Presentation on theme: "Agricultural modelling and assessments in a changing climate Olivier Crespo Climate System Analysis Group University of Cape Town."— Presentation transcript:
Agricultural modelling and assessments in a changing climate Olivier Crespo Climate System Analysis Group University of Cape Town
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) Keep in mind that crop models are
Crop model Weather Decision thresholds Crop response Resources consumed Calendar applied Biophysical conditions Decision rules Limitations Environment definition Controllable variables Uncontrollable variables Outcome Inputs and Outputs of a model
Biophysical model Decision model Plant Air Soil Model the decision making process of crop actions : sowing, irrigation, fertilisation, harvest … A crop 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 The biophysical 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 The decisional part of the model
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 Example of decision rule
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 More about the inputs
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 More about the outputs
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) Crop models Pros and Cons to keep in mind
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 Useful for operational 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 Useful for tactical 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 Useful for strategic decisions
Crop impact assessment e.g. permanent yield reduction Resources availability e.g. water competition Adaptation alternatives e.g. alternative crops, relocation Vulnerability Copping potential The strategic time scale is particularly relevant for CC
which makes its prediction ability a useful tool for : Exploitation: Improving current systems Optimising the outcomes Exploration: Assessing innovative systems Assessing uncontrollable variable impacts A model can be simulated
Example of soil data Multiple layers describing Structure and texture Fine, coarse, sand, silt, clay Soil water description Lower limit, drained upper limit Soil carbon, nitrogen ratio Soil pH
Example of plant data e.g. APSIM big advantage is to provide lots of plant modules, so that you probably will find what you need Plant Available Water Capacity (PAWC) Water uptake limits
Example of air data Mainly your weather data set. Depending on the time step of the model Hourly, daily, monthly The usual suspects Min and max temperatures, rainfall, solar radiation/ETo Sometimes Wind, relative humidity,...
Example of rules block Loop More open to innovating managements While (1 month around my usual planting date) Test irrigation rule Test fertilisation rule Sequential Control of operation number 20 < DAS < 30 : test irrigation rule 30 < DAS < 40 : test fertilisation rule...
Back to the simplified crop model Crop model Weather Decision thresholds Crop response Resources consumed Calendar applied Biophysical conditions Decision rules Limitations
Useful tool for agriculture and CC Agricultural system Weather Biophysical conditions Actions Crop response Resources status historicalfuture currentinnovative Coping with impacts Adapting to expectations
In any case simulated / potential achievable actual Example : Yield Best possible in the model But there is also weeds, pests, diseases... Best possible in the field, Field constraints, limitations...
Biophysical model Decision model Time step : hourly, twice a day, daily, weekly, monthly.. Variable state Actions Interactions