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

8

96

6

6

1 0

3

6

5

S o i l . a s c

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 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