Yuan, Yongping - Soil and Water Conservation Society

ANNAGNPS APPLICATION FOR BEASLEY
LAKE WATERSHED CONSERVATION
PRACTICES ASSESSMENT
USDA-ARS-National Sedimentation Laboratory
Yongping Yuan, Martin Locke, Ron Bingner

Introduction
• The impairment of water quality from
nonpoint source pollution continues to be a
societal and environmental concern.
• To address nonpoint source pollution
problems, the USEPA mandates states to
develop TMDLs and the NRCS promotes
conservation practices and/or BMPs.
• In 2003, the USDA began implementing the
Conservation Effect Assessment Project
(CEAP).

Objectives of CEAP
• The principal focus of the CEAP is to assess
environmental benefits derived from implementing
USDA conservation programs supported by the
USDA in the 2002 Farm Bill.
• Watershed Assessment Studies (WAS) are a key
element of the CEAP.
• One of the CEAP-WAS goals is to utilize models to
simulate environmental effects of conservation
practices and evaluate the capabilities of
watershed models to assess the effect of USDA
conservation practices.

CEAP Study Watersheds
Fourteen ARS maintained benchmark watersheds representing different
agroecosystems were selected across the country for the CEAP project

Beasley Lake Watershed
• Beasley Lake watershed was originally one of the
three watersheds studied in the Management
Systems Evaluation Area project (MSEA), which
sought to develop and assess alternative
innovative farming systems for improved water
quality.
• Beasley Lake Watershed is unique in that it
represents the large portion of the Mississippi
Delta cotton-soybean production fields that
produce a significant amount of sediment.
• Beasley Lake is an oxbow lake cutoff from the
Sunflower River.

Sunflower River Beasley Lake Forest Area

Watershed Characteristics
• The total drainage area of the BLW is
approximately 850 ha, and the lake area is
approximately 25 ha.
• The watershed topography is very flat, with a 5 m
difference in elevation from the top of the
watershed boundary to the lake surface.
• The watershed is a relatively closed system.
• Soil texture in the watershed varies from sandy
loam to heavy clay.

Watershed Characteristics
• Prior to 1995, conventional tillage was the
dominant practice applied to the watershed.
• Since its inclusion in the MDMSEA project in 1995,
reduced–tillage cotton, no–tillage soybeans and
edge-of-field practices were implemented.
• In 1995–1996, the U.S. Geological Survey (USGS)
installed a gauging station to monitor runoff,
sediment, nutrient and pesticide loadings at one of
the inlets to the Beasley Lake.

Watershed Characteristics
• From 1995 to 2002, 660 ha of the watershed were
cropped with cotton, corn and soybeans, and
cotton represented about 70% of total cropped
area.
• From 2003 to present, soybeans have been the
dominant crop (more than 50%), but cotton, corn,
and sorghum are also produced.
• From 2003, 91 ha were removed from crop
production and planted into native grass and
hardwoods under the Conservation Reserve
Program (CRP).

AnnAGNPS
• Annualized Agricultural Non-Point Source pollutant
loading (PL) model.
• Continuous simulation, watershed scale model.
• Simulates water, sediment, & chemical loadings.
• Can be used in gaged & ungaged, agriculturalrelated
watersheds containing mixed land use.
• Tracks loadings by source throughout the
transport process.

AnnAGNPS:
Processes
Uses NRCS Standards
Databases
• Weather Generation - GEM
• Runoff – SCS Curve Number
• Peak Runoff – TR-55
• Erosion - RUSLE
• Nutrients (N, P and C)
• Pesticides
• Soils - NASIS
• Crops and Operations –
Set by NRCS State
Agronomists
• HUWQ Databases –
Fertilizer, Pesticides,
Animal Wastes, etc.

Data Requirements
Soil Data
Cell Processes
runoff event
Reach
Topographic
Field Management
Climate
Agricultural
Landuse (crops)
Management
Schedule
Non-Agricultural
Landuse
Fertilizers
Irrigation
Pesticides
SCS CN

All Available AnnAGNPS Input Data Sections
AnnAGNPS
Identifier
Watershed
Data
Simulation
Period
Daily
Climate
Verification
Data
Global
Output
Gully
Point
Source
Feedlot
Feedlot
Management
Field Pond
Cell Data
Reach Data
Soils
Management
Field
Tile-Drain
Impoundment
Reach Channel
Geometry
Reach Nutrient
Half-life
Management
Schedule
Fertilizer
Application
Pesticides
Application
Management
Operation
Strip Crop Contours Crop Runoff Curve
Number
Irrigation
Fertilizer
Reference
Pesticides
Reference
Non-Crop
Required
Required if Referenced
Optional

AnnAGNPS: Output
Average annual contribution of each cell’s unitarea
loads to the outlet as well as by individual
event are available such as:
• sediment & chemical (nutrients and pesticides)
pollutants;
• yield of all constituents (water, sediment, &
chemicals) to its receiving stream;
• loading of all constituents at any location in the
stream network; and

Data Requirements
• Various GIS data layers of the watershed
including DEM, land-use, and soils.
• In field crop operation and management.
• Climate information.

Data Collections
• Elevation was obtained by GPS survey from 2005-2006.
• Soil survey was conducted by the NRCS district office in
spring of 2006.
• From 1996 to 2003, detailed records have been kept of
field management operations, including crop, planting
dates, fertilizer and pesticide applications, cultivation
events, and harvest dates.
• From 1996 to 1999, maximum and minimum temperatures,
precipitation and wind speed were recorded at the
Stoneville which is about 15 miles away from Beasley
Lake watershed. From 2000 to 2005, maximum and
minimum temperatures, precipitation and wind speed
were recorded at the Beasley Lake watershed.

Annual Average Soil Erosion from 1996-2003
Average Annual Soil Erosion from 1996 to 2003 (Mg/ha.)
0.045 - 0.108
0.108 - 1.565
1.565 - 2.231
2.231 - 2.395
2.395 - 3.235
3.235 - 3.816
3.816 - 5.764
5.764 - 7.386
7.386 - 8.326

ID
A
B
C
D
E
F
G
H
I
J
K
L
M
N
Scenario
Description
Existing (baseline) condition
7% of the watershed representing the highest eroding cropland areas (60.3 ha.) converted to no-till
soybeans
17% of the watershed converted to no-till soybeans
33% of the watershed converted to no-till soybeans
All cropland no-tilled soybeans
All cropland reduced tillage Soybean
All cropland conventional tillage Soybean
All cropland conventional tillage cotton
All cropland reduced tillage cotton
All cropland no-tilled cotton
7% of the watershed representing the highest eroding cropland areas (60.3 ha.) converted to grass land
17% of the watershed representing the highest eroding cropland areas (143.8 ha.) converted to grass
land
33% of the watershed representing the highest eroding cropland areas (281.2 ha.) converted to grass
land
All cropland converted to grass land

Comparison of results from alternative scenarios
Landscape Erosion/Sediment Load/Sediment Yield (T/ha/year)
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Landscape soil erosion
Sediment yield
Sediment load
A B C D E F G H I J K L M N
Scenario
A. Existing (baseline) condition.
B 7% of the watershed representing
the highest eroding cropland areas
(60.3 ha.) converted to no-till
soybean.
C 17% of the watershed (143.8 ha.)
converted no-till soybeans.
D 33% of the watershed (281.2 ha.)
converted to no-till soybeans.
E All cropland no-tilled soybeans.
F All cropland reduced tillage
Soybean.
G All cropland conventional tillage
Soybean.
H All cropland conventional tillage
cotton.
I All cropland reduced tillage cotton.
J All cropland no-tilled cotton.
K 7% of the watershed representing
the highest eroding cropland areas
(60.3 ha.) converted to grass land.
L 17% of the watershed representing
the highest eroding cropland areas
(143.8 ha.) converted to grass land.
M 33% of the watershed representing
the highest eroding cropland areas
(281.2 ha.) converted to grass land.
N All cropland converted to grass
land.

Conclusions
• The model demonstrated that applications of
various areas of no-tillage or grassland to the
watershed could reduce sediment loadings to a
range of 15-69 percent of the existing condition.
• Converting 7% of the highest eroding cropland
cells to no tillage soybean practice would reduce
sediment load by 15%.
• A more efficient way is to convert the 7% of the
highest eroding cropland cells to grass that would
reduce sediment load by 19%.

Conclusions
• For the same tillage system, planting soybeans
resulted in less sediment loading to the lake than
planting cotton because soybeans produces more
residue that provides a better protection of the soil
than cotton.
• Converting all the cropland to no-tillage soybeans
would reduce sediment loading by 77%; whereas
converting all the cropland to no-tillage cotton
would reduce sediment loading by 64%.

Future Work
• Linking AnnAGNPS model with a lake water
quality model and/or establish links
between sediment load and biologic
impairment/aquatic habitat of the Beasley
Lake watershed will be one of the future
enhancements of AnnAGNPS.

Acknowledgements
• Special thanks go to John Massey and
Calvin Vick for their field data collection.