Modeling of Land Production Potential for Irrigated Winter Wheat in Aghili Plain, Khuzestan Province

Document Type : Research Paper

Authors

1 PhD student, Tehran University, and Assistant Professor of Soil and Water Research Institute

2 Professor, Tehran University

3 Associate Professor, Tehran University

Abstract

The study area is located in south west of Iran, Agili plain, Gotvand, Khuzestan province. Based on Shoushtar synoptic station, the climatic type of the area is semi-desertic. The maximum daily air temperature is 46.6 °C in June and the minimum daily air temperature is 8.1 °C in December. The annual rainfall is about 324 mm. The aim of this research was to elaborate an approach for the prediction of the land production potential for irrigated winter wheat, taking into account the environmental condition in the study area. The results showed that irrigated potential yield based on crop growth model method was 8041 kg/ha in the study area and land production potential for irrigated wheat, taking into account the effect of soil limitations, ranged between 2454 to 6687 kg/ha with Square Root method, and between 2296 to 6756 kg/ha for Storie method. The reduction in yield was due to high calcium carbonate, poor drainage, salinity and alkalinity, and management method. For evaluation of the model, three different regression methods, namely, standard multiple regression, stepwise regression, and curve estimation were used for comparison of the yield predicted  by the model with the yield harvested by the farmers. Based on the results, coefficient of determination (r2) for predicted yield with Storie, square root, and characteristics methods were 0.83, 0.80, and 0.33. These results show that the predicted yield can estimate the observed yield with 83 percent accuracy for Storie method, 80 percent for Square Root method, and 33 percent for Land Characteristics. Meanwhile, the Root Mean Square Error was, respectively, 598, 648, and 1258 kg/ha. Based on the results, it can be concluded that Storie method can predict yield better that the other methods because of higher coefficient of determination and lower RMSE.

Keywords

References

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Iranian Journal of Soil Research
Volume 27, Issue 4
March 2013
Pages 427-439

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