Mapping spatial distribution and geographic shifts of East African highland banana (Musa spp.) in Uganda.

East African highland banana (Musa acuminata genome group AAA-EA; hereafter referred to as banana) is critical for Uganda's food supply, hence our aim to map current distribution and to understand changes in banana production areas over the past five decades. We collected banana presence/absence data through an online survey based on high-resolution satellite images and coupled this data with independent covariates as inputs for ensemble machine learning prediction of current banana distribution. We assessed geographic shifts of production areas using spatially explicit differences between the 1958 and 2016 banana distribution maps. The biophysical factors associated with banana spatial distribution and geographic shift were determined using a logistic regression model and classification and regression tree, respectively. Ensemble models were superior (AUC = 0.895; 0.907) compared to their constituent algorithms trained with 12 and 17 covariates, respectively: random forests (AUC = 0.883; 0.901), gradient boosting machines (AUC = 0.878; 0.903), and neural networks (AUC = 0.870; 0.890). The logistic regression model (AUC = 0.879) performance was similar to that for the ensemble model and its constituent algorithms. In 2016, banana cultivation was concentrated in the western (44%) and central (36%) regions, while only a small proportion was in the eastern (18%) and northern (2%) regions. About 60% of increased cultivation since 1958 was in the western region; 50% of decreased cultivation in the eastern region; and 44% of continued cultivation in the central region. Soil organic carbon, soil pH, annual precipitation, slope gradient, bulk density and blue reflectance were associated with increased banana cultivation while precipitation seasonality and mean annual temperature were associated with decreased banana cultivation over the past 50 years. The maps of spatial distribution and geographic shift of banana can support targeting of context-specific intensification options and policy advocacy to avert agriculture driven environmental degradation.

Vapour pressure deficit determines critical thresholds for global coffee production under climate change.

Our understanding of the impact of climate change on global coffee production is largely based on studies focusing on temperature and precipitation, but other climate indicators could trigger critical threshold changes in productivity. Here, using generalized additive models and threshold regression, we investigate temperature, precipitation, soil moisture and vapour pressure deficit (VPD) effects on global Arabica coffee productivity. We show that VPD during fruit development is a key indicator of global coffee productivity, with yield declining rapidly above 0.82 kPa. The risk of exceeding this threshold rises sharply for most countries we assess, if global warming exceeds 2 °C. At 2.9 °C, countries making up 90% of global supply are more likely than not to exceed the VPD threshold. The inclusion of VPD and the identification of thresholds appear critical for understanding climate change impacts on coffee and for the design of adaptation strategies.

Micro-livestock in smallholder farming systems: the role, challenges and opportunities for cavies in South Kivu, eastern DR Congo.

Livestock play multiple roles for smallholder farmers in sub-Saharan Africa. Mixed crop-livestock systems are common in South Kivu, eastern DR Congo, but herd sizes are small and numbers of large livestock (i.e. cattle) have declined, due to high population density, recent conflicts and extreme poverty. Over half of the farmers keep cavies, a type of micro-livestock fitting the circumstances of smallholders and a valuable asset especially for the poorest households. To characterize cavy husbandry practices, detailed monthly on-farm data on cavy numbers, weights, herd dynamics and feeding practices were collected over 15 months and from households in two contrasting sites in South Kivu. Cavy herds contained on average 10 animals and strongly varied in size over time and between households. The main reasons for keeping cavies were meat consumption, especially for children, and the opportunity to generate petty cash. A large difference was observed in adult cavy live weights between the sites (an average of 0.6 and 1.0 kg per animal in Kabamba and Lurhala, respectively) and attributed to differences in cavy husbandry and genetics. In both sites, quantities of fresh fodder on offer were larger than fodder demand by 50-100%, but no correlation was found between amount of fodder on offer and cavy weight. Farmers faced several constraints to cavy production, including substantial declines in cavy herd size due to predation or theft and a lack of knowledge regarding breeding and feeding. Hence, the introduction of cages to limit mortality and fodder cultivation to improve feed quality were opportunities for improving cavy production. Overall, micro-livestock present a promising entry-point for development initiatives, also outside DR Congo, because of their potential to decrease poverty and improve human nutrition.

Using household survey data to identify large-scale food security patterns across Uganda.

To target food security interventions for smallholder households, decision makers need large-scale information, such as maps on poverty, food security and key livelihood activities. Such information is often based on expert knowledge or aggregated data, despite the fact that food security and poverty are driven largely by processes at the household level. At present, it is unclear if and how household level information can contribute to the spatial prediction of such welfare indicators or to what extent local variability is ignored by current mapping efforts. A combination of geo-referenced household level information with spatially continuous information is an underused approach to quantify local and large-scale variation, while it can provide a direct estimate of the variability of welfare indicators at the most relevant scale. We applied a stepwise regression kriging procedure to translate point information to spatially explicit patterns and create country-wide predictions with associated uncertainty estimates for indicators on food availability and related livelihood activities using household survey data from Uganda. With few exceptions, predictions of the indicators were weak, highlighting the difficulty in capturing variability at larger scale. Household explanatory variables identified little additional variation compared to environmental explanatory variables alone. Spatial predictability was strongest for indicators whose distribution was determined by environmental gradients. In contrast, indicators of crops that were more ubiquitously present across agroecological zones showed large local variation, which often overruled large-scale patterns. Our procedure adds to existing approaches that often only show large-scale patterns by revealing that local variation in welfare is large. Interventions that aim to target the poor must recognise that diversity in livelihood activities for income generation within any given area often overrides the variability of livelihood activities between distant regions in the country.

Drivers of household food availability in sub-Saharan Africa based on big data from small farms.

We calculated a simple indicator of food availability using data from 93 sites in 17 countries across contrasted agroecologies in sub-Saharan Africa (>13,000 farm households) and analyzed the drivers of variations in food availability. Crop production was the major source of energy, contributing 60% of food availability. The off-farm income contribution to food availability ranged from 12% for households without enough food available (18% of the total sample) to 27% for the 58% of households with sufficient food available. Using only three explanatory variables (household size, number of livestock, and land area), we were able to predict correctly the agricultural determined status of food availability for 72% of the households, but the relationships were strongly influenced by the degree of market access. Our analyses suggest that targeting poverty through improving market access and off-farm opportunities is a better strategy to increase food security than focusing on agricultural production and closing yield gaps. This calls for multisectoral policy harmonization, incentives, and diversification of employment sources rather than a singular focus on agricultural development. Recognizing and understanding diversity among smallholder farm households in sub-Saharan Africa is key for the design of policies that aim to improve food security.