Socioeconomic value of EO agriculture | Part I
This is the first in a series of three articles which intend to summarise some of the key findings of three related deliverables produced within the e-shape project. These deliverables looked into some of the socioeconomic benefits Earth Observation (EO) can bring to selected sectors, with the first focusing on agriculture. For more information on e-shape, please visit the project website. Before diving into the socioeconomic benefits, let’s first explain the methodology and approach to the analysis in these reports.
EO-based services can significantly help actors in different domains and along the respective value chains to address the challenges that shape their own operational reality. To fully understand this value it is essential to identify the decisions and processes undertaken by the different actors in the value chain and pinpoint how the availability of EO data or derived services generates value. Thus, the starting point of the analysis in these socioeconomic benefits reports was an extension of the methodological framework developed within the Sentinel Benefits Study (SeBS). The SeBS reports provide a contribution to the body of knowledge of the European EO community when it comes to quantifying and presenting the benefits EO solutions enable. SeBS defines value chains in detail and analyses how EO benefits the involved companies, businesses, government stakeholders and, eventually, even society, the economy and the environment at large. Studying each link of the value chain, SeBS case studies develop solid argumentation around the benefits the different actors experience thanks to the use of EO-based services, and where possible quantify these benefits.
Taking benefits studied in each SeBS case, we then tried to generalise and extrapolate so as to present benefits tracked back to EO-based services for whole sectors. In doing so, we combine two approaches to estimating benefits; “bottom-up” and “top-down”. A “bottom-up” approach gains a very good understanding of benefits and value manifested at the micro level i.e. in a single value chain with a relatively small number of stakeholders. This approach only gains a limited understanding of the overall value and benefits manifested at the macro level i.e. at a regional, national or supranational level. A “top-down” approach is the opposite, it gains a good understand of benefits and value manifested at a regional, national or supranational level, but only a limited understanding of the benefits and value manifested at the micro level. Thereby, by combing both we end up somewhere in the middle, where we take well understood micro-level cases, link and group them by application and then build a picture of various market segments. Taking the market segments as “building blocks” of the overall market, we aim to illustrate the potential magnitude of the overall macro-level benefits. For more detail on this approach please refer to the full analysis.
It can be quite difficult for farmers to know exactly what fields and which crops need their attention. Variations in both crop health and yield can materialise within the same field, soil conditions across farms can be difficult to monitor, and knowing exactly the correct amount and timing for the application of inputs such as fertilisers, pesticides, fungicides is no easy task.
These, and many other similar challenges are shaping the farmers’ everyday reality, especially in relation to the most important interventions throughout the year (spreading/sowing, fertilising, spraying, irrigating, harvesting, etc.). Understanding what is happening, where and when is thus of utmost importance. To that end, EO data can allow for the collection and interpretation of a wide range of information on the different conditions that affect crop growth and quality (e.g. soil composition and moisture, weather and climate aspects, crop health, surface temperature, etc.).
EO data can be particularly helpful in agricultural applications thanks to its ability to generate information in the form of widespread indices such as the Normalised Difference Vegetation Index (NDVI). NDVI techniques measure the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs). Healthy vegetation (chlorophyll) reflects more near-infrared and green light compared to other wavelengths, and as a result, plant health status and vegetation coverage can be inferred easily.
Precision Agriculture (PA) consists in the application of the “right treatment in the right place at the right time”. Enabled by a combination of EO data, GNSS and various other technologies (e.g. proximal and remote sensors), PA enables fine-scale, site-specific management of agricultural production. This is implemented through an approach referred to as “Variable Rate Application” (VRA). Thus, by taking into account the variabilities of their fields observed through EO data, precisely guiding their farming machinery and accurately applying different inputs, farmers have been able to minimise soil compaction, reduce the use of fuel, pesticide and fertilisers, and increase productivity. Other significant benefits include the reduction of environmental impacts and increased work safety.
EO can allow for soil moisture and condition monitoring over vast expanses through the use of active sensors, capable of emitting their own energy (in the form of electromagnetic radiation). Satellites carrying such sensors send a pulse of energy from the sensor to the earth and then receive the radiation that is reflected or backscattered from the ground. The signal received by these microwave sensors is sensitive to the amount of water contained in the first few centimetres of the soil and therefore can be used to help infer soil moisture and condition status. Typically used sensors in this category are radar, scatterometers and lidar. Satellites carrying such sensors – for example Synthetic Aperture Radar (SAR) satellites – are unaffected by cloud coverage.
Within the European agricultural sector, the Common Agricultural Policy (CAP) is by far the most overarching and important regulation in existence. A core role of the CAP is to provide farmers with income support, through both direct payments and through remunerations for maintaining environmentally friendly practices. One such remuneration is known as “greening”, which supports farmers who adopt environmentally friendly practices, such as the maintenance of biologically diverse farms and areas of permanent grassland. The traditional way in which CAP greening compliance checks were conducted involved inspections being carried out on-the-spot (at the farm) by inspectors, however, since 2018, this all changed thanks to EO.
The introduction of EU Regulation No 2018/746 in 2018 both allowed for and strongly encouraged EU member states to use satellite data in their CAP monitoring and verification activities, meaning both time and money could be saved. Automatic and continuous monitoring of European farmland and associated farm activities can be achieved through the use of EO, helping to holistically enforce CAP regulation and maintain environmentally friendly farming all across the continent.