Introduction


Accurate land suitability evaluation is an important basis for sustainable agricultural land management and improved crop productivity. Conventional parametric land evaluation methods commonly represent the effects of soil limitations using predefined, piecewise-linear relationships between soil properties and suitability scores. Although these relationships provide a continuous representation of suitability, their fixed linear form may not adequately capture nonlinear crop responses to variation in soil properties. Fuzzy logic provides an alternative framework by expressing the degree of suitability through continuous membership functions whose shape and parameters can be adjusted to represent nonlinear responses. However, the parameters of these functions are commonly defined using expert knowledge or predefined threshold values, which may not fully reflect crop responses under specific field conditions. A data-driven calibration approach can reduce reliance on manual parameter setting by optimizing membership-function parameters against observed crop performance. Therefore, the objective of this study was to develop a data-driven fuzzy soil suitability model for irrigated grain maize by optimizing the parameters of fuzzy membership functions using the Differential Evolution (DE) algorithm, and to examine the contribution of individual soil properties to the relationship between soil suitability and observed maize yield.


Materials and Methods


The study was conducted using observations from 100 irrigated grain-maize farms distributed across four agricultural regions of Iran, located in Khuzestan, Fars, Qazvin, and Kerman provinces. Climatic suitability was considered similar and suitable for maize across the study areas; therefore, the analysis focused on soil-related limitations. The soil and land characteristics considered as model inputs were soil pH, electrical conductivity (EC), exchangeable sodium percentage (ESP), calcium carbonate equivalent (CCE), gypsum content, soil texture, drainage condition, and slope. Actual maize yield, calculated as the average yield of each farm over five years, was used as an independent criterion for evaluating the relationship between the soil suitability index and crop performance and as the objective variable for data-driven optimization; it was not used as a direct input for calculating the suitability index. A classical parametric suitability model based on the framework of Sys et al. (1991) was first implemented as the reference approach. Subsequently, a fuzzy model was developed in which continuous membership functions were defined for the quantitative soil properties according to the agronomic response of maize, while qualitative variables were represented using expert-defined suitability functions. The parameters of the membership functions were then optimized using the DE algorithm by maximizing the coefficient of determination (R²) between the resulting fuzzy soil suitability index (FSI) and observed maize yield. The initial fuzzy model was also evaluated before optimization to quantify the effect of parameter optimization. Sensitivity analysis was conducted by removing each soil variable individually from the optimized model and assessing the resulting change in R². Permutation-based importance analysis was additionally used to examine the independent contribution of each predictor by randomly permuting its values while keeping the other predictors unchanged. Alternative aggregation operators were examined using the generalized power mean, and the effect of assigning variable-specific weights on the resulting suitability index was also evaluated.


Results and Discussion


The results showed that data-driven optimization of the membership-function parameters improved the relationship between the fuzzy soil suitability index and observed maize yield. The coefficient of determination increased from 0.45 for the initial fuzzy model to 0.69 after optimization, whereas the classical parametric suitability approach produced an R² of 0.59. Accordingly, the optimized fuzzy model provided a stronger explanation of the observed variation in maize yield than both the initial fuzzy formulation and the classical parametric approach. These results indicate that, in addition to adopting continuous fuzzy membership functions, appropriate calibration of their parameters is important for representing the field-level response of maize to soil limitations. Sensitivity analysis and permutation-based importance analysis did not produce identical rankings of soil properties, reflecting differences in the information captured by the two approaches; nevertheless, both analyses highlighted the greater contribution of several chemical soil properties to the observed relationship between soil suitability and maize yield. The complementary nature of the two analyses provides additional information on the influence of individual soil properties without implying that either analysis alone represents a unique causal ranking of the variables. Evaluation of alternative aggregation operators showed that the generalized power mean approached the geometric mean, with the optimized power parameter close to zero. Consequently, the geometric mean was retained for the final suitability index. Furthermore, applying variable-specific weights did not increase the coefficient of determination of the fuzzy model, indicating that weighting the membership functions did not provide an improvement over their unweighted geometric aggregation under the conditions of this study.


Conclusion


This study developed a data-driven framework for fuzzy soil suitability assessment of irrigated grain maize by optimizing membership-function parameters using the Differential Evolution algorithm and observed field yield. The results showed that optimization increased the coefficient of determination between the soil suitability index and observed maize yield from 0.45 to 0.69, compared with 0.59 obtained using the classical parametric approach. The findings demonstrate that data-driven adjustment of membership-function parameters can improve the ability of a fuzzy soil suitability index to explain variation in observed maize yield under field conditions. Sensitivity and permutation-based importance analyses provided complementary information on the contribution of individual soil properties, with several chemical properties showing greater influence than the evaluated topographic factors. The geometric mean remained the appropriate aggregation operator after evaluation of alternative power-mean formulations, while variable-specific weighting did not improve the model’s R². Overall, the proposed framework provides a data-driven approach for reducing reliance on manual specification of membership-function parameters and may be considered in future soil suitability studies for other crops and regions where reliable field-performance data are available.