eChapters

1. Introduction

The agricultural sector plays a pivotal role in driving economic development in India, a nation with a population of 1.39 billion, making it the world’s second most populous country according to the United Nations’ World Population Prospects 2019. With 54.6 percent of the workforce engaged in agriculture as per the 2011 Census, and contributing 18.6 percent to the Gross Value Added (GVA) in 2021-22, its significance cannot be overstated. Notably, women play a substantial role in this sector, with rural females exhibiting a higher workforce participation rate of 41.8 percent compared to 35.31 percent for urban women (MoSPI, 2017). In rural communities, agriculture and allied sector is the primary source of livelihood that includes 80 percent of all economically active women, out of which 33 percent constitute agricultural labour force and 48 percent are self-employed farmers. Rural women are engaged at all levels of the agricultural value chain; i.e., production- pre-harvest, post-harvest, processing, packaging, marketing to increase productivity in agriculture (Nilam Patel etal, 2022). As per Pingali et al. (2019), the ratio of women to men working in the agricultural sector has increased over the time and made a greater amount of contribution to GDP per capita. They are the momentous demographic group for a sustainable food system (FAO, 2011). Empowering women in agriculture through reforms aimed at ensuring equal access to resources, skill development, and opportunities is projected to increase agricultural output in developing countries by 2.5 to 4 percent (FAO, 2011).

1. Introduction

Fish forms an integral component of the Indian food basket and it is more so for the population living in the eastern and north eastern States of India. The eastern states of India include States such as West Bengal, Bihar, Jharkhand and Odisha. As per a current estimate of 2023 (https://www.indiacensus.net), approximately a population of 31.66 crore, i.e. 22.15% of the country’s total, live in these four States. Due to the suitable geographical terrain, these states have been bestowed upon a large number of riverine stretches, a large number of natural reservoirs of varied sizes, beels and floodplain wetlands, and a huge plain terrain supporting ponds and tank resources. Due to the very nature of the land and the popularity of fish among the populace, these eastern Indian states have also served as the main stage for the development of fisheries and aquaculture during the last centuries in the country. Further, many of the epoch making technologies for the development of fisheries in the inland waters as well as aquaculture in the ponds have originated from this region.

1. Introduction

India has 536.76 million livestock as per 20th Livestock Census 2019. Livestock plays a vital role in improving the socio-economic conditions of rural masses especially for landless and marginal farmers; as well as an important role in the national economy. Livestock production and agriculture are closely related and vital for the overall food security. Agriculture diversification through animal husbandry is one of the primary factors of growth in rural incomes. A higher public investment in livestock sector is the need of the hour for doubling farmers’ income. Future economic growth would have to come from improvements in animal productivity. India is bestowed with a great degree of livestock biodiversity which can be elucidated in hosting more than 200 breeds across all the species. Along with producing A2 milk, desi cattle are renowned for withstanding heat, illness resistance, immunity to ticks and parasites and low maintenance requirements. The various indigenous breeds of agricultural animals are mostly the consequence of evolutionary processes. They were improved through field progeny testing & selection. Conservation and propagation of most precious high producing elite buffalo germplasm though the cloning technique is the contribution of the scientists to the buffalo rearing farmers of the country. The Indian Jamunapari goat is one of the ancestors of the American Nubian. Goat milk is an excellent source of vitamin A, is high in digestible protein, contains significantly lower levels of alpha-S1-casein, contains slightly less lactose, and has more non-digestible carbohydrates than cows’ milk.

1. Introduction

Cattle are one of the important livestock species of the country which forms an integral part of Indian agriculture since ages. Human progress depends on the utilisation of animals and natural resources in a balanced way. Cow, in particular, is considered as a part of the family in India. Cattle have been of the utmost importance for the sustenance of agriculture, nutrition supply and religious practices in India. Frequent references on cattle farming are found in Indian mythological books and historical writings regarding their utilization in agriculture as draught power, milk production and source of manure. Approximately 200 million Indians are involved in livestock farming, including around 100 million dairy farmers. Roughly 80% of bovines in the country are low on productivity and are reared by small and marginal farmers. Most of the local cows are managed on a low input and low output system for production of milk for the household nutritional requirement and sale surplus milk to dairy co-operatives or local milk sellers. Poor genetic makeup of Indian livestock is the main reason for their lower productivity as compared to the recorded potential of animals in temperate climate. With the change in food consumption pattern due to urbanization, increase in health awareness, reduced requirement of draught animal power due to mechanisation, etc., the demand for milk and milk products have increased many folds.

1. Introduction

Poultry industry in India stands out as a major success story. It’s phenomenal that India’s poultry production whichstarted justas a backyard venture before 1960s, has shaped up into a proven agribusiness, where its annual turnover exceeds Rs 30,000 crores. Current global ranking of India is that of number three, in terms of egg production (behind China [1st] and USA [2nd]), and of18th position in Broiler production. Today, Poultry is one of the fastest growing industries of the agricultural sector in India at present. The annual growth of broilers and egg production is 8-10% compared to 1-2% for production of agricultural crops [3].

Poultry production forms an important Agrarian Avenue providing valuable animal proteins for the fast-growing human population of the developing India, with promises of additional income to small and marginal farmers.Rural and backyard poultry farming is very common among landless and marginal farmer families in India and is a rewarding source of additional income. It involves low investment with high returns which can be easily operated by women, children and elder members of the farm families. Poor households generate good income through backyard poultry farming which provides meat and egg rich in protein and energy. Agriculture including animal husbandry provides 30-40% employment in terms of man/woman days in a year. Scarcity of land holdings, land fragmentation and seasonal agriculture aresome of the reasons that crop production only is unable to provide cent percent employment to the rural youths (men and women).

Women Folk: A Precious Resource

India’s ancient history ascribes a divine status to women. Laxmi, Durga and Saraswati are the three great goddesses of Prosperity, Power and Wisdom respectively. The ancient scriptures declared that ‘God lives where women are worshipped’. However, a multitude of derogatory attributes have been ascribed to women in the post-vedic period. According to Manu, “a woman must never be independent. In childhood, she must be subject to her father, in youth to her husband, and when her lord is dead, to her sons”. The passive role of women, unfortunately thus visualized, later guided the future course of action leading to their present status of backwardness in general and neglect in the streams of development and scientific arena in particular.

The term ‘gender’ was first used by Ann Oakley and others in 1970s to describe the characteristics of men and women which are socially determined in contrast to biological differences. The term ‘gender’ further explains, a neutral term meaning either men or women or both in a particular context. The goal is to improve the status of disadvantaged class and get rid of socially created and approved discriminations. The green revolution in India changed the face of agriculture but apparently it contributed to two general trends (FAO, 1996) where wealthy have benefited more from technological change in agriculture than the less well off andsecondly men have benefited more than women.

Introduction

Challenges in agriculture and natural resource management in the past have prompted changes in agricultural education, research, and extension globally. The sustainable development of agriculture in a country largely depends on the effectiveness of its agricultural extension strategies, encompassing approaches, service delivery, methodology, and processes. The desired outcome of agricultural development is farmers’ outcomes, achievable through engagement, learning, and participation in research and extension processes. Agricultural extension played a significant role in the green revolution by providing farming communities with appropriate technologies and optimal cropping practices. This helped countries transition from being food deficit to food exporting (Ratnakar, 2016). Additionally, extension services play a crucial role in expanding knowledge, skills, and attitudes about the management of socio-economic-agricultural practices by understanding the existing techno political ecosystem and exploring a basket of technology alternatives for making informed decisions.

1. Introduction

India is a land of great diversities in terms of people, culture, languages and the style of living, among many other varied characteristics in the country. Based on the climatic, geographical, and cultural features, the country is broadly divided into six zones - the Northern, Southern, Eastern, Western zone, Central and the North-Eastern zone. Eastern zone comprises Bihar, Jharkhand, West Bengal, Odisha, Chhattisgarh, Assam and eastern UP (27districts of UP). This document exclusively focuses on the four states namely Bihar, Jharkhand, Odisha and West Bengal in the Eastern Zone with an attempt to look into the bottlenecks inhibiting the rural development of the zone.The f inancial institutions for agriculture in India was developed with the purpose of financing the needy farmers and providing them the facilities to increase the efficiency of agriculture. As a result, NABARD was formed in 1982. Thus, it was developed as an apex bank to help and support the agriculture section in India.This chapter seeks to evaluate the role of various financial institutions which play a major role in raising the standard of living of nearly one-fourth of India’s population in the eastern region of our vast country.

1. Introduction

The National Agroforestry Policy of India is a comprehensive policy framework designed to improve agricultural livelihoods by maximizing agricultural productivity including animal productivity along with tree cover and mitigating climate change. The Government of India launched the policy in February 2014 during the World Congress on Agroforestry, held in Delhi. The focus of the policy is to address Agroforestry, which is an integrated system of practicing agriculture, livestock farming and forest activities on the same unit of land, storing high carbon, there by mitigating climate change. Agroforestry systems have the potential to be more beneficial than traditional agriculture and forest production practices. They may be able to give enhanced productivity, as well as social, economic, and environmental advantages, as well as a broader variety of ecological products and services. It’s important to remember that these advantages are contingent on appropriate farm management. In 2014 India became the first nation in the world to adopt an inter-sectoral policy on agroforestry, which seeks to identify the bottlenecks in expansion on agroforestry in the country and pathways to remove the constraints in a systematic manner.

1. Introduction

India’s horticulture sector stands as a beacon of promise and potential within the agricultural landscape, showcasing a rich diversity of fruits, vegetables, tubers, spices, flowers, medicinal and ornamental plants as well as herbs. This sector provides livelihoods for millions of farmers and plays a crucial role in ensuring food security and nutritional well-being. Amidst the challenges posed by climate change and environmental degradation, the resilience of horticultural crops becomes increasingly significant. Diversifying towards resilient crops and sustainable practices not only mitigates risks but also enhances the sector’s adaptability to changing climatic conditions. Investment in research and development to breed climate-resilient varieties and promote agroecological approaches is imperative. Furthermore, promoting organic and niche horticulture products presents exciting opportunities for Indian farmers to capture premium markets both domestically and internationally. With growing consumer awareness and demand for healthy and nutritionally rich food, India’s horticulture sector is well-positioned to become a global leader in organic and exotic produce.The recent trend of exploring new crops within Indian horticulture promises to diversify farmers’ income sources and address emerging challenges such as climate change and shifting consumer preferences.

The land is one of the basic natural resources for agricultural growth and development of a country. Investment on agricultural land improvement by way of land leveling, contour bunding, sinking of well for irrigation and resorting to other measures like treating the soil to reduce acidity etc. have been continuous effort of agriculturists for increasing productivity.In addition, new variety of seeds which are high yielding and pest resistant are sought after by farmers for increasing production. Efficient land management encompassing soil and water conservation is the building block for sustainable agricultural development.

Land tenure rules define the ways in which property rights to land are allocated, transferred, used, or managed in a particular society. Ownership of land plays a significant role for improvement of land and its management. If the land is tenanted,the tiller of the land has less stake on long term measures on land.For centuries, the land tenure system in India was based on intermediaries known as Zamindary system.In Odisha, the tenurial system has based on the Bengal Rents Act, the Bengal Tenancy Act, the Bihar & Orissa Tenancy Act and finally, the Orissa Tenancy Act.In addition, theCentral Province Tenancy Act and the Central Province Land Revenue Act were in vogue in the Sambalpur district when the Orissa province was formed. Further, theGanjam district was under the Madras Estates Land Act(MELA). All these laws defined and classified the tenurial system.If one examines the OT Act one would find series of intermediaries between the proprietor and actual tiller of land.Tenure-holders had under-tenure holders. Raiyats had under-Raiyats. Tenants also inducted sub-tenants.

Introduction

Agriculture has been the cornerstone of human civilisation for millennia, providing sustenance and livelihoods to communities around the world. As the global population continues to grow, reaching an estimated 9.7 billion by 2050, the need to ensure food security becomes increasingly urgent. However, this burgeoning demand for food coincides with numerous challenges such as climate change, dwindling natural resources, and the pressures of sustainable farming practices. To address these complex issues and meet the world's food requirements sustainably, modern agriculture must embrace innovative technologies. One such game-changing technology that has emerged in recent years is Artificial Intelligence (AI), which is revolutionising various industries, including crop production. AI is the simulation of human intelligence processes by machines, particularly computer systems. It encompasses a range of capabilities, including learning, reasoning, problem-solving, perception, and language understanding. When applied to agriculture, AI holds immense promise to transform how we grow crops, optimise resource utilisation, and enhance overall productivity (Fan et al., 2012). By leveraging AI's power, farmers can make data-driven decisions, improve efficiency, and implement precision farming techniques tailored to specific field conditions. By employing AI technologies, it is possible to mitigate the impact on natural ecosystems and enhance worker safety. This, in turn, can contribute to keeping food prices down and ensuring that food production remains in step with the growing global population (Ranjan et al., 2023).

Introduction

Biofortification, also known as biological fortification, involves enhancing the nutritional content of food crops to improve bioavailability for the human population. This is achieved through the use of modern biotechnological techniques, conventional plant breeding, and agronomic practices. Unlike our current agricultural system, which primarily focuses on increasing grain yield and crop productivity, biofortification addresses the nutritional needs of the human population. The prevailing approach has led to a rapid increase in micronutrient deficiency in food grains, contributing to higher rates of micronutrient malnutrition among consumers. Biofortification presents a one-time investment that offers a cost-effective, long-term, and sustainable solution to combat hidden hunger (Sow and Ranjan, 2021a). Once biofortified crops are developed, there are no ongoing costs associated with purchasing fortificants and adding them to the food supply during processing. Agronomic biofortification methods involve the physical application of nutrients to temporarily enhance the nutritional and health status of crops. The consumption of such crops subsequently improves human nutritional status. Successful implementation of the biofortification initiative requires farmers to receive support for using micronutrient fertilisers on fortified cultivars and marketing assistance. Despite challenges, the biofortification of millets holds a promising future in addressing the issue of malnutrition.

Introduction

Crop modeling in agriculture involves utilising quantitative data on eco physiological processes to forecast plant growth and development, leveraging environmental conditions and management inputs. These models simulate how crops respond whether in terms of growth, yield, or other factors to a dynamic interplay of elements like management practices, water, weather, and soil parameters throughout a growing season. They serve as computational tools emulating the intricate growth patterns of crops by mathematically representing various facets within the cropping system (Nath et al., 2023). The roots of crop modeling trace back to the 1960s, when researchers began integrating physical and biological principles to model agricultural systems. These models heavily rely on measurable inputs, obtained via sensors, machinery, or manual measurements, to predict desired outputs such as plant growth, crop yield, soil nitrogen levels, or crop staging.

Crop Modeling

Crop modeling involves the creation of mathematical or computational models that represent the growth, development, and yield of crops. These models are based on scientific principles and empirical data, aiming to simulate and predict various aspects of crop behaviour under specific conditions. The primary purpose of crop modeling is to provide a theoretical framework for understanding and predicting crop responses to environmental factors, management practices, and genetic variations.

Introduction

Sustainable agriculture focuses on practices that ensure the long-term well being of the environment, society, and economy. In India, the government has instituted various policies and initiatives to encourage sustainable farming practices and enhance farmers' income. Embracing sustainability is crucial for making agriculture both profitable and environmentally friendly for future generations, and technology emerges as the key enabler for achieving this goal. Technological advancements empower farmers to make informed decisions regarding optimal planting and harvesting times, as well as the judicious use of water and fertilisers. This leads to reduced waste, increased crop yields, and ultimately higher profits for farmers. Additionally, technology plays a significant role in mechanisation, encompassing the use of modern agricultural implements that multitask while minimising soil impact (McFadden et al., 2023). Addressing major challenges such as crop residue management through environmentally friendly technological methods not only enhances efficiency and productivity but also reduces the physical labor required, transforming farming into a less strenuous occupation.

Introduction

The managemment of pests and diseases in agriculture is challenging for farmers. Antibiotics and chemical pesticides are commonly used in traditional methods, which can be harmful to human health, the environment, and the long-term sustainability of agricultural practices. This article explores innovative approaches to pest and disease management in agriculture that are changing the game (Lykogianni et al., 2021). Techniques for controlling diseases and pests in field crops are necessary to keep agricultural systems healthy and productive. Farmers and agricultural professionals can prevent, monitor, and control pests and diseases that can significantly affect crop yields in a variety of ways. These administrators are practicing strategies to find a balance between restricting the use of chemical inputs, promoting sustainable agriculture, and maintaining financial viability.

Principle of Integrated Pests and Disease Management (IPDM)

• Suppression and Prevention: The elimination and prevention of pests are the first tenets of IPM. IPM aims to prevent a single pest from dominating a cropping system or causing damage, not eliminate all pests (Barzman et al., 2015). Three distinct sub-principles are combined in this principle: crop rotation and management, ecology, and a multi-pest strategy in combination The papers in this Special Issue discuss each of these guiding principles. A genuine illustration of the mix of strategies and multi-pest approaches is crafted by Poggi et al. (2021), who discussed ways to control wireworms in field crops. A risk assessment based on the production context (such as crop, climate, soil characteristics, and landscape) and monitoring of adult and/or larval populations should be the first steps in any new agro-ecological strategy.

Introduction

The realm of agricultural policies and environmental policies stands as two critical pillars influencing the sustainability and equilibrium of our ecosystem. Agricultural policies primarily focus on regulations and measures instituted by governments or institutions to manage agricultural practices, production, distribution, and rural development. Conversely, environmental policies aim to safeguard and conserve natural resources, biodiversity, and ecosystems while mitigating the adverse impacts of human activities on the environment.

Overview of Agricultural and Environmental Policies

Agricultural policies often encompass subsidies, price controls, land use regulations, and trade agreements aimed at bolstering agricultural productivity, ensuring food security, and supporting rural economies (Fischer & Edmeades, 2010). Conversely, environmental policies encompass a broad spectrum of regulations, such as pollution control measures, wildlife protection, habitat conservation, and climate change mitigation strategies (Boyd & Banzhaf, 2007).

Importance of Understanding Their Interactions

Understanding the interactions between agricultural and environmental policies is crucial due to their interconnectedness and potential trade-offs. Agricultural activities significantly impact the environment through deforestation, water pollution from agricultural runoff, greenhouse gas emissions, and loss of biodiversity (Pretty et al., 2008). Conversely, environmental conditions greatly influence agricultural productivity and sustainability, including soil quality, water availability, and climate patterns. Recognition of these interdependencies is vital for devising coherent policies that foster sustainable agricultural practices without compromising environmental integrity (Tilman et al., 2011). The challenge lies in formulating policies that strike a balance between agricultural production goals and environmental conservation imperatives.

Introduction

Throughout their existence on this planet, human beings have adapted to the diverse environments they inhabit. Depending on the topography and prevailing natural conditions, individuals have successfully navigated the challenges posed by nature and their surroundings. As life has progressed, humans have gained insights into nature, its resources, and the prudent utilisation thereof. This understanding encompasses various natural phenomena, the associated challenges, and the methods to mitigate them. Over time, humans have evolved into repositories of such knowledge, collectively known as 'indigenous knowledge' or 'traditional wisdom,' passed down from one generation to the next. Each specific region possesses its unique set of indigenous knowledge reflecting the harmonious coexistence of humans with their environment.

Indigenous agricultural knowledge (IAK) stands as a crucial force in the realm of agricultural development. It represents a systematic and collective body of knowledge, practices, and beliefs that evolve through adaptation processes and are transmitted across generations through cultural channels, elucidating the intricate relationship between living beings and their environment. With this collective wisdom, farmers have adeptly cultivated food crops in diverse environmental conditions and seasonal variations, relying on indigenous techniques in the absence of external inputs, resources, and scientific knowledge. This includes refraining from the use of synthetic agrochemicals and instead embracing practices like crop rotations and soil fertility restoration through closed nutrient cycles (Melash et al., 2023).

Introduction

Embarking on the journey of legume cultivation in India reveals a nuanced landscape marked by both challenges and promising horizons. The agricultural scenario is akin to orchestrating a symphony, where legumes play a vital role, contributing melodies of resilience and significance. Unveiling this leguminous symphony in India is an exploration into the intricate interplay of obstacles and potential triumphs within the agricultural sector. As we delve into an overview of legumes, commonly known as pulse crops, their significance in Indian agriculture becomes apparent. Belonging to the Fabaceae family, these plants possess a distinctive feature - the ability to harness nitrogen from the atmosphere. This unique characteristic renders them pivotal components in sustainable agricultural systems, impacting not only food and nutritional security but also influencing the broader scope of soil fertility enhancement and economic prosperity. The journey through legume cultivation in India unfolds as a harmonious narrative, weaving together the challenges faced and the promising prospects that lie ahead in this agricultural symphony (Sow and Ranjan, 2021).

Introduction

Millet is a small grain cereal grass bearing coarse seeds, and since ancient times has been considered a valuable and nutritious grain. It includes jowar, bajra, ragi and small millets. Small millets include Barnyard millet, proso millet, kodo millet, little millet and foxtail millet. It is rich in fiber, protein, iron, calcium, magnesium, zinc, vitamin B, has a low glycemic index and has antioxidant, anti-inflammatory and anti-allergic properties (Ranjan et al., 2023). These have been added to the modern lifestyle as healthy foods because they are beneficial for human health due to the presence of nutritional ingredients. The nutritional quality of food is the most important parameter for maintaining human health and comprehensive physical health. Good nutrition is the driving force for growth and maximisation of human genetic potential (Radhila et al, 2011). Food quality in the diet must be considered to maintain optimal human health and fitness to address the deep-seated problem of malnutrition. Diversification of food production at both the national and household levels should be encouraged, in addition to increasing domestic productivity and technology (Singh and Raghuvanshi, 2016). Some agricultural foods are not used by humans as staple foods due to human ignorance. Millet is one of them. Millet is used as food for animals and birds. Millet has many nutritional and medical functions (Yang et al, 2012).

Introduction

Plant Parasitic Nematodes (PPN) belonging to the kingdom Animalia are considered the most serious threat to agriculture and horticulture crops worldwide. The estimated yield loss on average is about 173 billion USD (12.3%), among which developing countries are more prone to nematode infestation (14.46%) than developed countries (8.8%). Nevertheless, these PPN aggravate the loss by forming disease complexes with other soil-borne pathogens (Jones et al., 2013). PPN gain entry onto the host roots through their secretome which secretes various kinds of proteins that are mandatory for the expression of disease symptoms in host plants. These proteins are generally called Nematode-Associated Molecular Patterns (NAMPs). The management of nematodes through synthetic nematicides paves the way for the production of commodities with toxic residues that may have severe ill effects after consumption. However, the use of resistant plants is another strategy to combat major PPN and suppress their population in soil and roots. Recent research progress has revealed the various defence mechanisms exhibited by plants against the invasion of PPN. Here we will discuss in brief about the various mechanisms followed by the host plants to recognise the invading PPNs and the resulting host immune response in response to recognition of various effector proteins of PPN.

Introduction

Agrochemicals are categorised based on the specific target organisms they are intended to control, including insects, weeds, and fungi. Among these target organisms, weeds significantly contribute to the highest economic losses due to their interference in crop production. Herbicides, the predominant class of agrochemicals globally, account for 48% of the total expenditure. The largest consumers of agrochemicals are Europe, Asia, and the United States. Herbicides have various biological and chemical properties (Roy et al., 2023a). They exhibit variations in behaviour and spectrum weed control. These properties play an important role in herbicide selection, method of utilisation and application, post-application effects such as toxicity and residue accumulation in the soil. The crucial factor for any herbicide application is the behaviour of the herbicide in the soil and its transformation or modification once it reaches the soil (Miller and Westra, 1914). The fate of herbicides and their efficiency are regulated by herbicide properties, soil characteristics and climatic conditions.

Introduction

Agriculture faces a massive challenge: by 2050, it needs to sustainably feed over 9 billion people while supporting economic growth and protecting the environment. Meeting this demand requires a staggering 70% increase in food production from 2006 levels, mainly by making the most of existing farmland and resources.

Precision agriculture offers a promising way forward. It's about smartly managing soil and crops using technology and information to boost profits while being eco-friendly. This approach involves giving the precise amount of resources—like water, nutrients, and pesticides—at the right time and place to optimise farming. It's all about understanding and using the differences within f ields to improve crop growth and protect the environment. Traditionally, understanding these differences involved collecting tons of soil and plant samples for lab analysis. But this can be costly. So, finding better, more efficient ways to assess these differences in soil and plant growth is crucial.

Precision agriculture relies on high-tech tools like GPS, GIS, yield monitors, and remote sensing to measure differences in soil and crop growth across f ields. By integrating these technologies with tools that adjust resource usage based on these differences (called variable rate technologies), farmers can apply fertilisers and water precisely where they're needed, optimising crop yields (Ranjan et al., 2022a). This method has three main parts: collecting data, understanding that data, and then making smart decisions based on it, all tailored to specific areas and timings. New technologies like yield monitors, satellites, and handheld sensors have made data collection faster and easier. But the real challenge is making sense of all this information and using it effectively for better farming. This review delves into how these technologies can enhance agricultural production and the hurdles they bring.

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.