Why we need a new climate classification for agriculture (CCA) R Gommes, M Bernardi, F Nachtergaele,...
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Transcript of Why we need a new climate classification for agriculture (CCA) R Gommes, M Bernardi, F Nachtergaele,...
![Page 1: Why we need a new climate classification for agriculture (CCA) R Gommes, M Bernardi, F Nachtergaele, FAO J. Grieser, DWD/GPCC WORKSHOP ON CLIMATIC ANALYSIS.](https://reader035.fdocuments.net/reader035/viewer/2022062621/551c17ba550346a84f8b571d/html5/thumbnails/1.jpg)
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)
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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
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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.
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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
![Page 5: Why we need a new climate classification for agriculture (CCA) R Gommes, M Bernardi, F Nachtergaele, FAO J. Grieser, DWD/GPCC WORKSHOP ON CLIMATIC ANALYSIS.](https://reader035.fdocuments.net/reader035/viewer/2022062621/551c17ba550346a84f8b571d/html5/thumbnails/5.jpg)
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
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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
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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)
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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)
![Page 9: Why we need a new climate classification for agriculture (CCA) R Gommes, M Bernardi, F Nachtergaele, FAO J. Grieser, DWD/GPCC WORKSHOP ON CLIMATIC ANALYSIS.](https://reader035.fdocuments.net/reader035/viewer/2022062621/551c17ba550346a84f8b571d/html5/thumbnails/9.jpg)
CMB_Monthly Rainfall Profiles 7 Classes
0
50
100
150
200
250
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
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Cambodia rainfall7 classes
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Tanzania rainfall types
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50
100
150
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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
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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
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Burkina Faso
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Length of growing period
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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?)
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LCCS: opening screen
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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
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LCCS:dichotomous phase
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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
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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
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Thank you!
Source of farmers: 1634 etching by Rembrandt (Het Rembrandthuis Museum, Amsterdam)