LOS EMPRENDEDORES DE POLÍTICA

The study analyses some relevant differences between conventional and organic Italian agriculture. The farm structures report different patterns in terms of frequency and geographical location between the two regimes. Also arable crop patterns, inside each farm structure, show differences among regimes. On the other hand, performances of farm indicators record too much variability inside each farm structure and regime. Therefore it is not clear how to link the whole performance level to the regimes. The farms with husbandry report the most relevant differences among regimes, according to farm structure distribution, arable land management and some indicator levels.

The farm typologies well describe the high variability inside agricultural situations reported by FADN data, especially at a national level. This farm classification is a useful tool to describe farm and territory, and it can be supported by other classifications as size and UDE.

Even the arable land analysis could have a high potential to describe farm and territory patterns, also in terms of dynamics. The arable land analysis made by FADN data is not able to detect crop rotational schemes, but it reports crop patterns, which are an important aspect of farm management and landscape.

This analysis tries to investigate the agronomical situation and considers a vast level of variability over time and space, at a national level. Each step of the analysis, which groups together similar farms or crops, affects the results. For this reason the crop classification constitutes the core of analysis; but other classifications as phyto-climatic zone, slope or rotational scheme definition have a relevant role, too.

Some data classifications, as slope and farm location, come from FADN data. Is not easy improve their detail, because it would mean to change the FADN database. For other classifications the analysis is more malleable and it could be quite easy to operate improvements. It is the case of crop classification and rotational scheme attribution. The possibility to improve these aspects is an important characteristic of the theoretical approach, proposed in this study.

The irrigation is not considered in this study because the MAD model does not consider it. However, not to consider irrigation could be one of the most important limitations of analysis. Farm structures are affected by its presence; water consumption is one of the most important productive aspects in agriculture and it is described by several indicators. Moreover, the presence of crop on arable fields is strictly dependent from water availability, which can sensibly alter rotational schemes. For all these aspects, in future the irrigation could be taken into account, also because FADN reports types of irrigation for each crop.

Adding information to farm typologies could also allow to have a more precise estimation of several indicator levels. By considering type of soil (at least granularity), slope and presence of irrigation it could be possible to develop a model to better estimate soil dynamics, in terms of soil organic matter levels and nutrient availability. This information, integrated in MAD, could be used also for production estimation. On the other hand, soil fertility deeply affected also rotational schemes. Since 2008 RICA database collects also information about soil conditions.

Another relevant aspect from an agronomical point of view is meteorological conditions. But their high variability among seasons, years and space does not allow any generalisations. Phyto- climatic zones description is an attempt to describe global climatic conditions which are quite stable over time. But at a farm level meteorological data is required, especially to improve model of soil dynamics.

Also, the social farm aspect could be improved to explain the whole farm and context description. Age and instruction level of farmers are useful data reported in FADN database since 2008. Farm labour is described by gender, origin country, wage and other aspects.

As previously mentioned, this analysis could consider also farm dynamics. In this study only stable farms or arable lands are taken into account. But if the historical sequences to analyse are longer than few years, making a separation of the period in homogeneous group would be interesting. The ARPEnTAge software recognises homogeneous periods inside a unique land cover sequence. Indeed, it has been developed in France to investigate Teruti data (Mari, Lazrak, & Benoit, 2013). By combining this methodology and the analysis reported in this study, farm and territory dynamics would be possibly investigated in terms of pattern and surface variation.

Even though the technical coefficients and some classifications could be improved, the approach can remain the same. By describing the whole farm area and farm activities is possible to analyse the farm and its connections to the context. And also by describing the context is possible to investigate farm processes at a more detailed level.

On the other hand, the farm analysis could be easily oriented to several other topics, just by modifying some technical coefficients. The FADN data availability at an european level and the plasticity of analysis suggest that the farm typologies and crop patterns descriptions can be useful methods to investigate agriculture and territory, at a farm or local level. Indeed the results of these analyses could support farm and land managements.