eChapters

Introduction

The importance of soil in agriculture cannot be overstated, as it serves as the foundation for crop growth and sustenance. However, many regions grapple with problematic soils characterised by issues such as poor fertility, erosion, contamination, and structural degradation. In recent years, significant steps have been made in addressing these challenges through innovative approaches and technologies. This change in outlook includes the combination of imaginative advancements, protection procedures, and biological standards to enhance farming efficiency while limiting ecological effects (Singh et al., 2011).

Farmers are challenged with the management of problematic soils characterised by fertility issues, erosion, contamination, and structural degradation. Sustainable agricultural techniques depend on the efficient management of problematic soils, which is also critical to environmental sustainability, global food security, and farming systems' ability to withstand climate change. The idea of changing soil and harvest management practices a complex system that incorporates accurate horticulture, agroecology, and computerised cultivating innovations (Fu et al., 2021).

Introduction

According to the United Nations (UN, DESA 2019) the world population will reach around 9.7 billion by 2050. The Food and Agriculture Organisation (FAO) has predicted that the world food grain production must increase by 70% by the year 2050 to feed this ever-growing population (Bernard et al., 2017). United Nations World Population Prospects (2022) also predicted the same (Fig 1). However, it is a matter of great concern that the global food grain production has not increased to any significant level (Fig 2) in the past 15 years. As seen in Fig 2, the global food grain production trend has reached a plateau. The main reason behind this fact is the degradation of this valuable natural resource (soil) beyond the tolerance limit due to anthropogenic activities such as imbalanced fertiliser use with the application being largely skewed in favour of nitrogen (N) fertilisers. As per the available data from 2020-21, in nutrient terms, India consumes a total of 32.5 million tonnes (Mt) of fertilisers swhich are distributed over a stable net cultivated area of 141 million hectares (Mha) (Singh et al., 2022). Associated with this is the expenditure of a huge amount of energy for producing these fertilisers, which principally comes from fossil fuel burning, pumping a gigantic quantity of groundwater to solubilise these fertilisers and so on. Not only nitrogen, but the usage of phosphatic and potassic fertilisers has also witnessed an upsurge in the last 50 years (Fig 3).

Introduction

Soil fertility is a key factor in successful crop production measures the ability of the soil to supply nutrients to plants. Soil fertility and fertilisers are closely related concepts. Soil fertility acts as a "SOURCE" from which plants can absorb nutrients for maximum yield, while manure acts as a "SOURCE" from which we can continuously absorb various nutrients and also add to the soil. The importance of soil fertility and fertiliser management has been increasingly recognised in all countries to meet the demand for food and other agricultural commodities. Intensive use of fertilisers and cultivation of high yielding varieties has undoubtedly increased food production and reduced food scarcity, but it has also brought with it a host of problems related to soil fertility, soil and water pollution (Sow et al., 2023a). On the other hand, rapid depletion of nutrients due to overuse, widespread N, P, K, and S deficiency, along with micronutrient deficiencies, especially Zn and Boron, have been observed in many soils.

Introduction

Water is a prominent element in the daily life of human beings as well as in agriculture. In better crop production, the quantity of water, but the quality of water is also important. With respect to the current scenario of water harvesting or water conservation there are some techniques adopted in Indian agricultural practices, such as the use of drip irrigation to minimise water surplus, rain fed agricultural practices, storing rain water in different storage components, using mulching (either natural or plastic), proper tillage practices, selecting crops which need less amount of water, using laser leveller for better water uses to encounter surplus water flow to the lower elevated portion of the field etc. Water conservation techniques and measures are directly related to the type of meteorological factors of specific watershed. For the arid and semi arid regions proper method of tillage practice improves water use while in conventional practices of farming consumes more than required water quantity. Some crops are water-intensive where the quantity of the available water is crucial. Sources of water can be ground water, rain water and irrigation water. In sustainable crop production, the right amount of water is necessary for the optimal physiological growth of specific crops. Although the availability of water depends upon the geographical location, for example in the Gangetic plains of India there is less need for water conservation agricultural purposes. On the other, in the region of Deccan Plateau of India, availability of water is comparatively lower than in the Gangetic plains. It is also important that water conservation techniques can be adopted irrespective of the geographical locations. Water is conserved in nature itself but the availability of water depends on the hydrological cycle. Therefore, conservation has become a trending topic. Water conservation can be improved through sustainable agricultural practices such as proper scheduling of irrigation, selection of crops based on sustainability, monitoring soil moisture content, maintaining records of crop growth and physio-chemical data, implementing effective drainage systems, intercropping operations and utilising recycled water for other purposes.

Introduction

Climate change has brought about unprecedented weather variability, disrupting established patterns and posing significant challenges to global agriculture. The Intergovernmental Panel on Climate Change (IPCC) reports unequivocal evidence of increasing temperatures and altered precipitation patterns (IPCC, 2021). These changes impact crop production, water availability, and overall food security, necessitating innovative approaches to ensure sustainable agriculture in the face of evolving climatic conditions. To address these challenges, accurate crop yield forecasting becomes paramount. Reliable predictions empower farmers, policymakers, and agricultural stakeholders to make informed decisions regarding resource allocation, risk management, and adaptation strategies (Lobell et al., 2012; FAO, 2019). This chapter explores the intricate interplay between weather variability, climate change, and the development of effective crop yield forecasting models, offering a comprehensive perspective on how sustainable agriculture can be achieved in the midst of these dynamic environmental shifts.

1.1. Introduction

Research in simple terms refers to search for knowledge. It is a scientific and systematic search for information on a particular topic or issue. It is also known as the art of scientific investigation. Several social scientists have defined research in different ways.

In the Encyclopaedia of Social Sciences, D. Slesinger and M. Stephenson (1930) defined research as “the manipulation of things, concepts or symbols for the purpose of generalizing to extend, correct or verify knowledge, whether that knowledge aids in the construction of theory or in the practice of an art”. According to Redman and Mory (1923), research is a “systematized effort to gain new knowledge”. It is an academic activity and therefore the term should be used in a technical sense. According to Clifford Woody (Kothari, 1988), research comprises “defining and redefining problems, formulating hypotheses or suggested solutions; collecting, organizing and evaluating data; making deductions and reaching conclusions; and finally, carefully testing the conclusions to determine whether they fit the formulated hypotheses”

Statistics is a branch of science which deals with the collection, classification, description and interpretation of data obtained by conducting surveys and experiments. The essential purpose of it is to describe and draw valid inferences about numerical properties of populations” (Ferguson 1966). The experience in statistics application indicates that a single statistical method can be used in different research areas for dealing with different types of problems. Various possibilities which application of a statistical method provides in certain research areas should be considered as an adequate way of dealing with a problem of the research. However, it should be noted that statistics is not a method by which it is possible to solve all the problems in a research. Nachmias and Nachmias (2009) stated that there are basically two types of statistics which include descriptive and inferential statistics. Descriptive statistics enable the researcher to summarize and organize data in an effective and meaningful way. It involves the use of tables, charts, graphs, mean, modes, median, standard scores and correlation to treat collected data. Inferential statistics is concerned with making inferences from a unit of a population. Inferential statistics is concerned with making inferences from a unit of a population. Inferential statistics allow the researcher to make decisions or inferences by interpreting data patterns. Researchers use inferential statistics to determine whether an expected pattern designated by the theory and hypotheses is actually found in the observations. To decide whether this hypothesis is true, researchers might survey the respondents and then use descriptive statistics to make comparison between these groups and would employ inferential statistics to determine whether the differences between the groups are significant or not.

In statistics, sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Two advantages of sampling are that the cost is lower and data collection is faster than measuring the entire population. Each observation measures one or more properties (such as weight, location, colour) of observable bodies distinguished as independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly stratified sampling. Results from probability theory and statistical theory are employed to guide the practice. In business and medical research, sampling is widely used for gathering information about a population. Acceptance sampling is used to determine if a production lot of material meets the governing specifications.

The sampling process comprises several stages

• Defining the population of concern

• Specifying a sampling frame, a set of items or events possible to measure

• Specifying a sampling method for selecting items or events from the frame

• Determining the sample size

• Implementing the sampling plan

• Sampling and data collecting

Multivariate analysis is essentially the statistical process of simultaneously analyzing multiple independent variables with multiple dependent (outcome or criterion) variables using matrix algebra. During the last two or three decades, multivariate statistical analysis has become increasingly popular. The theory has made great progress, and with the rapid advances in computer technology, routine applications of multivariate statistical methods are implemented in several statistical software packages. The traditional approach to the teaching of multivariate statistical analysis, as exemplified by Anderson (1958), relies heavily on advanced matrix mathematics. On the other hand, Hury and Riedwyl (1988) suggest that it is possible to understand most of the basic ideas underlying multivariate statistical analysis without a mastery of such mathematics, provided that these are conveyed with the help of real data sets. In this chapter we propose a non-mathematical data-driven approach for teaching multivariate statistical methods to students. Despite this, we are mindful of the need for students to know some basic linear algebra and univariate statistical concepts. Such basic knowledge provides students with the foundation necessary for the application of the appropriate multivariate statistical procedures and for the interpretation of results.

A linear programming problem aims to maximize or minimize a linear function subject to the linear constraints. But in many cases, the objective function may not be linear or all/some of the constraints may not be linear or both the objective and the constraints may not be linear. Such an optimization problem is called a nonlinear programming problem (NLPP). In this chapter, we discuss about NLPP’s. Nonlinear programming is the process of solving an optimization problemdefined by a system of equalities and inequalities, collectively termed constraints, over a set of unknown real variables, along with an objective function to be maximized or minimized, where some of the constraints or the objective function are nonlinear. It is the sub-field of mathematical optimization that deals with problems that are not linear. A typical non-convex problem is that of optimizing transportation costs by selection from a set of transportation methods, one or more of which exhibit economies of scale, with various connectivity and capacity constraints. An example would be petroleum product transport given a selection or combination of pipeline, rail tanker, road tanker, river barge. Owing to economic batch size the cost functions may have discontinuities in addition to smooth changes. In experimental science, some simple data analysis (such as fitting a spectrum with a sum of peaks of known location and shape but unknown magnitude) can be done with linear methods, but in general these problems, also, are nonlinear.

Statistics as a wide subject is used in almost all disciplines especially in research studies. Statistics provides scientific tools for representative data collection, appropriate data analysis and summarization of data and inferential procedure for drawing valid conclusion in the face of uncertainty. Each and every researcher should have some knowledge in Statistics and must use statistical tools in his or her research, one should know about the importance of statistical tools and how to use them in their research or survey. The quality assurance of the work must be dealt with: the statistical operations necessary to control and verify the analytical procedures as well as the resulting data making mistakes in analytical work is unavoidable. The emergence of statistical software in the twenty-first century has helped different researchers in the physical and social science to improve in the quality of research. Most renowned researchers in adopting this software in their data analysis have been able to identify the immense contribution to research findings (Adetola, 2013). Any quantitative research cannot be done effectively without Statistical Software. Moreover, it enables research data for easy presentation. It helps professionals to interact with data thereby paving way for creativity and innovation. Some are user friendly interface with drop-down tips for beginners (ATS, Ucla Edu, 2014). Advances in technology have improved all our lives (Akindutire, 2013) and has allowed experts greater freedom to come out with results within a twinkle of an eye than ever before where it takes time to finish analysis. This same technology has offer tremendous opportunity to research and keep research as a more interesting field of study. This study is significant, because it is an attempt to measure the direct impact of different statistical software on research analysis.

7.1. People’s Participation in Observation, Analysis and Planning for Rural Development

Why should people participate?

There is no controversy as to whether there should be people’s participation in the development programmes in the development programmes designed to meet the basic needs of the people. The post-mortem of any unsuccessful programmes has shown that they have invariably failed when they failed to provide an adequate place and importance to people’s participation in the planning and analysis.

There is a body of philosophy and a set of assumptions behind people’s participation in rural development. These are;

i) It should be people’s programme with government’s participation;

ii) Community needs to build up their own capacity, agents have got a catalytic role;

iii) Self- imposed changes have got permanence as compared to those imposed from outsides

iv) Holistic approaches are far better than the fragmented approaches;

v) People need certain help to solve unique problems;

8.1. Introduction

Sometimes writing reference list could be so confusing since there are a lot of different rules. Each universities or even each faculty use different rule in writing reference list. The different rule of writing reference list is normal because they refer to different style guide. There are a lot of styles guide available. MLA (Modern Language Association of America) style; the Chicago Manual of Style; the Oxford Guide to Style are some examples of style guide. Difference reference of style guides chosen will result in totally different way of writing reference list. The most widely accepted style guides in social science is the APA (American Psychological Association) style. In some higher education journals (e.g. Journal of Studies in International Education, Higher Education) it is stated clearly that author must submit their paper according to APA style. Referencing in a general sense means to give credit to someone for using his or her own ideas or thoughts in a research activity. Referencing helps in gaining the originality of the ideas and thoughts used in the research activity. Failure to reference is treated as disrespect to the original author or writer and seen as a major misconduct in the area of academic research writing. Generally students made the mistake of not mentioning proper referencing at the end of their research projects, essays or any other piece of work. This may lead to cancellation of the written matter. If we do not reference properly the written matter is treated as if it is copied from somewhere. The research paper or material cannot be submitted for further research or reading purpose. We find a lot of data when we search for something.

9.1. Introduction

The human population of the world will reach nearly 10 billion by 2050, boosting agricultural order-in a situation of humble financial development (FAO, 2017). At present, approximately 37.7% of total land surface is being utilized for crop production. From employment generation to contribution to National Income, agriculture sector is contributing a significant role in economic prosperity of the developed nations and is playing an active part in the economy of the developing countries as well. The growth of agriculture has resulted in a significant increase in the per-capita income of the rural community. Thus, a greater emphasis on agricultural sector is the need of the hour. For countries, like India, the agricultural sector accounts for 18% of GDP and provides employment to 50% of the country’s workforce. Development in the agricultural sector will enrich the rural development which will lead toward rural transformation and ultimately the desired structural transformation (Mogili and Deepak, 2018; Shah et al., 2019). The role of Artificial Intelligence (AI) has been obvious in the agricultural sector recently. The sector needs the impetus from AI based technology to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology.

10.1. Introduction

Decision-making is a fundamental aspect of human life, influencing choices in both personal and professional settings. Every individual, whether consciously or unconsciously, engages in decision-making daily, ranging from simple tasks like selecting a meal to complex strategic planning in organizations. However, decision-making becomes increasingly challenging when multiple conflicting criteria are involved. The presence of numerous factors, each with varying degrees of importance, often creates ambiguity and uncertainty, making it difficult to arrive at the most suitable option. To address these challenges, structured decision-making methodologies have been developed to provide a systematic and logical framework for evaluating alternatives.

One such widely recognized method is the Analytical Hierarchy Process (AHP), introduced by Thomas L. Saaty in the 1970s. AHP was designed to assist decision-makers in breaking down complex problems into a structured and systematic hierarchy. This approach allows for a step-by-step evaluation of different factors, making it easier to analyze their relative importance. By structuring a problem into different levels - such as goal, criteria, sub-criteria, and alternatives - AHP provides clarity in decision-making and enhances consistency in judgments. The method is particularly beneficial when dealing with decisions that involve both tangible and intangible factors, as it allows subjective preferences to be quantified systematically (Saaty, 2008).

11.1. Introduction

Fuzzy Cognitive Mapping (FCM) is a powerful soft computing technique that integrates elements of fuzzy logic and cognitive mapping to model complex systems. It allows for the representation of causal relationships between variables in an interpretable and flexible manner, making it particularly valuable for analyzing and simulating real-world dynamic systems. FCM has its roots in cognitive science, systems thinking, and artificial intelligence, enabling researchers and practitioners to model uncertainties and interdependencies effectively (Tyrovolas et al., 2023). The approach is particularly advantageous when dealing with complex, ill-structured problems where traditional mathematical modeling techniques fall short. By capturing expert knowledge and combining it with computational techniques, FCM helps in making informed predictions and policy decisions.

It is widely applied across multiple domains, including environmental management for sustainability assessments (Özesmi and Özesmi, 2004), decision support systems in business and governance, healthcare for medical diagnostics and treatment planning (Apostolopoulos et al., 2024), and socio economic planning for policy analysis and forecasting (Gray et al., 2015). Furthermore, its adaptability has led to its increasing integration with artificial intelligence, machine learning, and big data analytics, making it a promising approach for tackling contemporary challenges.

12.1. Introduction

Artificial Neural Networks (ANNs) are computational models inspired by the way biological neural networks in the human brain process information. They consist of interconnected nodes or “neurons” arranged in layers: an input layer, one or more hidden layers, and an output layer. Each neuron in the network processes data and passes the results to the next layer. ANNs are designed to recognize patterns, learn from data, and make predictions or classifications based on that learning.

In an ANN, the connections between neurons are weighted, meaning each connection has a strength or value that determines the influence of one neuron on another. Through a process called training, the network adjusts these weights to minimize errors in its predictions. This is typically done using techniques like backpropagation, where the error is propagated backward through the network to update the weights (Abiodunet al., 2018).

ANNs are highly versatile and have been applied across various domains, including image and speech recognition, natural language processing, and decision-making systems. Their ability to model complex relationships in large datasets makes them a crucial component in the field of machine learning and artificial intelligence (Ahmed et al., 2023).

13.1. Introduction

The Delphi technique is a systematic, interactive forecasting method that involves gathering expert opinions to make informed decisions, predict future trends, or address complex problems. The core idea behind this technique is to use the knowledge and insights of experts in a structured manner to produce well-informed and reliable outcomes. By combining expert judgment with iterative feedback, the Delphi method allows for deeper analysis, greater accuracy, and a better understanding of complex issues that cannot be easily solved through traditional data analysis or quantitative methods (Ismail and Taliep, 2023).

Originally designed for defence-related forecasting, the Delphi technique has since evolved into a versatile tool used across a wide range of fields, such as business, healthcare, education, public policy, environmental science, and social research. Its versatility stems from its ability to gather expert opinions in a manner that allows for rigorous analysis of complex issues, without the constraints of traditional group meetings or face-to-face discussions.

Project managers need to make sense on the many data that they have. Analytical tools are used in project management to achieve such need. Such tools are used to create a forecast of potential outcomes based on the variations present in the environmental and project variables. There are different types of analytical tools used and one of the most common tools is regression analysis.

14.1. Multiple Regression

Concept: Multiple regression is the most commonly utilized multivariate technique. It examines the relationship between a single metric dependent variable and two or more metric independent variables. Multiple regressions are often used as a forecasting tool. Goal is to use the linear composite of two or more continuous and/or categorical variables (predictors) to: 1) predict scores on a single continuous variable (criterion), or to 2) explain the nature of the single continuous criterion variable from what is known about the predictor variables. In prediction, the criterion is the main emphasis because decisions are made on its value, but often times, it is difficult to directly measure or obtain a subject’s actual score on the criterion; therefore, it is important to estimate or predict one’s criterion score based on the value of the predictor scores.

15.1. Introduction

Meta-analysis is a statistical technique used to combine the results of multiple studies to produce a single, more precise estimate of the effect size of a particular intervention or association. It is a powerful tool in research, particularly in f ields such as medicine, psychology, education, and social sciences, where it is often necessary to synthesize evidence from numerous studies to draw meaningful conclusions. This chapter provides a comprehensive overview of meta-analysis, including its history, methodology, advantages, limitations, and applications.

15.2. Historical Background

The concept of meta-analysis dates back to the early 20th century, but it was not until the 1970s that the term was coined by Gene V. Glass. Glass defined meta-analysis as “the statistical analysis of a large collection of analysis results from individual studies for the purpose of integrating the findings” (Glass, 1976). Since then, meta-analysis has evolved significantly, with advancements in statistical methods and software making it more accessible and robust.

15.3. The Need for Meta-Analysis

15.3.1. Accumulation of Evidence

In many fields, particularly in medicine and psychology, a large number of studies are conducted on the same or similar research questions. However, individual studies may have small sample sizes, leading to low statistical power and inconclusive results. Meta-analysis allows researchers to pool data from multiple studies, increasing the overall sample size and statistical power, thereby providing a more reliable estimate of the effect size.

16.1. Introduction

Game theory is a mathematical framework designed for understanding and analyzing strategic interactions among rational decision-makers. It has applications in various fields such as economics, political science, psychology, biology, and computer science. The foundational premise of game theory is that the outcome of a decision made by one agent depends on the decisions made by others. This chapter provides a comprehensive overview of game theory, covering its fundamental concepts, types of games, solution concepts, and applications.

16.2. Fundamental Concepts

16.2.1. Players

In game theory, a player is any individual or entity that makes decisions within the context of the game. Players are assumed to be rational, meaning they aim to maximize their own utility or payoff based on their preferences and the information available to them.

17.1. Introduction

Grounded Theory (GT) is a systematic methodology in the social sciences that involves the construction of theories through methodical gathering and analysis of data. Developed by Barney Glaser and Anselm Strauss in 1967, GT is widely used in qualitative research to generate theories that are grounded in empirical data. Unlike traditional research methods that test hypotheses, GT allows theories are to emerge from the data itself, making it a valuable tool for exploring complex social phenomena.

This chapter provides an in-depth exploration of Grounded Theory, including its origins, key principles, methodologies, applications, and criticisms. By the end of this article, readers will have a thorough understanding of GT and its significance in qualitative research.

Important features

• Cattle produce 81% of world milk production, followed by buffaloes with 15 percent, goats with 2 percent and sheep with 1 percent; camels provide 0.5 percent. The remaining share is produced by other dairy species such as equines and yaks.

• About one-third of milk production in developing countries comes from buffaloes, goats, camels and sheep. In developed countries, almost all milk is produced by cattle.

• Milk from dairy species other than cattle represents 40 percent of milk production in Asia, 23 percent in Africa, 3 percent in Europe and 0.5 percent in the Americas; it is almost non-existent in Oceania (FAO)

Taxonomy of Cattle

Cattle belong to the kingdom Animalia, phylum Chordata, class Mammalia, order Artiodactyla, family Bovidae, genus Bos, and species taurus or indicus. Cattle were originally identified by Carolus Linnaeus as three separate species: Bos taurus, Bos indicus, and Bos primigenius. Bovines, which include cattle, bison, African buffalo, water buffalos, and certain antelopes, belong to the subfamily Bovinae.

The buffalo belonged to the Bovidae family, Bovinae subfamily and the scientific name is Bubalus bubalis. This is descended from the Bubalus arnee or wild Indian buffalo, and is widely dispersed throughout Southern Asia. The world population of buffaloes is approximately 204 million head: more than 98% are in Asia; 0.8%are in Africa, particularly in Egypt; 0.9% are in South America; and 0.2% are in Europe (FAO, 2024).

The countries with the largest numbers of dairy buffaloes are India, Pakistan, China, Nepal and Egypt. In Pakistan and Nepal there are more dairy buffaloes than dairy cows. Water buffaloes are the main source of milk in South Asia. India is the largest water buffalo milk producer compared to Pakistan, where buffaloes produce more milk than cattle.

 Scientific classification
 Genus: Bubalus 
 Species: bubalis
 Wild water buffalo: Bubalus arnee
 Domestic water buffalo: Bubalus bubalis
 Lowland anoa: Bubalus depressicornis
 Mountain anoa: Bubalus quarlesi
 Tamaraw: Bubalus mindorensis
 Cebu tamaraw: Bubalus cebuensis

According to the karyotyping the domestic sheep (Ovis aries) has 54 chromosomes. The following is a brief summary of their chromosome composition:

Autosomes: There are one pair of sex chromosomes, 52 autosomes and three pairs of these autosomes are submetacentric (having a centromere slightly off centre), while the rest are acrocentric (with the centromere near one end).

Sex Chromosomes: The X chromosome is acrocentric and the Y chromosome varies from submetacentric to metacentric.

Important features

• According to the Food and Agriculture Organization Corporate Statistical (FAOSTAT) Database (2019) and database of the United Nations Food and Agriculture Organization, the top three countries by the number of heads of sheep were: China (163.48 million heads), India (74.26 million) and Australia (65.75 million). • India has second largest sheep population in the World.

• Sheep milk is an important product in the Near East and North Africa (9% of total milk production) and sub-Saharan Africa (4%).

• The countries with the most dairy sheep are China, Sudan, Turkey and Algeria (FAO).

• Major sheep milk producers are China, Turkey and Greece.

• More than half of the world’s sheep population is found in developing countries. Sheep are more prevalent than goats in cold climatic conditions. The major produce from Sheep production are milk, meat, skin, fibre and manure. However, most of the small-scale producers in developing countries raise sheep for meat or sale.

In India, goats are known as “poor man's cow” because they are an important source of income for poor and landless people. They can be reared on cheaper resources easily than other livestock.

Goats come under the genus Capra have evolved from five wild species, including Capra hircus (which includes Bezoar), Capra ibex, Capra caucasica, Capra pyrenaica, and Capra falconeri. This evolutionary pattern contributes to their phenotypic diversity and adaptability to various environments. The chromosome profile of goats (specifically the Capra hircus species) reveals some interesting features which are as follows:

Chromosome Number
The modal chromosome number for goats is 60.
These chromosomes can be categorized into different types based on their centromere position:
Autosomes: These are either acrocentric or telocentric.
X Chromosome: It is acrocentric.
Y Chromosome: It is submetacentric.

Identification

Quinacrine-mustard staining and fluorescence microscopy allow us to identify 28 out of 30 chromosome pairs in goats.

Origin of Fowl

Domestication of poultry seems to have been undertaken in south-east Asia. The chickens were brought to India by 1000 BC, and later on they spread north, westwards and reached Greece by 525 BC. By the beginning of Christian era, the birds were already popular in West Asia and East Europe, and then gradually reached South Africa, Australia, Japan, USSR and USA.

Red Jungle fowls are the ancestors of the present-day poultry breeds. There are four known species of wild fowl and they belong to the same genus “Gallus” meaning a cock. The four species are as follows:

The Red Jungle fowl is supposed to be the main contributor for the development of modern-day poultry and is widely distributed throughout Burma, China, India, Philippines, Sumatra and Thailand. The plumage (colour of the feather) of females resembles that of Brown Leghorn, the males have orange red feathers in hackle, wing bow and saddle regions, while the breast is black. Eggs are buff (off-white) in colour. The legs are slate (dark bluish grey) coloured. The comb is all red.

Pork production in India is limited, representing only 9% of the country’s animal protein sources. Production is concentrated mainly in the northeastern states of the country and consists primarily of backyard and informal sector producers. The contribution of pork products in terms of value works out to 0.80% of total livestock products and 4.32% of the meat and meat products. The contribution of pigs to Indian exports is very poor.

The chromosomes of the domestic pig (Sus scrofa domesticus) are known to be prone to reciprocal chromosome translocations and other balanced chromosome rearrangements with concomitant fertility impairment of carriers. The domestic pig has diploid chromosome (2n) number 38. The domestic pig karyotype shows a tendency for balanced structural chromosomal rearrangements, such as reciprocal, Robertsonian, and tandem translocations, as well as inversions. There is extensive polymorphism in the heterochromatin, with G-C-rich heterochromatin located in centromere regions of biarmed chromosomes and less G-C-rich heterochromatin in the centromere regions of one-armed chromosomes.

The diploid chromosome number of the pig is 2n = 38, consisting of 18 autosomal chromosome pairs, which vary in length and morphology (12-bi-armed and 6 one-armed pairs), and two sex chromosomes, XX or XY. Robertsonian translocation is known to occur in the acrocentric chromosomes 13, 14, 15, 16, 17, and 18. In these cases, the chromosomes fuse at the centromeric region, resulting in the production of two derivative chromosomes, a large bi-armed chromosome, and a secondary short chromosome often lost in subsequent cell divisions. Thus, the karyotype of a domestic pig carrying Robertsonian translocation has a distinct 2n = 37 diploid chromosome number, with the noticeable addition of a novel large bi-armed chromosome.

Camels could be exploited for augmenting the country’s milk production capacity as a supplementary milk resource in India for the benefit of increasing human population. Since milk production potential of camels in India have remained unexploited and considering its therapeutic utility it can fetch good prices. Camels are raised for milk, meat, fibre (wool and hair), transport and other work; their dung is used as fuel. Milk is often the most important camel product and is the staple food of nomads.

The camel is an important species of the Indian fragile desert ecosystem, a proven icon of adaptation with its unique bio-physiological characteristics and has formidable ways of living in harsh situations of arid and semi-arid regions. The proverbial Ship of Desert earned its epithet on account of its indispensability as a mode of transportation and draught ability in desert. However, its utilities are subject to continuous social and economic changes. The camel has also played a significant role in civil law and order, defence and battles from the ancient times till date. Camels formed an important component of the Mauryan Army (C.322-232 BC) and continued through the Mughal period (1200-1700 AD) to the present times.

In some developing countries, milk from horses (Equus caballus) and donkeys (Equus asinus) is an essential food for poor farmers. Generally, horses are more commonly used for dairy purposes in cool areas and donkeys in dry semi-arid regions. Milking equines is time-consuming and has to be repeated five or six times a day. In addition, an equine will not release milk unless it is stimulated by the presence of its foal. Mare’s milk is commonly consumed in the steppe areas of Central Asia, where a traditional lactic-alcoholic beverage called koumiss is produced through fermentation. Horse milk is also an important source of animal protein for pastoralists in Mongolia. The consumption of donkey milk has become very marginal. In some African communities, donkey milk is consumed for medical purposes.

Equus is a genus of mammals in the family Equidae, which includes horses, asses, and zebras. Within the Equidae, Equus is the only recognized existing genus, comprising seven living species. Like Equidae more broadly, Equus has numerous extinct species recognized only from fossils. Equines are odd-toed ungulates with slender legs, long heads, relatively long necks, manes (erect in most subspecies), and long tails. All species are herbivorous, and mostly grazers, with simpler digestive systems than ruminants but able to subsist on lower-quality vegetation. The chromosome number of a horse is 64 (2n).

Donkeys can also be termed as ass (Equus africanus asinus) and is a domesticated member of the horse family, Equidae with chromosome number 62. The Wild ass of Africa, E. africanus is the wild ancestor of the donkey. For approximately 5000-7000 years, the donkey has been used as a working animal.

An adult male donkey is a jack or jackass, an adult female is a jenny or jennet, and an immature donkey of either sex is a foal. Jacks are often mated with female horses (mares) to produce mules, the less common hybrid of a male horse (stallion) and jenny is called hinny. NBAGR-ICAR has registered three breeds of donkey till date.

1. Kachchhi

• These donkeys are found in Kachchh district of Gujarat.

• Coat colour is mainly grey (dorsal surface grey and ventral surface white) followed by white, brown and black.

• Forehead is convex. Nasal bone is straight.

• Height at wither ranges from 77 to 115 cm.

• Docile in temperament.

• Only donkeys are used for agricultural purposes like inter cultivation for weed removing.

• Also utilized for transportation as donkey cart, as pack animal during pastoralist migration, etc.

The full name of an organism technically has eight terms. For the dog it is: Eukarya, Animalia, Chordata, Mammalia, Carnivora, Canidae, Canis, and lupus. Notice that each name is capitalized except for species, and the genus and species names are italicized. Scientists generally refer to an organism only by its genus and species, which is its two-word scientific name, or binomial nomenclature. Therefore, the scientific name of the dog is Canis lupus. The name at each level is also a taxonomy Carnivora is the name of the taxon at the order level; Canidae is the taxon at the family level, and so forth. Organisms also have a common name that people typically use, in this case, dog. Note that the dog is additionally a subspecies: the “familiaris” in Canis lupus familiaris. Subspecies are members of the same species that are capable of mating and reproducing viable offspring, but they are separate subspecies due to geographic or behavioural isolation or other factors. Dogs, which are under the species Canis lupus familiaris, are known to have a total of 78 chromosomes (2n). The diploid genes can be classified into 38 pairs with two sex chromosomes.

The yak is a bovine species that provides livelihoods for people in high altitude conditions of extreme harshness and deprivation. Yaks live predominantly on the “roof of the world”, as the Qinghai-Tibetan Plateau is often called, and provide milk, meat, hair and down fibre, hides, draught power and dung (principally used as fuel). The yak has physical and physiological characteristics that allow it to thrive at high altitudes (low oxygen) and in extreme cold (at temperatures as low as -40 °C) and to survive feed shortages in the winter (FAO).

The milk yield of the yak cow is often no more than the amount suckled by the calf and is not comparable to the milk yield of dairy cattle. However, although milk offtake for human consumption may be at the expense of the calf, yak milk is important for households. In economic terms, milk is often the most important yak product. Yak milk is generally produced by small-scale farmers in traditional systems where management is highly influenced by climate and seasons. Lactation is seasonal and yak cows produce between 150 and 500 litres; yields vary by breed and location. Lactation can generally continue into a second year without another calving. During the winter, yak cows do not go dry and continue to produce a small amount of milk, with milk yields as low as 2 to 4 litres/month. During the second year of lactation, milk yields are between half and two-thirds of those in the first year. Currently, there are no specialized dairy yak breeds (FAO).

Some have described Mithun as domesticated Gaur others as a hybrid descent from the crossing of gaur bulls and common cows. Considered to have originated along the Assam and Myanmar border. • Mithun, also known as ‘Cattle of Mountain” is an important bovine species of north-eastern hill region of India and also of China, Myanmar, Bhutan and Bangladesh. This magnificent massive bovine is presently reared under free-range condition in the hill forests at an altitude of 1000 to 3000 m above mean sea level.

• Mithun plays an important role in the socio-economic and cultural life of the local tribal population.

• Presently, this animal is mainly reared for meat, which is considered to be more tender and superior over the meat of any other species.

• Mithun milk, though produced less in quantity, is of high quality and can be used for preparation of various milk products.

• Leather obtained from this species has been found to be superior to cattle.

Using the FAO criteria, Bargur and Siri cattle breeds and Attapadi goat breeds were categorized under vulnerable status of risk; Krishna Valley, Mewati, Pullikulam and Punganur cattle breeds, Chilika and Toda buffalo breeds and Karnah and Poonchi sheep breeds under endangered status of under risk and Vechur cattle under critical status of risk. All these breeds need immediate steps for rescue by using suitable conservation methodology. Breed-wise census should be continued in future so that trends in breed populations may also be estimated for fine tuning of risk assessment of breeds.

Rate of inbreeding in Indian livestock breeds: The rate of inbreeding estimated for different breeds of cattle was well below 0.5% (needed for a breed ‘not at risk’) except for Mewati (1.193%), Pullikulam (0.72%) and Vechur (17.893%). The rate of inbreeding in buffalo breeds was also well below 0.5% except in Chilika buffalo, where it was 0.731%. The rate of inbreeding of these breeds may be reduced by increasing the number of breeding males and in Vechur also the breeding females. The rate of inbreeding in all the breeds of sheep (Indian or exotic), goat and pig were estimated to be less than 0.5%. In these species, a good balance of breeding males and females is always expected.

20th Livestock Census of India

India has highest cattle population (192.49 million), buffalo population (109.85), in the world producing 24% is the highest milk yield (221.1 million tons in the year 2021-2022, and per capita availability was 444 gm/day) in the world (Source NDDB). India produces 43% of world buffalo meat production. Unfortunately, 80% cattle and 70% buffaloes are non-descriptive and yield very low amount of milk.

After independence Indian Council of Agricultural Research (ICAR) has developed serval projects and plans for research and characterization of livestock species all over the country. Our country holds enormous genetic biodiversity in our domestic animals, result in increasing number of registered breeds in every decade. Breed registration Committee on its online meeting held on 5th December, 2023 approved registration of eight new breeds of livestock. This includes Aravali Chicken (Gujarat) Andamani Duck (Andaman and Nicobar Islands), Anjori Goat (Chhattisgarh), Andamani Goat (Andaman and Nicobar Islands), Bhimthadi Horse (Maharashtra), Andamani Pig (Andaman and Nicobar Islands), Macherla Sheep (Andhra Pradesh), Frieswal Cattle (Uttar Pradesh and Uttarakhand). Endangered, critical and vulnerable breeds of livestock also have been identified by ICAR which need more scientific research and multiplication breed.

Q1. Fill in the blanks with appropriate answers.

1. India ranks …first…. position in cattle population in the world.

2. India ranks … first …. position in buffalo population in the world.

3. India ranks … first ……. position in total milk production in the world.

4. …Buffalo…….. species produce the highest amount of milk in India.

5. Frieswal cattle is a cross of …Holstein Friesian….and ……Sahiwal…

6. Mehsana buffalo is a cross of …Murrah….and ……Surti……

7. India ranks … first …. position in goat population in the world.

Introduction

India was a self-sufficient nation in its food production before independence but after the occurrence of various famines, the government in late 1960s started focusing more on the crop production i.e.,green revolution (NAAS 2019). Use of hybrids, fertilizers and plant protection chemicals increased and major investments were made on irrigation structures which led to a massive jump in the production of crops. But with time, the negative externalities caused by green revolution were vividly noticeable. The indiscriminate use of chemicals has debilitated the environment by causing soil depletion, pollution, deforestation, climate change etc. thus affecting the health of human kind. The lofty prices of inputs led the farmers to resort to debt thus falling into debt trap and suicides. In order to address this problem an alternative to the conventional farming is required which is more sustainable and climate resilient. In the mid-1990s, natural farming viz., Zero Budget Natural Farming (ZBNF) was demonstrated by Sh. Subhash Palekar also known Subash Palekar Natural Farming (SPNF). It is an agroecology based diversified farming system which involves no financial outlay to purchase essential from outside and makes farming profitable. Mishra (2018) studied on Zero Budget Natural Farming (ZBNF) and found that crops grown under ZBNF evinced higher yield than the crops grown under non-ZBNF.

Introduction

In one or another way, agriculture is integral to the physical and economic survival of every human being. The United Nations forecasts that the global population will reach more than 9 billion by 2050. To feed everyone, food production will have to increase 70 per cent. Helping the world’s farmers to achieve this target is challenging in itself, but beyond providing food, agriculture sustains the economies of most countries in significant ways, especially in the developing world (Anonymous, 2018). To meet this challenge in a fast-changing world, new tools, processes and ways of organizing-i.e. innovations-are needed to make agriculture more efficient in economic, social and environmental terms (Hall, Mytelka and Oyeyinka, 2006). Past efforts have tended to focus solely on technology. Previous approaches to technological change in agriculture have been based around supplying technology in a simplistic, top-down manner. These approaches had some success for relatively simple innovations in conducive settings. However, these simplistic approaches are not adequate for the task of meeting the complicated challenges facing agriculture today. Historically, new agricultural technologies have been developed by public research organizations and then transferred to farmers by public extension services (Sulaiman, 2015).

Developing countries with agrarian economy have been facing challenges arising from climate change causes and consequences. The altered patterns of climate are affecting the yield potential of crops thereby affecting agricultural production, farmers’ income, and livelihoods. Out of the 17 Sustainable Development Goals (SDGs) of the United Nations, the 13th SDG on ‘Climate action to combat climate change and its impacts,’ emphasises the importance and urgency of addressing the effects of climate change in developing nations. It is projected that climate change would have the most detrimental effect on agriculture, the primary source of income for small and marginal farmers in developed nations (Azadi et al., 2021). Building the ability of small and marginal farmers will be crucial to global food security over the next decade (FAO, 2020). World Bank (2018) reported that 800 million South Asian population are at risk of experiencing a decline in their standard of living and incomes due to rising temperatures and more erratic rainfall hampering the agriculture production system. Millions of low-income smallholder farmers, particularly those who rely on agriculture for a living and an income in South Asia, would be directly impacted by a reduction in productivity leading to food supply shortages and price increases.

1. Introduction

Agriculture has always been greatly dependent on climate patterns and variations, and is considered to be particularly at risk to the influences of climate change (James et al., 2014). Climate change is manifest in the form of reduction in rainfall and drought experienced in the last decade, pushing the agrarian community into real distress (MSSRF, 2020). To reduce the adverse impact of climate change on agriculture, adaptation is considered a crucial component of any policy response related with agriculture and climate change (Deressa et al., 2009). Adaptation to climate change involves changes in agricultural management practices in response to changes in climate conditions (Akinnagbe and Irohibe, 2014). Having knowledge about climatic adaptation practices by the farming communities would make them better equipped to deal with climate change related challenges (Mwazi&Ndokosho, 2011). Though smallholder cultivators are one of the most vulnerable groups to climate change, efforts to support their adaptation strategies are hindered by the lack of information on how they are experiencing and responding to climate change (Harvey et al., 2018). Senapati (2019) argues that farm households with access to weather-related information and extension agency contact have lesser level of vulnerability to climate change and enhanced adaptive capacity. Kavi Kumar (2009) contends the need to have region specific adaptation measures in farming as the key to address challenges of climate change.

Introduction

We are faced with the dual challenges of Climate change and food insecurity irrespective of the countries and regions across the globe. Asia Pacific region is one of the most climate change vulnerable regions in the world (Prabhakar & Matsumoto, 2010) on account of its relatively higher & dense population depending on climate related sectors and living in climate change vulnerable locations. In particular, the agriculture production system is facing serious repercussions due to increasing weather vagaries since past few decades. Agriculture being highly dependent on climatic condition is like gambling at the hands of erratic rainfall or droughts leading to crop loss on a regular basis in terms of reduced yield, increased pest resurgence and lack of safety net in case of weather based disasters all of which prominently affects the small holder farm families. Indian subcontinent has been facing extreme climatic events since the past decades in form of floods, erratic rainfall, high temperature, shrinking water sources, etc. which have caused deprivation in agriculture in economic and non-economic terms. In a series of researches and studies conducted over the years; the need for enhanced adaptation research and policymaking capacity in developing Asia has been reflected. Hence, adaptation research has to take an important place at global, national and local level.

India is endowed with diverse agro- climatic conditions which facilitate to cultivate various horticultural crops all through the year in one part of the country or others, but the quality of fruits under open field conditions particularly in high value crops (vegetables & flowers) by and large fall short of domestic and international markets standards. Some time there no guarantee of consistent production of from open cultivation, as the crop is exposed to number of environmental factors that changes frequently. Hence it is imperative to increase the productivity and quality of produce to meet the demand of quality conscious consumers. A break through in production technology that integrates market driven quality parameters with the production system simultaneously ensuring a vertical growth is warranted. In this context, most important technology is the Controlled Environment Agriculture (CEA) i.e. Protected Cultivated Technology (PCTs) such as greenhouse, net house, glasshouse and low tunnel. “Protected cultivation” means modification of plant environment to achieve optimum plant growth avoiding the harmful effect of climatic factors.

1. Introduction

The agriculture sector in Asia and the Pacific plays a vital role in the rural economy, making significant contributions to both GDP and employment. This sector serves as a cornerstone for food production, economic growth and poverty reduction in the region. Understanding the importance of farming in the region’s rural economy, as well as the need for viable farming systems particularly for smallholder farms, is essential for sustainable development.

Importance of Farming in the Rural Economy

Farming has a substantial impact on the rural economy of Asia and the Pacific. It serves as a primary source of income and livelihood for rural populations, contributing significantly to the region’s Gross Domestic Product (GDP). The agricultural sector encompasses a wide range of activities, including crop production, livestock farming, fisheries, and forestry, providing diverse opportunities for economic development.

According to the Food and Agriculture Organization (FAO), farming accounts for 29 percent of the region’s GDP (FAO, 2021). This demonstrates the significant economic value that agriculture brings to the region, making it a crucial sector for sustainable development. The revenue generated from agricultural activities supports not only farmers but also other stakeholders in the value chain, including agro-industries, distributors, and exporters.

Introduction

Increasing use and rise in the cost of chemical fertilizers, pollution due to their application and decline in soil fertility and micro flora are worrying facts to the farming community. Increased awareness about organic produce among the consumers is another reason to find out an alternative to chemical fertilizers. Vermicompost production on farm and its use in crop production is one way of mitigating these problems. Among the different methods, Windrow method of vermicomposting is the production of compost by piling organic matter or biodegradable waste, such as animal manure and crop residues as long rows (Windrows) on the surface of soil under the shade of trees without any permanent structures and later releasing required quantity of earthworms in wet beds. This vermibed technology is simple and cost effective and sustainable to adopt.

The prototypes have been exhibited in the premises of Dairy unit, University of Agricultural Sciences, Dharwad since long and many farmers seeing these were of the opinion that technology was simple and easy to adopt using the farm generated biomaterial waste with no additional cost. During 2020-21, when farmers of remote villages Dharwad district were interacted, it was noticed that still the technology was not followed by the farmers.Therefore, a team of Green Foundation based in Dharwad made an attempt to reach the farmers of remote villages of Dharwad district to make aware of this technology. After interactions with different categories of farmers, a modest attempt has been made to identify small and large farmers of remote villages viz. Basavarsikoppa and Holtikoti of Dharwad district during 2021-22 to sensitize and demonstrate windrow method of vermicompost production. The results, experiences of the farmers and finally the impact of the technology are discussed here in this presentation.

Introduction Large cardamom is an important cash crop cultivated in Sikkim. It is also cultivated in Darjeeling district of West Bengal and some parts of North-eastern state like Arunachal Pradesh, Nagaland and Manipur. It is a low volume, high value non-perishable crop grown organicallyat different altitudes under the trees. The State produces about 4,970 tonnes annually from an area of 23,312 hectares (Spice board, 2021). Among the four districts of Sikkim, North and East Sikkim district contributes largest in terms of area and production of large cardamom (GoS, 2016). Off-late, climate change has become a major concern for the large cardamom growers as many researchers reported its contribution to the decline of its production. Sikkim, as Himalayan states, is endowed with varied agro-climate ranging from tropical in the South to alpine in the North. Shrestha et al. (2010) reported that warming in the Himalayas was between 0.15°C and 0.60°C per decade in the last three decades. Seetharam (2012) revealed that in Gangtok, there is a decreasing trend in maximum temperature while an increasing trend in minimum temperature. The study also depicted the decreasing trend of annual rainfall amount in the study area. In a recent study, Kumar et al. (2020) reported that the warming was more prominent in the last decades in Gangtok and Tadong station, better reflected in the minimum temperature with varied precipitation across the observed period from 1961 2017.

1. Introduction

Biochar, produced from pyrolytic decomposition of lignocellulosic biomass, is a solid hard product and a unique green material for soil amendments. Its use has been identified in Amazon fertility as ancient indigenous practice. Through ‘pyrolysis’ (heating without oxygen) process (‘pyro’ which means caused by heat whereas ‘lysis’ means dis-integration), there are three types of yields we get: a solid product (biochar), a gaseous product (syngas/ synthesis gas), and a liquid (bio-oil). The solid item, biochar, is applied for soil amendments. It can be used in agricultural, industrial, and environmental as a functional material. Solid char, a common pyrolytic outcome is charcoal, typically used to designate the solid char product that is ignited for fuel, whereas “biochar” designates the unique solid char purposively used as a soil amendment agent. On the other hand, the gaseous and liquid counterparts of pyrolysis have commercial applications like green energy, feedstock raw materials, and precursors (Fig.1).

Introduction

Agriculture has historically been the primary source of income in India. It has the potential to alleviate poverty, increase income, and enhances food security for 80% of both the world’s poor and people who reside in rural areas primarily work in agriculture. One of the most effective strategies to eradicate extreme poverty, increase shared prosperity, and feed an estimated 9.7 billion people by 2050 is agricultural development. Compared to other modes, the agro - based sector’s progress is two to four times more successful in enhancing the earnings of the poorest people (www.worldbank.org).

Nearly 5 billion hectares, or 38% of the world’s land surface, is used for agriculture. Around one of this is devoted for farming, and the remaining two thirds are pastures and meadows for grazing animals. Over 10% of cropland is assigned to permanent crops like cocoa plantations, oil palm plantations, and fruit tree orchards. Yet another 21% of land is primed for irrigation, a crucial agricultural land management technique.

Introduction

Agriculture has historically been the primary source of income in India. It has the potential to alleviate poverty, increase income, and enhances food security for 80% of both the world’s poor and people who reside in rural areas primarily work in agriculture. One of the most effective strategies to eradicate extreme poverty, increase shared prosperity, and feed an estimated 9.7 billion people by 2050 is agricultural development. Compared to other modes, the agro - based sector’s progress is two to four times more successful in enhancing the earnings of the poorest people (www.worldbank.org).

Nearly 5 billion hectares, or 38% of the world’s land surface, is used for agriculture. Around one of this is devoted for farming, and the remaining two thirds are pastures and meadows for grazing animals. Over 10% of cropland is assigned to permanent crops like cocoa plantations, oil palm plantations, and fruit tree orchards. Yet another 21% of land is primed for irrigation, a crucial agricultural land management technique.

Introduction

Despite recent significant increases in global food production Sustainable development goal- “End hunger, achieve food security and improved nutrition, and promote sustainable agriculture” seems unachievable. Globally, 1 in 9 people are undernourished, the vast majority of whom live in developing countries. Under nutrition causes wasting or severe wasting of 52 million children worldwide. It contributes to nearly half (45%) of deaths in children under five – 3.1 million children per.Today, the majority of the world’s hungry people—nearly 870 million—live in sub-Saharan Africa and South Asia. Climate change impacts on food security and livelihoods are already alarming and affecting millions of smallholder farmers in sub-Saharan Africa (FAO, 2016).

In view of today’s 795 million hungry and the additional 2 billion people expected by 2050, there is an urgent need for a change of global food and agriculture systems in order to sustainably increase food production (UN, 2016). This will exert intense pressure on agro-ecosystems that are already overburdened, particularly in developing countries as most of those increases in production have to occur in this part of the world. The environmental impacts of satisfying this increased food demand will be further aggravated by climate change. With increased frequency and severity of droughts, extreme heat conditions and over dependence on rainfed agriculture, there is a growing agricultural productivity crisis, dwindling household food availability and the economic prosperity of countries whose national economies are dependent on agriculture. Considering that climate change impacts are felt differently within regions, context-specific adaptation measures are required to reduce risks and build adaptive capacity of smallholder farmers (Altieri, M.Aet al.2017).