Farmer’s data sourcing: A best practise example for mapping soil properties in permanent crops in South Tyrol (northern Italy)

In agriculture, detailed knowledge of soil properties is a key element for high-quality food production. However, soil data at a single parcel scale are generally unavailable. In this study, a best practice framework is presented where, through an operational chain from an individual farmer through a centralised database and with a geostatistical approach, new knowledge has been generated that enables application far beyond a single soil sample at the parcel scale. This study was carried out in intensively managed permanent crops in South Tyrol, Italy, where 16,000 soil samples taken in the framework of an integrated production program have b...  more

In agriculture, detailed knowledge of soil properties is a key element for high-quality food production. However, soil data at a single parcel scale are generally unavailable. In this study, a best practice framework is presented where, through an operational chain from an individual farmer through a centralised database and with a geostatistical approach, new knowledge has been generated that enables application far beyond a single soil sample at the parcel scale. This study was carried out in intensively managed permanent crops in South Tyrol, Italy, where 16,000 soil samples taken in the framework of an integrated production program have been used to show the capability to predict accurate soil property maps. Geospatialisation was conducted using Kriging interpolation. Finally, the resulting maps of soil texture, soil organic matter (SOM), and pH are shown and discussed. The results showed that combining agricultural production guidelines, a long-term data collection program, farmers, public administration services, and scientific analysis can provide a successful framework for digital soil mapping. The large number of samples combined with their spatial distribution has contributed to the robust estimation of the soil texture, pH, and SOM prediction. The maps show the complex interplay of fluvial processes, topography, and anthropogenic influences on the variability of soil texture, pH, and SOM. Finally, this study was focused on a fixed time span and a subset of the available agronomic variables. Thus, the long-term soil monitoring program and the combination of all the available variables will allow digital assessment of the spatial patterns of nutrient availability, ecological risk assessments, change detection studies, and an overall long-term plan for soil security.