eChapters

Introduction

Nanofertilizers are products of nanotechnology that are created or modified from conventional fertilizers, bulk fertilizer materials, or plant extracts. They use different methods such as chemical, physical, mechanical, or biological processes to produce nanosized particles that can enhance soil fertility, crop productivity, and quality (KhetiGaadi 2022). Nanofertilizers have a size range of 0.1–100 nm, which is similar to the size of plant cell wall (20 nm), making them easier to be absorbed by plants (Tiwari 2022).

Introduction

Nanopesticides are pesticides that use nanomaterials or nanotechnology to improve their effectiveness and safety in agriculture. Nanomaterials are materials that have at least one dimension between 1 and 200 nm. Nanomaterials have different properties than their bulk counterparts, such as higher surface area, reactivity, and mobility. Nanopesticides can be formulated in various ways, such as nanosized active ingredients, molecular active ingredient nanocarrier complexes, or active ingredients encapsulated or coated by nanomaterials (Deka et al., 2021; Xu et al., 2022).

Introduction

Crop models can be broadly classified into two categories: dynamic or process-based crop simulation models (CSMs), and reduced form statistical models. Since simulation models are based on various plant-related processes, they develop or alter in tandem with plant growth (Chetty 2009); as a result, they are inherently nonlinear (Olmstead 2009). To run, calibrate, and validate, they require a significant amount of date input. However, statistical models are helpful in situations where there is a lack of data because they describe the relationships between a subset of variables while keeping the others constant. (Chetty 2009).

Introduction

The term “Remote Sensing” was coined in 1950s by Ms. Evelyn Pruitt, a geographer with the U.S. Office of Naval Research, and the term is now commonly used to describe as the science and art of identifying, observing, and measuring an object on the earth surface, without having any direct contact. This process involves the detection and measurement of radiation of different wavelengths reflected or emitted from distant objects or materials through which they may be identified and categorized by class/type, substance, and spatial distribution (National Aeronautics and Space Administration—NASA). Remote sensing technology has been widely applied in various applications such as water resources, agriculture, geological studies, forestry, land use, resource exploration, etc.

Elements of Remote Sensing

The remote sensing process involves an interaction between the incident radiation and the targets of interest, exemplified by the use of imaging systems in which the following seven elements are involved; however, remote sensing also involves sensing of emitted energy and using non-imaging sensors

Introduction

Watershed management has emerged as an important research area in recent years. Understanding the morphology of the catchment is one of the widely used methods for watershed management. Morphometry is the measurement and mathematical analysis of the configuration of the earth’s surface, shape, and dimension of its landforms (Agarwal 1998; Obi Reddy et al., 2002). The study of watershed is very useful in drainage basin evaluation, silt erosion control, flood frequency analysis, watershed prioritization, natural resources management, and conservation (Waikar and Nilawar, 2014). The significance of morphometric analysis is highlighted in the basins that are either un-gauged or difficult to access. The morphometry is usually analyzed through measurement of linear, aerial, relief, gradient of channel network, and contributing ground slope of the basin (Magesh et al., 2012). The hydrological behavior of a basin can be easily determined through the areal parameters (Mahala, 2020.) The evolutionary history of any basin can be best understood through the implication of different relief morphometric measures of drainage basin (Sharma and Sarma, 2013).

GIS and RS techniques are proven convenient methods for morphometric characterization of sub-watersheds and prioritization of watersheds. As the satellite images provide a synoptic view of a large area, the RS and GIS methods become very useful in the analysis of drainage basin morphometry (Rai et al., 2017). Thus, the morphometric analysis using spatial techniques has emerged to be the fast and efficient method as compared to most of the conventional methods (Rao et al., 2010) such as manual extraction of drainage network and assigning the stream order from a published Survey of India topographic map. The present study aims at using remote sensing and GIS technology to compute various parameters of morphometric characteristics of the Chaliyar River basin.

Introduction

Groundwater resources are under stress globally due to various reasons including increasing water demand, impending climate change, industrialization, land use/land cover changes, etc. Adequate sustainable development strategies have to be adopted for the management of groundwater resources. Irrespective of the developmental status of countries all over the world, groundwater serves as a crucial resource for meeting fundamental needs. However, increasing population, water demand, surface water contamination, and recent patterns of climate unpredictability have caused increasing reliance on groundwater as reported by several researchers (Adimalla and Taloor 2020; Aladejana et al. 2020; Cuthbert et al. 2019; Green 2016; Hamed et al. 2018; Jasrotia et al. 2019; Khalaj et al. 2019; Li 2016; McGill et al. 2019; Morsy et al. 2017; Taloor et al. 2020).

Even in India, water scarcity has been reported in many parts, though the country receives relatively higher rainfall. However, the rainfall pattern has changed over the last few years. The country has been experiencing significant spatiotemporal changes in rainfall patterns, leading to fewer rainy days and more extreme rainfall, which many studies have attributed to global climate change and anthropogenic influences. The rainfall variability has substantial impacts on water availability, agricultural productivity, and welfare of the 1.2 billion people in India because agriculture in India feeds about 17.2% of the global population and more than 56% of the agricultural land is rain-fed. Rapid and heavy patterns of rainfall result in the draining of most of the rainfall as surface runoff particularly on higher slopes, and reaches the surface water bodies, thereby reducing the infiltration and subsequent groundwater recharge. These variations in rainfall have ultimately affected the groundwater behavior and availability in many river basins.

Introduction

Climate change and human activities have emerged as significant factors affecting water resources in diverse regions (Kumar et al. 2022; Haddeland et al. 2014). The escalation in human water consumption for domestic and industrial purposes enhanced by the impacts of climate change has resulted in an overall decline in water availability (Liu et al. 2017; Paul et al. 2021). Rising temperatures and altered precipitation patterns directly influence water availability for ecosystems and agriculture potentially leading to greater evapotranspiration rates, intensified rainfall events, and prolonged droughts (Sharannya et al. 2021; Myers et al. 2023). Furthermore, urbanization, deforestation, and agricultural practices alter landscapes, affecting water resources in these areas.

Water shortages pose threats to ecosystem health, and socio-economic development and disrupt agricultural production (Piao et al. 2010). Thus, ensuring sustainable and efficient water usage necessitates accounting for water usage and quantifying it in a changing environment. To comprehensively assess the significance of water resources in the environment, Falkenmark (1995) introduced the concept of blue water (BW) and green water (GW) water, particularly for managing water resources in water-stressed regions. Green water refers to the portion of precipitation that infiltrates into the unsaturated soil layer and is subsequently utilized by plants through transpiration and soil evaporation. In fact, “blue water” represents surface and groundwater flow in rivers, lakes, and aquifers (Abate et al 2024; Sharannya et al. 2021). While numerous studies have investigated the spatial and temporal variation of BW and GW using the Soil and Water Assessment Tool (SWAT) (Feng et al. 2021; Li et al. 2009; Lyu et al. 2019), fewer publications have focused on assessing these variations and uncertainties at the basin scale under varying climate scenarios (Sharma et al. 2023). Hydrological models such as SWAT, serve as valuable tools for simulating past and future hydrological changes in the watersheds using projected climate data. SWAT specifically can simulate both blue and green flows at the watershed scale.

Introduction

Rainfall is one of the climatic variables that varies very highly spatially and temporally. Understanding this micro-level variation is a crucial indicator for planning future water requirements and preparing for management strategies. Numerous researchers have delved into the distribution, variability, and trends of rainfall across diverse spatial scales, ranging from global to regional and basin levels. Nair et al. (2014) analyzed the rainfall trend and seasonality index for Kerala state in India over the last 100 years. The rainfall trend was found to be decreasing significantly in almost all the regions of the state. Nischitha et al. (2013) provide insights into the spatial and temporal variability of daily monsoon rainfall in the Tunga and Bhadra River basins in Karnataka state. Paradkar et al. (2019) evaluated the pattern of weekly rainfall by using statistical parameters such as mean, standard deviation, skewness, and kurtosis for five stations in Pratapgarh district.

Geographical Information System (GIS) proved to be an effective tool in handling information and analyzing rainfall patterns, providing visual representations, and facilitating the decision-making process (Kadir et al. (2016) and Rao et al. (2017)). Anand et al. (2020) analyzed the long-term spatial and temporal trends of rainfall in the Lower Bhavani basin in Tamil Nadu, India using GIS and statistical methods. Earls et al. (2007) compared different interpolation methods such as Inverse Distance Weighting, Spline, and Kriging (ordinary and universal) to generate a 30 m representation of the rainfall data. Spatial interpolation techniques perform differently and their evaluation should not rely solely on quantitative assessment. Wu & Hung (2016) compared the performance of three spatial interpolation techniques i.e., IDW, kriging, and spline using quantitative assessment, 2D visualization, and 3D visualization. IDW and kriging performed similarly in terms of quantitative assessment, while spline performed worse. IDW and spline performed similarly in 2D visualization, while kriging showed different behavior. Kriging was observed to be an inexact interpolation in 3D visualization, while spline tended to create extreme values along the edges of the study area. Some studies discovered that the accuracy of Kriging in the study area is higher than that of IDW or Thiessen interpolations (Earls et al. 2007; Song et al. 2008; Chen et al. 2010). GIS applications have the capability to integrate spatial data and non-spatial data like rainfall and to visualize, model, and analyze how rainfall distribution varies across space and over time, providing insights into climate change, water resource management, agricultural planning, and disaster management. The present study aims to analyze annual rainfall data pertaining to Kerala state in the GIS environment.

Introduction to GIS

QGIS (Quantum GIS) is an open-source Geographic Information System (GIS) software has been used to showcase various exercises pertaining to viewing, editing, analyzing, and printing of the geospatial data. QGIS helps to allow the users to easily access and manage geographic data stored in folders on local disks or relational databases available on the user’s network. QGIS supports raster, vector, and mesh layers. QGIS also allows using data from external sources with the help of web services such as Web Map Service (WMS) and Web Feature Service. QGIS is a constantly developing software as anyone can add new features or update existing tools. Newer versions of QGIS are frequently released on the official website. It is a cross-platform software that can be installed in Windows, Linux, and MacOS.

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.

Introduction

The foundation of agricultural productivity lies in the realm of weather and climate, a reality unaltered by technological progress. The consequences of climate change on agriculture represent a worldwide apprehension, particularly significant for India. Around 54.6% of the Indian population is engaged in agriculture and allied activities, contributing to 17.4% of the country’s total gross value added. The agriculture sector is battling a multitude of challenges, such as increasing food demand, depletion of agricultural lands and water resources, and the devastating effects of climate change. It is high time that we acknowledge and address these issues to safeguard the future of our food systems and ensure food security for all.

Climate change refers to the long-term variations in weather patterns and temperature over extended periods. Climate change occurs when earth’s climate system experiences variations in weather patterns that can last for decades or millions of years. These changes can arise naturally due to phenomena like volcanic eruptions or changes in solar activity. However, since the 1800s, human activities accelerate the natural pace of climate change, primarily due to the combustion of fossil fuels like coal, oil, and gas. These activities release greenhouse gases like carbon dioxide (CO2 ), methane (CH4 ), and nitrous oxide (N2 O) into the atmosphere, trapping heat and leading to a gradual increase in global temperatures. One of the most remarkable characteristics of climate change is the increase in temperature, which is widely referred to as global warming (Houghton, 2009). According to the report of Intergovernmental Panel for Climate Change, the release of greenhouse gases caused by human actions has contributed to around 1.1°C of warming since the period between1850-1900. The report also predicts that the global temperature will likely increase to 1.5°C or more in the next 20 years (IPCC, 2021).

Introduction

Soil is a silent foundation and a dynamic part of Earth’s complex ecosystem. It sustains life, influences plant growth and is involved in various environmental processes. However, the balance of this vital resource is increasingly threatened by the pervasive effects of climate change, manifested by rising temperatures, changing rainfall patterns and changes in atmospheric conditions. As global temperatures rise and weather patterns change, the relationship between soil and climate poses transformative challenges with far-reaching consequences. This research addresses the complexity of this interaction and examines how climate change affects agriculture, soil health, composition and functionality. Soil health, a holistic concept that includes physical, chemical and biological aspects of soils, is closely linked to climate change. A notable consequence of climate change is the increasing frequency and intensity of extreme weather events such as droughts and floods. These events disrupt the delicate balance of soil ecosystems and lead to erosion, loss of topsoil and reduced fertility. Additionally, increased temperatures contribute to soil moisture depletion, reducing water availability for plants and microorganisms and further compromising soil health. A central issue at the interface between soil science and climate change is the role of soil as a carbon sink. Soils store large amounts of carbon in the form of organic material, thereby influencing the environment.