5/05/2023. Project: Predic-Pro "Provision of Remotely-sensED Information to infer Cereals PRODuction"

Organization: Ministerio de Ciencia e Innovación.

Referencia: CPP2021-008733.

Financiacion: MCIN/ AEI/10.13039/501100011033/ y por la Unión Europea NextGenerationEU/PRTR.

Duration: 2022-2025

Summary: The world population faces the challenge of increasing food production in a sustainable manner through the rational use of natural resources, reducing the use of production inputs and increasing their efficiency. The green revolution of the last century generated a negative impact on the environment and people's health due to the crops’ intensification. Nowadays, international policymakers are trying to avoid this negative impact, favoring environmental policies to achieve fairer, healthier and more sustainable food. In fact, these are the foundations of the European Union's “Farm to Fork” strategy. Consequently, new agricultural technologies that mitigate the adverse effects of a highly productive agriculture and that integrate a new and more efficient agronomic management to preserve natural resources (soil and water) are required. One of these technologies is Earth Observations (EO) applied to Agronomy. The rise of EO space missions has become clear in recent decades with the development of the European Union's Copernicus program. This program enables the monitoring of crops’ biophysical properties with an unprecedentedly high temporal (5 days), spatial (10m pixels) and spectral (more than 10 electromagnetic bands) resolution. The technological advance that this program has entailed is evident given that the new Common Agricultural Policy (CAP) will rely on data acquired by the Copernicus program to monitor agricultural practices. However, this technology is still far from being useful for the farmers. PREDIC-PRO aims to develop the foundations that will build the link between an established technology with a high degree of technological development and the end user. Given this background, the ultimate goal of PREDIC-PRO is to generate predictive yield models based on remote sensing data to optimize agronomic management and improve the final production of wheat, barley and rice crops, within the framework of sustainable agriculture. The specific objectives are the following: (1), build an extensive agronomic database of yield maps in three cereal-producing provinces in Spain: Seville, Valladolid and Valencia; (2), apply EO data to plant production of these crops; (3), establish a monitoring protocol for these crops at the regional level; (4), propose a predictive model of crop yields to estimate the total production of these cereals in each region; (5), prescribe an agronomic management of crops in accordance with the objectives of this project; (6) test the robustness of the models by implementing them at national scale to provide national forecasts. Particularly, field yield maps will be collected by a novel technology onboard cereal harvesting machines that will be matched to the images acquired by the Copernicus satellites (Sentinel-1, radar, and Sentinel-2, optical). Based on these data, a descriptive analysis will be carried out using statistical methods; subsequently, mathematical models will be adjusted with predictive analysis using artificial intelligence systems; and finally, they will be in turn the input towards a prescriptive analysis of the agronomic management of crops that will be fully transferable to the Spanish agri-food sector. With the proposed modeling, we will develop a tool that will estimate the final yield improvement compared to the forecasted yield obtained by the models 90, 60 and 30 days before harvest. In this way, farmers will be able to carry out corrective measures in the agronomic management of crops that will result in an optimization of the production process, improving the efficiency in the use of agricultural inputs, thus promoting the use of digital technologies in the agri-food sector, one of the strategic lines of the Spanish Plan Estatal de Investigacion Cientifica, Técnica e Innovacion.