Narrowing yield gaps does not guarantee a living income from smallholder farming-an empirical study from western Kenya.

Crop yields in sub-Saharan Africa need to increase to keep pace with food demands from the burgeoning population. Smallholder farmers play an important role in national food self-sufficiency, yet many live in poverty. Investing in inputs to increase yields is therefore often not viable for them. To investigate how to unlock this paradox, whole-farm experiments can reveal which incentives could increase farm production while also increasing household income. In this study we investigated the impact of providing farmers with a US$ 100 input voucher each season, for five seasons in a row, on maize yields and overall farm-level production in two contrasting locations in terms of population density, Vihiga and Busia, in western Kenya. We compared the value of farmers' produce with the poverty line and the living income threshold. Crop yields were mainly limited by cash constraints and not by technological constraints as maize yield immediately increased from 16% to 40-50% of the water-limited yield with the provision of the voucher. In Vihiga, at best, one-third of the participating households reached the poverty line. In Busia half of the households reached the poverty line and one-third obtained a living income. This difference between locations was caused by larger farm areas in Busia. Although one third of the households increased the area farmed, mostly by renting land, this was not enough for them to obtain a living income. Our results provide empirical evidence of how a current smallholder farming system could improve its productivity and value of produce upon the introduction of an input voucher. We conclude that increasing yields of the currently most common crops cannot provide a living income for all households and additional institutional changes, such as alternative employment, are required to provide smallholder farmers a way out of poverty.

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.

Understanding spatial patterns of soils for sustainable agriculture in northern Ethiopia's tropical mountains.

Knowledge of the geographical distribution of soils is indispensable for policy and decision makers to achieve the goal of increasing agricultural production and reduce poverty, particularly in the Global South. A study was conducted to better understand the soilscapes of the Giba catchment (900-3300 m a.s.l.; 5133 km2) in northern Ethiopia, so as to sustain soil use and management. To characterise the chemical and physical properties of the different benchmark soils and to classify them in line with the World Reference Base of Soil Resources, 141 soil profile pits and 1381 soil augerings at representative sites were analysed. The dominant soil units identified are Leptosol and bare rock (19% coverage), Vertic Cambisol (14%), Regosol and Cambisol (10%), Skeletic/Leptic Cambisol and Regosol (9%), Rendzic Leptosol (7%), Calcaric/Calcic Vertisol (6%), Chromic Luvisol (6%) and Chromic/Pellic Vertisol (5%). Together these eight soil units cover almost 75% of the catchment. Topography and parent material are the major influencing factors that explain the soil distribution. Besides these two factors, land cover that is strongly impacted by human activities, may not be overlooked. Our soil suitability study shows that currently, after thousands of years of agricultural land use, a new dynamic equilibrium has come into existence in the soilscape, in which ca. 40% of the catchment is very suitable, and 25% is moderately suitable for agricultural production. In view of such large suitable areas, the Giba catchment has a good agricultural potential if soil erosion rates can be controlled, soil fertility (particularly nitrogen) increased, available water optimally used, and henceforth crop yields increased.

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.

Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development.

Creating typologies is a way to summarize the large heterogeneity of smallholder farming systems into a few farm types. Various methods exist, commonly using statistical analysis, to create these typologies. We demonstrate that the methodological decisions on data collection, variable selection, data-reduction and clustering techniques can bear a large impact on the typology results. We illustrate the effects of analysing the diversity from different angles, using different typology objectives and different hypotheses, on typology creation by using an example from Zambia's Eastern Province. Five separate typologies were created with principal component analysis (PCA) and hierarchical clustering analysis (HCA), based on three different expert-informed hypotheses. The greatest overlap between typologies was observed for the larger, wealthier farm types but for the remainder of the farms there were no clear overlaps between typologies. Based on these results, we argue that the typology development should be guided by a hypothesis on the local agriculture features and the drivers and mechanisms of differentiation among farming systems, such as biophysical and socio-economic conditions. That hypothesis is based both on the typology objective and on prior expert knowledge and theories of the farm diversity in the study area. We present a methodological framework that aims to integrate participatory and statistical methods for hypothesis-based typology construction. This is an iterative process whereby the results of the statistical analysis are compared with the reality of the target population as hypothesized by the local experts. Using a well-defined hypothesis and the presented methodological framework, which consolidates the hypothesis through local expert knowledge for the creation of typologies, warrants development of less subjective and more contextualized quantitative farm typologies.

Climate change and maize yield in southern Africa: what can farm management do?

There is concern that food insecurity will increase in southern Africa due to climate change. We quantified the response of maize yield to projected climate change and to three key management options - planting date, fertilizer use and cultivar choice - using the crop simulation model, agricultural production systems simulator (APSIM), at two contrasting sites in Zimbabwe. Three climate periods up to 2100 were selected to cover both near- and long-term climates. Future climate data under two radiative forcing scenarios were generated from five global circulation models. The temperature is projected to increase significantly in Zimbabwe by 2100 with no significant change in mean annual total rainfall. When planting before mid-December with a high fertilizer rate, the simulated average grain yield for all three maize cultivars declined by 13% for the periods 2010-2039 and 2040-2069 and by 20% for 2070-2099 compared with the baseline climate, under low radiative forcing. Larger declines in yield of up to 32% were predicted for 2070-2099 with high radiative forcing. Despite differences in annual rainfall, similar trends in yield changes were observed for the two sites studied, Hwedza and Makoni. The yield response to delay in planting was nonlinear. Fertilizer increased yield significantly under both baseline and future climates. The response of maize to mineral nitrogen decreased with progressing climate change, implying a decrease in the optimal fertilizer rate in the future. Our results suggest that in the near future, improved crop and soil fertility management will remain important for enhanced maize yield. Towards the end of the 21st century, however, none of the farm management options tested in the study can avoid large yield losses in southern Africa due to climate change. There is a need to transform the current cropping systems of southern Africa to offset the negative impacts of climate change.

Environmental conditions and human drivers for changes to north Ethiopian mountain landscapes over 145 years.

As quantitative or spatially distributed studies of environmental change over truly long-term periods of more than 100 years are extremely rare, we re-photographed 361 landscapes that appear on historical photographs (1868-1994) within a 40,000 km(2) study area in northern Ethiopia. Visible evidence of environmental changes apparent from the paired photographs was analyzed using an expert rating system. The conditions of the woody vegetation, soil and water conservation structures and land management were worse in the earlier periods compared to their present conditions. The cover by indigenous trees is a notable exception: it peaked in the 1930s, declined afterwards and then achieved a second peak in the early 21st century. Particularly in areas with greater population densities, there has been a significant increase in woody vegetation and soil and water conservation structures over the course of the study period. We conclude that except for an apparent upward movement of the upper tree limit, the direct human impacts on the environment are overriding the effects of climate change in the north Ethiopian highlands and that the northern Ethiopian highlands are currently greener than at any other time in the last 145 years.