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This deliverable D7.6 of the LIFT project is the final scientific deliverable of the project that was carried out during four years from May 2018 till April 2022. The deliverable summarises the methodologies used and the key results for the main research activities that were carried out in LIFT: definition of ecological agriculture; adoption of ecological approaches; farm performance of ecological agriculture; territorial sustainability of ecological agriculture; trade-offs and synergies across sustainability dimensions and scales; impact of policies; role of stakeholders. Recommendations in terms of policies, data and research needs, are then provided.

In light of the ambitions of the European Union (EU) to achieve an ecological transition of its agricultural sector it is crucial to assess and continuously monitor (i) the uptake of main ecological approaches by farms and (ii) associated effects on farm performance, considering all sustainability dimensions (economic, environmental, social) jointly. Given these needs, in the present deliverable D5.1 of the LIFT project, we develop a novel indicator system, which combines the LIFT farm typology and farm performance data, covering all sustainability dimensions. The approach compares performance of farms in five ecological groups (referred to as ecological farming approaches or ecological farming systems) from the LIFT farm typology (Conservation Agriculture, Low-Input farming, Integrated/Circular farming, Organic farming, Agroecology) as well as possible combinations of these groups with a less ecological group, referred to as Standard farming. This allows us to depict whether ecological farms perform differently or have different trade-offs and synergies than standard farms. Based on this system, we carry out a farm sustainability performance assessment with the two main data sources in the LIFT project, namely Farm Accountancy Data Network (FADN) data and data from the LIFT large-scale farmer survey, covering main farm types present in the European Union (EU) in several case study regions/countries. Additionally, we present in-depth analyses of further specific aspects, namely (i) the extension of the developed indicator framework to bio-economic models, (ii) the integration of the consumption and provision of ecosystem services into the developed indicator system through composite agri-environmental performance (AEP) indicators, derived from the body of secondary literature and region-specific stakeholder input, and (iii) working conditions and employment on farms in the context of an ecological transition. Overall, our results show the importance of considering trade-offs and synergies both within and between farm sustainability dimensions, in the assessment of farm level sustainability performance of ecological farming approaches. Our results also highlight that in many cases the effects of an increasing uptake of ecological approaches are heterogenous and need to be investigated further. We clearly point out the assumptions associated with our approach as well as its limitations. Given these limitations, the LIFT farm sustainability performance assessment developed here is nevertheless well suited for large-scale and long-term monitoring. This is based on readily available FADN data and, in the near future, could be based on Farm Sustainability Data Network (FSDN) data, providing an in-depth exploratory view for policy makers and researchers regarding farm level sustainability performance of ecological approaches in the EU farming sector. We outline several possible avenues for further research, namely (i) the inclusion of other data sources, (ii) the usage of econometric methods to facilitate causal inference, (iii) the broader usage of the developed composite AEP indicators, and (iv) further in-depth studies regarding the social sustainability dimension. Finally, in terms of policy recommendations we point out the importance of (i) flexible policy measures, able to properly address region-specific needs of farms, (ii) sound data as a basis for evidence-based policy, and (iii) investigating the ecological transition of the EU farming sector in more detail also at regional level, e.g. via living labs.

This paper assesses the potential market-mediated impacts, including global Land Use Change (LUC) and GHG emissions, from increased subsidies to pastureland-based livestock sectors in the EU, through a “tax recycling strategy” simulated against a baseline under SSP2 up to 2030. The budget neutral increase in the level of pastureland subsidy rates in different Member States is achieved by a decrease in land subsidies to other cropping activities. We employ an integrated CGE-MRIO approach, in which we link a recursive dynamic version of the well-known GTAP-CGE model, called GTAP-RDEM to the FABIO MRIO. This approach allows to take advantages from both methods. FABIO offers better resolution with regards to agricultural sectors than in the GTAP database, while the combined use of this MRIO with a CGE model allows to consider price and income dependent feedbacks, required for policy analyses and long run assessments of changes in the global economy. Results show that the redistribution of land-based subsidies provokes significant changes in agricultural markets across the EU. Pastureland areas and cattle production increases in almost all EU Member States, whereas crop land and crop production decreases. The resulting increase in crop prices translates into reduced output of intensive animal production sector, mainly pig and poultry, which rely on concentrate feed to a larger extent compared to cattle. As a result of the decrease in cropland area and overall crop production in the EU, most EU countries increase imports of grain, oilseeds, and cakes from major agricultural producers, essentially soybean cake from Brazil and North America. This generates significant LUC and related GHG emissions that spill outside the EU, mainly in major feed exporters while some emission saving is observed at global level.

This deliverable presents the results of the research carried out in WP6 task 6.2 of the LIFT project, on the impact of policies on the adoption of ecological approaches and on the performance and sustainability of ecological agriculture. We first provide a short synthesis of the policy implications of the studies carried out in WP2, WP3 and WP4 of the LIFT project. These studies highlight some drawbacks of currently implemented schemes, such as the current Common Agricultural Policy (CAP) first and second pillar subsidies that may not be adequate for extensive technologies. In addition, these studies advocate policy compensation schemes that take into consideration the income forgone, given the regional potential, both in terms of agricultural production and environmental endowments. We then focus on the effect of Payments for Environmental Services (PES) using meta-analysis and quasi-experimental methods for about 150 PES-schemes implemented worldwide. We find that the effect of PES largely depends on their characteristics. Among others, eligibility of Ecosystem Services (ES) providers, contract length, reference design, payment constraint, monitoring system and the implementation zone of the PES schemes appear to be correlated with the probability of achieving positive environmental results. Nevertheless, the effectiveness of the PES-schemes investigated in this meta-analysis is shown to especially depend on the monitoring system implemented to ensure compliance and on the eligibility of ES providers. Using econometric analysis on French farm data, we also find that farmers’ incomes are not affected by their ecological practices, once the extra cost of these practices has been covered by the Agri-Environmental Schemes (AES) payment or promote some efficiency gains. The real cost of the transition is therefore on average well compensated by these payments. It does not imply that farms earn extra profit, and thus appears to respect World Trade Organisation (WTO) rules.

International audience; Legumes, which currently show low production levels in the European Union, can reduce negative environmental externalities of agricultural systems by lowering nitrogen (N) fertilization and increasing protein self-sufficiency. This has led to the introduction of coupled support in France, in contrast to Germany. However, the German implementation of the Nitrates Directive is more favorable for legumes. Our study assesses economic and environmental impacts of these two policies affecting legume production. We employ the bio-economic model FarmDyn, representing French and German dairy farms. The results suggest that relatively low levels of coupled support can lead to modest increases in legume production, but that more substantial changes require considerable subsidies. Allowing the French farm to apply manure on legumes, as is already possible in Germany, fosters legume production while considerably reducing the use of synthetic N fertilizer and imported protein-rich feed. However, environmental benefits are limited.

Risk and risk preferences belong to the key determinants of investment-based technology adoption in agriculture. We develop and apply a novel approach in which an inverse second order stochastic dominance approach is integrated into a stochastic dynamic farm-level model to quantify the effect of both risk and risk aversion on the timing and scale of agricultural technology adoption. Our illustrative example on short rotation coppice adoption shows that risk aversion leads to technology adoption that takes place earlier, but to a smaller extent. In contrast, higher levels of risk exposure lead to postponed but overall larger adoption. These effects would be obscured if technology adoption is not analyzed in a farm-scale context or considered as a now-or-never decision, the still dominant approach in the literature., Q Open, 1 (2)

In bio-economic farm models, crop choices are generally depicted as shares of land types which are aggregates of plots with similar characteristics. The ongoing process of digitalization allows access to highly detailed, spatially explicit farm data and facilitates to represent single plots instead. In our paper, we examine how different approaches to model crop choices influence the results of an arable farm in a bio-economic model. Three possible approaches are considered: ‘single plots’ with one crop per season, crop shares of land differentiated by soil type, called ‘categorized’, and crop shares on all arable land, termed ‘aggregate’. The analysis is conducted using a highly detailed, spatially explicit dataset of 8,509 arable farms located in the German federal state of North Rhine-Westphalia. Our analysis indicates that the ‘aggregate’ and ‘categorized’ land endowment approaches produce similar simulation results, which however diverge from the ‘single plot’ approach. We find that on average, crop choices per farm differ by 11% between the spatially explicit ‘single plot’ and the ‘aggregate’ land endowment approach in our case study region. Total work requirements are found to be on average 10% higher in the ‘aggregate’ approach compared to the ‘single plot’ approach, while energy requirements are relatively similar (average difference of 2.2%). Among other factors, we find the difference to be highly correlated with the number of plots a farm is endowed with. For instance, the average difference in crop choices increases from the sample average of 11% to 20.8% for those farms that are endowed with less than 10 plots (~ 50% of the case study population). Differences in simulated farm profits when comparing the ‘aggregate’ land endowment approach to the ‘single plot’ approach are found to range between -306 €/ha to 434 €/ha (mean: 4.57 €/ha, median: - 9.93 €/ha, S.D.: 71.47 €/ha). Our results suggest that for bio-economic farm analyses focusing on aggregate results over a larger sample of farms, both the ‘aggregate’ and ‘categorized’ land endowment approaches are sufficiently accurate in case of similar average numbers of plots per farm as in our study. If single farm results or variability in the population are targeted, we propose to incorporate the ‘single plot’ approach in bio-economic farm analyses. The same holds for decision support systems focusing on individual farm responses to policy changes or technology adoption.