Presentation given at Wageningen University after finishing a half year research experience in Madagascar.

1. Welcome
Estimating parameters of the RUSLE for rain-fed crops under
Conservation Agriculture in Madagascar
16-12-2010
Colloquium
Freddy van Hulst
Supervision WUR: Jan de Graaff - Saskia Visser
CIRAD: Krishna Naudin - Eric Scopel

2. Contents
 The big picture CA2AFRICA
 The study area
 The model RUSLE
 Objectives
 Methods and results
 Potential erosion
 Effect of CA
 Conclusion and discussion

3. The big picture
 Conservation Agriculture (CA)
 no tillage
 permanent soil cover
 crop rotations
 CA2AFRICA: Why is adoption of CA limited so far
in Africa?
 Amongst others: understanding effect of CA on soil
loss

4. The study area
Average annual rainfall: 1051 mm
Soil type: Loam, sandy clay loam
Slopes: 0-25 %
Main crops: Rice and Maize

5. The model
RUSLE
 Empirical model to
 Quantify soil loss
 Evaluate relative impact of management
 Range of application
 Field level: rill and interrill erosion
 Can be aggregated to watershed level
 Original from USA  adaptation necessary

6. The model
 Soil loss A = R · K · LS · C · P
 Rainfall erosivity R
 Soil erodibility K
 Slope length & steepness LS
 Crop cover C
 Conservation practices P
potential
erosion
effect of
management

7. Objectives
 Potential erosion parameters: R, K and LS
 Compare estimation methods
 Determine values
 Management parameters: C and P
 Evaluate impact of CA on soil loss, relative to a
traditional farming system

8.  Estimations based on either hourly, daily, monthly
or yearly rainfall data
 Selected method: Regression formula from daily
effective rainfall (Rk) Yu (1998)
Potential erosion Rainfall erosivity R
 Justification:
 Credible outcome: match with literature
 Available data matches necessary data
 Model applicable for different climates

9. Potential erosion Rainfall erosivity R
Yearly R: 8487 MJ·mm·ha-1
h-1

10. Potential erosion Soil erodibility K
 Estimations based on
 RUSLE nomograph (2x)
 Regression from world soils
 Regression from tropical soils
 Selected method: average
Why: no reference in literature

11. Potential erosion Soil erodibility K

12. Potential erosion Slope length & steepness LS
 Estimation based on slope length and steepness
ARNOLDUS (1977)
 3 scenario’s:
Low LS Medium LS High LS
0,6 1,5 4
Length (m) 20 60 40
Steepness (%) 6,4 8,5 18
For example:

13. Potential erosion R · K · LS

14. Effect of CA Crop cover C
 3 rotations
Dolichos lablab
Weeds
Upland rice
Stylosanthes
guianensi
Maize

15. Effect of CA Crop cover C
C-factor divided into:
 Crop component (Cc)
 Based on canopy cover
 Mulch component (Cm)
 Based on residue cover (F) and type (b)

16. Effect of CA Crop cover C

17. Effect of CA Conservation Practices P
 Estimation based on literature:
 Traditional: non-mechanical tillage on contour, P=0.5
 CA: no-tillage, P=0.1

18. Effect of CA Potential · C · P
Based on CA stylo CA cowpea Traditional
Monthly interval 4 ton/ha 17 ton/ha 188 ton/ha
Yearly interval 2 ton/ha 9 ton/ha 108 ton/ha

19. Conclusion
 Estimating RUSLE parameters possible, but
validation still necessary.
 Soil loss estimates at monthly time interval about
2 times higher compared to yearly time interval.
 CA farming systems reduce soil loss with 98%
(stylo) and 91% (cowpea) compared to a
traditional farming system.

20. Discussion
 Difference between monthly and yearly interval
 P uncertain, least reliable factor
 Long road from results to application by farmer
 Will farmers produce same C and P?

21. Merci!

22. Calculation of potential erosion (ton/ha)
R K
LS Potential erosion
Low Medium High Low Medium High
jan 2122 0,086
0,6 1,5 4
110 274 731
feb 1680 0,078 78 196 522
mrch 1566 0,069 65 163 435
apr 307 0,023 4 11 29
may 42 0,014 0 1 2
june 17 0,013 0 0 1
july 9 0,013 0 0 0
aug 25 0,014 0 0 1
sep 8 0,012 0 0 0
oct 182 0,018 2 5 13
nov 831 0,044 22 55 146
dec 1700 0,071 73 182 485
monthly
calc sum 355 887 2366
yearly calc
total 8487 0,038 0,6 1,5 4 193 483 1288

23. Calculation of actual erosion (ton/ha)
Potential
erosion
C not weighted for R* P Actual erosion
CA stylo CA cowp Tradit CA Trad CA stylo CA cowpea Traditional
jan 274 0,116 0,266 0,450
0,1 0,5
3,17 7,29 61,67
feb 196 0,033 0,034 0,075 0,64 0,66 7,33
mrch 163 0,001 0,000 0,200 0,02 0,00 16,33
apr 11 0,004 0,027 0,399 0,00 0,03 2,16
may 1 0,062 0,046 0,476 0,01 0,00 0,21
june 0 0,032 0,074 0,604 0,00 0,00 0,10
july 0 0,062 0,100 0,748 0,00 0,00 0,06
aug 0 0,067 0,125 0,748 0,00 0,01 0,19
sep 0 0,066 0,129 0,748 0,00 0,00 0,05
oct 5 0,057 0,133 0,692 0,03 0,07 1,70
nov 55 0,019 0,150 0,779 0,11 0,82 21,26
dec 182 0,006 0,462 0,850 0,11 8,40 77,30
monthly calc
total
4 17 188
yearly calc
sum** 483 0,041 0,185 0,447 0,1 0,5 2 9 108*) Average for years 1-2, stylo 1-4
**) C weighted with R