ILRI chapters in books and reports
Permanent URI for this collectionhttps://hdl.handle.net/10568/131
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Item EcoHealth projects in South-East Asia(Book Chapter, 2025-07-03) Lam, Steven; Hung Nguyen-VietItem ILRI One Health research for development portfolio to address food system challenges(Book Chapter, 2025-07-03) Hung Nguyen-Viet; Lam, StevenItem CGIAR Initiative on One Health(Book Chapter, 2025-07-03) Hung Nguyen-Viet; Hoffmann, VivianItem Capacitating One Health in Eastern and Southern Africa (COHESA)(Book Chapter, 2025-07-03) Caron, Alexandre; Richards, Shauna; Iraki, B.; De Nys, H.; Karembu, M.; Knight-Jones, Theodore J.D.Item One Health at scale: social-ecological system health(Book Chapter, 2025-07-03) Caron, Alexandre; Garine-Wichatitsky, M. deItem Next-generation tools for nutrition-inclusive breeding for cereals(Book Chapter, 2025-01-23) Choudhary, S.; Anbazhagan, Krithika; Kholova, J.; Murugesan, T.; Kaliamoorthy, S.; Chadalawada, K.; Prasad, Kodukula V.S.V.; Nankar, A.N.; Mani, V.; Chandra, M.; Banoriya, R.; Vadez, V.Addressing global malnutrition requires improving the nutritional quality of major crops and promoting nutritionally rich crops. However, breeding for improving nutritional traits is challenging, particularly in the absence of rapid and precise phenotyping of these parameters. Quick phenotyping is crucial as it allows breeders to select lines with high nutritional value alongside yield and other important traits while advancing the generations. Traditionally, grain nutritional and quality assessments have relied on wet-lab analytical services, which are slow, costly, and often inaccessible. To overcome these limitations, rapid and cost-effective sensor-based technologies have emerged as a promising solution. Interdisciplinary research combining sensor technology, AI, biochemistry, and crop science has significantly advancing the grain composition analysis, and post-harvest trait evaluation. Tools like near-infrared spectroscopy (NIRS), X-ray fluorescence (XRF), and computer tomography (CT) are increasingly getting utilized to ensure quality standards in trade, nutrition, and food safety. These technologies focus on key traits precisely, time, and cost-effectively, with early findings highlighting their potential for scalable solutions. Such advancements are essential for nutrition-sensitive breeding and improving food safety, quality-based payments for farmers, and supporting global efforts against malnutrition. The swift adoption of these technologies in breeding programs, supported by public-private partnerships, is crucial for sustainable development.Item AI-enabled UAV borne hyperspectral imaging for crop-livestock farm management(Book Chapter, 2025-03-13) Sankararao, A.U.G.; Rajalakshmi, P.; Choudhary, Sunita; Kholova, Jana; Jones, Christopher S.Crop-livestock farming plays a crucial role in global agricultural communities by integrating crop production with livestock farming to create a sustainable and diversified farming system. However, this industry has become increasingly scrutinized due to environmental impact, climate change, and land degradation. As per present reports, crop residues, a significant livestock feed resource, are in shortage and have poor nutritional value. Moreover, various factors like heatwaves, drought, and diseases can negatively impact forage quality and reduce productivity. Conventional methods of assessing forage/crop residue quality face significant challenges, including labor-intensive, costly, time-consuming, and error-prone. UAV-based imaging can boost multi-dimensional crop improvement programs due to advantages like wider coverage, short revising times, high spatial resolutions, and ease of operation. Hyperspectral imaging (HSI) sensors provide enriched spectral information, enabling more precise investigations into feed quality evaluation, forage management, and livestock health. Artificial intelligence and machine learning (AI/ML) approaches can effectively analyze high-dimensional HSI data and extract meaningful insights. Integrating UAV-based HSI and AI/ML techniques is crucial to enhance crop-livestock farm management. This chapter explores the potential of UAV-based HSI and AI/ML for crop-livestock farm research and management, focusing on animal and forage health monitoring, and enhancing feed quality. We also emphasize AI/ML-based data analytics and algorithm development on UAV-borne HSI data to revolutionize crop-livestock farming.Item Animal models in influenza research(Book Chapter, 2025-02-01) Zhou, J.; Hemmink, Johanneke D.; Whittaker, C.J.; Shelton, H.A.; Peacock, T.Animal model systems for human and animal influenza virus infection and transmission have been established to address research questions which cannot be addressed using in vitro models. Several animal models are widely used, including mice, guinea pigs, hamsters, ferrets, pigs, poultry, nonhuman primates, and others. Each animal model has its own strength and weaknesses, which should be taken into consideration to select the appropriate animal model to use. This chapter will describe standard protocols relevant for in vivo experiment, including procedures required prior to the start of the animal experiment and sample processing. The animal models described in this chapter are mice, hamsters, guinea pigs, ferrets, pigs, and chickens.Item Achieving gender equity in Kenya’s informal dairy sector(Book Chapter, 2024-10-14) Jumba, Humphrey; Alonso, Silvia; Galiè, Alessandra; Kilonzi, Emily; Sharma, Garima; Omondi, Immaculate A.Item Endupoto: Securing pastoralist women’s land rights through collective land use planning and customary occupancy certificates in Tanzania(Book Chapter, 2024-12) Flintan, Fiona E.Item Brucellosis: A neglected zoonosis(Book Chapter, 2024-11-22) Deka, Ram P.; Kumar, M.S.; Sanjumon, E.S.; Biswas, R.Brucellosis is one of the most widespread bacterial zoonotic diseases around the world. The disease has huge significance in both animal and humans due to its effect on productive and reproductive performances, economical loss, international trade, and public health consequences. The disease is reported globally with the exception of a few high-income countries that have successfully eliminated the disease. The occurrence of the disease shows variability among endemic regions and factors such as misdiagnosis and underreporting may have masked the actual number of cases, which could be substantially higher in number. Human brucellosis cases are globally distributed, exhibiting varying prevalence rates in Asian and African regions, and the incidence is more noticeable in low- and middle-income countries (LMICs) where lesser regulated livestock production, trading, handling, and consumption practices are followed. The human infection burden is significant, with a real incidence between 5,000,000 and 12,500,000 cases per year. The disease is also considered as an occupational hazard and people involved in animal farming, trading, diagnostic, treatment, and consumption of animal-origin contaminated foods are prone to acquiring the infection. The identification of risk factors for brucellosis in livestock is of paramount importance in preventing the dissemination and persistence of the disease. Larger livestock population, dominance of smallholder farming, poor infrastructure, and husbandry practices, weak surveillance systems, lack of awareness, limited access to healthcare, inadequate control programs, consumption of unpasteurized dairy products, close interactions with animals, and poor diagnosis facilities increase the risk of brucellosis in humans. Serological tests are the most used tests for the diagnosis of Brucella infection throughout the globe, mainly in LMICs. These serological tests are easy to perform, less expensive, and give fast results; however, they are prone to false positive/negative reactions due to the cross reactions with other microbes. The confirmatory diagnosis is achieved through either the isolation of Brucella spp., but it is expensive, slow, cumbersome, and require good biosafety level in laboratory, which makes it difficult to many low-income countries to effectively detect the disease. In case of humans, diagnosis of brucellosis is considered even more difficult because of its confusion with few other diseases that could exhibit similar signs/symptoms with brucellosis. On many occasions, physicians' term similar disease condition as pyrexia of unknown origin (PUO) or fever of unknown origin (FUO) in the absence of confirmatory diagnosis. Brucellosis control is a costly, time-consuming, labor and resource-intensive initiative which cannot be afforded by many LMICs. Control measures may vary from country to country based on socioreligious and farming practices and availability of resources in terms of money, materials, and manpower. The common control measures include vaccination and test-and-slaughter which are used either alone or in combination. Combining vaccination along with test-and-slaughter, personal protection in humans, and maintaining sanitary condition is an ideal control strategy. However, in LMICs, the control strategy involving vaccination, testing, quarantine, and implementing compensation policies in cases of slaughter is not as effective or practical. These factors combined with the lack of knowledge among the general masses contribute to the neglected status of brucellosis. The effective control of the diseases can be achieved through a robust, collective, and coordinated effort among human and animal health professionals following One Health approach.Item An Intersectional Approach to Agricultural Research for Development (AR4D)(Book Chapter, 2025) Tavenner, Katie; Crane, Todd A.; Bullock, Renee; Galiè, Alessandra; Campos, Hugo; Katothya, GeraldOriginating nearly 40 years ago in black feminist thought, the concept of intersectionality has become established as an analytical lens and social theory to account for and better understand multiple and compounding identities and how they influence discrimination and privilege. Within agricultural research for development (AR4D), intersectional approaches are relatively novel compared to traditional gender and social analyses, and to date there are limited tools and empirical studies in AR4D that have adopted such an approach. Without a strong conceptual and methodological foundation, future intersectional approaches in AR4D risk treating multiple identities as standalone “tick box” variables, and not as a holistic way of understanding and addressing these multiple sources of marginalization. To emphasize the potential value-addition of deeper engagement with intersectionality, this chapter outlines the state-of-the-field on intersectional analyses in AR4D and how they are situated within wider gender mainstreaming in international development. Using an empirical case study on index-based livestock insurance (IBLI) in Northern Kenya, the chapter demonstrates an intersectional analysis in AR4D, based on a new conceptual framework and method (Tavenner et al. Gend Technol Dev 26(3):385–403, 2022). This chapter explores how AR4D can deepen its understanding of intersectionality and the potential integration of this concept in a meaningful way that supports addressing multiple layers of inequalities and marginalization in agricultural research methods and practice.Item Food systems, food safety and One Health(Book Chapter, 2024-08-20) Grace, DeliaThis short chapter focuses on foodborne diseases, the global health burden of which rivals that of malaria or tuberculosis. Most of this burden is from the consumption of animal-source foods and fresh produce sold in the informal markets of low- and middle-income countries. The chapter considers the causes of foodborne diseases, their One Health characteristics, their health and economic burden, their trends, their associated food matrices, their associated myths and, finally, their management. Considerable progress has occurred with the management of these conditions in high-income settings; some of the lessons learned there can also be applied in low- and middle-income countries.Item Methods to Reduce the Dimension of Multivariate Models(Book Chapter, 2023-10-13) Mrode, Raphael A.; Pocrnic, IvanItem Single-step Approaches to Genomics(Book Chapter, 2023-10-13) Mrode, Raphael A.; Pocrnic, IvanItem Genomic Prediction and Selection(Book Chapter, 2023-10-13) Mrode, Raphael A.; Pocrnic, IvanItem Non-additive Animal Models(Book Chapter, 2023-10-13) Mrode, Raphael A.; Pocrnic, IvanItem Enzymes in Quality and Processing of Tropical and Subtropical Fruits(Book Chapter, 2012-08-17) Liavoga, Allan; Matella, Norm JosephItem Genetic and Genomic Models for Multibreed and Crossbred Analyses(Book Chapter, 2023-10-13) Mrode, Raphael A.; Pocrnic, IvanItem Genetic Evaluation with Different Sources of Records(Book Chapter, 2023-10-13) Mrode, Raphael A.; Pocrnic, Ivan