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• W4382807078 abstract "Purpose: The objective of the study is to validate the technology of liquid nitrogen source by spraying of foliar absorption in comparison to the standard source urea of root absorption in the oat crop. Adapt the fuzzy logic model and training an artificial neural network to simulate oat productivity, under conditions of nitrogen use with the combined action of rainfall and thermal sum accumulated during the crop cycle.&#x0D; &#x0D; Method/design/approach: The study was conducted in Augusto Pestana, RS, Brazil, in a randomized block design with four replications in a 2x4 factorial, for 2 nitrogen sources (liquid and solid) with 4 doses (0, 30, 60 and 120 kg ha-1), respectively. Solid (urea) and liquid (N-Top®) nitrogen sources were applied at the phenological stage of expanded oat fourth leaf.&#x0D; &#x0D; Results and conclusion: The liquid source nitrogen technology presents results similar to the use of the standard urea source, confirming the possibility of using the nutrient by foliar absorption. The simulation of grain yield by fuzzy logic with the input variables nitrogen dose, thermal sum and precipitation is not adequate for the formulated rule base. The input variables used in the artificial neural network proved to be appropriate in the simulation of oat productivity, with consistent simulation results.&#x0D; &#x0D; Originality/value: unprecedented research that seeks to validate the technology of foliar nitrogen absorption in real conditions of oat cultivation in guaranteeing productivity and less environmental damage. And the use of artificial intelligence as a resource to simulate grain productivity involving biological and environmental indicators in the main oat producing region of Brazil." @default.
• W4382807078 created "2023-07-02" @default.
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• W4382807078 date "2023-05-09" @default.
• W4382807078 modified "2023-09-28" @default.
• W4382807078 title "Liquid Source Nitrogen as a More Sustainable Technology for oat Fertilization with Computational Simulation Resource" @default.
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• W4382807078 doi "https://doi.org/10.24857/rgsa.v17n2-008" @default.
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