Development of yield forecast model using multiple regression analysis and impact of climatic parameters on spring wheat
Keywords:
yield, forecast modelling, multiple regression, climatic parameters, spring wheatAbstract
Understanding the impacts of climate change in agriculture is important to ensure optimal and continuous crop production. The agricultural sector plays a significant role in the economy of Upper Midwestern states in the USA, especially that of North Dakota (ND). Spring wheat contributes most of the wheat production in ND, which is a major producer of wheat in the USA. This study focuses on assessing possible impacts of three climate variables on spring wheat yield in ND by building a regression model. Eighty-five years of field data were collected and the trend of average minimum temperature along with average maximum temperature, average precipitation, and spring wheat yield was analyzed using Mann-Kendall test. The study area was divided into 9 divisions based on physical locations. The minimum temperature plays an important role in the region as it impacts the physiological development of the crops. Increasing trend was noticed for 6 divisions for average minimum temperature and average precipitation during growing season. Northeast and Southeast division showed the strongest increasing trend for average minimum temperature and average precipitation, respectively. East-central division had the most decreasing trend for average maximum temperature. A significant relationship was established between spring wheat yield and climatic parameters as the p-value is lower than 0.05 level which rejects the null hypothesis. The regression model was tested for forecasting accuracy. The percentage deviation of error for the model is approximately ±30% in most of the years. Keywords: yield, forecast modelling, multiple regression, climatic parameters, spring wheat DOI: 10.25165/j.ijabe.20191204.4477 Citation: Mistry P, Bora G. Development of yield forecast model using multiple regression analysis and impact of climatic parameters on spring wheat. Int J Agric & Biol Eng, 2019; 12(4): 110–115.References
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[15] Bora G C, Bali S, Mistry P. Impact of climate variability on yield of spring wheat in North Dakota. American Journal of Climate Change, 2014; 3(4): 366.
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[17] Pirttioja N, Carter TR, Fronzek S, Bindi M, Hoffmann H, Palosuo T, Ruiz-Ramos M, Tao F, Trnka M, Acutis M, Asseng S. Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces. Climate Research, 2015; 65: 87–105.
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[19] Schlenker W, Roberts M J. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proceedings of the National Academy of sciences, 2009; 106(37): 15594–15598.
[2] Ortiz R., Sayre, K D, Govaerts B, Gupta R., Subbarao G V, Ban T, Hodson D, Dixon J M, Ortiz-Monasterio J I, Reynolds M. Climate change: can wheat beat the heat? Agriculture, Ecosystems & Environment, 2008; 126(1-2): 46–58.
[3] Nkeme K K, Ndaeyo N U. Climate change coping strategies among peasant farmers in Akwa Ibom State, Nigeria. International Journal of Basic and Applied Science, 2013; 2(1): 24–28.
[4] United States Department of Agriculture. Agricultural statistics 2009. United States Government Printing Office, Washington, DC, 2009.
[5] North Dakota Wheat Commission. Available: http://www.ndwheat.com/ buyers/default.asp?ID=294. Accessed on [2015-09-21].
[6] McCarthy J J, Canziani O F, Leary N A, Dokken D J, White K S. Climate change 2001: Impacts, adaptation and vulnerability. Cambridge University Press, 2001.
[7] United States Department of Agriculture: National Agricultural Statistics Service. Available: http://www.nass.usda.gov/Quick_Stats/Lite/.
Accessed on [2015-07-12].
[8] National Oceanic and Atmospheric Administration’s (NOAA). National Climatic Data Center. Available: http://www7.ncdc.noaa.gov/CDO/ CDODivisionalSelect.jsp. Accessed on [2015-05-16].
[9] United States Environmental Protection Agency. Data quality assessment: statistical method for practitioners. Office of Environmental Information, Washington, DC. 2006; Available: http://www.epa.gov/quality/qs-docs/ g9s-final.pdf. Accessed on [2015-08-12].
[10] Karmeshu N. Trend detection in annual temperature & precipitation using the Mann Kendall Test–a case study to assess climate change on select states in the northeastern United States. Master of Environmental Studies Capstone Projects. University of Pennsylvania, USA, 2012, 08.
[11] Lobell D B, Burke M B. On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology, 2010; 150(11): 1443–1452.
[12] Myers R H. Classical and modern regression with applications. Belmont, CA: Duxbury Press, 1990.
[13] Smith G S. Changes in North Dakota hard red spring wheat varieties, 1900-1977. North Dakota Farm Research, 1978; 35:16–21.
[14] Gunderson J J, Carr P M., Martin G B. Variety trial yields: a look at the
past 65 Years. Technical Report, Dickinson Research Extension Center, North Dakota State University, 2007; Available: http://www.ag.ndsu.edu/ archive/dickinso/research/2007/pdf/agron07a. pdf.
[15] Bora G C, Bali S, Mistry P. Impact of climate variability on yield of spring wheat in North Dakota. American Journal of Climate Change, 2014; 3(4): 366.
[16] Lobell D B, Burke M B. Why are agricultural impacts of climate change so uncertain? The importance of temperature relative to precipitation. Environmental Research Letters, 2008; 3(3): 034007.
[17] Pirttioja N, Carter TR, Fronzek S, Bindi M, Hoffmann H, Palosuo T, Ruiz-Ramos M, Tao F, Trnka M, Acutis M, Asseng S. Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces. Climate Research, 2015; 65: 87–105.
[18] Lobell D B, Asseng S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environmental Research Letters, 2017; 12(1): 015001.
[19] Schlenker W, Roberts M J. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proceedings of the National Academy of sciences, 2009; 106(37): 15594–15598.
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Published
2019-08-01
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Mistry, P., & Bora, G. C. (2019). Development of yield forecast model using multiple regression analysis and impact of climatic parameters on spring wheat. International Journal of Agricultural and Biological Engineering, 12(4), 110–115. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/4477
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Natural Resources and Environmental Systems
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