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 Mostly mechanistic (describe the processes) Mostly dynamic (across time) Mostly deterministic (no randomness)

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

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

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

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

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

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

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

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

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)

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

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

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

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

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

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 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 ...

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

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

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

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

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