eChapters

Livestock farming is an important activity to a large population of small and marginal farmers as well as landless agricultural labourers of Tamil Nadu. It also provides employment opportunities for most of the unemployed and underemployed in the rural areas. Tamil Nadu is bounded by the Eastern Ghats on the north, by the Nilgiris, the Anamalai Hills and Kerala on the west, by the Bay of Bengal in the east, and by the Indian Ocean on the south. Tamil Nadu is the eleventh largest state in India by area. The land area has been classified into seven agro-climatic zones based on soil characteristics, rainfall distribution, irrigation pattern, cropping pattern and other ecological and social characteristics. Tamil Nadu endowed with 13.97 per cent of recognised breeds of the domestic ruminants (cattle, buffalo, sheep and goats) with 22.55 millions of population (19^thLivestock Census-2012).

Chapter 6 delves into the critical role of monsoon rainfall in shaping the economies and lives of numerous countries across the globe. While the concept of a country being only dependent on monsoons is a simplification, this chapter focuses on regions where the seasonal monsoon rains are the paramount source of freshwater for agriculture, industry, and daily life. These monsoon-dependent nations, predominantly located in South Asia, Southeast Asia, Australia, parts of Africa, and even areas of South America, experience a dramatic annual cycle of wet and dry periods dictated by the shifting monsoon winds. The arrival and intensity of the monsoon rains are not merely meteorological events; they are pivotal determinants of agricultural yields, food security, economic stability, and the overall well-being of billions of people. Given this profound dependence, accurate and timely forecasting of monsoon rainfall is of paramount importance. The second part of this chapter explores the current state of the art in monsoon forecasting. We will examine the sophisticated methodologies and technologies employed by meteorological agencies and research institutions worldwide to predict the onset, duration, intensity, and spatial distribution of monsoon rains. This includes an overview of empirical models leveraging historical climate data, the increasing reliance on complex dynamical models that simulate atmospheric and oceanic processes, and the integration of advanced statistical techniques like Bayesian methods and downscaling to enhance forecast accuracy and regional specificity. Understanding the strengths and limitations of these cutting-edge forecasting techniques is crucial for effective water resource management, agricultural planning, disaster preparedness, and ultimately, for building resilience in monsoon-dependent regions.

Experiment

An experiment is a planned inquiry to obtain new facts or confirm or deny the result of previous experiment. Such inquiry can aid in an administrative decision, such as recommending a variety, a feed, a cultural practices, a fertilizer, a pesticide or a fungicide etc.

Type of Experiments

    1.     Preliminary experiments : Here investigator tries out large number of treatments in order to obtain leads for future work. Here replications are not taken for such treatments tried e.g. number of pesticides for control of diseases - take single observation for each treatment and take decision that some of them worth trying for use.

Agriculture has long been intertwined with weather, where accurate forecasts are essential for planning everything from planting schedules to irrigation management, harvesting, and market timing. Over the decades, statistical methods have evolved from simple empirical models to sophisticated tools that integrate vast amounts of historical data, real-time observations, and remote sensing information (Fig. 4.1).
This chapter provides an in-depth exploration of the statistical techniques that underpin modern agricultural forecasting. In doing so, it examines their applications across various agricultural domains—including staple crops, fisheries, livestock, and horticulture/plantation systems—while also addressing the challenges and uncertainties inherent in weather prediction. Furthermore, the chapter discusses how the rise of big data and artificial intelligence (AI) is reshaping traditional approaches, particularly in the context of food production and agricultural resilience.
In the following sections, we explore
• Statistical Techniques: An overview of regression models, time-series analysis, and probabilistic methods used in weather forecasting.
• Applications in Agriculture: Tailoring forecasting models for crops, fisheries, livestock, and horticultural/plantation systems.

8.1 Introduction
Having explored the intricate processes of meteorological data collection, storage, and the crucial steps of quality control and preprocessing, we nowturn our attention to how this vast and meticulously  prepared information is actually analyzed and interpreted. This brings us to the realm of statistical methods, which are indispensable tools for making sense of the inherent variability, uncertainty, and sheer volume of meteorological data. Meteorological phenomena are complex, influenced by numerous interacting factors, and often exhibit chaotic behavior. Statistical methods provide the rigorous framework necessary to quantify these complexities, identify underlying patterns, assess relationships between different atmospheric variables, and ultimately, draw robust conclusions. From summarizing observed climate patterns to evaluating forecast accuracy, and from detecting long-term trends to predicting future weather events, statistics permeate nearly every aspect of modern meteorology.
This chapter will introduce the fundamental statistical concepts and techniques applied in meteorological analysis. We will begin with descriptive statistics to summarize and visualize data, then move to inferential statistics to draw conclusions about atmospheric processes from samples. We will delve into methods for understanding relationships, such as correlation and regression analysis, and explore techniques for analyzing time series data to identify trends and periodicities. Furthermore, we will touch upon spatial statistics for gridded data and the growing role of machine learning approaches. By mastering these statistical tools, meteorologists can transform raw data into meaningful insights, enhance predictive capabilities, and advance our scientific understanding of the Earth’s atmosphere.

8.1 Introduction

The knowledge of statistics and probability is very helpful for correct prediction of hydrological data and correct prediction of hydrological data is useful in giving good performance in all water resources projects. Since hydrological data are highly stochastic varying both in space and time, it is essential to predict them as correct as possible. The hydrologic evens do not depend on physical and chemical laws but depend entirely on nature. They cannot be predicted exactly by using any relationship but can be predicted at different probability levels by fitting suitable probability distribution functions. Application of statistics and probability in the field of hydrology can help to predict the hydrologic events in a better way. In hydrology, objectives of statistics are as follows (Suresh, 2008):

Rainfall, runoff, evaporation, and other hydrological variables exhibit considerable natural uncertainty and therefore cannot be treated as entirely deterministic phenomena. Statistical and probability methods make it possible to organize observations, describe their variability, identify relationships, estimate extreme events, and predict values associated with selected probabilities. Hydrological data may be analysed daily, weekly, monthly, seasonally, or annually and expressed through averages, maxima, minima, dispersion, skewness, and frequency. This chapter introduces statistical terminology, correlation, regression, probability distributions, plotting positions, goodness-of-fit tests, model-evaluation tools, reliability, standard error, and risk analysis. These techniques are essential for flood-frequency analysis, planning, and hydraulic design.

Abstract

Alleviating plant salt stress and remediating saline soils are of great economic interest. Beneficial microbes such as arbuscular mycorrhizal (AM) fungi, plant growth promoting rhizobacteria (PGPR), etc. are associated with many plant species including trees. These beneficial microbes can cope up with salinity and help the plants to survive in such soils. Diversity status of beneficial microbes from the samples collected from different salt affected areas was investigated by many researchers. They screened and identified the efficient PGPR isolates isolated from saline areas for plant growth hormone (IAA) production and phosphate solubilization. The significance and highlights of the recent research as well as the past understanding of beneficial microbes and their role in reclamation of saline areas is discussed in this paper.

A forest is a large area dominated by trees. Many definitions of forest are used throughout the world, incorporating factors such as tree density, tree height, land use, legal standing and ecological function. Although a forest is usually defined by the presence of trees, under many definitions an area completely lacking trees may still be considered a forest if it grew trees in the past, will grow trees in the future, or was legally designated as a forest regardless of vegetation type. According to the Food and Agriculture Organization, forests covered 4 billion hectares or approximately 30% of the world’s land area in 2006. Forests are the dominant terrestrial ecosystem of earth, and are distributed around the globe. Forests account for 75% of the gross primary production of the earth’s biosphere, and contain 80% of the earth’s plant biomass

Classification Of Forest

Forests have been classified in different ways. Forests can be classified on the basis of method of regeneration, age, composition, object of management, ownership, canopy density, stand density or growing stock.

Abstract

The arid Kachchh district, located at the western most part of the Gujarat State in India, is known for its large extent of area under wetland habitat, which is represented by numerous seasonal water bodies and coastal habitats. Vast stretch of grasslands also available in the northern border of the district, the Banni (23⁰ 19’ to 23⁰ 52’ N; 68⁰ 56’ to 70⁰ 32’ E), which encompasses an area of 2,617 km². There are many depressions in Banni, locally known as Dhandh (shallow lakes). During monsoon, rainwater gets collected in these depressions and form shallow lakes. About 34 smaller and bigger size Dhandhs are present in the Banni. Banni communities are highly dependent on the Dhandhs for various purposes. Due to the erratic rainfall in arid Kachchh, Maldharis is highly unstable for livestock in search of fodder and water during drought years. During the normal rainfall years, livestock from neighboring talukas of Kachhh and even from other districts depends on this grassland and Dhandhs. During last decade, the Banni grasslands have seen serious environmental changes due to the invasion of an exotic plant Prosopis juliflora and increasing the salinity are the major issues of Dhandh degradation. Thus, the Banni is gradually losing its capacity to sustain human and livestock population.

Main objectives of the present paper is to examine the local perceptions of wetland and grassland change, socio-economic status, and factors that cause degradation of the wetland and vegetation resources in the Banni grassland.

Data mining is a powerful technology which helps in extracting future trends and behaviour from large databases. Thereby it helps decision makers to make knowledge-driven decisions. One common form of real-world data is time series. The suitable models are required to analyze time series data. This chapter provides a short introduction to stochastic system, uncertainty, basic statistics time series analysis and some stochastic models.

Introduction

Livestock plays an important role in the sustainable livelihood of poor people of rain-fed agro-ecosystem, because of inherent risk involved in the crop farming due to uncertainty of rainfall and occurrence of recurrent droughts. They provide income and increased economic stability, and often the most important “cash crops” in small-scale mixed farming systems. Livestock sector in India is highly livelihood intensive and most rural households own livestock of one species or the other and earn supplementary incomes from them. Livestock holding is less iniquitous than land holding and income from livestock is more equitably distributed. The sector contributes 25.6 per cent of the value of output at current prices of total value of output in agriculture, fishing & forestry sector and in GDP, it was 4.11% in 2012-13 (19^th livestock census, 2012) therefore, development of the livestock sector is the critical pathway to rural prosperity

As per the 19^th livestock census, the total livestock population consisting of Cattle, Buffalo, Sheep, Goat, Pig, Horses & Ponies, Mules, Donkeys, Camels, Mithun and Yak in the country is 512.05 million numbers in 2012. The total Bovine population (Cattle, Buffalo, Mithun and Yak) is 299.9 million numbers in 2012 which shows a decline of 1.57% over previous census. In percentage basis distribution of livestock were: 37.28% were cattle, 21.23% buffaloes, 12.71% sheep, 26.40% goats and 2.01% pigs. The corresponding figures as per the 18th Livestock Census were 37.58%, 19.89%, 13.50%, 26.53% and 2.10%. The number of milch animals (in-milk and dry) in cows and buffaloes has increased from 111.09 million to 118.59 million, an increase of 6.75%. The number of animals in milk in cows and buffaloes has increased from 77.04 million to 80.52 million showing a growth of 4.51%. The Female Cattle (Cows) Population has increased by 6.52% over the previous census (2007) and the total number of female cattle in 2012 is 122.9 million numbers. The Female Buffalo population has increased by 7.99% over the previous census and the total number of female buffalo is 92.5 million numbers in 2012. The buffalo population has increased from 105.3 million to 108.7 million showing a growth of 3.19%. The total sheep in the country is 65.06 million numbers in 2012, declined by about 9.07% over census 2007. The Goat population has declined by 3.82% over the previous census and the total Goat in the country is 135.17 million numbers in 2012. The total poultry population in the country has increased by 12.39% over the previous census and the total poultry in the country is 729.2 million numbers in 2012. The total number of animals in milk in the country is 116.77 Million numbers.

Introduction

Most of the technological advances in the recent past in Indian Horticulture have been done for favourable agro-climatic regions. However, the vast and diverse land-scape of hot desert, arid, semi-arid and tribal area is not yet receiving logical thought for resource based horticultural development (Samadia and Haldhar, 2017). Although, the semi-arid regions of the country have now became potential hub for aonla, bael, ber, pomegranate, jamun, guava, fig and custard apple. There has been a steady increase in area and production of these fruits particularly under aonla, ber, custard apple and pomegranate in the country as a result of identification and development of suitable varieties and production technologies. The average productivity of these fruits in India is, however, low as compared to many developed countries. The main reasons for low productivity are old and senile orchards, low yielding varieties, poor orchard management and inadequate technological up-gradation and adoption by the growers. Presently, the continuing decline in the availability of cultivable land, rising energy and land costs together with the increased demand of fruit and fruit products, have given thrust to the concept of high density planting (HDP) in fruit crops. High density planting gives earlier production and return per unit area, shortens juvenility, eco-friendly, provides efficient land use and better use of resources like light, water and fertilizers, efficient pesticides application, besides, in this system the harvesting becomes easy(Mishra and Goswami, 2016). Among the factors such as cultivar, rootstock, quality of planting material and cultivation practices contribute to high yield of fruit trees, however, the number of trees^-1 is the most important factor which brings about radical increase in fruit production. Therefore, high density planting has great potential for increasing productivity in fruit crops (Mishra et al., 2003). The success of this technology in most of the fruit crops is dependent on the use of methods to control shoot growth and maximize light interception as the trees begin to bear fruit. The main aim of HDP is to achieve the twin requisites of productivity by maintaining a balance between vegetative and reproductive growth without impairing the plant health and fruit quality. The underlying principle of HDP is to make the best use of vertical and horizontal space per unit time and to harness maximum possible return per unit of inputs and natural resources.

Introduction

Energy conservation is the practice of using less energy to lower costs and reduce environmental impact.

A majority percentage of energy used in buildings is still from non - renewable and fossil fuel resources. Lighting and Appliances are affecting energy demand in domestic sector.

Reasons for increased energy demand

• Increase in electrical appliance ownership

• Increasing income level and reliable access

• Rise of Lighting and appliance loads

13.1 Introduction

Greenhouse gas (GHG) emissions have become an increasingly important topic worldwide due to their effects on global warming and climate change. The effects of GHG emissions on the ecological and socioeconomic vulnerability have already been noticed and will continue to grow regionally and globally in the years to come (IPCC 2007). Carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), hydrofluorocarbons, perflourocarbons, and sulfur hexafluoride are the important GHGs that are monitored by the United Nations Framework Convention on Climate Change (UNFCCC 2008). Global GHG emissions due to human activities (anthropogenic) have grown since the beginning of the industrial revolution with an increase of 70% between 1970 and 2004 (IPCC, 2007).

6.1 Introduction

Stratigraphy is the branch of geology which deals with the study of rock layers (strata) and layering (stratification) in their chronological order. It is primarily used in the study of sedimentary and layered volcanic rocks in order to interpret geological history and age. The term historical geology is also used as a synonym of stratigraphy but in a wider sense, it deals with the whole sequence of events that make up the history of the earth from its origin and age relations of rock strata, evolution through its life form, and structures. The scope of stratigraphy is very wide; it involves description and classification of layered sedimentary rocks and also of igneous & metamorphic rocks. The study involves correlation between different areas like lithological characters, fossil content, geophysical and geochemical properties including absolute radiometric dating.

6.2 Principles of Stratigraphy

Stratigraphy is based on the following few principles:

6.2.1 Law of superposition

In a sequence of horizontal sedimentary rock layers, the younger layers are on the top and the older layers are at the bottom. To put it another way, in a normal sequence, each bed is younger than the bed beneath it. Geologists today use it as the basis for a bed-by-bed analysis of geological history in a given area. This is important to stratigraphic dating, which assumes that an object cannot be older than the materials of which it is composed. This law known as "Law of Superposition" was first proposed by the scientist Nicolas Steno in the 17th century.

5.1 Introduction

A steam can be defined as a flow channel in to which the surface runoff flowing over the land surfaces and base flow/ground flow flowing below the ground surfaces meet. It receives the total flow from a specified basin. Streamflow representing the runoff phase of the hydrologic cycle is the most important basic data for hydrologic studies. Interestingly, stream flow is the only part of the hydrologic cycle that can be measured accurately. Stream flow is a historical data and like rainfall and runoff, it is a stochastic hydrologic variable. It is also called as discharge or rate of flow. Discharge is defined as the volume of water flowing through a cross section in a unit time.

Abstract

In order to estimate the irrigation need of the crop, stress degree day index computers from canopy and air temperature could be used. During rabi manon, two varieties (Seeta, Brassica Juncea) and (Benoy, Brassica Camperteris) were grown. The canopy temperature of the crop was measured on 45th, 60th  and 75th  days after sowing and stress degree day (SDDI) was also measured. About 67, 70 and 51 per cent activation between SDDI and dry matter production could be explained at 45, 60 and 75 DAS of the crop growth period. Reflectance by mustard canopy was 940 measured. Late sown crop reflected less radiation with the advancement of growth. Extinction coefficient was also measured and the increase in the value of extinction coefficient upon irrigation showed canopy development and better utilization of increasing radiation by the crop. 

Introduction

According to the Forest Survey of India (FSI) “all lands, more than one hectare in area, with a tree canopy density of more than 10% is defined as Forest” (FSI 2009). The status of forests and forest management systems contribute to the vulnerability of forests to climate change. Indian forests are extremely diverse and heterogeneous. Forests and Climate Change are intimately intertwined. Forests have regulated the climate, rain, groundwater, soil of the earth over millennia. Their transpiration act as a regulator of the balance of oxygen and carbon-di-oxide: the world’s forests and forest soils currently store more than one trillion tons of carbon – twice the amount found floating free in the atmosphere. The forest and soils are home of biological diversity.

7.1 Introduction

The earth's crust is made up of igneous, sedimentary and metamorphic rocks. Each variety of rocks exhibit some columnar joints in Igneous rock, characteristic structures like bedding in sedimentary rocks and fabrics like schistosity and gneissosity in metamorphic rocks.

As a result of tectonic activities in nature, rocks may suffer deformation, rupture, dislocation of beds (Fault) or other planar structures.. The whole study of such deformation is known as structural Geology. It has been observed that sedimentary rocks are most suitable for studying structures, because these rocks are deposited in quiet environment, and initially the beds arranged in horizontal position. As a result of stress due to kinematic movements/tectonism, the horizontal beds tend to deviate from their horizontal disposition to dipping position or get folded or faulted. Thus the rock develops various kinds of structures. There are following structures present in sedimentary rocks: