Greenhouse emissions from agricultural as a consequence of human activities are causing climate change. Small changes in agricultural practices have a large potential for reducing greenhouse gas emissions. Intention of Agriculture Experts in order to mitigation climate change Can play an important role in finding appropriate solutions for each region in climate change mitigation and deliver them to their farmers. However Intentions to take mitigation actions are, most likely, influenced by a range of factors. The purpose of this study was to investigate of factors influencing on Agriculture Experts in order to mitigation greenhouse gases. This research in terms of purpose is an applied research and in terms of methodology is survey research. The statistical population of this research consisted of 520 Agriculture Experts in Khuzestan Province. A sample of 320 persons was selected through Random Stratified Sampling method. The survey instrument was a questionnaire which its validity confirmed by experts. The reliability of the questionnaire using Cronbach's alpha coefficient (α= 0/67-0/87) has been approved. Our analysis reveal, direct experience were predictors of risk perception, self-efficacy and environmental attitude. The changes in self-efficacy and environmental attitude were also strong predictors of changes in intention.  The variables of self-efficacy and environmental attitude could predict 61 percent of variations in willingness to mitigating climate change. Therefore experience indirectly affected on the mitigation intention. Lastly we provide recommendations on how these findings can be used to climate change mitigation.
 

This research study aims to investigate the effect of climatic variables, specifically precipitation, temperature, and humidity, on changes in vegetation indices of orange orchards in Hassan Abad, Darab County, using satellite data. Consequently, observational data, including orange tree phenology data and meteorological data from the agricultural weather station, were collected over a period of more than 10 years (2006 to 2016). MODIS images from 2006 to 2016 were referenced based on territorial data and 1:25000 maps from the Iran National Cartographic Center. These images were used to calculate remote sensing vegetation indices, namely the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The results demonstrated that the variables of maximum humidity, minimum temperature, and precipitation have a significant positive effect on the NDVI variable. Additionally, the variables of maximum temperature and minimum humidity have a significant negative effect on both the NDVI and EVI. To determine the significance of each independent variable in predicting the dependent variables, the artificial neural network method was employed. The findings showed that the climatic elements of precipitation, minimum temperature, maximum temperature, minimum humidity, and maximum humidity had the greatest effect on EVI, with values of 0.39, 0.3, 0.13, 0.1, and 0.06 respectively. Moreover, the effect of these variables on the NDVI index is equal to their coefficients, which are 0.2, 0.28, 0.22, 0.11, and 0.17 respectively. Finally, the ARMAX regression method was used to improve the explanatory power of the model. The results indicated that this method enhanced the explanatory power of the model and reduced the forecasting error.