Why we need a new climate classification for agriculture (CCA)

R Gommes, M Bernardi, F Nachtergaele, FAOJ. Grieser, DWD/GPCC

WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE

(14-17 June 2005, Bologna, Italy)

Some history... (1)

Koeppen (1918-1936) and Trewartha (1968): threshold-based vegetation-oriented

Ivanov (1948): threshold-based, sums of temperature, moisture index

GKU = Annual rain / f [(annual t)2, annual RH])

desert: GKU<0.13; rainforest: GKU>1.5 Thornthwaite (1931): Precip. Effectiveness Ratio

PER = 115 * [ inches rain / (Fahrenheit-10)]10/9

PEI = Sum monthly PER; TEI = Sum TER

Some history... (2) Thornthwaite (1948): introduces ET and Moisture Index

monthly PE = 1.6 daylength (10 T / HI) a

HI = (T / 5) 1.514

a = 0.49 + 0.017 HI + 0.00077 HI2 + etc.

Budyko (1955) introduces radiation Seljaninov (1966-72): “agricultural classification” Vegetation

v period defined as Tav day > 10C. Later “adjusted” with Drought Index

GTKv = 10 Sum Rain / SDD above 10C

GTKv >1.5: need to drain

GTKv <0.5 : need to irrigate

Sasko, Kloskov, Papadakis, Botanists (Gaussen, de Martonne, Emberger), Lang, FAO (AEZ/LGP) etc.

Non-linearity of response

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1

Intensity of environmental factor

Re

sp

on

se o

f s

yst

em

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

Imp

act

/

Pro

bab

ility

Response

Probability

Impact

Temporal variability(Zimbabwe rain 1981-2002)

0

50

100

150

200

250

300

350

400

Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May June

Mon

thly

rai

nfal

l (m

m)

Average

Monthly Min

Monthly Max

Driest year (1991-92)

Wettest year (1973-74)

ETP

Temporal variability(Zimbabwe rain 1981-2002)

0

50

100

150

200

250

300

350

July Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June

Rai

nfa

ll a

mo

un

t m

m

1991-921982-831994-951986-871972-731981-821979-801990-912000-011988-891992-931976-771999-001970-711984-85Class 1Class 4Class 10

Yield = -1.80 StD

Yield = 0.21 StD

Yield = 1.19 StD

The climate “complex”

Tmin Tmax Rain SunFrac ETPCloud cover

Vapour Pressure

Tmin 1.00

Tmax 0.84 1.00

Rain 0.36 0.54 1.00

SunFrac 0.01 0.12 0.18 1.00

ETP 0.62 0.50 -0.05 -0.03 1.00

Wind Speed

-0.27 -0.22 -0.28 -0.02 0.05 1.00

Vapour Pressure

0.78 0.94 0.65 0.14 0.35 -0.25 1.00

(286 Latin-American stations, average

March data)

Correlations between variables

Windspeed

Sunshine fractionRain

ETP

Vap.press.

Tmax

Tmin

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

-1 -0.5 0 0.5

Component 1 (49.2 % of variance)

Co

mp

on

en

t 2 (

18

.8 %

of v

ari

an

ce)

CMB_Monthly Rainfall Profiles 7 Classes

0

50

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300

350

011

013

022

031

033

042

051

053

062

071

073

082

091

093

102

111

113

122

class 1

class 2

class 3

class 4

class 5

class 6

class 7

Cambodia rainfall7 classes

Tanzania rainfall types

0

50

100

150

200

250

300

350

jul1

jul2

jul3

aug1

aug2

aug3

sep1

sep2

sep3 oc

t1oc

t2oc

t3no

v1no

v2no

v3de

c1de

c2de

c3jan

1jan

2jan

3fe

b1fe

b2fe

b3m

ar1

mar

2m

ar3

apr1

apr2

apr3

may

1m

ay2

may

3jun

1jun

2jun

3

class 1

class 2+7

class 3

class 4

class 5

class 6

class 8

class 9

class10

URT

0

50

100

150

200

250

300

350

jul1

jul2

jul3

aug1

aug2

aug3

sep1

sep2

sep3 oc

t1oc

t2oc

t3no

v1no

v2no

v3de

c1de

c2de

c3jan

1jan

2jan

3fe

b1fe

b2fe

b3m

ar1

mar

2m

ar3

apr1

apr2

apr3

may

1m

ay2

may

3jun

1jun

2jun

3

class 1

class 2+7

class 3

class 4

class 5

class 6

class 8

class 9

class10

Burkina Faso

Length of growing period

CCA philosophy Agronomically significant classifiers Avoidance of redundant classifiers Variability essential ingredient Organism independent but relevant for

crops, animals, forest, diseases etc Hierarchical (e.g. mappable) and scale

independent (global, topo, micro) Transparent links with other classifications

(compatible? Maybe include other systems?)

LCCS: opening screen

CCA based on Land Cover Classification System approach

1.Dichotomous phase2.Modular-Hierarchical phase

1.Climate variables (thresholds)2.Indicators

1.production potential2.development (phenology)3.others (NDVI, hotspots...)

3.Specific-technical variables (non-climatic)

3.Accomodates a priori and a posteriori classes

LCCS:dichotomous phase

CCA principles Core of CCA: independent of other

classifications (e.g. soils, landscape, economics, irrigation potential, inputs)

No fuzzy boundaries Year (perennial) and growing season-based

(annual) re-definition of variables: nb of rainy days

and rain per rainy day rather than Rmonth

Low level of “French indices” e.g. Turc's Thermal Factor (Tf = P / (T2 -10 T +200)) or Thornthwaite' s Precipitation Effectiveness Ratio

Why a new CCA? New uses of CCA, for instance in climate

change studies New data grids, data processing and

classification techniques are available A generic Agricultural Climate Classification

System (ACCS) can be developed that incorporates other systems and all necessary tools (PCA, NHC)

Double entry: find locations based on climate (as defined in ACCS; iso-climates), or determine which climatic conditions are associated with e.g. blue cabbage wasp

Thank you!

Source of farmers: 1634 etching by Rembrandt (Het Rembrandthuis Museum, Amsterdam)