Now there is no dearth of experts on climate change, nor even on global warming. Our global leaders are busy with international conferences; experts are predicting what is going to happen to agriculture or livelihood through applying AI devices and perditions. Now we have started taking notes through a blend of participatory and non-participatory approaches, during 2023 on what is the real time perception which is driving our farmers to set on their ground activities in terms of climate change, economic uncertainty And agricultural productivity. While the focus is on climate change, the farmers identified uncertainty of market is even more pernicious; if global warming has been the crux of discourses and geo-political concerns to most of the environmentalists, farmers are really concerns, as the data visualization goes deeper, depicts their increasing helplessness over increasing joblessness. Hence, we applied mental Modelling and Fuzzy logics to capture the real interpretation based on ongoing perception and simulation eying on the future consequences.
As we feel the real crisis that crippled into farmers psychic ecosystem is the perceived uncertainty. To them not only climate has turned uncertain and unpredictable; the market response, cost and return of cropping, livelihood and agricultural occupation have isochronously turned chaotic, cryptic, fragile and uncertain. Only from the state of West Bengal a close to five millions of migrant workers , drifted off their agricultural occupation, have been found to wander across during Covid pandemic just to survive.
Climate change is a complex and dynamic phenomenon that has significant impacts on agriculture, especially in terms of uncertainty and variability. Agricultural dynamics refer to the various factors that affect agricultural production, including climate, soil, water availability, pests, and diseases, as well as market forces and government policies. Climate change has significant impacts on agricultural dynamics. Variations in temperature, patterns of rainfall, and occurrences of extreme weather can influence crop yields and livestock production in both beneficial and detrimental ways (UCAR, 2021). The effect of climate change on farming can differ based on the geographical location and the specific agricultural practices employed. For example, in some areas, increased rainfall may lead to increased crop yields, while in other areas; droughts may lead to crop failures and loss of income for farmers. Additionally, rising temperatures can cause reduced crop yields and decreased quality of some crops. Uncertainty is a term used to describe a situation where there is a lack of information, knowledge, or predictability about a particular outcome or event. In agricultural dynamics, uncertainty can refer to a range of factors that can affect the production, distribution, and consumption of agricultural products. One of the most significant sources of uncertainty in agriculture is the weather. The productivity of crops can be significantly influenced by weather conditions, including rainfall and temperature and farmers need to effectively adjust their agricultural practices to adapt to these changing weather patterns The social, ecological and economic interpretation of climate change by farmers involves understanding the interactions between the natural environment, social systems, and economic factors (Spash, Clive L, 2011). Farmers need to consider these interactions when making decisions about their farming practices. For example, changes in climate can affect the availability of water, soil fertility, and crop yields, which can in turn affect social and economic factors such as food security and income. Farmers need to take into account the wider consequences of their agricultural practices on society and the environment which includes considering the impacts on biodiversity, ecosystems, and human well-being (Warsaw et al., 2021).
Climate change is a complex and dynamic phenomenon that has significant impacts on agriculture, especially in terms of uncertainty and variability. Agricultural dynamics refer to the various factors that affect agricultural production, including climate, soil, water availability, pests, and diseases, as well as market forces and government policies. Climate change has significant impacts on agricultural dynamics. Variations in temperature, patterns of rainfall, and occurrences of extreme weather can influence crop yields and livestock production in both beneficial and detrimental ways (UCAR, 2021). The effects of climate change on farming can differ based on the geographical location and the specific agricultural practices employed (Wikipedia Contributors, 2019). For example, in some areas, increased rainfall may lead to increased crop yields, while in other areas, droughts may lead to crop failures and loss of income for farmers. Additionally, rising temperatures can cause reduced crop yields and decreased quality of some crops. Uncertainty is a term used to describe a situation where there is a lack of information, knowledge, or predictability about a particular outcome or event. In agricultural dynamics, uncertainty can refer to a range of factors that can affect the production, distribution, and consumption of agricultural products. One of the most significant sources of uncertainty in agriculture is the weather. The productivity of crops can be significantly influenced by weather conditions, including rainfall and temperature and farmers need to effectively adjust their agricultural practices to adapt to these changing weather patterns (Guntukulaand Goyari, 2020). For example, droughts or floods can reduce crop yields, while frost or hail can damage crops(Lamaoui et al., 2018).
In the context of a thesis, theoretical orientation refers to the conceptual or theoretical framework or perspective that informs and shapes the overall approach and analysis of the research (Natalie, 2022). It involves selecting and adopting a specific set of theories, concepts, or models that provide a foundation for understanding and interpreting the research topic. The theoretical orientation helps to guide the methodology, and provide a coherent framework for organizing and analysing the data. Theoretical orientation in a thesis serves several purposes. It helps to establish a theoretical basis for the study, providing a context for the research and connecting it to existing knowledge and theories in the field. It also helps to guide the selection of appropriate research methods and data analysis techniques. The theoretical orientation shapes the overall structure and content of the thesis, ensuring that the study is grounded in relevant theories and concepts and contributes to the existing body of knowledge. By adopting a specific theoretical orientation, the researcher can provide a clear and systematic framework for understanding and interpreting the findings of the study. This orientation also helps to establish credibility and validity for the research, as it demonstrates the researcher’s awareness of and engagement with the existing theoretical perspectives in the field. This chapter addresses crucial terminology and concepts essential for effectively communicating the ideas of the current study. It lays the groundwork for empirical investigation and aids in the selection of predictor variables. Additionally, this section involves a scholarly and theoretical exercise that influences the operational definition and content of the research. It combines conceptual inputs, axiomatic inputs, logical discussions, and the pursuit of knowledge to construct a theory that aligns with the research objectives and designs.
The Review of literature is a systematic, explicit and reproducible method of identifying, evaluating and synthesizing the existing body of a completed and a recorded work produced by researchers, scholars and practitioners. The Review of literature is also a resource paper which includes the current knowledge including substantive findings as well as the theoretical and the methodological contributions to a particular topic. These are available mostly in academic and scholastic journals. Review of literature provide the basics for research in nearly every academic f ield. This chapter is an extensive survey of all past studies relevant to the field of investigation which gives knowledge of what others have found out in the related field of study. In any scientific investigation or research, a comprehensive presentation of the review of literature is a statutory requirement. The aim of review of literature is to highlight what has been done so far in the field of interest and how our findings are related to earlier research. Its main functions are to develop a better understanding of the problem to be investigated, delineate a new area of study and avoid unnecessary duplication, decide on the tools and techniques to be adopted including developing some new ones and also to relate the present study with the previous ones by finding out the areas of agreement and disagreement.
The research setting provides specific details about the location where a study is conducted. This study area typically encompasses a particular geographical region, such as a state, district, block, or gram panchayat, chosen according to the research requirements. The researcher must have a thorough understanding of the locality, including its communication methods, in order to effectively access all parts of the region for data collection. In social science research, a clear comprehension of the characteristics, attitudes, and behaviours of the individuals within the study area is essential. Without this understanding, accurately conceptualizing, perceiving, and interpreting data becomes challenging. To obtain a comprehensive understanding of the implications and behavioural intricacies of the local people representing a broader community in the referenced study area, researchers must have a solid grasp of these factors. Therefore, this chapter critically analyses the socio-demographic background of the residents in a rural setting. The term “research setting” denotes the context in which research inputs and elements are absorbed, interacted with, and mutually contribute to the system’s overall performance. The research setting is crucial because it shapes and influences the interactions of various factors and components. Therefore, conducting a study on how farmers perceive persuasive issues and other topics requires a local context that takes into account the unique natural environment, demographics, crop ecology, institutional framework, and socio-cultural factors. For example, when writing a paper about the social behaviour of chimpanzees, it is important to include details about the research setting. This may include information such as where the chimpanzees were studied (in the wild or in captivity), the number of chimpanzees that were observed, whether they were part of the same social group, the geographic location, the time of the study, the season/weather conditions, the availability of resources such as food, water, and shelter, and any environmental factors that were present.
The research methodology constitutes a comprehensive and well-structured plan of action for conducting the investigation and serves as a blueprint for the research process. Its purpose is to gain an understanding of the underlying concepts, methods, and techniques employed to define the study, gather relevant information, analyse data, and interpret the findings, thereby revealing concrete evidence and formulating theories. In this chapter, an in-depth exploration of the research methodology is undertaken to comprehend the concepts, methods, and techniques employed for designing the study, gathering data, conducting data analysis, and interpreting the results. To facilitate clarity and ease of comprehension, the chapter has been divided into the following sub-headings: 1. Research Design 2. Locale of research 3. Pilot study 4. Sampling Design 5. Variables and their Empirical measurement 6. Methods of Data collection 7. Preparation of Interview Schedule 8. Item analysis and Expert’s rating for devising scoring technique for variables 9. Pre-testing of Interview Schedule 10. Data Processing and Analysis for FCM 11. Statistical Tools used for Data Analysis 12. Software used for Data Analysis
This chapter focuses on presenting empirical findings from data analysis to provide sufficient interpretation so to draw reliable conclusions. To enhance and complement the textual contents of the study, tables and models are frequently utilized. Additionally, the discussion serves to contextualize the study’s findings and establish connections with the research work. Result: The Table 13 depicts the distributive characteristics of the variables for the 90 respondents under the study. Revelation: The pattern of distribution of the variable, Age (x1 ) depicts that the maximum age of the respondents is 70 and the minimum is 26. The mean value of age of the respondents is 42.98 with standard deviation of 10.77. The distribution of the variable is strongly consistent with a CV value of 25.06 per cent. The pattern of distribution of the variable, Education (x2 ) depicts that the maximum education of the respondents is 5 and the minimum is 2. The mean value of education of the respondents is 4.09 with standard deviation of 0.82. The Coefficient of Variation is 19.96 per cent which indicates the distribution of variable is highly consistent.
7.1 Summary Climate change is a complex global phenomenon that poses significant challenges for agriculture including uncertainty about future weather patterns, soil conditions, and other environmental factors. Farmers are facing the difficult task of interpreting and responding to these changes in order to maintain their livelihoods and ensure food security for growing populations. In this context, mental modelling has emerged as a useful approach for understanding how farmers interpret and respond to climate change. Mental models are cognitive representations of how individuals understand and make decisions about complex systems, and can be used to identify key drivers of behaviour and decision-making. The present study has explored the social, ecological, and economic factors that influence farmers’ mental models on perception of climate change, level of uncertainty and trend of agricultural change in the selected study area. It argues that farmers’ mental models are shaped by a range of factors, including their own experiences and observations, social networks and interactions, access to information and resources, and broader economic and political contexts. The present study has also highlighted the role of uncertainty in shaping farmers’ mental models and decision-making. Uncertainty about the future impacts of climate change can create significant challenges for farmers, as they must make decisions based on incomplete information and uncertain predictions.
Despite the meticulous methodology and extensive analysis employed throughout this research project, it is crucial to acknowledge and address the limitations that arose during the study. By recognizing these limitations, we not only ensure a transparent evaluation of the research process but also shed light on potential areas for future investigations to refine and expand upon the findings. In this section of the chapter, the limitations encountered in this thesis are highlighted as follows: 1. Due to the complexity of the concept, obtaining quantitative responses proved to be exceedingly challenging. 2. The development of improved scales for scoring could have enhanced the research if implemented. 3. The inclusion of a greater number of relevant variables could have provided a more comprehensive and in-depth understanding of the research topic. 4. Deploying mathematical optimization and simulations could have effectively integrated and co-integrated the seemingly disparate aspects of climate change perception, uncertainty perception, and perception of agricultural dynamics. 5. Presently, farmers are hesitant to engage in conversations and provide data to individuals outside their close social circles.
As the present study draws to a close, it opens up a myriad of possibilities for future research and exploration in the field of the present study. The insights gained and the findings presented in this thesis may provide a foundation for building upon and extending the existing body of knowledge. By identifying key areas that require additional attention and offering suggestions for future studies, this section of the chapter aims to inspire and guide researchers in their pursuit of enhancing our understanding and driving progress in this dynamic and evolving field. The present study leaves behind the following domains which may be researched out in future: 1) The present research can be replicated with varied geo-spatial situations viz., in different locations and settings, with different respondents, with spatio-temporal expansion and methodological innovation. 2) PRA tools and materials can be deployed to collect more specific data to compensate for the inadequate information to go for the classical statistical approaches. 3) Multi-criteria decision analysis (MCDA) techniques can be integrated with mental modelling and fuzzy logic cognitive mapping so to enhance the decision-making process. 4) To realize the full potential of these frameworks, collaboration and knowledge sharing among researchers, practitioners, policymakers, and farmers are essential. Continued research and development efforts are needed to refine and validate the models, enhance user-friendliness, and adapt them to diverse agricultural systems and contexts.
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