BovDairy - efficient chain

Abstract

Dairy farms are one of the most important economic activities in the world. Approximately 2.5 billion people living in rural areas earn their livelihoods from agriculture and approximately three quarters of the world's added agricultural value is generated in developing countries. In many of these countries, the agricultural sector contributes up to 30% of the Gross Domestic Product. Agribusiness, in turn, is considered an activity with numerous and growing risk factors. Climate change, price fluctuations and changes in institutional measures increase the variability of farm income, which can have a negative effect. Because of this, a multi-step, layered risk approach is needed, which must be considered separately. Normal risks are generally managed on the farm with different cost strategies by farmers, while market and catastrophic risks are managed by the private sector or the state. In addition, benchmarking can be an effective approach to increase farm efficiency. Understanding price behavior, however, is a critical element in making decisions under uncertain conditions that significantly influence the return of dairy market participants. The increase in variability in the world price of milk and in the world price of feed in recent years has increased the need for research on price dynamics and forecasting. Expensive, inadequate and low quality food, lack of pastures, green grass, inadequate capital, knowledge about production costs, weak extension services, high fees and unavailability of veterinarians, are some of the main problems for milk producers. In this way, appropriating an effective risk management strategy can help farmers to protect themselves from such events and help to stabilize their income. In this sense, the objective of this project is to complete the research, development and innovation cycle of the BovDairy solution, building a digitalized milk supply chain, which will connect rural producers, field agents, milk processing industry and the final consumer, thus making it possible, through the use of efficiency indicators throughout the chain, to increase and improve the quality standards of the milk produced, assist the chain to apply good production practices and meet market demands for traceability, food security and use of natural resources in a sustainable way. In addition, the solution will also allow better Risk Management for stakeholders in this segment, guiding them with activities to be in Compliance with the Normative Instructions of the Ministry of Agriculture, Livestock and Supply for the milk production chain. Thus, it is expected to promote greater efficiency of all links in the chain, either through greater productivity on the farm, in extensionism and application of best practices by field agents, promoting greater security and price predictability, risk and loss management and compliance with current regulations. Therefore, the application of modern Data Science techniques, machine learning, among other tools, will be fundamental for the success of the project, connecting and benchmarking the platform. (AU)