THE USE OF DATA-DRIVEN FARMING IN PREVENTING LOW PATHOGENIC AVIAN INFLUENZA

La gripe aviar (IA) es una enfermedad respiratoria altamente contagiosa y mortal que cuesta la vida a miles de animales al mes en todo el mundo. El objetivo actual es detectar precozmente la presencia de la enfermedad para poner en marcha medidas de prevención y tratamiento antes de que los brotes se extiendan y sea necesario el sacrificio masivo. Mediante la investigación de los síntomas, los factores de riesgo y las limitaciones tecnológicas y normativas, el objetivo del estudio es desarrollar un protocolo eficaz que satisfaga esta necesidad y ahorre dinero y tiempo a los ganaderos y contribuya a maximizar la producción de alimentos, que será esencial para seguir el ritmo de la superpoblación entrante. La agricultura basada en datos será el núcleo de la metodología empleada para interpretar y recopilar big data procedente del seguimiento de diversos factores internos y externos que implican la presencia de la enfermedad. Esta disciplina ayudará al ganadero en la toma de decisiones sobre medidas de prevención eficaces para controlar la enfermedad. El estudio es principalmente una investigación bibliográfica que basa los resultados en anteriores herramientas de prevención y detección de IA utilizadas en el ámbito de la vigilancia de síntomas y la gestión de datos en tiempo real. Lo innovador es el enfoque colectivo con el que se aplica el protocolo, más que una herramienta para granjeros individuales. El objetivo es la producción avícola en Francia, centrándose en las zonas de riesgo de difusión y complementándose con la cooperación de granjeros de explotaciones vecinas o comercialmente relacionadas. Las variables elegidas para el seguimiento pretenden garantizar una detección precoz de los pollos infectados y una aplicación útil de las medidas de prevención. La falta de pasos experimentales supone una limitación al estudio, pero sus bases se fundamentan en casos similares donde la metodología ha demostrado ahorrar tiempo, dinero y, lo que es más importante, reducir la mortalidad de la producción. <br /

A Survey on Automation Challenges and Opportunities for IoT based Agriculture

Agriculture automation is a major concern and a contentious issue in every country. This study provides a comprehensive assessment of the obstacles and potential associated with automating agricultural practises using IoT (Internet of Things) technology. It begins with an introduction that highlights the prior work and discusses the proposed proposal, which is centred on IoT and machine learning applications and breakthroughs in irrigation systems. The report digs into several IoT applications in agriculture, including crop and soil management, drone field surveillance, cattle and resource management, and pesticide/fertilizer tracking. It delves into the breakthroughs made possible by IoT and machine learning, particularly in smart irrigation systems, livestock monitoring, drone technology, precision agriculture, and integrated pest management. The paper thoroughly examines the challenges associated with automating irrigation practises, such as interoperability, data storage, connectivity, hardware and software maintenance, security concerns, data collection, environmental variability, cost, infrastructure, privacy, and adoption by small-scale farmers. The survey finishes by synthesising the important findings and emphasising the crucial need of overcoming these problems in order to successfully adopt IoT-driven agriculture automation

The Digitalisation of African Agriculture Report 2018-2019

An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

Insights from quantitative and mathematical modelling on the proposed 2030 goal for gambiense human African trypanosomiasis (gHAT)

Gambiense human African trypanosomiasis (gHAT) is a parasitic, vector-borne neglected tropical disease that has historically affected populations across West and Central Africa and can result in death if untreated. Following from the success of recent intervention programmes against gHAT, the World Health Organization (WHO) has defined a 2030 goal of global elimination of transmission (EOT). The key proposed indicator to measure achievement of the goal is to have zero reported cases. Results of previous mathematical modelling and quantitative analyses are brought together to explore both the implications of the proposed indicator and the feasibility of achieving the WHO goal. Whilst the indicator of zero case reporting is clear and measurable, it is an imperfect proxy for EOT and could arise either before or after EOT is achieved. Lagging reporting of infection and imperfect diagnostic specificity could result in case reporting after EOT, whereas the converse could be true due to underreporting, lack of coverage, and cryptic human and animal reservoirs. At the village-scale, the WHO recommendation of continuing active screening until there are three years of zero cases yields a high probability of local EOT, but extrapolating this result to larger spatial scales is complex. Predictive modelling of gHAT has consistently found that EOT by 2030 is unlikely across key endemic regions if current medical-only strategies are not bolstered by improved coverage, reduced time to detection and/or complementary vector control. Unfortunately, projected costs for strategies expected to meet EOT are high in the short term and strategies that are cost-effective in reducing burden are unlikely to result in EOT by 2030. Future modelling work should aim to provide predictions while taking into account uncertainties in stochastic dynamics and infection reservoirs, as well as assessment of multiple spatial scales, reactive strategies, and measurable proxies of EOT

Agricultural 4.0 Leveraging on Technological Solutions: Study for Smart Farming Sector

By 2050, it is predicted that there will be 9 billion people on the planet, which will call for more production, lower costs, and the preservation of natural resources. It is anticipated that atypical occurrences and climate change will pose severe risks to agricultural output. It follows that a 70% or more significant rise in food output is anticipated. Smart farming, often known as agriculture 4.0, is a tech-driven revolution in agriculture with the goal of raising industry production and efficiency. Four primary trends are responsible for it: food waste, climate change, population shifts, and resource scarcity. The agriculture industry is changing as a result of the adoption of emerging technologies. Using cutting-edge technology like IoT, AI, and other sensors, smart farming transforms traditional production methods and international agricultural policies. The objective is to establish a value chain that is optimized to facilitate enhanced monitoring and decreased labor expenses. The agricultural sector has seen tremendous transformation as a result of the fourth industrial revolution, which has combined traditional farming methods with cutting-edge technology to increase productivity, sustainability, and efficiency. To effectively utilize the potential of technology gadgets in the agriculture sector, collaboration between governments, private sector entities, and other stakeholders is necessary. This paper covers Agriculture 4.0, looks at its possible benefits and drawbacks of the implementation methodologies, compatibility, reliability, and investigates the several digital tools that are being utilized to change the agriculture industry and how to mitigate the challenges.Comment: 9 pages, 4 figures, under reviewing proces

Identifying target areas for risk-based surveillance and control of Transboundary Animal Diseases: A seasonal analysis of slaughter and live-trade cattle movements in Uganda

Abstract Animal movements are a major driver for the spread of Transboundary Animal Diseases (TADs). These movements link populations that would otherwise be isolated and hence create opportunities for susceptible and infected individuals to meet. We used social network analysis to describe the seasonal network structure of cattle movements in Uganda and unravel critical network features that identify districts or sub-regions for targeted risk-based surveillance and intervention. We constructed weighted, directed networks based on 2019 between-district cattle movements using official livestock mobility data; the purpose of the movement (‘slaughter’ vs. ‘live trade’) was used to subset the network and capture the risks more reliably. Our results show that cattle trade can result in local and long-distance disease spread in Uganda. Seasonal variability appears to impact the structure of the network, with high heterogeneity of node and edge activity identified throughout the seasons. These observations mean that the structure of the live trade network can be exploited to target influential district hubs within the cattle corridor and peripheral areas in the south and west, which would result in rapid network fragmentation, reducing the contact structure-related trade risks. Similar exploitable features were observed for the slaughter network, where cattle traffic serves mainly slaughter hubs close to urban centres along the cattle corridor. Critically, analyses that target the complex livestock supply value chain offer a unique framework for understanding and quantifying risks for TADs such as Foot-and-Mouth disease in a land-locked country like Uganda. These findings can be used to inform the development of risk-based surveillance strategies and decision making on resource allocation. For instance, vaccine deployment, biosecurity enforcement and capacity building for stakeholders at the local community and across animal health services with the potential to limit the socio-economic impact of outbreaks, or indeed reduce their frequency