Factors limiting the implementation of mechanical harvesting of sugarcane in Campos dos Goytacazes, RJ, Brazil

Solano, Cristóbal Soto; Ponciano, Niraldo José; Azevedo, Hamilton Jorge de; Souza, Paulo Marcelo de

doi:10.1590/0034-737x201764010006

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…index (Solano et al, 2017). It can also assist in the calculation of outsourcing contracts for mechanized sugarcane cutting operations in processing plants, since supporting parameters for defining these contracts are scarce.…”

Section: Resultsmentioning

confidence: 99%

Operating cost of sugarcane harvester in function of agricultural productivity and harvester age

Banchi

García

Grespan

et al. 2019

Rev. bras. eng. agríc. ambient.

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The brazilian agriculture has expanded and improved its techniques in the last decades as well as the mechanization of sugarcane cultivation. Overall, the mechanization cost of this cultivation is the highest of the total cost of production in relation to other crops. That cost consists of several elements such as the cost of the harvester. This study aimed to develop a mathematical model that represents the operational cost of the harvester in relation to its operating life and agricultural productivity, parameters that are associated with its operational capacity. Simulations of this cost were conducted, raging the operating life of harvesters between 0 and 17,900 h, and the agricultural productivity of the cultivation from 50 to 130 Mg ha-1. The results indicated an operating cost between R$ 7.23 (BRL) and R$ 26.43 Mg-1. It was verified that the operational cost is inversely proportional and nonlinear to productivity, and also directly proportional and nonlinear in relation to the age of the harvester.

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Section: Resultsmentioning

confidence: 99%

Operating cost of sugarcane harvester in function of agricultural productivity and harvester age

Banchi

García

Grespan

et al. 2019

Rev. bras. eng. agríc. ambient.

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“…Recommendations presented in the literature which were reviewed in this paper (Benedini and Conde, 2008;Bharati et al, 2017;Paixão et al, 2016;Solano et al, 2017) included the objective function (1) which aimed to minimize the number of harvest machine maneuvers in the plot j and, simultaneously, take advantage of the plantation area with constraints on the dimensions and area of the plots. Constraint (2) imposed boundaries, lower (Area low ) and upper (Area upp ), on the areas of the plots.…”

Section: Plot Generation Problemmentioning

confidence: 99%

“…This protocol established the complete elimination of the practice of sugarcane burning in 2017 (Cervi et al, 2015). Solano et al (2017) identified the main technical factors that affect the implementation of mechanized harvesting systems. The authors identified important technical constraints in the system of planting, particularly the size and shape of the plots, the appropriate row spacing and the selection of sugarcane varieties suitable for mechanical harvesting.…”

Section: Introductionmentioning

confidence: 99%

A methodology to determine size and shape of plots for sugarcane plantation

Cherri

Vianna

Ramos

et al. 2019

Sci. agric. (Piracicaba, Braz.)

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Brazil is the largest sugarcane producer in the world and the leader in the production of sugar and ethanol. Although sugarcane has become an important factor in the Brazilian economy, cultivation has presented many issues, for example, the problems due to burning before the manual harvest. The Brazilian authorities have approved a law that prohibits this practice and mechanized harvesting has thus become the most fitting approach. Given this development, areas for sugarcane plantation must be properly rebuilt to accommodate the new way of harvesting. The main characteristic demanded of sugarcane plots to use harvesting machines is that they must be rectangular. In the present paper, we propose a methodology for dividing the plantation area into plots and planning their allocation so as to accommodate mechanized harvesting. In view of the requirement for plots to be rectangular, we represented this problem as a two-dimensional cutting problem, and to find a solution we adopted the AND/OR graph approach. The computational experiments were conducted using real cases, and the proposed strategy was shown to perform well.

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Remote Sensing Applications in Sugarcane Cultivation: A Review

et al. 2021

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A large number of studies have been published addressing sugarcane management and monitoring to increase productivity and production as well as to better understand landscape dynamics and environmental threats. Building on existing reviews which mainly focused on the crop’s spectral behavior, a comprehensive review is provided which considers the progress made using novel data analysis techniques and improved data sources. To complement the available reviews, and to make the large body of research more easily accessible for both researchers and practitioners, in this review (i) we summarized remote sensing applications from 1981 to 2020, (ii) discussed key strengths and weaknesses of remote sensing approaches in the sugarcane context, and (iii) described the challenges and opportunities for future earth observation (EO)-based sugarcane monitoring and management. More than one hundred scientific studies were assessed regarding sugarcane mapping (52 papers), crop growth anomaly detection (11 papers), health monitoring (14 papers), and yield estimation (30 papers). The articles demonstrate that decametric satellite sensors such as Landsat and Sentinel-2 enable a reliable, cost-efficient, and timely mapping and monitoring of sugarcane by overcoming the ground sampling distance (GSD)-related limitations of coarser hectometric resolution data, while offering rich spectral information in the frequently recorded data. The Sentinel-2 constellation in particular provides fine spatial resolution at 10 m and high revisit frequency to support sugarcane management and other applications over large areas. For very small areas, and in particular for up-scaling and calibration purposes, unmanned aerial vehicles (UAV) are also useful. Multi-temporal and multi-source data, together with powerful machine learning approaches such as the random forest (RF) algorithm, are key to providing efficient monitoring and mapping of sugarcane growth, health, and yield. A number of difficulties for sugarcane monitoring and mapping were identified that are also well known for other crops. Those difficulties relate mainly to the often (i) time consuming pre-processing of optical time series to cope with atmospheric perturbations and cloud coverage, (ii) the still important lack of analysis-ready-data (ARD), (iii) the diversity of environmental and growth conditions—even for a given country—under which sugarcane is grown, superimposing non-crop related radiometric information on the observed sugarcane crop, and (iv) the general ill-posedness of retrieval and classification approaches which adds ambiguity to the derived information.

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Factors limiting the implementation of mechanical harvesting of sugarcane in Campos dos Goytacazes, RJ, Brazil

Cited by 3 publications

References 2 publications

Operating cost of sugarcane harvester in function of agricultural productivity and harvester age

Operating cost of sugarcane harvester in function of agricultural productivity and harvester age

A methodology to determine size and shape of plots for sugarcane plantation

Remote Sensing Applications in Sugarcane Cultivation: A Review

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