Zobrazeno 1 - 9 of 9 pro vyhledávání: '"Mehrdad Esfandiari"'
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Akademický článek
The effects of drought and salinity on KS and RAW managerial coefficients in the efficient water management in maize farms
Autor: Faramarz Zargar Yaghoubi, Mahdi Sarai Tabrizi, Ali Mohammadi Torkashvand, Mehrdad Esfandiari, Hadi Ramezani Etedali
Publikováno v: Applied Water Science, Vol 14, Iss 8, Pp 1-13 (2024)
Abstract This study aimed to investigate the simultaneous effects of drought and salinity on irrigation management coefficients in maize farms. A three-year field research was conducted in the form of a 3 × 3 factorial experiment with a randomized c
Externí odkaz: https://doaj.org/article/1596beea4f184397bb64895753acaed6
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2
Development support vector machine, artificial neural network and artificial neural network – genetic algorithm hybrid models for estimating erodible fraction of soil to wind erosion
Autor: Alireza Nouri, Mehrdad Esfandiari, Kamran Eftekhari, Ali Mohammadi Torkashvand, Abbas Ahmadi
Publikováno v: International Journal of River Basin Management. :1-10
The index of Erodible Fraction (EF) component against wind is well known as a criteria of soil wind erodibility. This study was conducted to estimate the index of EF by utilizing support vector machine (SVM), artificial neural network (ANN) and genet
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f1473fbad64ee21cfcee0b8bfdf657c
https://doi.org/10.1080/15715124.2022.2153856
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4
Prediction of Saffron Yield Based on Soil Properties as a Way to Identify Susceptible Lands of Saffron
Autor: Fatemeh Tashakkori, Mehrdad Esfandiari, Abbas Ahmadi, Ali Mohammadi Torkashvand
Publikováno v: Communications in Soil Science and Plant Analysis. 52:2471-2481
Saffron (Crocus sativus L.) is one of the most important crops produced globally, and in only a limited number of countries. Determining the best conditions for cultivating this crop is important. ...
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9280c46fed8a2ad26be18de18f3e6054
https://doi.org/10.1080/00103624.2021.1929284
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5
Prediction of Saffron Yield Based on Soil Properties Using Artificial Neural Networks as a Way to Identify Susceptible Lands of Saffron
Autor: Fatemeh Tashakkori, Abbas Ahmadi, Mehrdad Esfandiari, Ali Mohammadi Torkashvand
Publikováno v: Communications in Soil Science and Plant Analysis. 52:1326-1337
Saffron (Crocus sativus L.) is one of the most important global crops produced only in a limited number of countries. Determining the best conditions for cultivating this crop is important and the ...
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::982f458e5b55262856ba1cebe28ff75d
https://doi.org/10.1080/00103624.2021.1879128
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6
Comparison of Different Methods of Estimating Saffron Yield Based on Soil Properties in Golestan Province
Autor: Abbas Ahmadi, Ali Mohammadi Torkashvand, Fatemeh Tashakkori, Mehrdad Esfandiari
Publikováno v: Communications in Soil Science and Plant Analysis. 51:1767-1779
Evaluation of the relationship between soil properties and saffron yield estimation may contribute to agricultural planning in finding suitable lands for the growth of this valuable product. This s...
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0baed899d5546dd967cfa15853b9e0a1
https://doi.org/10.1080/00103624.2020.1798988
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7
Morphological, physical, and clay mineralogy of calcareous and gypsiferous soils in North of Lorestan, Iran
Autor: Ebrahim Pazira, Kolsum Rahman Salari, Mehrdad Esfandiari, Mohammad Amir Delavar
Publikováno v: Canadian Journal of Soil Science. 99:485-494
There is limited information about the genesis, classification, and properties of calcareous and gypsiferous soils of western Iran. This study investigated the morphological, physical, and mineralogical characteristics of soils on different physiogra
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bbac205bcca87ce61ceae63503fb3461
https://doi.org/10.1139/cjss-2018-0141
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8
Spatial prediction of WRB soil classes in an arid floodplain using multinomial logistic regression and random forest models, south-east of Iran
Autor: Mehrdad Esfandiari, Mohammad Reza Pahlavan-Rad, Seyed Javad Forghani, Ali Mohammadi Torkashvand
Publikováno v: Arabian Journal of Geosciences. 13
In the current study, the variations of soil classes at the first and second levels of WRB (World Reference Base for soil resource) soil classification system were investigated by two machine learning including multinomial logistic regression (MLR) a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a89449e4f2fed1a810d66806de5c4d3d
https://doi.org/10.1007/s12517-020-05576-4
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