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[en] The aim of this study was to find a model able to extract the net time per unit of net worked area from different agricultural field basic shapes (square, circle, rectangle and triangle) considering the following variables: field gross area, working speed, number of turnings (these depending on the effective working width), side length parallel and orthogonal to working direction, and working direction type. Being this a non-linear problem, an approach based on artificial neural networks is proposed. The model was trained using an artificial dataset calculated for the various shapes (internal test) and then tested on 47 different agricultural operations extracted by a real field dataset for the estimation of the net time (external test). The net time records obtained from both, the trained model and the external test, were correlated and the performance parameter r was extracted. Both regression coefficients (r), for the training and internal test, appear to be excellent being equal to 0.98 with respect to traditional linear approach (0.13). The variable “number of turnings” scored the highest impact, with a value equal to 44.34% for the net time estimation. Finally, the r correlation parameter for the external test resulted to be very high (0.80). This information is very valuable of the use of information management system for precision agriculture.
[en] The estimation of operating costs of agricultural and forestry machineries is a key factor in both planning agricultural policies and farm management. Few works have tried to estimate operating costs and the produced models are normally based on deterministic approaches. Conversely, in the statistical model randomness is present and variable states are not described by unique values, but rather by probability distributions. In this study, for the first time, a multivariate statistical model based on Partial Least Squares (PLS) was adopted to predict the fuel consumption and costs of six agricultural operations such as: ploughing, harrowing, fertilization, sowing, weed control and shredding. The prediction was conducted on two steps: first of all few initial selected parameters (time per surface-area unit, maximum engine power, purchase price of the tractor and purchase price of the operating machinery) were used to estimate the fuel consumption; then the predicted fuel consumption together with the initial parameters were used to estimate the operational costs. Since the obtained models were based on an input dataset very heterogeneous, these resulted to be extremely efficient and so generalizable and robust. In details the results show prediction values in the test with r always ≥ 0.91. Thus, the approach may results extremely useful for both farmers (in terms of economic advantages) and at institutional level (representing an innovative and efficient tool for planning future Rural Development Programmes and the Common Agricultural Policy). In light of these advantages the proposed approach may as well be implemented on a web platform and made available to all the stakeholders.