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

Agricultural modelling and assessmentsin a changing climate

Olivier Crespo

Climate System Analysis GroupUniversity 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

Biophysicalmodel

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

Weather

Decision thresholds

Crop response

Resources consumed

Calendar applied

Biophysical conditions

Decision rules

Limitations

Inputs and outputs

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