Post on 14-Dec-2015
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