The supply of glutamic acid-based biostimulants may represent an innovative technology to increase oat grain yield and quality. The objective of this study is to measure the effect of different biostimulants based on concentrations of glutamic acid and nutrients and their application on indicators of productivity and industrial and chemical quality of oat grains. The study was conducted in 2016 and 2017 in a randomized block design with four replications, considering 10 combinations of treatments for different application conditions and types of glutamic acid-based biostimulants, which were: 1. Control; 2. Zinplex (seed) + Biomol (grain filling); 3. Zinplex (seed) + Glutamin Extra (grain filling); 4. Zinplex (seed) + Biomol (thinning); 5. Glutamin Extra (1st fungicide application) + Glutamin Extra (2nd fungicide application); 6. Biomol (1st fungicide application) + Biomol (2nd fungicide application); 7. Zinplex (seed) + Vorax (grain filling); 8. Vorax (1st fungicide application) + Vorax (2nd fungicide application); 9. Biomol (thinning) + Vorax (grain filling) and 10. Biomol (thinning) + Glutamin Extra (grain filling). The foliar application of biostimulants with the presence of glutamic acid and nutrients may have positive effects on variables related to productivity and industrial and chemical quality of oat grains, however, depending on the agricultural year conditions. The application of Glutamin Extra in the 1st and 2nd fungicide application shows the best results in the vast majority of grain yield and quality variables, but the costs involving only biostimulants do not guarantee economic viability.

Fuzzy logic can simulate wheat productivity by assisting crop predictability. The objective of the study is the use of fuzzy logic to simulate wheat yield in the conditions of nitrogen use, together with the effects of air temperature and rainfall, in the main cereal succession systems in Southern Brazil. The study was conducted in the years 2014, 2015 and 2016, in Augusto Pestana, RS, Brazil. The experimental design was a randomized block design with four repetitions in a 4 x 3 factorial scheme for N-fertilizer doses (0, 30, 60, 120 kg ha-1) and nutrient supply forms [100% in phenological stage V3 (third expanded leaf); (70%/30%) in the phenological stage V3/V6 (third and sixth expanded leaf) and; fractionated (70%/30%) at the phenological stage V3/E (third expanded leaf and beginning of grain filling)], respectively, in the soybean/wheat and corn/wheat systems. The pertinence functions and the linguistic values established for the input and output variables are adequate for the use of fuzzy logic. Fuzzy logic simulates wheat grain yield efficiently in the conditions of nitrogen use with air temperature and rainfall in crop systems.

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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

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.