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.
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Tittonell - Tradeoffs in resource management
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
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3. TSBF, 2007 Diversity: the agricultural context Viable farm sizes Minimum farm sizes Tittonell et al., AgSys 2010 (Intensification)
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. 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. ‘ 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
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
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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
18. Where do organic resources come from? Diverse livestock production systems Cattle densities
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. Allocation of manure to different crops Manure allocation strategies (10 year simulations) Productivity of Maize and Napier Effects on soil fertility
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
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Notes de l'éditeur
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…