1. Modelagem para recomendação de calagem e adubação para as principais regiões produtoras de grãos do Brasil na linguagem SQL.
- Author
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Alfonso Priebe, Henrique, Araújo Oliveira, Robert Rafael, Rodrigues Luz, Adriano, Silveira Dornelles, Eduardo, da Silva Bezerra, Gustavo Alves, Vargas Dutra, Diones, Maran, Vinícius, and Avelar Ferreira, Paulo Ademar
- Subjects
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SOIL testing , *FERTILIZER analysis , *CONCEPTUAL models , *KNOWLEDGE base , *INFORMATION resources management - Abstract
The agribusiness has a significant importance to the Brazilian economy. In this case, technologies are needed to increase the efficiency of the productive system, but there are few technological tools that perform interpretations of soil analysis and fertilizer recommendations efficiently and comprehensively. In this context, the AgroFert system proposes to make corrective and fertilizer recommendations based on soil analyzes for several regions and grain crops of Brazil, through two systems: web and mobile, which uses a knowledge base in liming and fertilization. Therefore, this work aims at presenting the conceptual and logical modeling process of a database that have rules of information management to interpret the results of soil analysis reports and to recommend soil correctives and fertilizers. Rules were created that use comparisons, filtering, mathematical operations and logical operations, modeled in the SQL language, and when executed, generate interpretations of soil analysis and recommendations for correctives and fertilizers. The AgroFert covers the states of Rio Grande do Sul, Santa Catarina, Paraná, Minas Gerais and São Paulo and the biome of Cerrado. In addition to the conceptual modeling, 22 operations were created, totaling approximately 400 lines of code, and it was possible to recommend fertilizers for 13 grain crops. The results of the tests showed that the computerization of the data present in the RS / SC and Paraná manuals facilitated the interpretation of the data present in soil analysis reports, besides increasing the precision and agility in the recommendations of corrective and fertilizer producers. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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