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| Mamann,Ângela T. W De; Silva,José A. G. da; Scremin,Osmar B.; Mantai,Rubia D.; Scremin,Ari H.; Dornelles,Eldair F.. |
| ABSTRACT Nitrogen use efficiency in wheat biomass and grain yields can be favored by the biopolymer hydrogel. The objective of the study was to analyze the use of the biopolymer hydrogel applied to the seed in the optimization of fertilizer-N on wheat biomass and grain yields, under different conditions of agricultural year and succession systems of high and reduced release of residual-N. In the study, two experiments were conducted, with different farming systems, soybean/wheat and maize/wheat, one to quantify the biomass yield rate and the other to determine grain yield. The experiments were conducted in the years 2014 and 2015, in a randomized block design with four replicates in a 4 x 4 factorial scheme, corresponding to hydrogel doses (0, 30, 60 and... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Triticum aestivum; Simulation; Farming systems; Water stress. |
| Ano: 2017 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662017001000697 |
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| Dornelles,Eldair F.; Kraisig,Adriana R.; Silva,José A. G. da; Sawicki,Sandro; Roos-Frantz,Fabricia; Carbonera,Roberto. |
| ABSTRACT Artificial intelligence may represent an efficient strategy for simulation and optimization of important processes in agriculture. The main goal of the study is to propose the use of artificial intelligence, namely artificial neural networks and genetic algorithms, respectively, in the simulation of oat grain yield and optimization of seeding density, considering the main succession systems of southern Brazil. The study was conducted in a randomized complete block design with four replicates, following a 4 x 2 factorial scheme, for seeding densities (100, 300, 600 and 900 seeds m-2) and oat cultivars (Brisasul and URS Taura), in succession systems of corn/oats and soybean/oats. A multi-layered artificial neural network and a genetic algorithm were... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Avena sativa; Artificial neural networks; Genetic algorithms; Innovation. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000300183 |
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