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Abstract
ABSTRACT 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.
Highlights
The prior and precise knowledge of agricultural harvests is a strategic issue in world agriculture (Gomes et al, 2014; Marolli et al, 2018)
The pertinence functions and the linguistic values established for the input and output variables are adequate for the use of fuzzy logic
In the agricultural year 2016 (Figure 1A), the maximum temperature observed at the time and after application of N-fertilizer was not high (± 15°C) and had favorable soil moisture conditions due to the rainfall that occurred in the days prior to fertilization
Summary
The prior and precise knowledge of agricultural harvests is a strategic issue in world agriculture (Gomes et al, 2014; Marolli et al, 2018). Mathematical models that seek efficient simulations, describing complex interactions with the nonlinear effects of agroecosystems are nincreasingly being sought (Rosa et al, 2015; Mantai et al, 2017). In this perspective, fuzzy models are techniques that allow the description of complex systems, produced from rules elaborated by specialists, providing their experience to the elaboration of an inference system of the type “If ” (Barros & Bassanezi, 2010; Silva et al, 2014). The proper timing of nitrogen supply does not always coincide with adjusted conditions of soil moisture and air temperature, changing the efficiency of nutrient utilization and, the expected productivity (Acosta et al, 2014; Arenhardt et al, 2015)
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