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| Conceição,Chaiane G. da; Robaina,Adroaldo D.; Peiter,Marcia X.; Parizi,Ana R. C.; Conceição,João A. da; Bruning,Jhosefe. |
| ABSTRACT Common bean crop plays an important role in the world, not only in economic aspects but also in social development. The objective of this study was to evaluate the grain yield and the economically optimal water depth which reflects the maximum technical efficiency of the common bean crop. The experiment was conducted in greenhouse, in Alegrete - RS, from February to May 2016. A completely randomized design was used, consisting of five water replacement treatments (25, 50, 75, 100 and 125% crop evapotranspiration - ETc) and four replicates. Based on the obtained results, both water deficit and water excess directly affected the final grain yield of the crop. Maximum grain yield was 3,554.1 kg ha-1, obtained by applying 492.72 mm (100% ETc). On the... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Phaseolus vulgaris L.; Water management; Yield components. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000700482 |
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| Pereira,Tonismar dos S.; Robaina,Adroaldo D.; Peiter,Marcia X.; Torres,Rogerio R.; Bruning,Jhosefe. |
| ABSTRACT The aim of this study was to present and to evaluate methodologies for the estimation of soil resistance to penetration (RP) using artificial intelligence prediction techniques. In order to do so, a data base with values of physical-water characteristics of the soils available in the literature was used, and the performances of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were evaluated. The models generated from the ANNs were implemented through the multilayer perceptron with backpropagation algorithm of Matlab software, varying the number of neurons in the input and intermediate layers. For the procedure from SVM, the RapidMiner software was used, varying input variables, the kernel function and the coefficients of these... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Soil compaction; Machine learning; Support vector machines; Artificial neural networks. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000100142 |
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