Post on 02-Jul-2015
description
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