Assessing accuracy of barley yield forecasting with integration of climate variables and support vector regression
Languages of publication
Investigations of the relation between crop yield and climate variables are crucial for agricultural studies and decision making related to crop monitoring. Multiple linear regression (MLR) and support vector regression (SVR) are used to identify and model the impact of climate variables on barley yield. The climate variables of 36 years (1982–2017) are gathered from three provinces of Iran with different climate: Yazd (arid), Zanjan (semi-arid), Gilan (very humid). Air temperature by high correlation coefficient with barley yield was introduced as the dominant climate variable. According to evaluation criteria, SVR provided accurate estimation of crop yield in comparison with MLR. The diversity of climate impressed the estimated yield in which UI, decreasing from Gilan to Yazd provinces, was 47.77%. Support vector machine (SVM) with capturing the nonlinearity of time series, could improve barley yield estimation, with the minimum UI for Yazd province. Also, the minimum correlation coefficient between the observed and simulated yield was found in Gilan province. Based on GMER calculations, SVM forecasts were underestimated in three provinces. All findings show that SVM is able to have high efficiency to model the climate effect on crop yield.
- Adams R.M., Hurd B.H., Lenhart S., Leary N. 1998. Effects of global climate change on agriculture: an interpretative review. Climate Change 11: 19–30.
- Agrawal R., Jain R.C., Mehta H.C. 2001. Yield forecast based on weather variables and agricultural inputs on agroclimatic zone basis. Ind. J. Agr. Sci. 71: 487–490.
- Barnwal P., Kotani K. 2013. Climatic impacts across agricultural crop yield distributions: An application of quantile regression on rice crops in Andhra Pradesh, India. Ecological Economics 87: 95–109.
- Chen, K.Y., Wang C.H. 2007. A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan. Expert Syst. App. 32: 254–264.
- Cimen M., Kisi O. 2009. Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey. J. Hydrol. 378: 253–262.
- De Leona M., Jalaob E. 2013. A prediction model framework for crop yield prediction. The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS), 3–6 December 2013 Cebu, Philippines, 1–16.
- Elavarasan D., Vincent D.R., Sharma V., Zomaya A., Srinivasan K. 2018. Forecasting yield by integrating agrarian factors and machine learning models: A survey. Comput. Electron. Agric. 155: 257–282.
- Eyshi Rezaei E., Webber H., Gaiser T., Naab J., Ewert F. 2015. Heat stress in cereals: Mechanisms and modelling. European J. Agr. 64: 98–113.
- Farooq M., Bramley H., Palta J.A., Siddique K.H.M. 2011. Heat stress in wheat during reproductive and grain-filling phases. Crit. Rev. Plant. Sci. 30: 491–507.
- Ghosh K., Balasubramanian R., Bandopadhyay S., Chattopadhyay N., Singh K.K., Rathore L.S. 2014. Development of crop yield forecast models under FASAL – a case study of Kharif rice in West Bengal. J. Agrometeor. 16: 1–8.
- Gornott C.H., Wechsung F. 2016. Statistical regression models for assessing climate impacts on cropyields: A validation study for winter wheat and silage maize in Germany. Agric. Forest Meteorol. 217: 89–100.
- Goyal M.K. 2014. Monthly rainfall prediction using wavelet regression and neural network: an analysis of 1901–2002 data, Assam, India. Theor. Appl. Climatol. 118: 25–34.
- Guo W.W., Xue H. 2012. An incorporative statistic and neural approach for crop yield modelling and forecasting. Neural Comput. Applic. 21: 109–117.
- H amidi O., Poorolajal J., Sadeghifar M., Abbasi H., Maryanaji Z., Faridi H.R. and Tapak L. 2014. A comparative study of support vector machines and artificial neural network for predicting precipitation in Iran. Theor. Appl. Climatol. 119: 723–731.
- IPCC. 2007 a. Summary for policymakers. In: Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change [S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, H.L. Miller (eds)]. Cambridge University Press, Cambridge.
- IPCC. 2007 b. Summary for policymakers. In: M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. Van der Linden, C.E. Hanson (eds). Climate change 2007: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, 7–22.
- Jayakumar M., Rajavel M., Surendran U. 2016. Climate-based statistical regression models for crop yield forecasting of coffee in humid tropical Kerala, India. Int. J. Biometeorol. 60: 1943–1952.
- Kecman V. 2000. Learning and Soft Computing, Support Vector Machines, Neural Network and Fuzzy Logic Models. MIT Press, ISBN 0-262-11255-8. 608 p.
- Keerthi S.S., Lin C.J. 2003. Asymptotic behaviors of support vector machines with Gaussian Kernel. Neural Computation 15: 1667-1689.
- Lecerf R., Ceglar A., Lopez-Lozano R., Van Der Velde M., Baruth B. 2019. Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe. Agric. Syst. 168: 191–202.
- Manatsa M., Nyakudya I.W., Mukwada G., Matsikwa H. 2011. Maize yield forecasting for Zimbabwe farming sectors using satellite rainfall estimates. Nat. Hazards 59: 447–463.
- Mishra S., Mishra D., Santra G.H. 2017. Adaptive boosting of weak regressions for forecasting of crop production considering climatic variability: An empirical assessment. J. King Saud University–Compu. Info. Sci. https://doi.org/10.1016/j.jksuci.2017.12.004.
- Misra D., Oommen T., Agarwal A., Mishra S.K., Thompson A.M. 2009. Application and analysis of support vector machine based simulation for runoff and sediment yield. Biosyst. Engin. 103: 527–535.
- Modarres R. 2009. Multi-criteria validation of artificial neural network rainfall-runoff modeling. Hydrol. Earth Syst. Sci. 13: 411–421.
- Oguntunde P.G., Lischeid G., Dietrich O. 2018. Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis. Int. J. Biometeorol. 62: 459–469.
- Parviz L., Paymai M. 2017. Comparison of the efficiency of classical and fuzzy regression models for crop yield forecasting with climatological aspect. Agric. Fores. 63(1): 235–248.
- Qader S.H., Dash J., Atkinson P.M. 2018. Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq. Sci. Total Environ. 613–614: 250–262.
- Terzi O. 2013. Daily pan evaporation estimation using gene expression programming and adaptive neural-based fuzzy inference system. Neural Comput. Appl. 23: 1035–1044.
- Toreti A., Maioranoa A.,, De Sanctis G., Webber H., Ruane A.C., Fumagalli D., Ceglar A., Niemeyer S., Zampieri M. 2109. Using reanalysis in crop monitoring and forecasting systems. Agric. Syst. 168: 144–153.
- Tripathy M.K., Mehra B., Chattopadhyay N., Singh K.K. 2012. Yield prediction of sugarcane and paddy for the districts of Uttar Pradesh. J. Agrometeor. 14: 173–175.
- Twarakavi N.K., Misra D., Bandopadhyay S. 2006. Prediction of arsenic in bedrock derived stream sediments at a gold mine site under conditions of sparse data. Natural Resourc. Res. 15(1): 15–26.
- Vapnik V.N. 1995. The Nature of Statistical Learning Theory. New York, Springer.
- Xiao G., Zhang Q., Li Y., Wang R., Yao Y., Zhao H., Bai H. 2010. Impact of temperature increase on the yield of winter wheat at low and high altitudes in semiarid northwestern China. Agric. Water Manage. 97: 1360–1364.
- You L., Rosegrant M.W., Wood S., Sun D. 2009. Impact of growing season temperature on wheat productivity in China. Agric. Forest Meteorol. 149(6–7): 1009–1014.
- Z aynoddin M., Bonakdari H., Azari A., Ebtehaj I., Gharabaghi B., Riahi Madavar H. 2018. Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall in a tropical climate. J. Environ. Manage. 222: 190–206.
- Z hang T., Zhu J., Wassmann R. 2010. Responses of rice yields to recent climate change in China: an empirical assessment based on long-term observations at different spatial scales (1981–2005). Agric. Forest Meteorol. 150: 1128–1137.
Publication order reference