Societal Impact Statement DNA fingerprinting is becoming the standard measurement procedure in crop adoption studies in the Global South, yet the lack of systematic and intentional sampling regarding which farmer to go to the plot with may bias results from these methods. We introduce a methodological innovation, Sampling for Socially Inclusive Adoption Studies (SSAS), to offer a more inclusive sampling strategy. We find that SSAS enables research around how respondent socio-demographic differences, respondent selection approaches, and intra-household dynamics shape DNA fingerprinting-based adoption studies. SSAS can serve as a tool for more participatory, nuanced, and context-sensitive research design for these studies.Summary The lack of intentional sampling of farmers may explain the disconnect between genomic and self-reported data in DNA fingerprinting-based crop varietal adoption studies. We introduce a methodological innovation, Sampling for Socially Inclusive Adoption Studies (SSAS), that interlinks socio-demographic information, a measurement of the information endowment of a respondent, intra-household decision making, and plot level DNA fingerprinting sampling. SSAS comprises two main parts; the first is an intra-household survey, administered to two individual adult decision makers within a farming household, which generates an estimate of their knowledge of the crop studied to determine who will be engaged for leaf tissue sampling. The second part is collecting leaf tissue in the field with the chosen respondent. We piloted the SSAS method with a small set of common bean growers in Costa Rica. The pilot data showed that application of SSAS generated data that would guide breeding programs to identify respondents for DNA fingerprinting studies and relate their results to decision making dynamics and household headship within households. To our knowledge, this is the first DNA fingerprinting approach to be developed and piloted directly by a National Agricultural Research Institution. We provide practical guidance for applying SSAS in resource-constrained institutions and offer a path forward for wider adoption of DNA fingerprinting methods. SSAS is a tool for exploring broader dynamics of knowledge, networks, intrahousehold dynamics, and inequality within agricultural production, opening up entry points for more participatory, nuanced, and context-sensitive research design.

Soil erosion threatens mixed farms in marginal areas, endangering their cultural and economic role in territories where pastoralist systems are already under pressure for climatic, socioeconomic, and generational factors. The rise in extreme rainfall events worsens soil loss on farmland, underscoring the need to co-develop practices that boost climate resilience in agriculture. This study helps fill the gap in understanding how the integration of farmers' perceptions with spatial modeling can inform land management strategies. We combined farmers' perceptions, model predictions, and farm management to provide an integrated assessment of the soil erosion. We represented the geographical distribution of soil erosion risk through geographical information systems-based RUSLE modeling. Farmers' perceptions on soil erosion were assessed through surveys and fuzzy cognitive mapping conducted across 25 sheep farms. Our model shows that 37% of cropland is at risk, mainly due to land topography and soil cover. Fuzzy cognitive maps reveal that farmers are aware of the main environmental and human-linked soil erosion drivers. Farmers recognize cropping system design, especially using perennial forage instead of annual crops, as key to reducing soil erosion, and also see temporary ditches, reduced tillage, and agroforestry as effective measures. Utilizing a multivariate ordinal logistic regression, we showed that sheep farmers with a higher education level tend to perceive higher soil erosion risk. The number of conservation measures adopted increases when farmers are more aware of soil erosion issues, when they identify a higher number of fuzzy cognitive map connections, and when the predicted soil erosion risk is higher. Farmers' perceptions of erosion risks and soil conservation measures aligned with model predictions on soil erosion, highlighting the importance of systematically involving farmers in research and policy design. Their detailed mental models enhance environmental models and should be considered in the European Common Agricultural Policy for sustainable rural development.

Finger millet is a climate-resilient crop providing food and nutrition security and income In Uganda. However, the current productivity of finger millet in farmers’ fields is low and among other factors, this is due to the poor adoption of improved varieties. With this study we aim to identify and profile varietal traits preferred by finger millet farmers and consumers in Uganda. We specifically focus on how these traits vary among women and men in the Ugandan finger millet value chain. We collect data using semi-structured questionnaires among 170 households growing millet in Bushenyi, Lira, and Nwoya districts, and we triangulate questionnaires replies with qualitative information from 11 focus group discussions and 3 key informant interviews. Using descriptive statistics and probit regression models, we find that the majority of the farmers (97%) prefer growing landrace varieties of finger millet compared to only 3% growing improved varieties. The most preferred varieties were Kaguma in Bushenyi, Ajuko Manyige in Nwoya, Kal Atar, and Okello Chiba in Lira. Farmers’ choice of variety depends on a combination of traits including agronomic, marketing, and consumption traits. Gender, marital status, education levels, and occupation are the major socio-demographic factors that influence specific preferences related to finger millet variety. This study lays a foundation for designing a gender-responsive finger millet product profile to guide the development and release of new varieties by the finger millet crop improvement program.

Economic and population growth increasingly pressure the Earth system. Fertile soils are essential to ensure global food security, requiring high-yielding agro-technological regimes to cope with rising soil degradation and macro-nutrients deficiencies, which may be further exacerbated by climate change. In this work, we extend the AgriLOVE land-use agent-based model (Coronese et al., 2023) to investigate trade-offs in the transition between conventional and sustainable farming regimes in a smallholder economy exposed to explicit environmental boundaries. We investigate the ability of the system to favor a sustainable transition when prolonged conventional farming leads to soil depletion. First, we showcase the emergence of three endogenous scenarios of transition and lock-in. Then, we analyze transition dynamics under several behavioral, environmental and policy scenarios. Our results highlights a strong path-dependence of the agricultural sector, with scarce capacity to foster successful transitions to a sustainable regime in absence of external interventions. The role of behavioral changes is limited and we find evidence of negative tipping points induced by mismanagement of grassland and forests. These findings call for policies strongly supporting sustainable agriculture. We test regulatory measures aimed at protecting common environmental goods and public incentives to encourage the search for novel production techniques targeted at closing the sustainable-conventional yield gap. We find that their effectiveness is highly time-dependent, with rapidly closing windows of opportunity.

Matching crop varieties to their target use context and user preferences is a challenge faced by many plant breeding programs serving smallholder agriculture. Numerous participatory approaches proposed by CGIAR and other research teams over the last four decades have attempted to capture farmers’ priorities/preferences and crop variety field performance in representative growing environments through experimental trials with higher external validity. Yet none have overcome the challenges of scalability, data validity and reliability, and difficulties in capturing socio-economic and environmental heterogeneity. Building on the strengths of these attempts, we developed a new data-generation approach, called triadic comparison of technology options (tricot). Tricot is a decentralized experimental approach supported by crowdsourced citizen science. In this article, we review the development, validation, and evolution of the tricot approach, through our own research results and reviewing the literature in which tricot approaches have been successfully applied. The first results indicated that tricot-aggregated farmer-led assessments contained information with adequate validity and that reliability could be achieved with a large sample. Costs were lower than current participatory approaches. Scaling the tricot approach into a large on-farm testing network successfully registered specific climatic effects of crop variety performance in representative growing environments. Tricot’s recent application in plant breeding networks in relation to decision-making has (i) advanced plant breeding lines recognizing socio-economic heterogeneity, and (ii) identified consumers’ preferences and market demands, generating alternative breeding design priorities. We review lessons learned from tricot applications that have enabled a large scaling effort, which should lead to stronger decision-making in crop improvement and increased use of improved varieties in smallholder agriculture.

Participatory approaches for crop variety testing can help breeding teams to incorporate traditional knowledge and consider site-specific sociocultural complexities. However, traditional participatory approaches have drawbacks and are seldom streamlined or scaled. Decentralized on-farm testing supported by citizen science addresses some of these challenges. In this study, we compare a citizen science on-farm testing approach - triadic comparisons of technology options (tricot-PVS) - with the benchmark state-of-the-art group-based participatory variety testing approach (group-PVS) over a set of socioeconomic outcomes. We focus on on-farm testing of common bean (Phaseolus vulgaris L.) in the Trifinio area of Central America. We measure the impact of these two approaches on bean growers in terms of on-farm diversification and food security. We use data from 1978 smallholder farmers from 140 villages, which were randomly assigned to tricot-PVS, group-PVS or control. Utilizing a difference-in-difference model with inverse probability weighting and an instrumental variable approach, we observe that farmers involved in group-PVS, and tricot-PVS had comparable levels of on-farm varietal diversification with respect to control farmers. Nonetheless, group-PVS appears to be significantly more effective in boosting household food security, which can be attributed to improved agronomic management of the crops. This study contributes to the next generation of innovations in exploring trait preferences to produce more inclusive, demand-driven varietal design that democratize participatory varietal selection programs.

Crop trait and varietal preferences are socially shaped, varying by gender, experience, and on-farm roles. This drives preference heterogeneity, between households but also within households. Adhering to the common practice of only interviewing the household head as a representative of households, leads to breeding programs collecting trait preferences that do not represent the experiences of other members within that household. This dearth of data on trait preferences of multiple household members could be hindered by the lack of robust and agile methods to collect this data. Here we present a method that explores intra-household differences between husbands and wives in trait preferences through choice experimentation, coupled with questions that capture decision-making, experience and time spent on farm to explore how these drive preferences. Dissecting crop management into three dimensions, we explore what drives intra-household heterogeneity in varietal preferences between husbands and wives, as well as, decision-making, crop experience and time spent working on the crop. We present preliminary results from testing this combined protocol with 270 cowpea growing households (540 respondents) in Senegal. The findings from this work hold promise to inform crop breeding programs on the value of intra-household analysis for trait priority setting, while offering a new method which is applicable by National Agricultural Research Organizations globally.

Demand-led approaches to crop breeding involve ranking priorities across different disciplines and stakeholder categories, but the implications of decisions made during varietal development are frequently understood only years later. Breeding teams must work a priori to rank crop improvement priorities and product concepts considering the context of the current, and ideally future, environmental, production and market conditions that a variety will be entering upon release. We propose PEEP (Participatory Ex-antE framework for Plant breeding), a new ex-ante framework, as a methodological tool for priority setting in plant breeding. PEEP leverages two elements: the usage of a heterodox methodological approach and the strong emphasis on the participation of knowledge-rich stakeholders. PEEP ranks crop improvement impacts based on a heterogenous set of environmental, social, and economic benefits and it employs a recursive and tailored multi-stakeholder approach to relate crop improvement impacts and product concepts. PEEP builds on the need to engage technical as well as practical knowledge and utilizes a tailored engagement strategy for each knowledge-rich stakeholder involved. The outcome is an assessment that ranks crop improvement impacts and breeding product concepts according to designed set of criteria. PEEP is scalable, gender inclusive, and crop agnostic. The results of PEEP are ex-ante recommendations for breeding teams in National Agriculture Research centers (NARs) and CGIAR centers alike. This methods manuscript describes the theoretical foundations of PEEP and its four phases of implementation.

This paper examines the relationship between mobile internet, employment and structural transformation in Rwanda. Thanks to its ability to enable access to a wide range of ICT technologies, internet coverage has the potential to affect the dynamics and the composition of employment significantly. To demonstrate this, we have combined GSMA network coverage maps with individual-level information from national population censuses and labour force surveys, creating a district-level dataset of Rwanda that covers the 2002 to 2019 period. Our results show that an increase in mobile internet coverage affects the labour market in two ways. First, by increasing employment opportunities. Second, by contributing to changes in the composition of the labour market. Education, migration and shifts in demand are all instrumental in explaining our findings.

This paper presents a dynamic agent-based model of land use and agricultural production under environmental boundaries, finite available resources and endogenous technical change. In particular, we model a spatially explicit smallholder farming system populated by boundedly-rational agents competing and innovating to fulfill an exogenous demand for food, while coping with a changing environment shaped by their production choices. Given the strong technological and environmental uncertainty, agents learn and adaptively employ heuristics which guide their decisions on engaging in innovation and imitation activities, hiring workers, acquiring new farms, deforesting virgin areas and abandoning unproductive lands. Such activities in turn impact farm productivity, food production, food prices and land use. We firstly show that the model can replicate key stylized facts of the agricultural sector. We then extensively explore its properties across several scenarios featuring different institutional and behavioral settings. Finally, we simulate the model across different applications considering deforestation and land abandonment; human-induced soil degradation; and climate impacts. AgriLOVE offers a flexible simulation environment to study the endogenous emergence of different agricultural production regimes from the interaction of spatially dispersed farms subject to resource constraints, spatial influence and climate change.

The perception on precipitation variability is key to food security of smallholder farmers in the current changing climate. In rainfed areas, households able to interpret correctly short-term precipitation deviations and extremes do retain a significant advantage in terms of resilience. Membership into informal associations (such as idir) foster the exchange of traditional and local knowledge. However, despite the communitarian nature of these rural societies, we know little about how the share of local knowledge influences risk perceptions on rainfall. In our study, we combine data from agronomic and socioeconomic surveys together with daily rainfall estimates to explore links between the households’ risk perceptions and the social dimension of local knowledge. We build a panel dataset, interviewing 280 smallholder households in the Ethiopian highlands in the spring of 2013 and 2019, while characterizing the frequency and intensity of rainfalls during the crop growing seasons. By analyzing varietal and soil management choices, we identify a novel indirect measure of farmer’s risk perception on rainfall abundance and scarcity. This measure shows high heterogeneity among neighboring households. Regressing the perception indices on rainfall parameters, we find that changes in volatility and maximum are rarely perceived by farmers. We further interact changes in the rainfall parameters with idir membership, to see whether the share of local knowledge mediates risk perceptions on rainfall frequency and intensity. Findings reveal that idir membership mediates the risk perception on rainfall parameters and that members comprehend better changes in rainfall patterns among crop growing seasons. Our findings suggest that the share of local knowledge in informal institutions like idir should be considered for risk reduction and programs of climate change mitigation.

This study assesses the impact of a participatory development program called Seeds For Needs, carried out in Ethiopia to support smallholders in addressing climate change and its consequences through the introduction, selection, use, and management of suitable crop varieties. More specifically, it analyzes the program’s role of boosting durum wheat varietal diversification and agrobiodiversity to support higher crop productivity and strengthen smallholder food security. The study is based on a survey of 1008 households across three major wheat-growing regional states: Amhara, Oromia, and Tigray. A doubly robust estimator was employed to properly estimate the impact of Seeds For Needs interventions. The results show that program activities have significantly enhanced wheat crop productivity and smallholders’ food security by increasing wheat varietal diversification. This paper provides further empirical evidence for the effective role that varietal diversity can play in improving food security in marginal environments, and also provides clear indications for development agencies regarding the importance of improving smallholders’ access to crop genetic resources.

Soil fertility is key to sustainable intensification of agriculture and food security in sub-Saharan Africa. However, when soil nutrients are not adequately managed, smallholder farming practices slowly erode soils to almost inert systems. This case study contributes to the understanding of such failures in marginal areas. We integrate agronomic and social sciences approaches to explore links between smallholder households’ farming knowledge and soil fertility in an ethnopedological perspective. We interview 280 smallholder households in two areas of the Ethiopian highlands, while collecting measures of 11 soil parameters at their main field. By analyzing soil compositions at tested households, we identify a novel measure of soil management ability, which provides an effective empirical characterization of the soil managing capacity of a household. Regression analysis is used to evaluate the effects of household knowledge on the soil management ability derived from laboratory analysis. Results highlight the complexity of knowledge transmission in low-input remote areas. We are able to disentangle a home learning and a social learning dimension of the household knowledge and appraise how they can result in virtuous and vicious cycles of soil management ability. We show that higher soil management ability is associated with farmers relying to a great extent on farming knowledge acquired within the household, as a result of practices slowly elaborated over the years. Conversely, lower soil management ability is linked to households valuing substantially farming knowledge acquired through neighbors and social gatherings. The present study is the first to formulate the concept of soil management ability and to investigate the effects of the presence and the types of farming knowledge on the soil management ability of smallholder farmers in remote areas. We show that farming knowledge has a primary role on soil fertility and we advise its consideration in agricultural development policies.