eChapters

Land is merely an area of the earth’s surface comprising all physical, chemical and biological environments that influence the land use (FAO ,1976). Land refers not only to soil but also to climate, landforms, hydrology, geology, natural vegetation including fauna and flora. The land use simply means management of a land for specific uses to meet the human needs in all rural, urban and industrial context, whereas the land use plan refers to a coherent set of decisions about the ways to achieve the desired level preferably on economic background. Thus, the land use planning is the systematic evaluation and assessment of a land and associated attributes for the purpose of identifying the best land use options beneficial to landowners or users without degrading the land resources or environment. It includes not only the landowner or users, but also the policy or decision makers as well as economic experts for financial measures from planning stage or logistic planning to execution.

Land cover

Relates to the type of feature present on the surface of the earth e.g., Fields, trees, concrete high ways.

Land use

Relates to the human activity associated with a specific piece of land. The same land would have a land cover consisting of roofs, pavements, grass and trees. The land use in an area is really an intervention by the people over the existing natural eco-environment for satisfying their needs.

Land cover is distinct from land use despite the two terms often being used interchangeably. Land use is a description of how people utilize the land and socio-economic activity - urban and agricultural land uses are two of the most commonly recognised high-level classes of use. At any one point or place, there may be multiple and alternate land uses, the specification of which may have a political dimension.

Drought, a sporadic yet impactful event, presents a significant challenge to agricultural systems and economies globally. A notable observation in this context is the tendency of policymakers to readily claim success for their governance during years of favorable weather, while conversely attributing agricultural failures in drought years primarily to natural causes. This inclination often masks an underlying lack of consistent focus and investment in proactive drought-proofing measures. This international comparison examines the contrasting approaches to drought management between developed and developing nations, highlighting how this lack of focus, coupled with varying capacities and resources, shapes their respective policy responses. Understanding these disparities is crucial for fostering more effective and equitable drought resilience measures worldwide, moving beyond the simplistic attribution of agricultural successes to policy and failures solely to weather

Issues of conservation have assumed great importance in view of widespread resource degradation and the need to reduce production costs, increase profitability and make agriculture more competitive. The growing concerns for sustainable agriculture have been seen as a positive response to limits of both low-input, traditional agriculture and intensive modern agriculture relying on high levels of inputs for crop production. Sustainable agriculture relies on practices that help to maintain ecological equilibrium and encourage natural regenerative processes such as nitrogen fixation, nutrient cycling, soil regeneration, and protection of natural enemies of pest and diseases as well as the targeted use of inputs. Agricultural systems relying on such approaches are not only able to support high productivity, but also preserve biodiversity and safeguard the environment. Thus, Conservation agriculture has come up as a new paradigm to achieve goal of sustained agricultural production. It is a major step toward transition to sustainable agriculture. 

The ancient partnership between agriculture and weather forecasting, initially rooted in observational knowledge and rudimentary tools, has been fundamental to sustaining human populations for millennia. Traditional agricultural meteorology has diligently documented climate patterns, analyzed weather phenomena, and established empirical relationships between meteorological variables and crop yields. This foundational knowledge remains invaluable. However, the stark realities of the 21st century, dominated by the accelerating and often unpredictable impacts of climate change, necessitate a radical departure from conventional thinking. The comfortable reliance on historical data and linear projections is increasingly inadequate in a world characterized by unprecedented c limate variability, intensifying extreme weather events (as you‘ve extensively researched), and the urgent need for enhanced food security for a growing global population.
• The limitations of traditional agricultural meteorology become increasingly apparent when confronted with the complexities of climate change. For instance, relying solely on historical rainfall patterns for irrigation planning becomes precarious when those patterns are drastically altered by shifting climate regimes. Traditional pest and disease forecasting models, often based on established temperature and humidity thresholds, may fail to accurately predict outbreaks as these thresholds themselves are disrupted by climate anomalies. The need for real-time, localized, and predictive insights into the dynamic interplay between the atmosphere and agricultural systems has never been more critical.
• This is where the transformative power of emerging technologies steps in. The next generation of agricultural meteorologists stands at a pivotal juncture, tasked with leveraging advancements in diverse technological domains to revolutionize the field. Consider the sheer volume of data now available from a constellation of sources – weather satellites providing high-resolution global coverage, dense networks of ground-based sensors capturing hyperlocal environmental conditions, agricultural drones offering detailed field-level monitoring, and IoT devices collecting real-time soil and plant health parameters. This deluge of information dwarfs anything

Introduction

The basic principle of conservation agriculture is to minimize soil disturbance in order to stabilize the soil structure, increase the fertility and balance the eco-system. Applied together, conservation agriculture practices – no tillage, permanent soil cover, use of crop cover and crop rotation, residue management- have complementary positive outcomes; no tillage maintains soil structure and biological activity; a permanent soil cover protects the soil surface from erosion and creates a stable and favourable micro-climate; cover crops and residue management provides organic matter, reduce soil erosion and improves soil fertility. Management of the soil water balance should be considered as a positive for cover crops and improvement of soil water infiltration, reduction in soil water evaporation, and decreasing water extraction from the soil would impact subsequent plant growth.

Weather wisdom refers to the ability to understand, interpret, and apply knowledge of weather patterns, forecasts, and conditions to make informed decisions in various aspects of life. This concept encompasses a comprehensive understanding of meteorological principles, as well as the ability to analyze and apply weather information in a practical and effective manner. At its core, weather wisdom involves a grasp of basic meteorological concepts, such as atmospheric pressure, temperature, humidity, wind, and precipitation. This foundational knowledge serves as a framework for understanding more complex weather phenomena, such as storms, droughts, and heat waves.In addition to a solid understanding of weather fundamentals, weather wisdom also requires the ability to interpret forecasts, warnings, and advisories issued by meteorological agencies. This involves being able to comprehend and analyze weather forecast models, as well as understanding the limitations and uncertainties associated with weather prediction (Fig. 2.1). Furthermore, weather wisdom involves familiarity with local weather patterns, climate, and microclimates. This knowledge is essential for making informed decisions, as local weather conditions can vary significantly from regional or national forecasts. The application of weather knowledge is a critical component of weather wisdom. This involves being able to apply weather information to various aspects

Conservation agriculture (CA) refers to a set of agricultural practices involving three basic principles of proven scientific soundness. These include continuous minimum mechanical soil disturbance; permanent organic soil cover with crop residues or cover crops; and diversified, efficient, economically viable, appropriate spatial and temporal crop sequences providing opportunities for saving on inputs (labor, fuel, machinery etc.), improving resource use efficiency (water, nutrient, energy etc.) and mitigating GHGs emission with climate change adaptation. Recently research efforts have been made to refine the crop management technologies which are more resource efficient, improve production and income, and reduce GHGs emission compared to the conventional practices. Resource conserving technologies (RCTs) are increasingly being adopted by farmers in the rice-wheat belt of the Indo-Gangetic Plains because of advantages of saving on labor, water, fuel and cost along with timeliness in operations particularly early planting of wheat.

Introduction

Spatial variation structure of soil properties is a key input for digital soil mapping and generally determined through geostatistical techniques (Webster and Oliver 2007). In geostatistics, spatial variation is expressed by semivariogram ?(h) , which measures the average dissimilarity between data separated by a vector h. It is generally computed as half of the average squared difference between the components of data pairs:

Introduction

Spatial datasets always exhibit two common features (i) the occurrence of a few very large concentrations (hot-spots) and (ii) the presence of data below the detection limit (black spots). Extreme values can strongly affect the characterization of spatial patterns, and subsequently the prediction. Several approaches exist to handle strongly positively skewed histograms (Saito and Goovaerts 2000). One common approach is to first transform the data (e.g. normal, Box Cox or lognormal), perform the analysis in the transformed space, and back-transform the resulting estimates. Such transform, however, does not solve problems created by the presence of numerous black spot data, since either it yields a spike of similar transformed values or, in the case of the normal-score transform, it requires a necessarily subjective ordering of all equally-valued observations. Moreover, except for the normal score transform (Deutsch and Journel 1998), it does not guarantee the normality of the transformed histogram, which is required to compute confidence intervals for the estimates. Last, the back-transform of estimated moments is not straightforward and can introduce bias if not done properly (Saito and Goovaerts 2000); for example, lognormal kriging estimates cannot simply be exponentiated. Another way to attenuate the impact of extreme values is to use more robust statistics and estimators. The non-parametric approach of indicator kriging (IK) falls within that category (Journel 1983, Goovaerts 2001). The basic idea is to discretize the range of variation of the environmental attribute by a set of thresholds (e.g. deciles of sample histogram, detection limit, regulatory threshold) and to transform each observation into a vector of indicators of non-exceedence of each threshold. Kriging is then applied to the set of indicators and estimated values are assembled to form a conditional cumulative distribution function (ccdf). The mean or median of the probability distribution can be used as an estimate of the concentration of the material in question (Barabas et al. 2001, Cattle et al. 2002, Goovaerts et al. 2005).

Introduction

Agriculture expanded and forest dwindled. That eventually occurred with the expansion of civilization. Clearing of forests, granted in past, continued in modern times. Cutting of forests reached to such an alarming level that thinking of people turned back to create more forests. Sometimes over extraction of forest resources made forests unstable to yield perpetually according to necessity in future for which forests are turned unsupportive for livelihood and people tried for innovative means to enhance forest productivity in future (Nair 1993). Also with time agriculture faced the similar unfortunate condition towards intensive cultivation. So, peoples’ thinking turned back in agriculture how to make it stable and more supportive for livelihood year after year. Culturally human societies are connected both with agriculture and as well as forests from the time immemorial.

Introduction

Rice is grown 43.7 m ha land in India. It is cultivated in 39.51 m ha under kharif and 4.26 m ha under rabi season. As a whole, rice production in India was 104.32 m t (2011-12) with an average productivity of around 2.6 t ha-1. Majority of rice grown in India in the eastern part of the country comprises the states of West Bengal, Orissa, Chattisgarh, Assam, Bihar and Jharkhand. Maximum area under rice in eastern India in West Bengal (5.94 m ha) with a production of 15.04 mt. Productivity was maximum in West Bengal followed by Jharkhand, Assam, Bihar, Orissa and Chattisgarh (DOSE, MOA, GOA, 2011). There is a plenty of scope to increase the productivity of these region with proper policies and technological interventions. Rice area in eastern region (23.4 m ha) would be divided in different ecologies, namely, irrigated (23.2%), rainfed upland (17.9%), rainfed lowland (43.1%) and flood prone areas (25.1%).

Natural disasters are of meteorological or hydrometeorological origin. Disasters such as earthquakes and tsunamis are occasional, but rainfall-related hazards, flood or drought, are very frequent and thus call for proper monitoring of large-scale rainfall variation, and better manage- ment and planning to combat the problem(s). Variation in rainfall is very large spatially and temporally compared to any other atmospheric parameter, such as pressure or temperature. The large temporal variability in the Indian rainfall has produced frequent flood/drought and periods of above/below normal rainfall for several centuries past. Areas of high rainfall and runoff are flooded in the summer monsoon season whereas others face water stress and drought conditions. The influence of global warming on rainfall variations over India is reported in numerous observational (Singh et al., 1991, 1992) and modelling studies (Lal, 1994). 

Climate risk management in the semi-arid tropics (SAT) is one of the major challenges to achieving food security and development in India and large parts of sub-Saharan Africa and also in the case of Australia. Climate-induced production risk associated with the current season-to-season variability of rainfall is a major barrier in making rainfed agriculture sustainable and viable farm business. Since season outcomes are uncertain, even with the best climate information, farmers have limited flexibility in applying management with confidence. In fact in risky environments, farmers most often respond by adapting a risk averse strategy and are reluctant to invest in even risk reducing measures (Leathers and Quiggin 1991). In the SAT agro-ecologies, there are a limited range of enterprise or crop options to consider which may be further restricted by cultural traditions, food preferences or market opportunities.While there are fundamental differences between large scale commercial farms in Australia compared to the predominantly smallholder resource poor farms found in India, when it comes to climate risk management in the SAT, there are many commonalities. The purpose of this paper is therefore to (i) establish a framework for managing climate variability and transforming farming systems to be more resilient and sustainable for future climates; and (ii) provide some case study examples from climate risk management in low rainfall cropping system in Australia and consider how they may be applied in smallholder systems of the SAT.

Introduction

Microorganisms generally present in all the environments are the most abundant and diverse living organisms present on earth. Microorganisms have great impact on the biosphere due to their inbuilt ability to transform the key elements of life into forms usable by other organisms and plants. Because of heterogeneity in nature of soils from variations in physical, chemical and biological properties the various natural habitat are microbially most diverse environments on the earth maintaining the stability between complex microbial communities and their function (Daniel 2005). One gram of soil contains about 10 billion microorganisms of possibly thousands of different species but microbial diversity in general, and particularly in extreme environments, and its role in ecology is poorly understood (Rossello-Mora and Amann 2001).

Deserts present a unique environment with very little precipitation and large variations in day and night temperatures, making it inhospitable environment for living organisms. Still a number of microbial species do survive in those harsh environments. The populations of aerobic bacteria in deserts across the world are reported to vary from < 10 in Atacama desert to 1.6 × 10⁷ cfu g^–1 in soils of Nevada (Skujnis 1984), and India is not exception to that with the bacterial population in the range of 1.0 × 10³ to 8.35 × 10⁶ cfu g^–1 sediment or ml^–1 water in the samples collected from cold deserts of north western Himalayas (Yadav et al. 2015).

Organic matter and its composition 
Decomposition  
Types microorganisms involved 
Enzymes involved in decomposition process?

Abstract

The damages caused by the tsunami surge of 26, December 2004 could not be recuperated. There is no second opinion for the above statement as attested by the consequences of tsunami surge on the coastal landforms, human lives, properties, biological assets, etc., The attempt made to evaluate the damages caused by the tsunami surge along the study area divulges various factors regarding the response of the coastal environment to this weird phenomenon. Kanyakumari district witnessed the tidal inundation of 50 – 450 m, the least along Colachel and the maximum in Kanyakumari and Muttom coasts. Run-up elevations were seen even up to 6 m in Manakudi and Colachel coasts. In major parts, the beaches of the study area had endured lofty erosion of beach sediments. High occurrence of Casualty was reported in the Coasts of Colachel, Kotilpadu, Manakudi and Azhikkal. Similarly, loss of man-made structures were also observed to be high along the Coast of Manakudi, Colachel, Kotilpadu, Azhikkal and Muttom. Costly articles like boats, fishing nets, etc., were also damaged in Kanyakumari, Pallam, Muttom, Kadiapatanam, Kesavanputhenthurai and Colachel. The quality of water has also been found to be affected with high level of pH and TDS in the coasts of Manakudi and Colachel. Such Colossal damages in the area is attributed to its geo-position exactly at the receiving point of the diverted tsunami surge by Sri Lanka. Bathymetry also seems to have played a crucial role in determining the intensity and angle of the seismic wave propagation in the area.

Keywords: Tsunami, India, Damages in Kanyakumari, Evaluation, Map, GIS.

1. Introduction
Agro-industrial waste plays a crucial role in bioremediation, the process of usingbiological organisms to remove or neutralize pollutants from contaminatedenvironments (Garg, 2020). With the rise of industrialization and agriculturalactivities, there’s a corresponding increase in waste production. However,these waste materials, often rich in organic compounds, can be repurposedas valuable resources as mentioned in scientific studies (Sahota and Sharma,2020; Yadav et al., 2022).

Introduction 
Sustainability refers to the ability to maintain a balanced and healthy equilibrium by addressing economic, social, and environmental demands over time. Sustainable development is a broad and evolving concept that aims to meet present needs without jeopardizing the ability of future generations to meet theirs. It seeks to balance short-term progress with long-term viability. The United Nations’ Sustainable Development Goals (SDGs), also known as the Global Goals, are a set of 17 goals and 169 targets that all UN Member States aim to achieve by 2030. SDG 3, which emphasizes ensuring healthy lives and promoting well-being for all at all ages, places a strong focus on health. Within SDG 3, reducing maternal mortality and decreasing neonatal and child mortality are key priorities, represented by Targets 3.1 and 3.2, respectively. The global goal for SDG 3.1 is to lower the maternal mortality ratio (MMR) to fewer than 70 per 100,000 live births by 2030. For individual countries, a supplementary target is set to reduce their national MMR to below 140 per 100,000 live births. A nation’s progress is fundamentally tied to the health and well-being of its mothers, which is a crucial factor in achieving equitable resource distribution, reducing poverty, and promoting sustainable development. Tragically, approximately 536,000 women die each year from maternal health complications, with 99% of these deaths occurring in developing nations. India, contributing about 25% of global maternal deaths, faces a critical challenge, with around 136,000 maternal deaths occurring annually. The country has set an ambitious goal to reduce its MMR to below 100 deaths per 100,000 live births. To address these issues and support sustainability, Reproductive and Child Health (RCH) indicators play an essential role in monitoring maternal and child health (MCH) outcomes. These indicators include measures such as immunization coverage, institutional delivery rates, home delivery rates, and antenatal care coverage. By tracking these metrics policymakers can assess the effectiveness of maternal health services and identify areas that require improvement. Utilizing RCH indicators to inform policies and programs contributes to better health outcomes for mothers and children and helps reduce mortality rates, which aligns with the objectives of SDG 3. Certain regions of India have made notable strides in improving MCH. For example, the northeastern state of Assam saw significant improvements in RCH indicators between 2010 and 2015. During this time, the rate of institutional deliveries increased substantially ensuring more women had access to safe childbirth services. The government has also strengthened maternal health care by implementing various MCH programs, including the RCH initiatives and other flagship schemes.

The State of Andhra Pradesh in the present form came into being on June 2, 2014 as a result of the Andhra Pradesh Reorganization Act. The state has 13 districts out of which nine are along the coast of Bay of Bengal and four are land locked in the southern part. A part of Khammam district, which is now in the Telangana state, was also added to the East and West Godavari districts of the State of Andhra Pradesh. The existing data for these two districts do not include this part and this fact is to be borne in mind. The state has a geographical area of 1.602 lakh km2 with a population of 49.83 million. The state in the current form is more agrarian compared to the undivided state as about 62 per cent of population depends on agriculture as a source of livelihood. Yet, the contribution of agriculture to the state’s gross domestic product is only 17 per cent. Though the state is known as ‘rice bowl of India’, not all is well with agriculture in the state. Some parts, especially the coastal districts, are prone to incidence of cyclones and floods while some other districts, Rayalaseema districts, are chronically drought-prone. 

6.1. Calculation of Energy Requirements for Field Operations
The energy needs for a certain field activity can be determined by adding the amounts of human, bullock, mechanical, and/or electric energy consumed.

Operational Costs for Various Power Sources
Manual power

In the case of human labour, the wages of unskilled employment are charged on an hourly or daily basis (as determined by the location).In the case of research farms, the costs for operating a pair of bullocks are determined on the basis of the cost of a pair of bullocks, the salary of an operator, the cost of bullock feed, and any other expenditures (as imposed in a given location). For the farmer’s fields, however, the real hiring expenses are used as the foundation.