Food Security Discussion Series
Summary by Dr Jane O'Sullivan Dr Daniel Rodriguez


Thursday, 19 May
A/Professor Daniel Rodriguez, PhD

To mulch or to munch? Modelling the benefits and trade-offs in the use of crop residues in Kenya

Rural poverty traps in Africa are a wicked problem, combining challenges of low productivity environments, land degradation, climate variability, poor markets and limited household resources. In low-yielding rainfed cropping systems, production is confined to one growing season, so farmers are compelled to market their produce at the one time when there is a glut and low prices. Due to long supply chains and poor markets, inputs are often unavailable or too costly. New technologies involve costs and uncertainty, and farmers have little incentive or capacity to invest in them. They must juggle significant trade-offs in the allocation of limited resources, including labour, land, cash and biomass.

Biomass trade-offs have long posed challenges for researchers. Last year Daniel Rodriguez edited a special issue of Agricultural Systems (Vol 134, pp 1-128) on biomass use trade-offs in cereal cropping systems. The papers give many examples of the failure of prescriptive advice. A central problem is the diversity of situations of individual farms and households, which means that adopting the same strategy can result in very different outcomes, from positive to negative.

The project reported in this seminar involved detailed farm management surveys of some 3550 households across 580 villages in five East African countries. These were used to populate a dynamic model that allows the researchers to anticipate the response to interventions on individual farms in terms of production, incomes and environmental impacts such as soil erosion, and hence to quantify the benefits and trade-offs for alternative pathways out of poverty.

Simulations revealed a wide scatter of outcomes of retaining crop residues as mulch, or feeding all to livestock, compared with the baseline scenario (a mixed approach specific to each farm). In general, farms in the drier Eastern Kenya saw less benefit of soil erosion control and greater penalty of fodder reduction when residues were retained as mulch. Those in the moister west gained considerable soil protection while the impact on fodder was neutral on average. Mulching improved maize yields with high probability in both regions, but the penalty for not mulching was less in the east. Nitrogen fertiliser could compensate for the yield loss from residue feeding but did not reduce the impact on soil erosion. By combining retention of half the stubble with higher nitrogen fertilisation, yield effects were neutral or slightly improved and soil erosion was reduced. This strategy was more likely to improve farm income in the east, where farmers are currently less likely to retain residues.

The benefits and trade-offs for individual households differ across agroecologies and across household typologies. But the magnitude of benefits is generally small. Bigger impacts might be obtained, for instance, by diversifying off-farm income or market access.

Further development of the modelling methodology will move from modelling individual households to populations of households, allowing impacts of collective behaviours such as trading labour and specialisation. This will also inform prospects for infrastructure investments and market development. Many challenges remain including how to deal with the informal economy and adequately applying inter-disciplinary understandings.

Our discussion explored the use of data mining to identify the factors which determine outcomes for individual households. The tendency to use high-achieving ‘model farmers’ as examples others can follow has underappreciated the diversity of circumstances. But data mining can be a ‘fishing’ exercise if we don’t know the right questions to ask. Do we know enough about what initiates and propagates agricultural intensification?  It is best to identify and support real local opportunities. The nuanced understanding derived from big-data analysis and diversity modelling can help anticipate impacts of these initiatives.

Download Daniel's presentation here (7 MB PDF).


Discover more presentations from the GCI Food Security Discussion Series here.

Connect with us