Small farms and development in sub‑Saharan Africa: farming for food, for income or for lack of better options?

Open Access Article; Published online: 15 Oct 2021Most food in sub-Saharan Africa is produced on small farms. Using large datasets from household surveys conducted across many countries, we find that the majority of farms are less than 1 ha, much smaller than previous estimates. Farms are larger in farming systems in drier climates. Through a detailed analysis of food self-sufficiency, food and nutrition security, and income among households from divergent farming systems in Ethiopia, Ghana, Mali, Malawi, Tanzania and Uganda, we reveal marked contrasts in food security and household incomes. In the south of Mali, where cotton is an important cash crop, almost all households are food secure, and almost half earn a living income. Yet, in a similar agroecological environment in northern Ghana, only 10% of households are food secure and none earn a living income. Surprisingly, the extent of food insecurity and poverty is almost as great in densely-populated locations in the Ethiopian and Tanzanian highlands that are characterised by much better soils and two cropping seasons a year. Where populations are less dense, such as in South-west Uganda, a larger proportion of the households are food self-sufficient and poverty is less prevalent. In densely-populated Central Malawi, a combination of a single cropping season a year and small farms results in a strong incidence of food insecurity and poverty. These examples reveal a strong interplay between population density, farm size, market access, and agroecological potential on food security and household incomes. Within each location, farm size is a major determinant of food self-sufficiency and a household’s ability to rise above the living income threshold. Closing yield gaps strongly increases the proportion of households that are food self-sufficient. Yet in four of the locations (Ethiopia, Tanzania, Ghana and Malawi), land is so constraining that only 42–53% of households achieve food self-sufficiency, and even when yield gaps are closed only a small proportion of households can achieve a living income. While farming remains of central importance to household food security and income, our results help to explain why off-farm employment is a must for many. We discuss these results in relation to sub-Saharan Africa’s increasing population, likely agricultural expansion, and agriculture’s role in future economic development

Rapid characterisation of vegetation structure to predict refugia and climate change impacts across a global biodiversity hotspot

Identification of refugia is an increasingly important adaptation strategy in conservation planning under rapid anthropogenic climate change. Granite outcrops (GOs) provide extraordinary diversity, including a wide range of taxa, vegetation types and habitats in the Southwest Australian Floristic Region (SWAFR). However, poor characterization of GOs limits the capacity of conservation planning for refugia under climate change. A novel means for the rapid identification of potential refugia is presented, based on the assessment of local-scale environment and vegetation structure in a wider region. This approach was tested on GOs across the SWAFR. Airborne discrete return Light Detection And Ranging (LiDAR) data and Red Green and Blue (RGB) imagery were acquired. Vertical vegetation profiles were used to derive 54 structural classes. Structural vegetation types were described in three areas for supervised classification of a further 13 GOs across the region.Habitat descriptions based on 494 vegetation plots on and around these GOs were used to quantify relationships between environmental variables, ground cover and canopy height. The vegetation surrounding GOs is strongly related to structural vegetation types (Kappa = 0.8) and to its spatial context. Water gaining sites around GOs are characterized by taller and denser vegetation in all areas. The strong relationship between rainfall, soil-depth, and vegetation structure (R2 of 0.8–0.9) allowed comparisons of vegetation structure between current and future climate. Significant shifts in vegetation structural types were predicted and mapped for future climates. Water gaining areas below granite outcrops were identified as important putative refugia. A reduction in rainfall may be offset by the occurrence of deeper soil elsewhere on the outcrop. However, climate change interactions with fire and water table declines may render our conclusions conservative. The LiDAR-based mapping approach presented enables the integration of site-based biotic assessment with structural vegetation types for the rapid delineation and prioritization of key refugia

Evaluating combined effects of pesticide and crop nutrition (with N, P, K and Si) on weevil damage in East African Highland Bananas.

Banana weevil (Cosmopolites sordidus, Germar) is a major pest in East African Highland Banana. The influence of crop nutritional status on weevil damage is poorly understood. Nutrient availability affects the nutritional quality of plants for weevils and may affect weevil damage. Here, we evaluate the effect of insecticides alone and in combination with fertilisers (N, P, K and Si) on weevil damage using data from two experiments in central and southwest Uganda. In the first experiment, we varied chlorpyrifos and application rates of N, P and K. In the second experiment, we varied the application rates of K and Si. Treatment effects were analysed using generalised linear mixed models with a negative binomial distribution. In the first experiment, chlorpyrifos reduced and N increased weevil damage, while P and K had no significant effect. In the K or Si application rates reduced weevil damage compared with the control. We conclude that the combined application of chlorpyrifos with K and Si fertilisers can contribute to weevil damage control on sites with low nutrient availability and should form part of integrated weevil management in bananas. Future studies should assess how much reduction in insecticide use is possible in EAHB with judicious input rates

Means and ranges of R² values across continents for (quadratic) relationships between NDVI and MODIS-NPP per biome.

<p>Relationships were developed for each continent separately, shown here for the annual sum (AS), large seasonal integral (LI) and April-October (AO) NDVI metrics. For each biome, the overall best performing GIMMS3g NDVI metric (including January-June, JJ) is listed.</p

Cumulative frequency distribution of the coefficient of determination for Simple Linear Regression (SLR) and piece-wise regression (PWR) trends in land pixels for the Annual Sum (AS-NDVI), Large Integral (LI-NDVI) and April-October (AO-NDVI) GIMMS3g NDVI metrics.

<p>Cumulative frequency distribution of the coefficient of determination for Simple Linear Regression (SLR) and piece-wise regression (PWR) trends in land pixels for the Annual Sum (AS-NDVI), Large Integral (LI-NDVI) and April-October (AO-NDVI) GIMMS3g NDVI metrics.</p

Proportions of productive pixels (with a TBW value calculated for more than half of the years) with decoupled, diverging or following or trends.

<p>Decoupled trends combine a negative NDVI trend with a positive or neutral TBW trend, diverging trends combining a positive NDVI trend with a negative TBW trend, and pixels with following trends have NDVI trends in line with TBW trends. A proportion of 0.23, 0.72 and 0.05 of all productive pixels show positive, neutral or negative TBW trends respectively.</p

Trends in Global Vegetation Activity and Climatic Drivers Indicate a Decoupled Response to Climate Change - Fig 5

<p><b>a</b>, Latitudinal means of NDVI metrics based on AS-NDVI and AO-NDVI for the years 1982–2010 (for legend see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138013#pone.0138013.g005" target="_blank">Fig 5c</a>); <b>b,</b> mean year of breakpoint for pixels where PWR regression improved upon SLR; <b>c,</b> mean trends in three NDVI metrics for the first (S1) and second segment (S2) of PWR. Pixel trends were based on PWR where PWR improved the coefficient of determination with at least 0.1 and SLR trends otherwise; (<b>d)</b> as in (<b>c</b>) but now for TBW.</p

Differences in the estimated year of the breakpoint (panels a, c, e) and most recent trends (panels b, d, f) between the AO-NDVI (panels a, b), AS-NDVI (panels c, d) and LI-NDVI (panels e, f) metrics.

<p>Trends shown are the percentage change per year. Maps are combinations of SLR trends and the trend in the second segment, the PWR segment 2 trend was used when PWR explained at least 0.1 more variation than SLR. Pixels in bare areas or with a low fraction (< 0.1) of explained variation are shown as white.</p

Descriptive statistics for second order polynomial relationships between various metrics derived from GIMMS3G NDVI-metrics and NPP (g C m^-2 yr^-1 MOD17A3).

<p>Comparisons of one generic and biome/land cover (LC) specific relationships per continent.</p