عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Humanly induced climate change has been taking place since few decades ago, and developing countries are generally more vulnerable to the consequences of these changes, as they tend to rely on more climate sensitive sectors, such as subsistence agriculture, where they lack the resources to buffer themselves against the changes emerging by global warming. Agriculture is a risky job inherently, and climate change with unexpected future worsens the situation. Many of farmersâ decisions and its results are influenced by climate parameters such as precipitation, temperature, and humidity, therefore they should adapt themselves with climate changes. Adjustment with current climate variability and potential climate change is a prerequisite for sustainable development and all farmers consequently need to learn to cope with the predicted changes. In this regard farmers need help and support to make good decisions to adapt with changes. With assumptions about how people and societies will respond to climate change, policy makers could more accurately compare the costs and benefits of particular policies, also, by identifying why a particular group of people do what they do, it will make opportunities to intervene in the process, helping them make better decisions.
Materials & Methods
The study place is Marvdasht in Fars Province that is one of the leading regions in the agriculture sector and is confronted with serious drought as well as reducing the precipitation in recent years. This qualitative study used grounded theory principles to collect and analyze data and provide a paradigmatic model. Research sample includes two villages, which were selected purposefully: Esmaeilabad with highest climate changes and Chamesohrabkhani with lowest climate changes in Marvdasht. In order to gather the required data field observations and in depth focus group interviews were used. Nine farmers in Esmaeilabad as well as eight farmers in Chamsohrabkhani attended in interview sessions. Questions were grouped into 4 categories: 1) Farmersâ perceptions about climate changes, the situation of agriculture and their properties 2) farmersâ behavior toward adaptation, their future decisions and existed problems and obstacles 3) farmersâ resources for climate information and consultation to farm management decisions and 4) farmersâ perception about future of agriculture sector in this region.
Discussion of Results & Conclusions
The analytical process in grounded theory involves coding strategies: Open coding is the process of breaking down interviews, observations and other forms of appropriate data into distinct units of meaning which are labeled to generate concepts. The focus of axial coding is to create a model that details the specific conditions of phenomenonâs occurrence. Selective coding is the process of selecting the central or core category, systematically relating it to other categories, validating those relationships, and complete categories that need further refinement and development. Some major research results in this study according to the above coding system include: Farmers' perception about reduction in precipitation and warmer environment, decreasing in quantity and quality of crops, increasing crop pests and diseases, reduction of property and capital, also, improving farm production management and developing technology. These are the main adaptive activities used by farmers. Also, their main problems include: lack of credit and fund for investment, bureaucratic problems with public organizations. Based on research results, some concepts were extracted and categorized in coding process and a paradigmatic model was designed. Finally, some applicable suggestions to improve farmersâ adjustment with climate change are presented. In this regard, one useful activity could be change in crop system and using crops with low water usage, also replacing crops like rice (with high water consumption) with low water crops, and extending modern technologies such as modern irrigation methods.