Using well-established empirical and mechanistic models such as Ecocrop, Maxent, DSSAT to assess the impact of climate change on productivity and climate-suitability of crops and production systems.

1. Using empirical and mechanistic models to predict crop
suitability and productivity in climate change research
Anton Eitzinger A.Eitzinger@cgiar.org
P. Laderach, C. Navarro, B. Rodriguez
Decision and Policy Analysis DAPA, CIAT Nairobi, June 13th 2013

2. Why crop modeling in climate change?
… assessing the impact of climate change on
productivity and climate-suitability of crops and
production systems … and understand the limiting
factors
… using well-established empirical and mechanistic
models such as Ecocrop, Maxent, DSSAT, …..
that allow for the incorporation of spatial data and
fine-tuned biophysical data
How?

3. Stations by
variable:
• 47,554
precipitation
• 24,542
tmean
• 14,835
tmax y tmin
Sources:
•GHCN
•FAOCLIM
•WMO
•CIAT
•R-Hydronet
•Redes nacionales
-30.1
30.5
Mean annual
temperature (ºC)
0
12084
Annual
precipitation (mm)

4. B
PREC
• Generate
interpolated climate
surfaces using
ANUSPLIN-SPLINA
with weather station
data
• Cross validating (25
iterations
• uncertainty
TMP
Uncertainty of climate data and models

5. B
Validation of climate surface (25 iterations)

6. B
Compare original worldclim with interpolated

7. GCMs are the only way
we can predict the future
climate
Using the past to learn
for the future
GCM “Global Climate Model”

8. The Delta Method
• Use anomalies and discard baselines
in GCMs
– Climate baseline: WorldClim
– Used in the majority of studies
– Takes original GCM timeseries
– Calculates averages over a baseline and
future periods (i.e. 2020s, 2050s)
– Compute anomalies
– Spline interpolation of anomalies
– Sum anomalies to WorldClim

9. Climate data
• For current climate (baseline)
we used historical climate data from WorldClim
www.worldclim.org
• Future climate: global climate models (GCMs)
from IPCC (AR5) – SRES A2, A1B, ..
• Downscaling to provide higher-resolution (2.5 arc-
minutes ~ 5 kilometer)
http://ccafs-climate.org

10. EcoCrop
The database was developed 1992 by the Land and Water
Development Division of FAO (AGLL) as a tool to identify plant species
for given environments and uses, and as an information system
contributing to a Land Use Planning concept.
In October 2000 Ecocrop went on-line under its own URL
www.ecocrop.fao.org. The database now held information on more
than 2000 species.
In 2001 Hijmans developed the basic mechanistic model (also named
EcoCrop) to calculate crop suitability index using FAO Ecocrop
database in DIVA GIS.
In 2011, CIAT (Ramirez-Villegas et al.) further developed the model,
providing calibration and evaluation procedures.

11. open
Suitability modeling with Ecocrop
EcoCrop, originally by Hijman et al. (2001), was further developed, providing calibration and
evaluation procedures (Ramirez-Villegas et al. 2011).
It evaluates on monthly basis if there
are adequate climatic conditions
within a growing season for
temperature and precipitation…
…and calculates the climatic suitability of the
resulting interaction between rainfall and
temperature…
How does it work?

12. • database held information on more than 2000
species

13. What happens when Ecocrop model runs?
1
2
3
4
5
6
7
8
9
10
11
12
1 kilometer grid cells
(climate environments)
The suitability of a location (grid cell) for a crop
is evaluated for each of the 12 potential
growing seasons.
Growing season
0 24 100 80

14. For temperature suitability
Ktmp: absolute temperature that will kill the plant
Tmin: minimum average temperature at which the plant will grow
Topmin: minimum average temperature at which the plant will grow optimally
Topmax: maximum average temperature at which the plant will grow optimally
Tmax: maximum average temperature at which the plant will cease to grow
For rainfall suitability
Rmin: minimum rainfall (mm) during the growing season
Ropmin: optimal minimum rainfall (mm) during the growing season
Ropmax: optimal maximum rainfall (mm) during the growing season
Rmax: maximum rainfall (mm) during the growing season
Length of the growing season
Gmin: minimun days of growing season
Gmax: maximum days of growing season

15. • Growing season: xx days (average of Gmin/Gmax)
• Temperature suitability (between 0 – 100%)
• Rainfall suitability (between 0 – 100%)
• Total suitability = TempSUIT * RainSUIT
If the average minimum temperature in one of these months is 4C or less above Ktmp, it is
assumed that, on average, KTMP will be reached on one day of the month, and the crop will die.
The temperature suitability of that month is thus 0%. If this is not the case, the temperature
suitability is evaluated for that month using the other temperature parameters.
The overall temperature suitability of a grid cell for a crop, for any growing season, is the lowest
suitability score for any of the consecutive number of months needed to complete the growing
season
The evaluation for rainfall is similar as for temperature, except that there is no “killing” rainfall and
there is one evaluation for the total growing period (the number of months defined by Gmin and
Gmax) and not for each month.
The output is the highest suitability score (percentage) for a growing season starting in any month
of the year.

16. (climate) Suitability modelling
A1B / 2030current

17. current A1B / 2030
(climate) Suitability modelling

18. Change in climate-suitability
“assumptions on regional level”
losses gains

19. Change in climate-suitability Losses
gains

20. • Maximum entropy methods are very general ways to predict probability
distributions given constraints on their moments
• Predict species’ distributions based on environmental covariates
What is Entropy Maximization?
• You can think of Maxent as having two parts: a constraint
• component and an entropy component
• The output is a probability distribution that sums to 1
• For species distributions this gives the relative probability of observing
the species in each cell
• Cells with environmental variables close to the means of the presence
locations have high probabilities
MaxEnt model

21. B
21
Input: Crop evidence (GPS points)
19 bioclimatic variables of current (worldclim) & future climate
Output:
Probability of distribution of coffee (0 to 1)
MaxEnt model

22. Bioclimatic variables for suitability modeling
• Bio1 = Annual mean temperature
• Bio2 = Mean diurnal range (Mean of monthly (max temp - min temp))
• Bio3 = Isothermality (Bio2/Bio7) (* 100)
• Bio4 = Temperature seasonality (standard deviation *100)
• Bio5 = Maximum temperature of warmest month
• Bio6 = Minimum temperature of coldest month
• Bio7 = Temperature Annual Range (Bio5 – Bi06)
• Bio8 = Mean Temperature of Wettest Quarter
• Bio9 = Mean Temperature of Driest Quarter
• Bio10 = Mean Temperature of Warmest Quarter
• Bio11 = Mean Temperature of Coldest Quarter
• Bio12 = Annual Precipitation
• Bio13 = Precipitation of Wettest Month
• Bio14 = Precipitation of Driest Month
• Bio15 = Precipitation Seasonality (Coefficient of Variation)
• Bio16 = Precipitation of Wettest Quarter
• Bio17 = Precipitation of Driest Quarter
• Bio18 = Precipitation of Warmest Quarter
• Bio19 = Precipitation of Coldest Quarter
derived from monthly temperature & precipitation

23. Coffee suitability - Maxent Results Nicaragua

24. B
Results
Variable Adjusted
R2
R2 due to
variable
% of total
variability
Present
mean
Change by 2050s
Locations with decreasing suitability (n=89.8 % of all observations)
BIO 14 – Precipitación del mes más seco 0.0817 0.0817 24.8 24.49 mm -3.27 mm
BIO 04 – Estacionalidad de temperatura 0.1776 0.0959 29.1 0.83 0.166
BIO 12 – Precipitación anual 0.2057 0.0281 8.5 2462.35 mm -24.31 mm
BIO 11 - Temperatura media del cuarto más frío 0.2633 0.0576 17.5 20.11 ºC 1.86 ºC
BIO 19 - Precipitación del cuarto más frío 0.2993 0.0155 4.7 169.13 mm -7.08 mm
BIO 05 - Temperatura máxima del mes más cálido 0.3198 0.0102 3.1 28.45 ºC 2.30 ºC
BIO 13 - Precipitación del mes más húmedo 0.2838 0.0205 6.2 450.27 mm 10.72 mm
Otros - - 6.2
Coffee suitability - Maxent Results Nicaragua

25. B
a Average of Q1 of GCMs
b Average of GMSs
c Average of Q3 of GCMs
d Measure of agreement of
models
e standard deviation of GCMs
b
c
e
Uncertainty of model output (Maxent) using 19 GCMs SRES A2 – timeserie 2040 – 2069 (2050)

26. Decision Support System for Agro technology Transfer (DSSAT)
+

27. • For 2 DSSAT-varieties (IB0006 ICTA-Ostua, IB0020 BAT1289
– “INTA Fuerte Sequia”, “INTA Rojo”, and “Tío Canela 75” originating from Nicaragua
– “ICTA Ostua” and “ICTA Ligero” originating from Guatemala
– “BAT 304” originating from Costa Rica
– “SER 16”, SEN 56”, “NCB 226”, and “SXB 412” originating from CIAT, Colombia.
• Sowing on:
– Primera (Beginning of June)
– Postrera (Beginning of September)
• After recollecting data during 2011
results will be used
in a post-project-analysis
to calibrate 2 initial DSSAT varieties
run it again for trial sites and find
spatial and temporal analogues
Accompanying field trials in 5 countries to calibrate DSSAT

28. Planting date: Between 15th of April and 30th of June1
Variety 1: IB0006 ICTA-Ostua Variety 2: IB0020 BAT1289
Soil 1: IB00000005 (generic medium silty loam) Soil 2: IB00000008 (generic medium sandy loam)
Fertilizer 1: 64 kg / ha 12-30-0 6 to 10 days after germination and 64 kg / ha Urea (46% N) at 22 to
25 days after germination. Fertilizer 2: 128 kg/ha 18-46-0 Fertilizer application on sowing and 64
kg/ha UREA at 22 to 30 days after germination.
Weather data input:
Current climate
Average of 99 MarkSim
daily outputs
Future climate
Ensemble of 19GCM & 99
MarkSim outputs for 2020
& 2050
Runs: 17,800 points x 3
climates x 99 MarkSim-
samples x 8 trials
DSSAT “Tortillas on the Roaster” in Central America

29. Results: yield change for year 2020 (Primera) – 8 trials
Trial 3 – high performance / high impact
Variety 1: ICTA-Ostua
Soil 1: generic medium silty loam
Fertilizer 2: 128 kg/ha 18-46-0 Fertilizer application on sowing
and 64 kg/ha UREA at 22 to 30 days after germination
Trial 7 – medium high performance / less impact
Variety 1: ICTA-Ostua
Soil 2: generic medium sandy loam
Fertilizer 2: 128 kg/ha 18-46-0 Fertilizer application on sowing
and 64 kg/ha UREA at 22 to 30 days after germination

30. Statistical negative and positive outliers of predicted yield change by 2020

31. 31
Areas where the production systems of crops can be
adapted
Adaptation-Spots
Focus on adaptation of production system
Areas where crop is no longer an option
Hot-Spots
Focus on livelihood diversification
New areas where crop production can be established
Pressure-Spots
Migration of agriculture – Risk of deforestation!
Identifying Impact-Hot-Spots and select sites for socio-economic analysis

32. 32
• Beans as most important income (sell 70% of harvest)
• Climate variability (intense rain, drought), missing labor & credits,
high input costs, … forces them to changes
• Increasing livestock displace crops into hillside areas
• Half of farmer rent their land
• Distance to market is far
• Mostly no road access in rainy season
• They buy inputs/sell produce from/to farm-stores
(they call them: Coyotes)
Result: Sample-site 1 - Texistepeque (Las Mesas), Santa Ana ,El Salvador
Message 2: Adaptation Strategies must be fine-tuned at each site!
Las Mesas
Altitude: 667 m
(about 2188 feet)
Hot-spot -141 kg/ha
For 2020:
• 35 mm less rain (current 1605mm)
• mean temperature increase 1.1 C
For 2050:
• 73mm less rain ( -5%)
• mean temperature increase 2.3 C
• hottest day up to 35 C (+ 2.6 C)
• coolest night up to 17.7 C (+ 1.8 C)
Hot-spot

33. 33
Message 3: There can be winners if they adapt quickly!
Result: Sample-site 2 – Valle de Jamastran, Danlí, Honduras Adaptation-spot
Jamastran
Altitude: 783 m
(about 2568 feet)
Adaptation-spot -
115 kg/ha
• Active communities with already advanced agronomic
management of maize-bean crops
• Favorable soil conditions and management
• Long-term technical assistance / training
• Irrigation schemes (e.g. 50 mz of 17 bean producers)
• Diversification options (vegetables, livestock)
• Market channels through processing industries
• Advanced infrastructure (electricity, roads)
• Need to optimize water use efficiency
• Credit problems
For 2020:
• 41 mm less rain (current 1094 mm)
• mean temperature increase 1.1 C
For 2050:
• 80 mm less rain ( -7%)
• mean temperature increase 2.4 C
• hottest day up to 34.2 C (+ 2.6 C)
• coolest night up to 17 C (+ 2.1 C)

34. Decision support system modelling (for benchmark sites)
Agronomic management
Expert & farmer survey
Integrated crop-soil modeling
160 LDSF sample sites
Baseline
domains
Impact
2030 A1b
Experimental
[n] cultivars
[n] fertilizer application
[n] seasons
Application domains
Analysis of biophysical systems and simulating crop yield in relation to management factors. Combine these
models with field observations that allow adjustment of the models in the course of the growing season .
Future
24 GCM
A1B (IPCC)
Current
worldClim
Validation with
available station data
Daily weather generator
MarkSIM
Weather
station data
(daily)
Climate data
yield
soil management

35. • Downscaling is inevitable.
• Continuous improvements are
being done
• Strong focus on uncertainty
analysis and improvement of
baseline data
• We need multiple approaches to improve the
information base on climate change scenarios
 Development of RCMs (multiple: PRECIS not enough)
 Downscaling empirical, methods Hybrids
 We tested different methodologies
Conclusions climate data

36. Conclusions crop models
• Ecocrop, when there is a lack on
crop information, for global or
regional assessment
• Maxent, perennial crops with
presence only data (coordinates)
available
• DSSAT, only for few crops (beans,
maize, …), high data input demand
and calibrated field experiments are
necessary
• We need to communicate
uncertainty of model predictions
Empirical
models
Mechanistic
models