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.
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 efficiency of nitrogen use by oats in association with climatic conditions is fundamental to the development of more sustainable managements with yield and quality. The objectives of this study were to define the agronomic efficiency of nitrogen by the ratio of the dose provided and product obtained, estimate the maximum technical efficiency of the nutrient on grain yield; and for the optimum dose, simulate the expression of the straw and industry yields, protein and total fiber in different conditions of the agricultural year in a soybean/oat system. The study was conducted from 2011 to 2016, in Augusto Pestana, RS, Brazil, in a randomized block design with four repetitions in a 4 x 2 factorial referring to nitrogen doses (0, 30, 60 and 120 kg ha-1) and oat cultivars (Barbarasul and Brisasul) in a soybean/oat system. Nitrogen increased grain, straw, and industry yields and total grain protein, with agronomic efficiency of 7.8, 19.7 and 3.3 kg ha-1 and 0.10 g kg-1, respectively, with reduction of the total fiber in 0.05 g kg-1 per kg of N supplied. The dose of maximum technical efficiency in the expression of grain yield is dependent on the weather conditions during cultivation. In general, the maximum efficiency of grain productivity was obtained with 86 kg ha-1 of N, with linear equations showing increased productivity of straw and industry yield, total protein, and reduction of the fiber content of oat grains by nitrogen use.
A utilização de agrotóxicos e o modelo atual de desenvolvimento agrícola é responsável por impactos, danos ambientais e saúde. Para que a produção de grãos seja satisfatória, o uso correto de técnicas de manejo se constitui em fator decisivo no desenvolvimento e colheita de grãos. O cultivo e consumo da aveia branca tornou-se evidente nos últimos anos devido a sua versatilidade, porém, é uma cultura sensível a variações ambientais acometida por doenças fúngicas. Esta pesquisa objetivou analisar produções científicas publicadas em periódicos nacionais e internacionais sobre cultivo de aveia branca com redução do uso do agrotóxico, garantia de cultivo sustentável ao meio ambiente e segurança alimentar. A partir da pergunta: “O que tem sido publicado na literatura nacional e internacional sobre o cultivo de aveia branca para consumo humano com o uso reduzido de fungicida?”. Com os seguintes descritores: (Fungicide AND “avena sativa”) nas bases de dados WEB OF SCIENCE (coleção principal) e SciVerse Scopus (SCOPUS), SCIENCE DIRECT e MENDELEY. Total de 312 artigos encontrados e 15 selecionados e lidos na íntegra. Os resultados indicam que independente do uso ou não de fungicida a resistência genética das cultivares ás doenças fúngicas se constitui o fator decisivo para o cultivo.
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