Impact of Climate Change and Technological Advancement on Cotton Production: Evidence from Xinjiang Region, China

Document Type : Original Research

Authors
1 Inner Mongolia Honder College Arts and Sciences, Hohhot, China.
2 School of Economics and Management, Beijing Jiaotong University, Beijing, China.
3 Department of Botany, The University of Agriculture, Faisalabad, Pakistan.
Abstract
China is the largest producer of cotton crop, followed by the United States of America. China's 52% cotton is produced in Xinjiang Region. The agricultural sector depends on the climate, and it is substantially susceptible to future climate changes. Climate factors directly affect cotton production and, therefore, assessing the influence of these factors on the cotton output is imperative. This study empirically investigated the relationship between climate and non-climate variables on Xinjiang Region's cotton production over the last three decades. To this end, an econometric technique was employed and the "Autoregressive Distributed Lag Model" (ARDL) was used to analyze the long-run and short-run relation between the selected variables. Empirical results revealed that a 1% decrease in average temperature, labor force, and rainfall could decrease cotton production by 0.18, 1.94, and 0.18%, respectively, due to the significant negative relation. However, this study depicted 1% rise in average temperature, technological changes, and the cultivated area will increase cotton production by 0.07, 0.05, and 0.23%, respectively. In conclusion, the regional climate changes significantly affect cotton crop. Although the study analyzed the data from XUAR Region, this model can be applied to all developing countries. This research helps the policymakers and the respective government department to introduce, promote, and subsidize environment-friendly production inputs and make the long-term plan for farmers and stakeholders to educate, spread awareness, and help to adopt new skills to gain sustainable regional cotton productivity.

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Abbas, S. 2020. Climate change and cotton production: an empirical investigation of Pakistan. Environmental Science and Pollution Research 27:29580-29588.
Akaike, H. 1974. A new look at the statistical model identification. . IEEE transactions on Automatic Control 19: 7.
Bai, Y., S. Mao, L. Tian, L. Li, and H. Dong. 2017. Advances and prospects of high-yielding and simplified cotton cultivation technology in Xinjiang cotton-growing area. Scientia Agricultura Sinica 50(1):38-50.
Bank, W. 2006. Pakistan Strategic Country Environmental Assessment. World Bank.
Blok, V., T. B. Long, A. I. Gaziulusoy, N. Ciliz, R. Lozano, D. Huisingh, M. Csutora, and C. Boks. 2015. From best practices to bridges for a more sustainable future: Advances and challenges in the transition to global sustainable production and consumption: Introduction to the ERSCP stream of the Special volume. Journal of Cleaner Production 108:19-30.
Boyer, J. S. 1982. Plant productivity and environment. Science 218(4571):443-448.
Chen, C., Y. Pang, X. Pan, and L. Zhang. 2015. Impacts of climate change on cotton yield in China from 1961 to 2010 based on provincial data. Journal of Meteorological Research 29(3):515-524.
Dickey, D. A., and W. A. Fuller. 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association 74(366a):427-431.
Engle, R. F., and C. W. Granger. 1987. Co-integration and error correction: representation, estimation, and testing. Econometrica: Journal of the Econometric Society:251-276.
Hannan, E. J., and B. G. Quinn. 1979. The determination of the order of an autoregression. Journal of the Royal Statistical Society: Series B (Methodological) 41(2):190-195.
Hedenus, F., S. Wirsenius, and D. J. Johansson. 2014. The importance of reduced meat and dairy consumption for meeting stringent climate change targets. Climatic Change 124(1):79-91.
Huang, J., and F. Ji. 2015. Effects of climate change on phenological trends and seed cotton yields in oasis of arid regions. International journal of biometeorology 59(7):877-888.
Johansen, S., and K. Juselius. 1990. Maximum likelihood estimation and inference on cointegration—with appucations to the demand for money. Oxford Bulletin of Economics and statistics 52(2):169-210.
Latifmanesh, H., A. Deng, L. Li, Z. Chen, Y. Zheng, X. Bao, C. Zheng, and W. Zhang. 2020. How incorporation depth of corn straw affects straw decomposition rate and C&N release in the wheat-corn cropping system. Agriculture, Ecosystems & Environment 300:107000.
Li, Q., Y. Chen, Y. Shen, X. Li, and J. Xu. 2011. Spatial and temporal trends of climate change in Xinjiang, China. Journal of Geographical Sciences 21(6):1007-1018.
Lianmei, Y. 2003. Climate change of extreme precipitation in Xinjiang [J]. Acta Geographica Sinica 4:012.
Luo, M., T. Liu, F. Meng, Y. Duan, A. Bao, W. Xing, X. Feng, P. De Maeyer, and A. Frankl. 2019. Identifying climate change impacts on water resources in Xinjiang, China. Science of the Total Environment 676:613-626.
Pan, D., J. Yang, G. Zhou, and F. Kong. 2020. The influence of COVID-19 on agricultural economy and emergency mitigation measures in China: A text mining analysis. PloS one 15(10):e0241167.
Phillips, P. C., and S. Ouliaris. 1990. Asymptotic properties of residual based tests for cointegration. Econometrica: Journal of the Econometric Society:165-193.
Rashid, M., Z. Husnain, and U. Shakoor. 2020. Impact of Climate Change on Cotton Production in Pakistan: An ARDL Bound Testing Approach. Sarhad Journal of Agriculture 36(1).
Reddy, K. R., H. F. Hodges, and J. M. McKinion. 1995. Cotton crop responses to a changing environment. Climate change and agriculture: Analysis of potential international impacts 59:3-30.
Saliou, I. O., A. Zannou, A. K. Aoudji, and A. N. Honlonkou. 2020. Drivers of Mechanization in Cotton Production in Benin, West Africa. Agriculture 10(11):549.
Schlenker, W., and M. J. Roberts. 2008. Estimating the impact of climate change on crop yields: The importance of nonlinear temperature effects. National Bureau of Economic Research.
Schwarz, G. 1978. Estimating the dimension of a model. Annals of statistics 6(2):461-464.
Wang, Z., J. Chen, F. Xing, Y. Han, F. Chen, L. Zhang, Y. Li, and C. Li. 2017. Response of cotton phenology to climate change on the North China Plain from 1981 to 2012. Scientific reports 7(1):1-10.
Wu, J., and X. Chen. 2015. Present situation, problems and countermeasures of cotton production mechanization development in Xinjiang Production and Construction Corps. Transactions of the Chinese Society of Agricultural Engineering 31(18):5-10.
Xiang-ling, T., and L. Xin. 2011. The Relationship between climate change and yield of cotton in Shihezi region. Hubei Agricultural Sciences:08.
Zewdie, M. C., M. Moretti, D. B. Tenessa, Z. A. Ayele, J. Nyssen, E. A. Tsegaye, A. S. Minale, and S. Van Passel. 2021. Agricultural Technical Efficiency of Smallholder Farmers in Ethiopia: A Stochastic Frontier Approach. Land 10(3):246.
Zhenyong, D., Z. Qiang, P. Jinyong, L. Dexiang, G. Hui, W. Quanfu, Z. Hong, and W. Heling. 2008. Impact of climate warming on crop planting and production in Northwest China. Acta Ecologica Sinica 28(8):3760-3768.