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.

• http://ecocrop.fao.org

• 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