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Agricultural modelling and assessments in a changing climate

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Presentation on theme: "Agricultural modelling and assessments in a changing climate"— Presentation transcript:

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

9 Environmental conditions: Controllable variables:
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 Crop Consumption Calendar biomass, yield quantity, quality, N residue
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


18 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

19 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

20 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, ...

21 Example of rules block Loop Sequential
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 ...

22 Back to the simplified crop model
Weather Biophysical conditions Decision rules Crop model Crop response Resources consumed Decision thresholds Calendar applied Limitations

23 Useful tool for agriculture and CC
historical future Weather Biophysical conditions Agricultural system Crop response Resources status Actions Adapting to expectations Coping with impacts current innovative

24 Some crop models Water-balance models Plant-based models
AgroMetShell Aquacrop Plant-based models CERES STICS Soil-based models (ease modularity) APSIM DSSAT (?)

25 In any case simulated / potential Best possible in the model
achievable Best possible in the field, Field constraints, limitations ... actual But there is also weeds, pests, diseases ... Example : Yield

26 Time step : Actions Variable state Interactions
hourly, twice a day, daily, weekly, monthly .. Biophysical model Actions Variable state Decision model

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