eChapters

Feeding wild animals in captivity presents a fearful challenge. There arehundreds of species to consider, each representing millions of years of evolutionary adaptation to particular ecological system.

Natural foods range from Ants to Antelopes. (Antelope = African (deer like) ruminant, forexample-gazelles, gnus, kudus and impala) and Mouth structures varyfromEdentate (toothless)toCarnassial(specialized pre-molars and molars).

Stomach : These may be

a) Single

b) Quadruple.

Intestines: These may be

a) Tube likeor

b) Equipped with a caecal fermentation vat (vat = large vessel).

On the surface, the problem appears unsolvable, … it certainly is not easy. But the need persists to nourish all captive species as appropriately as possible … it is not feasible to wait until a DATABASE is developed comparable with that which exists for domestic animals.Actually domestic animal data can be very useful for the estimation of nutrient needs of wild counterparts.

Abstract

Inspired by the human eye to brain assessment process, computer vision technology has demonstrated its potential for monitoring and controlling of several operations in food industry. Computer vision technology is, therefore, applied extensively in food drying process for monitoring nutritional value and sensorial characteristics because of its promising characteristics such as reliable objectivity and repeatability. In this chapter, after providing a brief explanation of the computer vision technology and its prerequisites and principles, an example is given to elucidate how this technology is used for monitoring the visual attributes of foodstuffs during drying process. Overall, monitoring the visual properties of food products like size, shape, color, and texture in off-line mode are the most popular issues to fascinate researchers’ attention to apply the computer vision technology in food drying. Nevertheless, industrial applications of the computer vision technology for real-time monitoring and control of food drying facilities as final goals of its introduction in food drying industry seem to be limited. For future studies, it is suggested to apply this unique technology along with advanced soft computing methodologies for in-line monitoring and control of food drying process in order to achieve high quality dried products. Finally, outlooks and potential employment of this technology in food drying domain are discussed as well.

Introduction

Biodynamic farming represents a unique and comprehensive approach to agriculture that integrates scientific and spiritual dimensions to cultivate not just crops, but entire ecosystems. Emerging in the early 20th century as a response to the growing concerns over the industrialization of agriculture, this method is grounded in the principles of anthroposophy, a philosophical movement founded by Rudolf Steiner. Biodynamic farming is not merely a set of agricultural practices; it embodies a worldview that considers the farm as a living, self-sustaining organism, where all elements are interrelated and interdependent.

At its core, biodynamic farming seeks to create a harmonious balance between the physical, biological, and spiritual aspects of farming. It emphasizes the importance of the farm’s natural rhythms, including lunar and cosmic influences, which are believed to impact the growth and health of plants and animals. Farmers who practice biodynamics use a variety of unique preparations made from fermented herbs, minerals, and manure to enhance soil fertility and plant vitality, following a biodynamic calendar that guides the timing of sowing, planting, and harvesting.

This chapter introduces the principles involved in designing and erecting protected structures for floricultural production. It examines structural safety, dead and live loads, wind and snow loads, site selection, orientation, foundation, greenhouse components and suitable covering materials. The chapter also identifies flower and foliage crops suitable for protected cultivation and relates structural design to crop characteristics and climatic conditions. These principles help growers develop efficient, safe and productive greenhouse systems

It was in 1968 that the term Green Revolution was coined by Dr. William Gaud of USA to describe advances in agriculture arising from productivity improvement. Even in 1968, I concluded that if farm ecology and economics go wrong, nothing else will go right in agriculture.  I expressed my views in the following words in my lecture at the Indian Science Congress Session held at Varanasi in January 1968:

Exploitive agriculture offers great dangers if carried out with only an immediate profit or production motive. The emerging exploitive farming community in India should become aware of this. Intensive cultivation of land without conservation of soil fertility and soil structure would lead, ultimately, to the springing up of deserts. Irrigation without arrangements for drainage would result in soils getting alkaline or saline. Indiscriminate use of pesticides, fungicides and herbicides could cause adverse changes in biological balance as well as lead to an increase in the incidence of cancer and other diseases, through the toxic residues present in the grains or other edible parts. Unscientific tapping of underground water will lead to the rapid exhaustion of this wonderful capital resource left to us through ages of natural farming.

Introduction

The floodplain wetlands or beels are used as multiple use and multiple users system. The large numbers of the stakeholders participate in the beels both within fisheries and across other sectors like forestry, animal husbandry, agriculture, flood control etc. (Sugunan & Sinha 2001, Ramsar Convention Secretariat 2004). The different set of stakeholders constitutes a diverse set of interests, goals and priorities for the fisheries. Therefore, under the present context, the fisheries managers face an extremely complex situation in governing fisheries of the wetlands. Such complexities lead to sub-optimal or unwise use of the wetlands. There is growing concern over the depletion of resources, poor quality of aquatic environment, poor potential realization, poor fisheries management, inequities, conflicts, over exploitation etc of the beels (CIFRI 2000, Pomeroy & William 1994, Welcomme & Bartley 1998). These issues are the outcome of the complex fisheries exploitation system and research efforts are being made to find suitable management regimes to address such issues. The evaluation of outcomes, identification of the shortcomings and understanding decision making process have been an important researchable areas (Pomeroy & William 1994, ICLARM & NSC 1998). Evaluation of a system should include depiction of its goals or objectives and an assessment of how well they are met as well as obstacles faced in meeting them. Accordingly, it is important to identify the management context of the beel fisheries management viz., the formal goals of a fishery management regime; the formal or informal goals of the various actors within it; the processes by which goals are identified and contested; processes through which they are linked to other aspects of the regime- including knowledge production and the implementation of action measures (ICES 2000). Therefore, it is imperative to understand the objectives, decision making process and criteria for evaluation of the fisheries management of the wetlands as a step towards further research in designing effective management regimes. The paper attempts to delineate the system in the simplistic way as well as review the relevant literature to map the objectives, decision making arrangements and evaluation criteria for assessing the fisheries management regimes of the beels. The present paper has been made based on the experience and understanding developed in the context of the beel fisheries management of Assam.

Introduction

The Horticulture crops and commercial crops like sugarcane have become a key drivers for economic development in many of the states in India, and it contributes 29.5 per cent and 24.80 percent respectively to the GDP of agriculture, which calls for technology-led development. The research priorities for genetic resource enhancement and its utilization, enhancing the efficiency of production and reducing the losses in environment friendly manner and effectively channellising the marketing for the produce. Accordingly the research was taken up to study on the prioritization of research for the crops viz., sugarcane, banana and jasmine. The study deals with the knowledge, adoption and research priorities on selected crops’ cultivation which has already been occurred.

The prospects of Farm Animal Genetic Resources (FAnGR) are exceedingly uncertain. The FAnGR characterize the genetic makeup of the thousands of local breeds worldwide which are well-adapted for producing in harsh environments, and which for millennia have been essential in nourishing human communities. Currently, the genetic diversity in numerous livestock breeds across the world is threatened. Decades of consistent selection intended mainly for production increase has caused irreversible loss of genetic diversity (FAO, 2009).

Managing the National Food Security System

Rajiv Gandhi understood the wisdom of Gandhiji’s doctrine that Gram Swaraj is the pathway to Purna Swaraj and launched the Panchayati Raj movement. Schedule 11 of Constitution Amendment 73 entrusts panchayats with the responsibility of managing natural resources and fostering sustainable agriculture. Representative democracy through elected members, one third of whom are women, and participatory democracy through Gram Sabhas, are powerful tools for ensuring a pro-nature, pro-poor, pro-women, and pro-livelihood orientation to all rural and agricultural development programmes.
 
The National Commission on Farmers has emphasised the need for community-managed food and water security systems promoted with the help and oversight of Gram Sabhas.  The Gram Sabha can serve as a Pani Panchayat to ensure that rainwater is not only harvested, but is used in a sustainable and equitable manner.

The objective of the present study was to explore the therapeutic potential of Tribulus terrestris and ITK formulation against obese streptozotocin- induced type II diabetes and diabetic nephropathy in male Wistar rats. In Phase I study, 70% hot ethanolic extract of Tribulus terrestris fruits and hot aqueous extract of ITK formulation (ingredients gum acacia, black cumin, wheat and barley) was prepared and evaluated for in vitro antioxidant and antidiabetic activity of extracts. Extracts have potent antioxidant (studied by 2, 2-Diphenyl 1-picrylhydrazyl (DPPH) and 2, 2’-Azino-bis (3-ethylbenzothiazoline-6 sulphonic acid) (ABTS) radical scavenging assay), antidiabetic (determined by in vitro α-amylase and α-glucosidase inhibitory activity) and glucose uptake potential in cultured lymphocytes and were observed to possess carbohydrates, alkaloids, proteins and amino acids, tannins, flavonoids, saponins, fixed oils and resins in Tribulus terrestris fruits with additional glycosides in ITK formulation after screening by qualitative tests and further confirmed to possess active principles having antidiabetic, antioxidant, nephroprotective, cardioprotective and anti-inflammatory properties.

Pesticide use in agriculture has evolved at several stages in the last few decades. Pesticides, as defined by the FAO, are any substance or mixture of substances intended to prevent, destroy, or control any pest, including vectors of human or animal disease, unwanted species of plants or animals causing harm during or otherwise interfering with the production, processing, storage, transport, or marketing of food, agricultural commodities, wood and wood products, or animal feedstuffs, or substances that may be administered to animals. A plant growth regulator, defoliant, desiccant, or agent used to thin or prevent premature fruit fall, as well as substances applied to crops before or after harvest to prevent them from deteriorating during storage and transportation, are included in this category.

Today’s agrochemistry has a massive challenge: ensuring the availability of high quality active ingredients to enable long-term control of pest species, weeds, and agricultural diseases. The differential uptake and transfer of the active ingredient by target and non-target organisms can contribute to diverse biological activity.

Probability analysis measures the likelihood of uncertain climatic and agricultural events. It is particularly useful for assessing rainfall occurrence, drought, floods, heatwaves, cold events and other risks that cannot be predicted with complete certainty. This chapter introduces classical, empirical, subjective and conditional probability, probability distributions, return periods and recurrence analysis. It explains how historical climatic records can estimate the likelihood and severity of future events. These methods assist in crop selection, sowing decisions, irrigation planning, infrastructure design and weather-risk management. By converting uncertainty into measurable probabilities, the chapter provides a scientific basis for climate-informed agricultural planning, disaster preparedness and development of resilient farming systems.

Abstract

An attempt has been made in this paper to study probability model for modeling of spell distribution & Weather Cycles during the monsoon period (June to September) at Cochbehar, West Bengal. The daily rainfall data of 31 years (1970 to 2000), recorded at Cochbehar have been analyzed. In this study, it was found that daily rainfall during monsoon period follows Markov Chain model. Markov dependent truncated geometric distribution model is best fitted for studying the Dry spells, Wet spells & Weather Cycles during the monsoon period.

True/False

Q.1.    The set of all possible outcomes of a variable along with the frequency (the number of occurrence) of each outcome or a group of outcome is called “Frequency Distribution”.
Q.2.    The frequency of an occurrence of each outcome of a trail divided by total frequency is known as “Relative frequency” of the outcome.
Q.3.    The distribution of total probability “1” into probabilities of occurrence of each outcome of a trial, is known as probability distribution of the outcome.

Random variable: A random variable is a variable that assumes numerical values associated with the events of a random experiment whose outcomes cannot be predicted with certainty. For example, Height of rice plants in a field; seed weight; No. of tillers per rice plant.

A random variable is of two types:-

1. Discrete random variable: The random variable which takes only countable values i.e., integral values. For Example, Number of tillers per rice plant. Here there can be finite no. of values between any two values.
2. Continuous random variables: The random variable which takes values within intervals. These values cannot be counted but can only be measured. Here there can be infinite no. of values between any two values for example, Height of rice plants in field; seed weight.

11.1 Learning Objectives
After comprehending this unit, we will be capable to:
i) Understand the conception of probability distribution;
ii) Appreciate the significance and applications of Binomial distribution;
iii) Understand the importance and uses of Poisson distribution;
iv) Know about the normal distribution and its’ applications.

11.2 Introduction

A probability distribution is a mathematical function that describes the likelihood of obtaining the possible outcomes of a random variable. In other words, it provides the probabilities of different events or values that a random variable can take. The sum of all probabilities in a probability distribution is equal to 1.

A probability distribution is a statistical function that describes all the possible values and likelihoods that a random variable can take within a given range. This range will be restricted between the minimum and maximum possible values.

The probability distribution shows how the set of all possible mutually exclusive events are distributed, and can be presented as an equation, a chart or a table.

A variable which can take different values with given probabilities is called a random variable.

There are numerous probability distributions which may be distinguished by whether the random variable is discrete, taking only a finite set of possible values, or continuous, taking an infinite set of possible values in a range of values.

A discrete random variable with only two possible values is called a binary variable, e.g. pregnant or not pregnant, diseased or healthy.

Probability distributions for discrete random variables The probability distribution for a discrete random variable y is the table, graph or formula that assigns the probability P(y) for each possible value of the variable y.

It has been a general notion that if an experiment is conducted under identical conditions, values so obtained would be similar. But the experiences of people have dispelled this belief. Observations are always taken about a factor or character under study, which can take different values, and the factor or character is termed as variable.
Hence, we have a set of outcomes (sample points) of a random experiment. A rule that assigns a real number to each outcome (sample points) is called random variable. The rule is nothing about a function of the variable, say, X, that assigns a unique value to each sample point of the sample space.

The theory of probability has its origin in the games of chance related to gambling such as throwing a die, tossing a coin, drawing cards from a pack of cards etc. Jerame Cardon (1501-1576), an Italian mathematician was the first man to write a book on the subject entitled “Book on Games and Chance”, which was published after his death in 1663. The systematic and scientific foundation of the mathematical theory of probability was laid in mid-seventeenth century by two French mathematicians B. Pascal (1623-1662) and Pierre de Fermat (1601 1665).

Important terms and concepts

To understand probability, let us first get acquainted with some important terms and concepts associated with probability.

• Trial: An experiment is any natural or planned activity for which it is not possible to predict the particular result called a trial.

• Events: The possible outcomes of experiments of a trial are called events. For example: Tossing of a coin in an experiment and one toss of the coin is called trial. In tossing of a coin there are only two outcomes or events, i.e. head or tail.

Types of events

Events can be of following different types:

• Exhaustive events

• Equally likely events

• Mutually exclusive events

• Dependent events

• Independent events

• Null or impossible events

• Exhaustive events: It is the total number of all the possible outcomes of an experiment.

In day-to-day life, one comes across many statements which create doubt about certain happenings and one may want to determine the chance of occurrence of a particular happening, e.g., if one desires to know the chance of rain based on some metrological observations.  In the case of coin tossing, one wants to know what will be the chance of winning if a person always stakes for head in a fixed number of tossing. There can be innumerable such queries which one would like to make and meanwhile evaluate mathematically the chance of the occurrence of such possibilities.  This invoked the need of probability theory. The theory of probability came in existence when problems of games were referred to mathematicians like Karl Pearson, Laplace and others. Complicated applications involving advanced theory of probability and axionmatic approach of probability are not dealt within this book.