A B S T R A C TArtificial 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 implemented in Java programming language, and the results obtained from this implementation were compared with traditional polynomial regression. The use of artificial intelligence through neural networks and genetic algorithms allows the efficient simulation of oat grain yield and better optimization of seeding density in comparison to polynomial regression, considering the main succession systems in southern Brazil.Inteligência artificial na otimização da densidade de semeadura e simulação da produtividade da aveia R E S U M O O uso de inteligência artificial pode representar uma estratégia eficiente de simulação e otimização de processos importantes na agricultura. O objetivo deste estudo é propor o uso de inteligência artificial via redes neurais artificiais e algoritmos genéticos, respectivamente, na simulação da produtividade de grãos de aveia (Avena sativa) e na otimização da densidade de semeadura, nos principais sistemas de sucessão do sul do Brasil. O estudo foi conduzido em blocos ao acaso com quatro repetições em esquema fatorial 4 x 2, para as densidades de semeadura (100, 300, 600 e 900 sementes m -2 ) e cultivares de aveia (Brisasul e URS Taura), nos sistemas de sucessão milho/aveia e soja/aveia. Implementou-se uma rede neural artificial de múltiplas camadas e um algoritmo genético, em linguagem de programação Java, e comparou-se os resultados obtidos desta implementação com análises tradicionais de regressão polinomial. O uso de inteligência artificial via redes neurais artificiais e algoritmos genéticos permite simular com eficiência a produtividade de grãos de aveia e melhor otimização da densidade de semeadura na comparação com regressão polinomial, considerando os principais sistemas de sucessão no sul do Brasil.

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Understanding the magnitude of contribution and relationships of industrial quality components to yield by nitrogen stimulation can drive strategies with benefits to the food industry. The objective of this study is to measure and interpret the contribution and relationship dynamics of the components of oat industrial quality with grain and industry yield by nitrogen stimulation, partitioning the correlation values in direct and indirect effects by path diagnosis, in proposing strategies that promote benefits to the food industry. The study was conducted from 2011 to 2016, in a randomized block design with four replications in 4x2 factorial for nitrogen rates (0, 30, 60 and 120 kg ha-1) and oat cultivars (Barbarasul and Brisasul) in separate environments soybean/oat and corn/oat succession system. The increase of nitrogen promoted greater change in the mass of caryopsis in soybean/oat system and the thousand grain mass and number of grains greater than 2 mm in corn/oat system, with a tendency of reduction. In soybean/oat system, grain and industry yields can be simultaneously incremented by direct increase via one thousand grain mass and indirect increase by caryopsis mass. In corn/oat system the grain yield does not show any relationship with industrial quality variables. However, the industral productivity is benefited by the increase of the number of grains larger than 2 mm. The management proposition in the improvement of the grain and industry productivity characteristics by nitrogen is dependent on the high succession and reduced N-residual release systems

Resumo. Na agricultura, o desenvolvimento de modelos matemáticos tem contribuído para o conhecimento fisiológico das culturas, na busca de inovação e validação de novas tecnologias. O cultivo da aveia brancaé muito comum na região sul do país e sua produtividadé e fortemente dependente do uso de nitrogênio, que se perde facilmente no ambiente. O emprego de hidrorretentores deágua no solo pode ser uma alternativa inovadora buscando melhorar a eficiência do nitrogênio. Dessa forma, o uso da modelagem matemática pode melhorar o entendimento das variáveis de produtividade da aveia e suas relações com o clima e o manejo do nitrogênio e hidrogel. Nesse contexto, o objetivo de estudoé o uso de regressão por superfície de resposta na otimização da combinação ideal de nitrogênio e hidrogel sobre a maior produtividade de grãos de aveia no sistema de sucessão milho/aveia. O uso de distintas doses do hidrorretentor associadasà adubação nitrogenada em cobertura influência positivamente na produtividade de grãos de aveia. A dose ajustada de hidrogel e nitrogênioà máxima produtividade de grãos no sistema milho/aveiaé ao redor de 60 e 100 kg ha −1 , respectivamente.Palavras-chave. Avena Sativa, nitrogênio, hidrogel, superfície de resposta, sistema milho/aveia. 1

The artificial neural networks modeling might simulate the efficiency of wheat grain yield involving biological and environmental conditions during the development cycle.  Considering the main succession systems in wheat crop in Brazil, the study aimed to adapt an artificial neural network architecture capable of predict the wheat grain productivity throughout the growth cycle, involving nitrogen and non-linearity of maximum air temperature and rainfall. The field experiment was conducted in two successions systems (soybean/wheat and maize/wheat) in 2017 and 2018, the trial design was in a randomize blocs with eight replicate in the level 0, 30, 60, and 120 kg ha-1 N-fertilizer doses in the phenological stage of third fully expanded leaves. Every 30 day of the development cycle were obtained the biomass yield, maximum air temperature and accumulated rainfall information. The perceptron multi-layered artificial neural networks with backpropagation algorithm with network architecture 5-8-1 and 5-7-1 in soybean/wheat and maize/wheat system respectively, is able to simulate the wheat grain yield involving the nitrogen dose at top-dressing and the non-linearity of maximum air temperature and rainfall with biomass information obtained during the cycle crop.

Artificial neural networks simulating oat grain yield throughout the crop cycle, can represent an innovative proposal regarding management and decision making, reducing costs and maximizing profits. The objective of the study is to develop biomathematical models via artificial neural networks, capable of predicting the productivity of oat grains by meteorological variables, nitrogen management and biomass obtained throughout the development cycle, making it possible to plan more efficient and sustainable managements. In each cultivation system (soybeans/oats; maize/oats), two experiments were carried out in 2017 and 2018, one for analyzing grain yield and the other for cutting every 30 days to obtain biomass. The experiments were conducted in a randomized block design with four replications for four levels of N-fertilizer (0, 30, 60 and 120 kg ha-1), applied in the stage of the 4th expanded leaf. The use of the artificial neural network makes it possible to predict grain yield by harvesting the biomass obtained at any stage of oat development, together with the handling of the nitrogen dose and meteorological information during cultivation. Therefore, a new tool to aid the simulation of oat productivity throughout the cycle, facilitating faster decision making for more efficient and sustainable management with the crop.

The most efficient nitrogen management by adjusting the nutrient dose at sowing and top-dressing with the supply period can increase the oat yield with greater sustainability. Considering the main cereal succession systems in Brazil and independent of the agricultural year condition, the objective of the study was to propose combination of nitrogen adjusted dose at sowing and at top-dressing with the most adequate moment of supply over the biomass and oat grain yield. The experiment was conducted in the years 2015, 2016 and 2017, in Augusto Pestana, RS, Brazil. The experimental plot was a randomized block design with four replicates, in a 4 x 4 factorial model, and four nitrogen rates at sowing (0 - control sample, 10, 30 and 60 kg ha-1), changing the top-dressing dose at total of 70 and 100 kg ha-1 in soybean / oat succession system and maize / oats, respectively. Expecting 4000 kg ha-1 of grain yield, with top-dressing supply in four periods (0, 10, 30 and 60 days after the emergency). The nitrogen management in oat, the combination of the adjusted dose at sowing and at top-dressing with the supply season shows the need to combine the technical recommendations of fertilization with the meteorological conditions of cropping. The absence of nitrogen at sowing and total dose applied at top-dressing, 30 to 35 days after emergence, increased the biomass and grains yield, regardless of condition of the agricultural year and succession system

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