eChapters

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).

Mamata Banerjee has raised the important issue of land use and acquisition for public, political, professional and media debate. With increasing population pressure on land, this issue deserves careful consideration and rational discussion. The Singur dilemma in a wider sense relates to both land use policy and norms of compensation for land acquired by government for non-farm purposes. Land conflicts are also spreading in other parts of our country, as far example where land is needed for establishing Special Economic Zones (SEZs). The Maharashtra Government has wisely adopted the policy of holding a referendum to seek the views of farmers and others who will be dispossessed of their land for meeting the needs of SEZs. Pro-active consultations will help to avoid difficulties later. Water conflicts are also affecting important irrigation projects. Land conflicts are likely to do the same and may affect the balanced growth of the primary, secondary and tertiary sectors of the economy. How can we deal with this situation in a manner which leads to a win-win outcome for all the stakeholders ?

Introduction

Site-specific nutrient management (SSNM) is the dynamic, field-specific management of nutrients in a particular cropping season to optimize the supply and demand of nutrients according to their differences in cycling throughsoil–plantsystems(Sarkar etal.,2017).Thisnewmethodofnutrient recommendationsisprimarilybasedontheinitialnutrientstatus(INS)from thesoil andthecrop’sneedfornutrients toachieve thedesiredyield.

Introduction

The natural resources of arid regions particularly soil and water are limited and is often in a delicate environmental balance. Desert encroachment due to lack of conservation planning, and the dangers of destroying or depleting beyond recovery these productive resources, are evident at present time and may be disastrous if development is based on short term expediency rather than long term stability.

The arid zone of India is spread over 38.7 million ha area, out of which 31.7 m ha in under hot arid region and 7 m ha under cold region. The hot arid region occupies major part of north-western India (28.57 m ha) and occurs in small pockets (3.13 m ha) in south India. The north-western arid region occurs between 22o30' and 32o05' N latitudes and from 68o05' to 75o45' E longitudes, covering western part of Rajasthan (19.6 m ha, 69%), north-western Gujarat (6.22 m ha, 21%) and 2.75 m ha (10%) in south-western part of Haryana and Punjab (Faroda et al. 1999). Rainfall distribution is highly uneven over space and time (CV>60%). The region receives low rainfall (<100 mm to 500 mm), has high evapotranspiration and high temperature regime (Rao and Singh 1998). Groundwater is deep and often brackish. The western-central area is devoid of drainage system and surface water resources are meager (Fig. 20.1). Due to low and erratic rainfall, replenishment of water resources is also very poor. The entire Rajasthan state is being categorized as the driest state and water scarce (having per capita water availability below 1000 m3 year-1) since 1991 in the country. Increasing pollution by industrial units, big and small, unregulated mining and even over-extraction of water from deep wells also add to the water quality problem in number of districts. Rapid urbanization and industrialization make such existing differences even more

1. Introduction
Soil is an extremely complex ecosystem and performs many functions, i. e, biomass production; food contingency, fuel and fibre; buffering and transforming actions; breakdown of organic matter; nutrients recycling; carbon sequestration; providing a biological habitat; source of raw materials; and regulation of water quality and supply (Weil and Brady, 2017; Telo da Gama, 2023). However, because of natural as well as anthropogenic reasons, there is a widespread degradation of soils. Soil degradation means deterioration in overall soil functioning which leads to slowdown of distribution of ecosystem services because of the impairment of soil health through anthropogenic perturbations. Therefore, farmers and researchers are very much concerned about soil health and crop productivity. Soil health status and its quality are often used interchangeably and have been defined by research community differently but recently the word soil health is acquiring popularity due to its flexibility in allowing diverse stakeholders, including policymakers to use the term in their own way. Recently, soil health is a topic of much discussion and research (Bünemann et al., 2018). Soil health is described as “the continued capacity of soil to function as a vital living ecosystem that sustains plants, animals, and humans” (USDA-NRCS, 2019). A broader, ecologically based approach is “the capacity of soil to function as a vital living system, within ecosystem and land-use boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and promote plant and animal health” (Doran et al., 2000). Soil health refers to the actual capacity of a soil to function, contributing to ecosystem services. Many have defined it as “fitness for use” while the rest regard as “capacity of the soil to function”. Lehmann et al. (2020) stated that the concept of soil health “connects agricultural and soil science to policy, stakeholder needs and sustainable supply-chain management” and compared the soil health concept to soil fertility, soil quality and soil security. Furthermore, they came to a conclusion that soil health is narrower concept than soil security, but more comprehensive than soil quality and even broader than soil fertility, in relation to the current sustainable development goals (SDGs) as proposed by United Nations. From an overall perspective. Soil health comprises of refinement and unification of the chemical, physical, physico-chemical, biophysical, biochemical indicators of soil that supports productivity and environmental quality (Figure 1). Soil can be classified as healthy or unhealthy based on its ability to sustain plant and animal productivity while maintaining air and water quality.

1. Introduction
Sustainable crop production describes farming practices that do not negatively impact the environment, biodiversity, natural agroecosystem, or the quality of the crops produced. Growing crops sustainably improves the system’s capacity to sustain long-term, steady, and uniform levels of food production and quality without raising the need for or increasing the demand for agricultural inputs necessary for effective management. Sustainable crop production deals with keeping the soil healthy with the integrated approach of maintaining organic matter, protecting biodiversity while reducing use of pesticides, ensuring food safety and food quality. It is an approach to grow food to feed our population without adversely affecting the natural ecosystem, soil and biodiversity, natural resources, environment etc. Since the soil is one of the key natural resources for food production, establishing and preserving soil health is crucial for agricultural sustainability and ecosystem function. The distinction between soil quality and health is quite narrow, and the phrases are sometimes used interchangeably. The “fitness for use,” “capacity of a soil to function,” or “the ability of the soil to serve as a natural medium for the growth of plants that sustain human and animal life” are some basic definitions of soil quality (Karlen et al., 1992; Pierce & Larson, 1993; Doran & Parkin, 1994; Acton and Gregorich, 1995; Karlen et al., 1997). Assessment of soil health by analyzing different associated attributes is necessary to monitor its status. However, soil health or quality evaluation focuses on the physical, chemical, and biological parameters which are highly interlinked to one another. All the parameters have their significant effect in governing overall soil health. This chapter will discuss the chemical parameters only which are very much important for the sustainable crop production and have very crucial role in determining the soil health. It will cover important chemical parameters, essential plant and soil nutrients, heavy metals, agrochemicals etc. which having significant effect on soil health determination

Introduction

In India, hot arid ecosystem covering an area of 31.7 million ha lies at north western part of the country and 62% of this area falls in western Rajasthan covering 12 districts i.e. Jodhpur, Jaisalmer, Jhunjhunu, Sikar, Hanumangarh, Sri Ganganagar, Bikaner, Barmer, Pali and Sirohi. The major landforms in arid western Rajasthan are dune and inter-dune plains having sandy to sandy-loam textured soils. Temperature remains high for most part of the year reaching up to 42-45°C during prolonged summer months (April to July), whereas annual rainfall is very low and highly sporadic (100-400 mm) mainly occurs during August and September. Annual potential evapotranspiration ranges from 1500-2000 mm. Loss of fertile top soil from denuded soil surface through wind erosion is frequent during summer months. The region is subjected to frequent occurrence of droughts, once in every three years, which further intensifies the process of desertification (Samra et al. 2006). Shorter growing period, sandy soils, very low available water capacity, inadequate nitrogen and phosphorus availability are the major constraints. Soils are prone to severe wind erosion, terrain deformation and nutrient depletion. The region is mostly mono cropped and double cropping is being practiced extensively in canal and tubes well command area. Extensive fallowing in the past even in very good rainfall years because of uneven undulating topography and severe windstorm are the cause of concern. Ground water is deep and is usually brackish, declining at alarming rate due to excessive withdrawal for irrigation and other purposes. The region, however, sustains on perennial vegetation of Prosopis cineraria, Tecomella undulata, arid shrubs and grasses, which are rich resources of fodder for animal because cropping is affected by vagaries of monsoon. During last two to three decades, a 2 to 3-fold increase in irrigated areas with cultivation of both kharif and rabi crops has been recorded and most of these increments are resulted through conversions from rainfed areas, grazing lands and sand dunes into irrigated areas.

1. Introduction
As climate change advances, risk of managing soil and atmosphere with regard to emission and pollution is going out of hand. Many researches and reports say that currently the global warming is 1.1°C more than the pre-industrial levels, which is not far behind the target of 1.5°C which was agreed by nations under the Paris agreement in 2015 (Janki M, 2023). Achieving that will likely involve removing carbon dioxide from the atmosphere, according to the Intergovernmental Panel on Climate Change. But strategies like capturing and storing the carbon emissions from biofuel-burning power plants, or planting new forests to absorb carbon, can create their own problems. Recently UN security general said we are shifting to the “Era of global boiling” from global warming. Globally countries are witnessing unbearable heat waves, forest fires, irregular rainfall, flashfloods, coral bleaching and unpredictable weather patterns which often resulting in devastating effects on the coastal and hilly regions of the country. Around nine tipping points have been identified so far globally. Climate tipping points are threshold that when crossed leads to irreversible changes in the climate system and will have a severe impact on the living organisms on the earth. All these variations in climate are driving by increasing greenhouse gas (GHG) emissions and global warming. We cannot change the state of atmosphere to as existed thousands or millions of years ago. Only option left with us is to reduce emissions and prevention of further global warming and climate change. As we are already existing in some level of changed climate, responding to climate change involves a two-pronged approach, reducing emissions of and stabilizing the levels of heat-trapping greenhouse gases in the atmosphere through mitigation and secondly, adapting to the climate change. Because climate change is irreversible process.

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).

Introduction

Over the past few decades, soil resources have witnessed many changes driven by demographic pressure and climate change. It is necessary to have up to date soil information to monitor the state of soils health with time. Soil resource inventory provides information about the potentialities and limitation of soils for its proper utilization. Soil survey offers an accurate and scientific inventory of different soils, their properties and extent. An in-depth knowledge of the type of soils and their spatial distribution is a prerequisite for developing rational land use planning. Remote sensing is the science of obtaining information about the earth’s surface without actually being in contact with it. On the other hand, proximal sensing refers to remotely sensed measurements that are taken at the field or laboratory level. Both these processes involve an interaction between incident radiation and the targets of interest. Fundamentally, remote sensing data products are a representation of the functional response of objects to energy sources. These are typically spectra, which are acquired as a discrete series of reflectance measurements taken at different wavelength intervals, or bands. In the shorter wavelengths of the electromagnetic spectrum (visible part), features can be detected by virtue of reflected solar energy, while in the longer wavelengths (microwave, thermal parts), sensing of emitted energy works. Conventional soil sampling, and subsequent laboratory analysis are generally time consuming and costly. In this context, remote sensing plays a crucial role for studying soil resources. The term remote sensing is generally used for airborne and space borne acquisitions, whereas proximal sensing refers to ground-based laboratory and field measurements. Advances in the quantitative disciplines like remote sensing and proximal sensing have laid the foundations for a spatial exploration of soil-system dynamics within a landscape context (Pennock and Veldkamp 2006).

Introduction

The life support systems of a country and socioeconomic development of its people largely depend on soils. The soils are the most valuable natural resources for the society. It caters the basic needs of mankind by producing food, fibre and timber. It is therefore essential to know the distribution and extent of different soils and their qualities, potentialities and constraints for sustainable utilization of this valuable natural resource (Shyampura and Sehgal 1995). Information on distribution, potential and constraints of major forest soils are essential to design most appropriate soil management systems in order to increase productivity of forests as forests also offer excellent potential for poverty reduction and rural economic growth in India. However, unlike agriculture lands forest soils usually draw less attention and are ignored in spite of the fact that they nourish the lungs of the Earth. Soil quality is the most important factor in forest management decisions. Soils will determine productivity of a particular forest and management strategy. Knowing about forest soils can serve as a basis for forest management decisions, including land acquisition, species selection for planting, site preparation requirements, watershed development, fertilization prescriptions, stand density/ composition, and harvest timing, as well as decisions affecting land ownership and use.

Introduction

In order to cater to the nutritional needs of a rapidly growing population, it is imperative to increase food grain production on the same land while maintaining the soil’s productive capacity. This necessitates comprehensive research to establish a scientific foundation for enhancing and sustaining food production and soil productivity with minimal environmental impact (Rathore et al., 2010). Balanced nutrition extends beyond the application of nitrogen, phosphorous, and potassium in specific quantities through fertilizers. It entails ensuring that the soil contains the right proportions of available nutrients to meet the crop’s requirements for optimal yield (Rathore et al., 2010).

The arid region in India is spread over in 38.7 million hectare land. Out of the total 31.7 m hectare lies in hot arid region and remaining 7 m hectare comes under cold arid region. The hot arid region occupies major part of northwestern India (28.7 m ha) and a small pocket (3.13 mha) in southern India. The north western arid region occurs between 22°30’ and 32°05’ N latitude and 68°05’ to 75°45’ E, covering western part of Rajasthan, north western Gujarat and south western parts of Haryana and Punjab. About 62% area of arid region falls in western Rajasthan and 20% in Gujarat. Haryana and Punjab together constitute 7% area of arid region. The state wise distribution of arid region is shown in Table 3.1. Further our discussion is based on the majority area of arid region, belonging to Rajasthan, Gujarat, Punjab and Haryana.

Abstract

The groundwater behaviour in hard rock aquifer systems has always remained enigmatic owing to the heterogeneity of the aquifer systems. This is the reason why there are a lot of failures in groundwater targeting and management in hard rock aquifer systems even after the employment of many modern technologies. For precise groundwater targeting in hard rock aquifer systems various parameters like lithology, the type and the degree of structural deformation, the degree of weathering, geomorphology, etc., will have to be analysed which involves spatial analysis of voluminous data. In the sphere of spatial modelling, GIS plays an important role. Hence, a newer study has been carried out in parts of Madurai, Dindigul and Theni districts, Tamil Nadu, to identify the groundwater provinces and their functions using GIS. In the said study, GIS images were generated on the groundwater targets through GIS integration of transmissivity, permeability, storage co-efficient and water level. Such GIS image showing groundwater maxima was overlaid with GIS image of fracture maxima, subsurface geology maxima and the geomorphology maxima and thus by various GIS based spatial manipulations, aquifers controlled by structure, geomorphology, subsurface geology, and combinations of the above various variables were brought out. This has provided potential method for precise targeting of groundwater.

Key words : Remote Sensing, Ecosystem, Environmental Pollution, Biodiversity Conservation, Coastal Environmental Degradation, Wetland.

Introduction

National prosperity and human resources development are primarily shaped by the availability of natural resources and their use. None the less, the benefits are quite often negated by the resultant environmental degradation concomitant with their utilisation for economic values. The problems of environmental degradation have assumed critical dimensions in many parts of India due to resources exploitation; industrialisation and urbanisation, unchecked population growth and a never ending quest for energy to meet the increasing demands. Moreover, improper utilisation of resources has resulted in such environmental problems as soil erosion, waterlogging, floods, droughts and pollution of various types besides socio-economic disparity among people.

After the 1972 Stockholm conference on Human Environment and Development, there has been a phenomenal increase in the worldwide interest in the environmental issues associated with development. Consequently, the focus of development has shifted to environment friendly and sustainable developmental planning in recent years. The Rio Summit (June 3-14, 1992) further emphasised the fact that earth is an integral and interdependent system, which require sustainable developmental planning. During deliberations of the Rio Summit (Earth Summit), it was emphasised that satellite imagery will be quite helpful in monitoring earth’s environment.

India has a long tradition of environmental protection and has enacted laws during the last three decades to give further impetus to the conservation of natural resources and protection of environment. Some of the important laws are:

Introduction

All spatial data stored in a GIS are associated with a map projection, either implicitly (so-called “unprojected” data stored with raw longitude, latitude coordinates) or explicitly (where the data have been transformed into a known map projection). Map projections are a particularly important feature of spatial data because they introduce error and distortion, since a sphere cannot be transformed to a plane without some stretching and twisting of the sphere’s surface.

The typical GIS package supports anywhere from 5 to 50 map projection types, and most projections may be modified or customized by varying their parameters. This flexibility is important for two reasons First, in order to use two or more layers together in a GIS for visualization or analysis, all data must be projected in the same way. Second, certain types of analyses, such as the measurement of area, are only valid when using certain types of projections (and conversely, are completely wrong when using an inappropriate projection).

Introduction

Spatial data is different from a non-spatial data in the sense that it always has a spatial reference system which is nothing but a pair of coordinate values and a system of reference for these coordinates. For example, the locations of benchmark soil series of India can be defined as pairs of longitude and latitude in decimal degree values with respect to the prime meridian at Greenwich and zero latitude at the equator. The World Geodetic System (WGS84) is a frequently used system of reference on the Earth. The different types of spatial data are: (i) point, which is a single point location, such as a GPS reading or a geocoded address (ii) line, which is defined as a set of ordered points, connected by straight line segments (iii) polygon, an area, marked by one or more enclosing lines, possibly containing holes and (iv) grid, a collection of points or rectangular cells, organised in a regular lattice. Among the above said spatial data, the first three are vector data models and represent entities as exactly as possible, while the fourth data model is a raster data model, representing continuous surfaces by using a regular tessellation. In this chapter, we discuss how a spatial data can be handled in R. There are several packages available in R in which spatial data analysis can be handled e.g. sp, rgdal, raster etc. All the contributed packages for spatial data analysis in R address two broad areas: moving spatial data into and out of R, and analysing spatial data in R. In the following section, the sp package for spatial data handling is briefly discussed.

Remote sensing images have an important characteristic known as spatial frequency. Spatial frequency is defined as the number of changes in pixel value per unit distance. Few changes in pixel or brightness value over an area in an image are known as low spatial frequency area like agricultural fields, forests, water bodies, etc. If the changes are large, then it is known as high frequency area like roads, built up areas, etc. Spatial filtering is an approach for extracting quantitative spatial information by taking into account the neighbouring pixel values along with the independent pixel value. Therefore, filtering is a technique for modifying or enhancing an image. Filtering is done to improve the interpretability of raster image or to extract certain information from them. It is a neighbourhood operation because the value of any pixel in the output image is determined by applying certain algorithm on the neighbourhood pixels of the corresponding input pixel. The nature of algorithm to be applied depends upon the information which user wants to derive from the raster image. Filtering operations are mainly done for smoothening, sharpening, noise removal and edge enhancement. A filter is a kernel of small array which is applied to input pixel and its neighbouring pixels in the raster. There are many types of filters which can be applied to a raster image in R. These types with their respective R – code examples are explained further.

4.1. Data Set Generation for Modelling
We have generated 54 transfomed spectra by applying different combination of parameter. Now this spectrum is to be tagged with respective soil sample and desired soil properties for which spectral modeling has to be done. Here we are generaring data tagged with CaCO3.
For that we have to use cbind() fuction. Here we are keeping soil properties in first column and spectra after that column. We are storing data in R memeory and naming data in such way that we can identify properties and transfoemed spectra attached with that data. After generating data for modeling, data is divided into calibration and validation data set by using random sampling method or Kedard-
Stone method
# Data set for CaCO3 with raw spectra ####
Data.CaCO3.pc.113=cbind(data10nm_$CaCO3_equivalent_per, First_ derivative10nm113) #case 1st
Here in case 1st, CaCO3 indicating properties and 113 indcating without smoothed 1st derivative, 1st order polynomial 3rd point spectra is tagged with these soil samples. Same was followed with other cases also

For applying spectral data models with other machine learning and regression algorithms we have to follow all steps upto Chapter 4 till the partition of whole datainto calibration and validation data set. After that different algorithms is used to develop others models so that,the best model among the different algorithms can be identified and used for better perdiction of soil properties.
5.1 Principal Component Regression (PCR)
Principal Component Regression (PCR) is a regression technique that combines Principal Component Analysis (PCA) and multiple linear regression. It is particularly useful when dealing with multicollinearity in the predictor variables, as it transforms the predictors into a set of uncorrelated components before performing the regression.
set.seed(123)
pcr.CaCO3.pc.223SGS<- train(CaCO3.pc ~., Train_Data.CaCO3.pc.223SGS,
method =”pcr”, trControl = custom,
tuneLength=25, importance = TRUE)
Explaination for code train(CaCO3.pc ~ ., data = Train_Data.CaCO3.pc.223SGS, ...)
Formula (CaCO3.pc ~ .): Specifies the model formula where CaCO3.pc is the
dependent variable and . indicates that all other columns in Train_Data.CaCO3.
pc.223SGS are independent variables.
Data (data = Train_Data.CaCO3.pc.223SGS): The dataset used for training the model.
method = “pcr”:Specifies that the model to be used is Principal Component Regression (PCR).
trControl = custom:Specifies the training control method. custom should be an object of class trainControl created earlier, defining the resampling method (e.g., cross-validation).This example sets up 10-fold cross-validation. tuneLength = 25:Specifies the number of different values for the number of principal components to consider. The package caret will try 25 different values to find the optimal number of components.