1. Making agriculture sustainable is a global challenge. In the European Union (EU), the Common Agricultural Policy (CAP) is failing with respect to biodiversity, climate, soil, land degradation as well as socio-economic challenges.2. The European Commission's proposal for a CAP post-2020 provides a scope for enhanced sustainability. However, it also allows Member States to choose low-ambition implementation pathways. It therefore remains essential to address citizens' demands for sustainable agriculture and rectify systemic weaknesses in the CAP, using the full breadth of available scientific evidence and knowledge.3. Concerned about current attempts to dilute the environmental ambition of the future CAP, and the lack of concrete proposals for improving the CAP in the draft of the European Green Deal, we call on the European Parliament, Council and Commission to adopt 10 urgent action points for delivering sustainable food production, biodiversity conservation and climate mitigation. Market measures : 5.3 % Coupled payments : 10.2 % Direct payments (decoupled) : 40.5 % Greening (ineffective)* : 18 % Greening (effective)* : 3 % AECM** : 6.3 % ANC : 3.7 % RDP + other expenditure : 13.1 % Pillar 1 Pillar 2 | 307 People and Nature PE'ER Et al.
In this paper we analyse the economic and environmental impacts of CAP greening introduced by the 2013 CAP reform using the CAPRI model. CAPRI captures the farm heterogeneity across the EU and it allows to depict the implementation of the greening measures in high detail while integrating the environmental effects and the market feedback of the simulated policy changes. The simulated results reveal that the economic impacts (land use, production, price and income) of CAP greening are rather small, although some farm types, crops (fallow land and pulses) and Member States may be affected more significantly. The CAP greening will lead simultaneously to a small increase in prices and a small decrease in production. Farm income slightly increases because the price effects offset the production decline. Similarly to economic effects, the environmental impacts (GHG emissions, N surplus, ammonia emissions, soil erosion, and biodiversity-friendly farming practices) of CAP greening are small, although some regions may see greater effects than others. In general, the environmental effects at EU level are positive on a per hectare basis, but the increase in UAA can reverse the sign for total impacts. Overall, simulated GHG and ammonia emissions decrease in the EU, while the total N
Agricultural vegetation development and harvest date monitoring over large areas requires frequent remote sensing observations. In regions with persistent cloud coverage during the vegetation season this is only feasible with active systems, such as SAR, and is limited for optical data. To date, optical remote sensing vegetation indices are more frequently used to monitor agricultural vegetation status because they are easily processed, and the characteristics are widely known. This study evaluated the correlations of three Sentinel-2 optical indices with Sentinel-1 SAR indices over agricultural areas to gain knowledge about their relationship. We compared Sentinel-2 Normalized Difference Vegetation Index, Normalized Difference Water Index, and Plant Senescence Radiation Index with Sentinel-1 SAR VV and VH backscatter, VH/VV ratio, and Sentinel-1 Radar Vegetation Index. The study was conducted on 22 test sites covering approximately 35,000 ha of four different main European agricultural land use types, namely grassland, maize, spring barley, and winter wheat, in Lower Saxony, Germany, in 2018. We investigated the relationship between Sentinel-1 and Sentinel-2 indices for each land use type considering three phenophases (growing, green, senescence). The strength of the correlations of optical and SAR indices differed among land use type and phenophase. There was no generic correlation between optical and SAR indices in our study. However, when the data were split by land use types and phenophases, the correlations increased remarkably. Overall, the highest correlations were found for the Radar Vegetation Index and VH backscatter. Correlations for grassland were lower than for the other land use types. Adding auxiliary data to a multiple linear regression analysis revealed that, in addition to land use type and phenophase information, the lower quartile and median SAR values per field, and a spatial variable, improved the models. Other auxiliary data retrieved from a digital elevation model, Sentinel-1 orbit direction, soil type information, and other SAR values had minor impacts on the model performance. In conclusion, despite the different nature of the signal generation, there were distinct relationships between optical and SAR indices which were independent of environmental variables but could be stratified by land use type and phenophase. These relationships showed similar patterns across different test sites. However, a regional clustering of landscapes would significantly improve the relationships.
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