The maintenance of organic farming production schemes is a theme receiving a growing interest now that there are signs of a slowing in organic farming uptake in Italy. The present study develops a model based on a Bayesian network (BN) that is aimed at investigating the factors that affect the exit of a farm from the organic sector and to simulate the probability of maintaining an organic scheme for different farm types. The model is based on a database of organic farms, which has been integrated with qualitative information. Farm-type simulation and sensitivity analysis of most of the relevant variables have been carried out. Main results show that arable farm types are those with a higher probability to stay in the organic sector, while farmers’ age, province the farm is situated in and farm size are the factors mostly influencing probability scores.
A Bayesian network to predict farms surviving in the organic system: a case study from Marche, Italy
GAMBELLI, Danilo;
2010-01-01
Abstract
The maintenance of organic farming production schemes is a theme receiving a growing interest now that there are signs of a slowing in organic farming uptake in Italy. The present study develops a model based on a Bayesian network (BN) that is aimed at investigating the factors that affect the exit of a farm from the organic sector and to simulate the probability of maintaining an organic scheme for different farm types. The model is based on a database of organic farms, which has been integrated with qualitative information. Farm-type simulation and sensitivity analysis of most of the relevant variables have been carried out. Main results show that arable farm types are those with a higher probability to stay in the organic sector, while farmers’ age, province the farm is situated in and farm size are the factors mostly influencing probability scores.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
