Abbas Nasiri Dehsorkhi; Hassan Makarian; Mehrdad Mahlooji; SeyedHassan Mirhashemi; Siavash Bardehji; Sima Sadat Seyedi; Navid Kargar Dehbidi
Abstract
An experiment was conducted at the Faculty of Agriculture, Shahrood University, as a randomized complete block design with four replications to investigate the effect of ultrasonic waves and seed priming on some quality traits of cowpea under soil application of trifluralin. Nine treatments were: T1: ...
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An experiment was conducted at the Faculty of Agriculture, Shahrood University, as a randomized complete block design with four replications to investigate the effect of ultrasonic waves and seed priming on some quality traits of cowpea under soil application of trifluralin. Nine treatments were: T1: control, T2: ultrasonic waves, T3: ultrasonic waves + reduced herbicide dose (1 L ha-1), T4: ultrasonic waves + recommended herbicide dose (2 L ha-1), T5: hydro-priming, T6: hydro-priming + reduced herbicide dose, T7: hydro-priming + recommended herbicide dose, T8: reduced herbicide dose, T9: recommended herbicide dose. The results showed that the effect of treatments was significant on all traits except leaf phosphorus. The maximum chlorophyll a (1.30 mg g-1 FW), carotenoid (1.82 mg g-1 FW), leaf relative water content (79.9 %), and leaf nitrogen (3.97%) were obtained in ultrasonic treatment, which resulted in a significant increase of 28.7, 22.1, 7.9, and 18.5 percent, respectively, in comparison to the control. In comparison to the ultrasonic treatment, ultrasonic waves + recommended herbicide dose reduced chlorophyll b, RWC, and leaf nitrogen by 29.3, 21.1, and 35.3 percent, respectively. In comparison to herbicide application alone, the combination of ultrasonic waves and the recommended herbicide dose reduced chlorophyll a and total chlorophyll by 29.7 and 22.2 percent, respectively. Overall, the results of the present study showed that pretreating cowpea seeds with ultrasonic waves could increase photosynthesis pigments, relative water content, and leaf N (in the absence of herbicide use).
SeyedHassan Mirhashemi; Mehdi Panahi
Abstract
The need for a model for effective planning and management of water resources, particularly groundwater, is especially critical in light of water scarcity and aquifers. Given the importance of various factors in determining the amount of drop, this study used human and natural factors to predict the ...
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The need for a model for effective planning and management of water resources, particularly groundwater, is especially critical in light of water scarcity and aquifers. Given the importance of various factors in determining the amount of drop, this study used human and natural factors to predict the amount of aquifer drop in Qazvin. To accomplish this, the K-Means clustering algorithm was used first, followed by the tree algorithms CART, CHAID, C5.0, and QUEST to determine the optimal ratio between different fields. Accuracy values of 0.90, 0.96, 0.94, and 0.92 were obtained for the aforementioned tree algorithms. The values obtained for the CHAID algorithm's sensitivity, transparency, accuracy, precision, false-positive rate, false-negative rate, F-measure, geometric mean, and error rate demonstrate that this algorithm outperforms other algorithms. The amount of water in the irrigation network is the most influential human factor in model production, while the amount of temperature is the most influential natural factor. The proposed model enables more accurate prediction of aquifer changes and can be used by managers and farmers to improve aquifer management.
SeyedHassan Mirhashemi; Ali Asghar Bour
Abstract
Data mining algorithms were used in this study to predict Shiraz's monthly potential evapotranspiration. The CART (Classification and Regression Trees), M5P, K-star, M5Rules, and REP-Tree (Reduced Error Pruning Tree) algorithms were used to predict potential evapotranspiration. Meteorological data from ...
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Data mining algorithms were used in this study to predict Shiraz's monthly potential evapotranspiration. The CART (Classification and Regression Trees), M5P, K-star, M5Rules, and REP-Tree (Reduced Error Pruning Tree) algorithms were used to predict potential evapotranspiration. Meteorological data from the Shiraz weather station from 2001 to 2016 were used in this study. The CART algorithm performed better in estimating monthly averages, according to statistical indicators. The maximum amount of potential evapotranspiration was reached when the sunshine hours exceeded 9.5 hours and the wind speed exceeded 0.3 meters per second, according to the results. When there was less than 9.5 hours of sunshine and the air temperature was less than 2 °C, the potential evapotranspiration rate was the lowest. The sensitivity analysis revealed that the parameters of sunshine hours, air temperature, wind speed, and relative humidity had a positive effect on the CART algorithm's performance in estimating monthly evapotranspiration.
