Tittonell - Tradeoffs in resource management


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Presentation delivered at the CIALCA international conference 'Challenges and Opportunities to the agricultural intensification of the humid highland systems of sub-Saharan Africa'. Kigali, Rwanda, October 24-27 2011.

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  • I soon realised that these systems had many layers of complexity and heterogeneity, and an important dynamic pattern in time, and that all farmers were different in the way they managed their resources For instance, here we have two farms under the same AE conditions, market opportunities, position in the landscape… etc
  • And this analysis can be done for different scenarios, like here it was done for different financial scenarios… and then we can select the best options and try to see what were the combination of decision variables or management parameters that led to these solutions… for example, here I’m showing the case of only two parameters, investment in N or in labour for weeding, …explain…
  • Another approach I explored for analysing tradeoffs at farm scale is the link of the detailed model DYNBAL with an optimisation algorithm … something we call inverse modelling. We have a heterogeneous farm with different fields, a certain amount of cash at the beginning of the season to invest in fertilisers or in hiring labour for weeding or for and preparation, etc, and a certain amount of labour in the household that can be allocated to different activities and to different fields… All these decisions on how to invest you labour and your cash are expressed as parameters of MOSCEM, and the model searches for the best combination, out of all the possible combinations, to try to find the ones that give the best solutions, that it, the maximum productivity with minimum losses… all these points along the Pareto frontier represent the best trade-off between these two objectives…
  • We need not only research on how nutrients are used by crops; but also how do these nutrients get there?
  • As I said, the reason to develop the model FIELD was to use it linked with livestock and household models in the farm-scale model FARMSIM. Here we see FIELD and the major interactions with the other farm components. These are all summary models but they are all dynamic, and they operate linked to one another and communicating their results after every time step, so feedbacks between for example crops and livestock can be captured… And this model allows analysing tradeoffs and interactions between fields within a farm…
  • This is where I ended up, highly complex systems, multiple crops (show tea, banana, tree plantations, annual crops in the top… almost everything here is human-made)… subsistence and semi-commercial agriculture, where production decisions cannot be separated from household decisions… lot’s of problems: overpopulation, degradation of natural resources, ethnical issues, land tenure, political instability…
  • Tittonell - Tradeoffs in resource management

    1. 1. Tradeoffs in the design of integrated resource management strategies for smallholder farming systems THEME II – System integration CIALCA Conference Kigali, 24 October 2011 Pablo Tittonell 1,2 1 Centre de coopération Internationale en Recherche Agronomique pour le Développement Montpellier, France 2 Tropical Resource Ecology Program, University of Zimbabwe, Harare
    2. 2. Introduction <ul><li>Theme II. System integration </li></ul><ul><ul><li>1. Relevant properties of the systems of interest; </li></ul></ul><ul><ul><li>2. Tradeoffs analysis and systems design; </li></ul></ul><ul><ul><li>3. Examples of systems approaches to ISFM   </li></ul></ul>Zones of relatively high agricultural potential and dense human population Humid highlands of SSA Smallholder mixed crop-livestock systems; annual and perennial crops; often cash crops Farming systems
    3. 3. TSBF, 2007 Diversity: the agricultural context Viable farm sizes Minimum farm sizes Tittonell et al., AgSys 2010 (Intensification)
    4. 4. Anisotropy and heterogeneity <ul><li>Anisotropy </li></ul><ul><li>Ecological niches </li></ul><ul><li>Landscape organisation </li></ul><ul><li>Connectivity </li></ul><ul><li>Contingency </li></ul><ul><li>Resource allocation </li></ul><ul><li>Local knowledge and perceptions of heterogeneity </li></ul><ul><li>Differential responses to interventions </li></ul><ul><li>Need to target technologies </li></ul>Ebanyat, 2010 Agroecosystems: complex socio-ecological systems Heterogeneity Soil C gradients in Mr. Oluka’s farm (Ouganda)
    5. 5. Heterogeneity and farmer diversity <ul><li>Esta foto muestra dos granjas contiguas, separadas por una cerca, e ilustra la diferencia entre campesinos. </li></ul><ul><li>Mientras que en el campo de la izquierda se ve un gradiente de productividad muy marcado, en el campo del vecino la productividad es más homogénea </li></ul>Tittonell et al., 2005a,b - AGEE Soil fertility gradients = ‘Soilscape’ + History of use + Current management
    6. 6. Long-term dynamics Tittonell et al. (2007), Agricultural Systems 94 Farm developmental cycle (Forbes, 1949)
    7. 7. A functional typology for East African highland systems Tittonell et al., AGEE 2005a,b; AgSys 2010 Wealthier households Mid-class to poor households
    8. 8. Typologies may become obsolete very soon… Assumptions: Policies and development interventions may impact on the right driving variables to move gradually from A to B A threshold may be there… A B Assumptions: Moving form A to B may not be so easy; these are two alternative system regimes; interventions need to provoke a ‘jump’ (hysteresis) Discontinuity, irreversibility… A B
    9. 9. ‘ Hanging in’ ‘ Stepping up’ ‘ Stepping out’ T3 T4 T5 T1 T2 Resources (natural, social, human) Performance (well-being) P’ P’’ R’’ R’ Functional farm types and system states
    10. 10. II. Tradeoffs analysis and systems design
    11. 11. Objective A Objective B A1 B1 B1 ’ B1 ” Substitution Complementarity Tradeoffs analysis Situations in which two or more competing/ conflicting objectives must be simultaneously satisfied to a certain degree <ul><li>Implications </li></ul><ul><li>There is a conflict: fulfilling one objective goes in detriment of the other(s) </li></ul><ul><li>A decision may affect two or more objectives at the same time </li></ul><ul><li>Choices are limited: e.g., by resources, by time, by cultural aspects, etc. </li></ul>
    12. 12. Tradeoffs in systems design Postulates: The expression of agroecosystem properties of interest can be influenced by design Agroecosystems are cybernetic systems (objective-structure-functioning): ecological engineering Rainy season Dry season Rainy season <ul><li>Rotation effects on pest and diseases </li></ul><ul><li>Fodder availability </li></ul><ul><li>C input and soil C stocks </li></ul><ul><li>Weed control </li></ul><ul><li>N fixation and nutrient cycling </li></ul><ul><li>Soil biological activity and physical properties </li></ul><ul><li>Erosion control </li></ul>Trade-offs between practical objectives Naudin et al. 2011
    13. 13. Tittonell et al. (2007), Agricultural Systems 95 Inverse modelling Paramètres Résultat Modélisation inverse Ensemble de paramètres = décisions de l’agriculteur Résultat Tradeoffs analysis: methods Modélisation directe
    14. 14. Analysing tradeoffs at farm scale Tittonell et al. (2007), Agricultural Systems 95 <ul><li>A spatially heterogeneous farm </li></ul><ul><li>A limited availability of cash </li></ul><ul><li>A limited availability of labour </li></ul><ul><li>Objectives: maximise food production, minimise N losses, etc… </li></ul>Simulated management decisions Trade-offs between objectives
    15. 15. Objectives: Learning from traditional agro-ecological knowledge systems Contribute to the sustainable intensification of low-input, smallholder farming systems Jared Diamond, Nature 418, 700-707(8 August 2002) Systems design: learning from indigenous agroecology Fernando Funes-Monzote Sustainable intensification of low-input systems
    16. 16. III. A systems approach to ISFM
    17. 17. A systems approach to ISFM Experimental field On-farm Landscape ?
    18. 18. Where do organic resources come from? Diverse livestock production systems Cattle densities
    19. 19. Integrated soil fertility management Farmers’ try-outs and adaption plots On-farm trials managed by researchers Improving livestock feeding and manure ‘production’ Improving compost management Manure storage: losses Nitrogen (kg SU -1 )
    20. 20. Allocation of manure to different crops Manure allocation strategies (10 year simulations) Productivity of Maize and Napier Effects on soil fertility
    21. 21. Prototyping: the ‘ideal’ farm NUANCES-FARMSIM: farm-scale, dynamic bio-economic model Tittonell et al., 2007a,b;2008;2009; van Wijk et al., 2009 Soil parameters Livestock parameters Climatic and management effects Crop responses across heterogeneous farms Activity calendars: seasonal labour and resource allocation Market prices and their variability
    22. 22. Thanks for your attention Summary <ul><li>Smallholder farming systems are diverse, spatially heterogeneous and highly dynamic </li></ul><ul><li>The expression of agroecosystem properties of interest can be influenced by design </li></ul><ul><li>Integrated analysis of farming systems allows studying the implications of proposed technologies across spatial and temporal scales </li></ul><ul><li>Tradeoffs emerge when resources are limited and two or more competing objectives must be simultaneously met </li></ul><ul><li>Farm-scale modelling offers ample opportunities for system integration and tradeoffs analysis to inform systems approaches to ISFM </li></ul>
    23. 23. <ul><li>Zingore, S., Tittonell, P., Corbeels, M., van Wijk, M.T., Giller, K.E., 2010. Managing soil fertility diversity to enhance resource use efficiencies in smallholder farming systems: a case from Murewa District, Zimbabwe. Nutrient Cycling in Agroecosystems, DOI 10.1007/s10705-010-9414-0. </li></ul><ul><li>Giller, K.E., Pablo Tittonell, Mariana C Rufino, Mark T van Wijk, Shamie Zingore, Paul Mapfumo, Samuel Adjei-Nsiah, Mario Herrero, Regis Chikowo, Marc Corbeels, Edwin C Rowe, Freddy Baijukya, Amos Mwijage, Jo Smith, Edward Yeboah, W J van de Burg, Ousmane M Sanogo, Michael Misiko, Nico de Ridder, Stanley Karanja, Cramer Kaizzi, James K'ungu, Moses Mwale, Dieudonne Nwaga, Cesare Pacini, Bernard Vanlauwe, 2010. Communicating complexity: Integrated assessment of trade-offs within African farming systems to support innovation and development. Agricultural Systems 104, 191–203. </li></ul><ul><li>Rufino, M.C., Dury, J., Tittonell, P., van Wijk, M.T., Herrero, M., Zingore, S., Mapfumo, P., Giller, K.E., 2011. Collective management of feed resources at village scale and the productivity of different farm types in a smallholder community of North East Zimbabwe. Agricultural Systems 104, 175–190. </li></ul><ul><li>Tittonell, P., Rufino, M.C., Janssen, B., Giller, K.E., 2010. Carbon and nutrient losses from manure stored under traditional and improved practices in smallholder crop-livestock systems – evidence from Kenya. Plant and Soil 328, 253–269 </li></ul><ul><li>Tittonell, P., Muriuki, A.W., Shepherd, K.D., Mugendi, D., Kaizzi, K.C., Okeyo, J., Verchot, L., Coe, R., Vanlauwe, B., 2010. The diversity of rural livelihoods and their influence on soil fertility in agricultural systems of East Africa - A typology of smallholder farms. Agricultural Systems 103, 83–97. </li></ul><ul><li>van Wijk, M.T., Tittonell, P., Rufino, M.C., Herrero, M., Pacini, C., de Ridder, N., Giller, K.E., 2009. Identifying key entry-points for strategic management of smallholder farming systems in sub-Saharan Africa using the dynamic farm-scale simulation model NUANCES-FARMSIM. Agricultural Systems 102, 89–101. </li></ul><ul><li>Tittonell, P., Corbeels, M., van Wijk, M.T., Giller, K.E., 2009. FIELD - A summary simulation model of the soil-crop system to analyse long-term resource interactions and use efficiencies at farm scale. European Journal of Agronomy 32, 10–21. </li></ul><ul><li>Tittonell, P., van Wijk, M.T., Herrero, M., Rufino, M.C., de Ridder, N., Giller, K.E., 2009. Beyond resource constraints – exploring the physical feasibility of options for the intensification of smallholder crop-livestock systems in Vihiga district, Kenya. Agricultural Systems 101, 1- 19. </li></ul><ul><li>Tittonell, P., Vanlauwe, B., Corbeels, M., Giller, K.E., 2008. Yield gaps, nutrient use efficiencies and responses to fertilisers by maize across heterogeneous smallholder farms in western Kenya. Plant and Soil 313, 19–37. </li></ul><ul><li>Tittonell, P., Corbeels, M., van Wijk, M.T., Vanlauwe, B., Giller, K.E., 2008. Combining organic and mineral fertilizers for integrated soil fertility management in smallholder farming systems of Kenya – explorations using the crop-soil model FIELD. Agronomy Journal 100, 1511-1526. </li></ul><ul><li>Chikowo, R., Corbeels, M., Tittonell, P., Vanlauwe, B., Whitbread, A., Giller, K.E., 2008. Aggregating field-scale knowledge into farm-scale models of African smallholder systems: Summary functions to simulate crop production using APSIM. Agricultural Systems 97, 151–166. </li></ul><ul><li>Tittonell, P., Shepherd, K.D., Vanlauwe, B., Giller, K.E., 2008. Unravelling the effects of soil and crop management on maize productivity in smallholder agricultural systems of western Kenya – an application of classification and regression tree analysis. Agriculture Ecosystems and Environment 123, 137-150. </li></ul><ul><li>Rufino, M.C., Tittonell, P., van Wijk, M.T., Castellanos-Navarrete, A., de Ridder, N., Giller, K.E., 2007. Manure as a key resource to sustainability of smallholder farming systems: analysing farm-scale nutrient cycling efficiencies within the NUANCES framework. Livestock Science 112, 273–287. </li></ul><ul><li>Tittonell, P., M.T. van Wijk, M.C. Rufino, J.A. Vrugt, K.E. Giller, 2007. Analysing trade-offs in resource and labour allocation by smallholder farmers using inverse modelling techniques: a case-study from Kakamega district, western Kenya. Agricultural Systems 95, 76–95. </li></ul><ul><li>Tittonell, P., Vanlauwe, B., de Ridder, N., Giller, K.E., 2007. Heterogeneity of crop productivity and resource use efficiency within smallholder Kenyan farms: soil fertility gradients or management intensity gradients? Agricultural Systems 94, 376-390 </li></ul><ul><li>Tittonell, P., Zingore, S., van Wijk, M.T., Corbeels, M., Giller, K.E., 2007. Nutrient use efficiencies and crop responses to N, P and manure applications in Zimbabwean soils: Exploring management strategies across soil fertility gradients. Field Crops Research, 100, 348 – 368. </li></ul><ul><li>Tittonell, P., Leffelaar, P.A., Vanlauwe, B., van Wijk, M.T., Giller, K.E., 2006. Exploring diversity of crop and soil management within smallholder African farms: a dynamic model for simulation of N balances and use efficiencies at field scale. Agricultural Systems 91, 71 – 101. </li></ul><ul><li>Tittonell, P., Vanlauwe, B., Leffelaar, P.A., Rowe, E., Giller, K.E., 2005. Exploring diversity in soil fertility management of smallholder farms in western Kenya. I. Heterogeneity at region and farm scale. Agriculture, Ecosystems and Environment 110, 149-165. </li></ul><ul><li>Tittonell, P., Vanlauwe, B., Leffelaar, P.A., Shepherd, K.D., Giller, K.E., 2005. Exploring diversity in soil fertility management of smallholder farms in western Kenya. II. Within-farm variability in resource allocation, nutrient flows and soil fertility status. Agriculture, Ecosystems and Environment 110, 166-184. </li></ul>