VTBeef: integration of metabolic, environmental and genetic data through Business Intelligence for the development of precision breeding solutions

• Grant number: 18/22537-0
• Support type: Research Grants - Innovative Research in Small Business - PIPE
• Duration: July 01, 2020 - March 31, 2021
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Animal Production
• Principal researcher: Adam Taiti Harth Utsunomiya
• Grantee: Adam Taiti Harth Utsunomiya
• Company: VT Beef Vitrine Tecnológica Agropecuária Ltda
• CNAE: Atividades de apoio à agricultura ; Atividades de apoio à pecuária ; Pesquisa e desenvolvimento experimental em ciências físicas e naturais
• City: Birigüi
• Assoc. researchers: José Fernando Garcia

Abstract

For decades, cattle-raising has advanced to the Center West and North of the Brazil, making the country the largest producer and exporter of beef in the world. However, economic and environmental pressures, together with the search for products with certification of origin and quality, have led beef production systems to rethink their strategies. Increased productivity (measured per kilogram of meat produced per hectare per year) is the main focus of this new thinking that, when associated with production systems that observe animal welfare and sustainable management of the environment, can have a positive impact on the good use of land with a consequence in the better perception of society on the livestock activity. Throughout the last decade, different animal monitoring systems have been developed that use hardware and software platforms capable of individually monitoring the activities carried out over the days. Examples of these technologies are autonomous weighing scales, necklaces with accelerometers and sensors that measure the movement and rumination patterns respectively, the troughs and drinking troughs that measure the individual consumption of food and water respectively, the implantable digital thermometers, among others. This new technological arsenal is capable of generating an enormous amount of data of great complexity, but the information generated by each of these systems is still very fragmented, generating difficulties for its full use in the day to day of the field. The present project aims to collect, store, process and interpret data obtained from different collector terminals of weight, food and water consumption, behavior patterns and body temperature, to generate business intelligence that can be applied in the day to day production systems generating the maximum efficiency of these. Thus, collected physiological and metabolic data will be associated with environmental and genomic data (information on single nucleotide polymorphism) and will constitute the prototype of the integrated database for the generation of strategic knowledge that should be disseminated by the new company (VTBeef) along the production chain through technical consultancies based on artificial intelligence to be developed with the prototype proposed in this project. (AU)