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Agricultural modelling and assessments in a changing climate Olivier Crespo

Agricultural modelling and assessments in a changing climate Olivier Crespo Climate System Analysis Group University of Cape Town. Keep in mind that crop models are. Partial : simplified representation of a system Biased : a specific perspective on the system

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Agricultural modelling and assessments in a changing climate Olivier Crespo

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  1. Agricultural modelling and assessments in a changing climate Olivier Crespo Climate System Analysis Group University of Cape Town

  2. Keep in mind that crop models are • Partial : simplified representation of a system • Biased : a specific perspective on the system • Mostly mechanistic (describe the processes) • Mostly dynamic (across time) • Mostly deterministic (no randomness)

  3. Inputs and Outputs of a model Uncontrollable variables Environment definition Weather Biophysical conditions Outcome Crop model Crop response Decision rules Resources consumed Controllable variables Calendar applied Decision thresholds Limitations

  4. A crop model Air Plant Biophysical model Model the decision making process of crop actions : sowing, irrigation, fertilisation, harvest … Decision model Soil

  5. The biophysical part of the model A biophysical model describes the chemical and biological subsystems of the crop model. It usually includes : • a soil model : water fluxes within soil layers, from soil to plant roots • an air model : wind, transpiration, evapotranspiration • a plant model : the plant growth according both to soil and air interactions

  6. The decisional part of the model A decisional model describes the decision making process. It usually consists in : • a sequence/loop • of decision rules if condition then action • where • condition: “variable (operator) threshold” • action: application details

  7. Example of decision rule • Sowing decision condition: Within D1 weeks surrounding my usual planting date, if D2 mm of rain falls within a week and D3 mm of rain falls in the 2 following weeks, then action: plant with D4 density, D5 deep, etc.. • You have control the rule structure and the rule variables Dx

  8. Inputs and outputs Weather Biophysical conditions Crop response Decision rules Resources consumed Decision thresholds Calendar applied Limitations

  9. More about the inputs • Environmental conditions: soil composition, water limitations • Controllable variables: biophysical (crop, cultivar), decision (rules, condition threshold), action (application details) • Uncontrollable variables: mostly the weather affecting the crop (temperatures, rainfall, solar radiation) but also soil inconsistency in the field, pest/disease spatialisation, ground level and natural pools

  10. More about the outputs • Crop biomass, yield quantity, quality, N residue • Consumption what sowing density, what amount of irrigation water, of fertiliser • Calendar when was the crop sown, what was the irrigation schedule, fertilisation

  11. Crop models Pros and Cons to keep in mind • Advantages : • Predictions based on physiological principles valid for different conditions • Complementary to field experiments number of conditions, possible corrections • More predictive indicators • Weaknesses : • Complex (to understand and to use) • Based on current understanding (limited)

  12. Useful for operational decisions At a few days time scale, it impact the execution of a decision: • Calculate non measured quantities e.g. soil water • Predict decision efficiency e.g. washed fertiliser • Test alternative applications e.g. irrigation amount

  13. Useful for tactical decisions At a few months time scale, it impact the procedure decisions: • Adapt the calendar e.g. regarding weather forecasts • Predict the outcome e.g. yield quantity and quality • Test alternative decisions e.g. alternative crop, irrigation schedule

  14. Useful for strategic decisions At a few years time scale, it impacts policy decisions: • Predict the outcome over years e.g. crop suitability in a region • Rotation management e.g. soil composition over the years • Regulation change assessments e.g. water demand, pesticide use

  15. The strategic time scale is particularly relevant for CC • Crop impact assessment e.g. permanent yield reduction • Resources availability e.g. water competition • Adaptation alternatives e.g. alternative crops, relocation • Vulnerability • Copping potential

  16. A model can be simulated which makes its prediction ability a useful tool for : • Exploitation: Improving current systems Optimising the outcomes • Exploration: Assessing innovative systems Assessing uncontrollable variable impacts

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