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The “Boots & Trousers” Method - an Extension to Dry Soils
Qualitative Field Observations of Shallow Soil Moisture Patterns
Michael Rinderer1, Daniela Müller1, Hans Komakech2, Hosea Sanga3, Tobias Siegfried4, Pascal Oechslin5, Benjamin Fischer1, Jan Seibert1
1
2
Department of Geography, University of Zurich, Zurich, Switzerland ([email protected]); Department of Water, Environmental Science and Engineering, The Nelson Mandela 3
4
5
African Institution of Science and Technology, Arusha, Tanzania; Pangani Basin Water Office, Moshi, Tanzania; hydrosolutions GmbH, Zurich, Switzerland; BGW AG, Switzerland/Austria
MOTIVATION:
iMoMo-PROJECT:
iM
“Boots & Trousers Method (Rinderer et al., 2012)
reliable, field method based on qualitative indicators
(visible, touchable, hearable)
good results in wet soil conditions
Innovative Monitoring and Modeling of Water
In
(w
(www.imomohub.ch/)
Is this
method
applicable
in dry
METHOD:
Africa?
- Interview farmers to adapt the exist. scheme to indigenous knowledge
owledge
- Mark 40 sampling points of different soil wetness
- Soil wetness classification by 16 farmers individually ( & 16 MSc-students,
udents, not shown)
own)
- Determine volumetric soil water content gravimetrically
Aim:
low-cost, high-tech and crowd-sourced ICT solutions for:
A
- better water management
- improved livelihoods
incorporating
measurements & tacit knowledge (soft data)
in
using
modern communication technology
u
Fieldsites:
Tanzania / Africa & Kyrgyzstan / Central Asia
F
Rinderer M., Kollegger A., Fischer B. M. C., Stähli M., Seibert J. (2012): Sensing with boots and trousers-qualitative field observations of shallow
w soil moisture patterns
patterns. In:
n: Hydr
Hydrological
Hyd
Processes
Processes, 26
26, p
p. 4112
4112-4120.
4120
WETNESS CLASSIFICATION SCHEME:
Wetness class
Qualitave indicator chriteria
1
very dry - "dust dry"
2
dry - but with some moist look
3
below normal - too dry to plant a crop
CONCLUSIONS:
4
normal - you want to plant seeds in there
5
above normal - scky, you can form a brick
6
wet - when you step on the soil water liquies , too wet to make a brick
7
very wet - you can see water ponding on the soil surface
RESEARCH QUESTIONS:
- Qualitative wetness indicators can capture shallow soil moisture patterns
- Farmers agreed to a large extent in wetness class assignments but
there is potential for improvement as ...
- Individual farmers rated dry classes to wet and wet classes too dry
- Individual farmers were off by several wetness classes
Follow-up Workshop:
- Interviews with farmers to identify better wetness class indicators
- Better training of farmers and repetition
tion
ion of
o the field test
4) Are qualitative wetness classes representative of
quantitative differences in soil water content?
1) How well do farmers agree among each other?
%
30
20.3
20
10
17.62
10
5.7
0
30
45.13
40
20
Vol. Water Content (Grav.) [%]
50
40
50
60
0
−6
0.17 1.17
−5
−4
3.69
2.52
−3
1.34 0.34 1.34 0.67
−2
−1
0
1
2
3
4
5
1
2
3
6
4
5
6
7
Qual. Wetness Class
Classification difference to mode [wetness classes]
Fig. 4: Spread of measured volumetric water content for soil samples of each wetness class:
(40 gravimetric soil samples, wetness classification by the author).
Fig. 1: Frequency of classification difference to the mode of class assignments
Results:
- In about 45% of all classification cases farmers assigned the same wetness class.
- 83% of all assignments are within the range of +/- one wetness class.
- But: 8% of all classifiactions were off by three or more wetness classes!
2) Which wetness classes are most difficult to assign?
Results:
- Qualitative wetness classes do reflect differences in median vol. water content except for the
driest and wettest classes.
- Considerable overlap of the IQR
(Inter Quartile Range).
3) Do individual farmers classify too wet/dry?
5
100%
80
6
Wetness classes
70
5
60
4
50
40
3
30
2
20
10
1
mode
1
10
20
30
40
0
Sampling points
4
3
6
2
Wetness classes
90
7
Relative frequency of wetness class assignments
7
5
1
4
0
−1
3
−2
2
−3
−4
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
all
−5
Test person #
Fig. 2: Spread of wetness class assignments for each sampling location
Fig. 3: Mean classification difference for each wetness class per farmer.
(Gray-shades indicating relative frequency of wetness class assignments, white rings showing the
reference which is the mode of wetness classification at each point).
Reference: gravimetric water content classified into qualitative wetness classes according to field-based scale.
(Red shades indicate mean classification of a wetness class to be too dry, blue shades to be too wet).
Results:
Results:
- No clear difference in spread of class assignments for different wetness classes.
- For a few sampling points the spread of assignment is larger than +/- two classes!
- In a similar test in Switzerland the agreement among class assignments was higher
and the driest and wettest classes showed smallest spread (Rinderer et al., 2012).
- Individual farmers classified dry sites too wet and wet sites too dry.
- Mean classification difference for each wetness class considering all farmers is within
the range of one wetness class, except class 1.
Acknowledgments:
Farmers of the Mungushi village / Tanzania for testing the classification scheme; Alfayo Miseyeki for translation; Devdi our safe driver!
Federal Department of Foreign Affaires - Swiss Agency for Development and Cooperation SDC for funding the iMoMo Project

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