The Effects of Site and Soil on Fertilizer Response of Coastal Douglas-fir

K.M. Littke, R.B. Harrison, and D.G. BriggsUniversity of WashingtonCoast Fertilization MeetingFebruary 15, 2012

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Introduction• Douglas-fir grows on many of the diverse

soil types of the coastal Pacific Northwest region

▫ Distinctive site, soil, and nutrient characteristics between different soil parent materials

• Douglas-fir productivity has been related to site, soil, water, and nitrogen characteristics

• Urea fertilizer has been found to increase Douglas-fir growth response 70% of the time

▫ Many factors involving water and nitrogen availability have been investigated as predictors of fertilizer response

▫ No consistent predictors have been found

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Objectives• Determine the best predictor variables of Douglas-fir

fertilizer growth response using boosted regression trees (BRT)

• Relate BRT results to actual values

• Map BRT results to identify spatial relationships in fertilizer response

Definitions:• Predictor variables: Climate, site, soil, water,

nitrogen, foliar, and productivity characteristics

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Study Sites• 60 paired-tree Douglas-

fir fertilization installations

• At or near canopy closure (14-28 years old)

• Similar spacing (750 trees per ha)

• Red markers – Glacial parent material

• Green markers – Sedimentary parent material

• Blue markers – Igneous parent material

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Paired-tree Design• 48 dominant/co-dominant

Douglas-fir trees chosen on a 15-meter grid

• Trees paired by most similar diameter at breast height and crown height

• 12-20 pairs per installation

• One tree per pair fertilized with 224 kg N ha-1 as urea

• One soil pit sampled per installation to one-meter

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Variables

Soil Characteristics

•Effective Depth

•A Horizon Depth

•Sand (5 & 50 cm)

•Clay (5 & 50 cm)

Soil Nutrients

•Forest Floor C:N Ratio

•Soil C:N Ratio

•Total Soil Nitrogen

•Soil Base Saturation

Soil Water

•Lowest Soil Moisture

(5 & 50 cm)

•Plant Available Water

(5 & 50 cm)

Foliar Characteristics

•Foliar Nitrogen

Concentration

•100 Needle Area

Climate

Characteristics

•Growing Degree Days

•Monthly Temperature

and Precipitation

•Seasonal Temperature

and Precipitation

•Precipitation as Snow

Site Characteristics

•Stand Density

•Slope

•Elevation

•Aspect

•Parent Material and Region

Two-year Tree Fertilizer Response

•Basal Area Response (%)

•Height Response (%)

•Volume Response (%)

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Boosted Regression Trees• Improves model accuracy over regression trees and multiple

regression• Combination of regression trees and machine learning• Produced 1000 simple trees that are combined to form each model• Found six best variables for basal area, height, and volume growth

response

x 1000 =

Predictor

Split

Low Response

High Response

Predictors

Resp

on

se

Splits

Predictors

Resp

on

se

Resp

on

seR

esp

on

se

Resp

on

seR

esp

on

se

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Results: BRT Partial Dependence Plots

Forest Floor C:N Ratio (23%)

Basal Area Mean Annual Increment (cm2/year) (18%)

April Temperature (C) (14%)

Base Saturation (%) (9%)

Growing Degree Days (18%)

February Precipitation (mm) (17%)

• Effect of predictor variables keeping other predictors average

• Fitted function▫ Shows the effect

of the predictor variable on the response variable

▫ Centered around the mean

• Relative influence shown for each predictor (%)

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Results: Fertilizer Basal Area Response (%) Model

• 63% deviance explained

R2 = 0.62

• Basal area response to fertilization increased with forest floor C:N ratio, growing degree days, and February precipitation

• Negatively related to basal area mean annual increment, April temperatures, and base saturation

Forest Floor C:N Ratio (23%)

Basal Area Mean Annual Increment (cm2/year) (18%)

April Temperature (C) (14%)

Base Saturation (%) (9%)

Growing Degree Days (18%)

February Precipitation (mm) (17%)

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Results: Fertilizer Height Response (%) Model

• 51% deviance explained

R2 = 0.51

• Fertilizer height response decreased with basal area and volume mean annual increment, June temperature, and February precipitation

• Positive influence of summer precipitation

• Low- and high ranges of soil clay content also yielded greater height responseClay Content (%) (16%)

Basal Area Mean Annual Increment (cm2/year) (24%)

Summer Precipitation (mm) (15%)

Volume Mean Annual Increment (cm3) (8%)

June Temperature (C) (19%)

February Precipitation (mm) (18%)

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Results: Fertilizer Volume Response (%) Model

• 77% deviance

R2 = 0.75

• Volume growth response to fertilization positively related to May precipitation, forest floor C:N ratio, and growing degree days

• Negatively related to basal area mean annual increment and April temperatures

• Low and high February precipitation led to greater volume response

Forest Floor C:N Ratio (14%)

Basal Area Mean Annual Increment (cm2/year) (27%)

April Temperature (C) (19%)

May Precipitation (mm) (14%)

Growing Degree Days (13%)

February Precipitation (13%)

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How do we interpret this model?• Volume response

• Find the range of the predictor variable that yields an above average response.

• Used 60 installations that formed the model

• Determine if the stand meets each predictor criteria (0 or 1).

• Multiply each criteria ranking by the relative influence of the predictor.

• Total all predictors to determine the model criteria for that stand

14%

27% 19% 14%

13% 13%

01 0 0

00

0

1 1

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Relative Influence

=

Predictor Criteria

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Example:

= 81/100

81% of the criteria

This stand should have a high probability of responding to fertilization

Three Criteria Levels:

> 66% = High Response

33-66% = Medium Response

< 33% = Low Response

1 * 14%

1 * 27%

0 * 19% 1 * 14%

1 * 13% 1 * 13%

1

0

1

1 1 1

*

*

*

* * *

+ +

+ +

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Model Criteria and Response Differences

• Installations with less than 1/3 of the model criteria had significantly lower response.

• High model criteria significantly separated the installations with the greatest fertilizer response.

Model CriteriaMean Volume Response (%)

Std. ErrorSignificanc

ep-value

Low (<33%)

4 1 a

<0.001Medium

(33-66%)11 2 b

High (>66%)

23 4 c

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Mapping Predictor Criteria

• Inverse distance weighting of each predictor variable

• Separated by predictor criteria (0 or 1)

• All six predictor variables mapped

• Intersected different predictor criteria polygons to produce polygons with unique model criteria

• Combined polygons into low, medium, and high criteria

• Spatially joined installations with the model criteria polygons

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Mapping Model Criteria

• High variability in response in some areas

• Northern Vancouver Island and southeastern Oregon have the highest model criteria

• Significantly greater fertilizer volume response on high mapped model criteria

Model Criteria

Mean Response

(%)

Std. Error

Sig. p-value

Low 5 1 a

<0.001Medium 11 2 a

High 25 4 b

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Discussion• Basal area mean annual increment was the most important

predictor of fertilizer growth response.

▫ Less than 23 cm2/year more likely to response to fertilization

• Basal area and volume response was positively related to forest floor C:N ratio (>30)

▫ Height response was not related to forest floor C:N ratio

• Greater response on stands with low April temperatures, high May precipitation, and low and high February precipitation

▫ Could help narrow down stands that will respond

• Boosted regression tree models translated model criteria for installations with low, medium, and high fertilizer response

• Mapping of model criteria identified hot-spots of fertilizer response in northern Vancouver Island and southeastern Oregon

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Questions?•Thanks to:

▫Stand Management Cooperative

▫Center for Advanced Forestry Systems

▫Agenda 2020