eChapters

Important Points to Remember

• The active factor of soil formation is : Climate
• Black soils (Vertisols) are formed mainly from the weathering of: Basalts
• Which soils have the highest cation exchange capacity : Black soils
• Plants wilt when soil water content goes below: 15 bar
• Available phosphorus in fertilizer is the fraction: Water soluble + Citrate soluble P

Important Points to be Remember

• Total number of essential nutrient elements required for plant growth -17
• Man made cereal – Triticale
• Father of India’s green revolution – Dr M.S. Swaminathan
• Na, Ve, Ni, Si, Co, Se and I - Beneficial nutrients
• Is added to prevent nitrogen losses from FYM decomposition-SSP

Important Points to be Remember

• Lysosome was discovered by – Duve (1955)
• The term chromosome was first named by – Waldayer
• Power house of the cell – Mitochondria
• Two lines different for a single locus is called – Isogenic line
• Ploidy level in seeds – Embryo – 2n, Endosperm – 3n

Important Points to be Remember

• Father of Indian plant pathology – E.J.Butler
• Father of Plant virology – Beijerinck
• Wheat rust is associated with Indian scientist – K.C.Mehta
• Kresek phase of rice is due to – Bacterial leaf blight
• Bakanae disease of rice is also known as – Foot rot and Foolish seedling disease

Important Points

Factors for Insects Abundance

• More number of species: In the animal kingdom more than 85 per cent of the species belongs to insect group. Total number of insects described so far is more than 9 lakhs.
• Large number of individuals in a single species: e.g., Locust swarm comprising of 109 number of individuals, occupying large area.
• Great variety of habitats: Insects thrive well under varied conditions.
• Long geological history: Insects were known to occupy this earth for more than 350 million years, which is a good track record. This has given the insects’ great variety of adoptions under different conditions.

Important Points to Remember

• The word ‘Extension’ is derived from Latin roots ‘ex’ and ‘tensio’.
• The use of the term ‘Extension’ originated in England in 1866 with a system of University extension which was taken up first by Cambridge and then Oxford Universities.
• James Stuart is considered as father of University Extension.
• Dr. K.N. Singh is known as father of Extension in India.
• Agricultural extension operates within a broader knowledge system that includes research and agricultural education.

Important Points

• A farmer with limited funds may operate in a range of increasing returns to capital is however, not an irrational producer.
• A firm is a particular business firm.
• A higher rate of accumulation of capital relative to the rate of population growth adds to the real stock of capital of a nation.
• A profit maximizing farmer will maximize the output for given level of outlay.
• An isolation curve is negatively inclined.

Important Points to Remember

• Statistics is a branch of mathematics that deals with the data collection, examination, description, and display of numerical data masses.
• Statistics is used in various aspects of life, like in robotics, data science, business, weather forecasting, sports, and much more.
• An ambiguous term. It often denotes the arithmetic mean, but it can also denote the median, the mode, the geometric mean, and weighted means, among other things. Beware if something reports “the average” without making it clear which average.
• The geometric mean of n numbers {x1, x2, x3, …, xn} is the nth root of their product: (x1×x2×x3× … ×xn)^1/n.
• The samples are one of the sub-set of the population, whereas a population is an overall set of things or individuals of a specified group.

Important Points to Remember

Apple

• Botanical name: Malus x domestica Borkh.
• Family: Rosaceae
• 2n: 34
• Origin: Asia Minor to Western Himalayas

Important Points to Remember

Cauliflower

• Botanical name: Brassica oleraceae var. botrytis
• Family: Crucifereae
• 2n = 18
• Type of vegetable: Leafy vegetable
• Origin: Mediterranean region

Important Points to Remember

ROSE

• Botanical name: Rosa spp.
• Family: Rosaceae
• 2n = 14
• Origin: North Hemisphere (Himalyas)
• Flower colour: Red (commonly)

Important Points to Remember

• The term Biochemistry (biosG = life) was first introduced by a German chemist Carl Neuberg in 1903. Biochemistry may be defined as a science concerned with the chemical nature and chemical behavior of the living matter.
• Major organic molecules present in the living beings are: carbohydrates, proteins (of course, including enzymes), lipids and nucleic acids.
• Carbohydrates are the most abundant organic molecules in nature. They are primarily composed of the elements carbon, hydrogen and oxygen.
• The name carbohydrate literally means ‘hydrates of carbon’. The simplest carbohydrates are the monosaccharides that have the general formula (CH2O)n where n is 3 or more.
• Carbohydrates may be defined as polyhydroxyaldehydes or ketones or compounds which produce them on hydrolysis. The term ‘sugar’ is applied to carbohydrates soluble in water and sweet to taste.

Important Points to Remember

• Microbiology is the study of living organisms of microscopic size majority of which are too small to be observed with naked eye, include bacteria, fungi, algae and protozoa and the infectious agents at the borderline of life that are called viruses.
• However, a few members of some microbial groups like algae and fungi are clearly visible without the aid of microscope.
• It is concerned with their form, structure, reproduction, physiology, metabolism and classification. It includes the study of their distribution in nature, their relationship to each other and other living organisms, their effects on human beings and on other animals and plants, their abilities to make physical and chemical changes in our environment and their reactions to physical and chemical agents.
• Most microorganisms are unicellular in which all the life processes are performed by a single cell. All living cells contain protoplasm which is a colloidal organic complex consisting of largely proteins, lipids and nucleic acids.
• The major groups of microorganisms include Prokaryotic and Eukaryotic microorganisms.

Important Points to Remember

• The word “biotechnology” was first coined in 1917 by a Hungarian agricultural engineer, Karl Ereky. He used it to describe a system for raising pigs on sugar beets as their primary food source. He defined Biotechnology as “all lines of work by which products are produced from raw materials with the aid of living things”. He defined such a new terminology since most of the industries use mechanical devices - machines - to make things, biotechnology uses living organisms to make products of economic value.
• Many different scientific fields are responsible for emergence of this biological revolution. They are biochemistry, molecular biology, microbiology, physiology, computer science and what not?
• Hence, a newer, refined, definition of biotechnology was offered by U.S. Office of Technology Assessment: “Any technique that uses living organisms to make or modify products, to improve plants or animals, or to develop microorganisms for specific purposes.”
• The United Nations Convention on Biological Diversity defines biotechnology as: “Any technological application that uses biological systems, living organisms, or derivatives thereof, to make or modify products or processes for specific use.”
• Schleiden and Schwann proposed cell theory in 1838.

Abstract

Agricultural development interventions need agricultural financing to increase productivity. This study investigates what factors influence farmers’access and use of agricultural loans, what challenges they face in obtaining and repaying loans, and how financial support affects their income outcomes. The study also suggests some policy actions to improve the supply and performance of agricultural loans for farmers.

1.1. Meaning

Agricultural finance generally means studying, examining and analysing the financial aspects pertaining to farm business, which is the core sector of India. The financial aspects include money matters relating to production of agricultural products and their disposal.

Abstract

This chapter examines how agricultural credit is a vital input for all agricultural development programmes. Private moneylenders used to be the main source of agricultural credit. However, this source was insufficient, costly and oppressive. To overcome this, a multi-agency approach involving cooperatives, commercial banks and regional rural banks has been adopted to provide cheaper, timely and adequate credit to farmers.

Abstract

Most farmers in India lack capital. They need credit at the right time, from the right agency, and in adequate amounts to be productive. From the farmer’s perspective. When a farmer applies for a loan from an Institutional Financial Agency (IFA), the lender must be convinced of the 3 R’s, 5 C’s, and 7 P’s, which indicate the economic viability of the proposed investments.

Abstract

Agriculture, like any other economic activity, needs finance. Crop productivity in India is low due to traditional farming practices and agricultural production must increase to achieve self-sufficiency and save foreign exchange. Therefore, credit is vital for enhancing agricultural production. Credit helps develop fixed farm assets and can be classified into short-term loans, medium-term loans, and long-term loans, according to studies on the relationship between agricultural growth and credit availability. However, credit withdrawal might cause a decline in conditions, while credit provision with stagnant production conditions won’t improve output. Since production conditions are dynamic, loan requirements would grow from year to year even if input stay constant.

Abstract

Rural indebtedness is an old problem in India. In the 19^th century commercial banking was absent in rural areas and farmer were entirely in the hand of usurious moneylender. Since the British rule, the central government has been trying to extend institutional lending to the rural agriculture sector. To provide credit to the agricultural sector, a multi-agency approach involving co-operative banks, scheduled commercial banks, and RRBS has been used in India. Adequate and timely loan availability at reasonable rates is the basis for agricultural finance policy. The institutional structure for agricultural lending has improved over time in terms of scope and reach. India’s higher education institutions have been focusing their efforts on advancing agricultural finance.

Abstract

Many obstacles hinder agriculture credit in rural regions: insufficient funding, failure to reach vulnerable rural residents, especially marginal and small farmers, and lack of local focus in agriculture credit distribution. India adopted a “Social and Development Banking” policy after nationalizing commercial banks in 1969. This policy aimed to solve some of these issues and boost the supply of formal credit to the agriculture sector. Rural communities would benefit from more loans at competitive interest rates due to this policy. In the 2000s, agriculture credit grew annually by 17.6%, much higher than the 2.6% growth rate in the 1990s. The increase in agriculture credit had three distinct features: indirect financing, a sharp rise in loans of Rs. 10 crore and above, and growing availability of agriculture credit through urban bank branches. The revival of agriculture lending in the 2000s mostly benefited small farmers and marginal farmers

Abstract

Financial analysis evaluates the performance and suitability of enterprises, projects, budgets, and other financial-related institutions. It determines if a business is secure, financially sound, liquid, or profitable enough to warrant a financial commitment. Accurate records and financial statements provide the essential information for analyzing the financial situation and trends of a farm operation. A financial analyst examines the income statement, balance sheet, and cash flow statement.

Abstract

This chapter explains SWOT analysis, a tool for corporate planning to evaluate how an organisation compares with its rivals. It stands for strengths, weaknesses, opportunities, and threats. Albert Humphrey is traditionally considered the originator of the plan in the 1960s, but this claim is disputed. No one agrees on who created it. It has become useful for differentiating from and finding a niche within the larger market. It is also called the SWOT Matrix. SWOT Analysis is a tool that can apply outside of business to analyse a person’s status against their competitors. The tool considers both internal and external factors. The terms “weaknesses” and “strengths” relate internally. The former is a characteristic of a company or other entity that gives it a disadvantage over rivals. The latter is a trait of that same entity, which gives it an advantage over its rivals. For external factors, “opportunity” are aspects of the larger environment that the entity can exploit to its benefit. However, “threats” are also realities in the larger environment, which could harm the entity.

Abstract

This chapter explains the cooperation which create social relations that help individuals achieve goals that they could not achieve alone. They also help farmers benefit from economies of scale and reduce risks, and empower their members economically and socially by involving them in decision-making processes.

Abstract

This chapter covers the main theories and academic perspectives used to study the role of different organizations in which they explain their vision, mission, object functioning, etc.

Weather and climate have profound influence on agriculture of a region. There are some differences between these two terms. Weather describes the state of the lower atmosphere of a place with respect to temperature, precipitation, wind direction, wind speed, humidity, cloudiness and pressure etc. over a short period of time. The short term variations over minutes to days are indicated as weather of a location. It says whether the atmosphere is hot or cold, rainy or dry, sunny or cloudy, still or cyclonic and so on usually for period of 24 hours. The weather may vary from place to place and from time to time due to difference in temperature, cloudiness, humidity, rain and other events of atmosphere. It may be sunny at one locality, but rainy at another locality.

Modern agriculture requires certain precise information on pattern of rainfall, temperature, occurrence of drought and flood etc. for formulating required strategy for crop production. It also requires knowledge about agro-climatic conditions of the region for deciding a planned cropping pattern. Therefore climatic classification and delineation of agro-climatic zones would be imperative for a country or state.

Climatic Factors and Crop Growth

Crop growth depends on internal and external factors. Internal factors are mostly genetic or hereditary factors which include high yielding ability, resistance to lodging, tolerance to adverse conditions such as drought, flood and salinity, tolerance to insect pests and diseases, and grain quality. The external factors include climate, edaphic, biotic, physiographic and socio-economic factors.

Climate and Agriculture

Soil, climate and water are three basic requirements of a crop to be grown in a particular region. Climate affects the temperature trends, seasons, length of the growing season, amount of sunlight and the seasonality of sunlight etc. A change in climate may cause recurrent droughts, floods and weather extremities that would affect the agricultural production, human livelihoods, health and biodiversity. The relationship between agriculture and climate is complex as it is dependent on climate and also plays a contributing role in emission of greenhouse gas (GHG). Greenhouse gases include carbon dioxide, nitrous oxide, methane, chlorofluorocarbons (CFCs), hydro-chlorofluorocarbons (HCFCs), hydro-fluorocarbons (HFCs), per fluorocarbons (PFCs) etc. , which play a significant role in climate change.

Agriculture and Food Security

Food security refers to a situation when people gain physical and access to enough food to meet the dietary requirements for a healthy life for all times. A household can have food security when all the members lead a hunger free life. It is estimated that nearly 800 million people in the world go to bed hungry every night. Food security exists when all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life (World Food Summit, 1996). This definition gives rise to four dimensions of food security: availability of food, accessibility (economically and physically), utilization (the way it is used and assimilated by the human body) and stability of these three dimensions.

Climatic factors like increase in temperature, carbon dioxide (CO₂) concentration, moisture, and other factors directly or indirectly affect agriculture, which have been elaborately dealt in previous chapters. Climate change causes weather variability that may result in low production, hike in price of agricultural commodities including food grains, vegetables, fruits, meat, egg and milk. It is now realised that climate resilient agriculture (CRA) will be imperative to reduce the vulnerability of agriculture to the climate change.

Introduction on Climate Smart Agriculture (CSA)

Extreme weather conditions like rainstorms, high wind pressures, high temperatures, flood and drought situations have profound influence on agriculture. Climate and agriculture are strongly interrelated universal processes and thus variations in climate influence agricultural activities (IPCC, 2007). The effects of climate change are seen in form of global warming, unseasonal and excess rainfall, and frequent natural calamities which affects agricultural production. At the same time emission of GHG to the atmosphere is a major source of climate change. The impact of climate change on crop yield, dairy and other livestock production including fishery will be felt in reduced production and consumption. Climate change will effect in additional increase in prices for main agricultural food crops such as rice, wheat, corn and soybeans (IFPRI, 2009).

Need for climate-smart agriculture strategy

Climate change has long-lasting and potentially detrimental consequences on the agriculture sector. Changes in weather events cause perturbations across agricultural ecosystems and have impact on farm productivity. Adopting the climate-smart agriculture approach has thus, become inevitable in order to build resilience in agriculture systems and cope up with climate change. With a view to implementing climate-smart agricultural practices to ensure climate resilience, certain broad strategies have to be adopted. The CSA approach is designed to identify and operationalize sustainable agricultural development within the explicit parameters of climate change (Climate Smart Agriculture Sourcebook.FAO.2013). It integrates the three dimensions of sustainable development (economic, social and environmental) by jointly addressing food security and climate challenges.

Climate-smart Agriculture Practices and Indicators

Considering the three pillars of climate-smart agriculture such as productivity, adaptation and mitigation, different technologies and practices have been evolved.

The CSA practices are grouped under soil and land management, water management, crop management, livestock management, forestry, fishery & aquaculture and energy management. The World Bank Group (2016) has suggested broad indicators of CSA practices (Table 7.1; Chapter 7)

Application of Climate-Smart Agriculture

Agricultural production systems are expected to change in response to climate change and ensure food security in different countries. Climate variability may further affect the agricultural production in future. To meet such challenges, there are many options to reduce the negative impacts of climate change on agricultural systems, make them resilient to climate change, and reduce emissions. Many technologies and practices can increase crop yields, farm income, and input-use efficiency, and may reduce GHG emissions (Khatri Chhetri et al. 2017). Climate-smart agriculture (CSA) is suggested to address the adverse effects of climate change and ensure food security. It is intended to achieve the three objectives of ensuring productivity, adapting and building resilience and reducing emission of GHG.

Services for CSA

Under the CSA projects and programmes, the services like supply of inputs and technology transfer through extension services are provided to the farmers. The inputs like seeds of crops are provided by the input dealers at the subsidized rate and seedlings of fruits and vegetables are provided by both private and government agencies to grow under general cultivation and poly-house cultivation technology. Similarly other services like soil testing facilities, issue of soil health cards, construction of micro-irrigation projects and rain harvesting structures are made available to farmers. Under insurance scheme, loanee farmers are supported to pay premium dues for availing crop insurance facilities. Other services like trainings at various levels, demonstrations, exposure visits, exhibitions, kisan melas, field days, monitoring and evaluation of all these activities under these programmes are provided to the farmers with collaboration of extension officers.

Domestic livestock plays a major role in meeting the food requirement of human beings. Especially with dwindling resources, improvement in production efficiency is primary to meet the ever-increasing demand for animal protein. The role of an animal breeder is to bring about a sustained improvement in the quality and quantity of such products. Over the years, there has been an enormous improvement in the production ability of livestock and statistical analysis has played a predominant role in the application of principles of animal breeding to improve the productivity of animals. The combination of genetics and statistics developed into a separate branch of science called biometry.

Maintaining the required type of information in the form of records is primary for any type of animal breeding analysis and this chapter explains the importance of records and ways to maintain them in the form of registers or computer databases. The formats for different types of registers to be maintained are also given as a template in appendix. Any breeding plan is formulated to improve the economic traits depending on the utility of species reared and the chapter lists important economic traits of livestock and poultry with definitions and formulae, where ever required.

Basic record maintenance is a must for any animal breeding program. Every farm or breeding program is expected to maintain records in the form of registers or computerized databases. The goal of animal breeding is to select mates with the objective to improve economically important traits. In this chapter, the most common economic traits in different species of livestock are described. Even though the list is not exhaustive the basic economic traits that have been worked for improvement are explained. Breeders also explore new composite traits which are a combination of these base traits.

Animal breeding data is unique in terms of volume, statistical analysis, type of information required etc. The large volume of data extended spatially and temporally warrants additional tools in the form of databases to handle data and enable accuracy, retrieval, efficient utilization and adaptability to software. This chapter explains the nature of animal breeding data and the importance of databases with examples and explanations.

Animals are raised for some utility including meat, milk, hide, wool etc., and efforts are on to continuously improve the quality and quantity of these products. Some of the important economic traits in this regard were already discussed in earlier chapters. Record maintenance is primary for decision-making with respect to the selection of animals. The details of record maintenance including the format of registers are already explained in chapter 2. Apart from the actual information required for economic traits, pedigree information forms an important part of animal breeding data.

Basic knowledge of statistics is important to understand animal breeding analysis. In this chapter, the hypothesis testing, principles of experimental design, types of data and probability distribution are described with examples using appropriate software.

Correlations and regression are important components of animal breeding analysis. The genetic correlation is an important genetic parameter that is useful in the planning of breeding programs involving multiple traits. In this chapter, the basic concepts of correlation and regression including method of estimation, assumptions and test of significance are presented. An example using SPSS software is also given at the end for better understanding.

It is known that the phenotype of an individual is a combination of genotype and environment. Animal breeding analysis is mainly done to partition the genetic part from the environmental part so as to assess the actual genetic worth of the animal. In this process correction for the known environmental part is integral to any animal breeding analysis. In this chapter, a list of common factors affecting important economic traits are described. The analytical part for these effects will be explained in later chapters.

Correction or standardization of data is very important in any animal breeding analysis. Unless the candidates under selection are placed on a common platform, various factors affecting the trait will lead to bias in assessing the performance of the individual with respect to a particular trait. For eg. a lamb born during a favorable season, a lactating cow in its third parity, first lactation yield of a cow with longer age at first calving.

The key genetic parameters available with the animal breeder to attain improvement are heritability, repeatability and genetic correlation. This chapter will mainly deal with the theoretical considerations of these parameters.

The goal of an animal breeder is to bring about permanent improvement in different economic traits, either individually (univariate) or simultaneously in multiple traits (multitrait). The phenotypic value available from the animal is a combination of genotypic value and environmental deviation. By permanent improvement in a trait, we mean the improvement in genotypic value, which could be carried over to future generations. The environmental deviations are spatially and temporally specific, and thus will not be the same over generations. The genotypic value, itself is made up of additive effect, dominance deviation and interaction deviation. The additive effect is a value of genes (the sum of the average effect of genes) and hence is more important for the breeder as only the genes are passed on to the next generation.

Estimation of variance is primary for any breeding program as all the genetic parameters are dependent on these estimates. The properties of an estimate, their assumptions, type of data, availability of data, and computational requirement make it important to understand the method of estimation of variance components so as to make a wise choice that would suit the breeder’s requirement. This chapter explains the methods of estimation of variance with merits and demerits of each and the reader could use these as a guide to choose the right method of estimation.

The genetic parameters are the basis for any animal breeding program and the important parameters such as heritability, repeatability and genetic correlation are ratios of (co)variances and their reliability depends on the accurate estimation of variance components. Estimates of variance components are used extensively in animal breeding.

Linear models have been extensively used in animal breeding. This chapter explains the basics of linear models including description; fixed, random and mixed models; model diagnostics and covariates. The concept of linear models in animal breeding is explained with representation and actual examples of use in different types of livestock.

Introduction to Linear Models

A model can be defined as a physical, mathematical or otherwise logical representation of a system, entity, phenomenon, or process. Linear models are a way of describing response variables in terms of a linear combination of the predictor variable. They provide a framework for a large set of models whose common goal is to explain or predict a quantitative dependent variable by a set of independent variables that can be categorical or quantitative.

Traits such as growth, lactation test day yield, semen evaluation are longitudinal traits measured repeatedly on the animal and random regression models (RRM) have been found to be suitable for modeling these traits, The methodology used, data requirement, assumptions, validity, software available and application of RRM are explained in this chapter.

Growth, test-day milk yield, and semen traits are examples of longitudinal traits measured repeatedly on the animal and random regression models (RRM) have been found suitable for modeling such traits. RRM is based on a covariance function which could be defined as “a continuous function to give the variance and covariance of traits measured at different points on a trajectory” (van der Werf, 2001). Kirkpatrick et al. (1990) showed that variance components for longitudinal data can be modelled through covariance functions. Random regression models (RRM) and the resulting covariance function have been recognized as ideally suited for the analysis of longitudinal data (Hill, 1998; Meyer, 1998).

Milk yield is one of the most important economic traits and modelling the lactation curve is important with respect to the unique shape with ascending, persistent and descending phases. The non-linear relationship of daily milk yield with days in milk has led to extensive research in modelling the lactation curve. The nature of the lactation curve, different models, identifying the best model of fit, factors affecting the curve parameters and orthogonal polynomials to model the curve are discussed in this chapter.

The aim of the breeder is to improve on various economic traits through selection. However, due to the lesser number of parents used, over a period of time, there creeps in the effect of inbreeding and loss of genetic variability which in turn causes depression and loss of fitness. This is more prominent in finite endangered populations and evaluation for genetic variability also forms an integral part of the animal breeding analysis.

Genetic variability is an integral part of domestic animal populations as fitness is directly related to the variability.

Meta-analyses has been used extensively to combine the results of existing studies and obtain powerful estimates of a parameter. In animal breeding, attempts have been made to obtain meta-analysis estimates for genetic parameters. This tool is more pertinent to animal breeding on account of heterogeneity in terms of location, sample size and other factors in the estimation of genetic parameters. This chapter explains the background, methodology, precautions and implications of meta-analysis with respect to genetic parameters.

A meta-analysis is a powerful tool of statistics that could combine several estimates from different studies for a parameter and the resulting weighted estimate could be used as a representative value for the particular parameter. However, not all the studies are similar with respect to location, sample size, and methodology used and thus various precautions are to be taken right from the selection of studies to incorporating of between-study heterogeneity in the models. In animal breeding, met-analysis is very much useful for obtaining meaningful estimates of genetic parameters and this chapter explains the methodology of meta-analysis in animal breeding.

This chapter describes various multivariate techniques which are useful in animal breeding. The genetic correlation with multiple response variables is already explained in the chapters on Correlation and Genetic Parameters. The other techniques including MANOVA, Principal Component Analysis, Factor Analysis, Cluster Analysis and Discriminant Analysis are explained.

Multivariate analysis is referred to a set of data with multiple response variables in general and such data is encountered often in animal breeding analysis. The simplest form of multivariate analysis is the genetic correlation where two or more traits are analysed together as response variables with factors affecting them on the right-hand side (RHS) and details of these are already explained in earlier chapters.

The large volume, type and nature of animal breeding data and the complex nature of analytical requirement warrants additional tools for handling, storage, retrieval and solving complex equations. Accuracy, speed, computational requirement, applicability, etc., has resulted in the development of numerous software exclusive for animal breeding data analysis. The database management system is already explained in the chapter on ‘Nature of animal breeding data and database management’. This chapter gives a note on the commonly used animal breeding software.