eChapters

Introduction

“If agriculture goes wrong, nothing else will have a chance to go right in the country”,these words by Dr MS Swaminathan known as the ‘Father of Green Revolution’ in India, itself highlights the importance of agriculture and its allied sectors in Indian economy. India is an agrarian nation with about 140 million farmers and net sown area equals to 42.4% of the total geographical area. Its agriculture sector has always played and will continue to play a major role in its economic growth. As per the First Advance Estimates of National Income, 2021-22 released by National Statistical Office (NSO), Ministry of Statistics & ProgrammeImplementation, the contribution of agriculture and its allied sectors stand at approximately 18.8 % of India’s Gross Value Added (GVA) at current prices during 2021-22, depicting a growth of 3.9%.In addition, agriculture is also the largest employment generating sector in India as approximately 55% of the total workforce is engaged in agriculture and its allied sector activities. It is the main source of income security of rural households in India. Since ancient times, agriculture and livestock keeping are complementary professions that rural households have undertaken for their survival.

Shrinking land resources, vulnerable market of cash crops along with climatic aberrations have adversely affected the Indian agriculture and as a result, small farmers are increasingly alienating from this profession. To sustain their livelihood, farmers are increasingly adopting the alternative sources and dairy farming is one of the best-suited businesses, which can rightly fit at this stage. It is fast emerging as a potential business in rural India, particularly in the peri-urban belt. Among the major inputs required to set up a dairy unit, round the years availability of good quality fodder is the foremost requirement. Green forages are rich and cheapest source of carbohydrates, protein, vitamins and minerals for dairy animals. The importance of forages in our country is well recognized since feeding forages alone accounts for over 70% of the cost of milk production. Hence by providing sufficient quantities of green fodder instead of costly concentrates and feeds to the milch animals, the economics of dairy unit can easily be maintained. Milch animals need around 40 kg of green fodder daily for better milk production and for maintaining good health of the animal. There is a large number of fodder crops grown in our country, the majority being dominated by pearl millet, sorghum, maize and oats in the non-leguminous and berseem and lucerne in the leguminous category. Pearl millet and sorghum are extensively grown as cereals in the southern and dryer regions of the country and the stover remained is used as animal fodder. However, the nutritional quality of the stover is very poor due to higher content of indigestible lignin and low levels of cellulose, whereas, the cultivated green fodders are mostly confined to the North-Western plain zones of Punjab, Haryana and Western Uttar Pradesh. The availability of round the years quality green fodder, therefore, seems to be a tough task. At present, the country faces a net deficit of 61.1% green fodder, 21.9% dry crop residues and 64% feeds (IGFRI Vision, 2030).

23.1 Introduction

Eggs are nutrient dense superfoods, naturally occurring vitamin pills. They contain a complete source of protein with all essential aminoacids and naturally occurring vitamin D. The adverse publicity of high cholesterol content in the egg limit their intake. Nowadays consumers need the products free from drugs/ pesticides and contain special health promoting substances. To reap the growing demand of health conscious consumers and to make available of their requirements, designer eggs are made. Designer eggs are those eggs their composition differ from the standard eggs by changing the feed of the hen.

Number of individuals in a population increase under favourable environmental conditions. Sudden or violent changes in the environment or in the biological characteristics of the individual could lead to decline. Fluctuation in the population is usually because of the effect of a combination of several factors responsible for their survival, growth and reproduction. It is necessary to understand the cause of such fluctuations for better scientific management of the population as well as the habitat (Acharya, 2000). In most of the studies, community has largely been expressed in terms of species richness, abundance, density and diversity. All these have been used as indicators of habitat quality, because an increase in the value of the component is generally thought to reflect larger amounts of the necessary resources to sustain larger population within a given area. Moreover, it is believed that if the factors determining the distribution of the animals are known, predictions can be made concerning the responses of animals to specific perturbations and hence, such animals can be used to monitor environment quality (Gokula, 1998).

In this chapter the data collected in Silent Valley and Mukkali on the abundance of birds are discussed. Species abundance relations of the community is also prepared and presented in this chapter.

METHODS

Abundance : Total number of birds seen in each month and seasons (summer and monsoon) were calculated using the census data.

Density : Density of birds in each month, in two seasons and individual density of selected species were also calculated. The density was estimated using the line transect method (Burnham et al., 1980). The Fourier series method, which is used for analysis, is not dependent on specific distribution assumptions about the detection probability of birds at various perpendicular distances from the transect line. In this method density is computed from ungrouped, perpendicular distances from transect.

ABSTRACT

Colchicine, one of the most widely used alkaloids is synthesized in Gloriosa species, a plant of great medicinal importance. The seeds, tubers and leaves of six different species of Gloriosa were phytochemically investigated for alkaloids and were found to contain colchicine and colchicoside. These alkaloids were isolated and quantified by HPLC analysis. This is the first report of alkaloids in Gloriosa plantii, Gloriosa lutea, and Gloriosa vuchuria. The colchicine and colchicoside content of the Gloriosa species was found to be organ dependent as there was statistically significant differences (P<0.05) between different organs but not among different species under study. The total alkaloids are organ as well as species dependent as there were statistically significant differences (P<0.05) between all the species as well as organs of different species of Gloriosa under study and its organs.

The screwpines are tropical shrubby plants grown for the highly scented flowers or the ornamental foliage. The ornamental foliage types are generally cultivated in the green house and protected from direct sun. The variegated types loose the colour in a rich soil and hence should be grown in a rather poor soil containing just sufficient nutrients and moisture. Some species are good for potting and indoor decoration. The common screwpine is used as a hedge also (Randhawa and Mukhopadhyay, 1986)

The screwpine (Pandanus), is highly polymorphic with numerous forms and varieties and selected in various countries for specific uses as maintained by vegetative propagation, e.g. P. leram (Plate 14), the Nicober-bread fruit, is grown in the Andaman Islands for its edible fruit providing a starchy food that is enjoyed locally; another species, P. utilis (Plate 15), on the remote Island of Mauritius, provides fibre from the leaves, used in making bags and a species from Polynesia, P.veitchii (Plate 16) is most commonly grown in green houses and outdoors as an ornamental, where the winter weather is mild and in the species the prop roots and branching stems are particularly well developed attracting attention from many people who might not otherwise notice the stranger from the tropics (Lorus and Milne, 1967). However, these three species give a striking foliage effect and especially useful in seaside gardens as P. utilis with green leaves; P. veitchii with banded white and P. leram with even longer leaves (Thomas, 1965). An unarmed form, forma laevis warb., is cultivated for the fragrant bracts of the inflorescence, while forma samak, a group of prickly Pandanus, is preferred for the tough leaves suitable for matting; another form with longitudinal yellow bands on leaves (forma variegatus) is grown for ornamental purposes (Deshaprabhu, 1966; Ambasta, 1986). In the Pacific Islands, some of the forms (e.g pulposus) are cultivated for their edible fruits (Purseglove, 1975; Ambasta, 1986). Burkill (1966) states that the common wild armed form of the sea-coasts was var. littoralis and that flower growers had selected various races for cultivation, which included the prickly var. samak and thronless var. laevis, grown in India, Malay and Indonesia, are used for making mats.

Species composition of birds in an area is related to the type of vegetation, height above the sea level, the availability of microhabitats and various other factors. The study site at Silent Valley covered all the representative habitats namely forest canopy, forest interior, forest edge, rocky patches, grass patches and foliage air interface met within a tropical forest. Riparian patches were excluded after an initial survey as it contained only a few species of forest birds. The Mukkali study site covered moist deciduous forest, burned forests, coffee estate, open rocky areas and agricultural land. Common species, Dominant species and Species richness of the two study areas were assessed and the data are presented in this chapter.

Information on the avifauna of an area is a prerequisite to assess the status of birds and the habitat quality with specific attention to indicator species including the rare, endangered and endemic species. Birds are one of the best indicators of the health of an ecosystem. They are highly mobile and easily observed indicators of change in the environment (Holmes et al., 1986). Many wetland species also play a role in the control of agricultural pests, while some species are themselves considered pest of certain crops. After fish, birds are probably the most important faunal group that attract people to wetlands. Loss of wetland habitats through direct and indirect modifications and non-sustainable harvesting of water birds for human needs have led to decline in several water bird populations and a number of species (Jin-Han Im et al., 2001).

Some of the most catastrophic declines of birds have taken place in the last few decades and the list of threatened species in the Asia- Pacific region has expanded rapidly to include species from a large range of water bird groups. While decline of some populations has been well documented, the fates of many others remain unknown. It is vital to understand the underlying causes in population changes and to attempt to control these trends in order to prevent key component of biodiversity of wetland habitat from being lost. The number of water birds using a particular habitat is related to types and quality of habitats, abundance and availability of food and level of disturbance. Monitoring of water birds can thus provide valuable information on the status of wetlands and can be a key tool for increasing the awareness of importance of wetland and conservation values. In this chapter, the status, occurrence and species composition of avifauna recorded from Kole wetlands are elucidated.

INTRODUCTION

Himachal Pradesh, the mountainous state is well known for its natural wealth. It is situated between 300 22’ 40"to 330 12’ 40" N latitude and 750 45’ 55" to 790 04’ 20" E longitude in the western Himalayas. The 12 districts viz., Bilaspur, Chamba, Hamirpur, Kangra, Kinnaur, Kullu, Lahaul & Spiti, Mandi, Sirmaur, Shimla, Solan, and Una of the state covered 10.5 per cent of the Indian Himalayan Region (IHR) and 1.69 per cent of country’s geographic area. State experienced high variation in an elevation and climatic conditions. An elevation of the stateranged from 350 to 6975 metres above mean sea level (amsl.) that increases from west to east and south to north.On the basis of elevation and slope,the state is divided into fourdistinct topographical regions: (1) Shiwalik Hills, (2) Mountains-Lesser Himalaya, Greater Himalaya and Trans Himalaya, (3) Valleys-Shiwalik dun valleys, fluvial, and glacio-fluvial valleys, and (4) Mountain Passes (Singh and Kumar, 2014). Whereas, physiographically, state is divided into four agro-ecological zones: the Shivaliks’ low, mid and high-hills; and the cold and dry or the alpine zone (Table 1). The 70% of the state area is steep to very steep, about 19% is moderate to moderately steep, and 11% is gentle to nearly level slopes.

Abstract

While multiple species interact with each other in variable environments, they employ a variety of interaction strategies to withstand variation in environmental conditions. Phenotypic plasticity is one such mean allowing species to cope with environmental challenges often posed by the variation in consumer pressure, disturbances, and predation risks. In response to these and other environmental cues, expressed phenotypes can alter the sign and strength of interactions among species as these responses are often reflected in increased or decreased species competitive or facilitative abilities. Besides phenotypic plasticity, species interactions also depend on continued variation in abundances or densities of interacting species commonly termed as density-dependent interactions. Such density-dependence follows the law of diminishing returns, which means that while an individual species adds a density-dependent weight to the performance of other species, with increasing accounts, at a certain level of interaction it provides diminishing returns resulting in decreased population growth of the species. Thus, species interactions in variable environments hold two interdependent opposing forces: the growth-escalating and the growth-diminishing forces. The first one is resulted from adaptive phenotypic responses to variable environments and the second one is resulted from dynamic changes of abundances or densities of interacting species. Based on this conceptual framework, we describe community dynamics that are shaped by the balance of these two forces in variable environment. By analyzing these dynamics, we then seek underlying mechanism of Grim’s classic humped-back model of productivity-diversity relationship and also provide an alternative explanation for the intermediate disturbance hypothesis.

Keywords: Competition, Facilitation, Functional niche, Interaction continuum, Phenotypic plasticity.

Introduction

The conventional approach of the human person for plant classification is comparing the color and shape of the leaves, but by using Artificial Intelligence analysis can provide quicker and more detailed results by analyzing the morphology of the leaves and provides more details about the leaf properties. Species Recognition has been a very important part of agriculture and there are thousands of species in plants which can be identified by a single person and even if it does so It will take a huge lot of time to do it.

In this chapter we will discuss and built a project related to identify species in flower with an image. In this project we will be using Artificial Intelligence different branches like machine learning, deep learning and computer vision to build such an amazing project. So, let’s begin

Formulating feed tailored to specific species is a crucial element of modern aquaculture, ensuring that each fish or shellfish receives the precise nutrients needed for healthy growth, reproduction, and overall well-being. Different aquatic species have unique dietary patterns, digestive systems, and nutrient requirements, making customized diets essential. Carnivorous species like trout and seabass need protein-rich, energy-dense feeds with animal-derived components such as fishmeal or poultry by-products. In contrast, omnivores like catfish and tilapia can digest plant-based proteins like soybean meal and groundnut cake efficiently. Herbivorous fish such as rohu and grass carp perform well on fiber-rich diets incorporating aquatic plant ingredients. Shrimp require feeds high in specific amino acids, cholesterol, and minerals such as calcium and magnesium to support molting and exoskeleton development. Larvae and juveniles across species need highly digestible feeds fortified with micronutrients and immune-enhancing

Tuberculosis

Tuberculosis has been recognized as one of the most important infectious diseases in the world because of its high global impact and its chronic debilitating character, particularly in the poor section of society. Milk-borne tuberculosis is directly or indirectly related to the consumption of raw milk from infected dairy herds.

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.

Data pre-processing is a crucial step in data analysis and machine learning that involves preparing raw data for further processing and analysis. Proper preprocessing helps in improving the quality of the data, which in turn enhances the performance of machine learning models. Smoothening VIS-NIR spectra enhances signal quality by reducing noise, crucial for better spectral data interpretation. Key methods include Savitzky-Golay and moving average filters, wavelet transform (WT), and frational derivatives.
Savitzky-Golay Filter: The Savitzky-Golay filter is widely used for smoothing spectral data. It preserves the original shape and features of the signal better than simple averaging filters.Savitzky-Golay Filter fits low-degree polynomials to successive subsets of data points using linear least squares, effectively preserving the signal’s original shape and features, making it ideal for spectroscopy. Moving Average:Moving Average is a simple method that reduces random noise by averaging neighboring data points. While effective, it can sometimes blur sharp features.
Wavelet Transform: Wavelet transform is effective for denoising spectral data, especially when dealing with non-stationary signals.Wavelet Transform (WT) decomposes spectral data into multiple scales, providing both time and frequency information. It is particularly useful for identifying features like peaks and trends. WT includes Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Maximal Overlap Discrete Wavelet Transform (MODWT). Fourier Transform Filtering: Fourier transform can be used to remove highfrequency noise components from the spectra.
Median Filter: Median filtering is useful for removing outliers and preserving edges in the spectral data.
Fractional derivative:Fractional derivative generalizes traditional derivatives to non-integer (fractional) orders, allowing for arbitrary order derivatives. This provides a flexible, nuanced approach to modeling and analyzing complex, nonlinear, or memory-dependent behaviors.

Introduction

The ultimate aim of agricultural research or any other related activity is to increase yield. Yield improvement could be obtained mainly through the enhancement of photosynthetic efficiency. Photosynthesis is a process in which plants convert light energy to chemical energy. Sun is the ultimate source of energy for photosynthesis. However only half of the total solar radiation (51%) reaches the earth’s surface of these only 1.2% reaches the plant canopy. Solar spectrum contains an array of other radiations which can be harmful for plant and human life. With increasing global climatic change amount of harmful radiation reaching the earth’s surface has increased and this is found to influence plant productivity. Under this scenario, protected cultivation is gaining popularity in many countries as it helps to reduce harmful effects of undesirable radiation, to allow cultivation of crops irrespective of seasons and also expand cultivation to non-traditional areas by artificially manipulating crop environment. In this context, spectral manipulation for crop production has emerged as an important area in crop production system

ABSTRACT

In the present paper, study of spectral reflectance measurements has been carried out for different type of snow and other ambient objects in snowbound areas of Indian Himalaya using Spectroradiometer (350-2500nm). The observations suggest that there are significant changes in the spectral radiances and reflectance values of snow and other ambient objects, which can be discriminated using satellite multi-spectral bands, however for characterizing the snow using satellite sensor, spectral bands having maximum radiances values should be greater than snow radiances and suitable spectral bandwidth are to be identified. The detail analysis of field collected reflectance suggest that, due to aging and grain size variation in snow, the maximum variations in reflectance was observed in the NIR region i.e. around 1040-1050nm. For contamination and snow depth, the maximum variation was observed in the visible region i.e. around 470nm and 590nm respectively. For the moisture changes, the maximum variations observed around 980 and 1160nm. The study also suggests, normalized difference snow index (NDSI) increases with the increase of aging, grain size and moisture content. This study can be helpful in selection of suitable spectral bands for snow cover applications using satellite

remote sensing and developing the algorithms for seasonal snow cover characteristics using multi-spectral and hyper-spectral data.

Introduction

Soil is one of the vital resource, and is the basis of the existence of mankind. Management of this resource including its conservation and utilization is of crucial importance. It is a base on which all life depends. In the recent past, with burgeoning populations and the national goals of seeking self-sufficiency in food and fibre production, this resource base is slowly being stripped. While natural systems often adapt to stress in a remarkable fashion, some relationships - once destroyed - can never be restored. Soil resources can be assessed and monitored through its spectral signatures gathered through remote sensing.

The term ‘remote sensing’ is commonly restricted to methods that employ electromagnetic energy (such as lights, heat, micro wave) as means of detecting and measuring target characteristics. Sensors acquire data as various earth surface features reflect or e mit electromagnetic energy (EM energy). Remote sensing is the technology that is now the principal method (tool) by which the earth’s surface and atmosphere (as targets or objects of surveillance) are being observed, measured, and interpreted from vantage points. Remote sensing of the earth traditionally has used reflected energy in the visible and infrared, and emitted energy in the thermal infrared and microwave regions to gather radiation that can be analyzed numerically or used to generate images whose tonal variations represent different intensities of photons associated with a range of wavelengths that are received at the sensor.