Modelling the impact of climate change on rangeland forage production using a generalized regression neural network: a case study in Isfahan Province, Central Iran
Zahra JABERALANSAR*, Mostafa TARKESH, Mehdi BASSIRI, Saeid POURMANAFI
Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, Iran
Modelling the impact of climate change on rangeland forage production using a generalized regression neural network: a case study in Isfahan Province, Central Iran
Zahra JABERALANSAR*, Mostafa TARKESH, Mehdi BASSIRI, Saeid POURMANAFI
Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, Iran
摘要 Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network (GRNN) model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data (including climate and topography data), and the performance of each network model was assessed using the mean estimation error (MEE), model efficiency factor (MEF), and correlation coefficient (r). The best neural network model was then selected and further applied to predict the forage production of rangelands in the future (in 2030 and 2080) under A1B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production (0–100 kg/hm2) will increase while the areas with moderate, moderately high and high levels of forage production (≥100 kg/hm2) will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.
Abstract：
Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network (GRNN) model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data (including climate and topography data), and the performance of each network model was assessed using the mean estimation error (MEE), model efficiency factor (MEF), and correlation coefficient (r). The best neural network model was then selected and further applied to predict the forage production of rangelands in the future (in 2030 and 2080) under A1B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production (0–100 kg/hm2) will increase while the areas with moderate, moderately high and high levels of forage production (≥100 kg/hm2) will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.
Zahra JABERALANSAR,Mostafa TARKESH,Mehdi BASSIRI等. Modelling the impact of climate change on rangeland forage production using a generalized regression neural network: a case study in Isfahan Province, Central Iran[J]. Journal of Arid Land, 2017, 9(4): 489-503.
Zahra JABERALANSAR,Mostafa TARKESH,Mehdi BASSIRI et al. Modelling the impact of climate change on rangeland forage production using a generalized regression neural network: a case study in Isfahan Province, Central Iran[J]. Journal of Arid Land, 2017, 9(4): 489-503.
2017, Vol. 9 
