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A pervasive challenge to ending hunger and reducing poverty is that farming practices known to sustainably increase production and reduce and reverse land degradation processes are not widely adopted by smallholder farmers. Current approaches to development-focused agricultural research typically focus on maximising the productivity of one component of the farming system and evaluating innovations based on averages and plot-scale metrics such as yield per hectare. Yet, for restorative farming practices to be adopted, they not only need to be productive and profitable they also need to be attractive within the broader context of smallholder livelihood systems. This thesis responds to the need for systemic approaches to evaluating innovations and attempts to embrace both the complexity and diversity of smallholder livelihood systems through its methods. Conducted in the context of a large-scale land restoration project working with over 2,500 farmers in the eastern drylands of Kenya, my overarching goal was to assess the impact of on-farm restoration practices (planting basins and tree planting) on the livelihoods of participating households, so as to improve the specificity of recommendations and scaling of restoration efforts. In Chapter 2, I present various analytical approaches to assessing the efficacy of planting basins for growing maize. In doing so, I demonstrate how even simple arithmetic and translating plot- scale metrics into farm-scale metrics can be a helpful first step towards more farmer-relevant assessments. In Chapter 3, I develop a farm-scale model that extends the results from Chapter 2 across multiple years and explores the impact of planting basins for two households with contrasting resource endowments. Chapters 4 and 5 take a more qualitative and inductive approach, broadening the scope of my assessment to include the role of intrahousehold gender dynamics and aspirations. Combining survey data on decision-making and labour participation with interviews and focus group discussions, I investigate how gender roles and relations influence the uptake of planting basins and tree planting. In Chapter 5, I explore the changing livelihood aspirations of rural women amidst intensifying male outmigration using a novel narrative-based survey tool, contributing to a more nuanced understanding of rural aspirations and the socio-cultural context within which they are embedded. Although planting basins provided impressive gains in average maize yield, there was strong variability in their performance. For some households, they provided substantial production increases, extra days of food and a potential income boost, while other households faced substantial losses. This variability in performance presents substantial risks for farmers when basins are promoted as a generalised recommendation. Furthermore, plot-scale metrics such as yield and intensity of adoption overlooked the wider role planting basins play within people's livelihoods, other than maximising yield (e.g., yield stability) and share of benefits amongst household members. Chapters 4 and 5 revealed that heterogeneity also exists in the aspirations of those within households and that women are likely to be important catalysts of agricultural innovation amid the increasing outmigration of men and feminisation of farm management. Attempting to apply a more systemic approach to evaluating restoration practices, this thesis is interdisciplinary in nature and broad in its scope and use of methods. It brings together alternative approaches to assessing agricultural innovations, and in doing so, stresses the need for development-focused agricultural research to step away from a fixation on differences in mean yield and to embrace variation in innovation performance and complexity of smallholder livelihoods, not avoid it.

Smallholder agricultural systems in Northwest Vietnam face a number of significant economic and ecological challenges. The move from shifting cultivation to more static farming methods, particularly maize farming, in this mountainous region has resulted in high levels of soil erosion. Agroforestry offers a potential mechanism to both stabilise these systems and to diversify production. The four chapters presented in this PhD thesis develop an integrated framework to scale up agroforestry options in Northwest Vietnam. Given the cultural and biophysical heterogeneity of this area it was assumed that there were no “one-size-fit-all” agroforestry solution. The concept of developing options that fitted, and were appropriate, to local contexts was used as a guiding principle. The framework was developed by mapping the extent and prevalence of soil degradation across the agricultural landscape and associating that with biophysical suitability mapping for different agroforestry options. Next the impact of social factors, including ethnicity and gender, were evaluated in terms of how they influence adoption of different agroforestry options. Finally the impact of social factors including ethnicity and gender affect adoption of different agroforestry options were assessed. Finally, shaded coffee agroforestry, an example of existing agroforestry system with potential for expansion, was explored as a case study to capture farmers’ existing knowledge of the costs and benefits of integration of trees on farmland to identify opportunities for knowledge exchange. The study provides the first systematic evaluation of soil erosion in the Northwest – showing that the area of cropland on erosion prone slopes has been underestimated (and is almost double the area identified by officially reported data). Agroforestry systems are an appropriate response option to this degradation as they have well established soil stabilisation benefits, particularly in contour planting patterns. Biophysical suitability analysis of different agroforestry options showed that 85 % of study site areas was viable for such agroforestry systems using high value tree species. The study found that farmers’ preferences for agroforestry adoption were highly influenced by social norms associated with different ethnic minorities who make up the majority of the farming population in this area (this study focused on the Kinh, Thai and H'mong groups). A case study looking at gender in H'mong communities found that H'mong men and women had very differentiated roles within the current agricultural systems and were subject to different constraints and interests in relation to agroforestry adoption opportunities. These social norms currently limit H'mong women’s full participation in agroforestry expansion. In terms of design, tree selection for agroforestry was significantly influenced by the economic value to the tree (either as timber or fruit) and accessibility to market. Detailed analysis of farmers’ understanding of the benefits of different trees in coffee systems explained why there were fewer native species in the areas with better road accessibility despite the farmers’ own valuations of non-native tree species limited ecological benefits to coffee trees. The native leguminous tree Leucaena leucocephala and fruit tree Dimocarpus longan were identified by farmers as providing the broadest range of environmental benefits to coffee systems. However, Leucaena leucocephala had limited economic value and was primarily found only in the areas far away from roads and market. On the contrary, Dimocarpus longan had high market value, was found in more than 50% of surveyed farms, mostly near and medium distance to market. These results help local extension institutions understand current selection criteria of the ‘right’ tree species according to the local context. An integrated framework for scaling out agroforestry adoption is proposed and discussed in the synthesis chapter. It is tailored to a Vietnamese context through provision of greater detail on the context, options and actors involved in potential agroforestry systems in Northwest Vietnam.

Globally, there is an emerging interest in scaling the adoption of land restoration interventions as a sustainable approach to improving food security. Past attempts to scale restoration interventions have tended to promote a few generic options that are often poorly tailored to their context. This has resulted in restoration interventions that are not locally adapted, acceptable or cost-effective. I explored the potential for utilising local knowledge to better inform land restoration options across scale. Local knowledge was elicited through a systematic knowledge-based systems approach involving smallholder farmers (n=482) using 'paired-landscape experimental design' for degrading and recovering systems in both Rwanda and Ethiopia. My findings demonstrate that in landscapes affected by degradation, livelihood systems operate across broad landscape scales beyond the farm boundary and have wide spatial livelihood system boundaries and as a result the thesis was able to capture locally informed scaling dimensions. This included identifying use of indicators, such as Dichogaster itoliensis (an earthworm new to science) whose burrowing behaviour is a local indicator of soil quality. Local knowledge can also be used to inform understanding about ecosystem service scaling processes (both spatial and temporal) but also for identifying critical knowledge gaps in farmer understanding about degradation processes. This local knowledge informs how farmers adapt and modify their land restoration interventions to better suit their needs and context; hence the acquisition and analysis of local knowledge provides an effective mechanism to track iterative development of adaptation measures and to evaluate both positive and negative consequences resulting from these actions. These findings support the 'options by context' approach to 'research in development' for adapting restoration technologies (with a focus here on agroforestry systems) to better suit the needs of smallholder farmers trying to recover their soils and points towards the need for further integration of local knowledge in the development of restoration activity.

There is increasing worldwide interest in agroforestry as a multifunctional land-use strategy that can improve farm productivity while generating multiple ecosystem services necessary for human populations trying to adapt to and mitigate climate change and restore degraded landscapes. However there are critical knowledge gaps in our scientific understanding of how a diversity of woody species under given management practices can be enhanced across different landscapes to deliver a range of these benefits. Reflections on the agenda for scaling up agroforestry suggest that there is no ‘one size fits all’ technology that can be promoted across large areas; instead menus of options have to be tailored to local contextual variations. With the increasing evidence that systematic acquisition of local knowledge is a valuable way of complementing scientific information, there is an urgent need to develop novel techniques to further elicit local knowledge and integrate it into all efforts to develop more inclusive agroforestry options, co-designing them to build resilience in landscapes and livelihoods. The four papers presented in this thesis draw from research in diverse smallholder landscapes in Sub-Saharan Africa. I used different participatory research methods to explore farmers’ tree management practices and knowledge of a diversity of trees, along with their functions and the agro-ecological interactions at play in land-use and livelihood dynamics. These contrasting contexts illustrate a diversity of major agricultural systems, encompassing cocoa farming in Côte d’Ivoire, coffee cultivation in Rwanda, and the management of native multiple purpose trees in the West African agroforestry parklands in Burkina Faso. The ‘options-by-context’ approach, applied through a multiple stakeholder engagement process to address the heterogeneity in the landscape and of land users in eastern Democratic Republic of Congo (DRC), holds general lessons for scaling up agroforestry. The key findings of the research show that farmers’ knowledge about a range of useful native and exotic trees, as well as the contextual variations associated with their management, is rich and complementary to science and can be articulated both qualitatively and quantitatively. The novel way of ranking trees by attributes and the explicit probability model, developed and tested in both the tropical highland context in Rwanda and in the semi-arid parklands in Burkina Faso, was found to be a quick and cost-effective way of classifying a broad range of trees managed by farmers based on ecological, management and utility attributes. The tree ranking estimates were consistent and in agreement with scientific assessments when they could be compared, thus allowing predictions for some of their agroecological effects. This knowledge can be explicitly integrated in tree-planting or agroforestry development initiatives to provide for more objective assessments of how a diverse range of tree species, largely unknown to science but important in farmers’ practice, might be expected to affect farm production and other ecosystem services. The stakeholder engagement approach used in eastern DRC was innovative as it built on explicit acquisition of local knowledge to facilitate a systematic consideration of trees at field, farm and landscape scales. This enabled the consideration of different options, in terms of practices/technologies but also market interventions and institutional reform against the contexts for which they were relevant (covering ecological, economic, social and cultural factors). We found that this approach led to a change in the attitudes and knowledge around tree planting by stakeholders, with an important shift away from the promotion of a handful of exotic tree species in woodlots, largely benefiting wealthier men, to recommendations for over 70 tree species, 30 of them native, with management practices that addressed the needs of women, various ethnic groups and different types of farmers. I conclude that a knowledge-intensive framework is required to design more inclusive, locally adapted and diversified tree-based options that aim to deliver both environmental and socio-economic benefits to a wider range of stakeholders. This framework explicitly looks at integrating local and scientific knowledge through the facilitation of broad-based stakeholder participation to identify agroforestry options for different contexts and the preconditions that may require interventions in the enabling environment. The next steps would be to investigate whether, by being sensitive to the needs and context of different smallholders, a knowledge-intensive framework leads to more effective scaling up of agroforestry than do conventional approaches to tree planting.

There are approximately 500 million smallholder farmer households worldwide, at least half of which live in poverty and food insecurity. Scientific research underpins development efforts by providing options for improved varieties, breeds, or practices (termed “interventions”); and by providing analyses of how to increase the adoption and impact of those interventions. One of the most widely used method of data collection to evaluate or predict the impact of interventions is the household survey, but critical evaluations of the effectiveness of household survey data and methodologies are rare. Lack of standardised questions make efforts to aggregate findings across datasets challenging, given that different surveys often yield widely different data, both in terms of content and quality, which severely limits the comparability of those data (see Chapters 2 and 3). Here I present an improved survey method to assess farm practices and food security for smallholder households in lower income countries, primarily tropical or sub-tropical. The tool is named the Rural Household Multi-Indicator Survey (RHoMIS). It makes use of recent advances in digital technologies, which enables quicker data collection and reporting than in previous generations of survey tools. The tool was designed to be rapid, lean, user-friendly, flexible and reliable (Chapter 3). The design ethic and advances in indicator formulation allowed data to be gathered on a wider range of topics over shorter time frames but still with adequate depth to permit effective analyses (Chapters 4, 5, and 6). During development RHoMIS was deployed by 13 organisations in 17 countries, with over 15,000 interviews conducted. The tool has the flexibility needed for application in many locations, sufficient standardisation to permit rapid analysis and data aggregation between sites, and enables more efficient characterisation of smallholder farming systems compared to previous efforts. Findings of analyses presented in this thesis stress the need to understand the heterogeneity of smallholders, and to plan or evaluate interventions for specific subsets of households. Analyses presented in the research chapters show that the farm strategy of input intensification is better suited to larger farms, crop diversification is better suited to smaller farms, and that the effects are strongly influenced by the degree of commercialisation of livelihoods (Chapter 3), the use of collected resources can strongly benefit the poorest households (Chapter 4), and that off-farm incomes in combination with farm intensification hold the potential to raise the prosperity of about 90% of the households studied in Chapter 5 . Furthermore households show different levels of interest in trialling and adopting new practices which are not necessarily related to their assets or farm types, in Chapter 2 about one quarter of households were identified as likely to trial new practices. In particular the analyses highlights that those experiencing (or at risk of) extreme food insecurity benefited most from opportunities for off-farm income, whereas moderately poor households benefited more from agricultural intensification. These findings indicate that for agricultural intensification measures to raise households out of food insecurity and poverty they must be targeted to the appropriate group of smallholders, and to succeed must be in combination with opportunities to earn off farm income.