workshop crop suitability modeling gms
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Ecocrop modeling
Overview of climate variability and likely climate change impacts on agriculture across the Greater Mekong Sub-region (GMS)
10 – 11 March, 2014, Hanoi, Vietnam
Eitzinger Anton, Giang Linh, Lefroy RodLaderach Peter, Carmona Stephania
outline
• What is Ecocrop?• FAO Ecocrop plant database• Suitability modeling with Ecocrop• Modeling Ecocrop with DIVA GIS• Calibrating ecological ranges (using literature)• Projecting suitability into the future
• 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.
• Common bean
• database held information on more than 2000 species
Suitability modeling with EcocropEcoCrop, 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?
What happens when Ecocrop model runs?1
2
3
4
5
67
8
9
10
11
12
12 potentialgrowing seasons
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
𝑇𝑘𝑖𝑙𝑙=4+Tkill ( initial )
𝑇𝑘𝑖𝑙𝑙𝑇𝑚𝑖𝑛
𝑇𝑜𝑝𝑚𝑖𝑛
𝑇𝑚𝑎𝑥
𝑇𝑜𝑝𝑚𝑎𝑥
𝑇 (𝑋 )−𝑇𝑚𝑖𝑛
𝑇𝑜𝑝𝑚𝑖𝑛−𝑇𝑚𝑖𝑛
=𝑇 𝑠𝑢𝑖𝑡 1−𝑇 (𝑋 )−𝑇 𝑜𝑝𝑚𝑎𝑥
𝑇𝑚𝑎𝑥−𝑇 𝑜𝑝𝑚𝑎𝑥
=𝑇 𝑠𝑢𝑖𝑡
𝑇 𝑠𝑢𝑖𝑡=0
𝑇 𝑠𝑢𝑖𝑡=100
For temperature suitabilityKtmp: 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 growFor rainfall suitabilityRmin: 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 seasonGmin: minimun days of growing seasonGmax: maximum days of growing season
P
P
P
P
• 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.
Results from GMS study
Crop climate- suitability change by 2050
Histograms of D:\_modeling_OUTPUT\sea\run-1.gdb\banana2chg in zones of D:\Anton\_DAPA\_Projects_ongoing\SEA-CCAFS\geodata\gms_mask.shp
KHM
2,000
1,800
1,600
1,400
1,200
1,000
800
600
400
200
0
LAO MMR THA VNM CHN
Histograms of D:\_modeling_OUTPUT\sea\run-1.gdb\potato2chg in zones of D:\Anton\_DAPA\_Projects_ongoing\SEA-CCAFS\geodata\gms_mask.shp
KHM
1,000
900
800
700
600
500
400
300
200
100
0
LAO MMR THA VNM CHN
Histogram: Banana Potato
Cambodia Laos Myanmar Thailand Vietnam China Cambodia Laos Myanmar Thailand Vietnam China
www.ciat.cgiar.orgScience to cultivate change
Use and Interpretation of EcoCrop• Purely Climatic Suitability:
• Does not include soils• Does not include pests and diseases
• Rainfall does not equal available water:• Irrigation• Soil water management (SOM, mulch, etc.)• Topography and soil type affect drainage
• Phenology: Different requirements at different stages of growth (especially for perennials)
• What is “most suitable” not necessarily the best to grow – markets, labour, farming system, etc.
www.ciat.cgiar.orgScience to cultivate change
Maize in Lao PDR• Maize in Lao PDR
Maize
-
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
www.ciat.cgiar.orgScience to cultivate change
Sugarcane in Lao PDR
Sugar Cane
-
2,000
4,000
6,000
8,000
10,000
12,000
14,000
North
ern
Regio
n
Phon
gsaly
Lua
ngnam
tha
Oud
omxa
y
Boke
o
Lua
ngpra
bang
Hua
phan
h
Xaya
bury
Centra
l Reg
ion
Vie
ntia
ne.C
Xie
ngkh
uang
Vie
ntia
ne
Borik
ham
xay
Kham
mua
ne
Sava
nnakh
et
South
ern
Regio
n
Sara
van
Seko
ng
Cha
mpas
ack
Atta
peu
www.ciat.cgiar.orgScience to cultivate change
Rubber and Oil Palm in Thailand
Other approaches for crop modeling
• 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
B
20
Input: Crop evidence (GPS points)19 bioclimatic variables of current (worldclim) & future climateOutput:Probability of distribution of coffee (0 to 1)
MaxEnt model
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
Coffee suitability - Maxent Results Nicaragua
B
Results
Variable AdjustedR2
R2 due to variable
% of totalvariability
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 0.166BIO 12 – Precipitación anual 0.2057 0.0281 8.5 2462.35 mm -24.31 mmBIO 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
Decision Support System for Agro technology Transfer (DSSAT)
+
Decision support system modelling (for benchmark sites)
Agronomic managementExpert & farmer survey
Integrated crop-soil modeling
160 LDSF sample sites
Baseline domains
Impact2030 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 .
Future24 GCM
A1B (IPCC)
CurrentworldClim
Validation with available station data
Daily weather generatorMarkSIM
Weather station data
(daily)
Climate data
yield
soil management
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
Empiricalmodels
Mechanisticmodels
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