Abstract
ABSTRACT Brazil is the largest sugarcane producer worldwide. In mills, sugarcane crops are sprayed with agrochemicals using self-propelled, tractor-driven, and aerial hydraulic sprayers. As sugarcane spraying is regularly performed over extensive areas, the machinery to be used should be planned and dimensioned to ensure timely operations without overloading and at lower costs. Therefore, the current study aimed to analyze the operational and economic performances of different hydraulic sprayers to perform sugarcane crop control. As long as meeting our goal in field conditions would be difficult, we opted to develop a computational model, named as “TratoCana”, using a spreadsheet and programming language. The model was used to assess economic and operational factors by generating scenarios and analyzing probable routine errors. In short, the results evidenced that initial value and operation speed are factors with the strongest impact on the costs of self-propelled sprayers and tractor-sprayer sets. Yet, the aerial application was mostly affected by fuel costs and crop row lengths. Moreover, the larger the spray tank volume, the lower the costs.
Highlights
During the 2017/2018 growing season, sugarcane grown area in Brazil was estimated at 8.76 million hectares, with a production estimate of 646.34 million tons (CONAB, 2017)
A sample scenario was developed for a fictitious sugarcane mill, considering an area of 22,000 ha, a mean sugarcane yield of 80.00 Mg ha-1, and a price paid to growers for delivered sugarcane of US$ 22.04 Mg-1, in accordance with UDOP (2016)
The mean values of climatic parameters used for the mill refer to the data recorded in Rio Largo County AL (Brazil) in 2014
Summary
During the 2017/2018 growing season, sugarcane grown area in Brazil was estimated at 8.76 million hectares, with a production estimate of 646.34 million tons (CONAB, 2017). These are extensive areas and have to be regularly sprayed for pest and disease control. According to Dash & Sirohi (2008), Sichonany et al (2011), and Akinnuli et al (2014), agricultural mechanized operations should be planned and managed previously for punctuality, without under- or overloading machinery and having minimal operational costs. The absence of planning and management in agricultural machinery has resulted in discrepant reductions and increases in operation times. Hansen et al (2007) studied a row crop harvesting pattern by combines for turning maneuvers, considering four platform sizes (6, 8, 12, and 16-row heads); they observed that the 12-row platform provided the shortest turn time in a row crop
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