Abstract
Food-animal production businesses are part of a data-driven ecosystem shaped by stringent requirements for traceability along the value chain and the expanding capabilities of connected products. Within this sector, the generation of animal health intelligence, in particular, in terms of antimicrobial usage, is hindered by the lack of a centralized framework for data storage and usage. In this Perspective, we delimit the 11 processes required for evidence-based decisions and explore processes 3 (digital data acquisition) to 10 (communication to decision-makers) in more depth. We argue that small agribusinesses disproportionally face challenges related to economies of scale given the high price of equipment and services. There are two main areas of concern regarding the collection and usage of digital farm data. First, recording platforms must be developed with the needs and constraints of small businesses in mind and move away from local data storage, which hinders data accessibility and interoperability. Second, such data are unstructured and exhibit properties that can prove challenging to its near real-time preprocessing and analysis in a sector that is largely lagging behind others in terms of computing infrastructure and buying into digital technologies. To complete the digital transformation of this sector, investment in rural digital infrastructure is required alongside the development of new business models to empower small businesses to commit to near real-time data capture. This approach will deliver critical information to fill gaps in our understanding of emerging diseases and antimicrobial resistance in production animals, eventually leading to effective evidence-based policies.
Highlights
The food-animal production sector, in industrially developed countries, is evolving into a more data-driven ecosystem in which data are adding value to the business process and to the entire food supply chain [1]
The use of antimicrobials in this sector, for both disease treatment and, in some countries, growth promotion, is Challenges to Agri-Data Digitization one of the areas directly affected by this evolution, with usage in livestock projected to increase by 67% by 2030 [4]
We identified 11 processes taking place between the time a decision-maker, either farm- or office-based, formulates business questions and the moment they can take evidence-based decisions (Figure 1). In this Perspective paper, we explore processes 3 to 10 in more depth, with a focus on the inherent challenges to the timely generation of animal health intelligence and how they disproportionally affect small-scale food-animal production businesses
Summary
The food-animal production sector, in industrially developed countries, is evolving into a more data-driven ecosystem in which data are adding value to the business process and to the entire food supply chain [1] This transformation is driven by technology and by an increasing requirement for traceability and accountability initiated by regulatory frameworks [2], which can differ between countries, or directly by consumers [3]. We identified 11 processes taking place between the time a decision-maker, either farm- or office-based, formulates business questions and the moment they can take evidence-based decisions (Figure 1) In this Perspective paper, we explore processes 3 (digital data acquisition) to 10 (communication to decision-makers) in more depth, with a focus on the inherent challenges to the timely generation of animal health intelligence and how they disproportionally affect small-scale food-animal production businesses. A large proportion of agri-food data are collected through sensors and robots at all production
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