southern Togo. Environ. Pollut. Clim. Chang, 2, 148. [CrossRef]. https://doi.org/10.4172/2573-458X.1000148
Koundouri, P. & Nauges, C. (2005). On production function estimation with selectivity and risk considerations. Journal of Agricultural and Resource Economics, 30(3), 597-608. https://doi.org/10.22004/ag.econ.30977
Krishnan, R., Sanjay, J., Chellappan Gnanaseelan, Milind Mujumdar, Ashwini Kulkarni. & Supriyo Chakraborty. (2020). Assessment of climate change over the Indian region - A report of the Ministry of Earth Sciences (MoES), Government of India, ISBN 978-981-15-4327-2 (eBook) https://doi.org/10.1007/978-981-15-4327-2
Kumar, A., Sharma, P. & Ambrammal, S.K. (2015). Effects of climatic factors on productivity of cash crops in India: Evidence from state-wise panel data. Glob. J. Res. Soc. Sci., 1, 9–18.
Kumbhakar, S. C. & Tveteras, R. (2003). Risk preferences, production risk, and firm heterogeneity. The Scandinavian Journal of Economics, 105(2), 275-293. https://doi.org/10.1111/1467-9442.t01-1-00009
Maddala, G.S. & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61, 631-652. https://doi.org/10.1111/1468-0084.0610s1631
Mahdiyeh Saei, Hamid Mohammadi, Saman Ziaee. & Sajjad Barkhordari Dourbash. (2019). The impact of climate change on grain yield and yield variability in Iran. Iranian Economic Review, 23, No.2, /511. DOI: http://dx.doi.org/10.15666/aeer/1605_66916707
Man-Keun Kim. & Arwin Pang. (2009). Climate change impact on rice yield and production risk. Journal of Rural Development, 32(2), 17～29. https://doi.org/ 10.22004/ag.econ.90682
Mann, H.B. (1945). Nonparametric tests against trend. Econometrica, 13, 245. http://dx.doi.org/10.2307/1907187 [CrossRef]
McCarl, B. A., Villavicencio, X. & Wu, X. (2008). Climate change and future analysis: Is stationary dying? American Journal of Agricultural Economics, 90(5), 1241-1247. https://doi.org/10.1111/j.1467-8276.2008.01211.x
Min, S.K., Legutke, S., Hense, A. & Kwon, W.T.. (2005). Internal variability in a 1000-yr control simulation with the coupled climate model ECHO-G. Part I: Near surface temperature, precipitation, and mean sea level pressure. Tellus A: Dynamic Meteorology and Oceanography, 57 (4), 605-621 https://doi.org/ 10.3402/tellusa.v57i4.14712
Mulungu, K., Tembo, G., Bett, H. & Ngoma, H. (2021). Climate change and crop yields in Zambia: Historical effects and future projections. Environ Dev Sustain, 23, 11859–11880. https://doi.org/10.1007/s10668-020-01146-6
Patakamuri, S.K., Muthiah, K. & Sridhar, V. (2020). Long-term homogeneity, trend, and change-point analysis of rainfall in the arid district of Ananthapuramu, Andhra Pradesh State, India. Water, 12, 211. https://doi.org/10.3390/w12010211 [CrossRef]
Poudel, S. & Kotani, K. (2013). Climatic impacts on crop yield and its variability in Nepal: Do they vary across seasons and altitudes? Clim. Change, 116, 327–355. DOI: 10.1007/s10584-012-0491-8 [CrossRef]
Raju Mandal. & Pratiti Singha. (2020). Impact of climate change on average yields and their variability of the principal crops in Assam. Indian Journal of Agricultural Economics, Vol.75, No.3, July-September
Ramaswami, Bharat. (1992). Production risk and optimal input decisions. American Journal of Agricultural Economics, 74(4): 860-869. https://doi.org/10.2307/1243183
Resop, J.P., Fleisher, D.H., Timlin, D.J. & Reddy, V. (2014). Biophysical constraints to potential production capacity of potato across the US eastern seaboard region. Agron. J., 106, 43–56. doi:10.2134/agronj2013.0277 [CrossRef]
Rosegrant Mark, W. & James A. Roumasset. (1985). The effect of fertiliser on risk: A heteroscedastic production function with measurable stochastic inputs. Australian Journal of Agricultural Economics, 29(2), 107-121. https://doi.org/10.1111/j.1467-8489.1985.tb00651.x
Roumasset, J. A. 1989. Fertilizer and crop yield variability: A review. Variability in Grain Yields: 223-233.
Saha, A., Havenner, A. & Talpaz, H. (1997). Stochastic production function: Small sample properties of ML versus FGLS. Applied Economics, 29(4), 459-469. https://doi.org/ 10.1080/000368497326958
Samira Shayanmehr, Shida Rastegari Henneberry, Mahmood Sabouhi Sabouni. & Naser Shahnoushi Foroushani. (2020). Drought, climate change, and dryland wheat yield response: An Econometric Approach. Int. J. Environ. Res. Public Health, 17, 5264; https://doi.org/10.3390/ijerph17145264
Sanjay, J., Krishnan, R., Ramarao, M.V.S., Mahesh, R., Bhupendra Singh, Jayashri Patel, Sandip Ingle, Preethi Bhaskar, Revadekar, J.V., Sabin, T.P. & Mujumdar M. (2017). Future climate change projections over the Indian Region, https://doi.org/10.48550/arXiv.2012.10386
Sarker, M.A.R, Alam, K. & Gow, J. (2014). Assessing the effects of climate change on rice yields: An econometric investigation using Bangladeshi panel data. Econ. Anal. Policy, 44, 405–416. http://dx.doi.org/10.1016/j.eap.201 4.11.004 [CrossRef]
Sarker, M.A.R., Alam, K. & Gow, J. (2019). Performance of rain-fed Aman rice yield in Bangladesh in the presence of climate change. Renew. Agric. Food Syst., 34, 304–312. https://doi.org/10.1017/S1742170517000473[Cross Ref]
Sen, P.K. (1968). Estimates of the regression coefficient based on Kendall’s Tau. J. Am. Stat. Assoc., 63, 1379–1389. http://dx.doi.org/10.1080/01621459.1968.10480934 [CrossRef]
Sinnarong, N., Chen, C.C., McCarl, B. & Tran, B.L. (2019). Estimating the potential effects of climate change on rice production in Thailand. Paddy Water Environ., 4, 1–9. https://doi.org/10.1007/s10333-019-00755- [CrossRef]
Srivastava, R., Talla, A., Swain, D. & Panda, R. (2019). Quantitative approaches in adaptation strategies to cope with increased temperatures following climate change in potato crop. Potato Res., 62, 175–191, https://doi.org/10.1007/s11540-018-9406-z [CrossRef]
Statistical Abstract (2020). Government of Andhra Pradesh
Surendra Singh, Alka Singh. & Sanatan Nayak. (2020). Future climate change impacts on crop productivity in coastal regions of India: A panel estimation. Climate Change, 6(21), 100-108. http://www.discoveryjournals.org/climate_change/current_issue/v6/n21/TOC21.pdf
Tveteras, R. (1999). Production risk and productivity growth: Some findings for Norwegian Salmon Aquaculture. Journal of Productivity Analysis, 12(2), 161-179. https://www.jstor.org/stable/41770884
Tveteras, R., & Wan, G. H. (2000). Flexible panel data models for risky production technologies with an application to Salmon Aquaculture. Econometric Reviews, 19(3), 367-389.
Vashisht, B., Nigon, T., Mulla, D., Rosen, C., Xu, H., Twine, T. & Jalota, S. (2015). Adaptation of water and nitrogen management to future climates for sustaining potato yield in Minnesota: Field and simulation study. Agric. Water Manag, 152, 198–206. https://doi.org/10.1016/j.agwat.2015.01.011 [CrossRef]
Wijngaard, J.B., Klein Tank, A.M.G., & Können, G.P. (2003). Homogeneity of 20th century European daily temperature and precipitation series. Int. J. Clim, 23, 679–692. https://doi.org/10.1002/joc.906 [CrossRef]
www.fao.org
Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002). The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol. Process, 16, 1807–1829. https://doi.org/10.1002/hyp.1095 [CrossRef]
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Climate change effects on Chickpea yield and its variability in Andhra Pradesh, India . (2023). Journal of Applied and Natural Science, 15(1), 178-193. https://doi.org/10.31018/jans.v15i1.4102
