Agricultural and biosystems engineering for a sustainable world. International Conference on Agricultural Engineering, Hersonissos, Crete, Greece, 23-25 June, 2008., 2008
Citation: Monitoring and controlling animal effluents in livestock farms: the METAMORFOSI project... more Citation: Monitoring and controlling animal effluents in livestock farms: the METAMORFOSI project/F. Mazzetto, A. Calcante, P. Sacco, F. Salomoni-In: Proceedings of the 16th Nitrogen Workshop Connecting different scales of nitrogen use in agricolture [sl]: Facoltà di Agraria ...
... ARABLE FARM PRODUCTION ACTIVITIES A GRIC U L TU RAL E N MANAGEMENT STRATEGY… LIVESTOCKS VITIC... more ... ARABLE FARM PRODUCTION ACTIVITIES A GRIC U L TU RAL E N MANAGEMENT STRATEGY… LIVESTOCKS VITICULTURE ORTICULTURE D EPARTMEN T OF A DECISION MAKING PR. BASED ON TARGETED INFORMATION MANAGEMENT DECISION D 2 ...
Agricultural and biosystems engineering for a sustainable world. International Conference on Agricultural Engineering, Hersonissos, Crete, Greece, 23-25 June, 2008., 2008
Citation: Monitoring and controlling animal effluents in livestock farms: the METAMORFOSI project... more Citation: Monitoring and controlling animal effluents in livestock farms: the METAMORFOSI project/F. Mazzetto, A. Calcante, P. Sacco, F. Salomoni-In: Proceedings of the 16th Nitrogen Workshop Connecting different scales of nitrogen use in agricolture [sl]: Facoltà di Agraria ...
... ARABLE FARM PRODUCTION ACTIVITIES A GRIC U L TU RAL E N MANAGEMENT STRATEGY… LIVESTOCKS VITIC... more ... ARABLE FARM PRODUCTION ACTIVITIES A GRIC U L TU RAL E N MANAGEMENT STRATEGY… LIVESTOCKS VITICULTURE ORTICULTURE D EPARTMEN T OF A DECISION MAKING PR. BASED ON TARGETED INFORMATION MANAGEMENT DECISION D 2 ...
Effluent management has become increasingly important among livestock farming activities, particu... more Effluent management has become increasingly important among livestock farming activities, particularly relating to the environmental impacts that could result from inadequate effluent management especially at the farm level. Due to this issue, the European Commission, with Directive 91/676/EC, aimed at protecting the environment while ensuring that farmers could achieve proper production levels. Due to this directive, nitrate vulnerable zones have been set with strict regulations regarding the timing and rates of nitrogen application in these zones. In the vulnerable zones, a specific threshold limits the application rate to 170 kg/ha per year for nitrogen whilst, in the remaining areas, the threshold is 340 kg/ha. The present study aimed to develop a variable-rate (VRT) system capable of automatically controlling the nitrogen distribution rate in the open field specifically designed for pressurized slurry tanker (the most diffused spreading technology used in Italy. For field-testing purposes, this system was mounted on a double-axis 10 m3 slurry tanker equipped with a crawling nozzle distribution unit. Field experiments were conducted at two typical forward speeds (2 and 3 km/h) and three different nitrogen application rates (170, 250, and 340 kg/ha). Based on the experimental results, the system was generally capable of limiting the differences between the nominal and measured application rates to less than 7% and the transverse field distribution resulted uniform throughout the working width of the machine and at all tested operating conditions, with maximum deviations of about 15% (limit value imposed by UNI EN 13406:2002 standard
The instant torque and brake specific fuel consumption (BSFC) of a farm-tractor engine are very i... more The instant torque and brake specific fuel consumption (BSFC) of a farm-tractor engine are very interesting parameters from a technical and economical point of view and allow advancing many considerations in the Engineering and Farm-mechanization fields related to the optimization of the engine power and consumptions. A direct access to the CAN-BUS system, where present, can be difficult; as a consequence, some practical solutions (sensors, numerical methodologies) aimed to deduce continuously but indirectly the engine performances are therefore proposed and discussed.
In particular, the focus of this study is to evaluate the possibility of using artificial neural networks (ANNs) trained with exhaust gas (EG) and motor oil temperature data, easy to be measured. Hence, the above-mentioned temperatures and several network architectures (different for neurons and hidden layers number, neuronal transfer functions) were evaluated in their reliability in estimating the torque and BSFC of different tractor diesel motors, giving also the readers some useful indications: determination coefficients were calculated with reference to the line “predicted values = experimental values”.
Lubricant temperature resulted to be totally unsuitable (very low and diversified R2).
ANNs using the EG temperature for torque estimations achieved higher average R2 than ANNs predicting BSFC, both in the training (>0.996 vs. >0.889) and in the prediction phase (>0.993 vs. >0.621). Consequently, EG temperature is strongly recommended for estimating both parameters even if preliminary evaluations should be performed for BSFC (engine characteristics have a significant influence on the predictions).
Finally, best R2 can be scored by using the Gaussian neuronal transfer function.
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Papers by Aldo Calcante
The present study aimed to develop a variable-rate (VRT) system capable of automatically controlling the nitrogen distribution rate in the open field specifically designed for pressurized slurry tanker (the most diffused spreading technology used in Italy. For field-testing purposes, this system was mounted on a double-axis 10 m3 slurry tanker equipped with a crawling nozzle distribution unit. Field experiments were conducted at two typical forward speeds (2 and 3 km/h) and three different nitrogen application rates (170, 250, and 340 kg/ha).
Based on the experimental results, the system was generally capable of limiting the differences between the nominal and measured application rates to less than 7% and the transverse field distribution resulted uniform throughout the working width of the machine and at all tested operating conditions, with maximum deviations of about 15% (limit value imposed by UNI EN 13406:2002 standard
In particular, the focus of this study is to evaluate the possibility of using artificial neural networks (ANNs) trained with exhaust gas (EG) and motor oil temperature data, easy to be measured. Hence, the above-mentioned temperatures and several network architectures (different for neurons and hidden layers number, neuronal transfer functions) were evaluated in their reliability in estimating the torque and BSFC of different tractor diesel motors, giving also the readers some useful indications: determination coefficients were calculated with reference to the line “predicted values = experimental values”.
Lubricant temperature resulted to be totally unsuitable (very low and diversified R2).
ANNs using the EG temperature for torque estimations achieved higher average R2 than ANNs predicting BSFC, both in the training (>0.996 vs. >0.889) and in the prediction phase (>0.993 vs. >0.621). Consequently, EG temperature is strongly recommended for estimating both parameters even if preliminary evaluations should be performed for BSFC (engine characteristics have a significant influence on the predictions).
Finally, best R2 can be scored by using the Gaussian neuronal transfer function.