Modeling the yield of rainfed wheat, barley and alfalfa products using support vector regression and genetic programming

Authors

1 MSc Graduate of Irrigation and Drainage Engin., Dept. of Water Engin., Faculty of Agric., Univ. of Tabriz, Tabriz, Iran

2 Assist. Prof., Dept. of Water Eng., Faculty of Agric., University of Tabriz, Iran

3 Professor, Dept. of Water Engin., Faculty of Agric., Univ. of Tabriz, Tabriz, Iran

Abstract

Climate change, the rise of global temperature, the water crisis, along with the growth of the world's population have made the world's food supply a challenge for researchers. For this reason, it is necessary to predict and simulate plant products in accordance with the climatic conditions. In this study, the relationships of meteorological parameters and standard precipitation index (SPI) and reconnaissance drought index (RDI) with yields of the rainfed wheat, barley and alfalfa plants were studied in three regions in East Azarbaijan province. For each of the temperature, rainfall, evapotranspiration and SPI and RDI parameters, the time intervals of three to nine months were considered in the period from 2004 to 2014. Then, using support vector regression (SVR) and genetic and programming (GP), the production amounts of the three studied plants were predicted. In addition, the accuracy of the mentoned methods in predicting the performance of dry crop products was evaluated using root mean squared error (RMSE) and mean absolute error (MAE) statistics. Results showed that in Tabriz for alfalfa, GP method with RMSE= 0.17 (kg ha-1), in Maragheh for the alfalfa, SVR with RMSE= 0.56 (kg ha-1) and in Sarab for barely, SVR method with RMSE=0.20 (kg ha-1) had more precise predictions. It can be stated that the use of climatic factors and drought indicators of autumn, winter and spring seasons have significant effects on increasing the accuracy of soft computing techniques in predicting the performance of rainfed products.

Keywords


Allen RG, Pereira LS, Raes D and Smith M, 1998. Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, FAO, Rome, Italy.
Arshad S, Morid S, Mobasheri MR and Agha Alikhani M, 2012. Monitoring and forecasting drought impact on dryland farming areas. International Journal of Climatology 33: 2068–2081.
Bannayan M, Sanjani S, Alizadeh A, Sadeghi Lotfabadi S and Mohamadian A, 2010. Association between climate indices, aridity index, and rainfed crop yield in northeast of Iran. Field Crops Research 118: 105–114.
Basak D, Pal S and Patranabis DC, 2007. Support vector regression. Neural Information Processing 11: 203-225.
Battisti R, Sentelhas P and Boote k, 2017. Inter-comparison of performance of soybean crop simulation modelsand their ensemble in southern Brazil. Field Crops Research 200: 28-37.
Borelli A, DeFalco I, Della CA, Nicodemi M and Trautteur G, 2006. Performance of genetic programming to extract the trend in noisy data series. Physica A: Statistical Mechanics and its Applications 370: 104-108.
Boser BE, Guyon IM and Vapnik VN, 1992. A training algorithm for optimal margin classiers. Pp. 144-152. In: D.Haussler (ed.), 5th Annual ACM Workshop on COLT, Pittsburgh.
Edwards DC and McKee TB, 1997. Characteristics of 20th Century Drought in the United States at Multiple Time Scales. Climatology Report Number 97-2, Department of Atmospheric Science, Colorado State University, Fort Collins.
Elagib NA and Elhag M, 2011. Major climate indicators of ongoing drought in Sudan. Journal of Hydrology 409: 612-625.
Hui JU, Er-da1 L, Wheeler T, Challinor A and Shuai J, 2013.Climate change modelling and its roles to Chinese crops yield. Journal of Integrative Agriculture 12: 892-902.
Kang Y, Shahbaz Khan and Xiaoyi Ma, 2009. Climate change impacts on crop yield, crop water productivity and food security – A review. Journal of Progress in Natural Science 19: 1665–1674.
McKee TB, Doesken NJ and Kleist J, 1993. The relation of drought frequency and duration to time scales. Pp. 379-384, 8th Conference on Applied Climatology, 17-22 January, Anaheim, California
Mishra AK and  Desai VR, 2005. Spatial and temporal drought analysis in the Kansabati river basin, India. International Journal of River Basin Management 3: 31-41.
Mosaedi A and Ghabaei Sough M, 2011. Modification of standardized precipitation index (SPI) based on relevant probability distribution function. Journal of Water and Soil 25(5): 1206-1216 (In Persian with English abstract).
Mosaedi A, Mohammadi Moghaddam S and Ghabaei Sough M, 2015. Modeling rain-fed wheat and barley based on meteorological features and drought Indices. Journal of Water and Soil 29(3): 730-749 (In Persian with English abstract).
Padakandla SJ, 2016. Climate sensitivity of crop yields in the former state of Andhra Pradesh, India. Journal of Ecological Indicators 70: 431–438.
Rahmani E, Liaghat A and Khalili A, 2008. Estimating barley yield in Eastern Azerbaijan using drought indices and climatic parameters by artificial neural network (ANN). Iranian Journal of Soil and Water Research 39(1): 47-56 (In Persian with English abstract).
Samadianfard S and Asadi E, 2018. Prediction of SPI drought index using support vector and multiple linear regressions. Journal of Water and Soil Resource Conservation 6(4): 1-16 (In Persian with English abstract).
Sette L and Boullart L, 2001. Genetic programming: principles and applications. Engineering Applications of Artificial Intelligence, 14(6): 727-736.
Steinmann A, 2003. Drought indicators and triggers: a stochastic approach to evaluation. Journal of the American Water Resources Association 39: 1217-1233.
Tietjen B and Jeltsch F, 2007. Semi-arid grazing systems and climate change: a survey of present modelling potential and future needs. Journal of Applied Ecology 44: 425-434.
Tsakiris G and Vangelis H, 2004. Towards a drought watch system based on spatial SPI. Water Resources Management 18: 1-12.
Tsakiris G and Vangelis H, 2005. Establishing a drought index incorporating evapotranspiration. European Water 9: 3–11.
Tsakiris G, Pangalou D and Vangelis H, 2007. Regional drought assessment based on the reconnaissance drought index (RDI). Water Resource Management 21: 821–833.
Valizadeh J, Ziaei M and Mazloumzadeh SM, 2014. Assessing climate change impacts on wheat production (a case study). Journal of the Saudi Society of Agricultural Sciences 13: 107–115.
Vapnik VN, 1995. The Nature of Statistical Learning Theory. Springer, New York.
Vapnik VN, 1998. Statistical Learning Theory. Wiley, New York.
Xiao G, Zhang Q, Li Y, Wang R, Yao Y, Zhao H and Bai H, 2010. Impact of temperature increase on the yield of winter wheat low and high altitudes in semiarid northwestern China. Agricultural Water Management 97: 1360–1364.
Zare Abyaneh H, 2013. Evaluating roles of drought and climatic factors on variability of four dry farming yields in Mashhad and Birjand. Water and Soil Science-University of Tabriz 23(1): 39-56 (In Persian with English abstract).
Zimmermann A, Webber H, Zhao G, Ewert F, Kros J, Wolf J, Britz W and Vries W, 2017. Climate change impacts on crop yields, land use and environment in response to crop sowing dates and thermal time requirements. Agricultural Systems 157: 81–92.