Analysis of cost and performances of agricultural machinery: reference model for sprayers
¹University of Padova, Department of Land, Environment, Agriculture and Forestry, Via dell'Università 16, IT35020, Legnaro, Italy
²Edizioni L'Informatore Agrario srl, Via Bencivenga - Biondani, 16, IT37133 Verona, Italy
*Correspondence: tatevik.yezekyan@phd.unipd.it
Abstract:
Management of agricultural operations is currently rapidly changing caused by increased attention to the concepts of sustainable development and sustainable intensification. Enhancement of productivity and efficiency of agricultural machinery are the leading factors in sustainable agriculture. The complete application and exploitation of engineering advances require the revision of traditional agricultural machinery management process. The definition of the farm fleet (tractors and implements), as well as machinery planning and management, must consider different parameters, including not only the cost of the machines but also their dimensions, weight, working width, needed power, etc. All of this information related to an agricultural machine is eventually influencing the impact on productivity, on the return on the investment, and also on the environment.
The present work is aimed at identifying the most relevant parameters which are influencing costs and performances of sprayers, including tank volume, maximum flow, needed power, weight and price. The different parameters are analysed in a correlation matrix, in order to allow identification of dependencies and to extract reference models.
The study is based on linear and multiple linear regression analysis carried out on technical specifications of about 700 models of sprayers. Relevant correlations were highlighted between price and weight, between weight and tank capacity and in some cases between power and weight. Following such correlations, models have been proposed, which can be implemented in order to support the decision making phases.
Key words:
Agricultural machinery, correlation analysis, optimization model, sprayer, stepwise regression