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Beyond resource constraints - Exploring the biophysical feasibility of options for the intensification of smallholder crop-livestock systems in Vihiga district, Kenya

Listed author(s):
  • Tittonell, P.
  • van Wijk, M.T.
  • Herrero, M.
  • Rufino, M.C.
  • de Ridder, N.
  • Giller, K.E.
Registered author(s):

    During participatory prototyping activities in Vihiga, western Kenya, farmers designed what they considered to be the ideal farm [Waithaka, M.M., Thornton, P.K., Herrero, M., Shepherd, K.D., 2006. Bio-economic evaluation of farmers' perceptions of viable farms in western Kenya. Agric. Syst. 90, 243-271]: one in which high productivity is achieved through optimising crop-livestock interactions. We selected four case study crop-livestock farms of different resource endowment (Type 1-4 - excluding the poorest farmers, Type 5, who do not own livestock) and quantified all relevant physical flows through and within them. With this information we parameterised a dynamic, farm-scale simulation model to investigate (i) current differences in resource use efficiencies and degree of crop-livestock interactions across farm types; and (ii) the impact of different interventions in farm Types 3 and 4 on producing the desired shifts in productivity towards the ideal farm. Assuming no resource constraints, changes in the current farm systems were introduced stepwise, as both intensification of external input use (fertilisers and fodder) and qualitative changes in the configuration of the farms (i.e. changing land use towards fodder production, improving manure handling and/or changing cattle breeds). In 10-year simulations of the baseline, current scenario using historical weather data the wealthiest farms Type 2 achieved food self-sufficiency (FSS) in 20% of the seasons due to rainfall variability, whereas the poorer Type 4 only achieved FSS in 0 to 30% of the seasons; soil organic C decreased during the simulations at annual rates of -0.54, -0.73, -0.85 and -0.84 t C ha-1 on farms of Type 1-4, respectively; large differences in productivity and recycling efficiency between farm types indicated that there is ample room to improve the physical performance of the poorer farms (e.g. light and water use efficiency was 2-3 times larger on wealthier farms). Simulating different intensification scenarios indicated that household FSS can be achieved in all farm types through input intensification, e.g. using P fertilisers at rates as small as 15 kg farm-1 season-1 (i.e. from 7 to 28 kg ha-1). Increasing the area under Napier grass from c. 20 to 40% and reducing the area of maize, beans and sweet potato in farms of Type 3 and 4 increased their primary productivity by c. 1 t ha-1 season-1, their milk production by 156 and 45 L season-1, respectively, but decreased the production of edible energy (by 2000 and 250 MJ ha-1 season-1) and protein (by 20 and 3 kg ha-1 season-1). By bringing in a more productive cow the primary productivity increased even further in Farm Type 3 (up to 5 t ha-1 season-1), as did milk production (up to c. 1000 L season-1), edible energy (up to c. 10,000 MJ ha-1 season-1) and protein (up to c. 100 kg ha-1 season-1). The impact of livestock management on the recycling of nutrients and on the efficiency of nutrient use at farm scale can be large, provided that enough nutrients are present in or enter the system to be redistributed. An increase in N cycling efficiency through improved manure handling from 25 to 50% would increase the amount of N cycled in the case study farms of Type 1 and 2 by only ca. 10 kg season-1, and only 1-2 kg season-1 in Type 3 and 4. The various alternatives simulated when disregarding resource constraints contributed to narrow the productivity and efficiency gaps between poorer and wealthier farms. However, the feasibility of implementing such interventions on a large number of farms is questionable. Implications for system (re-)design and intensification strategies are discussed.

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    Article provided by Elsevier in its journal Agricultural Systems.

    Volume (Year): 101 (2009)
    Issue (Month): 1-2 (June)
    Pages: 1-19

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    Handle: RePEc:eee:agisys:v:101:y:2009:i:1-2:p:1-19
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    1. Kiptot, Evelyne & Hebinck, Paul & Franzel, Steven & Richards, Paul, 2007. "Adopters, testers or pseudo-adopters? Dynamics of the use of improved tree fallows by farmers in western Kenya," Agricultural Systems, Elsevier, vol. 94(2), pages 509-519, May.
    2. Meinzen-Dick, Ruth & DiGregorio, Monica & McCarthy, Nancy, 2004. "Methods for studying collective action in rural development," Agricultural Systems, Elsevier, vol. 82(3), pages 197-214, December.
    3. Waithaka, M.M. & Thornton, P.K. & Herrero, M. & Shepherd, K.D., 2006. "Bio-economic evaluation of farmers' perceptions of viable farms in western Kenya," Agricultural Systems, Elsevier, vol. 90(1-3), pages 243-271, October.
    4. Thornton, P. K. & Herrero, M., 2001. "Integrated crop-livestock simulation models for scenario analysis and impact assessment," Agricultural Systems, Elsevier, vol. 70(2-3), pages 581-602.
    5. Giller, Ken E. & Rowe, Ed C. & de Ridder, Nico & van Keulen, Herman, 2006. "Resource use dynamics and interactions in the tropics: Scaling up in space and time," Agricultural Systems, Elsevier, vol. 88(1), pages 8-27, April.
    6. Herrero, M. & Gonzalez-Estrada, E. & Thornton, P.K. & Quiros, C. & Waithaka, M.M. & Ruiz, R. & Hoogenboom, G., 2007. "IMPACT: Generic household-level databases and diagnostics tools for integrated crop-livestock systems analysis," Agricultural Systems, Elsevier, vol. 92(1-3), pages 240-265, January.
    7. Bouman, B. A. M. & van Keulen, H. & van Laar, H. H. & Rabbinge, R., 1996. "The `School of de Wit' crop growth simulation models: A pedigree and historical overview," Agricultural Systems, Elsevier, vol. 52(2-3), pages 171-198.
    8. de Ridder, Nico & Breman, Henk & van Keulen, Herman & Stomph, Tjeerd Jan, 2004. "Revisiting a `cure against land hunger': soil fertility management and farming systems dynamics in the West African Sahel," Agricultural Systems, Elsevier, vol. 80(2), pages 109-131, May.
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