zhila ghorbani; Kiumars Sefidi; Mahshid Souri; Mehdi Moameri
Volume 28, Issue 3 , October 2021, , Pages 395-409
Abstract
Awareness of the extent and impact of vegetation from livestock grazing disorders can be a solution to develop rangeland ecosystem management strategies to achieve sustainability and continuous production in these ecosystems. In this study, the production of aerial and underground organs of grazable ...
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Awareness of the extent and impact of vegetation from livestock grazing disorders can be a solution to develop rangeland ecosystem management strategies to achieve sustainability and continuous production in these ecosystems. In this study, the production of aerial and underground organs of grazable livestock species in the southeastern rangelands of Sabalan under the influence of different grazing intensities and distance from the village as the focus of the crisis was investigated. In addition, the development and evaluation of ANFIS model was presented in order to predict the production of aerial and underground organs of food species and compare the results with the regression model. For evaluation of regression and ANFIS models the Root Mean Square Error (RMSE) and correlation coefficient (R2) were used. The results showed that different grazing intensities, distance from village and interaction between them were significant effect on the production of aerial and underground organs of palatability species at (p≤0.01). Also, with increasing grazing intensity, the production of aerial and underground organs of these species decreased. The results of ANFIS section showed that in low grazing intensity and distance of about 400 meters, the highest amount of production of palatable species is observed. The lowest production of these species is predicted to be close to the village (200 meters). Moreover, the highest amount of underground biomass at farther distances (600 meter) and lowest amount of that was observed at 200 meter. In addition, ANFIS model with higher accuracy (R2 = 0.98 and R2 = 0.95) and lower error (RMSE = 0.9792 and RMSE = 1.168) than less accurate regression model (R2 = 0.92 and R2 0.77) which also had more errors (RMSE = 2.2835 and RMSE = 3.8954), predicted the production of aerial and underground organs, respectively.
Fatemeh Bahmani; Hosin Pirisahragard; Jamshid Piri
Volume 26, Issue 1 , June 2019, , Pages 201-213
Abstract
Estimation of the soil temperature in arid regions is one of the most important issues in planning the projects of desertification, water resource management, and establishment of vegetation.The present study aimed to compare the accuracy of artificial intelligence methods in order to estimate soil daily ...
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Estimation of the soil temperature in arid regions is one of the most important issues in planning the projects of desertification, water resource management, and establishment of vegetation.The present study aimed to compare the accuracy of artificial intelligence methods in order to estimate soil daily temperatures using meteorological data (daily minimum and maximum of temperatures, sunshine, and evaporation), as well as, identifying the most important factors on soil temperature in Zabol and Shiraz synoptic stations. For this purpose, soil daily temperature was estimated at 5, 10, 20, 30, 50 and 100 cm depths by using the three-year period data (2011-2014) and artificial neural network, neuro-fuzzy adaptive genetic programming, and combined neural network-genetic algorithm approaches. Thae results were evaluated using the root mean square error (RMSE) and mean absolute error (MAE) and determination coefficient. Based on the results, there was more dependence between the air temperature and soil temperature at the topsoil so that, the highest and the lowest correlation between actual and simulated data were observed at 5 cm (mean R2=0.92) and 100 cm depths (mean R2=0.56), respectively. The accuracy of the methods used was different from each other in estimating the soil daily temperature. Based on results, in Zabol and Shiraz stations, combined neural networks - genetic algorithm approach and artificial neural network methods provided the most accurate estimation of soil daily temperature, respectively (the mean RMSE=3.69, 2.86 and mean MAE=3.23, 2.57 respectively). According to the results of the present study, it is suggested that in order to choose appropriate time and depth of seeding in the vegetation reclamation projects in arid regions, through considering of climatic conditions of each region, precise artificial intelligence techniques could be used to estimate the soil daily temperature.