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Innovative agri-environmental contracts are increasingly studied in the literature, but their adoption has been relatively slow and geographically scattered. Action-based agri-environmental measures remain the predominant policy mechanism across Europe. A three-round Policy Delphi study was conducted with policy makers, scientific experts, farmers’ representatives, and NGOs from across 15 different European countries, to investigate how and under which circumstances novel contractual solutions could be implemented more widely. The expert panel perceived result-based and collective contractual elements as the most promising. Although considered beneficial from several aspects, value chain contracts were perceived less relevant to the policy environment. The Common Agricultural Policy (CAP) Pillar 2 measures were highlighted by the experts as the key policy area to implement novel contracts by national or regional authorities, but Pillar 1 eco-schemes, being launched in the CAP 2023–2027, were also considered as a potentially suitable framework for testing and implementation. The Delphi panel envisaged innovative contracts should be adopted by governments in iterative steps and not as a complete substitute for current payment schemes, but rather as an additional incentive to them. Such an incremental approach allows contractual innovations to capitalise on existing best practices. But it also implies the risk that innovative contracts could remain marginal and fail to substantially change farmers’ behaviour, resulting in a failure to improve environmental conditions.

Solving grand environmental societal challenges calls for transdisciplinary and participatory methods in social-ecological research. These methods enable co-designing the research, co-producing the results, and co-creating the impacts together with concerned stakeholders. COVID-19 has had serious impacts on the choice of research methods, but reflections on recent experiences of “moving online” are still rare. In this perspective, we focus on the challenge of adjusting different participatory methods to online formats used in five transdisciplinary social-ecological research projects. The key added value of our research is the lessons learned from a comparison of the pros and cons of adjusting a broader set of methods to online formats. We conclude that combining the adjusted online approaches with well-established face-to-face formats into more inclusive hybrid approaches can enrich and diversify the pool of available methods for postpandemic research. Furthermore, a more diverse group of participants can be engaged in the research process.

The System of Environmental-Economic Accounting - Ecosystem Accounting (SEEA EA) provides an integrated statistical framework which organizes spatially explicit data on environmental quality, natural capital and ecosystem services and links this information to economic activities such as agriculture. In this paper we assess how the SEEA EA can support the monitoring and evaluation of environmental objectives of the Common Agricultural Policy (CAP). We focus on the Netherlands, for which an elaborate set of SEEA EA accounts has been published, and the themes of nitrogen pollution and farmland biodiversity. We studied the completeness of indicators included in the accounts, their quality and analysed how the accounts could support agri-environmental reporting, agri-environmental measures effectiveness assessments, and results-based payments to farmers. As a reference we used the Driving forces – Pressures – State – Impacts - Responses (DPSIR) framework. The Dutch SEEA EA accounts only include half of the indicators which we considered essential to assess the effects of farming on natural capital and ecosystem services for the two studied environmental themes. However, most gaps in the accounts could be filled with other publicly available environmental monitoring data. Regarding N pollution, the availability and reliability of indicators at landscape and farm scales are not sufficient to support the assessment of agri-environmental measures effectiveness and results-based payments to decrease N pollution. The accounts have a higher potential to support the assessment of measures to conserve farmland biodiversity, in particular due to high resolution maps of ecosystem extent and ecosystem services flows. The potential of the SEEA EA accounts may be more limited in other countries where ecosystem accounting has only recently started. However, the SEEA EA is also implemented at the European Union scale, so that SEEA EA indicators will gradually become available for all E

Governance is an essential element in land-use decision-making and ecosystem management choices and thus for ecosystem service provisioning. Although a community-based approach, i.e. governance involving actors from all spheres of society (the state, market and civil society), is considered most appropriate for natural resource management, there is a lack of knowledge about its actual effects on environmental outcomes and ecosystem service supply in particular. To obtain insight in the effect of governance on ecosystem service provision in our study region (Berg en Dal, the Netherlands), we constructed ecosystem service maps for the period 1995 to 2012 using land-use maps. Also an inventory of the implemented governance models was created, based on interviews with stakeholders, supplemented with literature research. Our results show that 1) governance in Berg en Dal changed from top-down to more community-based models during the studied period; and 2) that the potential and actual supply of the majority of the investigated regulating, cultural and habitat ecosystem services increased during the studied period, at the expense of agricultural production. The interviewed local stakeholders also indicated that they have the perception that the landscape has improved during the last two decades. Although there is a clear connection between governance and improved ecosystem service supply, more research is needed to further develop causal relationships explaining the indirect effects and non-linear behavior within ecosystem service governance systems.

There is a persistent narrative about the potential of Sub-Saharan Africa (SSA) to be a 'grain breadbasket' because of large gaps between current low yields and yield potential with good management, and vast land resources with adequate rainfall. However, rigorous evaluation of the extent to which soils can support high, stable yields has been limited by lack of data on rootable soil depth of sufficient quality and spatial resolution. Here we use location-specific climate data, a robust spatial upscaling approach, and crop simulation to assess sensitivity of rainfed maize yields to root-zone water holding capacity. We find that SSA could produce a modest maize surplus but only if rootable soil depths are comparable to that of other major breadbaskets, such as the US Corn Belt and South American Pampas, which is unlikely based on currently available information. Otherwise, producing surplus grain for export will depend on expansion of crop area with the challenge of directing this expansion to regions where soil depth and rainfall are supportive of high and consistent yields, and where negative impacts on biodiversity are minimal

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et al. (2013). One part of the activities consists of collecting and processing weather data as an input for crop simulation models in sub-Saharan African countries including Kenya. This publication covers daily weather data for 12 locations in Kenya for the years 1998-2012. The project looked for good quality weather data in areas where crops are pre-dominantly grown. As locations with good public weather data are sparse in Africa, the project developed a method to generate bias corrected weather data from a combination of observed data and other external weather data. The bias corrected weather data consist of daily TRMM rain data and NASA POWER Tmax, Tmin, and Tdew data. These data are corrected based on calibrations with short-term (<10 years) observed weather data.