Departamento de Engenharia Agricola
Minas Gerais, Brasil
Crop yield modeling in a climate
change perspective
Flavio Justino
fjustino@ufv.br
Outline
1. Background
2. Conceptual Crop Models and the DSSAT Crop
Model
3. Climate Projections
4. Crop Yields
5. Amazon Region
6. Africa
7. Future Directions
Food Security
The looming crisis
Rising global population
- from 6 to 9 billion by 2050
- already more people in urban than rural areas
- changing diet: more demand for meat & dairy
For the first time in human history more people
now live in towns and cities than in the
countryside
Water use in cubic metres per
Kg of product
7000
Urban population
16
6000
14
Rural population
5000
12
10
4000
8
3000
6
4
2000
2
0
1000
Beef
2050
2040
2030
2020
2010
2000
1990
1980
1970
1960
1950
0
Lamb
Cereal
Economic Growth
• Middle class increase
• Changing habits
China
Climate Risk
6
Crop modeling approach
Soil
Water Management
Weather
N Application + Organic
Crop
(Genetic Coefficients )
Duration of
Phases
Development
Photosynthesis
Respiration
CO2
Mass of Crop
Kg/ha
Leaf
Stem
Growth
Partitioning
Root
Fruit
INPUTS
File x
Experimental
Data File
File S
File w
File C
Soil Data
Weather Data
Cultivar Code
Crop
Models
File A
Crop Data
at Harvest
File T
Crop Data
during season
Output Depending on Option Setting and Simulation Application
Crop modeling complexity
Complexity
Availiabity of
data
Experiments
Model development
CO2 effect
Technological effect
MAIZE
CLIMATE
CO2 +
Climate
CO2 +
Climate +
Technology
2020
2050
2080
DSSAT (Crop Model)
• DSSAT: Decision Support System for Agrotechnology
Transfer
• DSSAT: a microcomputer software program combining
crop soil and weather data bases and programs to
manage them, with crop models and application
programs, to simulate multi-year outcomes of crop
management strategies.
• DSSAT allows users to ask "what if" questions and
simulate results by conducting, in minutes on a
desktop computer, experiments which would consume
a significant part of an agronomist's career.
Amazon Basin
• High Pressure to Crop Area Enlargement
• Soybeans and Maize
• Modeling Input
– Climate data (Hadley Centre Regional Model)
• Tx, Tm, Rg, Wind and Prec
– Optimum soil managements
– CO2 fertilization effect
– Thermal and water stress
Climate Projections
Maize Phenological Cycle
Productivity A2 B2 (Kg/ha)
Grain yield (%)
-70
-65
-60
-55
-50
-45
-40
-35
-30
-25
-20
-15
-10
Soybeans – Phenological Cycle
Crop cycle (%)
-40
-35
-30
-25
-20
-15
-10
-5
0
+5
+10
+15
+20
Real Productivity
Grain yield (%)
-60
-50
-40
-30
-20
-10
0
+10
+20
+30
+40
+50
+60
Africa
• RegCM3 Climate data
– A1B IPCC scenario
– Tx, Tm, Rg, Wind, Prec
– 50 Km x 50 Km --- Changed to 150 Km
• Modeling Input
– Climate data
• Tx, Tm, Rg, Wind and Prec
– Optimum soil managements
– CO2 fertilization effect
– Thermal and water stresses
Potential and Actual Projection
2011-2030
No water deficit
Water deficit
Potential and Actual Projection
2071-2090
No water
dependance
Water
dependance
Is sorghum an alternative?
• Semi-arid region
Regular
precipitation
Future X present
Future X present
Temperature and CO2 effects
A new player, O3
Atmospheric Environment 43 (2009) 604–618
To validate: Experiments
Exp (conti)
Further Developments
• Similar study to:
– Wheat
– Evaluation of the effect of ozone on crops
• Wheat, soybeans, maize
– Coupling DSSAT to Century
• Carbon balance and fertilizer effect due to crop
implementation