As actors in tropical agricultural commodity supply chains implement commitments to end deforestation, they risk exacerbating social inequities by excluding smallholder farmers, who are important producers of many tropical commodity crops. Here, we explore the potential for independent oil palm smallholders in Indonesia to participate in zero-deforestation supply chains. We find that these smallholders are underrepresented in the share of zero-deforestation compliant oil palm production. We then synthesize perspectives from key actors in the oil palm industry including smallholders and their representatives, palm oil producing and consulting companies, nongovernmental organizations, and academic researchers. Based on these perspectives, we find that challenges to smallholder supply chain participation include limitations in knowledge (e.g., smallholders may not know the location of protected forests), institutional issues (e.g., absence of trust between oil palm growing companies and smallholder farmers), and financial constraints (e.g., the opportunity cost of not clearing forest). To address these shortcomings, we encourage oil palm growing and milling companies to take the lead on incentivizing, supporting, and facilitating smallholder participation in zero-deforestation initiatives. Specifically, these companies could build and use their technical and political resources to identify and map all forests in their entire supply shed and ensure small producers have land rights that enable participation in zero-deforestation supply chains. These policy levers would need to be combined with economic incentives such as access to improved inputs or price premia for their products. However, we caution that smallholder integration into existing zero-deforestation supply chains alone is unlikely to result in significant additional forest conservation at scale in Indonesia due to selection bias, leakage, and existing land tenure norms. Community-led and jurisdictional or landscape-scale supply chain initiatives that acknowledge multi-commodity production are more likely to provide equitable and just avenues for Indonesian smallholder farmers to steward forest resources.

Agricultural scientists are pursuing sustainable intensification strategies to increase global food availability, but integration from research to impact at the local-level requires knowledge of demographic and human-environment to enhance the adaptive capacity of farmers cultivating <10 ha. Enhancing close collaboration among transdisciplinary teams and these smallholders is critical to co-elaborate policy solutions to ongoing food security crises that are likely to be attuned with local conditions. Human and socio-cultural aspects need to be considered to facilitate both adoption and dissemination of adapted management practices. Despite this well-known need to co-produce knowledge in human systems, we demonstrate the inequality of current agricultural research in smallholder farming systems with heavy focus on a few domains of the sustainable intensification agricultural framework (SIAF), ultimately reducing the overall impact of interventions due to the lack compatibility with prevailing social contexts. Here we propose to integrate agriculture and agronomic models with social and demographic modeling approaches to increase agricultural productivity and food system resilience, while addressing persistent issues in food security. Researchers should consider the scale of interventions, ensure attention is paid to equality and political processes, explore local change interactions, and improve connection of agriculture with nutrition and health outcomes, via nutrition-sensitive agricultural investments.

Land endowments held by African rural communities affect the constraints and incentives they face, which in turn influence their response to development and conservation programs and policies. Availability of land with different qualities varies at different social organizational scales. While much work has focused on differences among households within villages, there has been little empirical work on variation among villages despite the fact that villages are often the sites through which land is accessed. This reflects serious difficulties in delineating village territories. Using a newly developed approach, this study estimates per capita cropland and rangeland availability of villages within two study areas in northern Burkina Faso: the provinces of Seno and Yatenga. Using georeferenced village population data, "village influence zones" were estimated taking account of each village's population and that of surrounding villages. Combining these with land-cover classifications allowed for estimations of average per capita cropland and rangeland for 725 villages. Large variation of land endowments is observed at fine spatial scales within the two provinces. For a subset of twenty-four villages, these values were compared with informants' estimates of cropland (fraction of cropland fallowed, fraction of cropland manured, and fraction of cropland from which crop residues are harvested) and rangeland management (seasonal presence of village livestock in village territory) parameters. Rates of crop residue harvesting were found to increase with cropland scarcity and small ruminant presence during the cropping season declined with less rangeland accessibility. The results of this study raise questions about one-size-fits-all approaches to rural development by revealing fine-scaled mosaics of village land endowments and management needs.

The response of vegetation to variable flood regimes is an important research question for floodplains in semi-arid sub-Saharan Africa experiencing climate change. The Inland Niger Delta (IND), located within the Sahelian zone of Central Mali, is a large floodplain that has experienced a historically-significant period of recurrent drought from 1970 to 1994 followed by a recovery of floods since. Vegetation associations, as determined through 2014 fieldwork at 538 IND sites, were compared to the sites’ vegetation associations as mapped in 1982. Site-specific flood histories were constructed through the analysis of Landsat imagery across the 1982–2014 period. In more deeply flooded portions of the floodplain, observations of vegetation associations in 2014 show remarkable consistency with those existing in 1982. In areas flooded infrequently and for the shortest duration, vegetation trajectories are more complicated with evidence for bush encroachment noted at 4.5% of all sites. Flood history explains only a portion of the few site-specific changes observed over the study period. Significant changes were more likely to be associated with more recent flood history (post 1999) rather than the flood history during the preceding abnormally dry period (1982–1991). These findings point to remarkable resiliency of IND vegetation to widely fluctuating levels of flooding that are driven not only by rainfall and run-off across the watershed but also by land use, water withdrawals and damming upstream. Given the low species diversity of IND vegetation, its resiliency derives from the combination of the seasonal plasticity of perennial grass growth in relation to rainfall and flood regimes and the efficiency of their vegetative and reproductive propagation in the context of highly seasonal grazing. The co-occurrence of interannual resiliency of the IND vegetation with annual vegetation production varying significantly with annual rainfall-flood conditions is consistent with non-equilibrium ecosystem dynamics.

A growing number of companies have announced zero-deforestation commitments (ZDCs) to eliminate commodities produced at the expense of forests from their supply chains. Translating these aspirational goals into forest conservation requires forest mapping and monitoring (M&M) systems that are technically adequate and therefore credible, salient so that they address the needs of decision makers, legitimate in that they are fair and unbiased, and scalable over space and time. We identify 12 attributes of M&M that contribute to these goals and assess how two prominent ZDC programs, the Amazon Soy Moratorium and the High Carbon Stock Approach, integrate these attributes into their M&M systems. These programs prioritize different attributes, highlighting fundamental trade-offs in M&M design. Rather than prescribe a one-size-fits-all solution, we provide policymakers and practitioners with guidance on the design of ZDC M&M systems that fit their specific use case and that may contribute to more effective implementation of ZDCs.

Food security has been and will continue to be a major challenge in Ethiopia. The country's smallholder, rainfed agriculture renders its food production system extremely vulnerable to climate variability and extremes. In this study, we investigate the impact of past climate variability and change on the yields of five major cereal crops in Ethiopia-barley, maize, millet, sorghum, and wheat-during the period 1979-2014 using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. The model is calibrated at both the site and agroecological-zone scales. At the sites studied, the model results suggest that climate in the past four decades may have contributed to an increasing trend in maize yield, a decreasing trend in wheat yield, and no clear trend in the yields of barley and millet; cereal crop yield is positively correlated with growing season solar radiation and temperature, but negatively correlated with growing season precipitation. For modeled cereal crops across the nation during the study period, yield in western Ethiopia is positively correlated with solar radiation and day time temperature; in the eastern and southeastern Ethiopia where water is a limiting factor for growth, yield is positively correlated with precipitation but negatively correlated with solar radiation and both day time and night time temperature. The national average of simulated yields of most crops (except maize) showed an overall decreasing (although not statistically significant) trend induced by past climate variability and changes. Over a large portion of the highly productive areas where there is a negative correlation between yield and temperature, yield is simulated to have significantly decreased over the past four decades, an indication of adverse climate impact in the past and potential food security concern in the future.

In the tropics, extreme weather associated with global climate teleconnections can have an outsized impact on food security. In Ethiopia, the El Niño Southern Oscillation (ENSO) is frequently linked to drought-induced food insecurity. Many projections hold that El Niño events will become more frequent or more intense under climate change, suggesting that El Niño associated droughts may become more destructive. Agricultural vulnerability to extremes under climate change, however, is a function of exposure, sensitivity, and adaptive capacity. Sensitivity, in this context, can depend on sub-seasonal distribution of rainfall. This paper investigates crop sensitivity to sub-seasonal rainfall variability under climate change in a food insecure area of the Ethiopian highlands through analysis of process-oriented crop model results for the years 1981–2100 driven by 14 GCMs chosen for the ability to represent ENSO and rainfall characteristics of the study area. Further, adaptive capacity in the region is investigated with in-depth interviews and focus groups concerning the 2015 strong El Niño event. Crop model results for sorghum highlight that exposure and sensitivity to sub-seasonal extremes of low rainfall can diverge significantly from sorghum’s response to seasonal drought. Even though climate change will bring generally warmer and wetter seasons to the study area, there is an increased occurrence of sub-seasonal failure of rains early in the rainy season which will likely have negative impacts on sorghum yield. In-depth interviews show that biophysical constraints significantly reduce farmer adaptive capacity to this type of sub-seasonal extreme. This work highlights the need to consider sub-seasonal weather when assessing climate change threats to agriculture, particularly for subsistence farmers in the developing world.

Ethiopia is a largely agrarian country with nearly 85% of its employment coming from agriculture. Nevertheless, it is not known how much land is under cultivation. Mapping land cover at finer resolution and global scales has been particularly difficult in Ethiopia. The study area falls in a region of high mapping complexity with environmental challenges which require higher quality maps. Here, remote sensing is used to classify a large area of the central and northwestern highlands into eight broad land cover classes that comprise agriculture, grassland, woodland/shrub, forest, bare ground, urban/impervious surfaces, water, and seasonal water/marsh areas. We use data from Landsat spectral bands from 2000 to 2011, the Normalized Difference Vegetation Index (NDVI) and its temporal mean and variance, together with a digital elevation model, all at 30-m spatial resolution, as inputs to a supervised classifier. A Support Vector Machines algorithm (SVM) was chosen to deal with the size, variability and non-parametric nature of these data stacks. In post-processing, an image segmentation algorithm with a minimum mapping unit of about 0.5 hectares was used to convert per pixel classification results into an object based final map. Although the reliability of the map is modest, its overall accuracy is 55%—encouraging results for the accuracy of agricultural uses at 85% suggest that these methods do offer great utility. Confusion among grassland, woodland and barren categories reflects the difficulty of classifying savannah landscapes, especially in east central Africa with monsoonal-driven rainfall patterns where the ground is obstructed by clouds for significant periods of time. Our analysis also points out the need for high quality reference data. Further, topographic analysis of the agriculture class suggests there is a significant amount of sloping land under cultivation. These results are important for future research and environmental monitoring in agricultural land use, soil erosion, and crop modeling of the Abay basin.