The influence of climate change and country-based conflict on crop production: Evidence based on global panel data in the last decade
Abstract
In the past decade, the food crisis has become a special concern for the international community. This is in the spotlight as the earth ages, increasingly changing climatic conditions lead to erratic crop yields and worsening crop quality. On the other hand, this condition is exacerbated by the increasingly tense dynamics of international politics which leads to conflict between countries. For this reason, we investigated the relationship between these conditions using the linear mixed model method. In this article, the model obtained is able to describe the real conditions currently occurring regarding the relationship between climate change, conflict between countries and crop production. Among other things, it is known that the majority of continents are carrying out agricultural extensions and intensifying efforts to reduce CO2 emissions, to increase crop production. On the other hand, as time goes by, the model shows that environmental temperature fluctuations are getting bigger. Apart from that, conflict factors apparently exacerbate the effects of climate change which directly affects crop production. This article also provides suggestions for countries on a continent to increase crop production while maintaining climate balance.
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Barr, D.J., Levy, R., Scheepers, C., & Tily, H.J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255–278. https://doi.org/10.1016/J.JML.2012.11.001
Bates, D., Kliegl, R., Vasishth, S., & Baayen, R.H. (2015). Parsimonious Mixed Models. http://arxiv.org/abs/1506.04967
Brück, P.T., Habibi, N., Martin-Shields, C., Sneyers, A., Stojetz, W., & Weezel, S. van. (2016). The Relationship between Food Security and Violent Conflict. ISDC-International Security and Development Center. www.isd-center.org
Chandio, A.A., Ozturk, I., Akram, W., Ahmad, F., & Mirani, A.A. (2020). Empirical analysis of climate change factors affecting cereal yield: evidence from Turkey. Environmental Science and Pollution Research, 27(11), 11944–11957. https://doi.org/10.1007/s11356-020-07739-y
Culbertson, A.M., Martin, J.F., Aloysius, N., & Ludsin, S.A. (2016). Anticipated impacts of climate change on 21st century Maumee River discharge and nutrient loads. Journal of Great Lakes Research, 42(6), 1332–1342. https://doi.org/10.1016/J.JGLR.2016.08.008
Fitzgerald, T., Kuwayama, Y., Olmstead, S., & Thompson, A. (2020). Dynamic impacts of US energy development on agricultural land use. Energy Policy, 137, 111163. https://doi.org/10.1016/J.ENPOL.2019.111163
Hall, C., Dawson, T.P., Macdiarmid, J.I., Matthews, R.B., & Smith, P. (2017). The impact of population growth and climate change on food security in Africa: looking ahead to 2050. International Journal of Agricultural Sustainability, 15(2), 124–135. https://doi.org/10.1080/14735903.2017.1293929
He, W., Yang, JY, Drury, C.F., Smith, W.N., Grant, B.B., He, P., Qian, B., Zhou, W., & Hoogenboom, G. (2018). Estimating the impacts of climate change on crop yields and N2O emissions for conventional and no-tillage in Southwestern Ontario, Canada. Agricultural Systems, 159, 187–198. https://doi.org/10.1016/J.AGSY.2017.01.025
Jost, L., & Jansen, P. (2022). Using linear mixed models to analyze learning processes within sessions improves detection of treatment effects: An exemplary study of chronometric mental rotation. Methods in Psychology, 6. https://doi.org/10.1016/j.metip.2022.100092
Kalcic, M.M., Logsdon Muenich, R., Basile, S., L. Steiner, A., Kirchhoff, C., & Scavia, D. (2019). Climate Change and Nutrient Loading in the Western Lake Erie Basin: Warming Can Counteract a Wetter Future. Environmental Science & Technology, 53(13), 7543–7550. https://doi.org/10.1021/acs.est.9b01274
Martin-Shields, C. P., & Stojetz, W. (2019). Food security and conflict: Empirical challenges and future opportunities for research and policy making on food security and conflict. World Development, 119, 150–164. https://doi.org/10.1016/J.WORLDDEV.2018.07.011
Mason-D'Croz, D., Sulser, T.B., Wiebe, K., Rosegrant, M.W., Lowder, S.K., Nin-Pratt, A., Willenbockel, D., Robinson, S., Zhu, T., Cenacchi, N., Dunston, S., & Robertson, R.D. (2019). Agricultural investments and hunger in Africa modeling potential contributions to SDG2 – Zero Hunger. World Development, 116, 38–53. https://doi.org/10.1016/j.worlddev.2018.12.006
Matuschek, H., Kliegl, R., Vasishth, S., Baayen, H., & Bates, D. (2017). Balancing Type I error and power in linear mixed models. Journal of Memory and Language, 94, 305–315. https://doi.org/10.1016/J.JML.2017.01.001
Min, L., Da-wei, Z., Ming-jin, J., De-qiang, L., Xue-Hai, J., Guang-Mei, J., Li-Jiang, L., & Wei- Jia, Z. (2022). Dry matter production and panicle characteristics of high yield and good taste indica hybrid rice varieties. Journal of Integrative Agriculture.
Powell, J. P., & Reinhard, S. (2015). Measuring the effects of extreme weather events on yields. Weather and Climate Extremes, 12, 69–79. https://doi.org/10.1016/j.wace.2016.02.003
Prishchepov, A. v., Ponkina, E., Sun, Z., & Müller, D. (2019). Revealing the determinants of wheat yields in the Siberian breadbasket of Russia with Bayesian networks. Land Use Policy, 80, 21–31. https://doi.org/10.1016/J.LANDUSEPOL.2018.09.038
Richardson, K. J., Lewis, K. H., Krishnamurthy, P. K., Kent, C., Wiltshire, A. J., & Hanlon, H. M. (2018). Food security outcomes under a changing climate: impacts of mitigation and adaptation on vulnerability to food insecurity. Climatic Change, 147(1–2), 327–341. https://doi.org/10.1007/s10584-018-2137-y
RONG, L. bing, GONG, K. yuan, DUAN, F. ying, LI, S. kun, ZHAO, M., HE, J., ZHOU, W. bin, & YU, Q. (2021). Yield gap and resource utilization efficiency of three major food crops in the world – A review. Journal of Integrative Agriculture, 20(2), 349–362. https://doi.org/10.1016/S2095-3119(20)63555-9
Schauberger, B., Archontoulis, S., Arneth, A., Balkovic, J., Ciais, P., Deryng, D., Elliott, J., Folberth, C., Khabarov, N., Müller, C., Pugh, T.A.M., Rolinski, S., Schaphoff, S., Schmid, E., Wang, X., Schlenker, W., & Frieler, K. (2017). Consistent negative response of US crops to high temperatures in observations and crop models. Nature Communications, 8. https://doi.org/10.1038/ncomms13931
Schmitt, J., Offermann, F., Söder, M., Frühauf, C., & Finger, R. (2022). Extreme weather events cause significant crop yield losses at the farm level in German agriculture. Food Policy, 112. https://doi.org/10.1016/j.foodpol.2022.102359
Spence, N., Hill, L., & Morris, J. (2020). How the global threat of pests and diseases impacts plants, people, and the planet. Plants People Planet, 2(1), 5–13. https://doi.org/10.1002/ppp3.10088
Taghvaee, V. M., Nodehi, M., Saber, R. M., & Mohebi, M. (2022). Sustainable development goals and transportation modes: Analyzing sustainability pillars of environment, health, and economy. World Development Sustainability, 1, 100018. https://doi.org/10.1016/j.wds.2022.100018
Tóth, G., Hermann, T., da Silva, M.R., & Montanarella, L. (2018). Monitoring soil for sustainable development and land degradation neutrality. Environmental Monitoring and Assessment, 190(2). https://doi.org/10.1007/s10661-017-6415-3
Turner, J.A., Horita, A., Fielke, S., Klerkx, L., Blackett, P., Bewsell, D., Small, B., & Boyce, W.M. (2020). Revealing power dynamics and staging conflicts in agricultural system transitions: Case studies of innovation platforms in New Zealand. Journal of Rural Studies, 76, 152–162. https://doi.org/10.1016/j.jrurstud.2020.04.022
van Loon, M.P., Adjei-Nsiah, S., Descheemaeker, K., Akotsen-Mensah, C., van Dijk, M., Morley, T., van Ittersum, M.K., & Reidsma, P. (2019). Can yield variability be explained? Integrated assessment of maize yield gaps across smallholders in Ghana. Field Crops Research, 236, 132–144. https://doi.org/10.1016/J.FCR.2019.03.022
Wang, Z., Zhang, T. Q., Tan, C. S., Xue, L., Bukovsky, M., & Qi, Z. M. (2021). Modeling impacts of climate change on crop yield and phosphorus loss in a subsurface drained field of Lake Erie region, Canada. Agricultural Systems, 190. https://doi.org/10.1016/j.agsy.2021.103110
Webber, H., Lischeid, G., Sommer, M., Finger, R., Nendel, C., Gaiser, T., & Ewert, F. (2020). No perfect storm for crop yield failure in Germany. Environmental Research Letters, 15(10). https://doi.org/10.1088/1748-9326/aba2a4
Winter, B. (nd). Linear models and linear mixed effects models in R with linguistic applications. http://www.r-
Zhi, J., Cao, X., Zhang, Z., Qin, T., Qu, L., Qi, L., Ge, L., Guo, A., Wang, Sun, Y., Liu, W., Zhang, H., & Fu, X. (2022). Identifying the determinants of crop yields in China since 1952 and its policy implications. Agricultural and Forest Meteorology, 327. https://doi.org/10.1016/j.agrformet.2022.109216
DOI: http://dx.doi.org/10.24042/djm.v6i2.18928
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