Abstract
Water productivity (WP) of crops is affected by water–fertilizer management in interaction with climatic factors. This study aimed to evaluate the efficiency of a hybrid method of season optimization algorithm (SO) and support vector regression (SVR) in estimating the yield and WP of tomato crops based on climatic factors, irrigation–fertilizer under the drip irrigation, and plastic mulch. To approve the proposed method, 160 field data including water consumption during the growing season, fertilizers, climatic variables, and crop variety were applied. Two types of treatments, namely drip irrigation (DI) and drip irrigation with plastic mulch (PMDI), were considered. Seven different input combinations were used to estimate yield and WP. R2, RMSE, NSE, SI, and σ criteria were utilized to assess the proposed hybrid method. A good agreement was presented between the observed (field monitoring data) and estimated (calculated with SO–SVR method) values (R2 = 0.982). The irrigation–-fertilizer parameters (PMDI, F) and crop variety (V) are the most effective in estimating the yield and WP of tomato crops. Statistical analysis of the obtained results showed that the SO–SVR hybrid method has high efficiency in estimating WP and yield. In general, intelligent hybrid methods can enable the optimal and economical use of water and fertilizer resources.
Highlights
Tomato is one of the most important crops in Iran, with a cultivated area of 0.13 million hectares
Due to the need to replace complex crop models in evaluating yield and water productivity (WP) with simpler statistical models and limited previous studies, the purpose of this study was to propose and evaluate the efficiency of a hybrid method of season optimization algorithm (SO) and support vector regression (SVR) in modeling and estimating the yield and WP of tomato crops based on climatic factors, irrigation–fertilizer under drip irrigation, and plastic mulch and determining influential variables to estimate crop yield and WP
Applied water flow is the same in both irrigations treatments, but it was lower under PMDI mainly due to less water loss
Summary
Tomato is one of the most important crops in Iran, with a cultivated area of 0.13 million hectares. It is necessary to manage the allocated water to the tomato cultivation properly. This provides suitable conditions for the optimal use of water resources along with food security. Crop yield and water productivity are a function of different crop conditions, including climatic factors, and soil and water management. Because in different climatic conditions, the response of tomatoes to various inputs and simultaneous evaluation of water–fertilizer, planting time, plant density, and the type of soil under field conditions is time-consuming and costly, providing and using soft computing methods seems essential. Various studies on modeling and estimating yield and water productivity (WP)
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