Soil property map

The soil property map based on soil data analysis and various ancillary sources is the best and most cost-effective alternative when analytical soil maps do not exist.

Soil maps are indispensable monitoring tools and can support the estimation of many environmental impact indicators. For example, in monitoring, soil maps allow the Managing Authority to specify the spatial extent of measures by considering various conditionalities in the form of soil Good Agricultural and Environmental Conditions - GAECs 4, 5 and 6. In evaluation, soil maps provide data to estimate the two soil impact indicators, i.e., soil erosion (I.13) and soil organic carbon (I.12). Soil maps also support the assessment of other indicators such as water abstraction and water quality. Of course, soil properties change very slowly, and this change can be measured or become evident after a period that exceeds the period of an RDP. However, if, for example, the policy is successful in establishing cover crops on the fields most prone to soil erosion, this is evidence that the policy confronts soil erosion even though this may not be measurable in the seven years of an RDP’s life.

The tool claims that acquiring and operating a portable spectral sensor cannot be a barrier to adoption because it is low cost and easy to learn. The transferability of the tool to other regions and Member States depends on the availability of ancillary data and the ease and time with which machine learning algorithms can be trained in new data. Machine learning tools are used to transform the raw data collected through the in-situ soil scanning system to appropriate soil properties, including SOC, clay, pH and CaCO3. In addition, algorithms control the combination of point measurements with EO imagery towards delivering spatially detailed maps using a spiked bottom-up approach that needs calibration with local data. Thus the tool will require a period of testing and calibration before it can be functional.