eChapters

Introduction

Climate change, a global phenomenon of present time, has emerged as the greatest challenge of the world in the twenty-first century. Rising earth’s surface temperature is one of the most threatening factors coming into view. The substantial changes in the earth’s climate have an impact on ecosystems, economics and societies in a variety of ways (Sharma et al., 2024). It is one of the important environmental, agricultural, human and animal health issue. Climate change is an association of multidimensional effects involving physical characteristics, causes and consequences (Visschers, 2018). The climate change has resulted in to increasing global average temperature of air and ocean, melting of snow, rising of ocean level, change in the rainfall pattern and extreme whether events such as drought, flood, heat waves, storms etc.

Punjab, known as the “breadbasket of India,” relies heavily on livestock, particularly cattle and buffalo, for its agricultural prosperity. These animals play a crucial role in the region’s dairy industry, contributing significantly to milk production and rural livelihoods. However, like all livestock, cattle and buffalo in Punjab are susceptible to a range of diseases that can impact their health, productivity, and overall well-being. Timely and accurate diagnosis is essential for effective treatment and prevention. This article explores some of the most common diseases affecting cattle and buffalo in Punjab, along with the diagnostic procedures used to detect and manage these health issues, ensuring the sustainability and growth of the dairy sector.

Hydatidosis

Hydatidosis is a zoonotic disease caused by metacestode stages of parasite Echinococcus granulosus and is recognized as a major public health problem worldwide. This tapeworm is found in small intestine of the canid (definitive host) where it produce proglottids containing eggs. Upon shedding through faeces the later are ingested by the cattle, buffaloes and other intermediate hosts including man. These hatch in their intestine and the oncosphere invade circulatory system and lodge in various organs (liver, lung, brain etc.) where they develop into large thick walled unilocular cavity called hydatid cyst, which expands and can reach very large size and buds hundreds and thousands of protoscoleces endogenously. In a survey on hydatid cysts, a prevalence rate of around 5% has been recorded in more than 4000 meat samples of different animals in Punjab.

AMR is undoubtedly the most serious challenge ever faced by the world health community because of its multifaceted complex nature and its overwhelming impact over the human and veterinary communities. Livestock sector is no exception to have the serious and devastating impact from the emergence and spread of AMR. Because of a steady shift towards intensive farming and increased stocking density there has been increase occurrence of infectious diseases. Growing world population, rapid urbanization, and deforestation, shrinkage of grazing land all aided to this process. This leads to increased usage of antimicrobials in food animals – cause of concern for the scientific community as AMU is known the single most important driver of AMR.

The Central Nervous System not only consists of the Brain but also involves the Spinal cord.

The spinal cord is responsible for conveying information from the Brain to the rest of the body via the peripheral nervous system. The spinal cord can be divided into 4 functional segments.

1. C1-C5 (Upper Cervical Segments)

2. C6-T2 (Brachial Plexus)

3. T3-L3 (Thoraco - Lumbar)

4. L4-S3 (Lumbo-Sacral Plexus )

It is important to note that the motor and sensory function of the pelvic limbs are not only controlled by the Lumbo-Sacral plexus but also involve the Thoraco Lumbar segments (T3-L3) of the spinal cord. It is important to understand not only the neuroanatomy, but also the functional anatomy before approaching a neurological patient.

Cytauxzoonosis is an emerging tick-borne disease of domestic cats and wild felids caused by the protozoa of the genus Cytauxzoon. The common clinical findings in affected cats are similar to other hemoparasitic infections namely anorexia, depression, anemia, vomiting, icterus, and high fever. The first reporting of feline cytauxzoonosis with four fatal cases was done by Wagner in Missouri, USA in the year 1976.

Bobcats (Lynx rufus) are recognized as the natural wild reservoir of Cytauxzoon felis with prevalence reaching up to 100% in some enzootic areas (Zieman et al., 2017). This infection is highly fatal in domestic cats with 97% mortality without treatment (Meinkoth and Kocan, 2005). However, subclinical infection in some cats has also been evidenced i.e., they were infected with C? felis but did not display the acute clinical signs of the disease before diagnosis (Brown et al., 2010 and Rizzi et al., 2015).

Several other wild felids apart from bobcats and domestic cats have been found to be affected by fatal or nonfatal Cytauxzoon sp. infection. These include lions, jaguar, cougars, pumas, ocelots, Iberian lynx, meerkats, European wild cats and Asiatic wild cat, panthers, and tigers (Jakob and Wesemeier, 1996; Rotstein et al., 1999; Yabsley et al., 2006; Andre et al., 2009; Filoni et al., 2012 and Furtado et al., 2017).

Cardiomyopathies in acts can be classified broadly as Hypertrophic cardiomyopathy and non-hypertrophic cardiomyopathies. Usually, no alarming clinical signs are detected during routine examination in the preclinical stage except cardiac murmurs in some cats with HCM. This is because, that the cats are sedentary and excellent in hiding the signs of illness from the pet parents. Hence the onset is often acute with the sudden appearance of the clinical signs linked to congestive heart failure like severe dyspnoea, cardiogenic shock, aortic thromboembolism, arrhythmias and even syncope and death

Cats with CHF will be presented with severe dyspnoea, due to fulminant pulmonary oedema and or pleural effusion. The pleural effusion associated with CHF is usually a modified transudate, some cases it may be chylous .In echocardiography ,the size of the left atrium should be determined .If the LA size is normal ,then the dyspnoea may not be of cardiac origin .The presence of B-lines in the lung area in association with LA enlargement confirms cardiogenic pulmonary oedema. Stress, anaesthesia, I/V fluids and steroids leads to decompensation and fulminant pulmonary oedema. Thoracocentesis is will be helpful if there is dyspnoea due to significant pleural effusion. At least 300ml of effusion can be usually removed. The dyspnoeic cat is very fragile and should be handled carefully. Butorphanol may be given for anxiolytic effect. Oxygen administration is beneficial. Furosemide when given intravenously has an initial venodilator effect and should be repeated as required monitoring the sleeping respiratory rate (SRR). Addition of nitro-glycerine (vasodilator) reduces preload, decreases left atrial pressure and help in reducing pulmonary oedema. If the clinical signs are suggestive of cardiogenic shock or myocardial failure, Pimobendan I/V or Dobutamine CRI should be considered.

Tissue Doppler Imaging

Doppler echocardiography relies on detection of the shift in frequency of ultrasound signals reflected from moving objects. With this principle, conventional Doppler techniques assess the velocity of blood flow by measuring high-frequency, low amplitude signals from small, fast-moving blood cells. In TDI, the same Doppler principles are used to quantify the higher-amplitude, lower-velocity signals of myocardial tissue motion.

There are important limitations to TD interrogation. As with all Doppler techniques, TDI measures only the vector of motion that is parallel to the direction of the ultrasound beam. In addition, TDI measures absolute tissue velocity and is unable to discriminate passive motion (related to translation or tethering) from active motion (fiber shortening or lengthening). The emerging technology of Doppler strain imaging provides a means to differentiate true contractility from passive myocardial motion by looking at relative changes in tissue velocity.

Precision medicine and personalised treatment of systemic or tumour diseases gain their acceptance and application in veterinary care, too. The application of personalised medical protocols and companion diagnostics is easier and ethically well reasoned within the veterinary medical field – and it can offer many advantageous lessons to human clinical transformation, too [1]. In vitro companion diagnostics for testing the applicability and sensitivity levels of certain kinase inhibitor and immunotherapeutic drugs offer a greatly increased efficiency for veterinary cancer patients, too. As the understanding of cancer biology has advanced, the field of veterinary oncology has grown significantly, paralleling developments in human oncology. Precision medicine is an innovative approach to disease treatment and prevention that considers individual variability in genes, environment, and lifestyle. In the context of veterinary medicine, precision medicine aims to provide tailored therapies that are specifically designed for the unique genetic and molecular profile of each animal’s cancer. This approach moves away from the traditional “one-size-fits-all” model, instead focusing on targeted treatments that maximize efficacy while minimizing side effects. Companion diagnostics, such as molecular tests, help determine whether a dog or cat is likely to respond to a particular drug. For dogs and cats, precision medicine holds great promise in improving outcomes for cancers, which are among the leading causes of death in companion animals.

Introduction

One of the remarkable advancements in veterinary medicine recently is the application of interventional cardiology techniques to treat various congenital heart diseases. In dogs and cats, congenital heart diseases can be treated much more easily and with fewer complications using minimally invasive interventional approaches, rather than through traditional open-heart surgery. Among congenital heart diseases, treatment options are available for conditions such as PDA (Patent Ductus Arteriosus), AS (Aortic Stenosis), PS (Pulmonic Stenosis), VSD (Ventricular Septal Defect), ASD (Atrial Septal Defect), extrahepatic shunt, and stem cell applications. These diseases often cannot be significantly improved by medical treatment alone, making the use of the aforementioned interventional techniques essential for effective management.

Renal failure is one of the most commonly presented conditions in veterinary clinics world over. Although the potential for recovery is favorable, most animals die due to multisystemic effects induced by azotemia. Hence, extracorporeal removal of endogenous and exogenous toxins is very essential for expanding the window of recovery. Hemodialysis has been used in treatment of veterinary patients for the last 50 years and is still one of the most expensive and technically demanding treatment modalities in veterinary medicine. It is an advanced extracorporeal renal replacement therapy which has recently transitioned from a clinical curiosity to a more advanced standard for management of acute kidney injury (AKI) and chronic kidney disease (CKD) in companion animals particularly dogs and cats.

In addition to eliminating selective toxins that cause AKI, regardless of renal function, hemodialysis is also effective in the removal of pharmacokinetically active soluble uremic substances in CKD to enhance life expectancy and quality of life (Singh, 2021). Other common indications of hemodialysis in veterinary medicine include fluid overload, dysregulated body fluid composition, drug overdose or intoxications (Foster, 2020; Singh et al?, 2024). Medical management alone is often insufficient to control the clinical signs and metabolic abnormalities associated with uremia when AKI is severe or when there is involvement of acute on chronic kidney disease. Consequently, animals are at risk of death within a short therapeutic window, despite the potential reversibility of AKI and the improvement of life expectancy in CKD.

As the heart diseases in companion animals is becoming common as in humans, the research in canine cardiac diseases is one of the most extensive studies in the present scenario. As per the available literature, the overall occurrence of various cardiac abnormalities ranges from 0.37 percent to 1.67 percent in various parts of the country. Whereas, in western parts of the globe, nearly 8 million dogs suffer from heart disease accounting 10% of all dogs in the United States. Heart diseases are common among older dogs; up to 75% of senior dogs have some type of heart condition, and unfortunately, most go undiagnosed due to varied reasons. The recognition of canine cardiac diseases has been delayed, and ignored on account of lack of awareness and knowledge by the owner and inadequate diagnostic facility in our country. Physical examination, radiography, electrocardiography and echocardiography are most useful diagnostic procedures employed in diagnosing the cardiac diseases in dogs. However, majority of the acquired cardiac diseases are being managed medically with one or a combination of drugs. Improved diet management and health care of companion animals have led to increased life expectancy, as a result, cardiac diseases have become pivotal causes of death for both dogs and cats. For such reason, a major objective has become to increase the quality of life by reducing the expansion of cardiac diseases via early detection of diseases and provision of effective treatment.

Introduction

Canine atopic dermatitis (CAD) is a chronic inflammatory skin disease marked by itching (pruritus), redness (erythema), and recurrent infections. While traditionally considered an immune-mediated condition, recent studies highlight the significant role of skin barrier dysfunction in its development. A key aspect of CAD is the compromised skin barrier, which increases permeability to allergens, irritants, and microorganisms, thereby worsening the disease. The skin barrier is a complex, dynamic structure essential for protecting dogs from environmental challenges. The stratum corneum—the outermost layer of the epidermis—serves as a crucial defense against environmental insults, allergens, and pathogens. In dogs with atopic dermatitis, this barrier often becomes impaired, leading to increased transepidermal water loss (TEWL), greater allergen penetration, and secondary infections. This dysfunction both results from and contributes to the condition, creating a self-perpetuating cycle of barrier disruption, inflammation, and immune dysregulation.

Learning objectives

1. Recognise inflammatory cell types

2. Recognise common skin pathogens

3. Diagnostic test are best for each case

4. Gain experience in interpretation

5. Recognise important artefacts

″The most crucial step towards healing is having the right diagnosis″. While approaching a case, a clinician should follow appropriate steps in a systematic way for accurate diagnosis of dermatological disorders. A careful history is a first step to choose appropriate diagnostic tests.

Asiatic Elephant (Elephas maximus) is the national heritage animal of India. This particular species now exists in 13 Asian countries including India. As per 2017 census, India is home to an estimated figure of 29,964 elephants that is >60% wild population of the globe. Once distributed across the country, now they are squeezed to 14 states grouped in four regions ie., southern (14,612), north-eastern (10,139), east-central (3,128) and northern regions (2,085). There are 2675 captive elephants across different states under both private and government custody. This largest terrestrial mammal is protected under Seclude I species of Indian Wild Life Protection Act, 1972.

Both central and state Government are implementing short- and long-term programmes for health and well-being of captive as well as free-range elephants. The Central Government has established an ‘Elephant Cell’ at the Wildlife Institute of India (WII), Dehradun to provide technical inputs to the ministry for strengthening the efforts towards conservation of elephants. Add-on, the ‘Project Elephant’ was launched in 1991-92 in the Ministry of Environment, Forests and Climate Change to restore the elephant habitats and reduce their sufferings. Elephant habitats are notified as Elephant Reserves that are basic management units for elephant conservation and managements. A total of 33 Elephant Reserves has been notified in the country. A book titled, ‘Caring for elephants: Managing health and welfare in captivity’ is edited for field officers, veterinary professionals and elephant handlers.

Introduction

A complete neurological examination is warranted whenever an equine patient displays signs consistent with neurological disorder or to establish that the horse is neurologically normal (e.g., pre-purchase examination). Neurological examination of equine patient is challenging due to their large size, which prevents veterinarian from using many of the tests used for small animals. The key to a successful neurologic examination is to do the examination in a consistent and organized fashion. It is important to develop a routine, then do it the same way each time. This approach ensures that some parts of the examination are not forgotten and it also increases the consistency of the exam as you examine more number of equine patients. A standardized examination form is a valuable aid in this and can be concise. The goals of the neurologic examination are to determine whether disease of the nervous system exists, to localize the lesion to a particular area of the nervous system, and to describe and record the responses as a baseline for future evaluations. In this paper, a complete neurological examination is described with special emphasis on field conditions.

Introduction

Mithun (Bos frontalis), or ‘Gayal’ or ‘Eso’ or ‘Sial’ or ‘Sandong’ or ‘Wei’, is a rare bovine species, exclusively found in the northeastern hill region (NEHR) of India at an altitude of 300-3000 m. It plays an important role in the socio-economic and cultural life of indigenous tribes of Arunachal Pradesh, Nagaland, Mizoram and Manipur and is aptly referred to as the ‘Pride of Northeast India’ (Mukherjee et al. 2019). Besides India, mithun population is distributed to China, Bangladesh, Thailand, Bhutan, Myanmar, and Malaysia. The Indian mithun is a medium- to large-sized majestic ruminant with a characteristic jet-black body, an ash-colored forehead, and ‘white stockings’ on all the limbs. This unique animal has a special preference for salt feeding, is a skilled browser even in steep, hilly terrain, and does not need pasture land like cattle. Mithun is primarily reared under a free range system as an important source of lean meat, rich in amino acids, proteins, PUFA, etc. On September 1, 2023, the Food Safety and Standards Authority of India (FSSAI) officially certified the mithun as a food animal (Joshi et al., 2021; Lalchamliani et al., 2024; Ramesh et al., 2024). Compared to milk from other species, mithun milk has much larger quantities of fat, lactose, solids-not fat (SNF), calcium, important vitamins A, D, and E, essential amino acids, and polyunsaturated fatty acids (Devi et al., 2023).

Introduction

Avian medicine is a rapidly growing field, with an increasing number of exotic bird species being kept as pets. However, the unique anatomy, physiology, and behaviour of these birds present several challenges as well as new opportunities for veterinarians and researchers.Now a day’s birds are gaining popularity over dogs and cats because of various reasons listed below:

a. They are more suited in present life style as can be suited well in small spaces.

b. They require low maintenance with relative low cost of maintenance and live longer life.

c. They form very strong bonds with their owners.

Birds can be classified into different families, such as Psittaciformes, Passeriformes, Columbiformes, Piciformes, Anseriformes, Galliformes etc. containing numerous species (>10,000). The first documentation of parrot as a pet was found in Rig Veda, written more than 3000 years ago. Evidence of bird keeping is observed in ancient civilizations of Greece, Rome, China and Egypt. In Greek mythology, Raven (a crow) was mentioned as a symbol of good luck and was associated with God Apollo (God of prophecy). Crows are also considered as ancestors in Hindu mythology and food (pinda) is offered to crows during ritual of paying homage to dead persons. In most of the countries psittacine and passerine birds are preferred as pet birds.

Introduction

Canine epilepsy, a prevalent neurological disorder, significantly impacts the quality of life for both dogs and their owners. Traditional anticonvulsant therapies often yield inconsistent outcomes, necessitating the exploration of alternative treatments such as homeopathy [1][2]. Homeopathy’s individualized approach aligns with the principles of unicism, focusing on holistic care tailored to each patient’s unique symptom profile [3]. However, the subjective nature of remedy selection and response assessment poses challenges for practitioners.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) offers a promising avenue to address these challenges [4]. Predictive models can analyze complex datasets to uncover patterns that guide remedy selection and treatment strategies. This study evaluates the comparative performance of Logistic Regression, Random Forest, and XGBoost in predicting treatment outcomes for canine epilepsy managed with homeopathic remedies.

Introduction

Wound is a disruption to the epithelial integrity of the skin, extended in-depth to subcutaneous tissue and other body parts such as ligaments, muscles, blood vessels, nerves, and bone (Robson et al. 2001). An appropriate wound healing is essential for the restoration of disrupted anatomical continuity (Begum, 2000). We have observed increasing interest in research based on traditional medicines of the world in the recent era (Medina-Franco et al. 2013). Traditional and complementary medicine regains popularity not only in developing countries but also in developed countries. Common and known practices across the globe of traditional medicines include Herbal, Ayurveda, Siddha, Unani, Iranian, Islamic, Vietnamese, Chinese, Acupuncture, African and other pseudo-medical knowledge (Dorai 2012). According to World Health Organization (WHO), traditional medicine ‘the health practices, approaches, knowledge and beliefs incorporating plant, animal and mineral-based medicines, spiritual therapies, manual techniques and exercises, applied singularly or in combination to treat, diagnose and prevent illnesses or maintain well-being’. Saxena et al. (2013) reported that phytochemicals present in plants are liable for preventing disease and promoting health. Antibiotics used in the treatment of wound infections are now proved to have adverse effects in the human body and in pathogens where resistance is emerging. Attention has, therefore, turned towards extracting biologically active compounds from plant species having wound healing property (Pattanayak and Sunita, 2008).

Introduction

As India is the leading global economy with livestock contributing 4.66% in total GVA to Indian Economy (PIB, 2024) and ranks 1st in Milk Production, 2nd in Egg production, 5th in meat production (BAHS, 2024). Being the largest exporter of milk and meat in the world , this has led to the intensification of animal production due to the increasing demand of major livestock products (MLPs), which has led to an increasing overall use of antimicrobials. Antimicrobials are invariably used to preserve animal and public health, but also as growth promoters at a sub-therapeutic level. The overuse and misuse of antibiotics can contribute to antimicrobial resistance. The growing resistance to antimicrobial drugs could reverse the therapeutic benefits; animals’ resistance to antimicrobial drugs, reduces productivity and leads to economic losses. Unwanted antimicrobials residues may be present in products of animal origins, in animal waste contaminating soil and water and the environment. Seventy-five to ninety percent of antimicrobials used in livestock are excreted, mostly unmetabolized. AMR poses a serious threat to the safety and quality of animal origin food, food security and livelihoods. Unhealthy and unproductive animals are no longer able to generate food products of acceptable safety and quality for human consumption and can no longer contribute to income generation. Insufficient standards for residues in animal food products, reduces the livestock sectors potential of access to trade and increases public health risks (https://www.fao.org/antimicrobial-resistance/key-sectors/animal-production/ en/).

By 2050, the global population is projected to reach approximately 9.8 billion, requiring a 70% increase in food production to meet the growing dietary demands. The livestock sector will play a crucial role in addressing this rising need through sustainable animal production, contributing to food security, poverty alleviation, public health, and food safety. To tackle the challenges posed by population growth and to achieve the United Nations Sustainable Development Goals (SDGs), technological advancements and innovations will be essential in transforming the food and agriculture sectors.

In light of these challenges, Veterinary Services are more critical than ever. They are essential for enhancing livestock health and productivity, ensuring high-quality food that meets safety standards, and mitigating risks associated with animal diseases and public health. Additionally, Veterinary Services must contribute to global efforts in addressing issues such as food security, antimicrobial resistance, climate change, and the depletion of natural resources.

In today’s rapidly evolving world, food production is at the confluence of tradition and innovation. While age-old farming practices have sustained civilizations for millennia, the modern era demands an unprecedented level of precision and foresight. Weather intelligence—the practice of integrating detailed meteorological data with advanced analytics and decision support systems— is revolutionizing food production, from agriculture and fisheries to animal husbandry (Fig. 1.1). This chapter lays the foundation for understanding how accurate and timely weather data underpins food security, economic sustainability, and environmental stewardship. It further highlights global themes addressing the growing demand for food in any nation with a burgeoning population, the challenges of climate change, and the increasing frequency of extreme weather events. This introduction also outlines the objectives and structure of this book, which aims to empower decision makers with the knowledge needed to navigate a world where climate and food production are inextricably linked.
1.1 Context & Importance
1.1.1 The Changing Face of Food Production
Food production has always been sensitive to the whims of nature. Historically, farmers relied on folklore, seasonal patterns, and local wisdom to guide planting and harvesting cycles. With growth and development of atmospheric sciences from 19th century, farmers started depending upon weather forecasts in planning and management of agricultural production systems. However, with the advent of climate change, environmental degradation, and increasingly unpredictable

Meteorology is not merely the study of weather patterns; it is a critical science that intersects agriculture and allied sectors in numerous ways. In a world where food security is intimately tied to climatic variability, understanding the basic principles of meteorology is essential for both traditional farming practices and modern integrated food production systems. This chapter explores the foundational concepts of weather and climate, examines the atmospheric dynamics that directly impact agricultural systems, and contrasts historical meteorological practices with modern forecasting approaches. By delving into these topics, we can better appreciate how the environment influences agricultural productivity and how innovative meteorological tools can improve decision-making in the food production chain. Weather describes the state of the atmosphere at a specific time and place. It is the sum of short-term atmospheric conditions such as temperature, humidity, precipitation, wind, and visibility. In contrast, climate refers to the long-term average of weather patterns over a period of decades or more. Understanding this distinction is crucial for agriculture, as farmers and allied sector professionals must respond to immediate weather events (such as sudden frost or heavy rainfall) while also adapting to long-term climatic trends (like gradual temperature increases or shifting precipitation patterns).

Agriculture has long been at the mercy of the weather, and in today’s datadriven world, harnessing meteorological information is more critical than ever. Weather intelligence—the process of collecting, analyzing, and disseminating weather data—supports decision-making in diverse fields such as aviation, transportation, emergency management, and especially agriculture (Fig 3.1). This chapter explores how advanced data acquisition methods and robust database management techniques underpin agricultural weather intelligence. It covers the variety of data sources available, details strategies for integrating and ensuring the quality of vast datasets, and examines the forecasting techniques that transform raw data into actionable insights for precision forecasting and decision making in food production. Fig. 3.1. Weather Data Acquisition System In this chapter, we not only discuss the technological means by which weather data is collected—from satellites, ground sensors, and IoT networks—but also dive into the structure of weather databases. These repositories house surface weather observations, radar and satellite imagery, upper air measurements, and outputs from weather models. Further, we outline the different forecast types and forecasting methodologies that are essential to interpreting this data, ranging from nowcasting for immediate weather conditions to seasonal forecasting that supports long-term planning. Finally, the role of big data analytics in supporting precision forecasting and optimizing decision-making in agricultural production is examined, highlighting the integration of advanced techniques like numerical

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

This chapter integrates case studies, scientific examples, and policy perspectives to illustrate how AI and ML can transform agricultural weather intelligence. The discussion spans from advanced predictive models in developed nations to emerging applications in technology-limited regions, while also addressing the urgent need for resilient crisis management systems in the face of natural and human-caused conditions. Advanced scientific techniques can not only optimize food production and resource management but also play a critical role in recovery efforts during times of crisis. Agriculture is intrinsically linked to the natural world—a domain defined by fluctuating weather patterns, seasonal shifts, and unpredictable environmental factors. Today, Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing how we analyze and predict weather patterns, and their applications in agriculture are far-reaching (Fig. 5.1). From forecasting crop yields and fishery conditions to managing plantations and monitoring livestock health, these technologies are creating a paradigm shift in agricultural weather intelligence. Scientific organizations worldwide are leveraging AI and ML to refine predictive models, enable multi-variate analysis, and provide advanced warning systems for extreme events such as droughts, tsunamis, earthquakes, and even crises caused by human-made conflicts. Emerging nations with rapidly growing technological capabilities stand to benefit immensely from these innovations, tailoring solutions to local challenges while contributing to global food security and crisis resilience.

Agriculture has long served as the backbone of human civilization, enabling societies to grow and prosper. For generations, farmers have relied on experience, traditional practices, and historical weather data to inform decisions about crop cultivation and livestock management. Yet these conventional methods often left operations vulnerable to unpredictable environmental conditions. Today, the integration of the Internet of Things (IoT) is transforming agriculture by shifting the approach from reactive management to proactive, data-driven decision making. Modern IoT systems deploy a network of interconnected sensors that continuously capture critical environmental parameters such as soil moisture, temperature, humidity, light intensity, and nutrient levels. These sensors provide granular, real-time data that is transmitted to centralized analytics platforms where it can be processed and visualized. The resulting insights empower farmers, aquaculturists, and animal husbandry professionals to optimize every aspect of their operations — enhancing yields, reducing waste, and increasing sustainability. This chapter explores the deployment and impact of IoT in agricultural and allied systems. It provides detailed technical discussions on sensor networks, communication protocols, and power management; illustrates how real-time data integration with advanced analytics transforms decision making; and presents in-depth case studies from initiatives such as John Deere’s precision agriculture systems, Microsoft FarmBeats, and regional projects like SmartAgriHubs. Moreover, the chapter delves into the formidable challenges of IoT deployment, including those faced by underdeveloped regions—and concludes with a look at emerging trends and a synthesis of the future vision for agriculture.
6.1 The Role of IoT in Modern Agriculture
6.1.1 Revolutionizing Data Collection
The most significant impact of IoT in agriculture is its transformation of data collection. Traditional methods relied on periodic field observations, manual sampling, and regional weather reports that provided only a snapshot of environmental conditions. In contrast, IoT devices continuously monitor the microclimate across a farm. With sensors distributed throughout fields, livestock facilities, and aquaculture systems, farmers can now capture real-time data on temperature, humidity, soil moisture, light intensity, and more (Fig.6.1).

This chapter provides an in-depth exploration of advanced techniques and conceptual frameworks—here termed “Weather Intelligence”—and their potential applications in precision agriculture. Although the term is not yet standardized, it serves as an exploratory framework for integrating advanced forecasting, diverse datasets, and data analytics to capture both natural and anthropogenic influences on weather patterns. The discussion spans crop and livestock production as well as allied sectors such as fisheries, aquaculture, plantations, horticulture, and specialized areas like flower farming. By integrating real-time data from sensors, remote sensing platforms, and historical records, these exploratory systems aim to empower farmers and food producers to optimize irrigation, planting, harvesting, and overall resource management. Agriculture has always depended on weather, yet the rapid pace of climate change and the need for sustainable practices have driven a technological revolution in farm management. In today’s digital age, advanced forecasting techniques and data analytics are converging into a new conceptual framework we call “Weather Intelligence.” Although this term is exploratory and not yet widely adopted, it encapsulates the vision of integrating high-resolution satellite imagery, IoT devices, and machine learning algorithms to forecast local microclimatic conditions with unprecedented accuracy. These forecasts enable proactive adjustments to critical operations—such as irrigation scheduling, planting, and harvesting, thereby mitigating weather-induced risks and enhancing productivity. Moreover, it is increasingly evident that weather patterns are not only shaped by natural phenomena but also by human-induced risks such as wars, conflicts, and environmental abuse. Factors like landfills, water and sea pollution, toxic dumping, and unsustainable industrial practices are altering microclimates around the world. This complex interplay of natural and anthropogenic forces calls for the incorporation of a broader array of variables and larger datasets into forecasting systems. In this context, embracing Weather Intelligence as a dedicated field of study and application in agricultural and allied sciences becomes not only desirable but essential. This chapter aims to detail the evolution and current state of these integrated approaches, discuss their potential applications across various agricultural sectors, and outline strategies for precision decision making. While much of the content is exploratory in nature, it provides a conceptual framework for future research

Remote sensing and Geographic Information Systems (GIS) have emerged as critical tools in modern agriculture and weather intelligence. Their integration has revolutionized how we monitor, analyze, and manage not only food production but also urban development, natural resource conservation, and climate adaptation. In this chapter, we explore the fundamentals of remote sensing, the use of diverse sensing technologies (including satellite imagery, radar, LiDAR, and thermal imaging), and the incorporation of advanced data analytics and machine learning. We also examine GIS integration for spatial analysis and mapping, discuss emerging technologies like unmanned aerial vehicles (UAVs) and Internet of Things (IoT) sensors, and address economic, ethical, and policy considerations. Real-world case studies illustrate the practical applications of these technologies, while discussions on future directions highlight opportunities for continued innovation. Food production systems and its management today face unprecedented challenges—from climate variability and resource scarcity to urban expansion and shifting ecosystems. To address these challenges, modern strategies increasingly depend on synthesizing high-resolution environmental data with advanced spatial analysis. Remote sensing technologies provide a bird’s-eye view of vast landscapes, while GIS platforms enable efficient management and interpretation of these datasets. The integration of these technologies supports a host of applications, including real-time monitoring of weather phenomena, predictive modeling of environmental impacts, and strategic planning for sustainable development. With emerging trends such as drone-based monitoring, IoT sensor networks, and AIdriven analytics, the potential for remote sensing and GIS to transform weather intelligence and environmental management is growing exponentially. This chapter provides both the technical foundation and applied perspectives necessary to understand and leverage these powerful tools.

The content presented in this chapter is exploratory and reflects emerging trends in integrating weather intelligence with decision support systems. The term “weather intelligence” is introduced as a framework, with ongoing research expected to further refine and expand upon these ideas as technologies evolve. In an era where climate variability and extreme weather events increasingly threaten global food systems, the integration of advanced decision support systems (DSS) with what we are now calling weather intelligence has emerged as a transformative paradigm. This chapter explores how food production— encompassing agriculture, fisheries, and animal husbandry—can harness integrated weather intelligence to inform real-time crisis management, strategic planning, and risk mitigation. The discussion is exploratory in many respects, as the concept of weather intelligence represents an innovative framework that challenges traditional forecasting methods and decision support tools (Fig. 9.1). Drawing on multidisciplinary research and practical case studies, we examine the building blocks of decision support systems tailored to food production. We then discuss the integration of weather intelligence into crisis response and strategic planning, highlighting both established risk mitigation approaches and emerging tools designed to anticipate and respond to extreme weather events. By contextualizing these developments within the broader scope of sustainable food production, this chapter offers an in-depth analysis and a forward-looking vision for decision support and response management in a rapidly changing climate.

In an era defined by rapid technological advancements and an ever-changing climate, the need for precise, adaptive, and actionable weather intelligence has never been greater. As global weather patterns become more erratic and extreme events increasingly disrupt lives and livelihoods, the integration of real-time field response into weather intelligence systems represents a transformative opportunity. Just as companies like Google and Tesla leverage real-time user inputs to continuously update their software, refine artificial intelligence (AI) models, and provide timely road-and-traffic guidance, weather intelligence systems that are continuously refined by feedback from farmers, fishermen, and other local stakeholders offer the promise of dynamic, “live” insights that can revolutionize agricultural practices and community resilience (Fig.10.1). This chapter delves into the critical importance of collecting and synthesizing feedback from those who are on the frontlines of weather impacts. It explores the methodologies and benefits of integrating real-time field data into weather intelligence systems, drawing parallels to the iterative processes observed in hightech industries. In doing so, we highlight how these innovations can maximize

Early Warning Systems (EWS) have evolved from simple, rudimentary alert mechanisms into complex, multi-hazard, data-driven networks that play a critical role in disaster risk management and climate adaptation. In today’s rapidly changing climate, timely and accurate information is essential not only for saving lives but also for protecting livelihoods, reducing economic losses, and enhancing community resilience. The application of EWS varies significantly by region, geography, type of disaster, and across diverse sectors such as agriculture, aquaculture, horticulture, animal husbandry, fisheries, and allied sciences. Global initiatives - led by organizations such as the United Nations - aim to ensure that every person, particularly the most vulnerable, is equipped with lifesaving early warnings by 2027 (Fig.11.1). Complementary partnerships, such as that between the World Meteorological Organization (WMO) and the Food and Agriculture Organization (FAO), further strengthen these systems by integrating cutting edge forecasting with actionable advisories for critical sectors. A particularly groundbreaking effort is the United Nations’ Early Warnings for All (EW4ALL) initiative, launched in 2022 by UN Secretary General António Guterres, which seeks to provide comprehensive early warning coverage for hazardous weather, water, and climate events to every person on Earth. Underpinning these global efforts is Weather Intelligence - a transformative approach that harnesses advanced data analytics, real time monitoring, and machine learning techniques to refine forecasts and deliver precise, context specific alerts. By integrating diverse data streams and tailoring

The rapid pace of technological innovation, coupled with mounting environmental challenges and shifting societal expectations, is fundamentally reshaping the landscape of sustainable food production. In this chapter, we explore the future outlook of this dynamic field by examining emerging trends in weather intelligence, the transformative potential of innovations in artificial intelligence (AI), the Internet of Things (IoT), and data integration, as well as the evolving policy and ethical considerations that underpin sustainable agriculture. The integration of advanced technologies with traditional agronomic practices has led to the development of data-driven systems that not only forecast weather with greater accuracy but also optimize crop management, resource utilization, and risk mitigation. As we look toward the future, the convergence of these disciplines promises to usher in a new era of resilient, efficient, and environmentally conscious food production systems. We begin by outlining the current landscape and identifying key trends that are driving change. The discussion then transitions into an exploration of cuttingedge innovations in AI, IoT, and data integration that are revolutionizing weather intelligence. Subsequent sections address the policy frameworks and ethical dilemmas accompanying rapid technological adoption in agriculture, before concluding with a vision for future research directions and strategic planning. Finally, the chapter suggests additional topics—such as blockchain for supply chain transparency and urban vertical farming—to ensure a holistic view of sustainable food production in a rapidly changing world.
12.1 Emerging Trends in Weather Intelligence and Food Production
Over the past decade, advancements in sensor technologies, communication networks, and computational algorithms have revolutionized weather forecasting and climate monitoring. These breakthroughs are not only critical for predicting weather events but also serve as the foundation for proactive decision-making in agricultural management. High-resolution satellite imagery and remote sensing techniques now provide granular data about atmospheric conditions, soil moisture levels, and crop health (Fig.12.1).

Experimentation is fundamental to scientific investigation because it provides a systematic basis for studying natural and controlled systems and drawing conclusions from observations. In forestry and horticulture, experiments are often affected by developmental, environmental, genetic, sampling, and experimental variations. This chapter introduces the principles required for planning, conducting, analysing, and interpreting such experiments. It explains comparative experiments, the need for experimentation, experimental units, sampling units, plots, blocks, treatments, factors, levels, and sources of variation. Particular emphasis is given to the basic principles of experimental design—randomisation, replication, and local control—which help improve validity, precision, and coverage of experimental results.

Hypothesis testing provides a statistical framework for making inferences about populations from sample observations obtained through random experiments. This chapter introduces the scientific logic underlying statistical hypotheses and explains the formulation of null and alternative hypotheses. It discusses one-tailed and two-tailed tests, levels of significance, Type I and Type II errors, sampling distributions, test statistics, critical regions, p-values, and decision-making. The chapter also explains statistical significance and the interpretation of test results. Applications of Student’s t-test and related procedures are illustrated through forestry and biological examples, enabling researchers to determine whether observed differences between means can reasonably be attributed to treatments rather than random sampling variation.

Analysis of Variance (ANOVA) is an important statistical technique for separating variation attributable to different sources in an experiment. The chapter explains how total variation can be partitioned into controlled and uncontrolled components and how experimental error is used to assess treatment differences. The F-test provides the basis for testing the homogeneity of several treatment means simultaneously. The chapter discusses the assumptions underlying ANOVA, including independence, normality, additivity, and homogeneity of variance. It also introduces fixed- and random-effects models and explains their relevance to experimental analysis. Forestry and agricultural examples demonstrate the application of ANOVA for interpreting treatment and experimental effects.

Transformation of data is useful when the assumptions required for analysis of variance are not adequately satisfied. Forestry experiments may produce observations showing skewness, unequal variances, non-normality, or departures from additivity. This chapter explains the need for transforming observations to an appropriate scale before statistical analysis. It discusses the characteristics of an ideal transformation, including stabilisation of variance, improvement of normality, efficient estimation of means, and achievement of linear and additive effects. Various transformation techniques used in forestry experimental analysis are introduced with suitable examples. The chapter emphasizes that appropriate transformation can improve the validity, sensitivity, and interpretability of statistical tests.

When ANOVA indicates significant differences among treatment means, further analysis is required to determine which particular means differ significantly. This chapter deals with post-ANOVA procedures used for detailed comparison of treatment means. It introduces methods such as critical difference, least significant difference, and multiple comparison procedures. Particular attention is given to Scheffé’s method, Newman–Keuls method, and Duncan’s New Multiple Range Test. The chapter explains the principles, calculations, and interpretation of these techniques using forestry examples, including comparisons among tree species and provenances. It also discusses considerations for selecting an appropriate multiple-range or comparison method according to the objectives and number of comparisons involved.

Orthogonal designs provide an efficient framework for arranging treatments and sources of variation so that their effects can be estimated independently. This chapter introduces contrasts, orthogonal contrasts, and the concept of orthogonality in experimental design. Orthogonality helps prevent the effects of different factors from becoming entangled and facilitates systematic statistical analysis. The chapter discusses three basic experimental designs: Completely Randomized Design, Randomized Complete Block Design, and Latin Square Design. Their principles, applications, and suitability for forestry and agricultural experiments are explained. Examples involving seedlings, tree species, fertilizers, chemicals, wood pieces, and seeds demonstrate how appropriate design selection can reduce heterogeneity and improve experimental precision.

Factorial experiments provide an efficient method for studying two or more factors simultaneously at different levels. This chapter introduces the concept and notation of factorial arrangements and explains treatment combinations in experiments such as 2 × 2 and higher-order factorials. It distinguishes among simple effects, main effects, and interaction effects, emphasizing the importance of interactions in interpreting biological responses. Factorial experiments allow researchers to examine the effects of individual factors as well as their combined influence. The chapter also explains that factorial treatments may be arranged using different basic experimental designs, such as Completely Randomized, Randomized Block, or Latin Square designs, according to experimental objectives and conditions.

Loss of experimental observations is a common practical problem in forestry and agricultural experiments because seeds, seedlings, plants, and trees may be damaged by natural events, animals, anthropogenic activities, or other causes. This chapter presents the missing plot technique for estimating unavailable observations so that an experiment can continue to be analysed. It explains why missing observations can reduce the information and precision of an experiment and discusses alternative approaches for handling incomplete data. The chapter focuses particularly on the method of least squares and the iteration method. Applications include estimation of missing values in Randomized Block Designs and other experimental layouts used in forestry research.

Analysis of Covariance (ANCOVA) combines features of analysis of variance and regression analysis to account for the influence of a concomitant or covariate variable. In forestry and horticultural experiments, initial measurements or other associated characteristics may influence the response variable and contribute to experimental variation. This chapter explains how treatment effects can be evaluated after adjusting for the linear relationship with a covariate. It presents the ANCOVA framework, calculation of sums of squares, regression coefficients, adjusted error and treatment variation, F-tests, and adjusted treatment means. Examples involving seedling growth and physiological responses demonstrate how covariance adjustment can provide more precise comparisons among treatments.

Split-plot design is particularly useful when certain biological treatments cannot conveniently be applied to small experimental units. This chapter explains how large experimental plots can be divided into smaller sub-plots to accommodate subsidiary treatments. Main-plot treatments are randomly allocated to larger units, while sub-plot treatments are independently allocated within them. The design allows researchers to study main effects, sub-plot effects, and interactions, although greater precision is generally obtained for sub-plot comparisons than for main-plot comparisons. Examples include wood preservation and nursery experiments involving sowing methods and seasons. The chapter also explains the two error terms required for appropriate analysis of split-plot experiments.

Series of experiments are conducted at different locations, times, or combinations of locations and years to evaluate the spatial and temporal performance of treatments. Such experiments are especially important in forestry and agricultural research when conclusions are intended to apply across wider geographical or environmental conditions. This chapter explains combined analysis of a series of experiments after individual experiments have first been analysed according to their respective designs. Particular attention is given to testing the homogeneity of error variances, including Bartlett’s test. When variances are homogeneous, pooled analysis can assess treatment effects, location or year effects, and treatment-by-location or treatment-by-year interactions, thereby evaluating consistency of treatment responses.

Augmented designs are useful when a large number of standard varieties or treatments are available for replication but new or promising materials are available only in limited quantities. This chapter introduces augmented designs as modifications of standard experimental designs in which additional, usually unreplicated, entries are incorporated alongside replicated standard treatments. Applications include testing new varieties, seedlings, medicinal plants, growth regulators, seedling carriers, and progeny under forestry and agricultural conditions. The chapter explains the principles of augmented designs and their applications in experimental research. It also discusses augmented Completely Randomized Design, including its layout, analysis of variance, and comparison of replicated varieties and unreplicated seedlings.

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

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

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

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

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

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