A Knowledge Brokering Framework for Integrated Landscape Management

Introduction

Integrated Landscape Management

Around the world land, water, and ecosystems face greater pressure than ever before. The expansion and intensification of agriculture to feed the demands of a growing and increasingly wealthy global population has had profound effects on the functioning of natural ecosystems (Foley et al., 2005). Agriculture currently occupies almost 40% of the global land surface (World Bank, 2019). While that figure has remained relatively static since the early 1990s, the management of farmland has been intensifying around the world. For example, there was a 700% increase in global fertiliser use and a 70% increase in land under irrigation in the second half of the twentieth century (Matson et al., 1997; Tilman et al., 2001; Foley et al., 2005). Green revolution agricultural intensification has led to the homogenisation and simplification of farmed landscapes, increasing production to the detriment of other ecosystem services. If current trends in food consumption and waste continue, global food production will have to increase by around 70% by 2050, driving further intensification (Alexandratos and Bruinsma, 2012). Agriculture already contributes to large-scale water pollution (from nutrients, pesticides, pathogens and sediment), soil degradation (e.g., through erosion, loss of organic matter, and salinization), over-exploitation of water resources, and air pollution. It is responsible for around 24% of global anthropogenic greenhouse gas emissions (Smith et al., 2014). Agriculture and land conversion for agriculture are the biggest drivers of deforestation, environmental degradation and biodiversity decline at a global scale (FAO, 2016; Maxwell et al., 2016; Springmann et al., 2018; WWF, 2018; Sánchez-Bayo and Wyckhuys, 2019; Willett et al., 2019).

The challenge facing decision makers in community groups (e.g., farmers' cooperatives), local and national governments, non-governmental organisations (NGOs), development banks, and agricultural development programmes is stark. They need to tackle the environmental, social, and economic problems caused by unsustainable land management, whilst at the same time ensuring that farming provides economic sustainability, healthy diets, human well-being and social equity (Smith et al., 2017). A key part of the problem is that the interests of farmers and wider communities are often misaligned. While land management decision making takes place at the farm-scale, many of the cumulative impacts of these decisions are felt across landscape, national or global scales. Policymakers operating at these larger scales need to work with farmers to find solutions that deal with broad environmental, social and economic problems caused by unsustainable land management whilst addressing local farmer and community priorities. Their policies and programmes also need to consider global trends, e.g., in climate change, commodity prices, pests, diseases, migration and urbanisation, which in turn influence local outcomes (Hecht, 2010; Liu et al., 2013). Policies and programmes should be sensitive to local customs, social values and norms so that farmers are empowered to implement practices that are appropriate to local conditions. Such policies should also take account of potential interactions and unintended consequences. The environmental benefits or impacts of many of these interventions may be impossible to measure or attribute, and take decades to manifest.

In short, landscapes need to be managed for multi-functionality in terms of ecosystem functions, ecosystem services, and human well-being across multiple scales (OECD, 2001; DeClerck et al., 2016; Manning et al., 2018; Jones et al., 2019). Sustainable multifunctional landscapes are created and managed to simultaneously provide food security, livelihood opportunities, maintenance of species and ecological functions, and fulfil cultural, aesthetic and recreational needs (O'Farrell and Anderson, 2010). The need for such multifunctionality is increasingly recognized in order to halt and reverse declining trends in ecosystem service provision (OECD, 2001; O'Farrell and Anderson, 2010; DeClerck et al., 2016; Manning et al., 2018; Jones et al., 2019). Furthermore there is increased need for landscapes that assist species in responding to increasing climate pressures, facilitating movement and allowing them to establish populations in new emerging ecosystems (O'Farrell and Anderson, 2010; DeClerck et al., 2016).

The solutions to such complex land management challenges are unlikely to lie solely in new technologies. What is needed are systemic approaches; applying existing interventions in a more integrated, synchronised and targeted way (Jagustović et al., 2019). Principles and tools are emerging to help with this (Sayer et al., 2013).

A wide range of different terms have been used to refer to the integrated management of landscapes for multiple simultaneous objectives (Reed et al., 2016). Natural resource management is widely used to refer to the environmental and economic management of land, water and related resources (Denier et al., 2015). This includes the conservation of ecosystems, agriculture and broader land-use planning. So called landscape approaches to natural resource management often aim to address multiple environmental and livelihood considerations at large spatial scales, linking policy and practice, and taking human activities and institutions as integral parts of the system (FAO, 2012; Scherr et al., 2012; Milder et al., 2014; Sayer et al., 2017). Such approaches frequently incorporate multi-stakeholder processes to help coordinate land management to achieve environmental, social and economic outcomes simultaneously (Minang et al., 2015), and to reconcile the needs of people within the landscape with those outside, such as national governments (Denier et al., 2015). They often bring together policy and practice for competing land uses through adaptive management systems (Reed et al., 2014a). The term sustainable land management can refer to activities from farm to landscape-scale but is often used to refer to activities that focus on site level actions and particular stakeholder groups (Denier et al., 2015).

In this paper we use the term Integrated landscape management to refer to landscape-scale policy, planning or management initiatives that aim to simultaneously improve food production, environmental outcomes, rural livelihoods and governance (Estrada-Carmona et al., 2014; Milder et al., 2014). For the purposes of this paper, a landscape is defined as a geographically distinct area of land that is bigger than a single farm. This can be defined politically (e.g., district), hydrologically (e.g., river basin), agriculturally (e.g., agroclimatic zone), economically (by market), culturally (e.g., Globally Important Agricultural Heritage Sites), or ecologically (e.g., by ecosystem or by designated protected areas such as national parks). However the boundary is defined, we consider landscapes as socio-ecological systems in which social, cultural and economic elements interplay with ecological and biophysical ones (Sayer et al., 2013).

Although many traditional farming systems were built around multi-functionality, mainstream agricultural development and environmental conservation programmes and policies have tended to focus on single issues. Multi-objective integrated landscape management programmes, however, have been around for some time and are becoming more prevalent (Denier et al., 2015). For example, recent assessments of community-based natural resource management initiatives identified 428 integrated landscape initiatives that have been established to improve agriculture, livelihoods, and the environment in Africa (Milder et al., 2014), Latin America and the Caribbean (Estrada-Carmona et al., 2014), South and Southeast Asia (Zanzanaini et al., 2017), and Europe (García-Martín et al., 2016). Several of these initiatives are associated with specific integrated landscape management mechanisms, including biosphere reserves, model forests, biological corridors, integrated catchment management, and forest restoration for climate change mitigation (e.g., REDD+).

Evidence Into Policy

Many authors have argued that robust science is essential to inform effective integrated landscape management (Cash et al., 2003b; Collier et al., 2011; Gusmão Caiado et al., 2018). It can support decision making by quantifying uncertainty around interventions, highlighting previously unforeseen issues, reducing personal bias and increasing trust in a policy from the perspective of policymakers, lobby groups and the public. Science can also help to understand the consequences of different moral and ethical choices (Lubchenco, 1998). Science plays different roles for different types of decision makers. At an international level, it can facilitate dialogue and help set narratives, providing common ground between international players. Within national government ministries it informs the design and implementation of government policy. At the local level it can provide stakeholders with much needed practical evidence of what works.

A major barrier to the design of effective land management and conservation programmes is the limited accessibility and fragmentation of scientific evidence (Sunderland et al., 2009). Experimental research is by necessity reductionist, often focusing on one problem or intervention at a time, and ignoring the complex interactions between them. This can be difficult to reconcile with the broad, holistic framing of policy questions (Pullin et al., 2009). On top of this, evidence on the effectiveness of land management interventions is often site-specific and difficult to transfer from one location to another. Monitoring the impact of integrated landscape management programmes is technically difficult due to funding constraints, time lags and confounding effects between interventions (Estrada-Carmona et al., 2014; Milder et al., 2014; García-Martín et al., 2016; Zanzanaini et al., 2017). Despite rhetoric of integration, there is evidence that many researchers working in conservation and development are still firmly rooted in their disciplinary silos (Reed et al., 2016). Decision makers are unfortunately often left to themselves to piece together a systemic understanding of their specific situations.

This gap between science and policy is well-recognised and various authors have proposed ways to bridge it (Lubchenco, 1998; Wilson et al., 2007; Godfrey et al., 2010). Many involve collaboration between policymakers and evidence providers throughout the process of scoping, procuring, interpreting and synthesising applied research (Wilson et al., 2007; Godfrey et al., 2010). The challenges set out above, however, mean that traditional scientific approaches cannot alone provide all the answers. Instead, evidence used in policymaking needs to encompass judgments, opinions, beliefs, and analysis (Wilson et al., 2007). This broader view is often provided through professional consultancy (Funtowicz and Ravetz, 1994).

The high levels of uncertainty and high decision stakes inherent in many environmental issues mean that policy decisions need to be based on values as much as “facts.” Post-normal or transdisciplinary processes involve the stakeholders affected by the issues in dialogue with scientific experts and decision makers to explore solutions (Funtowicz and Ravetz, 1994). These approaches place formal scientific information alongside the diverse values, opinions and local tacit knowledge of actors who are affected by the issues (Funtowicz and Ravetz, 1994; Wilson et al., 2007). They bring together evidence from different natural and social science disciplines alongside lay knowledge, local priorities and values (Tress et al., 2005). Furthermore, they imply a broader role for science and new ways of working to lead and inform public dialogue in addition to informing policy (Lubchenco, 1998; Giller et al., 2007; Rietig, 2016).

Knowledge Brokering

In this paper, we use the term Knowledge Brokering to describe the full range of activities that bring together policymakers and researchers to develop a systems understanding of a given situation, and to develop evidence-informed policy. Knowledge brokering is a two-way participatory process in which researchers come together with policymakers, agriculture professionals and other stakeholders to share perspectives and learn from each other (Pretty, 1995). It covers a repertoire of activities including supplying knowledge (linking policymakers to experts), bridging (mediating and translating between science and policy), and facilitating interaction and collaboration between researchers and policymakers to co-produce knowledge (Turnhout et al., 2013). Knowledge brokers can include applied researchers, technical policy advisers (e.g., in government departments or NGOs, or the staff of third party institutions (e.g., think tanks or consultancies). In some cases, specific institutional structures either in research or policy organisations can fulfil this function (Godfrey et al., 2010).

Knowledge brokers can play an important role in bringing together strands of evidence to form a more holistic view of problems. They can support decision makers in accessing, interpreting and contextualising evidence, and support researchers to understand the policy context in which evidence is used. This requires skills in evidence synthesis and transdisciplinary approaches alongside non-scientific, translational skills (e.g., negotiation and group facilitation) to help break down boundaries between disciplines and professions (Schwartz et al., 2017). Knowledge brokers can help bridge between the world views of different interest groups, facilitating and supporting public and political dialogue (Giller et al., 2007).

As well as providing clear benefits for policymakers, knowledge brokering provides a mechanism to improve the impact and uptake of research. This is increasingly demanded by funders wanting to ensure value for money from their research investments, research organisations wanting to demonstrate impact and researchers themselves who are motivated to invoke positive changes as a result of their work. Identifying the appropriate tools to inform specific decisions in the management of complex socio-ecological systems poses a significant research challenge in its own right (Bennett et al., 2015). Many efforts to communicate knowledge to policymakers in the past have involved a unidirectional push of information through knowledge transfer or “scaling” initiatives (Godfrey et al., 2010). These have often been based on linear theories of change or impact pathways. This type of supply-driven approach has its limits. The common understanding of change as a linear process does not capture the complexity that is inherent in most development problems (Young et al., 2014). Co-designed solutions are more likely to have buy-in and a higher probability of self-sustained uptake. Supply-driven approaches to knowledge transfer often aim to influence decisions based on the findings of individual research projects. In contrast, demand-driven knowledge brokering approaches often draw on the weight of evidence built up on a given topic over multiple years. In such cases, knowledge brokers work with decision-makers and guide them through the evidence-base to support the design of effective interventions (Luedeling and Shepherd, 2016).

Aims

This paper is aimed at individuals and institutions working at the science-policy interface on integrated landscape management who act as knowledge brokers. It reviews evidence on what works to inform policy decision making to achieve multi-functional landscapes that deliver local, national and global sustainable development objectives. Our review is particularly focused on landscape-scale, multi-objective programmes and policies. We explore how to combine different types of evidence to inform the development of policies based on a systemic understanding of inherent complexity. This includes approaches to facilitate collaboration between researchers and decision makers, existing frameworks to organise evidence, and decision support approaches. From this, we set out guidelines for integrated landscape management knowledge brokering in integrated landscape management. We also propose a general decision support framework based on a typology of policy decisions that can be used to guide knowledge brokers and decision makers in how to answer complex questions that require the integration of multiple evidence strands.

Decision Making in Integrated Landscape Management

Actors and Their Decisions

One of the challenges of integrated landscape management is that control is spread over a wide range of actors. The way that land is used and managed is influenced by the interplay of policies and initiatives from local and national governments, non-governmental organisations, community initiatives, private companies, as well as a wide range of other external factors. Successful integrated landscape management requires coordination between stakeholders at all these levels. This can occur through participatory approaches or social learning frameworks (Estrada-Carmona et al., 2014; Milder et al., 2014).

This paper focuses on actors who are implementing multi-objective landscape-scale policies and programmes, including:

- Landscape-scale decision-makers (Local government; NGOs; community groups; local farmer groups; Integrated Landscape Management initiatives; private companies).

- National-scale decision-makers (Government ministries; NGOs; development banks; agricultural development programmes; industry groups such as farmers' unions).

- International-scale decision-makers (UN Organisations; intergovernmental panels; regional organisations such as the African Union; overseas development assistance programmes; private multinational companies).

Although actors in the groups listed above are often those with the power, resources and legitimacy to initiate integrated landscape management approaches and policies, there are many other stakeholders that influence or are influenced by their decisions. These groups need to be engaged through participatory approaches and governance structures. This is not without its challenges. Integrated landscape management programmes frequently cite barriers including difficulties in maintaining infrastructure and institutions, difficulties in engaging stakeholders (particularly from government and the private sector), coordination difficulties in moving from a sectoral to an integrated approach, entrenched power dynamics, and unsupportive policy frameworks. The resources, capacity and influence to work effectively at landscape-scale can preclude small organisations from taking part. Maintaining stakeholder engagement is frequently hampered by funding constraints and time lags in achieving results (Estrada-Carmona et al., 2014; Milder et al., 2014; García-Martín et al., 2016; Zanzanaini et al., 2017).

We use the term “policy” in a broad sense to cover laws, policies, programmes, investments, and other coordinated interventions to deliver integrated landscape management. Policymaking is frequently presented as a cyclical process through which decision makers iteratively prioritise, plan, implement, monitor, and evaluate. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), for example, considers a policy cycle of (i) agenda setting; (ii) policy design; (iii) policy implementation; and (iv) policy review (IPBES, 2016). They use this to categorise the potential uses of ecosystem service tools.

In practice, policymaking rarely happens in such a structured way. Instead, it tends to be opportunistic with steps occurring simultaneously or out of sequence. Processes can be complex and non-linear (Ademokun et al., 2016). However, even if the concept of a policy cycle is idealised in terms of sequence and process, it provides a good way of categorising the types of decisions that need to be made during the planning of a multi-objective, landscape-scale policy or programme. Viewed thus, the steps of a policy cycle can provide a typology of decisions faced by the actors described above. Various authors have proposed integrated landscape management decision support frameworks structured around a policy cycle (e.g., McGonigle et al., 2012; Young et al., 2014; Girvetz et al., 2017).

Knowledge brokers can play an important role in facilitating links between policymakers, scientists and wider actors for various types of land management decisions. We have broken down the relevant challenges facing policymakers into seven parts (Figure 1). These are grouped into the four components proposed by Girvetz et al. (2017). Although presented as a cyclical set of decision steps, the parts and components can stand alone.

FIGURE 1

Figure 1. Idealised policy/programme design cycle for integrated landscape management. This provides a typology of decisions and design phases. Decision approaches and toolkits to support each of the seven stages are sketched out in Table 3.

Component 1: Situation analysis. A first step in policy or programme design is one of prioritisation. This process may feed into the development of a business case or funding proposal. At this stage, the role of knowledge brokers is to bring together stakeholders to map systems, understand problems, explore conflicting interests, and envision futures. It includes 1a. Stakeholder engagement, identifying key actors and developing plans to involve them in problem framing, and 1b. Prioritisation, which can involve participatory approaches to identify issues, establish baseline information, map system relationships, understand conflicting interests, and envision futures.

Component 2: Prioritising interventions focuses on what to do and where to do it through 2a. Targeting and 2b. Identifying interventions. Knowledge brokers have a role here in linking with academia and joining up many fragmented studies on individual interventions to develop a holistic picture of what works where. Integrated landscape management can involve a wide range of potential technologies or interventions implemented systematically (WOCAT, 2019). These include:

° Farm management practices (e.g., integrated pest management practices, tillage practices, fertiliser management, irrigation practices).

° Land restoration interventions (e.g., radical terracing, bunds, crop targeting, agro-forestry, livestock exclusions).

° Forest management and restoration practices (e.g., natural regeneration, tree planting, harvesting timber and non-timber forest products).

° Farm system changes (e.g., switching from conventional to organic farming, integrated farm management, or holistic agroecological approaches).

° Land-use changes (e.g., crop changes, reverting from arable to grassland, agriculture to forest, or other uses).

The solution most appropriate to one landscape is not necessarily transferable to another. Targeting is therefore essential to ensure that the right interventions are taken up in the right place. This is not as simple as it seems, however, since most of the landscape, national or international scale decision-makers described above do not have direct control over resource use and management (including for example land, water, energy, and human resources). They need to apply policy levers [or Sustainable land management (SLM) approaches c.f. (WOCAT, 2019)] to encourage land management change. Maps, datasets and evidence to inform targeting often need to be compiled from many sources.

Component 3: Programme design and implementation. 3a. Programme design needs to be based on an understanding of local barriers, which can include education, access to extension, land tenure, access to credit, labour availability, access to tools, social networks and environmental factors (Teklewold et al., 2012; Kassie et al., 2013; Haile et al., 2017; Mutyasira et al., 2018). Potential levers therefore include supporting land managers with knowledge, access to materials, financial incentives or changes to legal and institutional settings (Table 1). 3b. Implementation needs to take account of the practical and economic barriers to policy levers. Knowledge brokers need to source evidence for programme design from social researchers, economists and other experts, while engaging those responsible for on-the-ground delivery to make sure that approaches are practical, locally relevant and likely to be effective.

TABLE 1

Table 1. Categories of levers and policy interventions to encourage changes in land use and management.

Component 4: Monitoring and evaluation. Finally, robust plans are needed for monitoring the social, economic and environmental impact of interventions. This is fundamental for identifying adjustments that are needed to secure sustainable development outcomes and to reduce or remove undesirable consequences (Selomane et al., 2015). Monitoring and evaluation increases the transparency of investments and helps institutions using public funds to be held accountable for expenditure (Piirainen, 2014). It also supports learning through an adaptive management process and should therefore be a core part of intervention planning.

Windows of Opportunity for Change

Researchers and knowledge brokers need to be aware of the wide range of political, economic, social, and environmental factors that limit the scope of actors to influence change. Governments, for example, are constrained by the need to ensure that policies are affordable, implementable and popularly acceptable, that laws are enforceable, and that appropriate institutions are in place to roll out large-scale programmes.

A window of opportunity for policy can be said to exist when action is politically desirable, when there is public perception of the problem and a desire to address it, and when cost-effective interventions exist (Kingdon, 1995, in Rucklehaus 2015).

In practical terms, national scale policies and laws often remain static for years, amended during short periods of intense activity. For example, in the period before elections, manifestos are developed which can set the policy direction over the term of a government. New governments may launch consultations, white papers or plans setting the trajectory for policy over longer timeframes. Other laws, directives, international agreements and targets may be reviewed periodically, for example every 5–6 years in the case of European Directives. International initiatives such as the UN Sustainable Development Goals set the agenda for years once agreed. These periods of review or negotiation can provide opportunities for tailored science-policy dialogue, for example exploring evidence on the effectiveness of potential new policy interventions (McGonigle et al., 2012). Such windows of opportunity are often predictable but can be hard to anticipate from the outside (Rose et al., 2017).

Opportunities for landscape management change can also arise from often unfortunate driver events, such as natural disasters, floods, serious land degradation, socioeconomic crashes, or political events that create pressure for change (Rose et al., 2017). Political factors can affect the response to such shocks. For example, donors were slow to act in response to the Ethiopian food crisis in 2000, because they hoped to use aid as leverage to quell an ongoing armed conflict in the region (Broad and Agrawala, 2000). Upon the food crises Ethiopia has drastically strengthened their National Social Safety Net programme and their Sustainable Land Management programme, which are now globally renowned and have buffered several new food crises (Berhane et al., 2013).

Researchers and knowledge brokers need to develop an awareness of these windows through regular and ongoing dialogue with policymakers. They need to predict them, respond to them rapidly as they occur, use them to frame research and knowledge products appropriately, and persevere with engagement between windows (McGonigle et al., 2012; Rose et al., 2017; Pali et al., 2018). Where possible, researchers need to work with policy makers and civil society organisations to create windows of opportunity through agenda-setting, coalition building, and policy learning (Ashford et al., 2006).

Integrating Different Types and Sources of Evidence

Types and Quality of Evidence

Evidence for integrated landscape management comes in many forms. It includes, for example, scientific evidence, anecdotal evidence, and evidence based on professional experience. Several authors have proposed ways to categorise evidence. These vary in relation to the field considered (Anon, 2014; Ademokun et al., 2016). Categories include:

- Data and statistics (quantitative and qualitative),

- Research evidence (experimental evidence including participatory research and citizen science),

- Process theory (systems maps, evidence-informed models, theories of change, or impact pathways),

- Observational evidence (practice informed or implementation evidence), and

- Local or indigenous knowledge (can include experiential, traditional, or lay knowledge about local conditions, effects, or practices).

Some types and sources of evidence carry more weight than others. For example, a survey of decision makers in the health sector found that systematic reviews are a particularly well-favoured source of evidence, followed closely by quantitative research. Evidence strength and consistency, data quality, bias and publication date were all cited as key factors affecting the influence of a study on policy (O'Donoughue Jenkins et al., 2016).

The weight attached to research evidence depends to a great extent on the design of studies. In the health sector, for example, Randomized Controlled Trials usually carry more weight than less rigorous experimental and quasi experimental designs, which in turn carry more weight than expert opinion (Hansen, 2014). DFID has set out guidance to assess the quality of individual studies and wider bodies of evidence based on their design, relevance and consistency (DFID, 2014).

While such hierarchies of evidence are useful to help interpret the weight of evidence on a particular intervention, integrated landscape management carries its own specific evidence challenges. Evidence is often highly fragmented, only covering certain parts of the system in question. Knowledge brokers and policymakers often need to contextualise evidence, rapidly filling gaps. In practice, this means building on all sources of information available, while taking account of the relative weighting of different information sources.

Large-scale field research on integrated landscape management carries further difficulties. The number of factors affecting environmental and social variables and the heterogeneity of landscapes make replication and control almost impossible. Time lags between intervention and effect, unpredictable interactions between factors and tipping points all add further challenges. Furthermore, many of the practices associated with integrated landscape management are highly site-specific and yet have cumulative off-site impacts. Many agroecological practices rely on farmers implementing combinations of practices based on a systems understanding. These are difficult to implement, and it is difficult to demonstrate impact.

Assessing the effectiveness of knowledge-based interventions (see Table 1) is particularly difficult when the expectation is for farmers to adopt complex management practices. A recent systematic review, for example, found relatively few high-quality studies demonstrating evidence of the effectiveness of farmer training to boost farmers' income and household food security. Simpler input innovations on the other hand, such as introducing new crop varieties, fertilisers or plant protection products, have been frequently shown to be effective at boosting household food security and nutrition (Stewart et al., 2015).

While longitudinal studies and Before-After-Control-Impact (Smith, 2002) designs may help to address some questions on the effectiveness of interventions at scale, researchers and policymakers are obliged to piece together knowledge from multiple sources, projecting large-scale and long-term effects using models and decision support tools, based on many assumptions. Complex land management policy decisions therefore almost always have to be made on incomplete or patchy evidence drawing on the full range of information available including data, research evidence, process theory, observational evidence, expert opinion, and local knowledge. Evidence needs to be consolidated, combining formal synthesis and systematic reviews (e.g., Sargeant et al., 2006), with modelling and expert elicitation, building on the best information available (Morgan, 2014). Developing this type of evidence narrative is a specialism in its own right, and a key role for integrated landscape management knowledge brokers.

Local or indigenous knowledge can be invaluable in informing integrated landscape management policymaking and can be used in combination with scientific evidence (Rathwell et al., 2015; Alexander et al., 2019). The value of such knowledge has been gaining increasing recognition, particularly among the conservation community where conservation organisations are increasingly asked by donors to take community-based approaches that consider indigenous knowledge. The knowledge and values of stakeholders are also vital in transdisciplinary or post-normal approaches that incorporate stakeholder engagement into the process of developing evidence (Funtowicz and Ravetz, 1994).

Examples from the literature show how evidence across all these types can potentially be misused, unintentionally or intentionally. Actors may preferentially “cherry pick” evidence that supports their own viewpoint (Rietig, 2016). Private sector interest groups can influence the use of evidence in policies and programmes (Waqa et al., 2017). Political lobbyists may use evidence to push a particular agenda (IOB, 2015). This is a particular problem in integrated landscape management where groups have conflicting interests and may present skewed evidence that supports their positions. The ability to access and use evidence to support a particular decision may also be unevenly distributed across interest groups leading to potential bias, creating a challenge for knowledge brokers.

While policymakers and lobbyists sometimes pick evidence to support their position, researchers themselves are not immune from bias. The personal values of scientists can, e.g., influence the way in which they frame research questions and portray results (Pretty, 1995). Bias can also come from a misunderstanding of the conclusions that can be drawn from a result, or extrapolation of results into different contexts beyond reasonable limits. “Expert” stakeholders are often overconfident in their own knowledge (Morgan, 2014). Techniques like calibration training can help stakeholders to better judge the limits of their knowledge (Whitney et al., 2018a,b). These potential sources of bias make the role of knowledge brokers especially important as neutral, honest brokers (Cvitanovic et al., 2016). However, knowledge brokering can never be fully neutral; selecting, which knowledge to share is inherently laden with values (Shaxson et al., 2012).

Barriers to the Use of Evidence

There are many potential ways in which decision makers can access evidence. They can get information directly from scientific literature, data interrogation, or internet searches; from evidence syntheses and policy briefs (e.g., Conservation Evidence, 2019; Sutherland et al., 2019); or indirectly from expert advisers, consultants, national research centres, lobby groups, and Non-Governmental Organisations.

Many decision makers value the supporting role of scientific evidence. A 2013 survey by the UK Department for International Development (DFID), for example, found that 87% of staff felt using evidence was important for their work (DFID, 2013). However, policymakers and practitioners relatively rarely access primary scientific literature (Sutherland et al., 2004; Sunderland et al., 2009). This can be due to a lack of scientific literacy, lack of access or because studies lack the interdisciplinarity to tackle the complexity of real-world policy problems (Sunderland et al., 2009; Oliver et al., 2014; Rose et al., 2017). Often decision makers simply lack time to analyse scientific literature, which is often written for academic dialogue rather than for informing policy. Secondary sources can be much more important for time-pressed policymakers. Experts consulted by policymakers thus play an important role as advisers or professional consultants (Funtowicz and Ravetz, 1994). The effectiveness of the relationships of such advisers with policymakers is enhanced over time as they build mutual trust (Lubell, 2007; Masset et al., 2011).

The use of evidence in policymaking seems rarely to be limited by a lack of available information. Rather, conflicting values, interests and world views are key limiting factors (Rose et al., 2017). Policymakers faced with a plethora of inconsistent and conflicting recommendations, tools and resources often rely on experiential knowledge rather than scientific evidence (Cvitanovic and Hobday, 2018). This has led to calls of “evidence complacency” from some in the research community (Sutherland and Wordley, 2017).

There are many reasons why evidence is not taken up. Time, ease of access, costs and institutional support are cited as common constraints by policymakers in various sectors across many countries (DFID, 2013; Oliver et al., 2014; Ntshotsho et al., 2015; O'Donoughue Jenkins et al., 2016; Rose et al., 2017; Waqa et al., 2017). The relevance of research evidence is also a barrier to its use (Oliver et al., 2014; O'Donoughue Jenkins et al., 2016). Sometimes studies are not well-aligned with the actual policy options that are being proposed or do not cover a wide enough range of outcomes. Evidence is sometimes presented in a way that does not take account of policy context, windows of opportunity or of the political or economic constraints facing decision makers (Elueze, 2016). This can be because researchers do not fully understand policy questions or fail to communicate evidence in a way that is aligned to immediate policy goals (UK National Audit Office, 2003). A lack of alignment with end users' needs has been cited as a key barrier in the uptake of decision support tools and ecosystem assessments (Förster et al., 2015; Gibson et al., 2017). Organisational structures and stakeholder priorities also have a strong influence with the result that decisions are influenced by a wide range of factors other than scientific evidence (Ntshotsho et al., 2015). Social factors have also been shown to play a key role in encouraging the use of evidence. Various studies have shown that developing strong and trusted relationships with regular contact between researchers and policymakers is highly important (Oliver et al., 2014; Cvitanovic and Hobday, 2018).

The factors affecting the use of evidence in policymaking can be summarised as follows:

- System-level factors: communication between scientists and policymakers; political system; citizens' demand for evidence use; other stakeholders; habit; timing; changes in administration; planning; sector; quality of research and data.

- Organisation-level factors: organisational culture; resources; library and information services; knowledge management processes; staff turnover rate.

- Individual-level factors: leadership; attitudes; knowledge of how to access research; skills in evaluating evidence; skills in communicating research; IT skills; professional experience; personal judgement.

(Ademokun et al., 2016)

Policymakers, short of time and inundated with confusing and conflicting messages, may be more prone to evidence fatigue than complacency. It can be hard to know where to start to access huge volumes of scientific literature on a given topic. Serendipity is often a key factor in the use and uptake of evidence (Oliver et al., 2014).

Researchers need to do more than put findings into easily accessible formats (Rose et al., 2017). They need to make themselves accessible to policymakers. In addition, policymakers need support in accessing relevant information, and dealing with conflicting and incomplete evidence. Institutions and research funders need to consider incentive structures to encourage academics to do this. Rewards and career progression in academia are currently primarily focused on peer reviewed papers rather than on policy focused outputs (Dilling and Lemos, 2011).

Bridging Between Evidence and Policy

The translation of evidence into effective policy is challenging and requires concerted effort from both decision makers and researchers to work across the boundaries of their respective communities with effective communication, translation and mediation (Cash et al., 2003b). This “boundary work” needs to ensure that scientific assessments and evidence syntheses are demand-driven, transparent, objective and technically accurate (Watson, 2005). Engaging key government, civil society and private sector actors in the generation and synthesis of evidence is key to achieving ownership and ensuring that science products are relevant, usable and accessible in the decision-making process. Above all, evidence should be presented in a way that is “policy relevant” rather than “policy prescriptive” (Watson, 2005).

Facilitating communication and collaboration between researchers and policymakers is a key role, but often one that is not formalised. In practice, this knowledge brokering role can be filled by scientifically literate policymakers, policy literate researchers, technical advisors, cabinets, or third party “boundary organisations” (including for example NGOs and consultancies) (Dilling and Lemos, 2011). Although this is an important discipline in its own right, the professional skills and career paths for knowledge brokers are not currently well-established or formalised.

Credibility, salience, and legitimacy are key factors determining the effectiveness of knowledge systems and knowledge brokers (Cash et al., 2003a; Turnhout et al., 2013). Knowledge brokers need to be able to act impartially. Mistrust between actors with conflicting agendas can create barriers to the design and adoption of policies (Yami and Van Asten, 2017). There are a number of key trade-offs that need to be considered when working at the science-policy interface. These include clarity vs. complexity (whether to focus on simple, clear messages vs. detailed and comprehensive systemic analyses); speed vs. quality (timely responses demanded by decision makers may lack robustness); push vs. pull (should the research agenda be set by policy demand or research supply?) (Sarkki et al., 2013).

The Knowledge Brokering Spectrum

A wide range of terms has been used to describe the function of facilitating access to knowledge. These include for example: Knowledge Transfer (KT), Knowledge Management (KM), Knowledge Translation (KTn), Knowledge Exchange (KE), Knowledge Brokering (KB), and Knowledge Mobilization (KMb). These can collectively be referred to as K^* (KStar) (Shaxson et al., 2012). This covers a spectrum of functions ranging from linear dissemination of knowledge from producer to user, to co-production of knowledge, social learning and innovation. Over recent years, there has been a change in emphasis from uni-directional KT to multi-directional KE and KB; from passive participation to functional participation to interactive participation between researchers and policymakers (Pretty, 1995).

There is a large overlap between the multiple frameworks that have been proposed for classifying knowledge brokering approaches (Pretty, 1995; Michaels, 2009; Reed et al., 2014b). Michaels et al. outlined six strategies ranging from the relatively passive publication of reports and briefs (inform) to more collaborative approaches to work with stakeholders to build capacity (Table 2) (Michaels, 2009). This echoes literature on public participation, which discusses engagement at different levels, e.g., communication, consultation and participation (Rowe and Frewer, 2005). Shaxson et al. (2012) set out four types of knowledge brokering roles including: information intermediary (informing, compiling and aggregating information), knowledge translator (disseminating, translating, communicating), knowledge broker (matchmaking, bridging, networking, and facilitating active engagement between stakeholders), and innovation broker (operating at the institutional level, building capacity, negotiating, collaborating, and establishing organisational functions).

TABLE 2

Table 2. Spectrum of knowledge brokering approaches. Elaborated from Michaels (2009).

Approaches from across this spectrum of engagement all have their place. In some cases, one-way communication is needed, for example to raise awareness of a new issue uncovered by research. In other cases a more participatory, capacity building approach is required. For example, in developing an integrated landscape management programme, participation ensures that the knowledge and values of different groups are taken into account, and that decisions are co-developed through an equitable and transparent process (Sayer et al., 2013). Various initiatives have aimed to develop policymakers' capacity to use evidence. For example, the Evidence-Informed Policy Making Toolkit (developed through the DFID Building Capacity to Use Research Evidence programme—BCURE) sets out a workshop format to guide practitioners in accessing and scrutinising scientific literature (for bias, quality etc.), helping people understand experimental design, statistics and experimental limitations (Ademokun et al., 2016). There is reliable evidence for education interventions, such as critical appraisal programmes, in increasing evidence uptake (Langer et al., 2016). However, institutional changes are also necessary. Incentives are required for policymakers to use evidence, and for researchers to translate their research into practice (Turnhout et al., 2013).

In any case, there needs to be a pull for knowledge from decision makers as well as a push from researchers. Interactions between researchers and decision makers need to be demand-driven, designed into research programme plans from the outset, and based on building long-term, trusting relationships with two-way dialogue (Reed et al., 2014b). Decision makers need tangible results as early as possible in order to maintain engagement (Reed et al., 2014b).

Co-creation of Knowledge

Policymakers also need to engage to set the direction of research. Two-way communication helps to translate general “policy” questions into more specific and actionable “research” questions (Cash et al., 2003b; Sutherland et al., 2006; Cvitanovic et al., 2016). Involving policymakers in the process of research design and implementation can be an effective way of developing a shared understanding of the context for research and enhancing its communication and uptake (McGonigle et al., 2014). This can be supported by the establishment of multi-stakeholder platforms (Hermans et al., 2017) or transdisciplinary research platforms (McGonigle et al., 2014), which provide a forum for decision makers and scientists to come together. Participatory systems mapping and decision analysis approaches provide other models through which a shared understanding can be built (Lanzanova et al., 2019).

Such collaboration encourages co-development of research questions, joint design of studies and co-production of knowledge. This boosts the experiential knowledge of decision makers while making sure that research is tailored to address specific current policy questions (Cvitanovic and Hobday, 2018). Such ongoing contact allows research to adapt to a rapidly evolving policy context. Stakeholder involvement in research design and implementation also provides a forum for researchers and policymakers to interact on a more informal and ad hoc basis, enabling policy questions to be rapidly discussed as they arise (McGonigle et al., 2014).

Collaboration between policymakers and researchers can also provide opportunities to incorporate research principles into the design and implementation of policies and programmes in a way that builds evidence on their effectiveness. Piloting, Randomized Controlled Trials or phase-in approaches, for example, can generate evidence that can be used to self-correct or make adjustment as policy roll-out progresses (HM Treasury, 2018). This blurring of the lines between science and policy provides a new potential role for researchers in directly engaging in policy design, potentially reframing policy as a scientific endeavour (Boyd, 2019).

Co-creation of knowledge has been shown to be valuable in a number of settings. For example, the iterative co-development of aquifer models between water managers and scientists from several disciplines in Nebraska was influential on decision making at multiple levels (Cash et al., 2003a). Iteration between researchers and decision makers, involving mediation, translation and co-production, has been shown to greatly enhance the usability of evidence on climate change (Dilling and Lemos, 2011). A study assessing the impact of Regional Integrated Sciences and Assessments workgroups (climate change research/policy boundary organisations) showed that they had improved climate literacy in policymakers, enabled coproduction of knowledge, and had produced credible, salient and legitimate evidence (Franklin et al., 2018).

However, there are trade-offs in knowledge co-production. Though it increases research relevance, it can potentially impact researchers' independence and credibility. Whilst coproduction should be incentivised, it is important to appreciate that some questions may not be able to be answered in this way (Turnhout et al., 2013).

Framing Evidence to Inform Integrated Landscape Management Policy

Although large numbers of decision support tools, case-studies and research programmes have been developed to inform integrated landscape management, there is little clear holistic guidance on the selection of appropriate approaches for given applications (Grêt-Regamey et al., 2017). This is significant because the choice of tool has a major effect on the way that problems are framed and hence the values, perspectives and assumptions built into analyses (Gasparatos and Scolobig, 2012). One way to overcome this problem is to use multiple analytical methods in combination through a “weight of evidence” approach (McGonigle et al., 2012). A combination of biophysical, economic and monitoring tools and indicators could give more nuance than a single tool. Very little work has been done to develop such multi-tool approaches, however (Gasparatos and Scolobig, 2012).

The guidance that does exist on tool selection tends to be rather general and focused on individual methods. A number of web-based tools are available to guide decision makers in selecting analytical approaches to target land management interventions. For example, the ValuES and WOCAT websites help signpost decision makers to relevant case-studies of ecosystem service evaluation in the case of the former, and sustainable land management practices in the latter (ValuES, 2019; WOCAT, 2019). WOCAT has also developed a toolkit to guide decision makers through the process of developing SLM strategies, although this does not specifically focus on evidence synthesis and contextualisation (Bastidas Fegan, 2019). The Overseas Development Institute ROMA framework provides guidance on policy engagement and influence through a flexible toolkit that combines workshops, rapid reviews, analysis and reflective learning to guide researchers and development practitioners hoping to influence policy in an iterative learning process (Young et al., 2014). The IUCN Restoration Opportunities Assessment Methodology (ROAM) is another example of a decision support toolkit targeted at forest restoration (IUCN and WRI, 2014).

To make evidence relevant to policymaking, it needs to be synthesised and packaged effectively. Dicks et al. (2014) proposed the 4-S hierarchy framework for organising scientific evidence. Scientific Studies are summarised by Systematic reviews. These feed into Summaries and Decision support systems to provide advice or guidance. However, high quality syntheses, e.g., through systematic reviews, are slow to do and may not be responsive enough for rapidly emerging policy windows (Rose et al., 2017). They also do not adequately address the inevitable gaps in evidence. There are often clear trade-offs between the confidence of an answer to a policy question and its timeliness (Sarkki et al., 2013). A rapid evidence appraisal or expert elicitation process over a few days is likely to have a more positive influence on policy than a high quality piece of analysis that arrives 6 months too late. Researchers need to be aware of how policymaking works in order to design approaches accordingly. Decision-analytic frameworks (e.g., decision analysis, cost–benefit analysis, cost-effectiveness analysis, and cultural prescriptive rules) can be used to bring evidence together to assess trade-offs (Watson, 2005).

Systems Mapping

One approach to contextualise evidence can be in developing systems maps, e.g., representing causal loops, or stocks and flows (Lane, 2016). This can take place as part of a participatory co-learning process, e.g., using workshops and interviews to build up a map (e.g., Lane, 2016; CECAN, 2019). It can also form part of a decision analysis approach. For example, Whitney et al. used expert elicitation to form a model of social, economic, political and environmental factors that affected nutrition in Uganda (Whitney et al., 2018b). The participatory approach that formed the model involved 23 experts answering questions over a week-long workshop.

A simpler approach could be the adoption of a linear conceptual model, such as the source-mobilisation-delivery-impact model developed for diffuse agricultural water pollution (Haygarth et al., 2005). In either case, evidence from research syntheses or models can be superimposed onto the map or conceptual model to link disparate pieces of research and to expose gaps. Expert elicitation, e.g., through Delphi processes, can be used to fill gaps and estimate uncertainty (Fischer et al., 2014; Whitney et al., 2018a).

Modelling and Decision Support Tools

A range of decision support tools, including frameworks, guidelines and models (De Ridder et al., 2007; Chazdon and Guariguata, 2018), have been developed to inform integrated landscape management. They have a range of different functions relevant to several of the different stages of policy or programme development shown in Figure 1. Such tools are particularly useful for scenario testing, targeting interventions, analysing trade-offs and synergies, and optimising solutions (Chazdon and Guariguata, 2018). They fall into categories including (1) Assessment frameworks, (2) Participatory tools, (3) Scenario analysis tools, (4) Multi-criteria analysis tools, (5) Cost-benefit analysis tools, (6) Accounting tools and indicator sets, and (7) Modelling tools (De Ridder et al., 2007). Sometimes simply informing decision makers on the existence and availability of tools can facilitate their uptake (Wood et al., 2018). The trade-off between the time required to use tools and the depth and quality of information they provide is a major factor affecting adoption (Bagstad et al., 2013). Hard or soft coupling of multiple biophysical, socioeconomic, and environmental models to evaluate complex systems offers a good deal of promise (Bagstad et al., 2013; Jones et al., 2017), as does combining macro-scale and sectoral models to identify specific intervention options within broader national or regional development scenarios (Allen et al., 2016).

Ecosystem Service Assessment Approaches

Approaches to identify and value ecosystem services can provide useful information to inform land management decisions (Carpenter et al., 2009; Primmer and Furman, 2012). A large number of tools and frameworks have been developed and applied to quantify and assess ecosystem services. These include ecosystem process models, ecosystem service models, integrated assessment models and ecosystem service valuation models, applicable at site (e.g., farm or town) or landscape (e.g., catchment or national) level (Bagstad et al., 2013; Wood et al., 2018). Such models can be used for scenario testing at every step of the policy cycle: exploratory scenarios at the agenda setting phase, target seeking scenarios at the design phase, ex-ante policy screening scenarios at the implementation phase and ex-post policy evaluation at the review phase (IPBES, 2016). Ecosystem service assessments have been applied from local to global scales and in many contexts.

Ecosystem service tools are very variable in their type and usability. Grêt-Regamey et al. (2017) evaluated 68 such tools. Most have been designed and applied in developed countries where spatial environmental data is relatively rich. They allow users to explore the impact of land management scenarios on one or more ecosystem services. The format of these tools is highly variable, for instance including interactive pdf documents, empirical models, or process-based models, available online or as standalone computer programmes. Some are spatially explicit, others not. Accessibility is also variable. Some tools are publicly accessible, others are described as prototypes. Some have user-friendly front-ends whereas others require users to be skilled in coding.

Tools need to be matched carefully to a given application (Gasparatos and Scolobig, 2012; IPBES, 2016). Several authors have suggested criteria for the selection of ecosystem service models and decision support tools, e.g., including the decision context, values of stakeholders, key ecosystem services, and time and funds available (e.g., Gasparatos and Scolobig, 2012; Christin et al., 2016).

IPBES has published guidance on how to apply ecosystem service assessments for policy (IPBES, 2016), and various models have been well-tested in a policymaking setting. For example, the Natural Capital Project has applied the InVEST model in a large number of case studies. This includes policy development in the context of spatial planning, payments for ecosystem services, impact assessments for development permits, hazard mitigation, adaptation to climate change, land restoration, and corporate risk management (Ruckelshaus et al., 2015). Other ecosystem service models have been applied to a variety of policy sectors, particularly including spatial planning, agriculture, water, conservation, forestry, soil and climate (Grêt-Regamey et al., 2017). These applications have varied in the level of policy engagement and the influence of the evidence generated.

Current ecosystem service assessment methodologies suffer from a number of limitations: (1) a lack of land cover data and relatively simplistic approaches limit spatially explicit recommendations, (2) uncertainty is rarely quantified, (3) relatively few are able to account for the demand for ecosystem services and their valuation, (4) many miss key services, and (5) they do not account for political and organisational aspects of decision making (Grêt-Regamey et al., 2017). A shortage of technical skills, lack of understanding about the benefits of ecosystem service modelling, lack of interaction between scientists and policymakers and a lack of guidance in model choice also limit the uptake and use of these types of tools (IPBES, 2016). Finally, models can be biased towards the biophysical rather than socio-economic aspects of socio-ecological systems (Rossing et al., 2007).

Partly because of the reasons set out above, ecosystem service models can generate results that lack relevance to policymakers, needing further work to contextualise them (Grêt-Regamey et al., 2017). Other authors have found little evidence of uptake and use of ecosystem service models. An analysis of 22 case studies in Europe and Latin America found that although most had engaged a wide range of stakeholders, there was little evidence that ecosystem service knowledge had been used systematically to select policy options (Saarikoski et al., 2018). Several authors have therefore suggested ways of embedding ecosystem service assessments in participatory processes that take into account the values and knowledge needs of different stakeholders (Förster et al., 2015; Martinez-Harms et al., 2015; Neufeldt et al., 2015; Rosenthal et al., 2015; Ruckelshaus et al., 2015; IPBES, 2016). Used in this way ecosystem service assessments can support conceptual learning between researchers, practitioners and stakeholders (IPBES, 2016; Saarikoski et al., 2018) and can be effective for informing policy change and integrated landscape management (Ruckelshaus et al., 2015).

A Proposed Integrated Landscape Management Evidence Framework and Toolkit

One way to make evidence more accessible and usable to policymakers is to provide guidance on which combinations of approaches can be used to answer particular types of policy question.

In Table 3 and Figure 2, we set out the blueprint for a “toolkit of toolkits.” This provides the structure for a future database of evidence sources, decision support tools and participatory approaches to inform decisions at each of the seven integrated landscape management stages set out in the section on Decision Making in Integrated Landscape Management. Each of the seven parts can act either as a standalone toolkit to address specific challenges or as part of a holistic toolkit that can be used in its entirety to guide decision makers through policy or programme design as a learning cycle. Although Figure 2 is presented as a cycle, there is no set starting point, and sections do not necessarily need to be followed in sequence.

TABLE 3

Table 3. Proposed decision support framework for knowledge brokering in integrated landscape management.

FIGURE 2

Figure 2. A knowledge brokering toolkit for Integrated Landscape Management: Examples of approaches to synthesise and apply evidence to inform seven types of policy decision relating to integrated landscape management.

The framework aims to provide a signposting tool to guide knowledge brokers and decision makers in the selection of the most appropriate evidence sources and analytical approaches to use for policy and programme design. We envisage this as a toolkit that is used by researchers, knowledge brokers and policy advisers to provide more holistic and responsive advice to integrated landscape management decision makers.

Table 3 sets out guiding questions for each of the seven stages. We also present an illustrative list of evidence sources, decision support tools and methodologies that can be used at each stage. These include (1) participatory approaches, (2) evidence review approaches, (3) models and decision support tools, and (4) protocols for the collection of new data (surveys, monitoring protocols etc.). The list is not intended to be definitive and the most appropriate choices are likely to vary significantly between different places depending on geographical factors and programme goals. Those selected and applied will also depend on time availability (e.g., rapid expert elicitation approaches vs.more in-depth, analytical ones), access to expertise, funding, data availability, and level of precision required as follows:

• Rapid response approaches (3–6 week timeframe): where time, data, funds, or other resources are limiting. Approaches are likely to mainly rely on rapid evidence assessment supplemented by participatory expert elicitation approaches.

• In-depth approaches (3–6 month timeframe): a more comprehensive approach where time is less limited. This is likely to involve more detailed desk-based literature reviews, and the application of existing models and decision support tools.

• Full assessment (1 year + timeframe): drawing on the full array of desk and field approaches where time and resources allow. Including the approaches set out above plus the collection and analysis of new empirical data.

Conclusion

Knowledge brokers working on integrated landscape management need to draw on a wide range of different approaches, evidence sources, and skills in order to translate research into actionable policy. They need to work in research-mode, helping to synthesise and contextualise fragmented evidence to inform policymaking, and in facilitator mode, bringing together experts from different disciplines with stakeholders from multiple interest groups to co-develop a shared systemic understanding (collaborating and building capacity) (Table 2). Table 4 sets out some of the key principles and skills for integrated landscape management knowledge brokers that we have drawn from the literature.

TABLE 4

Table 4. Ten proposed principles for knowledge brokering in integrated landscape management and 12 key skill-sets for knowledge brokers.

There will never be time to develop a perfect understanding of the complex socioecological interactions affecting integrated landscape management programmes and policies. Decision makers need support to make the best use of available evidence there is. Pragmatism and timeliness are key. Evidence syntheses need to be used in conjunction with models and participatory expert elicitation approaches.

The knowledge brokering process is admittedly challenging and has many potential conflicts. We offer guidance on how to make better use of evidence in the design of integrated landscape management policies and programmes. We recognise that a wide range of factors apart from evidence influence policy decisions. Amongst other things, these include politics, power dynamics, public opinion and organisational structures. The approaches that we present need to be applied within the context of these factors. Although the roles of politics and power dynamics are outside the scope of this paper, they have been explored by other authors (Estrada-Carmona et al., 2014; Milder et al., 2014; García-Martín et al., 2016; Zanzanaini et al., 2017).

It is clearly critical that community-based transdisciplinary approaches explicitly include relevant stakeholders. Decision-makers are often those with power and engaging with them should not lead to the exclusion of those without power. We should seek to include the knowledge, values and opinions of those affected by the issues together with those of scientific experts and decision makers (Funtowicz and Ravetz, 1994; Wilson et al., 2007). Participatory mapping, evidence synthesis and decision analysis approaches can all be employed in helping to achieve this.

The decision support framework presented here (Figure 2 and Table 3) aims to map types of evidence and approaches onto the different types of questions facing integrated landscape management decision makers. The framework set out in this paper will be populated and tested over the next 3 years through the CGIAR Water, Land and Ecosystems flagship programme on Enhancing the Sustainability of Agriculture.

The value of knowledge brokering is widely recognised but its provision is currently ad hoc. There is a need to formalise and strengthen capacity in knowledge brokering, recognising it as a specialism in its own right, and developing appropriate incentive structures and career pathways. For example, universities can offer training programmes aimed at equipping students with the skills needed for systems research. Research organisations can create knowledge brokering roles in order to maximise the impact of their work. Policy organisations and NGOs can build their own capacity, equipping scientific advisers with skills in networking, communication, facilitation and systems research alongside their traditional skillsets as subject matter experts in their own right. This requires a conceptual shift in the way that evidence is created, accessed and used. Research funders can also help by recognising the value of co-created, transdisciplinary approaches.

Author Contributions

The paper sets out ideas developed during the planning phase of the CGIAR Water, Land, and Ecosystems flagship programme on Enhancing the Sustainability of Agriculture. The toolkit concept set out in Figure 1 was developed by DM based on discussions with the team. Literature searches and summaries were undertaken by GN and RP. Text was written by DM, GN, and RP with inputs from the wider team. All authors contributed to manuscript revision, read, and approved the submitted version.

Funding

This work was funded by the CGIAR Water, Land, and Ecosystems programme, flagship programme on Enhancing Sustainability Across Agricultural Systems. The contribution of RP was supported by the University of Cambridge Doctoral Training Partnership funded by the UK Biotechnology and Biological Sciences Research Council.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank members of the CGIAR Water, Land, and Ecosystem programme management committee, steering group, and reviewers who have provided comments and feedback on the development of the flagship programme on Enhancing the Sustainability of Agriculture.

References

Adelle, C. (2015). Contexualising the tool development process through a knowledge brokering approach: the case of climate change adaptation and agriculture. Environ. Sci. Policy 51, 316–324. doi: 10.1016/j.envsci.2014.08.010

CrossRef Full Text | Google Scholar

Ademokun, A., Dennis, A., Hayter, E., Richards, C., and Runceanu, L.-E. (2016). Vakayiko Evidence Informed Policy Making Toolkit. Oxford. Available online at: http://www.inasp.info/uploads/filer_public/2013/04/22/what_is_the_evidence_on_eipm.pdf

Alexander, S. M., Provencher, J. F., Henri, D. A., Taylor, J. J., and Cooke, S. J. (2019). Bridging Indigenous and science-based knowledge in coastal-marine research, monitoring, and management in Canada: a systematic map protocol. Environ. Evid. 8:15. doi: 10.1186/s13750-019-0159-1

CrossRef Full Text | Google Scholar

Alexandratos, N., and Bruinsma, J. (2012). World Agriculture Towards 2030/2050. Italy: UN Food and Agriculture Organisation. Available online at: http://www.fao.org/fileadmin/templates/esa/Global_persepctives/world_ag_2030_50_2012_rev.pdf

Allen, C., Metternicht, G., and Wiedmann, T. (2016). National pathways to the Sustainable Development Goals (SDGs): a comparative review of scenario modelling tools. Environ. Sci. Policy 66, 199–207. doi: 10.1016/j.envsci.2016.09.008

CrossRef Full Text | Google Scholar

Anon (2014). Overview Paper What is Evidence-Based Policy-Making and Implementation? Cape Town: Department for Planning, Monitoring and Evaluation. Available online at: https://www.dpme.gov.za/keyfocusareas/evaluationsSite/Evaluations/What is EBPM 14 10 13_mp.pdf

Ashford, L. S., Smith, R. R., Souza, R., De Fikree, F. F., and Yinger, N. V (2006). Creating windows of opportunity for policy change : incorporating evidence into decentralized planning in Kenya. Bull. World Heal. Organ. 84, 669–672. doi: 10.2471/BLT.06.030593

PubMed Abstract | CrossRef Full Text | Google Scholar

Bagstad, K. J., Semmens, D. J., Waage, S., and Winthrop, R. (2013). A comparative assessment of decision-support tools for ecosystem services quantification and valuation. Ecosyst. Serv. 5, 27–39. doi: 10.1016/j.ecoser.2013.07.004

CrossRef Full Text | Google Scholar

Bastidas Fegan, S. (2019). The DS-SLM Sustainable Land Management Mainstreaming Tool - Decision Support for Mainstreaming and Scaling up Sustainable Land Management. Rome: UN Food and Agriculture Organisation. Available online at: http://www.fao.org/3/ca3761en/CA3761EN.pdf

Bennett, E. M., Cramer, W., Begossi, A., Cundill, G., Díaz, S., Egoh, B. N., et al. (2015). Linking biodiversity, ecosystem services, and human well-being: three challenges for designing research for sustainability. Curr. Opin. Environ. Sustain. 14, 76–85. doi: 10.1016/j.cosust.2015.03.007

CrossRef Full Text | Google Scholar

Berhane, G., Hoddinott, J., Kumar, N., Seyoum, A., Diressie, M. T., Yohannes, Y., et al. (2013). Evaluation of Ethiopia's Food Security Program: Documenting Progress in the Implementation of the Productive Safety Nets Programme and the Household Asset Building Programme. Addis Ababa: The Ethiopia Strategy Support Program II (ESSP II). Available online at: http://essp.ifpri.info/files/2013/05/ESSPII_EDRI_Report_PSNP.pdf

Boyd, I. (2019). Making science work for policy. Br. Ecol. Soc. Bull. 50, 14–15.

Broad, K., and Agrawala, S. (2000). The ethiopia food crisis–uses and limits of climate forecasts. Science 289, 1693 LP−1694. doi: 10.1126/science.289.5485.1693

PubMed Abstract | CrossRef Full Text | Google Scholar

Carpenter, S. R., Mooney, H. A., Agard, J., Capistrano, D., Defries, R. S., Díaz, S., et al. (2009). Science for managing ecosystem services: Beyond the Millennium Ecosystem Assessment. Proc. Natl. Acad. Sci. U.S.A. 106, 1305–1312. doi: 10.1073/pnas.0808772106

PubMed Abstract | CrossRef Full Text | Google Scholar

Cash, D., Clark, W. C., Alcock, F., Dickson, N., Eckley, N., and Jager, J. (2003a). Salience, credibility, legitimacy and boundaries: linking research, assessment and decision making. KSG Working Papers Series RWP02-046. doi: 10.2139/ssrn.372280. Available online at: https://ssrn.com/abstract=372280

CrossRef Full Text | Google Scholar

Cash, D. W., Clark, W. C., Alcock, F., Dickson, N. M., Eckley, N., Guston, D. H., et al. (2003b). Knowledge systems for sustainable development. Proc. Natl. Acad. Sci. U.S.A. 100, 8086–8091. doi: 10.1073/pnas.1231332100

PubMed Abstract | CrossRef Full Text | Google Scholar

CECAN (2019). Participatory Systems Mapping: A Practical Guide. Surrey: Centre for the Evaluation of Complexity Across the Nexus. Available online at: https://www.cecan.ac.uk/sites/default/files/2019-03/PSM Workshop method.pdf

CEH (2018). Dynamic Landscape Typology Tool. Defra Sustain. Intensif. Platf. Available online at: https://eip.ceh.ac.uk/apps/sustainable-intensification/info/ (accessed August 26, 2019).

Chazdon, R. L., and Guariguata, M. R. (2018). “Decision support tools for forest landscape restoration: current status and future outlook,” in Occasional Paper 183 (Bogor: CIFOR).

Google Scholar

Chen, Z., Wang, J., Deng, N., Lv, C., Wang, Q., Yu, H., et al. (2018). Modeling the effects of farming management practices on soil organic carbon stock at a county-regional scale. CATENA 160, 76–89. doi: 10.1016/j.catena.2017.09.006

CrossRef Full Text | Google Scholar

Christin, Z. L., Bagstad, K. J., and Verdone, M. A. (2016). A decision framework for identifying models to estimate forest ecosystem services gains from restoration. For. Ecosyst. 3:3. doi: 10.1186/s40663-016-0062-y

CrossRef Full Text | Google Scholar

Chukalla, A. D., Krol, M. S., Hoekstra, A. Y., and Centre, W. (2017). Marginal cost curves for water footprint reduction in irrigated agriculture : guiding a cost-effective reduction of crop water consumption to a permit or benchmark level. Hydrol. Earth Sysyt. 21, 3507–3524. doi: 10.5194/hess-21-3507-2017

CrossRef Full Text | Google Scholar

Collier, N., Campbell, B. M., Sandker, M., Garnett, S. T., Sayer, J., and Boedhihartono, A. K. (2011). Science for action: the use of scoping models in conservation and development. Environ. Sci. Policy 14, 628–638. doi: 10.1016/j.envsci.2011.05.004

CrossRef Full Text | Google Scholar

Conservation Evidence (2019). Conservation Evidence. Available online at: https://www.conservationevidence.com/ (accessed August 27, 2019).

Cvitanovic, C., and Hobday, A. J. (2018). Building optimism at the environmental science-policy-practice interface through the study of bright spots. Nat. Commun. 9:3466. doi: 10.1038/s41467-018-05977-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Cvitanovic, C., McDonald, J., and Hobday, A. J. (2016). From science to action: principles for undertaking environmental research that enables knowledge exchange and evidence-based decision-making. J. Environ. Manage. 183, 864–874. doi: 10.1016/j.jenvman.2016.09.038

PubMed Abstract | CrossRef Full Text | Google Scholar

De Ridder, W., Turnpenny, J., Nilsson, M., and Von Raggamby, A. (2007). A framework for tool selection and use in integrated assessment for sustainable development. J. Environ. Assess. Policy Manag. 9, 423–441. doi: 10.1142/S1464333207002883

CrossRef Full Text | Google Scholar

DeClerck, F. A. J., Jones, S. K., Attwood, S., Bossio, D., Girvetz, E., Chaplin-Kramer, B., et al. (2016). Agricultural ecosystems and their services: the vanguard of sustainability? Curr. Opin. Environ. Sustain. 23, 92–99. doi: 10.1016/j.cosust.2016.11.016

CrossRef Full Text | Google Scholar

Denier, L., Scherr, S., Shames, S., Chatterton, P., Hovani, L., and Stam, N. (2015). The Little Sustainable Landscapes Book: Achieving Sustainable Development Through Integrated Landscape Management. Oxford, UK: Global Canopy Programme. Available online at: https://globalcanopy.org/sites/default/files/documents/resources/GCP_LSLB_English.pdf

Google Scholar

DFID (2013). DFIDEvidence Survey Results Report November 2013. London. Available online at: https://assets.publishing.service.gov.uk/media/57a08a0ae5274a27b20003c3/61188-DFID_Evidence_Survey_2013_report_FINAL.pdf

Google Scholar

DFID (2014). Assessing the Strength of Evidence Background. London: Department for International Development. Available online at: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/291982/HTN-strength-evidence-march2014.pdf

Dicks, L. V., Walsh, J. C., and Sutherland, W. J. (2014). Organising evidence for environmental management decisions: a “4S” hierarchy. Trends Ecol. Evol. 29, 607–613. doi: 10.1016/j.tree.2014.09.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Dilling, L., and Lemos, M. C. (2011). Creating usable science: opportunities and constraints for climate knowledge use and their implications for science policy. Glob. Environ. Chang. 21, 680–689. doi: 10.1016/j.gloenvcha.2010.11.006

CrossRef Full Text | Google Scholar

Eigenbrod, F., Armsworth, P. R., Anderson, B. J., Heinemeyer, A., Gillings, S., Roy, D. B., et al. (2010). The impact of proxy-based methods on mapping the distribution of ecosystem services. J. Appl. Ecol. 47, 377–385. doi: 10.1111/j.1365-2664.2010.01777.x

CrossRef Full Text | Google Scholar

Elueze, I. N. (2016). Towards Evidence-Informed Agriculture Policy Making: Investigating the Knowledge Translation Practices of Researchers in the National Agriculture Research Institutes in Nigeria. PhD Thesis, University of Western Ontario, Electronic Thesis and Dissertation Repository. Available online at: https://ir.lib.uwo.ca/etd/4237

Google Scholar

Estrada-Carmona, N., Hart, A. K., DeClerck, F. A. J., Harvey, C. A., and Milder, J. C. (2014). Integrated landscape management for agriculture, rural livelihoods, and ecosystem conservation: an assessment of experience from Latin America and the Caribbean. Landsc. Urban Plan. 129, 1–11. doi: 10.1016/j.landurbplan.2014.05.001

CrossRef Full Text | Google Scholar

FAO (1996). Agro-ecological Zoning Guidelines. Available online at: https://www.mpl.ird.fr/crea/taller-colombia/FAO/AGLL/pdfdocs/aeze.pdf

Google Scholar

FAO (2012). “Mainstreaming climate-smart agriculture into a broader landscape approach,” in 2nd Global Conference Agriculture Food Security Climate Change, Vol. 26 (Hanoi).

Google Scholar

FAO (2016). State of the World's Forests 2016: Forests and Agriculture: Land-Use Challenges and Opportunities. Rome: UN Food and Agriculture Organisation. Available online at: http://www.fao.org/publications/sofo/2016/en/

Google Scholar

Fischer, G., Nachtergaele, F., Prieler, S., van Velthuizen, H. T., Verelst, L., and Wiberg, D. (2008). Global Agro-Ecological Zones Assessment for Agriculture (GAEZ 2008). Laxenburg; Rome: IIASA; FAO.

Fischer, R. H, Wentholt, M. T. A., Rowe, G., and Frewer, L. J. (2014). Expert involvement in policy development: a systematic review of current practice. Sci. Public Policy 41, 332–343. doi: 10.1093/scipol/sct062

CrossRef Full Text | Google Scholar

Foley, J. A., Defries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., et al. (2005). REVIEW global consequences of land use. Science 309, 570–575. doi: 10.1126/science.1111772

CrossRef Full Text | Google Scholar

Förster, J., Barkmann, J., Fricke, R., Hotes, S., Kleyer, M., Kobbe, S., et al. (2015). Assessing ecosystem services for informing land-use decisions: a problem oriented approach. Ecol. Soc. 20:31. doi: 10.5751/ES-07804-200331

CrossRef Full Text | Google Scholar

Franklin, A. L., Grossman, A., Le, J., and Shafer, M. (2018). Creating broader research impacts through boundary organizations. Public Adm. Rev. 79, 715–224. doi: 10.1111/puar.12985

CrossRef Full Text | Google Scholar

Funtowicz, S. O., and Ravetz, J. R. (1994). Uncertainty, complexity and post-normal science. Environ. Toxicol. Chem. 13, 1881–1885. doi: 10.1002/etc.5620131203

CrossRef Full Text | Google Scholar

García-Martín, M., Bieling, C., Hart, A., and Plieninger, T. (2016). Integrated landscape initiatives in Europe: multi-sector collaboration in multi-functional landscapes. Land Use Policy 58, 43–53. doi: 10.1016/j.landusepol.2016.07.001

CrossRef Full Text | Google Scholar

Gasparatos, A., and Scolobig, A. (2012). Choosing the most appropriate sustainability assessment tool. Ecol. Econ. 80, 1–7. doi: 10.1016/j.ecolecon.2012.05.005

CrossRef Full Text | Google Scholar

Gibbons, P., Zammit, C., Youngentob, K., Possingham, H. P., Lindenmayer, D. B., Bekessy, S., et al. (2008). Some practical suggestions for improving engagement between researchers and policy-makers in natural resource management. Ecol. Manag. Restor. 9, 182–186. doi: 10.1111/j.1442-8903.2008.00416.x

CrossRef Full Text | Google Scholar

Gibson, F. L., Rogers, A. A., Smith, A. D. M, Roberts, A., Possingham, H., McCarthy, M., and Pannell, D. J. (2017). Factors influencing the use of decision support tools in the development and design of conservation policy. Environ. Sci. Policy 70, 1–8. doi: 10.1016/j.envsci.2017.01.002

CrossRef Full Text | Google Scholar

Giller, K. E., Leeuwis, C., Andersson, J. A., Andriesse, W., Brouwer, A., Frost, P., et al. (2007). Competing claims on natural resources: what role for science? Ecol. Soc. 13:34. doi: 10.5751/ES-02595-130234

CrossRef Full Text | Google Scholar

Girvetz, E. H., Corner-Dolloff, C., Lamanna, C., and Rosenstock, T. S. (2017). “CSA-Plan”: strategies to put Cliamte-Smart Agriculture (CSA) into practice. Agric. Dev. 30, 12–16. Available online at: https://cgspace.cgiar.org/bitstream/handle/10568/81374/http://Ag4Dev30_2.pdf

Google Scholar

Godfrey, L., Funk, N., and Mbizvo, C. (2010). Bridging the science-policy interface: a new era for South African research and the role of knowledge brokering. S. Afr. J. Sci. 106, 1–8. doi: 10.4102/sajs.v106i5/6.247

CrossRef Full Text | Google Scholar

Gooday, R., Anthony, S., Chadwick, D., Newell-Price, P., Harris, D., Duethmann, D., et al. (2014). Modelling the cost-effectiveness of mitigation methods for multiple pollutants at farm scale. Sci. Total Environ. 468–469, 1198–1209. doi: 10.1016/j.scitotenv.2013.04.078

CrossRef Full Text | Google Scholar

Gooday, R., Anthony, S., Durrant, C., Harris, D., Lee, D., Metcalfe, P., et al. (2015). Developing the Farmscoper Decision Support Tool. Final Report for Defra Project SCF0104, Department for Environment, Food and Rural Affairs, London. Available online at: http://randd.defra.gov.uk/Document.aspx?Document=13269_SCF0104_FarmscoperExtensionFinalReport.pdf

Goswami, R., Chatterjee, S., and Prasad, B. (2014). Farm types and their economic characterization in complex agro-ecosystems for informed extension intervention: study from coastal West Bengal, India. Agric. Food Econ. 2:5. doi: 10.1186/s40100-014-0005-2

CrossRef Full Text | Google Scholar

Grêt-Regamey, A., Sirén, E., Brunner, S. H., and Weibel, B. (2017). Review of decision support tools to operationalize the ecosystem services concept. Ecosyst. Serv. 26, 306–315. doi: 10.1016/j.ecoser.2016.10.012

CrossRef Full Text | Google Scholar

Groot, J. C. J., Rossing, W. A. H., Jellema, A., Stobbelaar, D. J., Renting, H., and Van Ittersum, M. K. (2007). Exploring multi-scale trade-offs between nature conservation, agricultural profits and landscape quality-A methodology to support discussions on land-use perspectives. Agric. Ecosyst. Environ. 120, 58–69. doi: 10.1016/j.agee.2006.03.037

CrossRef Full Text | Google Scholar

Gusmão Caiado, R., Leal Filho, W., Quelhas, O., Luiz de Mattos Nascimento, D., and Ávila, L. (2018). A literature-based review on potentials and constraints in the implementation of the sustainable development goals. J. Clean. Prod. 198, 1276–1288. doi: 10.1016/j.jclepro.2018.07.102

CrossRef Full Text | Google Scholar

Haile, B., Cox, C. M., Azzarri, C., and Koo, J. (2017). “Adoption of sustainable intensification practices: evidence from maize-legume farming systems in Tanzania,” in IFPRI Discussion Paper 01696 (Washington, DC: IFPRI). Available online at: http://ebrary.ifpri.org/cdm/singleitem/collection/p15738coll2/id/132234

Hansen, H. F. (2014). Organisation of evidence-based knowledge production: evidence hierarchies and evidence typologies. Scand. J. Public Health 42, 11–17. doi: 10.1177/1403494813516715

PubMed Abstract | CrossRef Full Text | Google Scholar

Haygarth, P. M., Condron, L. M., Heathwaite, A. L., Turner, B. L., and Harris, G. P. (2005). The phosphorus transfer continuum: linking source to impact with an interdisciplinary and multi-scaled approach. Sci. Total Environ. 344, 5–14. doi: 10.1016/j.scitotenv.2005.02.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Hecht, S. (2010). The new rurality: globalization, peasants and the paradoxes of landscapes. Land Use Policy 27, 161–169. doi: 10.1016/j.landusepol.2009.08.010

CrossRef Full Text | Google Scholar

Hermans, F., Sartas, M., Van Schagen, B., Van Asten, P., and Schut, M. (2017). Social network analysis of multi-stakeholder platforms in agricultural research for development: opportunities and constraints for innovation and scaling. PLoS ONE 12:e0169634. doi: 10.1371/journal.pone.0169634

PubMed Abstract | CrossRef Full Text | Google Scholar

HM Treasury (2011). The Magenta Book - Guidance for Evaluation. London: HM Treasury.

Google Scholar

HM Treasury (2018). The Green Book: Central Government Guidance on Appraisal and Evaluation. London: HM Treasury.

Google Scholar

IOB (2015). Opening Doors and Unlocking Potential Key Lessons From an Evaluation of Support for Policy Influencing, Lobbying, and Advocacy (PILA). Policy and Operations Evaluation Department (IOB). Available online at: http://archief.iob-evaluatie.nl/en/PILA.html

IPBES (2016). “The methodological assessment report on scenarios and models of biodiversity and ecosystem services,” in Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, eds S. Ferrier, K. N. Ninan, P. Leadley, R. Alkemade, L. A. Acosta, H. R. Akçakaya, L. Brotons, W. W. L. Cheung, V. Christensen, K. A. Harhash, J. Kabubo-Mariara, C. Lundquist, M. Obersteiner, H. M. Pereira, G. Peterson, R. Pichs-Madruga, N. Ravindranath, C. Rondinini and B. A. Wintle (Bonn), 348.

Google Scholar

IUCN and WRI (2014). “A Guide to the restoration opportunities assessment methodology (ROAM): assessing forest landscape restoration opportunities at the national or sub-national level,” in IUCN Working Paper (Gland: Road-test edition).

Google Scholar

Jagustović, R., Zougmoré, R. B., Kessler, A., Ritsema, C. J., Keesstra, S., and Martin, R.eynolds (2019). Contribution of systems thinking and complex adaptive system attributes to sustainable food production: example from a climate-smart village. Agric. Syst. 171, 65–75. doi: 10.1016/j.agsy.2018.12.008

CrossRef Full Text | Google Scholar

Jones, J. W., Antle, J. M., Basso, B., Boote, K. J., Conant, R. T., Foster, I., et al. (2017). Toward a new generation of agricultural system data, models, and knowledge products: state of agricultural systems science. Agric. Syst. 155, 269–288. doi: 10.1016/j.agsy.2016.09.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Jones, S. K., Boundaogo, M., DeClerck, F. A., Estrada-Carmona, N., Mirumachi, N., and Mulligan, M. (2019). Insights into the importance of ecosystem services to human well-being in reservoir landscapes. Ecosyst. Serv. 39:100987. doi: 10.1016/j.ecoser.2019.100987

CrossRef Full Text | Google Scholar

Kassie, M., Jaleta, M., Shiferaw, B., Mmbando, F., and Mekuria, M. (2013). Adoption of interrelated sustainable agricultural practices in smallholder systems: evidence from rural Tanzania. Technol. Forecast. Soc. Chang. 80, 525–540. doi: 10.1016/j.techfore.2012.08.007

CrossRef Full Text | Google Scholar

Kingdon, J. W. (1995). “The policy window, and joining the streams,” in Agendas, Alternatives and Public Policies, eds. B. Kushner and R. Waite (New York, NY: Addison Wesley Educational Publishers, Inc.).

Google Scholar

Kuehne, G., Llewellyn, R., Pannell, D. J., Wilkinson, R., Dolling, P., Ouzman, J., et al. (2017). Predicting farmer uptake of new agricultural practices: a tool for research, extension and policy. Agric. Syst. 156, 115–125. doi: 10.1016/j.agsy.2017.06.007

CrossRef Full Text | Google Scholar

Lane, D. C. (2016). “Till the muddle in my mind have cleared awa”: can we help shape policy using systems modelling? Syst. Res. Behav. Sci. 33, 633–650. doi: 10.1002/sres.2422

CrossRef Full Text | Google Scholar

Langer, L., Tripney, J., and Gough, D. (2016). The Science of Using Science; Researching the Use of Research Evidence in Decision-Making. London: EPPI-Centre, Social Science Research Unit, UCL Institute of Education, University College London.

Google Scholar

Lanzanova, D., Whitney, C., Shepherd, K., and Luedeling, E. (2019). Improving development efficiency through decision analysis: reservoir protection in burkina faso. Environ. Modell. Softw. 115, 164–175. doi: 10.1016/j.envsoft.2019.01.016

CrossRef Full Text | Google Scholar

Li, H., Qiu, J., Wang, L., Tang, H., Li, C., and Van Ranst, E. (2010). Modelling impacts of alternative farming management practices on greenhouse gas emissions from a winter wheat–maize rotation system in China. Agric. Ecosyst. Environ. 135, 24–33. doi: 10.1016/j.agee.2009.08.003

CrossRef Full Text | Google Scholar

Liu, J., Hull, V., Batistella, M., DeFries, R., Dietz, T., Fu, F., et al. (2013). Framing sustainability in a telecoupled world. Ecol. Soc. 18:26. doi: 10.5751/ES-05873-180226

CrossRef Full Text | Google Scholar

Lubchenco, J. (1998). Entering the century of the environment: a new social contract for science. Science. 279, 491–497. doi: 10.1126/science.279.5350.491

CrossRef Full Text | Google Scholar

Lubell, M. (2007). Familiarity breeds trust: collective action in a policy domain. J. Polit. 69, 237–250. doi: 10.1111/j.1468-2508.2007.00507.x

CrossRef Full Text | Google Scholar

Luedeling, E., and Shepherd, K. (2016). Decision-focused agricultural research. Solutions 7, 46–54. Available online at: https://www.thesolutionsjournal.com/article/decision-focused-agricultural-research/

Google Scholar

Manning, P., Van Der Plas, F., Soliveres, S., Allan, E., Maestre, F. T., Mace, G., et al. (2018). Redefining ecosystem multifunctionality. Nat. Ecol. Evol. 2, 427–436. doi: 10.1038/s41559-017-0461-7

CrossRef Full Text | Google Scholar

Martinez-Harms, M. J., Bryan, B. A., Balvanera, P., Law, E. A., Rhodes, J. R., Possingham, H. P., et al. (2015). Making decisions for managing ecosystem services. Biol. Conserv. 184, 229–238. doi: 10.1016/j.biocon.2015.01.024

CrossRef Full Text | Google Scholar

Masset, E., Haddad, L., Cornelius, A., and Isaza-Castro, J. (2011). A Systematic Review of Agricultural Interventions That Aim to Improve Nutritional Status of Children. London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London.

PubMed Abstract | Google Scholar

Matson, P., Parton, W. J., Power, A. G., and Swift, M. J. (1997). Agricultural intensification and ecosystem properties. Science 504, 504–509. doi: 10.1126/science.277.5325.504

CrossRef Full Text | Google Scholar

Maxwell, S. L., Fuller, R. A., Brooks, T. M., and Watson, J. E. M. (2016). The ravages of guns, nets and bulldozers. Nature 536, 143–145. doi: 10.1038/536143a

PubMed Abstract | CrossRef Full Text | Google Scholar

McGonigle, D. F., Burke, S. P., Collins, a,. L., Gartner, R., Haft, M. R., Harris, R. C., et al. (2014). Developing demonstration test catchments as a platform for transdisciplinary land management research in England and Wales. Environ. Sci. Process. Impacts 16, 1618–1628. doi: 10.1039/C3EM00658A

PubMed Abstract | CrossRef Full Text | Google Scholar

McGonigle, D. F., Harris, R. C., McCamphill, C., Kirk, S., Dils, R., MacDonald, J., et al. (2012). Towards a more strategic approach to research to support catchment-based policy approaches to mitigate agricultural water pollution: a UK case-study. Environ. Sci. Policy 24, 4–14. doi: 10.1016/j.envsci.2012.07.016

CrossRef Full Text | Google Scholar

Michaels, S. (2009). Matching knowledge brokering strategies to environmental policy problems and settings. Environ. Sci. Policy 12, 994–1011. doi: 10.1016/j.envsci.2009.05.002

CrossRef Full Text | Google Scholar

Milder, J. C., Hart, A. K., Dobie, P., Minai, J., and Zaleski, C. (2014). Integrated landscape initiatives for african agriculture, development, and conservation: a region-wide assessment. World Dev. 54, 68–80. doi: 10.1016/j.worlddev.2013.07.006

CrossRef Full Text | Google Scholar

Minang, P. A., van Noordwijk, M., Freeman, O. E., Mbow, C., de Leeuw, J., and Catacutan, D. (Eds) (2015). Climate-Smart Landscapes: Multifunctionality In Practice. Nairobi: World Agroforestry Centre (ICRAF).

Google Scholar

Moglia, M., Alexander, K. S., Thephavanh, M., Thammavong, P., Sodahak, V., Khounsy, B., and Case, P. (2018). A Bayesian network model to explore practice change by smallholder rice farmers in Lao PDR. Agric. Syst. 164, 84–94. doi: 10.1016/j.agsy.2018.04.004

CrossRef Full Text | Google Scholar

Morgan, M. G. (2014). Use (and abuse) of expert elicitation in support of decision making for public policy. Proc. Natl. Acad. Sci. U.S.A. 111, 7176–7184. doi: 10.1073/pnas.1319946111

PubMed Abstract | CrossRef Full Text | Google Scholar

Mutyasira, V., Hoag, D., and Pendell, D. (2018). The adoption of sustainable agricultural practices by smallholder farmers in Ethiopian highlands: an integrative approach. Cogent Food Agric. 4, 1–17. doi: 10.1080/23311932.2018.1552439

CrossRef Full Text | Google Scholar

Neufeldt, H., Negra, C., Hancock, J., Foster, K., Devashree, N., and Singh, P. (2015). Scaling Up Climate Smart Agriculture: Lessons Learned From South Asia and Pathways for Success. Nairobi: World Agroforestry Centre. doi: 10.5716/WP15720.PDF

CrossRef Full Text | Google Scholar

Newman, K. (2014). What is the Evidence on the Impact of Research on International Development? London: UK Department for International Development (DFID).

Google Scholar

Ntshotsho, P., Prozesky, H. E., Esler, K. J., and Reyers, B. (2015). What drives the use of scientific evidence in decision making? The case of the South African Working for Water program. Biol. Conserv. 184, 136–144. doi: 10.1016/j.biocon.2015.01.021

CrossRef Full Text | Google Scholar

ODI (2019). ROMA. Available at: https://www.odi.org/features/roma/home (accessed April 25, 2019).

O'Donoughue Jenkins, L., Kelly, P. M., Cherbuin, N., and Anstey, K. J. (2016). Evaluating and using observational evidence: the contrasting views of policy makers and epidemiologists. Front. Public Heal. 4, 1–9. doi: 10.3389/fpubh.2016.00267

PubMed Abstract | CrossRef Full Text | Google Scholar

OECD (2001). Multifunctionality: Towards an Analytical Framework. Paris: OECD. doi: 10.1787/9789264192171-en

CrossRef Full Text | Google Scholar

O'Farrell, P. J., and Anderson, P. M. L. (2010). Sustainable multifunctional landscapes : a review to implementation. Curr. Opin. Environ. Sustain. 2, 59–65. doi: 10.1016/j.cosust.2010.02.005

CrossRef Full Text | Google Scholar

Oliver, K., Innvaer, S., Lorenc, T., Woodman, J., and Thomas, J. (2014). A systematic review of barriers to and facilitators of the use of evidence by policymakers. BMC Health Serv. Res. 14:2. doi: 10.1186/1472-6963-14-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Pali, P. N., Schut, M., Kibwika, P., Wairegi, L., Yami, M., van Asten, P. J. A., et al. (2018). Opportunities and pitfalls for researchers to contribute to the design of evidence-based agricultural policies: lessons from Uganda. Int. J. Agric. Sustain. 16, 272–285. doi: 10.1080/14735903.2018.1471830

CrossRef Full Text | Google Scholar

Piirainen, K. A. (2014). “Monitoring and evaluating investments,” in Finland as a Knowledge Economy 2.0: Lessons on Policies and Governance. Directions in Development, eds K. Halme, I. Lindy, K. A. Piirainen, V. Salminen, and J. White (Washington, DC: World Bank). doi: 10.1596/978-1-4648-0194-5

CrossRef Full Text | Google Scholar

Pretty, J. N. (1995). Participatory learning for sustainable agriculture. World Dev. 23, 1247–1263. doi: 10.1016/0305-750X(95)00046-F

CrossRef Full Text | Google Scholar

Primmer, E., and Furman, E. (2012). Operationalising ecosystem service approaches for governance: do measuring, mapping and valuing integrate sector-specific knowledge systems? Ecosyst. Serv. 1, 85–92. doi: 10.1016/j.ecoser.2012.07.008

CrossRef Full Text | Google Scholar

Pullin, A. S., Knight, T. M., and Watkinson, A. R. (2009). Linking reductionist science and holistic policy using systematic reviews: unpacking environmental policy questions to construct an evidence-based framework. J. Appl. Ecol. 46, 970–975. doi: 10.1111/j.1365-2664.2009.01704.x

CrossRef Full Text | Google Scholar

Rathwell, K. J., Armitage, D., and Berkes, F. (2015). Bridging knowledge systems to enhance governance of the environmental commons: a typology of settings. Int. J. Commons 9, 851–880. doi: 10.18352/ijc.584

CrossRef Full Text | Google Scholar

Reed, J., Deakin, L., and Sunderland, T. (2014a). What are “Integrated Landscape Approaches” and how effectively have they been implemented in the tropics: a systematic map protocol. Environ. Evid. 4, 1–7. doi: 10.1186/2047-2382-4-2

CrossRef Full Text | Google Scholar

Reed, J., Van Vianen, J., Deakin, E. L., Barlow, J., and Sunderland, T. (2016). Integrated landscape approaches to managing social and environmental issues in the tropics: learning from the past to guide the future. Glob. Chang. Biol. 22, 2540–2554. doi: 10.1111/gcb.13284

PubMed Abstract | CrossRef Full Text | Google Scholar

Reed, M. S., Stringer, L. C., Fazey, I., Evely, a,. C., and Kruijsen, J. H. J. (2014b). Five principles for the practice of knowledge exchange in environmental management. J. Environ. Manage. 146, 337–345. doi: 10.1016/j.jenvman.2014.07.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Rietig, K. (2016). The links among contested knowledge, beliefs, and learning in european climate governance: from consensus to conflict in reforming biofuels policy. Policy Stud. J. 46, 137–159. doi: 10.1111/psj.12169

CrossRef Full Text | Google Scholar

Rose, D. C., Mukherjee, N., Simmons, B. I., Tew, E. R., Robertson, R. J., Vadrot, A. B. M., et al. (2017). Policy windows for the environment: tips for improving the uptake of scientific knowledge. Environ. Sci. Policy. doi: 10.1016/j.envsci.2017.07.013

CrossRef Full Text | Google Scholar

Rosenthal, A., Verutes, G., McKenzie, E., Arkema, K. K., Bhagabati, N., Bremer, L. L., et al. (2015). Process matters: a framework for conducting decision-relevant assessments of ecosystem services. Int. J. Biodivers. Sci. Ecosyst. Serv. Manag. 11, 190–204. doi: 10.1080/21513732.2014.966149

CrossRef Full Text | Google Scholar

Rossing, W. A. H., Zander, P., Josien, E., Groot, J. C. J., Meyer, B. C., and Knierim, A. (2007). Integrative modelling approaches for analysis of impact of multifunctional agriculture: a review for France, Germany and The Netherlands. Agric. Ecosyst. Environ. 120, 41–57. doi: 10.1016/j.agee.2006.05.031

CrossRef Full Text | Google Scholar

Rowe, G., and Frewer, L. J. (2005). A typology of public engagement mechanisms. Sci. Technol. Hum. Values 30, 251–290. doi: 10.1177/0162243904271724

CrossRef Full Text | Google Scholar

Ruckelshaus, M., McKenzie, E., Tallis, H., Guerry, A., Daily, G., Kareiva, P., et al. (2015). Notes from the field: lessons learned from using ecosystem service approaches to inform real-world decisions. Ecol. Econ. 115, 11–21. doi: 10.1016/j.ecolecon.2013.07.009

CrossRef Full Text | Google Scholar

Saarikoski, H., Primmer, E., Saarela, S. R., Antunes, P., Aszalós, R., Bar,ó, F., et al. (2018). Institutional challenges in putting ecosystem service knowledge in practice. Ecosyst. Serv. 29, 579–598. doi: 10.1016/j.ecoser.2017.07.019

CrossRef Full Text | Google Scholar

Sánchez-Bayo, F., and Wyckhuys, K. A. G. (2019). Worldwide decline of the entomofauna: a review of its drivers. Biol. Conserv. 232, 8–27. doi: 10.1016/j.biocon.2019.01.020

CrossRef Full Text | Google Scholar

Sargeant, J. M., Rajic, A., Read, S., and Ohlsson, A. (2006). The process of systematic review and its application in agri-food public-health. Prev. Vet. Med. 75, 141–151. doi: 10.1016/j.prevetmed.2006.03.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Sarkki, S., Niemel,ä, J., Tinch, R., van den Hove, S., Watt, A., and Young, J. (2013). Balancing credibility, relevance and legitimacy: a critical assessment of trade-offs in science-policy interfaces. Sci. Public Policy 41, 194–206. doi: 10.1093/scipol/sct046

CrossRef Full Text | Google Scholar

Sayer, J.a, Margules, C., Boedhihartono, A. K., Sunderland, T., Langston, J. D., Reed, J., et al. (2017). Measuring the effectiveness of landscape approaches to conservation and development. Sustain. Sci. 12, 465–476. doi: 10.1007/s11625-016-0415-z

CrossRef Full Text | Google Scholar

Sayer, J., Sunderland, T., Ghazoul, J., Pfund, J.-L., Sheil, D., Meijaard, E., et al. (2013). Ten principles for a landscape approach to reconciling agriculture, conservation, and other competing land uses. Proc. Natl. Acad. Sci. U.S.A. 110, 8349–8356. doi: 10.1073/pnas.1210595110

PubMed Abstract | CrossRef Full Text | Google Scholar

Scherr, S. J., Shames, S., and Friedman, R. (2012). From climate-smart agriculture to climate-smart landscapes. Agric Food Sec. 1, 1–15. doi: 10.1186/2048-7010-1-12

CrossRef Full Text | Google Scholar

Schut, M., Klerkx, L., Rodenburg, J., Kayeke, J., Hinnou, L. C., Raboanarielina, C. M., et al. (2015). RAAIS: Rapid Appraisal of Agricultural Innovation Systems (Part I). A diagnostic tool for integrated analysis of complex problems and innovation capacity. Agric. Syst. 132, 1–11. doi: 10.1016/j.agsy.2014.08.009

CrossRef Full Text | Google Scholar

Schwartz, M. W., Hiers, J. K., Davis, F. W., Garfin, G. M., Jackson, S. T., Terando, A. J., et al. (2017). Developing a translational ecology workforce. Front. Ecol. Environ. 15, 587–596. doi: 10.1002/fee.1732

CrossRef Full Text | Google Scholar

Selomane, O., Reyers, B., Biggs, R., Tallis, H., and Polasky, S. (2015). Towards integrated social–ecological sustainability indicators: exploring the contribution and gaps in existing global data. Ecol. Econ. 118, 140–146. doi: 10.1016/j.ecolecon.2015.07.024

CrossRef Full Text | Google Scholar

Shaxson, L., Bielak, A., Ahmed, I., Brien, D., Conant, B., Fisher, C., et al. (2012). Expanding our understanding of K* (Kt, KE, Ktt, KMb, KB, KM, etc.). A concept paper emerging from the K* conference held in Hamilton, Ontario, Canada, April 2012. Hamilton, ON: United Nations University, Institute for Water, Environment and Health. Available online at: https://assets.publishing.service.gov.uk/media/57a08a6e40f0b649740005ba/KStar_ConceptPaper_FINAL_Oct29_WEBsmaller.pdf

Google Scholar

Smith, A., Snapp, S., Chikowo, R., Thorne, P., Bekunda, M., and Glover, J. (2017). Measuring sustainable intensification in smallholder agroecosystems: a review. Glob. Food Sec. 12, 127–138. doi: 10.1016/j.gfs.2016.11.002

CrossRef Full Text | Google Scholar

Smith, E. P. (2002). “BACI design,” in Encyclopedia of Environmetrics, eds A. H. El-Shaarawi and W. W. Piegorsch (Chichester: John Wiley & Sons, Ltd.), 141–148.

Google Scholar

Smith, P., Bustamante, M., Ahammad, H., Clark, H., Dong, H., Elsiddig, E. A., et al. (2014). “Agriculture, Forestry and Other Land Use (AFOLU),” in Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, eds O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, et al. (Cambridge, New York, NY: Cambridge University Press, 811–922.

Google Scholar

Springmann, M., Clark, M., Mason-D'croz, D., Wiebe, K., Leon Bodirsky, B., Lassaletta, L., et al. (2018). Options for keeping the food system within environmental limits. Nature. 562, 519–525. doi: 10.1038/s41586-018-0594-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Stewart, R., Langer, L., Rebelo da Silva, N., Muchiri, E., Zaranyika, H., Erasmus, Y., et al. (2015). The effects of training, innovation and new technology on African smallholder farmers' wealth and food security: a systematic review, 3ie Systematic Review 19. International Initiative for Impact Evaluation (3ie). London. doi: 10.23846/SRS006

CrossRef Full Text | Google Scholar

Sunderland, T., Sunderland-Groves, J., Shanley, P., and Campbell, B. (2009). Bridging the gap: how can information access and exchange between conservation biologists and field practitioners be improved for better conservation outcomes? Biotropica 41, 549–554. doi: 10.1111/j.1744-7429.2009.00557.x

CrossRef Full Text | Google Scholar

Sutherland, W., Dicks, L., Ockendon, N., Petrovan, S., and Smith, R. (2019). What Works in Conservation 2019. Cambridge: Open Book Publishers. doi: 10.11647/OBP.0179

CrossRef Full Text | Google Scholar

Sutherland, W. J., Armstrong-Brown, S., Armsworth, P. R., Brereton, T., Brickland, J., Campbell, C. D., et al. (2006). The identification of 100 ecological questions of high policy relevance in the UK. J. Appl. Ecol. 43, 617–627. doi: 10.1111/j.1365-2664.2006.01188.x

CrossRef Full Text | Google Scholar

Sutherland, W. J., Pullin, A. S., Dolman, P. M., and Knight, T. M. (2004). The need for evidence-based conservation. Trends Ecol. Evol. 19, 305–308. doi: 10.1016/j.tree.2004.03.018

PubMed Abstract | CrossRef Full Text | Google Scholar

Sutherland, W. J., and Wordley, C. F. R. (2017). Evidence complacency hampers conservation. Nat. Ecol. Evol. 1, 1215–1216. doi: 10.1038/s41559-017-0244-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Teklewold, H., Kassie, M., and Shiferaw, B. (2012). Adoption of multiple sustainable agricultural practices in rural Ethiopia. J. Agric. Econ. 64, 597–623. doi: 10.1111/1477-9552.12011

CrossRef Full Text | Google Scholar

Tilman, D., Fargione, J., Wolff, B., D'Antonio, C., Dobson, A., Howarth, R., et al. (2001). Forecasting agriculturally driven global environmental change. Science 292, 281–284. doi: 10.1126/science.1057544

PubMed Abstract | CrossRef Full Text | Google Scholar

Tress, B., Tress, G., and Fry, G. (2005). Integrative studies on rural landscapes: policy expectations and research practice. Landsc. Urban Plan. 70, 177–191. doi: 10.1016/j.landurbplan.2003.10.013

CrossRef Full Text | Google Scholar

Turnhout, E., Stuiver, M., Klostermann, J., Harms, B., and Leeuwis, C. (2013). New roles of science in society: different repertoires of knowledge brokering. Sci. Public Policy 40, 354–365. doi: 10.1093/scipol/scs114

CrossRef Full Text | Google Scholar

UK National Audit Office (2003). Getting the evidence : Using Research in Policy Making. London: UK National Audit Office.

Google Scholar

ValuES (2019). ValuES: Methods for Integrating Ecosystem Services Into Policy, Planning, and Practice. Bonn: GIZ. Available online at: http://aboutvalues.net/ (accessed August 26, 2019).

Google Scholar

Waqa, G., Bell, C., Snowdon, W., and Moodie, M. (2017). Factors affecting evidence-use in food policy-making processes in health and agriculture in Fiji. BMC Public Health 17:51. doi: 10.1186/s12889-016-3944-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Watson, R. T. (2005). Turning science into policy: challenges and experiences from the science-policy interface. Philos. Trans. R. Soc. B Biol. Sci. 360, 471–477. doi: 10.1098/rstb.2004.1601

PubMed Abstract | CrossRef Full Text | Google Scholar

Whitney, C., Shepherd, K., and Luedeling, E. (2018a). “Decision analysis methods guide; Agricultural policy for nutrition,” in Working Paper No. 275 (Nairobi: World Agroforestry Centre). Available online at: http://dx.doi.org/10.5716/WP18001.PDF

Google Scholar

Whitney, C. W., Lanzanova, D., Muchiri, C., Shepherd, K. D., Rosenstock, T. S., Krawinkel, M., et al. (2018b). Probabilistic decision tools for determining impacts of agricultural development policy on household nutrition. Earth's Futur. 6, 359–372. doi: 10.1002/2017EF000765

CrossRef Full Text | Google Scholar

Willett, W., Rockström, J., Loken, B., Springmann, M., Lang, T., Vermeulen, S., et al. (2019). Food in the anthropocene: the EAT-lancet commission on healthy diets from sustainable food systems. Lancet 393, 447–492. doi: 10.1016/S0140-6736(18)31788-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Wilson, D. C., Smith, N. A., Blakey, N. C., and Shaxson, L. (2007). Using research-based knowledge to underpin waste and resources policy. Waste Manag. Res. 25, 247–256. doi: 10.1177/0734242X07079154

PubMed Abstract | CrossRef Full Text | Google Scholar

WOCAT (2019). World Overview of Conservation Approaches and Technologies. Available at: https://www.wocat.net/en/ (accessed August 26, 2019).

Google Scholar

Wood, S. L. R., Jones, S. K., Johnson, J. A., Brauman, K. A., Chaplin-kramer, R., Fremier, A., et al. (2018). Distilling the role of ecosystem services in the Sustainable Development Goals. 29, 70–72. doi: 10.1016/j.ecoser.2017.10.010

CrossRef Full Text | Google Scholar

World Bank (2019). World Bank Data. Available online at: https://data.worldbank.org/indicator/AG.LND.AGRI.ZS?end=2016&start=1961

Google Scholar

WWF (2018). “The threats and pressures wiping out our world,” in Living Planet Report 2018: Aiming Higher, eds M. Grooten and R. E. A. Almond (Gland: WWF).

Google Scholar

Yami, M., and Van Asten, P. J. A. (2017). Policy support for sustainable crop intensification in Eastern Africa. J. Rural Studies. 55, 216–226. doi: 10.1016/j.jrurstud.2017.08.012

CrossRef Full Text | Google Scholar

Young, J., Shaxson, L., Jones, H., Hearn, S., Datta, A., and Cassidy, C. (2014). ROMA: A Guide to Policy Engagement and Influence. London: Overseas Development Institute. Available online at: https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinionfiles/9011.pdf

Google Scholar

Zanzanaini, C., Trãn, B. T., Singh, C., Hart, A., Milder, J., and DeClerck, F. (2017). Integrated landscape initiatives for agriculture, livelihoods and ecosystem conservation: an assessment of experiences from South and Southeast Asia. Landsc. Urban Plan. 165, 11–21. doi: 10.1016/j.landurbplan.2017.03.010

CrossRef Full Text | Google Scholar