Introduction: Climate change is one of the most important ecological problems of the 21^st century, which has significant impacts on water resources, soil, floods and soil erosion. Thus, adopting appropriate measures to reduce the concerned damages requires assessing the impact of climate change and the effectiveness of adaptation options. The climate change affects all economic sectors to some extent and has widespread consequences on natural ecosystems and is a very important factor in intensifying the occurrence of dust, biodiversity loss, pest outbreaks in ecosystems, threatening the biological functions of wetlands and changing the natural geographical distribution of animals. Therefore, the issue of climate change and its consequences as a global problem requires further investigation. Smart agriculture is an emerging concept that refers to the management of agricultural land with Artificial Intelligence (AI), Internet of Things (IOT), and robotics. The goal of using smart agriculture is to improve the quantity and quality of products and reduce the human labor required during the production process. The smart agriculture provides farmers with various technologies such as sensors, software, robots, connections, positioning, and data analysis. In this way, the farmers can control their land anywhere and make the best decisions with the useful information obtained from the tools. The goal of smart agriculture is to provide globally applicable principles for agricultural management for food security under the influence of climate change. One of the main features of smart agriculture is to meet three objectives: increasing food security through increased productivity and income, resilience and adaptation to climate change, and reducing greenhouse gas emissions. According to the mentioned materials, this research aimed to investigate the factors influencing the adoption of smart agriculture to respond to (in the face of) climate variability among the farmers of Fars province of Iran.
Materials and Methods: This study ws an applied research in terms of purpose and a descriptive-survey research in terms of methodology. The statistical population of the study included 287,863 farmers in Fars province. Morgan table was used to determine the sample size; and based on this table, the sample size was determined 384 people. Also, a simple random sampling method was used to select the sample size. The main tool for collecting research data was a questionnaire. In order to determine the construct validity, Average Variance Extracted (AVE) index was calculated; and to calculate the reliability of the questionnaire, Composite Reliability (CR) test and Cronbach’s alpha coefficient were used. Frequency distribution tables and central tendency indices including frequency distribution, standard and mean deviations were used to describe the research variables as well as Partial Least Squares (PLS) approach and coefficient of determination, CVCom and CVRed statistics were used to fit the model. SPSS22 and SMART PLS2 software were used to analyze the research data.
Results and Discussion: The results of the structural equation test for the relationship between the studied variables and adoption of smart agriculture for responding to (in the face of) climate variability showed that the significance of individual factors was 10.057, indicating a greater value than the critical limit of 1.96, and the factor loading value in the standard case was estimated 0.962, indicating that the relationship between the two variables was positive and in the direct direction, because the coefficient obtained was positive. For agricultural factors, the significance was 8.846, and the factor loading value in the standard case was calculated 0.883, indicating that the relationship between the two variables was positive and in the direct direction as well. For the social factors, it was equal to 6.693, and the factor loading value in the standard case was equal to 0.680, indicating that the relationship between the two variables was positive and in the direct direction too. Finally, for the economic factors, the significance was 4.916 and the factor loading value in the standard case was obtained as 0.425, indicating that the relationship between the economic factors and the adoption of smart agriculture tin the face of the climate variability was positive and in a direct direction as well, because the obtained coefficient was positive. The study findings showed that there were positive and significant relations of the social, agricultural and individual factors with the adoption of smart agriculture in the face of the climate variability. 
Conclusion and Suggestions: Based on the study results, indicating that there were some positive and significant relations between the studied economic, social, agricultural and individual factors and the adoption of smart agriculture in the face of the climate change (variability), it is suggested that the government take necessary measures to provide the economic and technical infrastructure needed to implement the smart agriculture; also, special and low-interest facilities be considered for farmers who are more inclined to adopt it. In addition, it is suggested that in order to promote the smart agriculture, in addition to focusing on educational and extension programs that can lead to changes in farmers’ attitudes and behaviors, a special attention should be paid to issues such as the cultural and social status of target groups as well as the individual characteristics of farmers, especially young and progressive farmers with higher levels of education.