Revista Brasileira de Engenharia Agrícola e Ambiental
URI permanente para esta coleçãohttps://thoth.dti.ufv.br/handle/123456789/10362
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Resultados da Pesquisa
Item Modeling of operational performance parameters applied in mechanized harvest of coffee(Departamento de Engenharia Agrícola - UFCG, 2016-10) Cunha, João P. B.; Silva, Fabio M. da; Andrade, Ednilton T. de; Carvalho, Luis C. C.In super-mechanized coffee harvesting system, all operations are performed mechanically. In order to improve the logistics of mechanized agricultural operations, the knowledge on the variables that affect the operational performance can generate models to accurately estimate these parameters. The use of response surface methodology (RSM) allows to verify the influence of different independent variables and the generated response to allow for a great value. This study aimed to verify, using RSM, the influence of speed, mean length of rows and the slope of the areas on the operational performance parameters in different mechanized operations in coffee production, such as: harvest, sweeping and gathering. The results show that the slope directly influences the operational performance of the mechanical harvesting of coffee. The RSM proved to be an important tool to verify the effect of variables on performance parameters, and the generated models showed high significance.Item Geostatistical analysis of Arabic coffee yield in two crop seasons(Departamento de Engenharia Agrícola - UFCG, 2017-06) Carvalho, Luis C. C.; Silva, Fabio M. da; Ferraz, Gabriel A. e S.; Stracieri, Juliana; Ferraz, Patrícia F. P.; Ambrosano, LucasTo make the coffee activity competitive, some farmers use precision coffee farming. Thus, it is possible to create thematic maps that guide management practices for regions where there are limitation for the plant development. The objective of this study was to identify the spatial dependence of coffee crop yield, in 2012 and 2013. The experimental area is located in a Haplustox in Três Pontas, Minas Gerais. One hundred sampling points were georeferenced for the collection of yield data through manual harvest. The difference of yield between crop seasons was also evaluated. Data were processed using geostatistical analysis. It was possible to identify and characterize the spatial dependence of all variables, as well as to create contour maps. There were differences between the 2012 and 2013 maps, due to the biennial coffee phenological cycle, which can be confirmed by the map of the difference between the crop seasons. It is recommended a crop management that considers the spatial variability of yield for greater economic return.