Crop growth simulation models

Camila Bonilla Cedrez

PhD candidate

University of California, Davis

Temperature

Radiation

Precipitation

Soil

Runoff

WHC

Evaporation

MicrobesNPK

SOM pH

NEVER ENDS !!!

Crop growth simulation: a tool of (Agro-) systems analysis

• System: limited part of reality that contains interrelated elements

• Model: a simplified representation of a system

Mechanistic models

Explaining the growth course from the underlying physiological processes in

relation to the environment

Types of crop simulation models

Crop growth simulation model: helps estimate crop yield as a function of weather conditions, soil conditions, and crop management practices

Statistical models

Describing the growth course with some empirical function

Why is crop growth simulation useful?

Source: van Ittersum et al. 2013

Production level (t/ha)

Pro

du

ctio

n s

itu

atio

n Yield Potential

Attainable

Actual

Defining factors

Limiting factors

Reducing factors

CO2Radiation

TemperatureCultivar features

WaterNutrients

WeedsPests and diseases

Pollutants

QUEFTS: Quantitative Evaluation of the Fertility of Tropical Soils

Smaling & Janssen, 1993. Sattari et al. 2014

Chemical soil properties Fertilizers * recovery

Potential supply of nutrients (N, P, K)

Actual uptakes

Possible yield ranges

Combining yield ranges to one Final yield

STEP 1

STEP 2

STEP 3

STEP 4

WOFOST: WOrld FOod STudies

• Is a dynamic explanatory model

State Variables Driving Variables Rate Variables

Quantitativei.e. biomass

External factors on the syst.

i.e. weather

Rate at which the state variables change at certain

moment

STATE (t + Δt) = State (t) + Rate (t) * Δt

RATE = State (t) * constant

WOFOST: WOrld FOod STudies

INPUT/OUTPUT: Crop, Soil and Weather

Daily weather data

INPUT

Soil data

Crop parameters

MODEL Yield level

OUTPUT

Spatial estimation of yield levels

Estimation of potential yield (g/m2) for 1 year for wheat for Tanzania

Known Daily Weather Station

A B

C

?

The problem

Daily weather station density is low

Sketch. No scales

How to estimate yield levels where there are no

weather stations?

How to estimate yield levels where there are no

weather stations?

Known Daily Weather Station

Possible methods:

1. Nearest neighbor2. Interpolate weather3. Interpolate yield with co-variables4. Daily weather simulator5. Metamodel

Sketch. No scales

A B

C

?

Nearest Neighbor

Known Daily Weather Station

Distance1 < Distance2 < Distance3

Sketch. No scales

d1d2

d3

AB

C

?

Nearest Neighbor

Known Daily Weather Station

A

B

C

?

Run crop model

Nearest neighbor

Distance1 < Distance2 < Distance3

x1, y1 x2, y2

x3, y3

?

Interpolate Weather

Known Daily Weather Station

30 (years) × 365 (days) × 5 (weather variables) = 54750 interpolations

A

C

B

?

Interpolate daily weather data

Run crop model

Fischer et al., 2002; Lobell & Field, 2007; Priya & Shibasaki, 2001

Interpolate Yield with co-variables

Known Daily Weather Station

AB

C

?

Run crop model

Interpolate with co-variables

De Wolf et al., 2002; Wu et al., 2006

Known Daily Weather Station

A B

C

?

The problem

Daily weather station density is low

Climate Data: High spatial resolution

How to estimate yield levels where there are no

weather stations?

Climate data

Daily weather simulator

Run crop model

Aggregate prediction

Run crop model Weather data

Metamodel

Andarzian et al., 2008; Deryng et al., 2011; Hijmans et al., 2000; Neumann et al., 2010; Nohebel, 1994; Perlman et al., 2014; Sparks et al., 2011; Van Bussel et al., 2011; Wilks & Wilby, 1999

Climate data

Prediction