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Spatial modeling of a soil fertility index using digital soil mapping (Case study from Honam watershed (Iran)) | ||
| Desert | ||
| مقاله 13، دوره 28، شماره 2، اسفند 2023، صفحه 365-380 اصل مقاله (750.38 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jdesert.2023.95754 | ||
| نویسندگان | ||
| Fatemeh Ebrahimi Meymand* 1؛ Hasan Ramezanpour2؛ Nafiseh Yaghmaeian3؛ Kamran Eftekhari1 | ||
| 1Department of Soil Identification Research and Land Evaluation, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. | ||
| 2Soil Science Department, College of Agriculture, University of Guilan, Rasht, Iran. | ||
| 32 Soil Science Department, College of Agriculture, University of Guilan, Rasht, Iran. | ||
| چکیده | ||
| Attempt to evaluate soil fertility was and still is one of the most challenging public importance. Soil nutrients are the key factors in soil fertility. For this reason, when constructing soil fertility potential, many researchers prefer to investigate soil nutrient status or use and assessment of qualitative research methods. Quantifying soil fertility is challenging since various factors such as numerous physical and chemical characteristics of soil might affect it. The proper selection of factors that may more accurately describe soil fertility is another issue. So, in this study, we developed a regional soil fertility index (SFI) based on different soil nutrients for quantifying soil fertility. After receiving fertility, a comparative study of machine learning techniques was carried out to construct its distribution map, using digital soil mapping (DSM). The spatial distribution of the SFI map showed that 55% of the studied area had poor fertility, 27.25% had moderately fertile soils, and only a tiny area had fertile soils. The results indicated that heavy soil texture and high calcium carbonate content were the most limiting factor and phosphorus and zinc were the most limiting nutrients across the studied area. Comparing machine learning techniques yielded the finding that the Random forest model has the best performance for predicting SFI (R2= 0.86) compared with the Decision tree (R2= 0.53) and Multi-linear regression (R2= 0.35). Therefore, specific soil fertility management practices and training farmers on the proper use of soil fertility management practices are recommended. | ||
| کلیدواژهها | ||
| Entisols؛ Google Earth Engine؛ Inceptisols؛ K-fold cross-validation؛ Soil fertility index | ||
| مراجع | ||
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