Javad Seyedmohammadi; Bahareh Delsouz Khaki; Fatemeh Ebrahimi Meymand; Zahra Mohammad Esmail; Rasoul Kharazmi; Mohsen Bagheri Bodaghabadi
Abstract
Climate has an important role in agricultural activities and can be examined from two perspectives. First, what locations are suitable for a specific plant, and second, what plants are suitable for a climate. The latter approach is less considered; thus in this study, it has been investigated by introducing ...
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Climate has an important role in agricultural activities and can be examined from two perspectives. First, what locations are suitable for a specific plant, and second, what plants are suitable for a climate. The latter approach is less considered; thus in this study, it has been investigated by introducing a standard method (Sys method). The study area is located in Mako city. Climatic suitability evaluation was performed by numerical (parametric) method using the Maku Synoptic Station data for pistachio, almond, pears, plum, sour cherry and sweet cherry. The findings showed that except for pistachio, with marginally suitable class (S3), other plants were classified as moderately suitable class (S2) but the value of climatic index for each crop was different. The introduced method made it possible to identify the most important effective climatic factors for the cultivation of each plant and determine the most limiting factor in the phenological period. Such findings showed that there is a close relationship between crops, phenological period, climatic characteristic and location. Based on this, the most suitable plant or plant species can be selected for a region, using the phenological period of the plant and climatic characteristics. However, for sustainable development, other components of the land, such as soil and/or topography, should also be evaluated. In general, the introduced approach can be used as an efficient tool, both for choosing the most suitable plants in a climate and for choosing the most suitable place (in terms of climate) for specific plants.
Fatemeh Ebrahimi Meymand; Hasan Ramezanpour; Nafiseh Yaghmaeian; Kamran Eftekhari
Abstract
In recent years, the use of digital soil mapping (DSM) based on machine learning algorithms with the aim of preparing soil maps has become widespread with the basis of soil class prediction with the help of modeling the relationships between them and environmental variables. One of this method's challenges ...
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In recent years, the use of digital soil mapping (DSM) based on machine learning algorithms with the aim of preparing soil maps has become widespread with the basis of soil class prediction with the help of modeling the relationships between them and environmental variables. One of this method's challenges is the imbalanced nature of soil distribution in landscape, which leads to overfitting and underfitting of classes, and as a result, reduces the accuracy of many used models. This study was conducted to evaluate the ability of two machine learning algorithms, including random forests and support vector machines, for the digital mapping of soil classes with an imbalanced data set. This study was conducted on 95 soil profile classes at the family level, in 4000 hectares of land in the Honam sub-basin, Lorestan province. The issue of imbalance in soil classes was investigated by using six data sets, including the original soil data set and five data sets created by several resampling approaches including two manual classifications and three over-sampling, under-sampling, and Synthetic Minority Over-Sampling Techniques in the R software. The results showed that despite the low values of overall accuracy, the Geographical distribution of soils with high frequency in the study area in digital soil map obtained from the random forest and the original data set as well as Synthetic Minority Over-Sampling Technique, with conventional soil map of study area is significant. Therefore, the low observation number of other soil classes and as a result incorrect training of models can be considered as one of the main reasons for the low accuracy of the used models.
