The Effects of Site and Soil on Fertilizer Response of Coastal Douglas-fir K.M. Littke, R.B. Harrison, and D.G. Briggs University of Washington Coast Fertilization Meeting February 15, 2012

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 2

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 3

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 4

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 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 5

Variables 6 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 (%)

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 7 x 1000 = Predictor Split Low Response High Response Predictors Response Splits Predictors Response

Results: BRT Partial Dependence Plots 8 Forest Floor C:N Ratio (23%) Basal Area Mean Annual Increment (cm 2 /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 (%)

Results: Fertilizer Basal Area Response (%) Model 9 63% deviance explained R 2 = 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 (cm 2 /year) (18%) April Temperature (C) (14%)Base Saturation (%) (9%) Growing Degree Days (18%) February Precipitation (mm) (17%)

Results: Fertilizer Height Response (%) Model 10 51% deviance explained R 2 = 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 response Clay Content (%) (16%) Basal Area Mean Annual Increment (cm 2 /year) (24%) Summer Precipitation (mm) (15%) Volume Mean Annual Increment (cm 3 ) (8%) June Temperature (C) (19%) February Precipitation (mm) (18%)

Results: Fertilizer Volume Response (%) Model 11 77% deviance R 2 = 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 (cm 2 /year) (27%) April Temperature (C) (19%)May Precipitation (mm) (14%) Growing Degree Days (13%) February Precipitation (13%)

How do we interpret this model? 12 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% Relative Influence = Predictor Criteria

Example: 13 = 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% * * * * * *

Model Criteria and Response Differences 14 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 Criteria Mean Volume Response (%) Std. ErrorSignificancep-value Low (<33%) 41a <0.001 Medium (33-66%) 112b High (>66%) 234c

Mapping Predictor Criteria 15 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

Mapping Model Criteria 16 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 Low51a <0.001 Medium112a High254b

Discussion Basal area mean annual increment was the most important predictor of fertilizer growth response. ▫Less than 23 cm 2 /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 17

Questions? Thanks to: ▫Stand Management Cooperative ▫Center for Advanced Forestry Systems ▫Agenda