Fuzzy Modeling of the Effects of Different Irrigation Depths on the Radish Crop. Part I: Productivity Analysis

Abstract

The productivity of a crop is related to the water demand inserted in its development. The measurement of water and its optimization directly influences the final costs of crop production for agricultural producers. In this sense, the objective of this study is evaluating the fuzzy modeling in estimating the productivity of the radish crop (fresh phytomass of the tuberous root) affected by different irrigation depths (25%, 50%, 75%, 100%, and 125%), based on evapotranspiration of the crop (ETc). To measure the results, two fuzzy systems (with triangular and Gaussian membership functions respectively) and a polynomial regression model were developed to perform model validation comparisons. The fuzzy modeling showed a better fit of the data compared to the polynomial regression model, with reduced errors (RMSE with values 6.3 and 6.9 in the fuzzy models versus 8.8 in the regression model) and higher correlation coefficient (0.54 and 0.5 fuzzy versus 0.1 regression). The triangular fuzzy model estimated the best crop yield (31.9 g of fresh phytomass) when using a 100% ETc depth. Also, the curve generated by the fuzzy model accurately represents all the productivity averages in each depth, in addition to this model presenting the smallest errors (compared to the triangular model and the regression model) and the highest R². However, the Gaussian fuzzy model proved to be more efficient in representing the agronomic reality, as it does not have peaks and valleys, and it is a smooth model in both growth and degrowth.

Fuzzy Modeling of the Effect of Irrigation Depths on Beet Cultivars

“…The coefficient of determination (R²) consists in evaluating the quality of the model's adjustment with the percentage or how much the model was able to explain the data collected experimentally (Ringle et al, 2014). Acoording Boso et al (2021a), the root mean square error (RMSE) and the mean absolute error (MAE), are measures of uncertainty in the model, where it indicates the size of the error caused by the model. The better the model fits, the smaller the error.…”

Lógica Fuzzy na Simulação da Produtividade de Grãos de Aveia pelo Nitrogênio, Soma Térmica e Precipitação Pluviométrica

“…Em trigo, [5] utilizando um modelo fuzzy simularam a produtividade biológica e grãos de trigo nas condições de uso de hidrogel, nitrogênio e temperatura máxima. Na cultura do rabanete [3] através da modelagem fuzzy estimaram o comportamento da…”

Multi‐criteria decision analysis integrated with GIS to determine land suitability for cultivation and best harvest time of vine

“…In this field, several studies have been conducted such as Mokarram et al 19 20 ; Mokarram and Mirsoleimani 21 ; Boso et al 22 23 .…”

“…In preparation of the final land suitability maps, several criteria are involved with varying units; therefore, in order to normalize the input data, it is more appropriate to place each data point between 0 and 1 based on their importance. In this field, several studies have been conducted such as Mokarram et al 19 ; Mokarram and Zarei 20 ; Mokarram and Mirsoleimani 21 ; Boso et al 22 ; Mokarram and Zarei. 23 However, considering that each of the selected criteria has a different weight for the purpose of preparing land suitability maps, it would be better to weigh them individually by using the AHP method.…”