Comparisons of N2O and CH4 fluxes as affected by land use systems and climate in small catchments in Korea

1 Comparisons of N 2 O and CH 4 fluxes as affected by land use systems and climate in small catchments in Korea Dissertation to attain the academic degree of Doctor of Natural Science (Dr. rer. nat) of the “Bayreuther Graduiertenschule für Mathematik und Naturwissenschaften” (BayNAT) of the University of Bayreuth presented by Sina Berger born January 31, 1987 in Eisenach (Germany) First Reviewer: Prof. Dr. Gerhard Gebauer Bayreuth, September 2012 2 1 Summary In the course of global and climate change humankind has to face extreme weather events with increased intensity and frequency and it has to deal with feeding an increasing number of people which is accompanied by shortage of resources such as water. Since half of humankind directly depends on freshwater and other ecosystem services provided by mountainous areas, it is essential to study such complex terrains and how natural as well as agricultural systems react to climatic and other anthropogenic changes. Emissions of greenhouse gases like Nitrous oxide (N 2 O) and Methane (CH 4 ) are of global concern, too, because they are involved in global warming and therewith: climate change. Major sources of N 2 O are agriculturally managed soils, and very important sources of CH 4 are rice paddies. Thus, it is of great importance to study intensively managed agricultural systems and the effects of the management practices on greenhouse gas emissions. The major focus of this thesis is to quantify dry crop fields’ and forests’ N 2 O emissions as well as rice paddies’ N 2 O and CH 4 emissions and to identify climatic as well as management related factors and underlying processes which are driving the N 2 O fluxes in a complex terrain. A prolonged early summer drought in 2010 led to significant N 2 O consumption in soil of three different forest sites. The following above-average monsoon rainfall period indeed turned the N 2 O consumption into emission but could not turn the N 2 O balance of a forest on sandy-loam substrate from negative into a positive one, which means that for the first time a negative N 2 O balance was observed for a forest soil during the growing season. The N 2 O emissions of those forest sites were clearly driven by soil moisture and temperature and there appeared to be an effect of the substrate on N 2 O emissions as well, as it is increasingly often observed that sandy-loam soils show significant N 2 O consumption. Plastic mulching – a worldwide used method in agriculture to increase crop production by enhancing soil temperature, creating more stable soil moisture conditions and restricting arable weed growth – turned out to have a mitigating effect on N 2 O emissions. DNDC (Denitrification and Decomposition) modeling results matched best with the measurement results when the maximum daily soil temperature and half of the daily precipitation was assumed to occur as dominating climate conditions underneath the impervious polyethylene (PE) film, suggesting that N 2 O production underneath the plastic cover was driven by soil moisture and temperature. N 2 O emissions from a non-fertilized soy bean field, which has Nitrogen fixation as an additional Nitrogen source, were similar to the N 2 O emissions from a radish field after application of an intermediate amount of N fertilizer of 200 kg ha -1 . 2 Comparing N 2 O and CH 4 emissions from rice paddies under different water management practices showed that intermittent irrigation (II) (no continuous flooding, no water logging) had the least global warming potential (GWP) which was only 30% of the global warming potential (GWP) of a traditionally irrigated (TI) paddy (continuous flooding and water logging). Another practice of 2.5 months of continuous flooding, followed by midseason drainage and reflooding which created moist but non-water logged conditions (FDFM) lead to 66% of the traditionally irrigated paddies combined CH 4 and N 2 O emissions. These results suggest that a trend towards less flooding has a great potential to mitigate greenhouse gas emissions from a sandy or sandy-loam substrate, respectively. Studying the three paddies’ subsoil conditions revealed that N 2 O production and consumption processes had mainly taken place between 25 and 50 cm soil depth judging by N 2 O concentrations and δ 15 N-N 2 O values along the soil profiles of all the investigated paddies as well as gene abundances of denitrifying and nitrifying bacteria of the FDFM paddy. Apart from these important findings on N 2 O flux dynamics of three different land use systems, it is noticeable that the N 2 O emissions of the study region are in general very low which is very pleasing and implies that the area deals with global change challenges and associated intensive agriculture in a way that comparatively only small amounts of N 2 O degas. But this raises the question after the “why?” considering that large amounts of fertilizer are applied on the fields. This thesis does not have a final answer to that question but it discusses whether the sandy substrate may play a major role for the N dynamics of the whole area. There is evidence that NO 3- - as the substrate for denitrification - leaches easily due to the soil conditions. To finally figure out why the N 2 O emissions are that low a more detailed investigation on the fate of NO 3would be desirable. 3 Zusammenfassung Im Zuge von Globalem Wandel und Klimawandel muss die Menschheit sich mit immer häufiger und heftiger werdenden extremen Wetterereignissen auseinandersetzen, sowie sie auch versuchen muss, eine immer zahlreicher werdende Weltbevölkerung zu ernähren bei zunehmender Verknappung von Ressourcen. Da die Hälfte der Menschheit angewiesen ist Ökosystemdienstleistungen aus den bergigen Gebiete der Erde, ist es essentiell, solche komplexen Landschaften zu studieren und zu verstehen, wie natürliche sowie auch landwirtschaftliche Ökosysteme sich auf Klimaänderungen und veränderte anthropogene Einflüsse einstellen. Emissionen von Treibhausgasen wie Lachgas (N 2 O) und Methan (CH 4 ) sind involviert in die Klimaerwärmung und den damit einhergehenden Klimawandel, was sie zu wichtigen globalen Angelegenheiten macht. Wichtigste Quellen von N 2 O sind landwirtschaftliche Böden, CH 4 entstammt zu großen Anteilen aus Reisfeldern. Daher ist es von größter Wichtigkeit, solche landwirtschaftlichen Systeme, im Hinblick der Management-Praktiken und deren Einfluss auf Treibhausgasemissionen, zu studieren. Das Hauptaugenmerk dieser Arbeit ist es, N 2 O Emissionen von landwirtschaftlichen und Waldböden zu quantifizieren, sowie auch N 2 O und CH 4 Emissionen von Reisfeldern und herauszufinden, welche Faktoren die Flüsse dieser Treibhausgase maßgeblich steuern. Die verlängerte Frühsommertrockenperiode des Jahres 2010 führte zu signifikanter N 2 OKonsumption in Böden dreier Waldstandorte. Die darauffolgenden überdurchschnittlich heftigen Monsunregenfälle verursachten dann zwar N 2 O-Emissionen, und leicht positive N 2 O-Bilanzen in zwei der Wälder, jedoch waren sie nicht ausreichend um die N 2 O-Bilanz des Waldes auf sandig-lehmigem Boden in eine positive umzukehren. Dies bedeutet, dass für einen Waldboden während der Vegetationsperiode zum ersten Mal eine negative N 2 O-Bilanz beobachtet wurde. Die N 2 O-Emissionen der Waldstandorte wurden gesteuert von Bodenfeuchte und Bodentemperatur und – wie zunehmend in der Literatur zu finden – schien es einen Einfluss der Bodentextur auf die N 2 O-Flüsse zu geben. Es stellte sich außerdem heraus, dass der Einsatz von Folie in der Landwirtschaft – eine weltweit immer häufiger eingesetzte Methode zur Steigerung der Ernten durch höhere Bodentemperaturen und stabilere Bodenfeuchte – eine lindernde Wirkung auf die N 2 OEmissionen der Felder hat. Modellierungen mit dem DNDC- (Denitrifikation und Dekomposition)-Model stimmten am besten mit den im Feld gemessenen N 2 O-Flüssen überein, wenn Tageshöchsttemperaturen und die Hälfte des Tagesniederschlages als dominierende Klimafaktoren unter der Folie angenommen wurden, was impliziert, dass die N 2 O-Produktion unter der Folie auch stark von Bodentemperatur und Bodenfeuchte 4 abhängig war. N 2 O-Emissionen eines ungedüngten Sojabohnenfeldes, waren ähnlich den N 2 O-Emissionen eines Rettichfeldes, welches eine mittlere Menge Stickstoff-Dünger von 200 kg N ha -1 bekommen hatte. Ein Vergleich von N 2 Ound CH 4 -Emissionen von Reisfeldern mit unter Bewässerungsstrategien ergab, dass eine zeitweise Flutung mit mehreren Trockenphasen das geringste Klimaschädigungspotential hat, welches nur 30% dessen beträgt, was ein traditionell bewässertes Reisfeld (fünf Monate kontinuierliche Flutung). Eine Intermediäre Bewässerungsstrategie (2.5 Monate Flutung, Austrocknung, Bewässerung ohne Stauen von Wasser) brachte im Vergleich zum traditionell gefluteten Reisfeld ein Klimaschädigungspotential von 60%. Diese Ergebnisse implizieren, dass ein Trend hin zu weniger Stauwasser auf Reisfeldern effektiv Treibhausgasemissionen senken kann, zumindest auf sandigen oder lehmig-sandigen Böden. Eine akribische Untersuchung der Reisfeldböden ergab, dass N 2 O-Produktion und Konsumption hauptsächlich in 25 bis 50 cm Tiefe stattgefunden haben; die N 2 O-Konzentrationen und δ 15 N-N 2 O-Werte dieser Tiefen von allen untersuchten Reisfeldern sowie auch Gen-Häufigkeiten von Denitrifizierern und Nitrifizierern des Reisfeldes mit der Intermediären Bewässerungsstrategie deuten darauf hin. Abgesehen von diesen wichtigen Erkenntnissen über N 2 O-Fluss-Dynamiken von drei verschiedenen Landnutzungssystemen, fällt auf, dass die N 2 O-Flüsse des Studiengebietes generell niedrig sind. Dies ist erfreulich und zeigt, dass das Gebiet mit jenen Herausforderungen, die der Globale Wandel mit sich bringt und die mit Landwirtschaft assoziiert sind, so eingestellt ist, dass zumindest keine großen Mengen an N 2 O produziert werden, was allerdings verwunderlich erscheint, führt man sich vor Augen welche großen Mengen an Dünger auf den Feldern ausgebracht werden. Die vorliegende Arbeit diskutiert an, ob möglicherweise der sandige Boden der Region eine schnelle Auswaschung der hochmobilen NO 3- -Ionen - dem Ausgangssubstrat für Denitrifikation - bewirken könnte, hat letztlich aber keine abschließende Antwort auf diese Frage. Um herauszufinden, wieso die N 2 O-Flüsse so gering sind, wäre es wünschenswert, NO 3- -Flüsse und das Schicksal der NO 3- -Ionen genauer zu untersuchen. 5 6 Acknowledgements This thesis would not have been possible without the help and support of numerous people. I need to thank everyone who supported me and contributed to this thesis and my well-being in whatever way during the writing of this thesis. First of all I have to express my deepest thankfulness to Gerhard Gebauer and John Tenhunen who got me involved in TERRECO, which gave my life a new and unexpected but amazing direction, opened the door to an incredible journey of world and self exploration and broadened my horizon with respect to so many, not only work-related things. I thank Gerhard Gebauer for greatly supervising and teaching me, giving me advice whenever I needed it and trusting in me and my skills even though my stubbornness sometimes might have been quite challenging to deal with. I thank Hojeong Kang, Inyoung Jang and Juyoung Seo for supporting me in Korea. Without them taking care of me and many logistics, my fieldwork wouldn’t have flown so smoothly. And I want to thank Ralf Kiese, who has the amazing ability to bring plenty of information within the shortest time possible into my head and therewith gave me lots of necessary input which improved my work and thesis a lot. A very special thank goes to my dear friend Eunyoung Jung for her great help in the field sites, but I thank her even more for always being there for me during good and (VERY!) bad times in Korea and accompanying me on my almost daily visits to the dentist in Yanggu during more than one HORRIBLE and pain-dominated month in May and June 2010, even though this time-consuming activity almost got her struggling with her own work. I also thank Julia Köpp a lot for restless maintaining and repairing the PreCon-GC-IRMS and for her essential help with analyzing my samples, for so many discussions about the interpretation of my data and teaching me everything I needed to know about isotope ratio mass spectrometry. And I thank her even more for being such a good friend who always listened to me whenever I was about to freak out because of too much work… I want to say thank you to all the other TERRECO members who helped me in the field and during the writing of my thesis by providing me with information and I also need to thank them for making my Korea experience so special. I thank the technicians Isolde Bauman, Christine Tiroch and Iris Schmiedinger for countless hours spent on maintaining the IRMS, analyzing my samples and for taking care of me. I am thankful to Margarete Wartinger and John Tenhunen for organizing fieldwork activities and taking care of shifting tons of necessary equipment between Germany and Korea. I have to thank my flatmate and best friend Julian Gaviria a lot for his patience and support especially during the last weeks! Without him always calming me down, listening to ALL of my problems, making me relax and discussing with me about space ships, stargates, Goa’uld and other alien invasions… I would have gone crazy. My deepest thank goes to my family for always having my back and great support, but especially to my mother, who is my idol, teacher and friend. She always gave me the feeling that I could do everything, that I could make all my dreams come true, that I could realize all of my crazy ideas; the only thing I would have to do would be to do my best, just as she always does every day. 7 Contents Summary 1 Zusammenfassung 3 Acknowledgements 6 List of Abbreviations 9 Chapter 1 On this thesis 13 Background 15 Objectives 24 Synopsis 26 Record of contributions to this thesis 31 References 33 Chapter 2 Forest soil N 2 O emissions as affected by early summer drought, heavy monsoon rains and other environmental factors Berger S, Jung E, Köpp J, Kang H, Gebauer G, 2012. Monsoon rains, drought periods and soil texture as drivers of soil N 2 O fluxes – soil drought turns East Asian temperate deciduous forest soils into temporary and unexpectedly persistent N 2 O sinks. Soil Biology & Biochemistry (published) 43 Chapter 3 N 2 O emissions from dry crop fields as affected by PE mulching, amount of fertilizer, crop type and climate Part A: Berger S, Kim Y, Kettering J, Gebauer G, 2012. Plastic mulching in agriculture - friend or foe of N 2 O emissions? Agriculture Ecosystems & Environment (Submitted, 24 August 2012, Resubmitted after revisions, 12 January 2013) 73 Part B: Kim Y, Berger S, Kettering J, Tenhunen J, Kiese R, 2012. The simulation of N 2 O emissions and nitrate leaching from different rates of N fertilizer in the radish field with the Landscape-DNDC model. (Manuscript in preparation) 98 Chapter 4 N 2 O and CH 4 emissions from rice paddies as affected by water management Berger S, Jang I, Seo J, Kang H, Gebauer G, 2012. A record of N 2 O and CH 4 emissions and underlying soil processes of Korean rice paddies as affected by different water management practices. Biogeochemistry (Submitted, 19 September 2012) 131 14 15 Background The most important greenhouse gases Greenhouse gases absorb infrared light in the atmosphere, thereby trap heat and cause a warming of the earth’s surface. In terms of their global warming potential the three important greenhouse gases are Carbon dioxide (CO 2 ), Methane (CH 4 ) and Nitrous oxide (N 2 O) (WMO 2006). In a 100-year horizon, unit masses of N 2 O and CH 4 are considered to have 298 and 25 times the global warming potential, respectively, as a unit of CO 2 because of their longer lifespan (IPCC 2007). Furthermore, N 2 O contributes to stratospheric ozone depletion (Cicerone 1987) and recently has even been identified as “the Dominant Ozone-Depleting Substance Emitted in the 21st Century” (Ravishankara et al. 2009). Other important gases are water vapor and halocarbon compounds but their emissions are not associated with agriculture and land use issues (Snyder et al. 2009). Even though the major greenhouse gas for the world’s economy is CO 2 , the most important greenhouse gas in agriculture is N 2 O (Snyder et al. 2009) as well as its emissions are of ongoing interest in forest research and therefore it becomes the major focus of this thesis. N 2 O N 2 O is released in relatively small amounts during the microbial soil processes denitrification, nitrification and nitrifier denitrification (Bange 2000, Snyder et al. 2009, Wrage et al. 2001, Kool et al. 2011) depending on Oxygen (O 2 ) concentrations in the soil, soil temperature and moisture, soil texture, amount of nitrate (NO 3- ) available for denitrification and amount of ammonium (NH 4+ ) available for nitrification (Firestone 1982, Granli and Bøckman 1994). Denitrification names the reduction from NO 3into dinitrogen (N 2 ) gas as described in the following pathway: NO 3-  NO 2-  NO  N 2 O  N 2 in an anoxic environment (Firestone 1982, Firestone and Davidson 1989, Robertson and Groffman 2007). The transformation of NO 3can be complete but it can happen that a small portion of N is emitted as N 2 O gas. Nitrification is the name of the conversion of NH 4+ into NO 2which is then transformed into NO 3- (Norton 2008). N 2 O as well as NO are by-products of the transformation from NO 2under oxygen-limited conditions (IFA/FAO 2001), but N 2 O emissions resulting from nitrification have also been reported under fully aerobic conditions (Bremner and Blackmer 1978). Nitrifier denitrification occurs when moisture conditions are suboptimal for denitrification, as a function of the soil moisture content, and likely of other environmental conditions as well. The 16 process is assumed to be a major contributor to N 2 O emission from soils with sandy texture and recently calls for more and more attention (Kool et al. 2011). Emissions of N 2 O mostly occur sporadic throughout the whole year and N 2 O emission peaks can be observed after previously well-aerated soils became moistened or saturated from precipitation or irrigation or during thawing of frozen soils (Snyder et al. 2009, Barton et al. 2008a, Goldberg et al. 2009). Forest soils’ N 2 O emissions are known to be influenced by soil moisture and temperature, soil type and texture, aeration, tree species composition, pH, C:N ratio, atmospheric nitrogen deposition (Schindelbacher et al. 2004. Skiba et al. 2009, Butterbach-Bahl et al. 2002, Menyailo and Huwe 1999, Yamulki et al. 1997, Kesik et al. 2006, Morkved et al. 2007, Weslien et al. 2009, Klemedtson et al. 2005, Pilegaard et al. 2006). Soil moisture and temperature often explain most of the temporal variation of the N 2 O fluxes in daily to weekly timescales (Omerci et al. 1999, Schindelbacher et al. 2004, Kesik et al. 2006, De Bruijn et al. 2009) but when it comes to comparing annual N 2 O emissions factors like nitrogen deposition and forest and soil type become much more important (Pilegaard et al. 2006). N 2 O emissions from croplands are known to be influenced by the amount of fertilizer applied (Cole et al. 1997, van Groeningen et al. 2010) and it is said that approximately 1% of the nitrogen fertilizer applied is emitted as N 2 O (IPCC 2006). In addition to those management related factors, which also include type of crop with major differences between legumes and other annual crops, environmental factors such as climate, soil texture, soil drainage and abundance of NO 3- -N and pH have been identified as the most important drivers of N 2 O fluxes (Eichner 1990, IFA/FAO 2001). Recently, N 2 O consumption is becoming a focal point of interest. Since the global N 2 O balance is still not closed, knowing of soils which act as N 2 O sinks could contribute to closing that balance (Billings 2008). The mechanisms behind this sink function and environmental factors leading to the sink function are still poorly understood. Chapuis-Lardy et al. (2007) summarized that it has mostly been reported under conditions of low mineral nitrogen availability and high soil moisture. However, significant consumption of N 2 O in forest soils has also been observed by Kellman and Kavanaugh (2008), Goldberg and Gebauer (2009a, b), Inclán et al. (2012) under drought conditions. 17 CH 4 CH 4 is produced by methanogenic bacteria during decomposition of organic material in a process which is called methanogenesis. Those bacteria use CO 2 as terminal electron acceptor and convert it into CH 4 (Thauer 1998). These bacteria require environments with no oxygen (a situation present in flooded soils) and abundant organic matter, both of which are characteristics of wetlands (Zehnder, 1978). The CH 4 emitted into the atmosphere is only a small fraction of the much larger amounts of the gas that are consumed in the soils due to CH 4 oxidation (Bartlett and Harriss 1993, Rothfuss et al. 1996, Gilbert and Frenzel 1998). Because CH 4 is such an important greenhouse gas and by being responsible for 10-25% of the global CH 4 emissions rice paddies are one of the major sources of CH 4 (Cicerone and Oremland 1988, Bartlett and Harriss 1993, Neue et al. 1997, Bousquet et al. 2006), much work has been and is still being done on CH 4 emissions from rice paddies. It turned out that CH 4 emissions can vary a lot with different water management strategies, mineralogy, rice cultivar, fertilization and local climate (Cai et al. 2001, Denier van der Con 2000, Neue et al. 1996, Liesack et al. 2000). Why studying in Korea? A huge percentage of humankind lives in mountainous areas, which account for 20% of the Earth’s terrestrial surface, and depends on freshwater and other ecosystem services provided by these regions (Millenium Ecosystem Assessment 2005). Studying complex terrain, its surface properties, gradients in climate, transfer of materials, soil properties, patterning of land use according to human preferences and the resulting impacts on the environment is crucial for management of Earth’s ecosystems and resources. The Republic of Korea is a predominantly hilly and mountainous, as well as densely populated and developed country with a very high Human Development Index (HDI) score and very high living standards (Human development report 2011). Large areas are under intensive agricultural use. In comparison to Germany, Korea houses 61% of Germany’s population on only 28% of Germany’s total area which leads to a high population density of 491 inhabitants per km 2 (Germany has 229 inhabitants per km 2 ) (Statistische Ämter des Bundes und der Länder: Bevölkerung am Monatsende, Korean Statistical Information Service). Thus, Korea is an interesting place to study as it can be regarded as a country which has to face and to deal with Global Change effects prior to other countries with a smaller population density, lower living standards, a larger area to flee from climate change driven natural catastrophes and extreme weather events and which are less exposed to such weather events. From studying Korea we could learn lessons for the whole world. 18 The TERRECO (Complex TERRain and ECOlogical Heterogeneity) project - a joint education and research activity between Germany and South Korea - aims to combine both, an achievement of a better understanding of the functioning of different land use and ecosystems in a complex terrain as well as an assessment of the ecosystem performances in terms of what we - the people - derive from them or how they cause or maybe mitigate environmental problems. Study site All the fieldwork for this thesis has been conducted in the Haean basin (see figure 1), which is located in Yanggu-county, Kangwon-province in the north-eastern part of South Korea between longitude 128° 5' to 128° 11' E and latitud e 38° 13' to 38° 20' N. The punchbowl shaped area with an average altitude of about 400m at the valley-sites is surrounded by mountains reaching up to 1320 m. The average annual air temperature is ca. 7.5°C at the mountain ridges and 10.5°C at the valley sites and the average precipitation amounts to 1577 mm (11-year average) with about 70% falling during the summer monsoon (Lee, Tenhunen, Geyer, Seo, Li and Kang, unpublished). The mountain ridges as well as the areas with steep slope are covered with forest vegetation dominated by Quercus dentata, Q. mongolica, Q. serrata, Betula davurica, and Tilia amurensis as major tree species and understory are Q. mongolica, Weigela florida, Stephanadra incisa, Ulmus laciniata, Symplocos chinensis, Euonymus alatus, Acer pseudosieboldianum, and Corylus heterophylla. The valley sites are very intensively agriculturally used. 25% of this cropland area is covered with rice paddies, dryland farms include radish (20% of cropland area), potato (15%), cabbage (15%), soy bean (5%) and Codonopsis pilosula and ginseng (together 5%) as well as relatively new plantings of fruit trees and miscellaneous other crops. The typical soils of the agriculturally used area as well as the forest soils are terric cambisols (IUSS Working group WRB 2006). Due to very high soil erosion in the cropland area during the monsoon season and in order to compensate for the resulting high soil loss, the local farmers add sandy soil on top of their fields every few years. This long-term agricultural management technique modifies the soils to anthrosols (IUSS Working group WRB 2006). With its landuse pattern (50% forest cover, rice accounting for 25% of the cropland area and other major crops accounting for the residual harvested area) the Haean basin is somewhat representative of the world’s pattern of landuse with 30% forest area and about 15% of the global cropland area used as rice fields (FAO 2005, Thenkabail 2010), which makes it a super study site when it comes to studying factors driving N 2 O emissions on a landscape scale, delivering meaningful results for the broader, global picture. Figure 1 : Satellite pictures of South Korea and the Haean Basin, fotograph of the Haean Basin. (Pictures were downloaded from http://www.worldofmaps.net/uploads/pics/satelliten September, 2012; downloaded from google http://www.bayceer.unibayreuth.de/terreco/de/top/gru/html.php?id_obj=67142 on 15 July, 2012) Forest soil N 2 O emissions as affected by environmental factors Forest soils’ N 2 O emissions increase with soil moisture, soil temperature and nutrient availability (Davidson and Kingerlee 1997 1999, Brumme e t al. 1999, Smith et al. 2003, al. 2004, Pilegaard et al. 2006, Kesik et al. 2006 stimulated by high amounts of nitrogen deposition, intermediated by increased inorganic N in the soil solution and a decrease in the soil C:N ratio (Butterbach 1998, Klemedtsson et al. 2005, Pilegaard et al. 2006, Horváth et al. 2006). In general one can say that deciduous forests have lower N Bahl et al. 2002, Menyailo and Huwe 1999). pH causes maximum N or lower, indicating that acid conditions favor N al. 2006, Morkved et al. 2007, Weslien important role in terms of driving N recent evidence that poor sandy soils have a lower capability to produce N loamy soils ha ve (Wlodarczyk et al. Gebauer (2009a, b), Inclán et al. ( fluxes on sandy loam soil, confirm that idea. 19 : Satellite pictures of South Korea and the Haean Basin, fotograph of the Haean Basin. http://www.worldofmaps.net/uploads/pics/satelliten - karte September, 2012; downloaded from google - maps on 17 September, 2012; downloaded from bayreuth.de/terreco/de/top/gru/html.php?id_obj=67142 on 15 July, 2012) O emissions as affected by environmental factors O emissions increase with soil moisture, soil temperature and nutrient Kingerlee 1997 , Ormeci et al. 1999, Papen and Butterbach t al. 1999, Smith et al. 2003, ButterbachBahl et al. 2004 Pilegaard et al. 2006, Kesik et al. 2006 , De Bruijn et al. 2009), as well as they are stimulated by high amounts of nitrogen deposition, intermediated by increased inorganic N in the soil solution and a decrease in the soil C:N ratio (Butterbach 1998, Klemedtsson et al. 2005, Pilegaard et al. 2006, Horváth et al. 2006). In general one can say that deciduous forests have lower N 2 O emissions than coniferous ones (Butterbach Bahl et al. 2002, Menyailo and Huwe 1999). pH causes maximum N 2 O fluxes at values of 5.9 or lower, indicating that acid conditions favor N 2 O production (Yamulki et al. 1997, Kesik et al. 2006, Morkved et al. 2007, Weslien et al. 2009). Soil texture has been assumed to play an important role in terms of driving N 2 O fluxes, too (Skiba et al. 2009), and there is increasing recent evidence that poor sandy soils have a lower capability to produce N ve (Wlodarczyk et al. 2011). Studies by Barton et al. ( 2008a Gebauer (2009a, b), Inclán et al. ( 2012 ), who all reported on very low and even negative N fluxes on sandy loam soil, confirm that idea. : Satellite pictures of South Korea and the Haean Basin, fotograph of the Haean Basin. karte -sued-korea.jpg on 17 maps on 17 September, 2012; downloaded from bayreuth.de/terreco/de/top/gru/html.php?id_obj=67142 on 15 July, 2012) O emissions increase with soil moisture, soil temperature and nutrient , Ormeci et al. 1999, Papen and Butterbach -Bahl Bahl et al. 2004 , Schindlbacher et , De Bruijn et al. 2009), as well as they are stimulated by high amounts of nitrogen deposition, intermediated by increased availability of inorganic N in the soil solution and a decrease in the soil C:N ratio (Butterbach -Bahl et al. 1998, Klemedtsson et al. 2005, Pilegaard et al. 2006, Horváth et al. 2006). In general one than coniferous ones (Butterbach - O fluxes at values of 5.9 O production (Yamulki et al. 1997, Kesik et et al. 2009). Soil texture has been assumed to play an O fluxes, too (Skiba et al. 2009), and there is increasing recent evidence that poor sandy soils have a lower capability to produce N 2 O than silty or 2008a ), Goldberg and ), who all reported on very low and even negative N 2 O 20 Because there are predicted changes in precipitation and temperature regimes which go along with an increasing occurrence of heavy rain events or extreme drought periods in the course of climate change (IPCC 2007), N 2 O emissions are expected to be enhanced in the future (Potter et al. 1996; Skiba et al. 1998). Thus, studies of effects of such extreme weather events on N 2 O emissions are absolutely necessary. During a long-term climate manipulation experiment in Germany it was found that a prolonged summer drought not only decreased N 2 O emissions but even lead to significant N 2 O consumption (Goldberg and Gebauer 2009a, b). However, the mechanism of that N 2 O sink function in dry soils could not be found, yet. Chapuis-Lardy et al. (2007) summarized that the rate of N 2 O consumption in soils (reduction to N 2 plus absorption by water) would depend on soil properties, such as the availability of mineral N (substrate for nitrification and denitrification), soil oxygen and water content, soil temperature, pH and redox conditions, and the availability of organic C and N, which are exactly the same parameters identified to drive N 2 O emissions. It is a current research challenge to clear up the processes and environmental factors responsible for the N 2 O uptake in soils. Dry crop fields’ soils’ N 2 O emissions as affected by management and environmental factors The N 2 O emitted from arable soils is known to increase linearly with amount of fertilizer applied (Eichner 1990, Kaiser et al. 1998). However, there is not yet a consensus reached on the type of N fertilizer which contributes the most to N 2 O emissions (Eichner 1990, Granli and Bøckman 1994, Snyder et al. 2009). Tenuta and Beauchamp (2003) suggested that ureabased N fertilizer would cause greater N 2 O emissions than other N fertilizers under aerobic conditions and that under conditions of higher soil moisture NH 4+ -based fertilizers would produce greater amounts of N 2 O. In contrast to that Harrison and Webb (2001) suggested that N 2 O emissions from urea under warm and wet conditions may exceed those of NH 4+ - based sources and that N 2 O emissions from NO 3- -based fertilizers would be greater than those from NH 4+ -based fertilizers. Bouwman (2002a), Tenuta and Beauchamp (2003), Velthoff et al. (2003), Venterea and Stanenas (2008) agreed that there are lower emissions for NO 3- -based fertilizer when compared to NH 4+ -based fertilizers and organic or syntheticorganic ones. Like for the fertilizer type’s influence on N 2 O emissions, there is no consensus yet on the tillage system’s influence on the amounts of N 2 O degassing from arable soils. Lal (2003), Gregorich et al. (2004), Venterea et al. (2005), Blanco-Conqui and Lal (2008) reported that 21 no or less tillage lead to increased N 2 O emissions when compared to conventional or intense tillage, whereas Robertson et al. (2000), Halvorson et al. (2008a, b) observed the opposite. Much work has been done on figuring out if N 2 fixing legumes, which have an additional N source, causes higher N 2 O emissions from soils than other crops. In general one can say that during N 2 fixation less N is available for nitrification and subsequent denitrification and the resulting N 2 O emissions during the time when the legumes are growing (Parkin and Kasper 2006) so that there are not necessarily greater N 2 O emissions from N fertilized nonlegume crops under similar climatic and management regimes (Helgason et al. 2005, Rochette and Janzen 2005, Parkin and Kaspar 2006, Stehfest and Bouwman 2006, Barton et al. 2008b). In addition to those management related N 2 O flux regulating factors, climatic factors also affect N 2 O emissions from dry crop fields. Soil moisture and temperature are known to increase N 2 O production (Dobbie et al. 1999, Ruser et al. 2006); however, it happened that no correlation between N 2 O emission rates and soil moisture or temperature is found (Flessa et al. 1995). Even if it has frequently been observed that rain events triggered N 2 O emissions from agricultural fields (Davidson et al. 1993, Scholes et al. 1997, Barton et al. 2008a), pH allows the most N 2 O production at slightly acidic values and less sandy soil texture does so, too (IFA/FAO 2001). Recently there is increasing use of an impervious polyethylene (PE) film (see figure 2) worldwide - but in East Asian countries such as Korea, China and Japan in particular - in order to increase crop production in the course of a growing world population and accompanying food scarcity (Kwon et al. 2006, Kyrikou and Briassoulis 2007). Due to a higher soil temperature and moisture underneath the PE mulch, conditions as in a greenhouse are created which promote crop growth, but that also raises the important question whether this method has negative side effects on the environment such as an increased N 2 O production. 22 Figure 2: Impervious polyethylene (PE) film applied on an agricultural field. It covers the ridges and leaves only little holes open where the crops can emerge. N 2 O and CH 4 emissions from rice paddies as affected by management practices Whereas rice paddies are one of the most important sources of atmospheric CH 4 (IPCC 1992, IPCC 2007), their contribution to global N 2 O emissions was considered to be rather insignificant (Granli and Bøckman 1994). Due to the strong anaerobic conditions of rice paddy soils under the traditional rice irrigation method of continuous flooding - which was the dominating practice until the early 1980s (Geng et al. 2001) - N 2 O as an intermediary product of denitrification would be further reduced to N 2 (Granli and Bøckman 1994). However, increasing water scarcity made and still makes farmers change their traditional irrigation practice to water-saving irrigation practices, including midseason drainages and non-water logged periods (Geng et al. 2001). It is well documented that such drainage, and the presence of non-water logging periods, enhance N 2 O emissions in contrast to continuous flooding (Cai et al. 1997, Zeng et al. 2000, Jiang et al. 2003, Li et al. 2004, Xu et al. 2004, Li et al. 2005) because of changes in several N 2 O production regulating factors, such as soil oxygen status, soil redox potential, moisture, temperature (Smith and Patrick 1983, Cai et al. 23 2001, Zou et al. 2005b, Johnson-Beebout et al. 2009, Liu et al. 2010, Peng et al. 2011). The good news about the new irrigation practices is that they significantly reduce CH 4 emissions; however, a clear trade-off relationship between CH 4 and N 2 O emissions was found (Yagi et al. 1996, Hou et al. 2000), which is why it is scientists’ challenge to find an irrigation method which would minimize the combined greenhouse effect by the two gases while ensuring maximum amounts of rice yields. Obviously, some factors other than water regime also affect rice paddies’ N 2 O and CH 4 emissions, such as fertilizer type, soil moisture and soil temperature (Bouwman et al. 2002b, Granli and Bøckman 1994). So does the application of urea-based fertilizer cause the greatest CH 4 emissions but less N 2 O emissions (Wang et al. 1992, Cai et al. 1997, Bufogle et al. 1998), in contrast to the effects of ammonium sulfate or ammonium bicarbonate fertilizer, which leads to higher N 2 O emissions but lower CH 4 emissions (Cai et al. 1997, Zheng et al. 2000) at identical water management systems. The lowest CH 4 and N 2 O emissions were observed after application of NO 3- -based fertilizer (Jugsujinda et al. 1995). This has to do with the redox potential which, after NO 3- -N application, was higher than -100mV (where CH 4 emissions occur), but lower than +200mV (where N 2 O emissions occur) so that neither CH 4 nor N 2 O emissions were promoted (Hou et al. 2000, Snyder et al. 2009). Furthermore, a significant positive relationship between N 2 O emissions and the WFPS (water filled pore space) ranging from 62.2 to 83.5%, while increasing the WFPS over 83.5% apparently reduces N 2 O emissions, was observed by Khalil and Baggs (2005), Sey et al. (2008), Peng et al. (2011). At soil temperatures between 25 and 40°C there is increasing N 2 O production (Granli and Bøckman 1994). Figure 3 : Amounts of cumulatively emitted N Just like large areas of the world, in the course of global and climate change the study area has to face extreme weather events such as more severe early summer drought followed by heavier monsoon rains. This thesis showed that of such weather events were accompanied by very low and to some extent even negative N during the growing season. Plastic mulching – a widely used practice in potential to mitigate N 2 O emissions, which should be subject to more studies. Intermittent irrigation was identified as the best water management practice for the study region’s investigated rice paddies as it the lowest N 2 O as well as CH literature but might be explained by the sandy soils and a high NO These findings are important climate change effects in a good way at least with regard to its greenhouse gas emissions. 30 : Amounts of cumulatively emitted N 2 O presented in mmol m -2 measured at the different sites during the growing seasons of 2010 and 2011. Just like large areas of the world, in the course of global and climate change the study area has to face extreme weather events such as more severe early summer drought followed by heavier monsoon rains. This thesis showed that of such weather events were accompanied by very low and to some extent even negative N 2 O balances of forest soils a widely used practice in agriculture worldwide – turned out to have a O emissions, which should be subject to more studies. Intermittent irrigation was identified as the best water management practice for the study region’s investigated rice paddies as it required the smallest amounts of water and caused O as well as CH 4 emissions, which for the N 2 O emissions is contrary to the literature but might be explained by the sandy soils and a high NO 3leaching potential. These findings are important and also suggest that the study region deals with global and climate change effects in a good way at least with regard to its greenhouse gas emissions. measured at the different sites during the Just like large areas of the world, in the course of global and climate change the study area has to face extreme weather events such as more severe early summer drought periods followed by heavier monsoon rains. This thesis showed that of such weather events were O balances of forest soils turned out to have a O emissions, which should be subject to more studies. Intermittent irrigation was identified as the best water management practice for the study required the smallest amounts of water and caused O emissions is contrary to the leaching potential. and also suggest that the study region deals with global and climate change effects in a good way at least with regard to its greenhouse gas emissions. 31 Record of contributions to this thesis Chapter 1 Chapter 1 and the summary of this thesis were written by me. This dissertation includes four manuscripts of which three were written by me and one was written by Youngsun Kim. One of the manuscripts written by me is already published, the second one is resubmitted after revisions and the third one is submitted. The manuscript by Youngsun Kim is in preparation for submission. The contribution of me and all co-authors is listed below. Chapter 2 Berger S, Jung E, Köpp J, Kang H, Gebauer G, 2013. Monsoon rains, drought periods and soil texture as drivers of soil N 2 O fluxes – soil drought turns East Asian temperate deciduous forest soils into temporary and unexpectedly persistent N 2 O sinks. Soil Biology & Biochemistry 57, 237-281. Berger S: 60% (concepts, field and laboratory work, interpretation, discussion and presentation of results, manuscript preparation) Jung E: 20% (concepts, field and laboratory work, discussion of results) Köpp J: 5% (laboratory work, interpretation and discussion of results) Kang H: 5% (field and laboratory work, logistics in Korea) Gebauer G: 10% (concepts, discussion of results, contribution to manuscript preparation) Chapter 3A Berger S, Kim Y, Kettering J, Gebauer G, 2012. Plastic mulching in agriculture - friend or foe of N 2 O emissions? Agriculture Ecosystems & Environment (Resubmitted after revisions, 12 January 2013) Berger S: 70% (concepts, field and laboratory work, interpretation, discussion and presentation of results, manuscript preparation) Kim Y: 15% (field and laboratory work, logistics in Korea) Kettering J: 5% (field work) Gebauer G: 10% (concepts, discussion of results, contribution to manuscript preparation) 32 Chapter 3B Kim Y, Berger S, Kettering J, Tenhunen J, Kiese R, 2012. The simulation of N 2 O emissions and nitrate leaching from different rates of N fertilizer in the radish field with the LandscapeDNDC model. (Manuscript in preparation) Kim Y: 55% (concepts, discussion of results, manuscript preparation) Berger S: 10% (field and laboratory work, discussion) Kettering J: 5% (filed and laboratory work) Tenhunen J: 5% (discussion of results) Kiese R: 25% (concepts, discusisons of reuslts, contribution to manuscript preparation) Chapter 4 Berger S, Jang I, Seo J, Kang H, Gebauer G, 2012. A record of N 2 O and CH 4 emissions and underlying soil processes of Korean rice paddies as affected by different water management practices. Biogeochemistry (Submitted, 19 September 2012) Berger S: 75% (concepts, field and laboratory work, interpretation, discussion and presentation of results, manuscript preparation) Jang I: 5% (field and laboratory work, logistics in Korea) Seo J: 5% (field and laboratory work, logistics in Korea) Kang H: 5% field work, logistics in Korea) Gebauer G: 10% (concepts, discussion of results, contribution to manuscript preparation) 33 References Bange JM, 2000. Global Change: it’s not a gas. Nature 408, 301-302. Bartlett KB, Harriss RC, 1993. Review and assessment of methane emissions from wetlands. Chemosphere 26, 261-320. Barton L, Kiese R, Gatter D, Butterbach-Bahl K, Buck R, Hinz C, Murphy D, 2008a. Nitrous oxide emissions from a cropped soil in a semi-arid climate. Global Change Biology 14, 177-193. 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Chemosphere 2, 207–214. 46 determined N 2 O concentrations and δ 15 N values along soil profiles in the dry and monsoon season for a sandy loam site. We observed N 2 O consumption at all of our study sites during early summer drought, which turned into N 2 O emission during the monsoon season. The N 2 O balance of the sandy loam site remained slightly negative during the entire vegetation period. Soil moisture explained most of the measured N 2 O fluxes. For a sandy-loam forest soil we calculated a switch between N 2 O emission and consumption at an intermediate soil moisture (pF level of 3.02) which corresponds to a water filled pore space (WFPS) of 36.34%, but at half an order of magnitude moister soil (pF level: 2.57; WFPS 50.31%) at a loamy site. N 2 O concentration and δ 15 N N2O values along the soil profiles suggest that those processes driving the N 2 O fluxes at the soil/atmosphere interface most likely occurred in the topsoil. Our results contribute to our knowledge on the global N 2 O budget, because monsoon affected forests cover large areas worldwide and their soils’ N 2 O emissions have so far been uninvestigated. Keywords: N 2 O emission, N 2 O consumption, soil profile, δ 15 N, heavy rainfall, sand, loam, Korea 47 1. Introduction N 2 O is a powerful greenhouse gas which contributes to the global warming effect (WMO 2006) and is also involved in the destruction of the stratospheric ozone layer (Cicerone 1987). Important sources of N 2 O are mainly agriculturally managed soils but also include (semi-)natural forest soils (Potter et al. 1996; Davidson and Kingerlee 1997; Pilegaard et al. 2006). Microbial denitrification, nitrification and nitrifier denitrification are the N 2 O producing processes (Kool et al. 2011). However, a significant N 2 O sink function has recently been observed in managed northern forests in Canada (Kellman and Kavanaugh 2008), and in European forests (Goldberg and Gebauer 2009a, b; Inclán et al. 2012). Those findings are now of importance for further improvement of predictions on Earth’s climate specifically under conditions of global climate change (Billings 2008), as soils as N 2 O sinks had not been taken into account for global N 2 O balances before. Still little is known about the underlying processes of this N 2 O sink function, which can temporarily be observed in different soils. Soil moisture and temperature have been identified as the most important drivers of N 2 O fluxes between forest soils and atmosphere (Butterbach-Bahl et al. 2004; Pilegaard et al. 2006; Kesik et al. 2006). It is also known that and an increasing amount of rainfall as well as increasing soil temperature are predicted to enhance N 2 O emissions (Potter et al. 1996; Skiba et al. 1998; IPCC 2001). IPCC (2007) predicted changes in precipitation and temperature regimes, which raised the question how such changes actually affect N 2 O emissions from forest soils. Goldberg and Gebauer (2009b) showed that an experimentally induced drought of 46 days could temporarily turn the soil of a coniferous forest in Germany from a source into a transient N 2 O sink. East Asian climate is even more extreme than the simulated one: yearly recurring heavy monsoon rainfall periods after eight months of fair to extreme drought (Qian et al. 2002; Yihui and Chan 2005). This provides an extreme case of drying and rewetting cycles of soils and therefore we considered it as an adequate framework to field-test the above mentioned experimental results. And we intended to go one step further by investigating the effects of the those long drought and heavy rainfall periods on forest soils distinguished by different soil textures, which appears of great importance especially when considering most recent findings by Wlodarczyk et al. (2011), who explicitly reported on loamy soils having a greater capacity to N 2 O production and consumption than sandy soils. Here we report on a monitoring study, to our knowledge investigating for the first time, how the N 2 O fluxes of East Asian forests respond to the extreme fluctuations in soil moisture which we expected to be caused by heavy monsoon rains. Over and above the N 2 O fluxes, 48 we determined several additional parameters such as soil moisture, soil temperature, soil and vegetation properties, N deposition and C/N ratio to see if there were any relationships with the occurring N 2 O fluxes. Because the soil texture can enhance or mitigate drought effects, due to differences in water holding capacity, aeration and O 2 availability etc., the measurements were carried out on three forest sites differing in their top soil texture characteristics: each one predominantly consisted of sand, sandy-loam or loam, respectively. We hypothesized that the sandy site as the location with the most aerated and most quickly drying soil would show the least N 2 O fluxes whereas the loamy soil with a greater water retaining capacity was expected to show higher emissions and less declining N 2 O emissions during the drought period. Furthermore, we attempted to identify the switching point from N 2 O emission to consumption and vice versa for each one of these soils. 2. Materials and Methods 2.1 Experimental sites The measurements were taken in three forests in the Haean Basin which is located northeast of the city of Chuncheon in Yanggu County, South Korea, between longitude 128° 5' to 128° 11' E and latitude 38° 13' to 38° 20' N, with a ran ge in altitude from ca. 400 to 1100 m a.s.l. The average annual air temperature is ca. 10.5°C at valley sites and ca. 7.5°C at the northern ridge line. Average precipitation is estimated at 1200 mm with 70% falling during the summer monsoon (Lee et al 2010, unpublished). The most important characteristics of the three sites are summarized in Table 1. The solar radiation (provided by the TERRECO-site (http://www.bayceer.uni-bayreuth.de/terreco/), downloaded on 10 January 2011) at the sites is shown in figure 1. According to the FAO soil classification (IUSS Working Group WRB 2006) the soils of our research sites can be classified as Cambisols, even though they are different in soil texture of the first 20 cm topsoil layer. Figure 1. Daily sum of solar radiation during the entire year at the three study sites. The grey box indicates the time period when the N 2.2 Measurement of soil moisture and soil temperature and determination of pF levels On the sandyloam site two ECH2O loggers and one ECH2O logger at both sandy and loamy site (EM50 Data logger, Decagon Devices, WA, USA) were installed at 10 cm dept logging volumetric soil water content [%] and soil temperature [°C] every 30 minutes from 10 May until 31 October 2010. Afterwards the mean daily water content and mean temperature of the soils at 10 cm depth were calculated. In addition, on all of the three sites three top soil samples were collected using a soil corer. The samples’ sand-, siltand clay contents as well as their bulk densities were determined in the laboratory of the Soil Physics Department at the University method was wet sieving for sand and laser particle analyzer "Mastersizer S MAM5004" (Malvern Instruments, Herrenberg, Germany) for silt and clay. The samples were prepared by humus destruction (H 2 O 2 ) and dispersion ((NaPO density data the computer program ROSETTA estimated the soil hydraulic parameters α and n which then defined the pF function (Schaap et al. 2001) for each site. Th soil water content value was read out of that curve. pF levels were determined because the topsoil characteristics of the study sites differed a lot and in order to make soil moisture site comparisons possible, stating soil moisture was needed. pF levels serve that purpose because they include soil characteristics such as soil texture and bulk density. 49 Figure 1. Daily sum of solar radiation during the entire year at the three study sites. The grey box indicates the time period when the N 2 O flux measurements were carried 2.2 Measurement of soil moisture and soil temperature and determination of pF levels loam site two ECH2O loggers and one ECH2O logger at both sandy and loamy site (EM50 Data logger, Decagon Devices, WA, USA) were installed at 10 cm dept logging volumetric soil water content [%] and soil temperature [°C] every 30 minutes from 10 May until 31 October 2010. Afterwards the mean daily water content and mean temperature of the soils at 10 cm depth were calculated. In addition, on all of the three sites three top soil samples were collected using a soil corer. and clay contents as well as their bulk densities were determined in the laboratory of the Soil Physics Department at the University of Bayreuth. The wet sieving for sand and laser particle analyzer "Mastersizer S MAM5004" (Malvern Instruments, Herrenberg, Germany) for silt and clay. The samples were prepared ) and dispersion ((NaPO 3 ) 6 ). Based on the texture and bulk density data the computer program ROSETTA estimated the soil hydraulic parameters and n which then defined the pF - water content - curve described by the Van function (Schaap et al. 2001) for each site. Th e pF level for each corresponding mean daily soil water content value was read out of that curve. pF levels were determined because the topsoil characteristics of the study sites differed a lot and in order to make soil moisture site comparisons possible, a more independent factor stating soil moisture was needed. pF levels serve that purpose because they include soil characteristics such as soil texture and bulk density. Figure 1. Daily sum of solar radiation during the entire year at the three study sites. The grey box O flux measurements were carried out. 2.2 Measurement of soil moisture and soil temperature and determination of pF levels loam site two ECH2O loggers and one ECH2O logger at both sandy and loamy site (EM50 Data logger, Decagon Devices, WA, USA) were installed at 10 cm dept h logging volumetric soil water content [%] and soil temperature [°C] every 30 minutes from 10 May until 31 October 2010. Afterwards the mean daily water content and mean temperature In addition, on all of the three sites three top soil samples were collected using a soil corer. and clay contents as well as their bulk densities were determined in of Bayreuth. The analysis wet sieving for sand and laser particle analyzer "Mastersizer S MAM5004" (Malvern Instruments, Herrenberg, Germany) for silt and clay. The samples were prepared Based on the texture and bulk density data the computer program ROSETTA estimated the soil hydraulic parameters θ r , θ s , curve described by the Van -Genuchten e pF level for each corresponding mean daily pF levels were determined because the topsoil characteristics of the study sites differed a lot a more independent factor stating soil moisture was needed. pF levels serve that purpose because they include soil 50 Table 1: Site characteristics of the studied forests, in Haean basin, South Korea. Site Location Aspect 2010 Soil Dominant Subdominant Understory Average monsoon precip. characspecies species Basal area tree &mean air temp. teristics Basal area Basal area height Sandy128°8'27.13"E 220° 1223 mm 60% sand 10.3 m -2 ha -1 10.15 m -2 ha -1 2.46 m -2 ha -1 9.9 m loam 38°18'57.067"N 8.5°C 31% silt (Quercus mongolica) (Quercus dentata, (Q. dentata, 650 m a.sl 9% clay Tilia mandshurica, Q. mongolica BD: 0.90 g cm -3 & others) & others) Sandy 128°6'0.86"E 70° 1616 mm 80% sand 16.13 m -2 ha -1 6.25 m -2 ha -1 1.02 m -2 ha -1 4.9 m 38°14'43.374"N 7.5 °C 15% silt (Q. mongolica) (Fraxinus rhynchophylla, (Acer pseudosieboldianum, 950 m a.sl 5% clay Euonymus hamilatonianus, Acer mono BD: 1.11 g cm -3 & others) & others) Loamy 128°7'50.091"E 70° 1326 mm 45% sand 11.43 m -2 ha -1 4.38 m -2 ha -1 8.28 m -2 ha -1 9.6 m 38°17'18.636"N 10.5 °C 42% silt (Quercus serrata, Q. dentata, (Rhododendron yedoense, 450 m a.sl. 13% clay Q. mongolica, Ulmus lacinata Euonymus alatus, BD: 1.07 g cm -3 Quercus aliena, & others) Lespedeza cyrtotrya Alnus japonica) & others) As dominant species we identified those which accounted for at least half of the canopy area. Temperature and rainfall data were downloaded from the TERRECO-site (http://www.bayceer.uni-bayreuth.de/terreco/) on 31st of January, 2011. Sand, silt and clay were classified according to EN ISO 14688. 51 2.3 N 2 O flux measurements N 2 O fluxes were measured from 14 May to 24 October of 2010 twice a week at the sandyloam site and in weekly intervals at the sandy and loamy sites using the closed chamber technique in conjunction with a photoacoustic infrared gas analyser (Multigas Monitor 1312, INNOVA, Ballerup, Denmark) as described by Yamulki and Jarvis (1999) and Goldberg et al. (2008b). The sandy-loam site contained 8 polyvinylchloride (PVC) cylinders, with a total height of 15 cm and a diameter of 19.5 cm, which were installed 7 cm deep into the soil. The sandy and loamy site contained 5 of such PVC cylinders. Those cylinders served as connection pieces where the chamber heads were attached to. 4 of PVC cylinders of the sandy-loam site, and three cylinders of the sandy site contained only few small herbs. The other 4 PVC cylinders of the sandy-loam site, 2 cylinders of the sandy site and all five cylinders at the loamy site did not contain herbs. The cylinders were installed in a way that the herb abundance inside the cylinders was representative of the forest soil. The N 2 O concentrations in the chambers’ headspaces were measured after 0, 8, 16, 24 and 36 minutes at the sandy-loam site and in 0, 10, 20, 30 and 40 minute intervals at the loamy and sandy site. The reproducibility of one single N 2 O concentration measurement was ± 32 ppb. From a linear increase or decrease of the N 2 O concentration in the chambers’ headspaces the N 2 O flux was calculated taking into account the total chamber volume which includes the chamber headspace volume (chamber head 4000 ml + each individual PVC cylinder’s volume of about 2000 ml), volume of the two 25 m long Teflon pipes (600 ml) and of the CO 2 and H 2 O gas traps (38.2 ml). According to the literature it is considered unlikely that daily or weekly measurements using manual chambers would sufficiently cover each after rain emission peak, especially in environments where N 2 O emissions are strongly influenced by a small number of rainfall events, which are particularly unpredictable; an accurate measurement of all the N 2 O fluxes ongoing would only be provided by an automated measurement system (Barton et al. 2008). We consider the climate that South Korea undergoes as suitable for N 2 O flux measurements using manual chambers as rain-events as well as dry after-rain periods lasted 3-4 days, so that there was sufficient time to measure N 2 O fluxes before, during and after each occurring and by weather forecast well-predicted weather-event. Cumulative N 2 O emissions were calculated as described by Tilsner et al. (2003), by multiplying the N 2 O emission rates of two consecutive measurement days with the corresponding time period. These time weighted N 2 O flux means were then summed up over the measurement period. 52 2.4 Gas sampling in the soil profiles Soil gas was collected from the sandy-loam site following the procedure described by Goldberg et al. (2008a) on 6 June in the early dry season, on 1 August in the monsoon season, and on 23 October 2010 during the autumn drought season. Sub-surface soil gas tubes were installed in 10, 30, 40 and 60 cm depth. There were three replicates for each depth. Three samples of ambient air were collected as well. Gas sampling glass bottles (with an inlet, an outlet, and a septum and defined volumes of about 100 ml) were first flushed with N 2 gas, evacuated using a membrane vacuum pump (KNF Neuberger N026.3AN.18, Freiburg, Germany) and after measuring the vacuum by using a pressure gauge (TensioCheck TC 03S, Tensio-Technik, Geisenheim, Germany), connected to an opened stopcock of a soil gas tube before its inlet was opened. 2.5 Measurement of soil air 15 N/ 14 N ratios and N 2 O concentrations To measure N 2 O concentrations and 15 N/ 14 N isotope ratios of the N 2 O in soil gas and air samples a gas chromatograph-isotope ratio mass spectrometer coupling was used which was linked to a pre-GC concentration device (PreCon-GC-IRMS) (IRMS: delta V plus; Thermo Fisher Scientific, Bremen, Germany; gas chromatograph: GC 5890 series II; Hewlett-Packard, Wilmington, USA; Pre-Con: Finnigan MAT, Bremen, Germany) as described in detail by Brand (1995). The method enables to determine isotope ratios with a precision of ± 0.15‰. They are presented as δ 15 N-values which are defined as: δ 15 N = (R sample /R standard -1) • 1000 [‰], (1) where R is the ratio of heavy isotope [atom percent, at %] to light isotope [at %] of the samples and the respective standard. The international standard is N 2 in the atmosphere (Mariotti 1983). N 2 O concentrations were calculated from the volume of the gas samples and the peak area in m/z on mass 44 with the help of a calibration curve. For further details on this method see Goldberg et al. (2008a). 53 2.6 Estimate of N deposition N deposition data based on direct measurements are not available for the three forest sites of our investigation. For this reason we chose a correlation approach (Emmet et al. 1998) to estimate N deposition for the three sites from 15 N enrichment factors. On each site five sun and five shade leaves from five tall Quercus mongolica trees which were present in the forest canopy were collected. Also, from five understory Q. mongolica trees five leaves which grew approximately 1.5 m above ground were sampled. The collected samples were herbarized immediately after the harvest, and two months later dried at 75°C for two days. Furthermore, at each site five top soil samples were collected with a soil corer. Roots were removed by hand and subsequently the samples were dried at 75°C. Afterwards both, leaf and soil samples were ground in a ball mill (Retsch Schwingmühle MM2, Haan, Germany), weighed into tin capsules and stored in a desiccator before further analysis. The relative N isotope abundance as well as C and N concentrations were measured with an elemental analyzer (Carlo Erba 1108, Milano, Italy) connected to a delta S isotope ratio mass spectrometer via a ConFlo III interface (both Finnigan MAT, Bremen, Germany). For further details see Bidartondo et al. (2004). From the soils’ C and N concentration C/N ratios were calculated. Isotope ratios are presented as δ values, which were calculated and defined according to the equation (1) given in 2.5. Mean 15 N abundances of the leaves and soil samples from each site were identified and a N enrichment factor was calculated by subtracting the average δ 15 N value of the leaves and the average δ 15 N value of the soil from each other. To calculate N deposition the N enrichment factors were inserted into an equation empirically found for the relationship between N enrichment and N deposition in a set of European forest sites: y = 0.1484x – 9.9472 (Emmet et al. 1998). Because the equation was only tested for coniferous forests, the results we derived were used carefully. 2.7 Statistical methods N 2 O flux curves were obtained by calculating mean N 2 O flux values ± 1SE for every day of measurement and linear interpolation between two consecutive measurement days. The mean flux is based on n=8 for the sandy-loam site and n=5 for the sandy and loamy sites. The soil profiles’ N 2 O concentrations and δ 15 N values are given as means of n=3 ± 1SE. Statistical analyses were performed using the software R 2.12.0 for Windows (R Development Core Team, 2010). Via t-Test (normally distributed data) or Mann-Whitney U- 54 test (not normally distributed data) it was tested whether the measured N 2 O fluxes are significantly different from 0 and whether the δ 15 N and N 2 O concentration profiles are significantly different from ambient air’s 15 N abundance and N 2 O concentration. Site comparisons with regard to N deposition and C and N concentrations were done via ANOVA or the non-parametric Kruskal-Wallis-test. A multiple regression analysis was used to identify significant correlations between N 2 O flux and other site parameters. Subsequently, Pearson correlations were done as posthoc tests. 3. Results 3.1 Soil moisture, soil temperature, C/N ratio and N deposition throughout the vegetation period Depending on their soil texture and their precipitation and temperature characteristics the three sites showed differences in soil moisture (Fig. 2, a-c; Table 2). With a mean pF level of 3.02 the sandy-loam site turned out to be the driest site throughout the measurement period, during early summer drought reaching maximum pF levels of 3.62 on 11 June and 1 July 2010. The loamy site had the moistest soil with a pF average of 2.52. On 10 and 29 June the site’s maximum pF level of 2.87 was reached, which means with a pF difference of 0.74 its soil was three-fourths orders of magnitude moister than the soils of the sandy-loam site during that drought period. The sandy site was of intermediate moisture (pF level 2.70). Whereas the sandy-loam and loamy sites showed huge moisture fluctuations throughout the measurement period, the sandy sites’ soil humidity remained more or less constant from 1 May until 31 October. During the monsoon period (2 July until 13 September) the sandy-loam and loamy sites’ soil moisture increased stepwise which is reflected by decreasing pF levels. The minimum pF level determined at the sandy-loam site was 2.44 and 2.11 at the loamy site. After the 2 ½ months of heavy monsoon rains the study sites’ soils dried up again. The soil temperature at all three sites increased gradually from the beginning of the measurement period until mid August and decreased afterwards. The mean soil temperature from May until October was highest at the loamy site (17.1°C; 450 m a.s.l.) and lowest at the sandy site (15.5°C; 950 m a.s.l.). The average soil temperature at the sandy-loam site (650 m a.s.l.) was 16.6°C. The sites slightly differed in C/N ratio depending on the soil depth (Table 2). Estimated N deposition ranges from 24 ± 13.8 kg N ha ± 15.3 kg N ha -1 at the most agriculture (Table 2). There is no statistically significant difference for N deposition between the three sites (P=0.102), but the diffe rence between the N deposition of the loamy site and the other two site’s N deposition can be regarded as a trend. Figure 2. Mean daily soil temperature [°C] and mean daily pF level [log cm] ( 1 ] (d-f) and cumulative N 2 O emission [mmol m loamy site (g, h, i ) as a function of time from 1 May until 31 October, 2010. The dashed vertical lines indicate beginning (2 July) and end (13 September) of the monsoon rains at the me Error bars in N 2 O fluxand cumulative N (n=8 at sandy-loam site, and n =5 at sandy and loamy site). 55 Estimated N deposition ranges from 24 ± 13.8 kg N ha -1 at the most remote sandy site to 51 at the most agriculture - affected loamy site in the middle of the Haean basin (Table 2). There is no statistically significant difference for N deposition between the three rence between the N deposition of the loamy site and the other two site’s N deposition can be regarded as a trend. Mean daily soil temperature [°C] and mean daily pF level [log cm] ( a-c ), N emission [mmol m -2 ] (g-i) at sandy-loam (a, d, g ), sandy ( ) as a function of time from 1 May until 31 October, 2010. The dashed vertical lines indicate beginning (2 July) and end (13 September) of the monsoon rains at the me and cumulative N 2 O emissiongraphs represent the standard error of the mean =5 at sandy and loamy site). at the most remote sandy site to 51 affected loamy site in the middle of the Haean basin (Table 2). There is no statistically significant difference for N deposition between the three rence between the N deposition of the loamy site and the other ), N 2 O flux [µmol m -2 h - ), sandy ( b, e, h) and ) as a function of time from 1 May until 31 October, 2010. The dashed vertical lines indicate beginning (2 July) and end (13 September) of the monsoon rains at the me asurement sites. graphs represent the standard error of the mean 62 signature of ambient N 2 O, which suggests an N 2 O flux from the atmosphere into the soil. On 1st of August (during the monsoon season) the N 2 O concentration and 15 N abundance pattern along the soil profile is not that clear. N 2 O concentrations in the soil gas only slightly higher than the N 2 O concentrations of the ambient air, as well as a 15 N-signature that was not significantly different from ambient-N 2 Os’ δ 15 N values allow the conclusion to be drawn that only very tiny N 2 O emissions have taken place on that sampling date, neither has occurred any N 2 O consumption along the soil profile. N 2 O concentrations and δ 15 N values along the soil profiles are in agreement with the N 2 O fluxes measured at the soil/atmosphere interface via chamber measurements and give further insights into the N 2 O production concerning processes, even though the values we observed are very different from other studies. Goldberg and Gebauer (2009a, b) detected N 2 O concentrations up to 5000 ppb and δ 15 N gradients between +7 and -24 ‰ in deeper soil layers (50 cm and deeper). Our study sites are exposed to fair or even severe drought during eight months a year, which means increased soil aeration and thus unfavorable conditions for N 2 O production by denitrification (Castaldi 2000) or nitrifier denitrification during most of the year. Drought is also known to reduce the amount of N cycled in the ecosystem by reducing the overall activity of N metabolizing microorganisms (Kieft et al. 1987). Given that the sandy-loam sites’ N 2 O emissions during the monsoon period were very low and that the fluxes at the soil/atmosphere interface result from dynamic production and consumption processes in the soil, it appears likely that just not much of such processes are going on in Korean deciduous forests’ soils and that a time span of 3-4 months of rainfall per year is too short to establish conditions favorable for N 2 O producing or consuming microorganisms at the site. Up to now there was the concept of not much of the N 2 O being produced within the soil column ever reaching the soil surface (Seiler and Conrad, 1981; Arah et al. 1991; Neftel et al. 2000; Goldberg and Gebauer, 2009a, b), but the concept of not having ongoing N 2 O production down to 60 cm soil depth is rather uncommon. Yoh et al. (1997) suggested that most of the N 2 O produced in the topsoil may easily escape to the atmosphere without residing in the soil for a long time, and since we identified the top-soil texture as a probable major factor driving our sites’ N 2 O fluxes between soil and atmosphere, since we detected N 2 O fluxes at the soil/atmosphere interface while neither having strongly increased or decreased N 2 O concentrations nor enriched or depleted 15 N N2O abundances along the soil profile, our data agree with this hypothesis. 63 4.4 Concluding remarks Given Korea’s frontier-like location between Pacific Ocean and Asia, it is not surprising that climate change hits the country harder than for example Europe. The mean annual temperature in Korea increased not only by 1°C as g lobally observed but by 1.8°C during the last 100 years, summers are now two to three weeks longer, moister and extreme rainfall events are occurring more frequent (Lee et al. 2012). Considering those facts – which are known to increase N 2 O emissions (Potter et al. 1996; Skiba et al. 1998; IPCC 2001, Butterbach-Bahl et al. 2004; Pilegaard et al. 2006; Kesik et al. 2006) – and that the 2010 monsoon season was unusually long and rain-laden (Zhao 2010, unpublished data), our data provide further insight in the N 2 O flux behavior of forest soils under global climate change. Taking into account all the previously discussed ideas and facts about dry forest soils acting as N 2 O sinks or at least not as huge N 2 O emitters, one would expect a climate change towards moister conditions to increase N 2 O emissions. But interestingly our study sites happened to have N 2 O balances which are extremely low or even negative which is an unexpected finding. Still 30% of the global N 2 O budget remains uncertain by either overestimating N 2 O sources or underestimating N 2 O sinks (Billings 2008). Our data now show that one year of extreme monsoon precipitation in Korea’s rapidly changing environments did not result in a strong N 2 O emission of forest soils which may suggest that forest soils as N 2 O sinks play a bigger role for the global N 2 O budget than considered up to now. Climate change is still proceeding and the development of Korea’s climate, which was observed during the last 100 years, will emerge in a more intensive way in the next 100 years (Lee et al. 2012), so it would be of great interest to put more effort in studying the N 2 O flux behavior of Korean forest’s soils and to also study forests of other East Asian countries such as Japan or the Eastern regions of China, which undergo the same climate. 64 Acknowledgements This work is part of the research group “TERRECO - Complex TERRain and ECOlogical Heterogeneity” and financially supported by the German Research Foundation (DFG). We thank Juyoung Seo and Injoung Jang for supporting us in the field, for taking care of sample logistics in Korea and for providing us with required equipment. We thank Sebastian Arnhold for providing data on soil texture and Steve Lindner for his help in the field. We are thankful to Peng Zhao for his patient assistance by removing millions of fine roots out of numerous soil samples by hand, and to Isolde Baumann and Christine Tiroch, who were of great assistance to us by measuring isotope abundances. We furthermore acknowledge the farseeing, careful and very professional coordination of the TERRECO fieldwork by John Tenhunen. 65 References Arah, J.R.M., Smith, K.A., Crichton, I.J., Li, H.S., 1991. Nitrous-oxide production and denitrification in scottish arable soils. Journal of Soil Science 42, 351-367. Barton, L., Kiese, R., Gatter, D., Butterbach-Bahl, K., Buck, R., Hinz, C., Murphy, D., 2008. Nitrous oxide emissions from a cropped soil in a semi-arid climate. 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World Meteorological Organization, Geneve, 4 pp. Yamulki, S., Jarvis, S.C., 1999. Automated chamber technique for gaseous flux measurements: evalutation of a photoacoustic infrared spectrometer-trace gas analyzer. Journal of Geophysical Research 104, 5463–5469. Yihui, D., Chan, J.C.L., 2005. The East Asian summer monsoon: an overview. Meteorology and Atmospheric Physics 89, 117-142 Yoh, M., Toda, H., Kanda, K., Tsuruta, H., 1997. Diffusion analysis of N 2 O cycling in a fertilized soil. Nutrient Cycling in Agroecosystems 49, 29-33. 70 71 Chapter 3 N 2 O emissions from dry crop fields as affected by PE mulching, amount of fertilizer, crop type and climate 78 Figure 1: Schematic drawing of the experimental design of the radish field site in 2010. 79 Figure 2: Scheme of a typical ridge cultivation system with plastic mulching in a temperate South Korean area with summer monsoon (Kettering unpublished). Shown are the distribution of N fertilizer in the system and width, height and distance of the ridges. 2.3 Experimental design in 2011 Before the experiment started, the field in which radish had grown the previous year, was ploughed by the farmer without applying any fertilizer in 2011. The ridge and furrow system was implemented (35 cm wide and 15-20 cm high (Fig. 2)), the ridges were covered with impervious black PE mulch that contained one row of holes every 25 cm along the ridge with a diameter of 6 cm. On May 29, soy beans were sowed on top of the ridges at a rate of two - three seeds for each hole. Some ridges remained uncovered. Weeding during the experiment was performed manually without the application of herbicides. This is not the common practice in this area as farmers usually seem to apply herbicides, but in order not to add any more chemicals and potentially N 2 O-emission-causing substances, it was decided to manually weed. The weeding was done one time, on June 15, 2011. N 2 O fluxes were measured using nine PVC cylinders: three surrounded soy bean plants which grew on ridges covered with PE mulch, three surrounded soy bean plants which grew on ridges which were not covered with PE mulch and three installed in the furrows which were randomly distributed next to PE-covered and non-PE-covered ridges. 2.4 Measurements of N 2 O fluxes N 2 O fluxes were measured every three to seven days from May 13 through October 22, 2010 at the radish field site and from May 16 through September 14, 2011 at the soy bean field site using the closed chamber method in conjunction with a photoacoustic infrared gas analyser (Multigas Monitor 1312, INNOVA, Ballerup, Denmark) as described by Yamulki and 80 Jarvis (1999) and Goldberg et al. (2008). Each site contained the amount of PVC cylinders described above with a diameter of 19.5 cm and a height of 15 cm, which were installed 7 cm deep in the soil. They served as connecting points to attach the chambers in whose headspaces the N 2 O concentrations were determined in 0, 10, 20, 30 and 40 minute intervals. The reproducibility of one single N 2 O concentration measurement was ± 32 ppb. From a linear increase or decrease of the N 2 O concentration in the chambers’ headspaces the N 2 O flux was calculated taking into account the total chamber volume which includes the chamber headspace volume, volume of the two 25 m long Teflon tubes and of the CO 2 and H 2 O gas traps. Cumulative N 2 O emissions were calculated as described by Tilsner et al. (2003a), by multiplying the N 2 O emission rates of two consecutive measurement days with the corresponding time period. These time weighted N 2 O flux means were then summed up over the measurement period. 2.5 Measurement of soil moisture and soil temperature To measure volumetric soil water content [%] and soil temperature [°C] ECH2O loggers (EM50 Data logger, Decagon Devices, WA, USA) were used. They logged soil moisture and temperature values every 30 minutes from May 13 through August 31, 2010 at the N200 treatment of the radish field and from May 16 through September 14, 2011 at the soy bean field. At the N200 treatment of the radish field one sensor was installed 5 cm deep in the furrow and a second sensor was installed 5 cm deep in one of the holes of the PE mulch. At the soy bean field one sensor was installed 5 cm deep in a furrow, one more sensor was installed 5 cm deep in one of the plant holes of a ridge that was covered with PE mulch and a third sensor was installed next to a plant of a ridge which was not covered with PE mulch. 2.6 Statistical methods N 2 O flux curves were obtained by calculating mean N 2 O flux values ± 1SE for every day of measurement and linear interpolation between two consecutive measurement days. The mean flux was based on n=3 for furrows, PE mulches and plant holes at each amount of fertilizer applied. Statistics were conducted with R 2.12.0. Via t-Test (normally distributed data) or Mann-Whitney U-test (not normally distributed data) it was tested whether the measured N 2 O fluxes are significantly different from zero and whether the soil moisture and temperature conditions underneath the PE mulch were different from those in the furrow. After the t-Test had not shown a difference between the soil temperatures of PE-mulched 81 ridges and furrows, a paired t-Test was conducted. To determine whether furrow-, PE mulchand plant hole N 2 O fluxes of the radish field’s N50, N200, N250 and N350 plots and also the N 2 O fluxes of the soy bean field’s PE-mulched and non-PE-mulched ridges, as well as soil moisture or soil temperature of the soy bean field’s furrows, PEand non-PE-mulched ridges were statistically different from each other, t-Tests, ANOVAs or the non-parametric KruskalWallis-tests were calculated. Pearson or Spearman analyses were performed to identify potential correlations between N 2 O fluxes and volumetric soil water content and soil temperature and between the cumulative N 2 O emissions and the amount of N fertilizer applied. 3. Results 3.1 N 2 O fluxes and cumulative N 2 O emissions at the radish field in 2010 With increasing amount of fertilizer applied there appeared to be a higher N 2 O emission rate of the plant hole-spots at all the plots` ridges. The N 2 O-emissions of the furrow showed a more complicated pattern: for those plots which had received a lower amount of N fertilizer (N50 and N200), the N 2 O emissions of the furrows exceeded the emissions of the plant holes. For the N250 and N350 plots the opposite N 2 O emission pattern could be observed. The N 2 O fluxes of ridges with PE mulch were almost zero for all of the treatments during the time of the experiment, except for June 23, when they reached their maximum (N50: 3.15 µmol m -2 h -1 ; N200: 1.85 µmol m -2 h -1 ; N250: 1.21 µmol m -2 h -1 ; N350: 2.84 µmol m -2 h -1 ). On that day, the plant holes and furrows also showed the highest N 2 O fluxes. Before June 16 and after July 24 only very tiny to zero N 2 O fluxes could be measured. There were significantly different N 2 O fluxes (*P<0.05) between PE mulch and plant holes in PE mulch as well as furrows for almost all of the plots (see Table 1 in the appendix for all statistical differences). No differences were found among ridges, furrows and PE mulches of the differently fertilized plots. The measurement period’s cumulative N 2 O emissions of the furrows and plant holes in PE mulch range between 2 to 6 mmol m -2 (equals 880.3 to 2640.8 g N 2 O ha -1 or 5.5 to 16.4 g N 2 O ha -1 d -1 ), whereas the highest cumulative N 2 O emissions degassed from the furrows of the N50 plots (6 mmol m -2 , equals 2640.8 g N 2 O ha -1 or 16.4 g N 2 O ha -1 d -1 ). Among all of the different amounts of fertilizer applied the N 2 O fluxes of the PE mulches integrated over time amounted to comparably low values of 0.2 to -0.8 mmol m -2 (equals 88.0 to -352.1 g N 2 O ha -1 or 0.5 to -2.2 g N 2 O ha -1 d -1 ). 82 Figure 3: N 2 O flux [µmol m -2 h -1 ] and cumulative N 2 O emission [mmol m -2 ] of the radish field site from May 13 until October 22, 2010. The first dotted line indicates the day when the N fertilizer was applied (June 1) and the second dotted line indicates the day when the radish was harvested, the PE mulch was removed and the ridge and furrow system was dissolved. Error bars in N 2 O fluxand cumulative N 2 O emissiongraphs represent the standard error of the mean (n=3). 3.2 Soil moisture and temperature of the PE There appeared to be higher temperature furrows; however, the mean soil temperature during the time of the experiment was 24.80°C (±2.14) in PEmulched ridges and 24.30°C (±1.58) and furrows (Fig. 4) which makes a very significant difference of 0 .5°C (**P = 0.005). In contrast, the mean volumetric soil water content in ridges and furrows differed with a mean value of 19.80% (ranging from 10% to 32%) in the furrows and 15.62% (ranging from 5% to 15%) in the ridges underneath the PE mulch, which mak es a highly significant difference (***P < 0.001) of 4.18%. Figure 4: Mean daily volumetric water content [%] and mean daily soil temperature [°C] from June 14 until August 31 of the N200 plot at the radish field site in 2010. 3.3 N 2 O fluxes and cumul ative N The N 2 O fluxes at the soy bean field site’s PE from slightly negative to 5.2 µmol m comparatively low: flux peaks occurred on May 22, June 6, July 6, August 23 and August 30; however, statistically significant differences between the PE ridges could not be f ound. For the furrows there is a similar pattern; however, their average N 2 O exchange at the soil/atmosphere interface most of the times was higher than that of the ridges (Tab. 2). For the cumulative N 2 O emissions the graph (Fig. 5) shows a difference bet of N 2 O degassed from PEcovered and non significant. Also, the amount of N cumulative N 2 O emissions of the PE 83 3.2 Soil moisture and temperature of the PE - mulched ridges and furrows at the N200 plot There appeared to be higher temperature fluctuations in the PEmulched ridges than in the furrows; however, the mean soil temperature during the time of the experiment was 24.80°C mulched ridges and 24.30°C (±1.58) and furrows (Fig. 4) which makes a very .5°C (**P = 0.005). In contrast, the mean volumetric soil water content in ridges and furrows differed with a mean value of 19.80% (ranging from 10% to 32%) in the furrows and 15.62% (ranging from 5% to 15%) in the ridges underneath the PE es a highly significant difference (***P < 0.001) of 4.18%. Figure 4: Mean daily volumetric water content [%] and mean daily soil temperature [°C] from June 14 until August 31 of the N200 plot at the radish field site in 2010. ative N 2 O emissions at the soy bean field in 2011 O fluxes at the soy bean field site’s PE -covered and non-PEcovered ridges ranged from slightly negative to 5.2 µmol m -2 h -1 (Fig. 5). During the time of the experiment they were comparatively low: flux peaks occurred on May 22, June 6, July 6, August 23 and August 30; however, statistically significant differences between the PE - mulched and non ound. For the furrows there is a similar pattern; however, their average O exchange at the soil/atmosphere interface most of the times was higher than that of the O emissions the graph (Fig. 5) shows a difference bet covered and non -PEcovered ridges which is not statistically significant. Also, the amount of N 2 O degassed from the furrows (Tab. 2) exceeds both the O emissions of the PE -covered and non-PE-covered ridg es. The N mulched ridges and furrows at the N200 plot mulched ridges than in the furrows; however, the mean soil temperature during the time of the experiment was 24.80°C mulched ridges and 24.30°C (±1.58) and furrows (Fig. 4) which makes a very .5°C (**P = 0.005). In contrast, the mean volumetric soil water content in ridges and furrows differed with a mean value of 19.80% (ranging from 10% to 32%) in the furrows and 15.62% (ranging from 5% to 15%) in the ridges underneath the PE es a highly significant difference (***P < 0.001) of 4.18%. Figure 4: Mean daily volumetric water content [%] and mean daily soil temperature [°C] from June 14 until August 31 of the N200 plot at the radish field site in 2010. O emissions at the soy bean field in 2011 covered ridges ranged (Fig. 5). During the time of the experiment they were comparatively low: flux peaks occurred on May 22, June 6, July 6, August 23 and August 30; mulched and non -PE-mulched ound. For the furrows there is a similar pattern; however, their average O exchange at the soil/atmosphere interface most of the times was higher than that of the O emissions the graph (Fig. 5) shows a difference bet ween the amount covered ridges which is not statistically O degassed from the furrows (Tab. 2) exceeds both the es. The N 2 O fluxes of 84 the non-PE-mulched ridges amounted to 3 mmol m -2 (equals 1320.4 g N 2 O ha -1 or 10.9 g N 2 O ha -1 d -1 ), which is 50% more than the emission from the PE-mulched ridges. The highest cumulative N 2 O emissions were found for the furrows (3.9 mmol m -2 equals 1716.5 g N 2 O ha - 1 or 14.2 g N 2 O ha -1 d -1 ). Figure 5: N 2 O flux [µmol m -2 h -1 ] and cumulative N 2 O emission [mmol m -2 ] of the soy bean field site from May 15 until September 14, 2011. Error bars represent the standard error of the mean (n=3). 85 Table 2: N 2 O flux [µmol m -2 h -1 ] and Standard Error (n=3) as well as cumulative N 2 O emission [mmol m -2 ] and Standard Error (n=3) of the soy bean field site’s furrows from May 15 through September 14, 2011. Those N 2 O fluxes are a mixture of N 2 O fluxes from furrows which were located next to PEmulched and such which were located next to non-PE-mulched ridges so that they cannot be included into Figure 5. Date Measured N 2 O flux [µmol m -2 h -1 ] ±1SE Cumulative N 2 O emission [mmol m -2 ] ±1SE 16.05.2011 -0.29 0.18 17.05.2011 0.40 0.56 0.00 0.00 22.05.2011 7.83 4.62 0.50 0.09 27.05.2011 0.23 0.41 0.98 0.19 30.05.2011 1.24 0.69 1.03 0.20 06.06.2011 3.03 2.12 1.34 0.32 08.06.2011 0.42 0.15 1.42 0.36 10.06.2011 0.95 0.09 1.46 0.39 13.06.2011 0.55 0.18 1.51 0.42 15.06.2011 1.02 0.08 1.55 0.43 21.06.2011 0.97 0.54 1.69 0.45 28.06.2011 1.95 1.58 1.93 0.50 02.07.2011 1.35 1.37 2.09 0.53 06.07.2011 3.30 2.53 2.31 0.54 10.07.2011 0.52 0.18 2.50 0.54 15.07.2011 0.50 0.43 2.56 0.56 21.07.2011 0.05 0.05 2.60 0.57 25.07.2011 -0.09 0.10 2.60 0.58 29.07.2011 0.14 0.30 2.60 0.59 02.08.2011 0.38 0.30 2.63 0.61 10.08.2011 0.53 0.49 2.71 0.63 15.08.2011 2.83 2.19 2.91 0.66 19.08.2011 -0.05 0.47 3.05 0.67 23.08.2011 2.16 1.09 3.15 0.69 30.08.2011 2.19 1.91 3.51 0.84 13.09.2011 0.15 0.36 3.91 1.09 3.4 Soil moisture and temperature of the PE mulched ridges, the non-PE-mulched ridges and furrows at the soy bean field The lowest mean soil temperature (21.47°C ±2.44) as well as the smallest temperature fluctuations occurred in the furrows (Fig. 6), whose soil temperatures were only by trend (P = 0.103) different from mean daily soil temperatures in the PE mulched and non-PE-mulched ridges. The temperature fluctuations and averaged mean daily soil temperature were very similar in PE-mulched (21.96°C ±2.57) and non-PE-mulched ridges (22.00°C ±2.56). Volumetric soil water content was very similar and statistically not differentiable in the furrows (30.22 ±0.11 %) and non-PE-mulched ridges (28.36 ±0.08 %), whereas the ridges which were covered with the PE film were much drier and statistically different (19.03 ±4.98 %), which is reflected in a highly significant statistical re Figure 6: Mean daily volumetric water content [%] and mean daily soil temperature [°C] from May 15 until September 14 at the soy bean field site in 2011. 3.5 Correlations between N 2 O fluxes and soil moisture, soil fertilizer applied Neither soil moisture nor soil temperature affected the N field site significantly (R 2 <0.1, 14 apparently triggered the N 2 more, smaller, N 2 O peak at the radish field site in 2010. No correlation could be found between applied N fertilizer amounts and sum of N from the radish field. 4. Discussion 4.1 General comments on crop yields of the study region The average yield of radish in the study area in 2010 was 33.1 t/ha and in 2011, 32.5 t/ha; average yield of soy beans was 1.85 t/ha in 2010 and 1.56 t/ha in 2011 (Yanggu County office statistic 2010, 2011, unpublished data sheets). For radish the average in the literature varies between 60 and 160 t/ha and for soy beans the average yields are 0.6 4.9 t/ha (Batti et al., 1983; Morgan and Midmore, 2003; Khairul Alam et al., 2010; Lindner, 2012, personal communication). Therefore, the yields on average whereas radish yields were below average. For other crops of the study area which also experience the PE mulching practice such as potato and cabbage, the yields are 86 were covered with the PE film were much drier and statistically different (19.03 ±4.98 %), which is reflected in a highly significant statistical re sult of P < 0.001, H = 86.684. Figure 6: Mean daily volumetric water content [%] and mean daily soil temperature [°C] from May 15 until September 14 at the soy bean field site in 2011. O fluxes and soil moisture, soil temperature and amount of N Neither soil moisture nor soil temperature affected the N 2 O fluxes at the radish or soy bean <0.1, P >0.05) even though the rain event from June 12 through June 2 O fluxes and the July 2 through July 5 rain event preceded one O peak at the radish field site in 2010. No correlation could be found between applied N fertilizer amounts and sum of N 4.1 General comments on crop yields of the study region The average yield of radish in the study area in 2010 was 33.1 t/ha and in 2011, 32.5 t/ha; average yield of soy beans was 1.85 t/ha in 2010 and 1.56 t/ha in 2011 (Yanggu County office statistic 2010, 2011, unpublished data sheets). For radish the average in the literature varies between 60 and 160 t/ha and for soy beans the average yields are 0.6 4.9 t/ha (Batti et al., 1983; Morgan and Midmore, 2003; Khairul Alam et al., 2010; Lindner, 2012, personal communication). Therefore, the yields of soy beans of the study region were on average whereas radish yields were below average. For other crops of the study area which also experience the PE mulching practice such as potato and cabbage, the yields are were covered with the PE film were much drier and statistically different (19.03 ±4.98 %), sult of P < 0.001, H = 86.684. Figure 6: Mean daily volumetric water content [%] and mean daily soil temperature [°C] from May 15 temperature and amount of N O fluxes at the radish or soy bean >0.05) even though the rain event from June 12 through June O fluxes and the July 2 through July 5 rain event preceded one No correlation could be found between applied N fertilizer amounts and sum of N 2 O emitted The average yield of radish in the study area in 2010 was 33.1 t/ha and in 2011, 32.5 t/ha; average yield of soy beans was 1.85 t/ha in 2010 and 1.56 t/ha in 2011 (Yanggu County office statistic 2010, 2011, unpublished data sheets). For radish the average yield data given in the literature varies between 60 and 160 t/ha and for soy beans the average yields are 0.6 - 4.9 t/ha (Batti et al., 1983; Morgan and Midmore, 2003; Khairul Alam et al., 2010; Lindner, of soy beans of the study region were on average whereas radish yields were below average. For other crops of the study area which also experience the PE mulching practice such as potato and cabbage, the yields are 87 well on average in comparison to other areas’ yields (Horton et al., 1988; Hassal and Associates, 2003; Rahemi et al., 2005; Bohl and Johnson, 2010). Also, it is known that the PE mulch - through performing as a greenhouse - in general has a positive effect on the plant productivity, which is the main reason why it is widely used worldwide. The purpose of our study was not to reconfirm it but we took the already wellinvestigated positive PE mulching effect on crop yields (Kyrikou and Briassoulis, 2007) as given and furthermore tried to broaden our knowledge on side effects of the PE mulch, such as its impact on N 2 O as its impact on N 2 O emissions. 4.2 Discussion of the results An unexpected result was that the soil moisture of the PE-mulched ridges of the radish field as well as those of the soy bean field was much lower than we had expected and as other publications predict (Kyrikou and Briassoulis, 2007; Nishimura et al. 2012). Nishimura et al. (2012) observed that during the summer the soil moisture under the PE mulch at their experimental site ranged from 26% to 33%, which is in contrast to the considerably lower soil moisture values underneath the PE mulch that we found at our study sites: during the early summer drought period in 2010 it ranged from 9% to 22% at the radish field site and during the early summer drought of the year 2011 it ranged from 12% to 20%. The reason for those low soil moistures could be the soil conditions of the study area. According to Kettering et al. (2013), the soils of the study region were very sandy, as were the soils of our experimental sites. Such soils show a fast infiltration and seepage of water; thus due to quick seeping of water it appears plausible to us that the PE mulch at our experimental sites could not keep the soil moisture high and the soils of our experimental sites were dryer as in the previous studies. This unexpected finding may be the main reason why our initial hypothesis could not be corroborated. We were assuming that plastic mulch films covering agricultural fields would lead to increased N 2 O emissions due to higher soil temperatures and moisture but the two experiments which we conducted were not in line with this hypothesis. The 2010 experiment at the radish field site provided an indication that ridges which are being covered with PE mulch films show very tiny N 2 O emissions from the PE mulch surface whereas the adjacent plant hole spots and furrows showed quite high emissions. This raised the question whether less N 2 O production occurred underneath the PE mulch film or there was horizontal diffusion of N 2 O from the ridge soil covered with the mulch film to the adjacent furrows and plant holes, so that most of the N 2 O produced underneath the PE mulch would have degassed from the furrows and plant hole spots. Recently, Nishimura et al. (2012) published that the N 2 O flux by permeation through the mulch film was much higher than that 94 Rochette, P., Janzen, H.H., 2005. Towards a revised coefficient for estimating N 2 O emissions from legumes. Nutr. Cycl. Agroecosys. 73, 171–179. Rodhe, H., 1990. A comparison of the contribution of various gases to the greenhouse-effect. Science, 248, 1217-1219. Ruser, R., Flessa, H., Russow, R., Schmidt, G., Buegger, F., Munch, J.C., 2006. Emission of N 2 O, N2 and CO 2 from soil fertilized with nitrate: Effect of compaction, soil moisture and rewetting. Soil Biol. Biochem. 38, 263-274. Scholes, M.C., Martin, R., Scholes, R.J., Parsons, D., Winstead, E., 1997. NO and N 2 O emissions from savanna soils following the first simulated rains of the season. Nutr. Cycl. Agroecosys. 48, 115– 122. Shepherd, M.F., Barzetti, S., Hastie, D.R., 1991. The production of atmosphereic NO X and N 2 O from a fertilized agricultural soil. Atmos. Environ. Part A 25, 1961-1969. Stehfest, E., Bouwman, L., 2006. N 2 O and NO emission from agricultural fields and soils under natural vegetation: summarizing available measurement data and modeling of global annual emissions. Nutr. Cycl. Agroecosys. 74, 207–228. Tilsner, J., Wrage, N., Lauf, J., Gebauer, G., 2003a. Emission of gaseous nitrogen oxides from an extensively managed grassland in NE Bavaria, Germany. I. Annual budgets of N 2 O and NO x emissions. Biogeochemistry 63, 229-247. Tilsner J., Wrage N., Lauf J., Gebauer G., 2003b. Emission of gaseous nitrogen oxides from an extensively managed grassland in NE Bavaria, Germany. II. Stable isotope natural abundance of N2O. Biogeochemistry 63, 249-267. Vitousek, P.M., Aber, J.D., Howarth, R.W., Likens, G.E., Matson, P.A., Schindler, D.W., Schlesinger, W.H., Tilman, D., 1997. Human alteration of the global nitrogen cycle: Sources and consequences. Ecol. Appl. 7, 737-750. WMO (2006). The State of Greenhouse Gases in the Atmosphere Using global Observations up to December 2004. WMO Greenhouse Bulletin 1. World Meteorological Organization,Geneve, 4pp Wrage, N., Velthof, G.L., van Beusichem, M.L., Oenema, O., 2001. Role of nitrifier denitrification in the production of nitrous oxide. Soil Biol. Biochem. 33, 1723-1732. 95 Yamulki, S., Jarvis, S.C., 1999. Automated chamber technique for gaseous flux measurements: evalutation of a photoacoustic infrared spectrometer-trace gas analyzer. J. Geophys. Res. 104, 5463– 5469. Zhang, S., Li, P., Yang, X., Wang, Z., Chen, X., 2011. Effects of tillage and plastic mulch on soil water, growth and yield of spring-sown maize. Soil Til. Res. 112, 92–97. 96 Appendix Table 1: Statistically significant differences between N 2 O fluxes of PE mulches, plant holes and furrows of the N50, N200, N250 and N350 plots of those measurement days when such differences occurred. * indicates P < 0.05, ** indicates P < 0.01 and *** indicates P < 0.001. N50 16.06. 2010 23.06. 2010 29.06. 2010 06.07. 2010 24.07. 2010 PE Plant PE Plant PE Plant PE Plant PE Plant mulch hole mulch hole mulch hole mulch hole mulch hole Plant * P = Plant Plant P = Plant P = Plant hole 0.036 hole hole 0.138 hole 0.173 hole FurFurFur- * P = P = Fur- * P = P = Furrow row row 0.044 0.158 row 0.017 0.093 row N200 12.06. 2010 19.06. 2010 22.06. 2010 03.07. 2010 07.07. 2010 PE Plant PE Plant PE Plant PE Plant PE Plant mulch hole mulch hole mulch hole mulch hole mulch hole Plant Plant P = Plant P = Plant P = Plant **P= hole hole 0.063 hole 0.121 hole 0.131 hole 0.003 FurP = Fur- * P = * P = Fur- * P = FurFurP = P = row 0.163 row 0.029 0.029 row 0.036 row row 0.151 0.074 12.07. 2010 21.07. 2010 PE Plant PE Plant mulch hole mulch hole Plant * P = Plant hole 0.022 hole Fur- * P = Fur- ***P< **P= row 0.018 row 0.001 0.006 N250 16.06. 2010 23.06. 2010 29.06. 2010 06.07. 2010 24.07. 2010 PE Plant PE Plant PE Plant PE Plant PE Plant mulch hole mulch hole mulch hole mulch hole mulch hole Plant Plant P = Plant * P = Plant P = Plant hole hole 0.057 hole 0.036 hole 0.056 hole FurFur- * P = * P = FurP = P = FurFurrow row 0.029 0.011 row 0.109 0.167 row row N350 16.06. 2010 23.06. 2010 29.06. 2010 06.07. 2010 24.07. 2010 PE Plant PE Plant PE Plant PE Plant PE Plant mulch hole mulch hole mulch hole mulch hole mulch hole Plant * P = Plant * P = Plant **P< Plant Plant ***P< hole 0.021 hole 0.026 hole 0.005 hole hole 0.001 FurFurP = Fur- * P = ***P< FurFurrow row 0.064 row 0.015 0.001 row row Given are those p-values which indicate a statistically significant difference as well as P-values which indicate a trend (P =/< 0.1). There were more measurement days but the table only provides the statistical results of such measurement days on which statistical differences between plant holes, PEmulch and furrows could be found. 97 98 Part B The simulation of N 2 O emissions and nitrate leaching from different rates of N fertilizer in the radish field with the Landscape-DNDC model Youngsun Kim 1,2 , Sina Berger 3 , Janine Kettering 4,5 , John Tenhunen 1 , Ralf Kiese 2 1 Department of Plant Ecology, University of Bayreuth, 95440 Bayreuth, Germany 2 Karlsruhe Institute of Technology, Institute for Meteorology and Climate Research, IMK-IFU, 82467 Garmisch-Partenkirchen, Germany 3 BayCEER-Laboratory of Isotope Biogeochemistry, University of Bayreuth, 95440 Bayreuth, Germany 4 Department of Agroecosystem Research, University of Bayreuth, 95440 Bayreuth, Germany 5 Present address: FLAD&FLAD Communication GmbH, 90562 Heroldsberg, Germany, [email protected] Manuscript in Preparation Abstract The Landscape-DNDC-model was used to estimate N 2 O emissions, nitrate concentrations and leaching from 50, 150, 250 and 350 kg N ha -1 fertilizer treatments of a radish field study site in Korea. Half of the daily precipitation and the daily average maximum temperature were assumed to be the climatic conditions in rows, which were covered with impervious black polytheylene (PE) mulch during the growing period of radish, in order to consider the effects of the plastic mulch on soil biogeochemistry in rows and differentiateN 2 O emissions, nitrate concentrations and leaching in interrows which were not covered with the plastic mulch. Simulation results showed that the model was capable of predicting the dynamics of N 2 O emissions, nitrate concentrations and leaching in rows and interrows. The simulated N 2 O emissions in rows were increased with increasing amount of N fertilizer applied but underestimated during the monsoon season. The model predicted more N 2 O emissions in rows than in interrows. About 0.94 and 0.97% of applied N fertilizer were lost by N 2 O emissions from rows and interrows, respectively. The simulation results of nitrate 99 concentrations at 45 cm depth in rows ranged from 137.1 (50 kg N ha -1 ) to 149.6 mg N l -1 (350 kg N ha -1 ). Lower nitrate concentrations were simulated for 45 cm depth in interrows as compared with rows, ranging from 91.1 (50 kg N ha -1 ) to 124.7 mg N l -1 (350 kg N ha -1 ). These results were in good agreement with measured nitrate concentrations at 45 cm depth in rows and interrows. ME was positive (ME > O) for all N treatments and r 2 was also high (eg., 0.89, 0.89 and 0.52 for 50, 150 and 250 kg N ha -1 ) at 45 cm depth of interrows. Nitrate leaching simulated by the model was increased following the rainfall events and applied N fertilizer rates. In general, interrows which were not covered with the plastic mulch show high nitrate leaching rates as compared with rows under the plastic mulch. The model simulated about 13.2% more nitrate leaching in interrows than in rows, ranging from 403.4 to 452.3 kg N ha -1 yr -1 . About 72.2% of applied N loss by nitrate leaching from interrows and about 62.5% N loss from rows were predicted by the model. Keywords: Landscape-DNDC, N fertilizer, Plastic mulch, N 2 O, Nitrate leaching 100 Introduction Agriculture is the major anthropogenic source of nitrous oxide (N 2 O) (McCraw and Motes 1991; Smith and Conen 2004). Of global anthropogenic greenhouse gas (GHG) emissions, agriculture accounts for about 60% of N 2 O (IPCC 2007b). N 2 O emissions are directly connected to the amounts of nitrogen application (Smith and Conen 2004) and have been increased by 11% since 1990, primarily due to the increase in fertilizer use and the aggregate growth of agriculture (IPCC 2007a). In Korea, agricultural N 2 O is estimated about 12 x 10 3 tons, which accounts for about 24% of the total N 2 O emissions. Agricultural soils are the major source of N 2 O emissions, contributing about 58.3% to the total agricultural N 2 O emissions (KEEI 2009). N 2 O emissions are influenced by environmental factors such as soil temperature and water content, radiation, pH, Eh, and substrate concentration gradient, as well as management practices such as plastic mulch, tillage, manure and fertilizer application and incorporation of crop residues (LI 2007; Smith et al. 2002). For many years the use of impervious black and clear plastic (polyethylene) films as a mulch has been widely utilized for various crops. In the traditional plastic mulching system the crop is sown under the plastic film which is held tightly across the soil surface by covering the edges. The crop emerges through perforations in the plastic mulch, which are usually made at the time of seeding. The plastic mulch is discarded after harvest and new a mulch is laid in following season (Fisher 1995). In arid and semi-arid regions, crop growth is limited by water. Amount of available water in soil can be increased by mulching (Wang et al. 2009). The plastic mulch is effective in reducing 3 to 11% crop water use and improves its efficiency by 25% (Chakraborty et al. 2010). The plastic mulch can also provide other benefits such as weed control, reduction in soil compaction and erosion, production of staple food crops (Fisher 1995), and an increase in soil temperature (Liakatas et al. 1986; Wan and El-Swaify 1999). The plastic mulch performs like a glass house by capturing and retaining daytime solar radiation and reducing heat loss at night, producing a mini-greenhouse effect (Kwabiah 2004). Many studies have reported that soil temperature is increased under the plastic mulch. The temperature of the black plastic mulch is greater than that of the soil surface during the day (Ham and Kluitenberg 1994). The net effect of the mulch is to increase the daily mean temperature of the soil by 3°C (Liakatas et al. 1986) and the maximum temperature is increased by 7°C for soil due to the plastic mulch (Kwabiah 2004). Less rainfall can pass through the root zoon under the plastic mulch because the rain that falls onto an impervious plastic film covering a planting bed can run into the furrows, immediately (Haraguchi et al. 2004). The total amounts of precipitation and surface runoff for two months under the full-mulching condition were 112.5 and 50.3 mm, respectively. Neglecting the water that is kept on leaves and the plastic film this result suggests that almost a half of the 101 rain that falls into the lysimeter infiltrated into the soil through a hole on a plastic film for seeding (Haraguchi et al. 2003). The black plastic mulch is the most commonly used for crop cultivation in Korea as well. The main purposes of using plastic mulch are weed control and water retention. The row is covered with the black plastic mulch before seeding or transplanting and the mulch is removed after harvest. The plastic mulch is one of the typical agricultural practices in Korea, however, the model is incapable of simulating the mulching effect, yet. Therefore, the daily mean maximum temperature as the daily mean temperature and a half of the precipitation as the total precipitation were hypothesized for the row condition. In contrast, the real climate information was used for the interrow condition. Objectives The process-based models can be used to predict the impact of various agricultural management practices on net greenhouse gas (GHG) emissions by analyzing the interactions between management practices, primary drivers such as climate, soil properties, crop types, etc., and biogeochemical reactions (Smith et al. 2010). So far the process-based model agricultural-DNDC (Denitrification and Decomposition) (Giltrap et al. 2010; Li et al. 1992a; Smith et al. 2002) and PnET-N-DNDC (Kesik et al. 2005; Kiese et al. 2011; Li et al. 2000) have been tested on the various types of ecosystems since its development. The Landscape-DNDC model, which is combined the Agricultural-DNDC with the Forest-DNDC, has been developed at Karlsruhe Institute of Technology (KIT), IMK-IFU in order to simulate the C and N turnover, GHG emissions, nitrate leaching and plant growth for arable, forest and grassland ecosystems on site and regional scales (Haas et al. 2012). However, little or no attention has been given to apply the DNDC model for arable or forest ecosystems of Korea. In this study, we applied the Landscape-DNDC to test the effects of different rates of N fertilizer on N 2 O emissions, nitrate concentrations and leaching and plant growth for arable fields in Korea. Materials and methods Site description The model was tested with data from summer radish fields (38.3°N, 128.14°E, 420 m a.s.l) in Haean-myun Catchment, located in the northeast of Yanggu County, Gangwon Province, South Korea. The annual average air temperature is 8.5°C and the annual precipitation is 102 approximately 1,500 mm (Fig. 1). More than half of the annual precipitation occurs during the monsoon season. Month Dec Apr Aug Dec Precipitation [mm] 0 20 40 60 80 100 120 140 160 Air temperature [oC] -20 -10 0 10 20 30 Precipitation (mm) Air temperature (oC) Fig. 1 Daily precipitation and daily average temperature at the study site. The data was collected from the automatic weather station on site in 2010. The soil is loamy sand at 0 - 40 cm depth and sandy loam from 40 - 60 cm (Kettering et al. Submitted to Nutrient Cycling in Agroecosystems 2012) and classified as Anthrosols (FAO 2006) with 80.7% sand; 16.3% silt; 3.0% clay; pH 5.08; bulk density 1.64 g cm -3 . The detailed information for soil characteristics of the study site is given in Table 1. Table 1 Soil properties for 50, 150, 250 and 350 kg N ha -1 treatments in radish fields at 0-20 cm depth soils N rates [kg N ha -1 ] OM [g kg -1 ] a pH BD [g cm -3 ] b SOC [%] c N [%] Sand [%] Silt [%] Clay [%] Average 29.7 5.08 1.64 0.21 0.038 80.7 16.3 3.0 50 0.29 0.037 80.3 16.7 3.0 150 0.20 0.036 79.5 17.4 3.2 250 0.21 0.041 81.6 15.6 2.8 350 0.20 0.038 81.6 15.5 2.9 a Organic Matter b Bulk Density c Soil Organic Carbon 103 N fertilizer treatments 186.7 kg N ha -1 fertilizer was manually applied to the total field as basal fertilizer two weeks before seeding and the field was plowed with about 15 - 20 cm depth at the time of the fertilizer application. To examine the impacts of different rates of N fertilizer on N 2 O emissions, nitrate leaching and crop growth, at first, the field was divided into four different N treatment plots with 196 m 2 size. Each N treatment plot had 4 replicated subplots (49 m 2 size in each). 50, 150, 250 and 350 kg N fertilizer were applied to the plots as a top dressing, respectively. All treatment plots were tilled again about one week after additional N fertilizer application in order to make rows and interrows. The rows were covered with the black plastic mulch prior to radish seeding and the mulch had continuously covered the row until harvest. About 2 or 3 seeds of summer radish (Raphanus sativus L.) were sown per one plant hole at rows in mid-June. Detailed information for crop management is shown in Table 2. Table 2 Crop managements and four different rates of N fertilizer application Seeding date [dd/mm] Basal fertilization Tillage Additional fertilization Harvest date [dd/mm] Date [dd/mm] N rate [kg N ha -1 ] Date [dd/mm] Depth [cm] Date [dd/mm] N rate [kg N ha -1 ] a 14/06 31/05 186.7 31/05 09/06 15 - 20 01/06 50/150/250/350 31/08 a 50, 150, 250 and 350 kg N ha -1 were applied to each N treatment plot with 4 replicates. Field measurements The field measurements were conducted in the radish field in 2010. The N 2 O fluxes were measured by the closed chamber in conjunction with a photoacoustic infrared trace gas analyzer (Multigas Monitor 1312, INNOVA, Ballerup, Denmark) (Berger et al. Submitted to Agriculture, Ecosystems & Environment 2012) from May (before seeding) to October (after harvest) in rows and interrows of each N treatment plot with 3 replicates. ECH 2 O loggers (5TE Soil Moisture Sensor, Decagon Devices, USA) connected with data loggers (EM50, Decagon Devices, USA) were installed in each N fertilizer treatment row in order to measure soil temperature and water content at 15 and 30 cm depth every 30 minutes with 2 replicates. Suction lysimeters connected with a soil hydrological monitoring network of standard tensiometers (Kettering et al. Submitted to Nutrient Cycling in Agroecosystems 2012) were installed at 15 and 45 cm depth in rows and at 45 cm depth in interrows in 50, 150, 250 and 350 kg ha -1 N treatment plots to estimate N losses in seepage water. The seepage samples were collected once a week. Suction lysimeters are able to be used to 110 50 kg N 15 20 25 30 35 40 Simulated_50% precipitation Simulated_annual precipitation Measured 150 kg N Volumetric soil water content at 15 cm depth [%] 15 20 25 30 35 40 250 kg N 15 20 25 30 35 40 350 kg N Month Dec Apr Aug Dec 15 20 25 30 35 40 a 50 kg N X Data 15 20 25 30 35 40 Simulated_50% precipitation Simulated_annual precipitation Measured 150 kg N Volumetric soil water content at 30 cm depth [%] 15 20 25 30 35 40 250 kg N 15 20 25 30 35 40 350 kg N Month Dec Apr Aug Dec 15 20 25 30 35 40 b Fig. 3 Measured (circle) and simulated (line) soil water content at 15 (a) and 30 cm (b) depth in rows with four different rates of N fertilizer. Solid lines represent the simulated soil water content with 50% of the precipitation in order to consider row conditions covered with the black plastic mulch during the whole growing periods of radish. Dotted lines indicate the simulated soil temperature with the annual precipitation. Radish biomass Radish is a cool-season and fast-maturing crop (El-Desuki et al. 2005) that grows well in spring and autumn (Sirtautas et al. 2011) in Korea. Korean ecotypes of radish are cold sensitive so that radish is cultivated during the autumn when ambient temperatures goes down to 5 - 6°C (Curtis 2003). Radish needs a high demand for nutrients even though it is a rapidly growing and a short duration crop (Akoumianakis et al. 2011; Hegde 1987). For example, radish requires 183 kg N, 120 kg P 2 O 5 , 232 kg K 2 O, 103 kg CaO, and 54 kg MgO ha -1 in order to produce 49,280 kg ha -1 (Park et al. 2006). 111 The Landscape-DNDC is capable to simulate aboveand belowground biomass separately. The aboveground biomass includes leaves and stems and the belowground biomass stands for roots. The simulation results of radish biomass for all N treatments were compared with the measured biomass at 25, 50 and 75 harvest days. Dry weights of measured and simulated radish biomass at the last harvest day (75 days after seeding) are listed in Table 5. Both measured and simulated radish biomass were increased as the increase of the N fertilizer application rates. This positive relationship between radish biomass and N application rates has been reported in several researches. Maximum radish root yield (16.6 kg) per plant was produced with 200 kg N ha -1 followed by 150 and 100 kg N ha -1 (Pervez et al. 2004). Radish root yield was 5.7 and 6.9 t ha -1 at 56 and 168 kg N ha -1 , respectively (Sanchez et al. 1991). The maximum yield (89.2 t ha -1 ) of total radish was recorded at 200 kg N ha -1 and the minimum yield (60.3 t ha -1 ) was produced at 50 kg N ha -1 treatments (Jilani et al. 2010). Simulated belowground biomass was underestimated for 50 kg N ha -1 treatment and slightly overestimated for 150, 250 and 350 kg N ha -1 treatments. In contrast, the model overestimated the aboveground biomass at 50 and 150 kg N ha -1 treatments and underestimated at 250 and 350 kg N ha -1 treatments. Total biomass indicates the sum of aboveand belowground biomass. The model overestimated the total biomass for 50 (2.0%) and 150 (2.0%) kg N ha -1 treatments. In contrast, the total biomass from 250 and 350 kg N ha -1 treatments was underestimated by 1.5 and 1.2%, respectively. Table 5 Measured and simulated radish biomass at the last harvest day (75 day) N rates [kg N ha -1 ] Aboveground [kg DW m - 2 ] a Belowground [kg DW m - 2 ] b Total [kg DW m - 2 ] c Measured Simulated Measured Simulated Measured Simulated 50 0.1264 0.1443 0.2724 0.2680 0.3988 0.4123 150 0.1547 0.1614 0.2971 0.2997 0.4518 0.4612 250 0.1817 0.1735 0.3217 0.3222 0.5034 0.4958 350 0.2005 0.1894 0.3393 0.3517 0.5399 0.5411 a Leaves and stems were included. b Roots were included. c Sum of aboveand belowground biomass Fig. 4 shows that the model overestimated both aboveand belowground biomass at first harvest day (25 day) and well predicted the last harvest day (75 day). At the second harvest day (50 day), the model overestimated the belowground biomass but underestimated the aboveground biomass over all N treatments. The growth and the development of simulated 112 radish were faster and they reached the mature stage earlier than the field radish so that the model might overestimate both aboveand belowground radish biomass at first two harvest days. In addition to this, the less available field data might also result in inaccurate predictions for radish biomass by the model in this study. 150 kg N Radish biomass [kg DW m -2 ] 0.0 0.2 0.4 0.6 50 kg N Radish biomass [kg DW m-2] 0.0 0.2 0.4 0.6 Simulated_Root Simulated_Foliage Simulated_Total Measured_Root Measured_Foliage Measured_Total 250 kg N Month Dec Apr Aug Dec 0.0 0.2 0.4 0.6 350 kg N Month Dec Apr Aug Dec 0.0 0.2 0.4 0.6 Fig. 4 Comparison of measured (circle) and simulated (line) radish biomass in rows with four different rates of N fertilizer. Bars represent standard errors of measurements. N 2 O emissions from agricultural soils N 2 O emissions depend on application of N fertilizer as well as other factors such as soil conditions and managements, precipitation and temperature (Roelandt et al. 2005). In this study, the measurements of N 2 O emissions were conducted at 50, 150, 250 and 350 kg N ha -1 treatments in rows and interrows and compared with the simulated N 2 O emissions. Both measured and simulated N 2 O emissions were increased as the increase of applied N fertilizer rates in rows; 350 > 250 > 150 > 50 kg N ha -1 treatments (Fig. 5). Similar results were shown in interrows, except for 50 kg N ha -1 treatment. Of all measurements of N 2 O emissions in interrows, the highest N 2 O emissions were observed in 50 kg N ha -1 treatment (77.97 ug N m -2 h -1 ) and followed by 350, 250 and 150 kg N ha -1 treatments. The model simulated more N 2 O emissions from 50 kg N ha -1 treatment (56.54 ug N m -1 h -1 ) than for 150 and 250 kg N ha -1 treatments as well. In contrast with measurements, the model predicted the highest N 2 O emissions from 350 kg N ha -1 treatment (73.08 ug N m -2 h -1 ). The high N 2 O emissions from measurements at 50 kg N ha -1 treatment might be caused by uncertainties in field measurements. The reason is that about 3.1 times more N 2 O emissions were observed in 50 kg N ha -1 treatment (435.3 ug N m -2 h -1 ) than in 350 kg N ha -1 113 treatment (147.6 ug N m -2 h -1 ) in 23 th of June. As compared with 150 and 250 kg N ha -1 treatments, N 2 O emissions from 50 kg N h -1 treatment were still 2.4 and 2.9 times high, respectively. In this sense, these high N 2 O emissions are also considered to result in the least correlation between measured and simulated N 2 O emissions from 50 kg N ha -1 treatment. Comparison between measurement and simulation results of N 2 O emissions from 50 kg N ha -1 treatment shows the highest RMSE (124.5) and the lowest r 2 (0.07) of all N treatments in interrows (Table 6). Except for 50 kg N h -1 treatments, N 2 O emissions from interrows were increased as the increase of N application rates. The simulation results showed that the model overestimated N 2 O emissions for all N treatments in rows. N 2 O emissions in interrows were overestimated as well, except for 50 kg N ha -1 treatment. The model underestimated N 2 O emissions from 50 kg N ha -1 treatment by 37.9%. As compared with simulated N 2 O emissions between rows and interrows, the model predicted N 2 O emissions better in rows than in interrows. r 2 was low and ME was negative (ME < 0) for almost all N treatments in interrows. Comparison between measured and simulated N 2 O emissions in rows and interrows showed that measured N 2 O emissions from rows were generally higher than from interrows, except for 50 kg N ha -1 treatment. In case of 50 kg N ha -1 treatment, about 2.8 times more N 2 O emissions were measured in interrows than in rows. In contrast, simulated N 2 O emissions from rows were always higher than from interrows for all N treatments. The second tillage and the plastic mulch are considered to induce the first high peak of N 2 O emissions in the measurements and the maximum peak of simulated N 2 O emissions is associated with N fertilizer application. Because added N fertilizer as a top dressing mixed into the soils during the second tillage for creating rows and interrows and then the rows were continuously covered with the black plastic mulch during the whole growing periods of radish in this study. The plastic mulch intercepts sunlight which warms the soil (McCraw and Motes 1991). The mulch keeps the soil warm and promotes N mineralization of the applied N fertilizer. The soil temperature at 5 cm depth under plastic mulch showed extensive diurnal fluctuation, mostly from 25 to 50°C in summer season (Nishimura et al. 2012) and the mean temperature under plastic mulch was 4°C higher than under bare soil ( Liakatas et al. 1986). In general, high soil temperature, high soil moisture and hence high decomposition rates promote high N 2 O emissions during the summer season (Li et al. 1992b). Since soil covered with plastic mulch right after fertilizer application is under high N content and low O 2 concentration, a significant amount of N 2 O can be produced and emitted to the atmosphere (Nishimura et al. 2012). Simulated N 2 O emissions were increased following the fertilizer application and then gradually decreased. N 2 O emissions are generally increased with the increase of rainfall flux (Li et al. 1992b) and the N 2 O emission peaks usually coincide with rainfall events (Smith et al. 2002). The rain that falls onto the impermeable plastic mulch is able to run into the 114 interrow immediately and the less rainfall can pass through the root zone in the row (McCraw and Motes 1991). The simulation results showed that the model was capable of simulating N 2 O emissions during heavy rainfall. As seen in Fig. 5, slightly increased N 2 O peaks were simulated during the monsoon season (June - August). Measured and simulated mean values of N 2 O emissions were presented in Table 6. 50 kg N 0 100 200 300 400 150 kg N N2O emissions from rows [ug N m-2 h-1] 0 100 200 300 400 250 kg N 0 100 200 300 400 350 kg N Month Dec Apr Aug Dec 0 100 200 300 400 Simulated Measured ↓↓ ↓↓ ↓↓ ↓↓ a 50 kg N 0 100 200 300 400 150kg N N2O emissions from interrows [ug N m-2 h-1] 0 100 200 300 400 250 kg N 0 100 200 300 400 350 kg N Month Dec Apr Aug Dec 0 100 200 300 400 Simulated Measured ↓ ↓ ↓↓ ↓↓ ↓↓ b Fig. 5 Measured (circle) and simulated (line) N 2 O emissions from four different rates of N fertilizer in (a) rows and (b) interrows. Arrows indicate time and date of N fertilizer application. Bars represent standard deviations of measurements. The model simulated N 2 O emissions in interrows with the same rates of N fertilizer and tilling events as rows. The only one difference between rows and interrows was that there was no crop in interrows. It means that there is no N loss by the plant uptake in interrows. Therefore, most of added N was lost by N 2 O emissions, nitrate leaching and ammonia volatilization in interrows as compared with rows. N 2 O emissions by both permeation through the plastic 115 mulch and the horizontal diffusion to the adjacent interrow may be important. The significant amounts of N 2 O emissions were observed from the unfertilized interrow between rows, which were covered with plastic mulch after fertilization, indicating the horizontal diffusion of N 2 O from rows to the adjacent interrow (Nishimura et al. 2012). In this study, the same rates of N fertilizer were added to rows and interrows because rows and interrows were created after fertilizer application. Therefore, it was not able to detect the horizontal diffusion of N 2 O in interrows in this study. Nitrate concentrations and nitrate leaching in agricultural soils Calculation of soil nitrate concentrations in soil layers takes into account the mineralization, nitrification and denitrification as well as nitrate leaching. In addition, nitrate deposition from the atmosphere is also considered in the first soil layer (Kiese et al. 2011). In this study, nitrate concentrations in seepage water were measured at 15 and 45 cm depth of rows covered with the black plastic mulch and at 45 cm depth of interrows without the plastic mulch for 50, 150, 250 and 350 kg N h -1 treatments and compared with the simulation results. The mean measured nitrate concentrations were increased as the increase of applied N fertilizer rates in both rows and interrows. The simulated nitrate concentrations at 45 cm depth of rows and interrows were increased as the increase of N fertilizer rates as well (Fig. 6). The simulation results of nitrate concentrations at 45 cm depth of rows ranged from 137.1 (50 kg N ha -1 ) to 149.6 mg N l -1 (350 kg N ha -1 ). The less nitrate concentrations were simulated for 45 cm depth of interrows as compared with rows, ranged from 91.1 (50 kg N ha -1 ) to 124.7 mg N l -1 (350 kg N ha -1 ). These results were in good agreement with measured nitrate concentrations at 45 cm depth of rows and interrows. The measurements for nitrate concentrations at 45 cm depth in rows were more than of interrows. In contrast, simulated nitrate concentrations at 15 cm depth in rows decreased as the increase of N fertilizer rates. The model simulated the high nitrate concentrations at 50 kg N ha -1 treatment (173.7 mg N l - 1 ) and the low concentrations at 350 kg N ha -1 treatment (158.1 mg N l -1 ). The model was able to predict nitrate concentrations in rows and interrows. For example, ME was positive (ME > O) for all N treatments and r 2 was also high (eg., 0.89, 0.89 and 0.52 for 50, 150 and 250 kg N ha -1 ) at 45 cm depth of interrows. The simulation results of nitrate concentrations in rows were generally in good agreement with measured nitrate concentrations as well. In case of rows at 45 cm depth, for instance, r 2 was 0.73 for 50 kg N ha -1 and 0.53 for 150 kg N ha -1 (Table 6). In addition, the model was capable of estimating nitrate concentrations following the rainfall events. The high nitrate concentrations were 116 observed in early growing stage of radish during the monsoon season than late growing stage over all N treatments. 50 kg N 0 200 400 600 800 150 kg N Nitrate concentrations at 15 cm depth of rows [mg N l-1 d-1] 0 200 400 600 800 250 kg N 0 200 400 600 800 350 kg N Month Dec Apr Aug Dec 0 200 400 600 800 Simulated Measured ↓↓ ↓↓ ↓↓ ↓↓ a 50 kg N 0 200 400 600 800 Simulated Measured 150 kg N Nitrate concentrations at 45 cm depth of rows [mg N l-1 d-1] 0 200 400 600 800 250 kg N 0 200 400 600 800 350 kg N Month Dec Apr Aug Dec 0 200 400 600 800 ↓ ↓ ↓↓ ↓↓ ↓↓ b 50 kg N 0 200 400 600 800 Simulated Measured 150 kg N Nitrate concentrations at 45 cm depth of interrows [mg N l-l d-1] 0 200 400 600 800 250 kg N 0 200 400 600 800 350 kg N Month Dec Apr Aug Dec 0 200 400 600 800 ↓↓ ↓ ↓ ↓ ↓ ↓ ↓ c Fig. 6 Comparison of nitrate concentrations between rows and interrows with four different rates of N fertilizer; measured (circle) and simulated (line) nitrate concentrations at (a) 15 cm depth in rows, (b) 45 cm depth in rows and (c) 45 cm depth in interrows. Arrows indicate time and date of N fertilizer application. Bars represent standard deviations of measurements. Nitrate leaching rate was most sensitive to fertilizer application rate and precipitation (Li et al. 2006). The amount and the time of N fertilizer application have significant impacts on the nitrate leaching (Hansen et al. 2000). The high input of N fertilizer and low use efficiency may certainly result in the increase of the N loss from leaching (Qiu et al. 2011). In general, nitrate leaching rates increased under high nitrogen level and the low N level led to lower nitrate leaching (Liu et al. 2003). However, the simulations of nitrate leaching in this study showed the different results. Simulated nitrate leaching was decreased as the increase of N fertilizer rates in rows and interrows; 350 < 250 < 150 < 50 kg N ha -1 treatments. Simulated annual nitrate leaching ranged from 352.6 (350 kg N ha -1 ) to 382.6 kg N ha -1 yr -1 (50 kg N ha -1 ) in rows. The model simulated about 13.2% more nitrate leaching in interrows than in rows, ranged from 403.4 (350 kg N ha -1 ) to 452.3 ha -1 yr -1 (50 kg N ha -1 ) in interrows. 117 Table 6 The model performance of Landscape-DNDC for simulation of radish fields with four different N treatments Mean Model Performance Measured Simulated ME RMSPE r 2 Soil temperature [°C] at 15 cm depth 50 kg N ha - 1 24.01 25.88 -2.62 2.23 0.28 *** 150 kg N ha - 1 23.69 25.84 -3.86 2.55 0.14 ** 250 kg N ha -1 23.91 25.81 -2.38 2.51 0.05 * 350 kg N ha - 1 23.96 25.79 -2.61 2.13 0.32 *** Soil temperature [°C] at 30 cm depth 50 kg N ha - 1 23.00 25.00 -4.91 2.11 0.66 *** 150 kg N ha - 1 22.85 24.95 -7.53 2.28 0.39 *** 250 kg N ha - 1 23.43 24.90 -2.80 1.67 0.50 *** 350 kg N ha - 1 23.37 24.86 -3.69 1.74 0.37 *** Soil water content [vol %] at 15 cm depth 50 kg N ha - 1 18.61 20.61 -0.23 3.58 0.22 *** 150 kg N ha - 1 19.30 22.37 -0.74 4.15 0.32 *** 250 kg N ha - 1 17.92 22.35 -2.05 6.58 0.02 350 kg N ha - 1 27.73 29.21 0.19 3.29 0.36 *** Soil water content [vol %] at 30 cm depth 50 kg N ha - 1 25.06 25.28 0.33 3.28 0.33 *** 150 kg N ha - 1 24.49 25.16 0.30 2.80 0.36 *** 250 kg N ha - 1 22.02 25.42 -0.38 4.32 0.48 *** 350 kg N ha - 1 18.68 24.92 -3.73 6.67 0.41 *** N 2 O emissions [ug N m - 2 h - 1 ] in Rows 50 kg N ha - 1 27.88 61.53 -0.35 50.83 0.27 150 kg N ha - 1 38.91 66.79 0.13 57.41 0.33 250 kg N ha - 1 58.83 73.48 0.27 91.13 0.34 350 kg N ha - 1 65.82 98.02 0.12 106.7 0.21 N 2 O emissions [ug N m - 2 h - 1 ] in Interrows 50 kg N ha - 1 77.97 56.54 0.04 124.5 0.07 150 kg N ha - 1 26.56 55.41 -0.24 55.22 0.11 250 kg N ha - 1 27.12 54.49 -0.24 50.08 0.15 350 kg N ha - 1 29.89 73.08 -0.99 63.31 0.16 Nitrate concentrations [mg N l - 1 ] at 15 cm depth in Rows 50 kg N ha - 1 79.81 173.7 -2.17 134.1 0.35 150 kg N ha - 1 92.50 172.1 -0.03 99.10 0.69 * 250 kg N ha - 1 143.2 165.4 0.37 69.03 0.59 * 350 kg N ha - 1 141.6 158.1 -0.14 84.57 0.34 Nitrate concentrations [mg N l - 1 ] at 45 cm depth in Rows 50 kg N ha - 1 61.24 137.1 0.69 44.29 0.73 ** 150 kg N ha - 1 52.01 125.8 -5.31 145.3 0.53 * 250 kg N ha - 1 107.6 139.9 -7.22 168.2 0.30 350 kg N ha - 1 116.9 149.6 -8.94 163.4 0.32 Nitrate concentrations [mg N l - 1 ] at 45 cm depth in Interrows 50 kg N ha - 1 53.00 91.05 0.43 44.54 0.89 *** 118 150 kg N ha - 1 56.75 96.87 0.38 50.05 0.89 *** 250 kg N ha - 1 104.8 118.3 0.41 43.36 0.52 * 350 kg N ha - 1 108.0 124.7 0.07 64.12 0.25 * P < 0.05, ** P < 0.01, *** P < 0.001 N fertilizer applied early in the crop growing stage has a high potential of being lost by leaching (Errebhi et al. 1998; Romic et al. 2003). The crop was not able to use up all nitrates, which usually linked to heavy rainfall, resulted in nitrate leaching (Romic et al. 2003). Frequent rainfall may cause rapid movement of nitrate from the rooting zone through the intermediate soil layer (Islam et al. 1994). The rainfall for 15 days induced more than 50.0% of nitrate leaching during the crop growing stage (Vázquez et al. 2006). The results from previous studies are in good agreement with simulated nitrate leaching in this study. Heavy rainfall in early growing stage of radish had significant effects on nitrate leaching. About 34.6% (510 mm) of the total precipitation was observed during the measurements of nitrate concentrations (from 30 th of June to 23 rd of August). The model simulated about 18.5% and 52.2% of the total nitrate leaching in rows and in interrows during this heavy rainfall, respectively. 150 kg N 0 5 10 15 20 25 250 kg N Month Dec Apr Aug Dec Nitrate leaching [kg N ha -1 ] 0 5 10 15 20 25 50 kg N 0 5 10 15 20 25 Row Interrow 350 kg N Month Dec Apr Aug Dec Nitrate leaching [kg N ha -1 ] 0 5 10 15 20 25 ↓ ↓ ↓ ↓↓↓ ↓↓ Fig. 7 Simulated nitrate leaching in rows and interrows with four different rates of N fertilizer. Solid lines represent nitrate leaching in rows with the black plastic mulch. Dotted lines indicate nitrate leaching in interrows without the black plastic mulch. Arrows indicate time and date of N fertilizer application. Several studies have shown that the plastic mulch has a positive effect on the reduction of nitrate leaching. Nitrate leaching in the plot with the mulch was less than without the mulch. The plastic mulch protects soil from the direct infiltration of precipitation so that nitrate 119 leaching from the root zone is reduced (Islam et al. 1994; McCraw and Motes 1991; Romic et al. 2003; Zhang et al. 2012). For example, a nitrate leaching rate of 7% from the total water was shown in the plot with mulching and 10% without mulching (Romic et al. 2003). This result is in good agreement of simulation results of nitrate leaching in this study. Comparison of simulated annual nitrate leaching between rows and interrows showed that the model was able to predict nitrate leaching under the plastic mulch (Fig. 8). The model simulated about 13.2% more nitrate leaching in interrows without the plastic mulch than in rows with the plastic mulch. The differences of nitrate leaching between rows and interrows ranged from 50.78 (350 kg N ha -1 ) to 69.65 kg N ha -1 yr -1 (50 kg N ha -1 ). N fertilizer rates [kg N ha-1] 50 150 250 350 Nitrate leaching [kg N ha-1 yr-1] 340 360 380 400 420 440 460 Row Interrow Fig. 8 Comparison of simulated annual nitrate leaching between rows and interrows with four different rates of N fertilizer Table 7 shows the annual N 2 O emissions and nitrate leaching from 50, 150, 250 and 350 kg N ha -1 treatments by the model. The total N 2 O emissions were high in rows with 350 kg N ha - 1 treatment (3.472 kg N yr -1 ) and in interrows with 350 kg N ha -1 treatment (3.155 kg N yr -1 ). The high nitrate leaching rates were shown both in rows and interrows with 50 kg N ha -1 treatments. About 0.94% of applied N fertilizer was lost by N 2 O emissions and more than a half of applied N fertilizer was lost by nitrate leaching in rows. As compared with rows and interrows, the model predicted more N lost by nitrate leaching in interrows (72.2%) than in rows (62.5%). Considering both the ratio of total N 2 O emissions and nitrate leaching to the total biomass, 250 kg N ha -1 was recommended to apply for the radish cultivation. 126 McCraw D and Motes J E 1991 Use of plastic mulch nad row covers in vegetable production. Oklahoma Cooperative Extension Service HLA-6034, Oklahoma State University, 1-7. Mosier A R and Freney J R 2002 NATURAL RESOURCE SYSTEM CHALLENGE: CLIMATE CHANGE, HUMAN SYSTEMS, AND POLICY-Vol. II - Nitrous Oxide Emission Reduction and Agriculture (Antoaneta Yotova ed) Encyclopedia of Life Support Systems (EOLSS). Nishimura S, Komada M, Takebe M, Yonemura S and Kato N 2012 Nitrous oxide evolved from soil covered with plastic mulch film in horticultural field. Biology and Fertility of Soils, 1-9. Park W, Jeong B, Song Y, Jeon H, Jeong K and Lee C 2006 Standard rates of fertilizer application for each crop Rural Development Administration, 87-89. 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Smith W N, Grant B B, Desjardins R L, Worth D, Li C, Boles S H and Huffman E C 2010 A tool to link agricultural activity data with the DNDC model to estimate GHG emission factors in Canada. Agriculture, Ecosystems and Environment 136, 301-309. Sommer S G, Schjoerring J K and Denmead O T 2004 Ammonia Emission from Mineral Fertilizers and Fertilized Crops. In Advances in Agronomy. pp 557-622. Academic Press. Vázquez N, Pardo A, Suso M L and Quemada M 2006 Drainage and nitrate leaching under processing tomato growth with drip irrigation and plastic mulching. Agriculture, Ecosystems & Environment 112, 313-323. Wan Y and El-Swaify S A 1999 Runoff and soil erosion as affected by plastic mulch in a Hawaiian pineapple field. Soil and Tillage Research 52, 29-35. Wang Y, Xie Z, Malhi S S, Vera C L, Zhang Y and Wang J 2009 Effects of rainfall harvesting and mulching technologies on water use efficiency and crop yield in the semi-arid Loess Plateau, China. 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Agriculture, Ecosystems & Environment 158, 164-171. 128 129 Chapter 4 N 2 O and CH 4 emissions from rice paddies as affected by water management 130 131 A record of N 2 O and CH 4 emissions and underlying soil processes of Korean rice paddies as affected by different water management practices Sina Berger 1 , Inyoung Jang 2 , Juyoung Seo 2 , Hojeong Kang 2 , Gerhard Gebauer 1 1 BayCEER - Laboratory of Isotope Biogeochemistry, University of Bayreuth, 95440 Bayreuth, Germany; 2 School of Civil and Environmental Engineering, Yonsei University, Seoul, Republic of Korea; Corresponding author: Gerhard Gebauer, BayCEER - Laboratory of Isotope Biogeochemistry, University of Bayreuth, 95440 Bayreuth, Germany, email: [email protected], phone: +49 (0)921/55-2060, fax: 49 (0)921/55-2564 Submitted to Biogeochemistry on 19 September 2012 Abstract Rice is staple food of half of mankind and paddy soils account for the largest anthropogenic wetlands on earth. Ample of research is being done to find cultivation methods under which the integrative greenhouse effect caused by CH 4 and N 2 O emissions would be mitigated. Whereas most of the research focuses on quantifying such emissions, there is a lack of studies on the biogeochemistry of paddy soils. In order to deepen our mechanistic understanding of N 2 O and CH 4 fluxes in rice paddies, we also determined NO 3and N 2 O concentrations as well as N 2 O isotope abundances and presence of O 2 along soil profiles of paddies which underwent three different water managements during the rice growing season(s) in (2010 and) 2011 in Korea. Largest amounts of N 2 O (2 mmol m -2 ) and CH 4 (14.5 mol m -2 ) degassed from the continuously flooded paddy, while paddies with less flooding showed 30-60% less CH 4 emissions and very low to negative N 2 O balances. In accordance, the global warming potential GWP was lowest for the Intermittent Irrigation paddy and highest for the Traditional Irrigation paddy. The N 2 O emissions could the best be explained 132 (*P<0.05) with the δ 15 N values and N 2 O concentrations in 40-50 cm soil depth, implying that major N 2 O production/consumption occurs there. No significant effect of NO 3on N 2 O production has been found. Our study gives insight into the soil of a rice paddy and reveals areas along the soil profile where N 2 O is being produced. Thereby it contributes to our understanding of subsoil processes of paddy soils. Keywords Nitrous oxide, 15 N, NO 3- , traditional irrigation, intermittent irrigation, Korea 133 Introduction Nitrous oxide (N 2 O) is a significant long-living greenhouse gas and it currently contributes about 6% to the annual increase in radiative forcing (WMO 2006). Worldwide, sources of N 2 O are dominated by agriculture (Potter et al. 1996, Robertson and Grace 2004), with the amount of N fertilizer applied as one of the key drivers of the N 2 O emission (Shepered et al. 1991). Rice is the staple food of almost 50% of the earth’s population and 20% of the agriculturally managed soils are rice planting areas (Frolking et al. 2002). Whereas rice paddies are known to be among the most important sources of methane (CH 4 ) (IPCC 1992, Neue and Sass 1998; Yan et al. 2009), their N 2 O emissions are considered negligible, in particular under conditions of 4-6 months of continuous flooding (Cai et al. 1997; Smith and Patrick 1983; Zou et al. 2005a) because such strong anaerobic conditions lead to a further reduction of the intermediary denitrification product N 2 O to N 2 , so that no degassing of N 2 O can occur (Granli and Bøckman 1994), whereas that irrigation method has the great disadvantage of producing great amounts of CH 4 (IPCC 1992; Neue and Sass 1998; Sass et al. 1999; Yan et al. 2009). However, it is the scientists aim to find irrigation methods which would cause the least integrative greenhouse effect by mitigating CH 4 and N 2 O emissions as much as possible by ensuring enough rice yields (Chapagain and Yamaji 2010; Miyazato et al. 2010; Sato et al. 2011; Peng et al. 2011). Controlled irrigation practices which leave rice paddies under nonwater logged conditions 40-80% of the time, are subject of plenty of studies (Cai et al. 1997; Wu 1999; Mao 2002; Zou et al. 2007; Quin et al. 2010; Peng et al. 2011). This not only saves water but also mitigates CH 4 emissions, however at stronger N 2 O emissions due to changes in soil oxygen status, soil redox potential, moisture, temperature etc. (Smith and Patrick 1983; Cai et al. 2001; Zou et al. 2005b; Johnson-Beebout et al. 2009; Liu et al. 2010; Peng et al. 2011). Such controlled irrigation methods are intermittent irrigation, flooding-midseason drainage-frequent water logging with intermittent irrigation (FDF), and flooding-midseason drainage-reflooding-moist intermittent irrigation but without water logging (FDFM) (Mao 2002; Wu 1999; Zou et al. 2007). This is a monitoring study comparing not only N 2 O and CH 4 fluxes at the soil/atmosphere interface of three rice paddies in South Korea, which were under different water management practices, but it even more focuses on a couple of biogeochemical soil factors which are known to affect N 2 O emissions, such as NO 3and N 2 O concentration, δ 15 N-N 2 O values, presence or absence of oxygen along soil profiles in addition to paddy water level and water temperature during the vegetation period of 2010 and 2011. The investigated water management practices were 1) ‘traditional irrigation’ (TI) with 5 months of flooding, 2) ‘flooding-midseason drainage-reflooding-moist intermittent irrigation without water logging’ 134 (FDFM) with only 2.5 months of continuous flooding before the drainage and 3) ‘intermittent irrigation’ (II) without continuous flooding. Our objectives were to test how the different water management practices would affect the biogeochemistry of the rice paddies with respect to N 2 O production and emission. According to the literature, we hypothesized that the most N 2 O would degas from the paddies experiencing less flooding and most CH 4 would be emitted from the continuously flooded paddy; but furthermore we were expecting to see great changes in N 2 O, NO 3and O 2 concentrations, as well as in δ 15 N-N 2 O values along the soil profiles in dependence of the water levels especially in the FDFM and II fields. 2. Methods 2.1 Study region and experimental sites All the field work was conducted in the mountainous Haean Basin between longitude 128° 5’ to 128° 11’ E and latitude 38° 13’ to 38° 20’ N in Yanggu County, Gangwon Province in the north-eastern part of South Korea. The average annual air temperature at the valley sites is 10.5°C and the average precipitation is approximately 1500mm, with 70% falling during the summer monsoon from June to August. Rice paddies cover over 507 ha which is 25% of the cropland area in the Haean Basin, which makes rice the most important crop of the region. Three rice paddies have been selected as research sites (see Table 1). The first one was undergoing flooding-midseason drainage-reflooding-moist intermittent irrigation without water logging (FDFM) with only 2.5 months of continuous flooding; the second one was exposed to intermittent irrigation (II) and the third one experienced traditional irrigation (TI) of 5 months of continuous flooding. Measurements have been taken from 11 May 2010 until 23 October, 2010 at the FDFM paddy and from 6 May until 15 September 2011 at all three paddies. The three paddies had a substrate of sandy-loam texture and the soils were characterized as terric cambisols or even as anthrosols (IUSS Working Group WRB 2007) because of an artificial long-term addition of sandy soil on the top of the fields. The paddies were treated in the following way: the first irrigation occurred between end of April and the first days of May. Between 8 and 10 May the paddies were fertilized (see Table 1) on their moist soils. Between 10 and 15 May the continuous flooding as well as the irrigation of the paddies started. The transplanting of the rice seedlings took place between 25 and 30 May. Herbicides and pesticides were spread by end of June. The harvest was between middle and end of October. The owners of the three paddies followed that traditional procedure in 2011 as well as the farmer of the FDFM paddy did in 2010. 135 In 2010 the measurements were taken at the edge of the FDFM rice paddy, and potential edge effects could not be excluded. To avoid those edge effects, the 2011 measurements were taken within the paddies 5-8 m away from the paddies’ edges. Walkways were used to access the experimental sites in 2011. These walkways aimed to minimize disturbances of the sites from stepping onto the soil leading to soil compaction and pushing of gas bubbles from the sub-soil. Furthermore, the paddies differed in their soil horizons which were investigated until 60 cm soil depth. The FDFM paddy had an Apg-horizon from 0 until 22 cm soil depth, followed by an Arp horizon. The II paddy had two different Apg-horizons (the first one reached from 0 to 11cm and from 11 to 34cm), followed by two different Arp-horizons. The II paddy’s sequence of horizons was quite different from the other two paddies’ sequence of horizons: the thin (015cm) Apg-horizon was followed by a thin (15-33cm) Arp-layer which was followed by two different B-horizons with Bg1 reaching from 33 to 55cm and Bg2 starting at 55cm, reaching deeper. Ap-horizons may be oxic or anoxic, Arp-horizons are characterized by the absence of free oxygen since they represent the puddled but compacted layer. B horizons may either be aerobic or anaerobic (IUSS working group WRB, 2006). Fig. 2: N 2 O flux and cumulative N 2 O emissions as a function of time from 11 May to 23 October 2010 and 5 May to 14 September 2011 at the experimental sites. Error bars in N 2 O fluxand cumulative N 142 O emissions as a function of time from 11 May to 23 October 2010 and 5 May to 14 September 2011 at the experimental sites. and cumulative N 2 O emissiongraphs represent the standard error of the mean (n=8). O emissions as a function of time from 11 May to 23 October 2010 and 5 May to 14 September 2011 at the experimental sites. graphs represent the standard error of the mean (n=8). 3.3 CH 4 fluxes and cumulative CH While the CH 4 fluxes of the II paddy were comparably low, there were quite huge amounts of CH 4 degassing from the TI and FDFM paddies (see Fig. 3). There was a decline to zero fluxes at the II and FDFM paddies on 12 July. During the measurement period, the paddy with the highest CH one with TI (14.5 mol m -2 ; equals 2328 kg FDFM paddy (9.6 mol m -2 ; equals 1541 kg CH emission balance was found for the II paddy with 4.4 mol m 5.89 kg CH 4 ha -1 d -1 ). Fig 3: CH 4 flux and cumulative CH the experimental sites. Error bars in N 3.4. Presence of O 2 along the paddies’ soil profiles The O 2 profiles of the rice paddies look very different (Fig. 4). The FDFM paddy did not seem to contain any O 2 from the starting point of the O abruptly huge amounts of O 2 The II and TI paddies had a more complex O had high to low amounts of O measurement period and almost no O O 2 in the topsoil during half of the O and 70 cm depth during more than half of the time of the investigation period. 143 fluxes and cumulative CH 4 emissions fluxes of the II paddy were comparably low, there were quite huge amounts of degassing from the TI and FDFM paddies (see Fig. 3). There was a decline to zero fluxes at the II and FDFM paddies on 12 July. During the measurement period, the paddy with the highest CH 4 emission balance was the ; equals 2328 kg CH 4 ha -1 or 19.4 kg CH 4 ha -1 d ; equals 1541 kg CH 4 ha -1 or 12.8 kg CH 4 ha -1 d emission balance was found for the II paddy with 4.4 mol m -2 (equals 706.42 kg CH flux and cumulative CH 4 emissions as a function of time from 29 May to 28 August 2011 at Error bars in N 2 O fluxand cumulative N 2 O emissiongraphs represent the standard error of the mean (n=5). along the paddies’ soil profiles profiles of the rice paddies look very different (Fig. 4). The FDFM paddy did not seem from the starting point of the O 2 investigation until mid of August when occurred from the topsoil until deep down in the paddy’s soil. The II and TI paddies had a more complex O 2 situation than the one with FDFM. Whereas II had high to low amounts of O 2 from the topsoil down to 30 cm depth during the whole and almost no O 2 occurred in the deeper soil layers, TI did not have any in the topsoil during half of the O 2 investigation period but it did have some O and 70 cm depth during more than half of the time of the investigation period. fluxes of the II paddy were comparably low, there were quite huge amounts of degassing from the TI and FDFM paddies (see Fig. 3). There was a decline to zero - emission balance was the d -1 ), followed by the d -1 ). The lowest CH 4 (equals 706.42 kg CH 4 ha -1 or emissions as a function of time from 29 May to 28 August 2011 at graphs represent the profiles of the rice paddies look very different (Fig. 4). The FDFM paddy did not seem investigation until mid of August when occurred from the topsoil until deep down in the paddy’s soil. situation than the one with FDFM. Whereas II from the topsoil down to 30 cm depth during the whole occurred in the deeper soil layers, TI did not have any investigation period but it did have some O 2 between 40 and 70 cm depth during more than half of the time of the investigation period. Fig. 4 : Presence or absence of oxygen (O 3.5 NO 3concentrations of the paddies’ soil profiles The NO 3pattern of the three paddies is similar (Fig. 5). All of them show high concentrations of NO 3- (about 40 mg/l; in 50 cm depth at the TI paddy there were 100mg/l) in June. From 10 June until 1 August, the NO 3 decreased to minimum values of 5 mg/l, then increased up to 55 mg/l and decreased again. The TI paddy showed slightly increased NO strong decrease down to 3 mg/l. There were no statistical differ concentrations of the three paddies at each measurement day. Fig. 5: NO 3concentration as a function of soil depth and time from 10 June to 11 September 2011 at the experimental sites. [For deviations from mean values see 144 : Presence or absence of oxygen (O 2 ) as a function of soil depth and time from 10 June to 13 September at the experimental sites. concentrations of the paddies’ soil profiles pattern of the three paddies is similar (Fig. 5). All of them show high concentrations (about 40 mg/l; in 50 cm depth at the TI paddy there were 100mg/l) in June. From 10 3 - concentrations along all depths of the FDFM an decreased to minimum values of 5 mg/l, then increased up to 55 mg/l and decreased again. The TI paddy showed slightly increased NO 3concentrations (35mg/l) on 17 July but then a strong decrease down to 3 mg/l. There were no statistical differ ences (P > 0.05) of the NO concentrations of the three paddies at each measurement day. concentration as a function of soil depth and time from 10 June to 11 September 2011 at the experimental sites. [For deviations from mean values see standard errors in Table 2 in the Appendix.] ) as a function of soil depth and time from 10 June to 13 pattern of the three paddies is similar (Fig. 5). All of them show high concentrations (about 40 mg/l; in 50 cm depth at the TI paddy there were 100mg/l) in June. From 10 concentrations along all depths of the FDFM an d II paddies decreased to minimum values of 5 mg/l, then increased up to 55 mg/l and decreased again. concentrations (35mg/l) on 17 July but then a ences (P > 0.05) of the NO 3concentration as a function of soil depth and time from 10 June to 11 September 2011 at standard errors in Table 2 in the 145 3.6 N 2 O concentrations and δ 15 N-N 2 O values along the paddies’ soil profiles In general, the N 2 O concentrations along the paddies’ soil profiles are quite low, except for beginning of June 2011 at the FDFM and II paddy, where maximum values of 6700 and 9900 ppb were reached (Fig. 6). At the other measurement days N 2 O concentrations in the soils of these two paddies were similar to the N 2 O concentration of ambient air (about 320 ppb). For the paddy undergoing TI, no huge changes in soil N 2 O concentrations could be observed; the lowest concentrations were around 460 ppb and the highest ones around 2085 ppb. The 2010 and 2011 N 2 O concentration profiles of the FDFM paddy differ a lot. Whereas there were huge fluctuations in 2011, they were almost stable in 2010, ranging from 425 to 1420 ppb. In addition to the statistical differences between the dates at each site, which are given in Fig. 6, comparisons of all three paddies’ N 2 O concentrations in June, July and August were done, too. They revealed that there were no statistical differences (P > 0.05) in June and July, but there were such differences in August (*P = 0.018, F = 5.706) with the paddy undergoing FDFM on the one hand having significantly higher N 2 O concentrations than the II paddy, but on the other hand not different from the TI paddy. The δ 15 N-N 2 O curves at all sites in 2011 as well as in 2010 varied statistically significant (Fig. 6). While the TI and II paddy’s curves started with δ 15 N values down to -11.85‰, for the following two dates the II paddy’s values increased up to -0.51‰ and the TI paddy’s values even turned into positive ones (3.8‰). For the FDFM paddy in 2011 the opposite pattern could be observed: the June initial values were positive or less negative ones (ranging from 1.51‰ down to -5.68‰), remained stable in July, but declined in August down to -11.84‰. A statistical comparison of the 2010 and 2011 δ 15 N-N 2 O values of the FDFM paddy showed that there were no statistical differences between June and July, but the differences were very significant (P = <0.001, t = -5.405) for the last measurement date, which was 24 August in 2011 and 23 October in 2010. A direct comparison of the δ 15 N values of each measurement day among the three paddies revealed that there were very significant differences in June (**P = 0.004, F = 9.109; FDFM different from II but not from TI), significant differences in July (P = 0.012; II different from TI but not different from FDFM) and highly significant differences in August (P = <0.001, F = 31.146; differences between all of the three paddies). Fig. 6: N 2 O concentrations and δ 15 N-N 2 O values on 6 June, 1 August and 23 October 2010 at the FDFM paddy and 14 June, 18 July and 24 August 2011 at the three experimental sites. Letters indicate statistical differences. 146 O values on 6 June, 1 August and 23 October 2010 at the FDFM paddy and 14 June, 18 July and 24 August 2011 at the Letters indicate statistical differences. [For deviations from mean values see standard errors in Table 3 in the App O values on 6 June, 1 August and 23 October 2010 at the FDFM paddy and 14 June, 18 July and 24 August 2011 at the [For deviations from mean values see standard errors in Table 3 in the App endix.] 147 3.7 Correlations of N 2 O fluxes at the soil/atmosphere interface and water level, CH 4 fluxes, the soils’ N 2 O concentrations and δ 15 N-N 2 O values For each experimental site a correlation between N 2 Oand CH 4 fluxes could be found, as well as there were correlations between N 2 O fluxes and water level at the individual sites. Over all sites correlations revealed relations between N 2 O fluxes and N 2 O concentrations in different soil depths as well as δ 15 N-N 2 O values at 50 cm depth (see Table 4). Table 4: Pand R 2 values of correlations of N 2 O fluxes with those parameters (CH 4 fluxes, water level, N 2 O concentration in 10, 20, 40 and 50 cm soil depth, δ 15 N value at 50 cm depth) which resulted in a statistical trend (P =/< 0.1) or were significant (*P < 0.05) or very significant (**P < 0.01). Correlations at individual experimental sites - N 2 O flux vs. : CH 4 flux (FDFM) CH 4 flux (II) CH 4 flux (TI) Water level (II) Water level (TI) P = 0.1089; R 2 = 0.39 *P = 0.0285; R 2 = 0.39 **P = 0.0092; R 2 = 0.81 P = 0.0839; R2 = 0.10 P = 0.0760; R2 = 0.10 negative correlation negative correlation positive correlation negative correlation negative correlation Correlations over all experimental sites - N 2 O flux vs. : N 2 O conc. 10 N 2 O conc. 20 N 2 O conc. 40 N 2 O conc. 50 δ 15 N 50 P = 0.1011; R 2 = 0.25 P = 0.1061; R 2 = 0.24 *P = 0.0450; R 2 = 0.30 *P = 0.0386; R 2 = 0.30 **P = 0.0079; R 2 = 0.47 positive correlation positive correlation positive correlation negative correlation negative correlation 4. Discussion 4.1 Evaluation of the N 2 O and CH 4 fluxes and emissions with respect to water management The first of the two initial hypotheses was that the most N 2 O would degas from the paddies experiencing less flooding and the least N 2 O but the highest amount of CH 4 would be emitted from the continuously flooded TI paddy. This hypothesis could not be corroborated for N 2 O, where the opposite result was found, but it could be corroborated for CH 4 . The TI paddy emitted the most N 2 O (2 mmol m -2 , equals 880.2 g N 2 O ha -1 or 6.57 g N 2 O ha -1 d -1 ) as well as the highest amounts of CH 4 (14.5 mol m -2 ; equals 2328 kg CH 4 ha -1 or 19.4 kg CH 4 ha -1 d -1 ), whereas the II paddy consumed exactly that amount of N 2 O and emitted only 30% of the CH 4 emitted from the TI paddy, which still sums up to a considerable amount of 4.4 mol m -2 (equals 706.42 kg CH 4 ha -1 or 5.89 kg CH 4 ha -1 d -1 ) during the measurement period. The FDFM paddy showed 65% of the TI paddy’s CH 4 emissions; its N 2 O emissions in 2011 summed up to almost zero, but in 2010, when the N 2 O flux measurements continued until end of October, the FDFM paddy consumed 1.47 mmol N 2 O m -2 , which corresponds to 72% of the amount that the II paddy consumed in 2011. 148 In general, previous studies have shown that N 2 O fluxes in rice paddies are strongly affected by source and rate of fertilizer applied (Clayton et al. 1997; Cai et al. 1997; Bouwman et al. 2002; Zou et al. 2005a; Ma et al. 2007) as well as by the irrigation method (Smith and Patrick 1983; Cai et al. 2001; Zou et al. 2005b; Johnson-Beebout et al. 2009; Liu et al. 2010; Peng et al. 2011), whereupon it’s the cumulative N 2 O emission that can be correlated with irrigation method (Zou et al. 2007), but it is not the N 2 O flux which is related to the water level, as our present result confirms, regardless of observations of N 2 O emission peaks during midseason aeration, which – when incorporated into statistics – do not bring significant results (Zou et al. 2007, Li et al. 2011, Yao et al. 2012). Traditionally irrigated paddies (which experience continuous flooding) have been found to show the least N 2 O emissions, which were consistent with the N 2 O emissions we measured in our experimental sites (Zou et al, 2007; Peng et al. 2011). For FDFM paddies (which are flooded for a shorter time of 2 to 3 months in the beginning of the rice growing period, experience midseason-drainage and stand moist but not flooded until the harvest) cumulative N 2 O emissions range between 1.21 and 6.17 kg N 2 O-N ha -1 (Zheng et al. 2000; Zheng et al. 2004; Zou et al. 2005a,b; Zou et al. 2007; Peng et al. 2011), which in any case exceeds the emissions we have measured. Other water management practices lead to cumulative N 2 O emissions of 0.17 to 2.5 kg N 2 O-N ha -1 (Cai et al. 1997; Cao et al. 1999; Zou et al. 2005; Peng et al. 2011). The N 2 O balances found in our study are considerably low, especially the ones of the II and FDFM paddies, which had been expected to be high, are lower than the TI paddy’s cumulative emission and even negative. N 2 O consumption in rice paddies was observed recently, too, (Ferré et al. 2012) occurred under flooded and water-logged conditions and might be explained by a more and more declining availability of NO 3- , which had served as electron acceptor before; and when nitrate became limited, microbes metabolized NO 2- , NO and N 2 O instead, resulting in the production of N 2 , which degassed from the soil into the atmosphere very quickly (Kögel-Knabner et al. 2010). This denitrification process, leading to decreased N 2 O emissions but increased N 2 O consumption and N 2 emission, would only occur under anoxic conditions (Khalil and Bags 2005; Sey et al. 2008; Kögel-Knabner et al. 2010) which we thought our II experimental site did not have to face; so we can only speculate that, either our rice paddy’s soil did remain wetter than we thought it was, or another process – metabolizing NH 4+ to NO 3and further to N 2 under aerobic conditions – could have taken place: nitrifier denitrification (Wrage et al. 2001; Kool et al. 2011). In fact, it is known that in rice paddy soils a tight coupling between nitrification and denitrification processes exists (Arth et al. 1998). Since it is also known that the application of NH 4+ fertilizer stimulates ammonium oxidizing bacteria (Cai et al. 1997; KögelKnabner et al. 2010), we assume that in our II site favorable conditions for NH 4+ oxidation and further processing under aerobic conditions to N 2 could be found, which may also have 149 lead to the use of N 2 O and its being processed to N 2 . The TI paddy experienced waterlogging during the whole rice growing season, whereas FDFM was flooded continuously for 2.5 months, so they underwent the procedure which typically leads to a thin layer of ammonium oxidizing bacteria in the upper few cm of the paddies’ soils and underneath, where it is supposed to be anoxic, there would be the denitrifying bacteria (FAO 2006; KögelKnabner et al. 2010) all together causing the processing of NH 4+ via NO 3to N 2 . This would have caused very low N 2 O fluxes, which is indeed what we have measured, except for two unexpected N 2 O emission peaks which boosted the TI paddy’s N 2 O balance. But fluctuations of the amount of N 2 O emitted from paddies with identical water management and fertilizer application have been observed before (Zheng et al. 2004) are not to be over-interpreted. The quite high CH 4 emissions at our sites may be explained by the NH 4+ fertilizer, too, because the presence of a high NH 4+ concentration also leads to a decreased CH 4 oxidation, which may cause higher CH 4 concentrations in the soil and finally leads to high CH 4 fluxes (Cai et al. 1997). On the other hand one must be aware that we measured CH 4 fluxes rather infrequent and seldom in contrast to our N 2 O emission measurements, so we may have missed CH 4 peaks as well as days with low CH 4 fluxes, which makes the CH 4 flux results less robust. Thus, both seems possible, increasing N 2 O emissions with increasing CH 4 emissions (as we found for the TI paddy) according to the preceding, to NH 4+ referring explanation, as well as the common opinion and our introductory hypothesis that contrary N 2 O and CH 4 fluxes would occur (as found for FDFM and II), meaning larger emissions of one gas would cause less emissions of the other one, as favorable conditions for the production of the two gases, are assumed to be mutually exclusive (Granli and Bøckman 1994, Klüber and Conrad 1998). When considering the combined Global Warming Potential (GWP) of CH 4 and N 2 O, calculated in units of CO 2 equivalents over a 100-year time horizon (based on a radiative forcing potential relative to CO 2 of 298 for N 2 O and 25 for CH 4 (IPCC 2001)), it turns out that the traditional irrigation lead to the highest GWP of 363.1 mol CO 2 eq m -2 , followed by FDFM, which lead to degassing of 240 mol CO 2 eq m -2 . Intermittent Irrigation turned out to have a GWP of 109 mol CO 2 eq m -2 . Thus, we would conclude that intermittent irrigation caused the least greenhouse gas emissions. 4.2 Evaluation of the N 2 O fluxes at the soil/atmosphere interface with respect to the soil parameters: presence or absence of O 2 , NO 3and N 2 O concentration and δ 15 N-N 2 O values We hypothesized great changes in N 2 O, NO 3and O 2 concentrations, as well as in δ 15 N-N 2 O values over time and along the soil profiles especially in the FDFM and II paddy, whereas we had expected the TI paddy to have rather stable soil conditions. This hypothesis could partly 150 be corroborated. In terms of O 2 presence the FDFM and II paddy behaved exactly as expected, so the FDFM paddy soil was anaerobic until mid August and afterwards experienced aerobic conditions, and II was infiltrated with O 2 from the top downwards during the whole measurement period. TI was riddled with O 2 in its deeper soil layers in particular, which at first sight appears odd, regarding that TI is the paddy with the smallest Ap-horizon (the horizon which contains oxygen (Frenzel et al. 1992; FAO 2006; Yu et al. 2007; KögelKnabner et al. 2010)), but at second sight one notices the paddy’s B-horizon which may have oxic conditions, too (Kögel-Knabner et al. 2010). We speculate that the TI paddy’s deeper soil layers contain O 2 because they may have access to ground water providing them with O 2 . In contrast to that, the other two paddies’ oxygen-containing horizons reach down to 20 and 35 cm, respectively, and get filled up with O 2 every time when the water level declines. Regarding the NO 3concentrations along the soil profiles we got anything but the expected result. Instead of great differences and concentration changes among study sites we found no statistical differences between NO 3concentrations of the experimental sites. Furthermore, no relations detected between N 2 O fluxes and NO 3concentrations of different soil layers makes us conclude that NO 3concentrations in the soils only play a minor role for the N 2 O production and exchange at the paddies’ soil/atmosphere interfaces. To our knowledge there are no other lysimeter studies investigating NO 3leaching from rice paddies, but there are such studies on DOC-leaching revealing that there are extremely large fluxes from topto subsoil (Michalzik et al. 2001; Katoh et al. 2004; Maie et al. 2004). In general, one assumes that the highly mobile NO 3can leach easily to deeper soil layers or is metabolized by microbes under anoxic conditions, quickly (Kögel-Knabner et al. 2010), which drastically reduces N fertilizer use efficiency in rice paddies in comparison to other agricultural systems (DeDatta 1981; Cao et al. 1984a,b; Roy and Misra 2003). Thus, we conclude that the water management of the three paddies had no effect on their NO 3concentrations throughout the measurement period. In the short term there might have been significant differences between the paddies’ NO 3concentrations, which we failed to detect, because NO 3is highly mobile and it might have leached or metabolized by microbes too quickly. With regard to N 2 O concentrations and δ 15 N-N 2 O values along soil profiles, introductorily it needs to be said that that high N 2 O concentrations together with depleted δ 15 N-N 2 O values are interpreted as N 2 O production, whereas low N 2 O concentrations and positive δ 15 N-N 2 O values are regarded as N 2 O consumption (Goldberg et al. 2010). The FDFM paddy showed high N 2 O concentrations in June and by end of August 2011, at the same time when its δ 15 NN 2 O values (in June in the deeper soil layers) were fairly negative (down to -11.84‰), which is regarded indicative of N 2 O production and further reduction to N 2 gas. The II paddy possessed high amounts of N 2 O (9977ppb) as well as fairly negative δ 15 N-N 2 O values (- 151 11.85‰) in June, which we also interpret as N 2 O production and subsequent reduction to N 2 gas whereas the rest of the measurement period showed δ 15 N-N 2 O values around -3‰ and N 2 O concentrations between 957 and 2106 ppb, indicating less N 2 O production than in June. The TI paddy’s soil was depleted in 15 N-N 2 O in June (-9.94‰), but comparably enriched (δ 15 N-N 2 O values up to 3.41‰), at comparably low N 2 O concentrations (567-3904ppb) throughout the measurement period, suggesting that after a short N 2 O production period in June, hardly any N 2 O had been produced anymore during the following measurement days. These profiles explain the N 2 O exchange we have measured at the soil/atmosphere interface to a good extend; so we identified the deeper soil layers’ (40-50 cm soil depth) N 2 O concentrations and 14 N/ 15 N ratios to have a significant effect on the N 2 O fluxes. Unpublished data on gene abundances of denitrifying and nitrifying bacteria at our FDFM paddy study site by Seo and Kang (2012) revealed a higher nirK / nosZ ratio at the subsoil (between 25 and 65 cm soil depth), suggesting that N 2 O might be produced in the subsoil, which supports our findings. Acknowledgements This work is part of the research group “TERRECO - Complex TERRain and ECOlogical Heterogeneity” and financially supported by the German Research Foundation (DFG). We truly thank Heera Lee, Youngsun Kim and Bora Lee for the great language help. We thank Andreas Kolb, who constructed the portable vacuum pump for us and Isolde Baumann for skilful assistance by measuring N 2 O isotope abundances. We furthermore acknowledge Sebastian Arnholds help with digging and interpreting soil profiles and we are very thankful to John Tenhunen, who professionally coordinated the TERRECO fieldwork.