Disciplinary convergence enhances the value of agronomic data

Abstract

From the paper notebook to artificial intelligence agents: in between, a long process of resistance, trials and errors, innovations, and devices that mark the pace of the digital revolution. In this search, two aspects become relevant: agrotechnologies (AgTech), which seek to provide various value solutions to agriculture, and users who still have not found the “shoe that fits.” The dizzying transformation of agriculture in the past 20 years, driven by exponential technologies, has fostered the creation of startups, technology-based companies, and digitalization ventures that go beyond the traditional profiles of agronomic professionals. Actors from other disciplines and specializations are emerging, reconfiguring the map of interactions in the agricultural sector. According to the mapping we are carrying out between CEPAL and INTA, the nearly 400 startups that are part of the Argentine AgTech ecosystem are changing the ways of doing and thinking about productive systems in our country. This diagnosis included an initial stage of defining survey categories, classification criteria, and scope delimitation. The analysis process of the collected information generates a thematic classification by application areas and also contributes differential technological aspects of each company, which will allow for a closer alignment of the technological offerings with producers. Agronomic data collected in the field or historical, real-time or predictive, or from various devices are the foundation that feeds the process of digital transformation and decision-making optimization for more efficient and sustainable agriculture. “The integration of agronomic data into digital platforms enables a paradigm shift: from intuitive decisions to data-driven agriculture, fostering sustainability and competitiveness” (Wolfert et al., 2020). The effort to collect and integrate data will not reach its full potential unless a real convergence of the different components is strengthened and consolidated: of technological tools with each other; of those resources with the various disciplines, professionals, institutions, and all ecosystem actors; and, in turn, with the adoption capacities of the generated solutions, so that data gain value and become much more than just data.