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«A Thesis Presented to the Faculty of the Graduate School of Cornell University In Partial Fulfillment of the Requirements for the Degree of Master of ...»

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wilcox.test(mean4.1.df[[1]]$mean_flxL, mean4.1.df[[2]]$mean_flxL, paired = TRUE) #p-value = 0.1043 after OL removal #######test 2 #test flux~position: not signifcant #V = 1064, p-value = 0.1615, p-value = 0.2212 after outlier removal wilcox.test(mean4.df$mean_flxL ~ mean4.df$pos, paired = TRUE, na.action = "na.pass") #or mean4.2.df - split(mean4.df, mean4.df$pos) #finds location of NA in "LOW" split NAvect2 - is.na(mean4.2.df[[2]]$mean_flxL) #set other pair to NA is.na(mean4.2.df[[1]]$mean_flxL) - NAvect2 wilcox.test(mean4.2.df[[1]]$mean_flxL, mean4.2.df[[2]]$mean_flxL, paired = TRUE) # p-value = 0.2212 after removing outlier # WILCOX signed rank test : paired combos~~~~~~~~~~~~~~~~~~~~~~~ mean4.3.df - split(mean4.df, mean4.df$ID2) ### HC vs HT #p-value = 0.1152 wilcox.test(mean4.3.df$HC$mean_flxL, mean4.3.df$HT$mean_flxL, paired = TRUE) ### HC vs. LT : significant!

#p-value = 0.02954, V = 195, p-value = 0.04946 after removing outlier

–  –  –

is.na(mean4.3.df[[1]]$mean_flxL) - NAvect3 wilcox.test(mean4.3.df$HC$mean_flxL, mean4.3.df$LT$mean_flxL, paired = TRUE) #### HC vs. LC p-value = 0.09458 wilcox.test(mean4.3.df$HC$mean_flxL, mean4.3.df$LC$mean_flxL, paired = TRUE) # HT vs LC p-value = 0.9074 wilcox.test(mean4.3.df$HT$mean_flxL, mean4.3.df$LC$mean_flxL, paired = TRUE) # HT vs LT : significant!

#p-value = 0.000444, p-value = 0.0008382 after removing outlier NAvect4 - is.na(mean4.3.df[[4]]$mean_flxL) is.na(mean4.3.df[[2]]$mean_flxL) - NAvect4 wilcox.test(mean4.3.df$HT$mean_flxL, mean4.3.df$LT$mean_flxL, paired = TRUE) # LC vs LT: significant!

#p-value = 8.472e-05, p-value = 0.0001637 after removing outlier NAvect5 - is.na(mean4.3.df[[4]]$mean_flxL) is.na(mean4.3.df[[3]]$mean_flxL) - NAvect5 wilcox.test(mean4.3.df$LC$mean_flxL, mean4.3.df$LT$mean_flxL, paired = TRUE) ############################################################################## ################################ #Kruskal-Wallis rank-sum test.

–  –  –

ID2.v - as.factor(flux.df$ID2) kruskal.test(fluxL.v, ID2.v) #Kruskal-Wallis chi-squared = 19.8283, df = 3, p-value = 0.0001842 #chi-squared = 18.7511, df = 3, p-value = 0.0003078 after removing OL ############################################ ## Default S3 method:

flux5.df -split(flux.

df, flux.df$ID2) HC.v - as.vector(flux5.df$HC$fluxL) HT.v - as.vector(flux5.df$HT$fluxL) LC.v - as.vector(flux5.df$LC$fluxL) LT.v - as.vector(flux5.df$LT$fluxL) #all four categories kruskal.test( list(HC.v, HT.v, LC.v, LT.v)) #Kruskal-Wallis chi-squared = 19.8283, df = 3, p-value = 0.0001842 #chi-squared = 18.7511, df = 3, p-value = 0.0003078 after removing OL # HC against rest kruskal.test( list(HC.v, c(HT.v,LC.v,LT.v))) # Kruskal-Wallis chi-squared = 0.4401, df = 1, p-value = 0.5071 #p-value = 0.4684 after removing OL

–  –  –

kruskal.test( list(HT.v, c(HC.v,LC.v,LT.v))) #Kruskal-Wallis chi-squared = 5.0185, df = 1, p-value = 0.02508 #p-value = 0.02851 after removing OL # LC against rest kruskal.test( list(LC.v, c(HT.v,HC.v,LT.v))) #Kruskal-Wallis chi-squared = 5.5363, df = 1, p-value = 0.01863 #p-value = 0.02125 after removing OL # LT against rest kruskal.test( list(LT.v, c(HT.v,HC.v,LC.v))) #Kruskal-Wallis chi-squared = 15.4428, df = 1, p-value = 8.504e-05 #p-value = 0.000153 after removing outlier #paired combos~~~~~~~~~~~~~~~~~~~~~~~ # HC vs HT p-value = 0.06907 kruskal.test( list(HC.v, HT.v)) # HC vs. LT p-value = 0.03665, p-value = 0.04927 after removing outlier kruskal.test( list(HC.v, LT.v)) # HC vs. LC p-value = 0.05824 kruskal.test( list(HC.v, LC.v))

–  –  –

kruskal.test( list(HT.v, LC.v)) # HT vs LT p-value = 0.0002058, p-value = 0.0003116 after removing outlier kruskal.test( list(HT.v, LT.v)) # LC vs LT p-value = 0.0001402, p-value = 0.0002136 after removing outlier kruskal.test( list(LC.v, LT.v)) ################################# kruskal.test(flux.df$fluxL ~ as.factor(flux.df$ID2), flux.df) #Kruskal-Wallis chi-squared = 19.8283, df = 3, p-value = 0.0001842, p-value = 0.0003078 after removing outlier kruskal.test(flux.df$fluxL ~ as.factor(flux.df$treatment), flux.df) #Kruskal-Wallis chi-squared = 2.1409, df = 1, p-value = 0.1434, p-value = 0.1712 after removing outlier kruskal.test(flux.df$fluxL ~ as.factor(flux.df$pos), flux.df) #Kruskal-Wallis chi-squared = 1.8647, df = 1, p-value = 0.1721, p-value = 0.204 after removing outlier kruskal.test(flux.df$fluxL ~ as.factor(flux.df$date), flux.df) #Kruskal-Wallis chi-squared = 11.3444, df = 11, p-value = 0.4149, p-value = 0.4306 after removing outlier

–  –  –

Like script 4, this script loads the flux output file generated by script 3. It first removes the hot-moment outlier, then explores the structure of the data before generating a linear model.

It runs an ANOVA and an LSD test on the model.

#clear all variables rm(list=ls()) # Getting the required packages, nlme to fit models with REML, others for skewness, and to run MYSQL library(nlme) library(moments) library(agricolae) #Load data files #set working directory to source of csv files setwd("./flux") #loads slope tables from csv files flux.df-read.csv("FT_flux_613.csv", header = TRUE, sep = ",", quote="\"", dec=".", fill = TRUE, comment.char="") #rename flux.df columns names(flux.df)[names(flux.df) == 'subplot'] - 'plot' names(flux.df)[names(flux.df) == 'treatment'] - 'cover' # create ID 2 for treatments and position flux.df$ID2 -as.factor(paste(flux.df$position, flux.df$cover, sep = ""))

–  –  –





flux.df - flux.df[flux.df$X != 208,] skewness(flux.df$fluxL) #0.2774125 shapiro.test(flux.df$fluxL) #W = 0.9909, p-value = 0.07308 #not great, but keep null hypothesis that data is normally distributed #looks pretty good: proceed without log transform!

############################################################################## ## ###################### Repeated measures fluxL ################### ############################################################################## ## #setting up corAR1 lag variables and playing with David's suggestions #AHT varID - 'AHT1' assign(paste(varID,".ts", sep = ""), ts(flux.df$fluxL[flux.df$ID == varID])) assign(paste(varID,".acf", sep = ""), acf(eval(as.name(paste(varID,".ts", sep = ""))), type = "correlation")) quartz() varID - 'AHT2' assign(paste(varID,".ts", sep = ""), ts(flux.df$fluxL[flux.df$ID == varID]))

–  –  –

"correlation")) #CLC quartz() varID - 'CLC1' assign(paste(varID,".ts", sep = ""), ts(flux.df$fluxL[flux.df$ID == varID])) assign(paste(varID,".acf", sep = ""), acf(eval(as.name(paste(varID,".ts", sep = ""))), type = "correlation")) quartz() varID - 'CLC2' assign(paste(varID,".ts", sep = ""), ts(flux.df$fluxL[flux.df$ID == varID])) assign(paste(varID,".acf", sep = ""), acf(eval(as.name(paste(varID,".ts", sep = ""))), type = "correlation")) #after viewing auto correlation function, it looks there is no temporal autocorrelation, so no need for repeated measures ######################################################################## #Correlation # Define correlation structure - regular time intervals cs1 - corAR1(form= ~ 1 | ID)#changed from plot to ID grouping flux.cor-gls(fluxL ~ cover * position, corr = cs1, data=flux.df) #examining the autocorrelation of the model, (0.08? ish?) we confirmed that there is very little auto correlation and we can simply use lm()

–  –  –

independent #we found that the interaction model is better than the additive, position or cover alone does not explain the data as well as the interaction flux.lm - lm(fluxL ~ cover * position, data = flux.df) #Residuals:

–  –  –

#(Intercept) 6.035e-08 1.582e-08 3.816 0.000167 *** # coverT -3.755e-08 2.237e-08 -1.679 0.094293.

#positionL -4.105e-08 2.237e-08 -1.835 0.067512.

#coverT:positionL 1.323e-07 3.169e-08 4.176 3.96e-05 *** #######significant interaction!!

# --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #Residual standard error: 1.342e-07 on 283 degrees of freedom #Multiple R-squared: 0.07543, Adjusted R-squared: 0.06563 #explains ~ 6.5% of variation in data #F-statistic: 7.696 on 3 and 283 DF, p-value: 5.835e-05 ###########################################################################

–  –  –

#cover 1 5.7400e-14 5.7403e-14 3.1876 0.07527.

#position 1 4.4400e-14 4.4386e-14 2.4647 0.11755 #cover:position 1 3.1400e-13 3.1398e-13 17.4349 3.963e-05 *** # Residuals 283 5.0964e-12 1.8008e-14 #--Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #check how good the model is:

par(mfrow=c(2,2)) plot(flux.lm$fitted,flux.lm$residuals,xlab="Fitted",ylab="Residuals") plot((flux.df$date),flux.lm$residuals,xlab="Date",ylab="Residuals") hist(flux.lm$residuals,main=" ", xlab="Residuals",ylab="Frequency") qqnorm(flux.lm$residuals,main=" ", xlab="Theoretical quantiles",ylab="Sample quantiles") skewness(flux.lm$residuals) #0.1854727 #what is the effect of the #cover:position interactions?

################################################################## df-df.residual(flux.lm)

–  –  –

#or:

#mean square error MSE MSerror - (as.numeric(1.342e-07)^2) #residual degrees of freedom df - 283 # Effect of #cover:position LSD.test(flux.df$fluxL, flux.df$cover:flux.df$position, df, MSerror, p.adj="none", group=TRUE) #Groups, Treatments and means

–  –  –

Like script 4 and 5, this script loads the flux output file generated by script 3. It converts the data units and adjusts data into the final form presented in the paper.

#clear all variables rm(list=ls()) #Load data files #set working directory to source of csv files setwd("./flux") #loads flux tables from csv files flux.df-read.csv("FT_flux_613.csv", header = TRUE, sep = ",", quote="\"", dec=".", fill = TRUE, comment.char="") #convert fluxes from grams N2O m^-2 min-1 to...... micrograms N2O-N m^-2 hr-1 flux.df$fluxL - flux.df$fluxL * (10^6) * (28/44) * 60 #convert dates from string to date class flux.df$date - strptime(flux.df$date, format = "%Y-%m-%d") #adjust datenumber to account for 1:49:30 pm time flux.df$date - ((flux.df$date) + ((49.5/60)+13)*3600) #convert soil Temps to C flux.df$Tavg - ((flux.df$Tavg - 32) /1.8)

–  –  –

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9. U.S. EPA. Global Mitigation of Non-CO2 Greenhouse Gases: 2010-2030. Office of Atmospheric Programs (6207J) Washington, DC 20005. 2013.

–  –  –

11. Molodovskaya M, Singurindy O, Richards BK, Warland J, Johnson MS, Steenhuis TS.

Temporal Variability of Nitrous Oxide from Fertilized Croplands: Hot Moment Analysis. Soil Sci Soc Am J. 2012;76(5):1728-40.

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13. Wagner-Riddle C. Estimates of nitrous oxide emissions from agricultural fields over 28 months. Canadian journal of soil science. 1997;77(2).

14. Christensen S, Tiedje JM. Brief and Vigorous N2o Production by Soil at Spring Thaw. J Soil Sci. 1990;41(1):1-4.

15. Dorsch P, Palojarvi A, Mommertz S. Overwinter greenhouse gas fluxes in two contrasting agricultural habitats. Nutr Cycl Agroecosys. 2004;70(2):117-33.

16. Wagner-Riddle C, Furon A, Mclaughlin NL, Lee I, Barbeau J, Jayasundara S, et al.

Intensive measurement of nitrous oxide emissions from a corn-soybean-wheat rotation under two contrasting management systems over 5 years. Global Change Biol. 2007;13(8):1722-36.

17. Singurindy O, Molodovskaya M, Richards BK, Steenhuis TS. Nitrous oxide emission at low temperatures from manure-amended soils under corn (Zea mays L.). Agriculture, Ecosystems & Environment. 2009;132(1-2):74-81.

18. Matzner E, Borken W. Do freeze-thaw events enhance C and N losses from soils of different ecosystems? A review. European Journal of Soil Science. 2008;59(2):274-84.

19. Teepe R, Brumme R, Beese F. Nitrous oxide emissions from soil during freezing and thawing periods. Soil Biol Biochem. 2001;33(9):1269-75.

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–  –  –

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2004;167(6):678-84.

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