Estimation of Sugarcane Production Potential Using Different Models in the Southern Lands of Khuzestan Province

Document Type : Research Paper

Authors

Assistant Professor, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

Abstract

Understanding the factors that limit prediction of regional performance of sugarcane crop and improve management practices is essential. The purpose of this study was to select a model that could estimate the potential of sugarcane production by considering the climatic, plant, and soil and terrain characteristics of the region. The study areas in the south of Khuzestan Province included the agro-industries of Amir Kabir, Miza Kuchak Khan, Dabal Khazaei, Salman Farsi, and Farabi. For this purpose, 100 sites in farms of different areas under sugarcane cultivation were studied based on diversity in soil characteristics. The method of this research was done in two hierarchies. In the first stage, estimation of sugarcane production potential using FAO growth model and, in the second stage, estimation of land production potential for sugarcane according to the effect of limiting factors in soil (as calculated using soil index with parametric method) that reduced yield in the first stage. To analyze the data, regression by standard methods, stepwise, and estimation curve were used. In standard and stepwise regression, soil properties were considered as an independent variable and the observed yield was considered as a dependent variable. In the linear, second and third degree estimation curve methods, the observed yield of sugarcane was considered as an independent variable and predicted yield was selected as dependent variable. The results of the first phase showed that the production potential of the FAO growth model was 95.8 tons/ha while the results of the second phase showed that the production potential of sugarcane in the region at different management levels was estimated from 18 to 69.3 tons/ha. Factors of yield reduction included limiting factors such as lime content, heavy and very heavy soil texture, drainage, salinity and sodicity, and lack of proper management. The results of standard and stepwise regression methods showed that the coefficient of determination was 0.52 and 0.49 and the standard error (ME) was 10.13 and 9.77 tons/ha, respectively. Soil properties could predict yield by standard method up to 52% and stepwise method up to 49%. In the estimation curve method for linear model, second degree, and third degree, the coefficients of determination were 0.74, 0.85, and 0.87, respectively, and standard errors were 7.8, 5.8 and 5.3 tons/ha. Therefore, the third degree estimation curve method, which uses the FAO growth model to predict crop yield, has higher accuracy and less error than the standard and stepwise regression model, which use only the effect of land characteristics on the observed yields.

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Main Subjects

References

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Iranian Journal of Soil Research
Volume 35, Issue 1
June 2021
Pages 59-74

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