Cyrille Rigolot

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

Significance We collated a unique dataset covering land use and production data of more than 13,000 smallholder farm households in 93 sites in 17 countries across sub-Saharan Africa. The study quantifies the importance of off-farm income and market conditions across sites differing strongly in agroecology and derives generally applicable threshold values that determine whether farm households have enough food available to feed their families. These results show there is a strong need for multisectoral policy harmonization and incentives and improved interconnectedness of people to urban centers and diversification of employment sources, rather than a singular focus on agricultural development of smallholder farmers in sub-Saharan Africa. 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.

Modelling of manure production by pigs and NH3, N2O and CH4 emissions. Part II: effect of animal housing, manure storage and treatment practices

A model has been developed to predict pig manure evolution (mass, dry and organic matter, N, P, K, Cu and Zn contents) and related gaseous emissions (methane (CH4), nitrous oxide (N2O) and ammonia (NH3)) from pig excreta up to manure stored before spreading. This model forms part of a more comprehensive model including the prediction of pig excretion. The model simulates contrasted management systems, including different options for housing (slatted floor or deep litter), outside storage of manure and treatment (anaerobic digestion, biological N removal processes, slurry composting (SC) with straw and solid manure composting). Farmer practices and climatic conditions, which have significant effects on gaseous emissions within each option, have also been identified. The quantification of their effects was based on expert judgement from literature and local experiments, relations from mechanistic models or simple emission factors, depending on existing knowledge. The model helps to identify relative advantages and weaknesses for each system. For example, deep-litter with standard management practices is associated with high-greenhouse gas (GHG) production (+125% compared to slatted floor) and SC on straw is associated with high NH3 emission (+15% compared to slatted floor). Another important result from model building and first simulations is that farmer practices and the climate induce an intra-system (for a given infrastructure) variability of NH3 and GHG emissions nearly as high as inter-system variability. For example, in deep-litter housing systems, NH3 and N2O emissions from animal housing may vary between 6% and 53%, and between 1% and 19% of total N excreted, respectively. Thus, the model could be useful to identify and quantify improvement margins on farms, more precisely or more easily than current methodologies.

Modelling of manure production by pigs and NH3, N2O and CH4 emissions. Part I: Animal excretion and enteric CH4, effect of feeding and performance.

A mathematical model was developed from literature data to predict the volume and composition of pigs excreta (dry and organic matter, C, N, P, K, Cu and Zn contents), and the emission of greenhouse gases (CH4 and CO2) though respiration and from the intestinal tract, for each physiological stage (post-weaning and fattening pigs and lactating and gestating sows). The main sources of variation considered in the model are related to animal performances (feed efficiency, prolificacy, body weight gain, etc.), to water and nutrient intakes and to housing conditions (ambient temperature). Model predictions were validated by using 19 experimental studies, most of them performed in conditions close to those of commercial farms. Validation results showed that the model is precise and robust when predicting slurry volume (R2 = 0.96), slurry N (R2 = 0.91), P (R2 = 0.95) and to a lesser extent dry matter (R2 = 0.75) contents. Faeces and urine composition (minerals and macronutrients) can also be precisely assessed, provided the composition and the digestibility of the feed are well known. Sensitivity analysis showed strong differences in CH4 emission and excretion amounts and composition according to physiological status, animal performance, temperature and diet composition. The model is an efficient tool to calculate nutrient balances at the animal level in commercial conditions, and to simulate the effect of production alternatives, such as feeding strategy or animal performance, on excreta production and composition. This is illustrated by simulations of three feeding strategies, which demonstrates important opportunities to limit environmental risks through diet manipulations.

MELODIE: a whole-farm model to study the dynamics of nutrients in dairy and pig farms with crops.

In regions of intensive pig and dairy farming, nutrient losses to the environment at farm level are a source of concern for water and air quality. Dynamic models are useful tools to evaluate the effects of production strategies on nutrient flows and losses to the environment. This paper presents the development of a new whole-farm model upscaling dynamic models developed at the field or animal scale. The model, called MELODIE, is based on an original structure with interacting biotechnical and decisional modules. Indeed, it is supported by an ontology of production systems and the associated programming platform DIESE. The biotechnical module simulates the nutrient flows in the different animal, soil and crops and manure sub-models. The decision module relies on an annual optimization of cropping and spreading allocation plans, and on the flexible execution of activity plans for each simulated year. These plans are examined every day by an operational management sub-model and their application is context dependent. As a result, MELODIE dynamically simulates the flows of carbon, nitrogen, phosphorus, copper, zinc and water within the whole farm over the short and long-term considering both the farming system and its adaptation to climatic conditions. Therefore, it is possible to study both the spatial and temporal heterogeneity of the environmental risks, and to test changes of practices and innovative scenarios. This is illustrated with one example of simulation plan on dairy farms to interpret the Nitrogen farm-gate budget indicator. It shows that this indicator is able to reflect small differences in Nitrogen losses between different systems, but it can only be interpreted using a mobile average, not on a yearly basis. This example illustrates how MELODIE could be used to study the dynamic behaviour of the system and the dynamic of nutrient flows. Finally, MELODIE can also be used for comprehensive multi-criterion assessments, and it also constitutes a generic and evolving framework for virtual experimentation on animal farming systems.

Closing system-wide yield gaps to increase food production and mitigate GHGs among mixed crop–livestock smallholders in Sub-Saharan Africa

In this study we estimate yield gaps for mixed crop–livestock smallholder farmers in seven Sub-Saharan African sites covering six countries (Kenya, Tanzania, Uganda, Ethiopia, Senegal and Burkina Faso). We also assess their potential to increase food production and reduce the GHG emission intensity of their products, as a result of closing these yield gaps. We use stochastic frontier analysis to construct separate production frontiers for each site, based on 2012 survey data prepared by the International Livestock Research Institute for the Climate Change, Agriculture and Food Security program. Instead of relying on theoretically optimal yields—a common approach in yield gap assessments—our yield gaps are based on observed differences in technical efficiency among farms within each site. Sizeable yield gaps were estimated to be present in all of the sites. Expressed as potential percentage increases in outputs, the average site-based yield gaps ranged from 28 to 167% for livestock products and from 16 to 209% for crop products. The emission intensities of both livestock and crop products registered substantial falls as a consequence of closing yield gaps. The relationships between farm attributes and technical efficiency were also assessed to help inform policy makers about where best to target capacity building efforts. We found a strong and statistically significant relationship between market participation and performance across most sites. We also identified an efficiency dividend associated with the closer integration of crop and livestock enterprises. Overall, this study reveals that there are large yield gaps and that substantial benefits for food production and environmental performance are possible through closing these gaps, without the need for new technology.

A whole farm-model to simulate the environmental impacts of animal farming system: Melodie

The ex ante environmental evaluation of farming systems is increasingly demanded when proposing new developments of animal farming systems. Modelling is a promising approach to reduce the cost and the delay in studying the relationship between farming management and risky emissions. The simulation of decision is essential to better analyze ex ante changes in farm management, but is rarely considered in environmental models. MELODIE simulates the flows of carbon, nitrogen, phosphorus, copper, zinc and water within the whole pig and dairy farm over the long term. MELODIE upscales dynamic models developed at the field or animal scale by considering the management of the whole farm system coherently with the livestock farming system. The model is structured according to an ontology of agricultural production systems to represent the interactions between the biotechnical system and the decision system. The biotechnical module simulates the nutrient flows at a daily time step for each entity of the sub-models (soil/crop, animal and manure processes). MELODIE represents decisions at two time scales: every year, for drawing annual activity plans and every day for the context-dependent application of this plan. Thanks to the interactions between the biotechnical system and the decision system at different time scales, MELODIE is able to run consistently under different long-term climate series. The goal is to study the emerging properties of the system. Besides, because the nutrient flows within the farm are dynamically simulated, it is possible to study both the spatial and temporal heterogeneity of the environmental risks. This approach enables a better understanding of variability in the farming system according to climate. MELODIE is intended for use in research, not as a decision support system for farm management. It is a framework for virtual experimentation on animal farming systems, and could be extended to deal with other issues than nutrient flows.