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Development of an algorithm to evaluate the influence of inputs on yield gaps, beside its return on investments

Grant number: 17/20624-0
Support Opportunities:Research Grants - Innovative Research in Small Business - PIPE
Duration: April 01, 2019 - May 31, 2020
Field of knowledge:Agronomical Sciences - Agronomy - Soil Science
Convênio/Acordo: FINEP - PIPE/PAPPE Grant
Principal Investigator:Yury Catalani Nepomuceno Duarte
Grantee:Yury Catalani Nepomuceno Duarte
Host Company:InCeres Desenvolvimento de Software e Processamento de Dados S/A
CNAE: Tratamento de dados, provedores de serviços de aplicação e serviços de hospedagem na internet
City: Rio das Pedras
Associated researchers:Felipe Gustavo Pilau ; José Paulo Molin ; Rafael Otto


Agriculture is a very important sector of the economy. However, the difficulty of the producer in determining Return on Investment (ROI) on the inputs used hampers the adoption of new technologies. This is because many factors interfere with crop yields, which makes it difficult to segment input interference on productivity. However, through the Yield Gap (YG) concept, it is possible to segment the influence of management and climate on the final productivities. YG is a successful approach used by FAO to analyze the influence of factors affecting agricultural productivity. Precision Agriculture (AP) allows the survey, monitoring and processing of a large volume of georeferenced information regarding fertility, soil texture and application of inputs. The database generated by the AP acts as a record of the management adopted in each field, allowing to know when, how much and what were the inputs applied. The ROI allows to know the financial return behind the use of an input, that is, the cost benefit of the inputs used. The elaboration of an algorithm capable of calculating and analyzing the information of the YG, AP and ROI can provide a significant improvement in the choice of more effective products to the producer besides helping the industry in its process of validation of new technologies. The objective of this work will be the improvement of InCeres Systems, through the elaboration of an algorithm capable of calculating ROI for each input used in agricultural production. In this way, the InCeres Systems will expand their scope and continue to generate information for the farmer at farm level on the (agronomic) effectiveness and (economic) efficiency of the inputs used. (AU)

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