East Africa Tradeoff Analysis Workshop Bio-physical working group.
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Transcript of East Africa Tradeoff Analysis Workshop Bio-physical working group.
East Africa
Tradeoff Analysis
Workshop
Bio-physical working group
Workshop Program
ProgramMonday Introduction to TOA approachTuesday AM Conceptual frameworkTuesday PM Introduction to TOA softwareWednesday/ Thursday AM
Disciplinary breakout groups
Thursday PM TOA applications to Machakos system
Friday Collaborating team work plans & presentations
Objectives
1. Get acquainted with the bio-physical datasets.
2. Learn how to set up data for the TOA model.
3. Backgrounds on Nutmon and DSSAT simulations.
4. Relevance of spatial variability.
5. Discuss the new advances to deal with data limitations and soil dynamics
6. Understand the process to run advanced scenarios.
Today’s program
8:30-9:00 General introduction9:00-10:00 The NUTMON toolbox10:00-10:30 Coffee
10:30-11:00 DSSAT11:00-12:15 Discussion questions12:30-13:30 Lunch
13:30-14:30 NUTMON exercises14:30-15:00 DSSAT and inherent productivities15:00-15:30 Coffee 15:30-17:00 Analysis of variance
Tomorrow’s program
8:30-9:00 General introduction9:00-10:30 Climate change scenarios10:30-11:00 Coffee
10:30-12:00 Drought resistant maize varieties12:00-12:30 Discussion
Conceptual framework
1
23
4
5
C l i m a t e . a s c
C L I M _ I D C L I M Z O N E1 1 c l i2 2 c l i3 3 c l i4 4 c l i5 5 c l i
C l i m a t e . d b f
* W E A T H E R D A T A : S a n G a b r i e l , c l i m a t e z o n e 1 ( 2 7 4 0 - 2 9 4 0 )
@ I N S I L A T L O N G E L E V T A V A M P R E F H T W N D H TC I S G 0 . 6 0 0 - 7 7 . 8 1 7 2 8 6 0 1 2 . 3 0 . 8 3 . 0 1 0 . 0
@ D A T E S R A D T M A X T M I N R A I N 8 5 0 0 1 2 0 . 5 1 6 . 8 6 . 4 0 . 0 8 5 0 0 2 2 2 . 4 1 7 . 4 3 . 0 0 . 0 8 5 0 0 3 2 0 . 6 1 8 . 8 7 . 4 0 . 0 8 5 0 0 4 1 7 . 5 1 6 . 0 8 . 2 0 . 0 8 5 0 0 5 2 0 . 2 1 9 . 0 3 . 0 0 . 0 8 5 0 0 6 6 . 1 1 9 . 4 2 . 4 8 . 0 8 5 0 0 7 7 . 7 1 5 . 6 8 . 8 4 . 6
1 c l i 8 5 0 1 . w t h
1
23
4
5
C l i m a t e . a s c
1
23
4
5
C l i m a t e . a s c
C L I M _ I D C L I M Z O N E1 1 c l i2 2 c l i3 3 c l i4 4 c l i5 5 c l i
C l i m a t e . d b fC L I M _ I D C L I M Z O N E
1 1 c l i2 2 c l i3 3 c l i4 4 c l i5 5 c l i
C l i m a t e . d b f
* W E A T H E R D A T A : S a n G a b r i e l , c l i m a t e z o n e 1 ( 2 7 4 0 - 2 9 4 0 )
@ I N S I L A T L O N G E L E V T A V A M P R E F H T W N D H TC I S G 0 . 6 0 0 - 7 7 . 8 1 7 2 8 6 0 1 2 . 3 0 . 8 3 . 0 1 0 . 0
@ D A T E S R A D T M A X T M I N R A I N 8 5 0 0 1 2 0 . 5 1 6 . 8 6 . 4 0 . 0 8 5 0 0 2 2 2 . 4 1 7 . 4 3 . 0 0 . 0 8 5 0 0 3 2 0 . 6 1 8 . 8 7 . 4 0 . 0 8 5 0 0 4 1 7 . 5 1 6 . 0 8 . 2 0 . 0 8 5 0 0 5 2 0 . 2 1 9 . 0 3 . 0 0 . 0 8 5 0 0 6 6 . 1 1 9 . 4 2 . 4 8 . 0 8 5 0 0 7 7 . 7 1 5 . 6 8 . 8 4 . 6
1 c l i 8 5 0 1 . w t h* W E A T H E R D A T A : S a n G a b r i e l , c l i m a t e z o n e 1 ( 2 7 4 0 - 2 9 4 0 )
@ I N S I L A T L O N G E L E V T A V A M P R E F H T W N D H TC I S G 0 . 6 0 0 - 7 7 . 8 1 7 2 8 6 0 1 2 . 3 0 . 8 3 . 0 1 0 . 0
@ D A T E S R A D T M A X T M I N R A I N 8 5 0 0 1 2 0 . 5 1 6 . 8 6 . 4 0 . 0 8 5 0 0 2 2 2 . 4 1 7 . 4 3 . 0 0 . 0 8 5 0 0 3 2 0 . 6 1 8 . 8 7 . 4 0 . 0 8 5 0 0 4 1 7 . 5 1 6 . 0 8 . 2 0 . 0 8 5 0 0 5 2 0 . 2 1 9 . 0 3 . 0 0 . 0 8 5 0 0 6 6 . 1 1 9 . 4 2 . 4 8 . 0 8 5 0 0 7 7 . 7 1 5 . 6 8 . 8 4 . 6
1 c l i 8 5 0 1 . w t h
Conceptual framework
7
8
1 32
8
96
6
6
1 0
3
6
5
S o i l . a s c
M U _ I D S O I L _ I D S _ V A L U E5 5 D f6 6 D m7 7 D p8 8 D v
S o i l . d b f
S O I L _ I D S O I L C O D E H O R _ I D H O R C O D E S L B7 D p 2 A 2 6 27 D p 3 B 1 1 4 47 D p 4 B 2 1 7 87 D p 6 B 4 2 0 05 D f 1 A 1 3 55 D f 3 B 1 8 35 D f 4 B 2 1 6 35 D f 5 B 3 2 0 0
P r o f d a t . d b f
H o r d a t . d b fH O R _ I D C O D E S L M H S L L L S L S I S L C F S L N I S L H W S L H B S C E C
1 A 1 - 9 9 0 . 3 3 3 4 . 4 0 0 . 4 4 5 . 8 4 . 6 2 0 . 82 A 2 - 9 9 0 . 2 5 3 7 . 8 0 0 . 5 4 5 . 2 4 . 5 2 7 . 93 B 1 - 9 9 0 . 3 9 2 5 . 2 0 0 . 2 7 5 . 8 5 . 0 3 2 . 24 B 2 - 9 9 0 . 1 3 2 1 . 3 0 0 . 0 5 6 . 2 4 . 8 6 . 15 B 3 - 9 9 0 . 4 4 3 4 . 8 0 0 . 1 4 5 . 6 4 . 7 2 5 . 56 B 4 - 9 9 0 . 4 3 3 1 . 0 0 0 . 2 7 5 . 5 4 . 6 2 5 . 4
7
8
1 32
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96
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6
1 0
3
6
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S o i l . a s c
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1 32
8
96
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1 0
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S o i l . a s c
M U _ I D S O I L _ I D S _ V A L U E5 5 D f6 6 D m7 7 D p8 8 D v
S o i l . d b fM U _ I D S O I L _ I D S _ V A L U E
5 5 D f6 6 D m7 7 D p8 8 D v
S o i l . d b f
S O I L _ I D S O I L C O D E H O R _ I D H O R C O D E S L B7 D p 2 A 2 6 27 D p 3 B 1 1 4 47 D p 4 B 2 1 7 87 D p 6 B 4 2 0 05 D f 1 A 1 3 55 D f 3 B 1 8 35 D f 4 B 2 1 6 35 D f 5 B 3 2 0 0
P r o f d a t . d b fS O I L _ I D S O I L C O D E H O R _ I D H O R C O D E S L B
7 D p 2 A 2 6 27 D p 3 B 1 1 4 47 D p 4 B 2 1 7 87 D p 6 B 4 2 0 05 D f 1 A 1 3 55 D f 3 B 1 8 35 D f 4 B 2 1 6 35 D f 5 B 3 2 0 0
P r o f d a t . d b f
H o r d a t . d b fH O R _ I D C O D E S L M H S L L L S L S I S L C F S L N I S L H W S L H B S C E C
1 A 1 - 9 9 0 . 3 3 3 4 . 4 0 0 . 4 4 5 . 8 4 . 6 2 0 . 82 A 2 - 9 9 0 . 2 5 3 7 . 8 0 0 . 5 4 5 . 2 4 . 5 2 7 . 93 B 1 - 9 9 0 . 3 9 2 5 . 2 0 0 . 2 7 5 . 8 5 . 0 3 2 . 24 B 2 - 9 9 0 . 1 3 2 1 . 3 0 0 . 0 5 6 . 2 4 . 8 6 . 15 B 3 - 9 9 0 . 4 4 3 4 . 8 0 0 . 1 4 5 . 6 4 . 7 2 5 . 56 B 4 - 9 9 0 . 4 3 3 1 . 0 0 0 . 2 7 5 . 5 4 . 6 2 5 . 4
H o r d a t . d b fH O R _ I D C O D E S L M H S L L L S L S I S L C F S L N I S L H W S L H B S C E C
1 A 1 - 9 9 0 . 3 3 3 4 . 4 0 0 . 4 4 5 . 8 4 . 6 2 0 . 82 A 2 - 9 9 0 . 2 5 3 7 . 8 0 0 . 5 4 5 . 2 4 . 5 2 7 . 93 B 1 - 9 9 0 . 3 9 2 5 . 2 0 0 . 2 7 5 . 8 5 . 0 3 2 . 24 B 2 - 9 9 0 . 1 3 2 1 . 3 0 0 . 0 5 6 . 2 4 . 8 6 . 15 B 3 - 9 9 0 . 4 4 3 4 . 8 0 0 . 1 4 5 . 6 4 . 7 2 5 . 56 B 4 - 9 9 0 . 4 3 3 1 . 0 0 0 . 2 7 5 . 5 4 . 6 2 5 . 4
Basic GIS data for Machakos
Basic GIS data for Machakos
See c:\to31_mk\arcview\machakos.apr
Data acquisition
• Digital soil mapping• Climate interpolation• Model callibration
Traditional soil survey
0
50,000
100,000
150,000
200,000
250,000
Scale
Number ofobservations
100 625 2,500 10,000
62,500
250,000
• Parent material
• Topography
• Tillage erosion
• Land management
• Climate
Causes of soil variability
Cause 1: Parent material
2r
R2
0
0.2
0.4
0.6
0.8
1 Crop growth
Pesticide leaching
Results
Cause 2: topography
2r
R2
0
0.2
0.4
0.6
0.8
1 Crop growth
Pesticide leaching
Results
Cause 3: Soil variability
Cause 3: Soil variability
Tillage erosion = f(distance, topography)
No tillage erosion
Strong tillage erosion
Cause 3: Soil variability
R2
0
0.2
0.4
0.6
0.8
1 Crop growth
Pesticide leaching
Results
Cause 4: Land management
Cause 4: Land management
R2
0
0.2
0.4
0.6
0.8
1 Crop growth
Pesticide leaching
Results
0
5
10
15
20
2500 2700 2900 3100 3300 3500
Altitude (m.a.s.l.)
Org
anic
mat
ter
(%)
Cause 5: Climate
R2
0
0.2
0.4
0.6
0.8
1 Crop growth
Pesticide leaching
Results
Results: detailed soil survey
Climate interpolation
(c)
MJ m2 day-1
< 12
13.0
14.0
15.0
16.0
17.0
18.0
> 19
(d)
mm
< 450
550
650
750
850
950
1050
>1150
(a)
ºC
<17.5
18.0
18.5
19.0
19.5
20.0
20.5
21.0
(b)
ºC
4.5
5.0
5.5
6.0
6.5
7.0
7.7
8.0
(c)
MJ m2 day-1
< 12
13.0
14.0
15.0
16.0
17.0
18.0
> 19
(c)
MJ m2 day-1
< 12
13.0
14.0
15.0
16.0
17.0
18.0
> 19
(d)
mm
< 450
550
650
750
850
950
1050
>1150
(d)
mm
< 450
550
650
750
850
950
1050
>1150
(a)
ºC
<17.5
18.0
18.5
19.0
19.5
20.0
20.5
21.0
(a)
ºC
<17.5
18.0
18.5
19.0
19.5
20.0
20.5
21.0
(b)
ºC
4.5
5.0
5.5
6.0
6.5
7.0
7.7
8.0
(b)
ºC
4.5
5.0
5.5
6.0
6.5
7.0
7.7
8.0
