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EnglishThe 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.
| Titolo: | A Bayesian network to predict farms surviving in the organic system: a case study from Marche, Italy |
| Autori: | |
| Data di pubblicazione: | 2010 |
| Rivista: | |
| 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. |
| Handle: | http://hdl.handle.net/11566/34700 |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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