An innovative methodological approach combining statistical typologies and stochastic frontier analysis was applied to data collected from 1840 mixed crop-livestock farms in six districts of Zimbabwe, representative of semi-arid areas of the country. The average annual cereal production was 362 kg farm-1, and the average annual livestock offtake was 0.64 ± 1.32 Tropical Livestock Units (TLU) farm-1. Our results demonstrate there is scope to increase cereal and livestock production by 90.7% and 111.9% relative to current production levels, respectively, with more efficient use of existing resources and technologies. Rainfall was found to have a strong effect on cereal production, highlighting the need for climate-smart practices. Livestock mortality (0.59 ± 1.62 TLU farm-1) was found to be in the same order of magnitude as livestock offtake (0.64 ± 1.32 TLU farm-1). Cereal production was supported by livestock, demonstrating the importance of crop-livestock interactions in these mixed farming systems. Three farm types were identified in our analysis. Crop-oriented mixed farms (31%) are likely to be the ones most responsive to crop-specific interventions e.g., crop rotation and integrated pest management. Livestock-oriented mixed farms (34%) are likely to benefit the most from livestock-specific interventions, e.g., home feed. Mixed farms dependent on off-farm activities (36% of the sample) may require nutrition-sensitive and laboursaving sustainable intensification technologies to benefit from their limited resources. Reducing cattle mortality is a priority for all three farm types. The method proposed here could be adapted to other contexts characterized by heterogeneous farming populations to target interventions.

Background. Evidence of the effectiveness of biofortified maize with higher provitamin A (PVA) to address vitamin A deficiency in rural Africa remains scant. Objectives: This study projects the impact of adopting PVA maize for a diversity of households in an area typical of rural Zimbabwe and models the cost and composition of diets adequate in vitamin A. Methods. Household-level weighed food records were generated from 30 rural households during a week in April and November 2021. Weekly household intakes were calculated, as well as indicative costs of diets using data from market surveys. The impact of PVA maize adoption was modeled assuming all maize products contained observed vitamin A concentrations. The composition and cost of the least expensive indicative diets adequate in vitamin A were calculated using linear programming. Results. Very few households would reach adequate intake of vitamin A with the consumption of PVA maize. However, from a current situation of 33%, 50%–70% of households were projected to reach =< 50% of their requirements (the target of PVA), even with the modest vitamin A concentrations achieved on-farm (mean of 28.3 μg RAE per 100 g). This proportion would increase if higher concentrations recorded on-station were achieved. The estimated daily costs of current diets (mean +/- standard deviation) were USD 1.43 +/- 0.59 in the wet season and USD 0.96 +/- 0.40 in the dry season. By comparison, optimization models suggest that diets adequate in vitamin A could be achieved at daily costs of USD 0.97 and USD 0.79 in the wet and dry seasons, respectively. Conclusions. The adoption of PVA maize would bring a substantial improvement in vitamin A intake in rural Zimbabwe but should be combined with other interventions (e.g., diet diversification) to fully address vitamin A deficiency.

Farm typologies are often used to reduce the complexity in categorising diverse farming systems, particularly in sub-Saharan Africa. The resulting typologies can then be used in multiple ways including designing efficient sampling schemes that capture the diversity in smallholder farms, prescribing the selection of certain farm types to which interventions can be targeted or upscaled, or to give context into derived relationships. However, the construction of farm typologies consists of many subjective decisions that are not always obvious or evident to the end-user. By developing a generalized framework for constructing farm typologies, we clarify where these subjective decisions are and quantify the impact they have on the resulting typologies. Further, this framework has been encapsulated in the open source RShiny App: TypologyGenerator to enable users to focus on the decisions and not the underlying implementation.

There is an urgent need for agricultural development strategies that reconcile agricultural production and biodiversity conservation. This is especially true in the Global South where population growth is rapid and much of the world’s remaining biodiversity is located. Combining conceptual thoughts with empirical insights from case studies in Indonesia and Ethiopia, we argue that such strategies will have to pay more attention to agri- cultural labour dynamics. Farmers have a strong motivation to reduce the heavy toil associated with farming by adopting technologies that save labour but can negatively affect biodiversity. Labour constraints can also prevent farmers from adopting technologies that improve biodiversity but increase labour intensity. Without explicitly accounting for labour issues, conservation efforts can hardly be successful. We hence highlight the need for biodiversity-smart agriculture, that is farming practices or systems that reconcile biodiversity with land and labour productivity. Our empirical insights suggest that technological and institutional options to reconcile farmers' socio-economic goals and biodiversity conservation exist but that more needs to be done to implement such options at scale.