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Modeling cost and energy demand in agricultural machinery fleets for soybean and maize cultivated using a no-tillage system

Tieppo, Rafael Cesar, Romanelli, Thiago Libório, Milan, Marcos, Sørensen, Claus Aage Grøn, Bochtis, Dionysis
Computers and electronics in agriculture 2019 v.156 pp. 282-292
agricultural machinery and equipment, agricultural mechanization, climate, corn, crop production, crops, deterministic models, energy balance, energy efficiency, energy use and consumption, equations, harvesting, mechanization, no-tillage, operating costs, prediction, sowing, soybeans, spraying, Brazil
Climate, area expansion and the possibility to grow soybean and maize within a same season using the no-tillage system and mechanized agriculture are factors that promoted the agriculture growth in Mato Grosso State – Brazil. Mechanized operations represent around 23% of production costs for maize and soybean, demanding a considerably powerful machinery. Energy balance is a tool to verify the sustainability level of mechanized system. Regarding the sustainability components profit and environment, this study aims to develop a deterministic model for agricultural machinery costs and energy demand for no-tillage system production of soybean and maize crops. In addition, scenario simulation aids to analyze the influence of fleet sizing regarding cost and energy demand. The development of the deterministic model consists on equations and data retrieved from literature. A simulation was developed for no-tillage soybean production system in Brazil, considering three basic mechanized operations (sowing, spraying and harvesting). Thereby, for those operations, three sizes of machinery commercially available and regularly used (small, medium, large) and seven levels of cropping area (500, 1000, 2000, 4000, 6000, 8000 and 10,000 ha) were used. The developed model was consistent for predictions of power demand, fuel consumption and costs. We noticed that the increase in area size implies in more working time for the machinery, which decreases the cost difference among the combinations. The greatest difference for the smallest area (500 ha) was 22.1 and 94.8% for sowing and harvesting operations, respectively. For 4000 and 10,000 ha, the difference decreased to 1.30 and 0.20%. Simulated scenarios showed the importance of determining operational cost and energy demand when energy efficiency is desired.