Modelling approaches to address crop-residue tradeoffs in mixed crop-livestock systems

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Presentation by Mark T. van Wijk, Mariana C. Rufino and Lieven Claessens (WUR) to the:CGIAR Systemwide Livestock Programme Livestock Policy Group meeting, 1 December 2009

Presentation by Mark T. van Wijk, Mariana C. Rufino and Lieven Claessens (WUR) to the:CGIAR Systemwide Livestock Programme Livestock Policy Group meeting, 1 December 2009

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  • Figure 11: Schematic conceptualisation of pathways towards intensification and their potential impact on household well-being. In this case, the impact of intensifying crop-livestock interactions (x-axis) on different aspects of household well-being (y-axis) such as food security or cash income follows a discontinuous trajectory in which input-intensification (quantitative) must be followed by qualitative changes in the productive structure of the farm to induce ‘jumps’ of the system towards higher states. Resource use efficiencies, the degree of complementarities between production activities, resource endowment and management intensity increase from system states I to III. Stressing factors (e.g. population density) and alleviation interventions may modify the slope of the trajectory towards higher levels of well-being for a certain degree of intensification.
  • At relatively low levels of intensification, we may find these elements in the dairy systems. To sustain milk production and cash flow, the soil productivity must be sustained. If there is investment capacity, fertilisers are used. But in the long term, attention needs to be paid to soil C. And therefore using crop residues and manure rationally may help to sustain the feed production on-farm.
  • Let’s imagine a more intensified farm, where the farmer is conscious of the need of keeping the soil productivity, increase herd size, and to supply feed to the cattle expand the cropping land, and the use of concentrates. Still has many decisions to make to allocate the resources.
  • Or may decide to purchase a piece of land and produce his own grass, for which investments are needed. This farm look much more complex in structure and in the type of decisions to be made than the first one.
  • Or may decide to purchase a piece of land and produce he own grass, for which investments are needed. This farm look much more complex in structure and in the type of decisions to be made than the first one.

Transcript

  • 1. Modelling approaches to address crop-residue tradeoffs in mixed crop-livestock systems Mark T. van Wijk, Mariana C. Rufino and Lieven Claessens Wageningen University, Plant Production Systems email: [email_address] CIP, Nairobi Presentation: CGIAR Systemwide Livestock Programme Livestock Policy Group, 1 December 2009
  • 2. Setup
    • Intro: role of crop residues within farming system
    • Something on trade offs
    • Our past work
    • Models and farm characterization
    • Role of models within SLP
  • 3. Aim of SLP project
    • Optimizing livelihood and environmental
    • benefits from crop residues in smallholder
    • crop-livestock systems in sub-Saharan
    • Africa and South Asia: regional case studies
  • 4. Theory of intensification (from a NRM perspective) After: McIntyre et al. 1992, Fernández-Rivera et al. 2002
  • 5. Crop residues Manure Feed Food Fertilisers Food + services Feed Market Food Livestock Grasslands Cropland Household
  • 6. Crop residues have different functions
    • Fodder for livestock  short term productivity livestock
    • Input for soil  long term productivity crops
    • These are just the first, direct effects. These,
    • in turn, will have cascading effects on
    • functioning of farming system and livelihood!
  • 7. Trade off
    • Limiting resources: changes in allocation of resources will positively affect one aspect of the system, and negatively another aspect
    • E.g., more crop residues to livestock can affect soil fertility negatively in the longer term
  • 8. Role of simulation models
    • Can help to analyse effects which are
    • difficult to measure
      • Long term effects
      • What-if questions
      • Risk analyses
  • 9. Some analyses performed in AfricaNUANCES project
    • Research project
      • Typology of farming systems
      • Data mining: e.g. experiments
      • Combined model of livestock, manure management, crop and soil
    • Used for
      • quantifying trade offs
      • identifying intensification strategies
  • 10. NUANCES-FARMSIM FIELD: dynamic, summary model of CROP and SOIL processes LIVSIM: individual based dynamic, summary model of livestock HEAPSIM: dynamic summary model of manure management and storage LABOURSIM and CASHSIM: summary models of socio-economic components and their interactions with production comp.
  • 11. Trade off analysis N losses at farm scale [kg season -1 ] Farm scale maize yield [kg season -1 ] Tittonell, Van Wijk et al, 2007, Agricultural Systems 0 1000 2000 3000 4000 5000 6000 7000 8000 80 100 120 140 160 180 200
  • 12.
    • Looking for best possible trade off (pareto solutions) between indicators
    • User should then make decision on what is preferable!
    Results of trade off analysis model input (parameters, management settings model system model output (set of indicators)
  • 13. Analysis with crop residues: results of a sensitivity analysis Van Wijk et al, 2009, Agricultural Systems
  • 14. Degree of crop-livestock integration Stocks, flows and assets System state II + + Management intensity + + System state III System state I Household “Well-being” Stress Alleviation Tittonell, Van Wijk et al, 2009, Agricultural Systems
  • 15. Models and Farm characterization
  • 16. milk feed $ feed $ $ feed crop residues nutrients $ fertilisers On-farm Rented land manure Investment capacity Labour availability Access to credit Access to information
  • 17. milk feed $ feed $ $ feed crop residues nutrients $ fertilisers On-farm feed crop residues nutrients $ fertilisers Where to invest How many cows? What type of feed? How much fodder produced on-farm? How long can this be sustained? manure Rented land
  • 18. milk feed $ manure feed $ feed crop residues nutrients ensiling feed $ fertilisers crop residues nutrients Rented land On-farm $ fertilisers
  • 19. Differences in: Resource endowment Decision making Farm household types
  • 20. Dury, Rufino, Van Wijk, De Ridder, Zingore and Giller, 2009 AEE submitted
  • 21. Analysis tools Parameters Input data Biophysical world Expert knowledge Experimentation Surveys Simulation models
  • 22. Analysis tools Parameters Input data Biophysical world Expert knowledge Experimentation Surveys Decision world Preferences Opportunities + Interviews Surveys Simulation models Optimisation tools
  • 23. Analysis tools Parameters Input data Biophysical world Expert knowledge Experimentation Surveys Decision world Preferences Opportunities + Interviews Surveys Preferences One decision maker (DM) Multiple DMs Influenced by the environment Opportunities Resources available Inputs prices Outputs prices Simulation models Optimisation tools
  • 24. Trade offs: multiple objectives!
    • Maximise gross margin
    • Maximise labour productivity
    • Minimise soil erosion
    • Minimise variability of production
    • Maximise social acceptability
    • Short term versus long term productivity
  • 25. Where do models fit within SLP?
    • Farm characterization:
      • Available resources
      • Farming strategies
      • Statistical and econometric analysis of current strategies
    • Data mining:
      • Data for parameterization & testing of models
    • Model analyses of existing and new farming strategies:
      • Long term effects
      • What-if questions of key indicators!
      • Risk analyses