OPAL-Life develops along with this action the first pilot version of the land use optimisation tool in collaboration with PeltoOptimi-project. This tool takes into account and integrates criteria that are critical to assess how cost-efficient and environmentally sustainable production is currently in each field of a farm. The outcome of the land use optimisation process is a farm map indicating intensified, extensified and afforested fields and/or their borderline cases. The current patterns of land use in a farm are considered as baseline against which the envisaged land use change impacts are monitored.

Development of the pilot version of the land use optimisation tool is done by the following methodology:

Yield gap analyses of participating farms

The partly dispersed, existing survey data on yields that are available for each farm is supplemented by farmer’s notes. These data points on recorded yields are used as references, when satellite data is applied to link existing yield data to satellite based and after comprehensive data processing generate estimates for biomass production for each field of each study farm for the last ten years.

Another highly relevant future method for agricultural monitoring is unmanned airborne vehicle (UAV) based local area remote sensing. UAV based spectral reflectance data are basically similar to optical satellite data, but provides better resolution and includes 3D structural information using stereo-photogrammetry. Provided estimates of production capacity of each field for a reasonably long time period serves as assessment base for existing yield gaps. UAV -flights have started in spring 2016 and have continued during the growing season 2017. Satellite data elaborated with UAV image data along with other relevant data sources used to assessment of yield potential will be analysed by the end of year 2019.

Annual variation in gaps and their linkages to weather conditions and constraints are assessed with datasets from Finnish Meteorological Institute. Production capacity of each field is compared to the mean as well as the best-quarter of the fields in a farm and the region. This assessment provides core information about the responsiveness and productivity of each field.

Read more about UAV-method from here (DroneFinland).

Other critical tool criteria

In addition to yield gaps we monitor other critical tool criteria. These criteria are: farm size (ha), field size (ha), field distance from the farm center (m), field shape, field slope (%), field proximity to waterway (m), soil type (organic, coarse mineral, two clay soils) and ownership (leased/owned) in addition to logistic advantages

Feedback based further development of the tool and/or field allocation process

After implementing first time the tool at farm scale, we progress with tool development by considering farmers experiences on how well the tool manages to demonstrate the productivity and other core criteria based differences in cost-efficient production capacities of the field parcels. We envisage that farmers bring their expertise, e.g. on understanding the other soil related constraints (soil degradation and compaction, low pH, high within-parcel variation in conditions, and soil texture), risk for heavy pathogen infestation, and poor subsurface drainage systems without sufficient incentives for renovations. Also the expertise and opinions of the policy developer target audience will be incorporated to the process at this point together with stakeholder insights.

Monitoring major drivers for poorly responsive, extensified or afforested fields

As agreed in theory about allocation of the fields into groups of intensified, extensified and afforested fields, we monitor the main reasons in each farm for poor responsiveness and conditions of field targeted for extensification. There are likely many reasons but the most dominating reasons are expected to be in soil compaction and degradation, low soil organic matter content, poor drainage systems and low pH, which are systematically monitored and supplemented with other relevant information available from the farmer.

Accuracy assessment and validation of the methods will be carried out based on in-situ measurements as well as using the UAV based data to validate satellite-based measurements.