Europe’s agricultural landscapes are supposed to become “greener”. Within the last years the European Union has implemented various new tools (laws as well as funding programs) in order to support a more sustainable and environmental friendly usage of our natural resources. The challenge lies in combining environmentally-friendly and sustainable management with the demands for high-quality and affordable agricultural products.
One of those tools that the EU has developed to achieve this goal is the so called “Greening” which is part of the recently reformed Common Agricultural Policy of the EU. From 2015 onwards parts of the subsidies farmers are receiving from the EU will only be paid if the farmers apply environmental friendly land management on their arable area. One core point of the Greening Directive is the designation of 5% of the arable area of a farm as ecological focus areas on which agricultural usage underlies strict regulations or is terminated completely.
But how are farmers supposed to know where to implement those ecological focus areas and which fields to keep in agricultural production?
Within the MELODIES project we aim to develop a service that will provide decision support for farmers to help them to identify areas within their arable land that are especially suitable to serve as ecological focus areas. We are addressing this issue by investigating productivity variations within the arable area – both, within and between fields – and linking this information with freely available land use and governmental data. With this information land management can be optimized and both – areas of high as well as areas of low productivity – can in the future be managed in a more economical feasible and environmental-friendly way.
What we can do so far – Analysing Productivity Patterns in Agricultural Fields
We are already able to identify spatial differences of productivity within agricultural fields. For this we are using time-series of Earth Observation (EO) data from which we are investigating plant-growth patterns indicating differing productivity within the agricultural unit. By using time series of five to ten years we can assure that the patterns we see are stable over time and independent of the planted crop species. One example-output of such an analysis can be seen in the map below (Fig. 1) To create this map the biomass production was investigated over a five-year time series using RapidEye satellite imagery, revealing strong inner-field variations in productivity.
Fig. 1: The TalkingFields Base Map shows persistent patterns of relative biomass within agricultural fields. Areas of relatively low productivity are indicated in brown and yellow whereas areas of high productivity are being displayed in blue.
The farmer can use this information in various ways. He can for example optimize his soil sampling strategy by investigating differences between the recognizable zones to identify potential reasons for the occurrence of those patterns. If the cause of the patterns is known the management can be adapted as well, e.g. by adjusting fertilization and plant protection. Modern agricultural machines are already able to vary management units within a few meters while using GPS sensors for orientation, a process called precision farming. Fertilizers and pesticides are not spread evenly over the entire area but the amount is adapted to the specific requirements of areas of differing growing conditions within the field. This means that already by knowing inner-field variability and applying precision farming techniques conventional land management, focussing on the production of large amounts of high-quality agricultural products, can become more environmental friendly by reducing the amount of necessary pesticides to the necessary minimum and avoiding over-fertilization of soils.
And where we want to go to – Analysing Ecological Potential within the Landscape
Within the MELODIES project we now go a step further and also provide farmers with detailed information about which parts of their agricultural area are suitable to be designated as ecological focus areas. In times of a growing global population it seems unwise to take highly productive areas out of agricultural usage. If we have to put areas out of usage we would therefore prefer to choose fields with poor growing conditions for agricultural crops. This is not only due to economic reasons, but makes a lot of sense from the ecological point of view as well. Bad growing conditions mean that higher amounts of fertilizers and pesticides as well as energy-consuming management techniques have to be applied on an area in order to produce yields that meet our demands concerning quality and quantity. The analysis of productivity differences between fields (Fig. 2) is therefore a part of the process in which we identify potential ecological focus areas within all fields of a farm.
Fig. 2: Computation of the varying yield potential between the different fields
In contrast to the wish of the farmer for an easy to manage and homogeneous areas, heterogeneity is desired in ecological focus areas. Differences in growing conditions offer higher habitat diversity to plants and animals. For example, an area with small scale variations in soil fertility and moisture, resulting in a pattern of dry and wet patches across the field, can offer the possibility to develop a valuable mosaic of different habitats if being taken out of production. Therefore, while searching for potential ecological focus areas, we use both – the information of inner-field variations in productivity as well as differing yield potential between fields (Fig. 3).
Combining Productivity Information with Open Data
There is one last, very important, requirement to a potential ecological focus area that has not been addressed so far – plants and animals have to be able to reach it. The spatial connection to surrounding habitats of high biological diversity such as forests, rivers and hedges ensures that species can colonize the area and travel between different habitats, thereby stabilizing the genetic variation of the population.
For this part of the analysis we are planning to use freely available geo-information data. There are already various map products available that show the spatial distribution of different habitat types. The spatial resolution and classification accuracy of those freely available products is increasing constantly (Gerardoand his colleagues are making sure of that). In the example below (Fig. 3) we used the information of theCorine Land Cover database to identify areas of potentially high ecological value that could serve as step stone habitats within the agricultural landscape. In this example one of the agricultural fields with low productivity and high inner-field variability is located in the direct neighbourhood of a forest. If this area would be taken out of agricultural production by designating it as an ecological focus area species such as birds and insects living in the nearby forest would not have to travel far to colonize this new area. Depending on how the area would be managed in the future it could offer differing living conditions to plants (in comparison to the nearby forest) and foraging opportunities to animals. A farmer might therefore wish to designate this field as an ecological focus area and to keep his more productive and easier to manage areas in conventional agricultural production.
Fig. 3: Workflow of MELODIES service – linking productivity information with open data
(Source – http://www.melodiesproject.eu/content/open-data-and-satellite-imagery-agriculture)