Title: Applying combinatorial neural model for vegetable production management

Authors: Edilson Ferneda, Hercules Antonio Do Prado, Ricardo Coelho De Faria

Addresses: Graduate Program on Knowledge and IT Management, Catholic University of Brasilia, SGAN 916, Modulo B, Sala A-108, 70.790-160 Brasilia, DF, Brazil. ' Graduate Program on Knowledge and IT Management, Catholic University of Brasilia, SGAN 916, Modulo B, Sala A-108, 70.790-160 Brasilia, DF, Brazil; Secretariat for Strategic Planning, Brazilian Agricultural Research Corporation, Embrapa, Parque Estacao Biologica – PqEB s/n, Asa Norte, 70770-901 Brasilia, DF, Brazil. ' Department of Economy, Catholic University of Brasilia – Campus I, QS 07 Lote 01 EPCT, Aguas Claras, Bloco K, Sala 203, 71966-700 Taguatinga, DF, Brazil

Abstract: This paper presents a study on the effects of the combination of different vegetables and some socio-economic variables in the economic results of the carrot cropping. Experts recognise that some combinations of cropping can produce synergistic effects that can lead to profits or losses, depending on the factors of production involved. However, the analysis of the results of cropping multiple varieties simultaneously involves a space of possibilities whose treatment is not trivial. Among the alternatives available for analysing such a problem, this study explored the Combinatorial Neural Model (CNM) which ensures the generation of hypotheses for all possible combinations, according to the parameters set to the model. The study is based on data collected from farms in Brasilia, Brazil. First, a preprocessing on the data is presented in order to provide information on the data nature and distribution, and some univariate and bi-dimensional analysis is shown. Next, the results generated by CNM are explored and the research limits and future work are discussed.

Keywords: reasoning-based systems; data mining; farming systems; agriculture; vegetable production management; socioeconomic variables; economics; carrot cropping; combinatorial neural model; Brazil.

DOI: 10.1504/IJRIS.2011.042267

International Journal of Reasoning-based Intelligent Systems, 2011 Vol.3 No.2, pp.132 - 138

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 02 Sep 2011 *

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