Spatial yield prediction of autumn rapeseed based on non-parametric methods

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ADAB H.; ATABATI A.; POURBAGHER KORDI S.M.; ARMIN M.; ZABIHI H.

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Journal: Journal of Plant Production Research Year:2019 | Volume:26 | Issue:3 Page(s): 199-217

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Title

Spatial yield prediction of autumn rapeseed based on non-parametric methods

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Keywords

K-Nearest Neighbors AlgorithmQ1

Random Forest AlgorithmQ1

Sabzevar regionQ1

Autumn rapeseed yieldQ1

Abstract

Background and Objectives: Modeling the correct relationship between environmental conditions and yields is a critical step to find how crop-planting choices in different regions of Iran. Spatial modeling in GIS is one of the most important strategies that can provide a basis for measuring environmental factors and land suitability for the cultivation of a particular product by combining statistical methods and spatial data. In this research, the link between water, soil and meteorological factors and rapeseed yields investigated during the growing season in sample farms. Materials and Methods: In this research, the position of 24 sample fields of rapeseed farming was recorded by Global Positioning System (GPS) and then actual yield was calculated. To explore how the environmental conditions and yields relationship has changed over space, we used ten environmental parameters influencing rapeseed productions yield, including elevation, slope, aspect, EC and pH groundwater resources, mean temperature, incoming solar radiation, potential evapotranspiration, wind exposition index, soil texture during the growing season. The values of each independent variables were extracted into samples by nearest neighbor method. Then, after normalizing the variables and taking into account the range of numbers, the samples were divided into two subsets: training (60%, 14 farms) and the testing dataset (40%, 10 farms) randomly. Two methods of nonparametric K of the nearest neighbor and random forest were then used to estimate rapeseed yield over the study area. Results: The results of mean absolute error percentage in the methods used showed that K is the nearest neighbor with 26% error and random forest with 11% error. The results of Nash– Sutcliffe efficiency index for validation data set represent the value of 0. 65 for K nearest neighbor and 0. 82 for random forest method. In general, the results indicate that the random forest method has a lesser error than the K nearest neighbor method in estimating the yield of rapeseed productions for the study area. Conclusion: Based on the results of this research, it can be concluded that among the variables used, two variables of wind supply index and average temperature had the most effect on the yield of rapeseed in comparison with other variables. Also, according to the final map, it was determined that suitable areas for rapeseed cultivation over Sabzevar region are located in the northern and northwestern regions. Low yield in the central regions of this part is mainly due to the excessive salinity of water and gypsum formations.

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APA: Copy

ADAB, H., ATABATI, A., POURBAGHER KORDI, S.M., ARMIN, M., & ZABIHI, H.. (2019). Spatial yield prediction of autumn rapeseed based on non-parametric methods. JOURNAL OF PLANT PRODUCTION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 26(3 ), 199-217. SID. https://sid.ir/paper/155747/en

Vancouver: Copy

ADAB H., ATABATI A., POURBAGHER KORDI S.M., ARMIN M., ZABIHI H.. Spatial yield prediction of autumn rapeseed based on non-parametric methods. JOURNAL OF PLANT PRODUCTION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2019;26(3 ):199-217. Available from: https://sid.ir/paper/155747/en

IEEE: Copy

H. ADAB, A. ATABATI, S.M. POURBAGHER KORDI, M. ARMIN, and H. ZABIHI, “Spatial yield prediction of autumn rapeseed based on non-parametric methods,” JOURNAL OF PLANT PRODUCTION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 26, no. 3 , pp. 199–217, 2019, [Online]. Available: https://sid.ir/paper/155747/en

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