WelcomeEstimating parameters of the RUSLE for rain-fed crops under Conservation Agriculture in Madagascar

16-12-2010ColloquiumFreddy van Hulst

Supervision WUR: Jan de Graaff - Saskia Visser CIRAD: Krishna Naudin - Eric Scopel

Contents The big picture CA2AFRICA The study area The model RUSLE

Objectives Methods and results

Potential erosion Effect of CA

Conclusion and discussion

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

The study areaAverage annual rainfall: 1051 mmSoil type: Loam, sandy clay loamSlopes: 0-25 %Main crops: Rice and Maize

The modelRUSLE

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

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

potentialerosion

effect of management

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

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

Potential erosion Rainfall erosivity R

Yearly R: 8487 MJ·mm·ha-1h-1

Potential erosion Soil erodibility K

Estimations based on RUSLE nomograph (2x) Regression from world soils Regression from tropical soils

Selected method: averageWhy: no reference in literature

Potential erosion Soil erodibility K

Potential erosion Slope length & steepness LS

Estimation based on slope length and steepness ARNOLDUS (1977)

3 scenario’s:Low LS Medium LS High LS0,6 1,5 4

Length (m) 20 60 40Steepness (%) 6,4 8,5 18

For example:

Potential erosion R · K · LS

Effect of CA Crop cover C

3 rotations

Dolichos lablab

Weeds

Upland riceStylosanthes guianensi

Maize

Effect of CA Crop cover CC-factor divided into:

Crop component (Cc) Based on canopy cover

Mulch component (Cm) Based on residue cover (F) and type (b)

Effect of CA Crop cover C

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

Effect of CA Potential · C · PBased on CA stylo CA cowpea Tradit ional

Monthly interval 4 ton/ha 17 ton/ha 188 ton/ha

Yearly interval 2 ton/ha 9 ton/ha 108 ton/ha

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.

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?

Merci!

Calculation of potential erosion (ton/ha) 

R KLS Potential erosion

  Low Medium High Low Medium Highjan 2122 0,086

0,6 1,5 4

110 274 731feb 1680 0,078 78 196 522mrch 1566 0,069 65 163 435apr 307 0,023 4 11 29may 42 0,014 0 1 2june 17 0,013 0 0 1july 9 0,013 0 0 0aug 25 0,014 0 0 1sep 8 0,012 0 0 0oct 182 0,018 2 5 13nov 831 0,044 22 55 146dec 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

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 Traditionaljan 274 0,116 0,266 0,450

0,1 0,5

3,17 7,29 61,67feb 196 0,033 0,034 0,075 0,64 0,66 7,33mrch 163 0,001 0,000 0,200 0,02 0,00 16,33apr 11 0,004 0,027 0,399 0,00 0,03 2,16may 1 0,062 0,046 0,476 0,01 0,00 0,21june 0 0,032 0,074 0,604 0,00 0,00 0,10july 0 0,062 0,100 0,748 0,00 0,00 0,06aug 0 0,067 0,125 0,748 0,00 0,01 0,19sep 0 0,066 0,129 0,748 0,00 0,00 0,05oct 5 0,057 0,133 0,692 0,03 0,07 1,70nov 55 0,019 0,150 0,779 0,11 0,82 21,26dec 182 0,006 0,462 0,850 0,11 8,40 77,30monthly 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