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Noori Mohsen, Farzaneh Salim, Seyed Sharifi Raouf, Keshtehgar Abbas, Ahmadi-Nouraldinvand Farnaz, Shahriari Alireza
Keywords: Macronutrients, Purslane, Prediction model, Soil elements, Support vector regression
The improvement of strategies for estimating the nutrient composition of arable crops is currently a fundamental prerequisite for sensible and consistent management of soil properties. Modeling methods can be used to assess the soil properties of arable land and to investigate crop properties via soil operations. Therefore, the main objective of this experiment was to investigate the use of Support Vector Regression to predict oil content and fatty acids in purslane seeds and the uptake of elements in response to soil macronutrient content under the influence of fertilizer types. Accordingly, two field trials were conducted with purslane using chemical and nanoparticulate fertilizers (nitrogen, phosphorus and potassium) and vermicompost (0, 5, 10 and 15 t ha−1). The results showed that the accuracy of the prediction model in response to soil potassium was more than 95% (R2 = 0.957) when the oleic acid datasets were used as input. Moreover, the best soil potassium content was between 160 and 300 mg kg−1. Similarly, the best oleic acid content was between 22.06 and 24.89% in response to soil potassium obtained with chemical fertilizers and NPK nanoparticles and 5 and 15 t ha−1 of vermicompost. The importance of oleic acid in response to soil potassium is also identified as a factor that significantly influences purslane prediction models. Higher levels of soil nutrients do not contribute to increased oil content and fatty acids in the seeds, and over-application may not be economical.
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Department of Plant Production and Genetic Engineering, University of Mohaghegh Ardabili, Ardabil, Iran