Drought, rising demand for water, declining water resources, and mismanagement have put society at serious risk. Therefore, it is essential to provide appropriate solutions to increase water productivity (WP). As an element of research, this study presents a hybrid machine learning approach and investigates its potential for estimating date palm crop yield and WP under different levels of subsurface drip irrigation (SDI). The amount of applied water in the SDI system was compared at three levels of 125% (T1), 100% (T2), and 75% (T3) of water requirement. The proposed ACVO-ANFIS approach is composed of an anti-coronavirus optimization algorithm (ACVO) and an adaptive neuro-fuzzy inference system (ANFIS). Since the effect of irrigation factors, climate, and crop characteristics are not equal in estimating the WP and yield, the importance of these factors should be measured in the estimation phase. To fulfill this aim, ACVO-ANFIS employed eight different feature combination models based on irrigation factors, climate, and crop characteristics. The proposed approach was evaluated on a benchmark dataset that contains information about the groves of Behbahan agricultural research station located in southeast Khuzestan, Iran. The results explained that the treatment T3 advanced data palm crop yield by 3.91 and 1.31%, and WP by 35.50 and 20.40 kg/m3, corresponding to T1 and T2 treatments, respectively. The amount of applied water in treatment T3 was 7528.80 m3/ha, which suggests a decrease of 5019.20 and 2509.6 m3/ha of applied water compared to the T1 and T2 treatments. The modeling results of the ACVO-ANFIS approach using a model with factors of crop variety, irrigation (75% water requirement of SDI system), and effective rainfall achieved RMSE = 0.005, δ = 0.603, and AICC = 183.25. The results confirmed that the ACVO-ANFIS outperformed its counterparts in terms of performance criteria.
As water scarcity becomes more acute in many parts of the world, increasing the effectiveness with which agricultural water resources are used is a priority for enhanced food security of water. Experiment was carried in the fromed out of randomized complete block design (RCBD) with three replications in Behbahan Agricultural Research Station in Khuzestan province southwest Iran, during the 2018 and 2019 growing seasons. To evaluate of Yield and Water Productivity of Sesame using Drip Tape Irrigation system, different water levels based on water requirements (40, 60, 80, and 100% of the water requirement) and three sesame varieties (Darab1, Dashtestan2, and Shevin) were considered as main plots and sub plots, respectively. According to obtained results, Comparison of the average interaction effects of irrigation and various varieties showed that the Darab1 variety, which had the best results with 100% water requirement treatment and had 73.3 seeds per capsule, 125.7 capsules per plant, 2.703 gr of 1000-seed weight, and 1314.5 kg.ha− 1 yields, was superior and came in first place. The analysis of the regression model's, beta coefficient and the Pearson correlation coefficient for the studied traits revealed a trend toward increasing beta coefficient and Pearson correlation coefficient values as water consumption increased from treatment 40–100% water requirement. The increasing trend in beta and Pearson correlation coefficients had a higher slope from 40 to 80% of water requirement levels and a lower slope from 80 to 100% of water requirement levels. At the level of 80% of water requirement, yield and water productivity had the highest beta coefficients (0.622 and 0.633) and Pearson correlation (0.712 ** and 0.730 **) with capsules per plant, respectively. Less fluctuation of beta and Pearson correlation coefficients from the level of 80 to 100% of water requirement compared to other irrigation levels caused the level of 80% of water requirement to be introduced as the optimal level of irrigation, and in conclusion, the highest water productivity was observed in Darab1 variety. Additionally, this research highlights the possibility of using Darab1 variety in study area and region with similar conditions.
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