Few longitudinal studies link agricultural biodiversity, land use and food access in rural landscapes. In this paper, we test the hypothesis that, in a context of economic change, cash crop expansion is associated with deforestation, reduced agrobiodiversity and changes in food access. For this purpose, we analysed data collected from the same 53 upland and floodplainmestizohouseholds in Ucayali, Peru, in 2000 and 2015. We found an emerging transition towards less diversified food access coupled with loss of forest cover and reduced agricultural biodiversity. In 2015, diets appeared to rely on fewer food groups, fewer food items, and on products increasingly purchased in the market compared to 2000. Wild fruits and plants were mentioned, but rarely consumed. Agricultural production systems became more specialised with a shift towards commercial crops. Peak deforestation years in the 15-year period appeared linked with incentives for agricultural expansion. Our results suggest an overall trend from diversified productive and “extractive” systems and more diverse food access, towards specialized productive systems, with less diverse food access and stronger market orientation (both in production and consumption). The assumption in the food and agricultural sciences that increased income and market-orientation is linked to improved food security, is challenged by our integrated analyses of food access, agrobiodiversity, land use and forest cover. Our results highlight the importance of longitudinal, multidimensional, systemic analyses, with major implications for land use, food and health policies. The potential risks of parallel homogenisation of diets and agricultural production systems require interdisciplinary research and policies that promote integrated landscape approaches for sustainable and inclusive food systems.

Land is an essential natural resource for climate mitigation and peace. It is commonly connected with sources of GHG emissions and with drivers of (violent) conflict. Therefore, climate mitigation and peacebuilding strategies are co-designing sustainable land-use systems (SLUS) with affected communities to integrate land-based climate mitigation and peacebuilding objectives. SLUS is practiced within agricultural production systems that meet sustainability principles (environmental, social, and economic). Nevertheless, there needs to be more program evaluation frameworks, especially measurable indicators, that integrate these two objectives (achieving peace and climate mitigation). This study aims to develop a methodology and criteria to evaluate the precise mechanisms of SLUS influencing greenhouse gas (GHG) emissions and drivers of conflict. A mix-method approach was used in two case study regions, Cesar and Caquetá, Colombia, where SLUS strategies where implemented. First, we conducted three workshops, two in-person and one virtual (n = 103). Secondly, we held semi-structured interviews (n = 115) to make an analysis of the conflict. Our research focused on the drivers of land-based emissions and conflict drivers targeted by the SLUS implementation. Lastly, through a household survey (n = 929), we illustrated the impacts of SLUS in peacebuilding at the farm level. Results show that SLUS, such as cocoa agroforestry, can contribute to climate change mitigation and deliver co-benefits in four core factors: (i) socio-economic inclusion by creating jobs and diversifying livelihoods, (ii) dialogue and conflict transformation by allowing negotiations around the participatory design of farms, including conservation agreements, (iii) natural resource governance, and (iv) cooperation by creating knowledge exchange and a community of practice.

In this article, we operationalized a sustainability framing based on the Sustainable Rural Livelihood Resources Framework (SLF), which consists of five capitals—human, physical, social, financial, and natural. We proposed a sustainability index (SI) for two landscapes dominated by two agricultural systems: cattle ranching and small-scale family agriculture. Farm variables within each capital were analyzed using confirmatory factor analysis. Key variables were identified and index values were calculated for each capital. These were combined through a set of simultaneous equations to estimate farm-specific capitals and SI from the observed farm variables. Principal component and cluster analyses were used to group the farms according to their index scores and to further compare their characteristics. Furthermore, with the purpose of comparing the index scoring with an independent metric, a landscape indicator, which comes from a continuous forest, was calculated. From the results, the capitals that contributed to a higher SI score the most were financial and physical. As cattle ranching was associated with higher economic returns and infrastructure investments, this livelihood was identified as the most sustainable. Yet, cattle ranching has been a deforestation driver in the region. These results are attributed to the current conceptual framework design, which gives greater weight to material and economic variables; therefore, it generates a weak sustainability measure. Although the framework allowed us to identify land-use alternatives that could improve SI scores (i.e., silvopastoral systems), corrections to the proposed framework and methodological approach will need to include additional environmental benefits currently unaccounted for. Farmers that use their farms for conservation purposes should be recognized and compensated. An improved environmentally focused SI operational framework could help to endorse and promote sustainable livelihoods and to generate a strong sustainability measure.

Climate variability largely affects agriculture in the developing world where rainfed agriculture is highly prevalent, and farmers rely on favourable climatic conditions to grow their crops. In Colombia, interannual climate variability can increase human vulnerabilities. Evidence on the vulnerability of farming households to climate variability at the local scale is, however, scarce. Here, we assessed the climate vulnerability and its determinants for a representative sample of 567 bean growing households in Santander, Colombia. We first applied Multiple Correspondence Analysis to calculate a vulnerability index and its components (exposure, sensitivity and adaptive capacity). The vulnerability index is in turn used to classify households into three vulnerability groups, namely, high, medium, and low. We then estimated a Generalized Ordered Probit Model to assess the probability of falling into each vulnerability category according to the household and farm management characteristics. We find that vulnerability is highly variable in the study region, with up to 65% of households classified as highly vulnerable. Geography, access to agronomic training, crop diversification, the percentage of household members making productive decisions and the gender of the household head are the most important factors determining the probability of being more or less vulnerable.

Variety selection and diversification are climate change adaptation practices pursued by Colombian common bean producers. We investigate the drivers behind common bean variety selection and diversification in one of the most important common bean production regions in Colombia —Santander. The effects of climate change on this region are expected to be elevation driven. Exploiting the relationship between elevation-driven weather variations and climate change perception in Santander, we estimate an alternative-specific conditional logistic regression model to identify the determinants of common bean variety selection from a survey of producers. Using an ordered-logistic regression model, we also investigate the drivers behind common bean variety diversification within this farming community. We find that farms' elevation, household composition, and seed certification are some of the most important drivers behind farmers' common bean variety selection in Santander. We also find that varieties that sell at higher prices and have shorter vegetative cycles tend to be more preferred by farmers. Finally, farmers who receive more help from family members and own a tractor tend to grow more than one variety in the same production cycle. Common bean breeding programmes can exploit these drivers to design communication strategies to maximize uptake of newly developed common bean phenotypes.

Voluntary uptake of climate-adapted beans is driven by farmers’ climate change perceptions. Identifying these perceptions and understanding their determinants help public agencies and seed suppliers design tailored engagement strategies to maximize uptake. We perform the first classification of climate change perceptions among farming communities in Colombia. A latent class analysis (LCA) is applied to a survey designed to capture the climate change perceptions of 566 bean farmers in the Colombian department of Santander. A Multinomial Logistic Model is estimated to determine the drivers behind the climate change perceptions identified. Farmers located at lower elevations and who are further away from their urban centres tend to be more concerned about the future economic consequences of climate change. These farmers also tend to seek climatic information for making productive activities. Accordingly, strategies aimed at maximizing the uptake of new drought-resistant bean varieties should focus on these farmers as they seem to be more receptive to uptake them. Moreover, engagement strategies containing information on management alternatives to appraise uncertainties and mitigate some of the severe effects of extreme weather events will generate increased uptake.