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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

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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.

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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

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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))

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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 = ""))

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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()

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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:

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#(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 ###########################################################################

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#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)

–  –  –

1. Adler PR, Del Grosso SJ, Parton WJ. Life-cycle assessment of net greenhouse-gas flux for bioenergy cropping systems. Ecol Appl. 2007;17(3):675-91.

2. Bessou C, Ferchaud F, Gabrielle B, Mary B. Biofuels, greenhouse gases and climate change. A review. Agronomy for Sustainable Development. 2011;31(1):1-79.

3. Hoefnagels R, Smeets E, Faaij A. Greenhouse gas footprints of different biofuel production systems. Renewable and Sustainable Energy Reviews. 2010;14(7):1661-94.

4. Sainju UM, Stevens WB, Caesar-Tonthat T, Liebig MA. Soil greenhouse gas emissions affected by irrigation, tillage, crop rotation, and nitrogen fertilization. Journal of environmental quality. 2012;41(6):1774-86. Epub 2012/11/07.

5. Forster P, V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, J.

Lean, D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz, R. Van Dorland. Changes in Atmospheric Constituents and in Radiative Forcing. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M.

Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 2007.

6. Conrad R. Soil microorganisms as controllers of atmospheric trace gases (H-2, CO, CH4, OCS, N2O, and NO). Microbiol Rev. 1996;60(4):609-+.

7. Ravishankara A. Nitrous oxide (N2O): The dominant ozone-depleting substance emitted in the 21st century. Science. 2009;326.

8. Smith P, D. Martino, Z. Cai, D. Gwary, H. Janzen, P. Kumar, B. McCarl, S. Ogle, F.

O’Mara, C. Rice, B. Scholes, O. Sirotenko. Agriculture. In Climate Change 2007: Mitigation.

Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, [B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds)].

Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 2007.

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.

12. Corre MD, vanKessel C, Pennock DJ. Landscape and seasonal patterns of nitrous oxide emissions in a semiarid region. Soil Sci Soc Am J. 1996;60(6):1806-15.

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.

20. Teepe R, Vor A, Beese F, Ludwig B. Emissions of N2O from soils during cycles of freezing and thawing and the effects of soil water, texture and duration of freezing. European Journal of Soil Science. 2004;55(2):357-65.

–  –  –

22. Ludwig B, Wolf I, Teepe R. Contribution of nitrification and denitrification to the emission of N2O in a freeze-thaw event in an agricultural soil. J Plant Nutr Soil Sc.

2004;167(6):678-84.

23. Dietzel R, Wolfe D, Thies JE. The influence of winter soil cover on spring nitrous oxide emissions from an agricultural soil. Soil Biology and Biochemistry. 2011;43(9):1989-91.

24. Pelster DE, Chantigny MH, Rochette P, Angers DA, Laganière J, Zebarth B, et al. Crop residue incorporation alters soil nitrous oxide emissions during freeze–thaw cycles. Canadian Journal of Soil Science. 2013;93(4):415-25.

25. Wagner-Riddle C, Thurtell GW. Nitrous oxide emissions from agricultural fields during winter and spring thaw as affected by management practices. Nutr Cycl Agroecosys. 1998;52(2Smith KA, Thomson PE, Clayton H, McTaggart IP, Conen F. Effects of temperature, water content and nitrogen fertilisation on emissions of nitrous oxide by soils. Atmos Environ.

1998;32(19):3301-9.

27. Cline MG, Bloom AL. Soil survey of Cornell University property and adjacent areas.

Ithaca: New York State College of Agriculture; 1965.

28. Parkin TB, Venterea RT. Sampling Protocols. Chapter 3. Chamber-Based Trace Gas Flux Measurements. Sampling Protocols RF Follett, editor. 2010: p. 3-1 to 3-39.

29. Molodovskaya M, Oberg G, Warland J, Richards BK, Steenhuis TS. Nitrous oxide from heterogeneous agricultural landscapes: Source contribution analysis by eddy covariance and chambers. Soil Sci Soc Am J Soil Science Society of America Journal. 2011;75(5):1829-38.

30. Rochette P, Bertrand N. Soil-Surface Gas Emissions, in: Soil Sampling and Methods of Analysis, 2n Ed: M.R. Carter and E.G. Gregorich eds. Canadian Journal of Soil Science. 2008.

31. Rochette P, Hutchinson GL. Measurement of Soil Respiration in situ: Chamber

Techniques. In: Hatfield JL, Baker JM, editors. Micrometeorology in Agricultural Systems:

American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America; 2005. p. 247-86.

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