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.
Fuzzy logic can simulate wheat yield by nitrogen and temperature nonlinearity, validating the use of hydrogel biopolymer. The objective of this study is to adapt the fuzzy logic model to the simulation of nitrogen biomass and wheat grain yield and non-linearity of the maximum air temperature, under the conditions of use of the hydrogel biopolymer, in high and low N-residual release systems. The study was conducted in 2014 and 2015, in Augusto Pestana, RS, Brazil (28 ° 26 ‘30’ latitude S and 54 ° 0 ‘58’ longitude W). The experimental design was a randomized block design with four replications in 5 x 5 factorial, for hydrogel doses (0, 30, 60, 90 and 120 kg ha-1), added in the furrow next to the seed, and N-fertilizer doses. (0, 30, 60, 90 and 120 kg ha-1), applied at the phenological stage V3 (third expanded leaf) as top-dressing, respectively. The pertinence functions together with the quantitative and linguistic values for the input and output variables are suitable for the use of fuzzy logic in the wheat yield simulation. The fuzzy model made it possible to estimate the values of biomass and wheat grain yield by nitrogen and non-linearity of the maximum air temperature under the conditions of use of the hydrogel biopolymer in high and low N-residual release systems.
Growth regulator in oat can reduce lodging with effects on yield indicators. The objective of the study is to define the optimum dose of growth regulator to reduce lodging in oats under different conditions of nitrogen (N) fertilization (reduced, high and very high) and the effects on yield indicators in the succession systems. In each succession system (soybean/oats and corn/oats), two experiments were conducted, one to quantify biomass yield and the other to estimate grain yield and lodging. In the four experiments, the design was randomized blocks with four replicates in 3 x 4 factorial scheme, for N-fertilizer doses (30, 90 and 150 kg ha-1) and growth regulator doses (0, 200, 400 and 600 mL ha-1), respectively. Growth regulator reduces lodging in oat plants, with the ideal doses of 500 mL ha-1 in the soybean/oat system and 400 mL ha-1 in the corn/oat system, regardless of the reduced, high and very high N doses. There is a linear reduction of biological and straw yields, and a quadratic trend in the expression of grain yield and harvest index as a function of the growth regulator doses, regardless of succession systems (soybean/oats and corn/oats).
The simulation of oat grain productivity does not contemplate the use of efficient models that involve important management with meteorological elements. The objective of the study is to propose a mathematical model capable of simulating the oat grain productivity through the management of nitrogen and growth regulator with variables related to the plant and to meteorological elements. In this study, two experiments were conducted in the years of 2013, 2014 and 2015: one to quantify biomass productivity and another to determine grain productivity and lodging at the management doses of nitrogen and growth regulator. The experimental design was a randomized block with four replications in a 4 × 3 factorial scheme for 0, 200, 400 and 600 mL•ha −1 growth regulator doses and 30, 90 and 150 kg•ha −1 nitrogen doses, respectively. During the crop cycles, the meteorological variables thermal sum, radiation and rainfall were quantified. The mathematical model proposed, which combines polynomial regression of the harvest index with multiple linear regression of the biological productivity, is efficient in the simulation of oat grains productivity with the use of growth regulator, nitrogen and meteorological elements. Thus, it adds to the conventional models of simulation and becomes an aid tool for making decisions regarding the management of oats culture.
A B S T R A C TWheat biomass yield focused on the production of quality silage is dependent on rainfall, temperature and nitrogen (N). The objective of the study was to validate the use of rainfall, thermal time and N as potential variables for the composition of the multiple linear regression model and simulation of wheat biomass yield for silage production under N supply conditions during the cycle, in the systems of succession. The study was conducted in 2012, 2013 and 2014, in randomized blocks with four replicates in 4 x 3 factorial, for N-fertilizer doses (0, 30, 60, 120 kg ha -1 ) and forms of N supply [single application (100%) in the stage V 3 (third expanded leaf); split application (70%/30%) in the stages V 3 /V 6 (third and sixth expanded leaves); split application (70%/30%) in the stages V 3 /E (third expanded leaf and beginning of grain filling)], respectively, in the systems soybean/wheat and maize/wheat. Rainfall and N are potential variables in the composition of the multiple linear regression model. Multiple linear regression models are efficient in the simulation of wheat biomass yield for silage under the N supply conditions during the cycle in the succession systems.Simulação da produtividade de biomassa do trigo pela soma térmica, precipitação e nitrogênio R E S U M O A produção de biomassa de trigo voltada para a elaboração de silagem de qualidade é dependente da precipitação, da temperatura e do nitrogênio. O objetivo no estudo foi validar o uso da precipitação, soma térmica e nitrogênio como variáveis potenciais para composição do modelo de regressão linear múltipla e a simulação da produtividade de biomassa do trigo na elaboração de silagem nas condições de fornecimento de nitrogênio durante o ciclo, nos sistemas de sucessão. O estudo foi conduzido em 2012, 2013 e 2014 em blocos ao acaso com quatro repetições em fatorial 4 x 3, para doses de N-fertilizante (0, 30, 60, 120 kg ha -1 ) e formas de fornecimento [único (100%) no estádio V 3 (terceira folha expandida); fracionado (70%/30%) no estádio V 3 /V 6 (terceira e sexta folha expandida) e fracionado (70%/30%) no estádio V 3 /E (terceira folha expandida e início do enchimento de grãos)] respectivamente, no sistema soja/trigo e milho/trigo. A precipitação e o nitrogênio são variáveis potenciais na composição do modelo de regressão linear múltipla. Os modelos de regressão linear múltipla são eficientes para simulação da produtividade de biomassa do trigo para silagem nas condições de fornecimento de nitrogênio durante o ciclo nos sistemas de sucessão.
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