Developing Regression Models for Sediment Yield and Nutrient Loss From Biomass Crops

1
Developing Regression Models for Sediment Yield
and Nutrient Loss From Biomass Crops

• E. Z. Nyakatawa1, D.A. Mays1 , and V.R.
  Tolbert2,
• 1Department of Plant and Soil Science, Alabama
  AM University, P.O. Box 1208, Normal, AL 35762
  2Bioenergy Feedstock Development Programs,
  Oakridge National Laboratory, Oakridge, TN 37831

2
INTRODUCTION
Soil Erosion An environmental problem.

• Siltation of rivers, dams, and lakes.
• Pollution of surface and ground water resources.

3

• Eutrophication of rivers and lakes.
• NO3- and soluble P (H2PO4-) in runoff water.
• NH4 and P attached to sediment.

4
Evaluating the sustainability of growing biomass
crops.

• Information needed
• How much runoff occurs.
• How much soil is lost as eroded sediment.
• Amount of nutrients, esp. N (NO3- and NH4 ) P
  are lost in runoff water and eroded sediment.

5
STUDY

• Co-operation between Tennessee Valley Authority
• (TVA), DOEs Bio-energy Feedstock Development
• Program (BFDP), Oakridge TN and Alabama
• AM University, Huntsville AL.
• Objective
• To assess the environmental impacts of bio-energy
• crop production on soil and water quality.

6

• Location
• Winfred Thomas Agricultural Research Station
  (WTARS), Hazel Green AL, from 1995 to 1999.
• Soil type
• Decatur silt loam (clayey, kaolinitic thermic,
  Typic Paleudults).

7
TREATMENT FACTORS

• No-till Corn (NTC)
• Switchgrass cover crop (SWG)
• Sweetgum with cover crop (SGC)
• Sweetgum without cover crop (SWC)

8
FIELD PLAN

• Eight runoff plots (four 0.18 ha and 0.43 ha)
  established.
• Treatments randomly assigned to plots with each
  treatment getting one large and one small plot
  (see diagram).

9
Plot layout, WTARS
10
FIELD PLAN

• Eight runoff plots (four 0.18 ha and 0.43 ha)
  established.
• Treatments randomly assigned to plots with each
  treatment getting one large and one small plot
  (see diagram).
• Earth berm 50 cm high around each plot to
  isolate them from surrounding fields.

11
DATA COLLECTED

• Rainfall.
• Runoff volume.
• Sediment yield.
• Nutrient loss.
• (nitrate, ammonium, and P).

12
Runoff and sediment collecting point
13
Setup for collecting runoff and sediment V flume
14
Housing for data loggers
15
Data collection and storage
16
Data collection and storage
17
Sweet gum plot
18
Switchgrass Plot
19
Corn plot
20
RESULTS AND DISCUSION
21
Rainfall and runoff, WTARS 1995-1999
22
Runoff NH4 as affected by no-till corn (NTC),
switchgrass (SWG), sweetgum wo/cover (SWC), and
sweetgum w/cover (SGC) treatments, WTARS, AL
23
Runoff NO3 as affected by no-till corn,
switchgrass, sweetgum wo/cover, and sweetgum
w/cover treatments, WTARS, AL
24
Runoff P as affected by no-till corn,
switchgrass, sweetgum wo/cover, and sweetgum
w/cover treatments, WTARS, AL
25
Sediment yield as affected by no-till corn,
switchgrass, sweetgum wo/cover, and sweetgum
w/cover treatments, WTARS, AL
26
Sediment NO3 as affected by no-till corn,
switchgrass, sweetgum wo/cover, and sweetgum
w/cover treatments, WTARS, AL
27
Sediment P as affected by no-till corn,
switchgrass, sweetgum wo/cover, and sweetgum
w/cover treatments, WTRS, AL
28

• Fitting regression models

• To relate sediment yield, NH4 and NO3
• losses to rainfall and runoff.
• Factors affecting sediment yield and
• nutrient loss
• Climate
• Soil type
• Topography (slope)
• Land use

29

• In this study
• Climate, soil type, and slope are similar for all
  treatments.
• Land use different as defined by each
  treatment.
• Therefore, interested in models for each
  treatment to be able to compare land use systems.

30
Strategy for Developing Regression Models
Start
Exploratory data analysis
Develop tentative regression models
Is one or more models suitable?
NO
Revise models/new ones?
YES
Identify most suitable model
Make inferences based on model
Stop
31

• Fitting the BEST model

• Statistics for developing regression
• models.
• Several regression analyses procedures.
• Proc Reg, Proc Glm, Proc rsreg, Proc Catmod etc
• Each procedure has special features for handling
  similar or different data types.
• Choice - largely on applicability of each proc
  and degree of complexity of data.
• However, individual preference also plays big
  part.

32

• Fitting the BEST model

• However, no matter how artistic model
  development can be, reality must be kept at
  manageable proportions !!
• Only a limited number of of independent or
  predictor variables (parameters) should be
  included in the model.
• Considerations in choosing model parameters
• Contribution to reducing variation in dependent
  variable.
• Degree to which parameter can be obtained
  efficiently (quickness, cost, accuracy).
• Importance of parameter to the study.

33
Scatter plots showing relationships between
sediment yield, rainfall and runoff volume
No-till corn
Switch grass
Sweetgum
Sweetgum w/cover
34
Scatter plots showing relationships between NO3
loss, rainfall and runoff volume
No-till corn
Switch grass
Sweetgum
Sweetgum w/cover
35
Scatter plots showing relationships between P
loss, rainfall and runoff volume
No-till corn
Switch grass
Sweetgum
Sweetgum w/cover
36
Scatter plots showing relationships between
runoff volume and rainfall
No-till corn
Switch grass
Sweetgum
Sweetgum w/cover
37

• In this study
• Proc REG choice over Proc GLM.
• Proc REG more diagnostic tools and
  capabilities.
• SAS ver. 6.02 introduced two new model-selection
  methods for fitting the best model.
• Adgjusted R2 (ADRSQ) and Mallows CP statistic.

38
SAS Procedure

• Proc rsquare cp mse adjrsq by trt by season
  trt
• rsquare regression SS/corrected total sum of
  squares
• this finds a no.of models with the highest R
  square.
• cp Mallows Cp
• (Residual SSp)/ (Res MS from full model) 2p-n
  
• this finds a no.of models with the lowest Cp.
• adjusted R2 (ADRSQ)
• 1 Error Mean Square/Total MS 1 (1-
  R2)(n-1)/(n-p)
• this finds a no.of models with the highest
  adjusted R square
• MSE mean square error.

39
SAS Model Statement

• Model sediment NH4 NO3 X1 X2 X3 Y1 Y2
  
  Y3 Z1
• Where
• X1 rainfall
• X2 rainfallrainfall
• X3 rainfallrainfallrainfall
• Y1 runoff
• Y2 runoffrunoff
• Y3 runoffrunoffrunoff
• Z1 rainfallrunoff.

40
Regression Models for Dependent Variable
SEDIMENT Number R-square Adjusted C(p)
MSE Variables in Model Model
R-square   1 0.06656206
0.05018596 1.11168 349275.40 X1
. ... . .
1 0.01977926 0.00258240
1.58914 366780.67 Y1 ------------------------
--------------------------------------------------
----------------------------- 2
0.08346260 0.05072912 0.08737 349075.66
X2 X3 . . .. .
. . ------------------------------------
--------------------------------------------------
------------------ 3 0.09370310
0.04426873 1.32144 351451.34 X1 X2 X3
. . . .. . .
--------------------------------------
--------------------------------------------------
----------------- 4 0.10033782
0.03369618 2.93841 355339.19 X1 Y1 Y2
Y3 . . . . .. .
. ----------------------------------
--------------------------------------------------
--------------------- 5 0.11168467
0.02788134 4.28335 357477.48 X2 X3 Y1
Y2 Y3 . . . . .. .
. ---------------------------------
--------------------------------------------------
--------------------- 6 0.11506849
0.01296101 6.08799 362964.14 X2 X3 Y1
Y2 Y3 Z1 -----------------------------------------
--------------------------------------------------
------------- 7 0.11659270
-.00465929 8.00000 369443.65 X1 X2 X3
Y1 Y2 Y3 Z1 --------------------------------
--------------------------------------------------
-----------------
41
-----------------------------TRT
NTC------------------------------
---------------------------TRT NTC
WINTER-----------------------------
----------------------------TRT NTC SPRING
-----------------------------
--------------------------TRT NTC SUMMER
----------------------------
42
-------------------------------- TRT SGC
-------------------------------
-------------------------- TRT SGC WINTER
-----------------------------
------------------------- TRT SGC SPRING
-----------------------------
------------------------ TRT SGC SUMMER
----------------------------
43
-------------------------------- TRT SWC
-------------------------------
-------------------------- TRT SWC WINTER
-----------------------------
------------------------- TRT SWC SPRING
-----------------------------
------------------------ TRT SWC SUMMER
----------------------------
44
-------------------------------- TRT SWG
-------------------------------
-------------------------- TRT SWG WINTER
-----------------------------
------------------------- TRT SWG SPRING
-----------------------------
------------------------ TRT SWG SUMMER
-----------------------------
45
SUMMARY

• For each treatment and dependent variable, best
  model varies with season (most likely reflecting
  rainfall variation).
• Seasonal changes also affect nutrient chemistry
  hence behavior (minerilization, immobilization,
  microbial activity)
• Therefore, have to develop models separately for
  each season..these can be called in succession
  during a simulation.
• Within treatments and seasons, model parameters
  seem to change depending on amount rainfall and
  initial soil moisture content (affects runoff,
  and behavior of soil particles) .