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introduction in a small soil sample without plant residues and then the final k could be calculated using a proportion between the soil weight and the weight of the plant residues in the monolith (Larionova et al., 2006): 160 k= (R2sms+R2rsmrs)/(R1sms+R1rsmrs) where, R1s and R2s are the respiration response of the soil prior and after the glucose addition respectively; R1rs and R2rs are the respiration response as a result of decomposition of plant residues prior and after the glucose addition and ms and mrs are the masses of soil and plant residues in the monolith. This however, couldn’ t be done in the field and implies a k determination in the laboratory. Another shortcoming of the method is a possible boosting of root respiration after the addition of glucose in water solution, which could be especially true under insufficient soil moisture conditions. This could result in further overestimation of microbial component of soil respiration. The glucose solution was always added in the way not to exceed the 60% of the soil water capacity, we were also measuring the effect of only water on total soil respiration rates. As could be seen from the figure 4, the main effect of water occurred during the first hour after the start of the treatment with the further stabilization of the respiration rate at the initial level. The effect was longer and more pronounced in July, under the limited natural soil content (data not shown), however the soil respiration have decreased and stabilized after 5 hours of experiment, when the glucose treatment on the contrary just reached its peak. 6.4.3. Regression analyses technique Although regression technique was nominated in the recent review of partitioning methods (Kuzyakov, 2006) as the most universal one due to its low cost, applicability to a wide range of ecosystems and the absence of the soil disturbance, its application in this study on a grassland community showed contrasting results. The main problem we faced with regression analyses was the absence of significant correlation between the root biomass and soil CO2 efflux. The correlation was weak for the biomass sampling day and the measurement days adjacent to the day of the biomass sampling, no correlation was observed further. Despite this fact, the method managed to detect some particular changes in the contribution of root and microbial respiration, which were observed also by the mesh exclusion technique (ex.: Fig. 9, peaks in root respiration in July). This could indicate that the augment of the measurement points may improve the correlation strength. The particularity of the grassland ecosystems is that the most of the belowground biomass is represented by the fine roots. Larionova et al.(2006) showed that fine root biomass is characterized by a greater variation in root specific activity, ranging from 0.2-1.2 mg C-CO2/g root while the
activity of the coarse roots, which account for the greatest part of belowground biomass in forest ecosystems is more stable and vary from 0.01 to 0.05 mg C-CO2/g root. Other studies have also demonstrated that specific root respiration and its Q10 decrease with increasing root diameter (Ryan et al. 1996; Pregitzer et al. 1998; Bahn et al. 2006), decreasing specific root length (Tjoelker et al. 2005) or the ratio of long to fine roots (Kutsch et al. 2001). Such effects may be partly related to a decrease in N concentration (Ryan et al. 1996; Pregitzer et al. 1998; Bahn et al. 2006) and an increase in tissue and plant age (Palta and Nobel 1989; George et al. 2003; Volder et al. 2004). In the course of the growing season belowground biomass at Amplero experienced significant changes (Fig.10). In spring, during the peak of the biomass growth, it has almost doubled in only one month. This means that one biomass sampling is not enough to account for the seasonal variation of different soil respiration sources; multiple sampling is needed for the reliable results. Regression technique is however an indirect method of respiration components estimation, and allow only an approximate quantification of root contribution to soil respiration. The theoretical basis of the regression technique is that the autotrophic component of soil respiration is proportional to the belowground biomass and in the absence of roots (i.e. x=0) the soil respiration is represented only by microbial component. However, recent studies have shown that root respiration is considered to be fuelled by non structural C, rather than structural C which is fixed in the cell wall of roots. Among the non structural C sources are starch, organic acids and soluble sugars as fructose, glucose and sucrose. Starch is formed as a transitory reserve when the supply of soluble sugars exceeds the actual demand within the plant (Hansen and Beck, 1994). Soluble sugars and organic acids are important substrates for root respiration. For example respiratory utilization of recently assimilated photosynthates such as sucrose in roots provides the essential energy for nutrient uptake, growth and maintenance as well as symbiotic processes and defences (Veen et al., 1980; Farrar, 1985; Bouma et al., 1996; George et al., 2003; Martinez et al., 2002; Xu et al., 2008). Moreover in has been shown that exogenously added sugars rapidly increase respiration rates of roots and shoots (Saglio et al., 1980; Azcon-Bieto et al., 1883; Journet et al., 1986). The above findings suggest that the non structural C content in roots could provide a better estimate of root respiration than bulk root biomass alone, since structural C fixed in the cell walls comprising the major root C pool cannot be utilized by roots and is only slowed utilized by rhizospheric microbes (Xu et al. 2008). Another limitation of the method, is that it doesn’ t consider a spatial variation of microbial respiration, whereas in some ecosystems (ex. the forest soils) due to the large special heterogeneity the microbial respiration may vary in space, limiting the reliability of the method. Rodeghiero and Cescatti (2006) for such cases proposed to introduce into the model additional 161
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explain diurnal and seasonal change
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sull’intensità del flusso respir
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8 3.2.4. Data analyses and definiti
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1. INTRODUCTION AND OVERVIEW 11
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times as much carbon as the terrest
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Fig. 3 Different soil C pools with
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1.3. Soil respiration sources Soil
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conditions, like soil type, plants
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For grasslands, the nature, frequen
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(a) (b) (c) Fig.7 Amplero (AQ, Ital
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magnitude of root respiration and o
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efficiency, indicating future posit
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approaches for studying of the cont
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Houghton R.A., 2003. Revised estima
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Vleeshouwers L.M., Verhagen A. 2002
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2.1. Introduction 36 Soil respirati
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38 In the year 2005 ten PVC collars
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2.2.4. Biochemical analyses Microbi
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42 Variation of soil respiration fr
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CO 2 efflux (mmol m -2 s -1 ) 44 CO
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46 All diurnal measurements of root
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48 Q10 R 2 p Rs 2.51 0.77 0.000 Ra
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the photosynthesis and its followin
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2.3.3. Inter-annual variation of so
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54 RS RHET 8 6 6 4 25 4 2 2 25 20 T
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2.4. Discussion 2.4.1. Partitioning
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measurements, the possibility to fi
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and Schlesinger, 1992; Moyano et al
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Janssens I.A., Pilegaard K., 2003.
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Xu X., Kuzyakov Y., Wanek W., Richt
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3.1. Introduction Soil respiration
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Our experiment was conducted in a m
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Lactic acid 13 C 13 CO2 Root-derive
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At the end of the process, the subl
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where, MAs - the mean age of the co
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13C of respiration ( o / oo ) 155 1
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Total Label Recovered (TLR, %) Ra R
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GPP (mmol m -2 s -1 ) 25 20 15 10 5
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frequent and possibly they missed t
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3.4.3. Destructive vs. Non-destruct
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Gavrichkova O., Kuzyakov Y., 2008.
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Chapter source: 90 4. THE EFFCT OF
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Bardgett et al. (1997) showed that
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94 Partitioning plots were installe
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Microbial C was calculated as a dif
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taken from 1 µg N-NO2 ml -1 soluti
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ates between clipped and unclipped
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Fig. 7 Total (Rs), root (Ra)- and m
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104 Rh, (CO 2 , mmolm -2 s -1 ) 7 6
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Fig. 11 Cumulative microbial respir
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108 After the calculation of synthe
Page 110 and 111: Fig. 16: a) Relationship between sy
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Page 114 and 115: mineral N available to plants and N
Page 116 and 117: Cambardella C.A., Elliott E.T., (19
Page 118 and 119: Rice C.W., Moorman T.B., Beare M, 1
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Page 122 and 123: 5.1. Introduction 122 Nitrogen (N)
Page 124 and 125: specie, c) an additional aim was to
Page 126 and 127: 5.2.4. Sampling and analyses 126 Na
Page 128 and 129: the differences between N and contr
Page 130 and 131: 130 specific root respiration (max
Page 132 and 133: Fig. 4 a) Intensity of 14 CO2 efflu
Page 134 and 135: Fig. 6 a) values above zero: root-d
Page 136 and 137: 136 Zea mays: A clear diurnal dynam
Page 138 and 139: plants. The growth stage could cont
Page 140 and 141: nitrates. At temperatures of 15 - 2
Page 142 and 143: experiment could be responsible for
Page 144 and 145: Horwath W.R., Pretziger K.S., Paul,
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Page 148 and 149: 148 6.1 . Introduction In studies o
Page 150 and 151: Regression analyses technique, whic
Page 152 and 153: 152 The respiratory response with g
Page 154 and 155: 154 100% 80% 60% 40% 20% 0% 85% 13%
Page 156 and 157: 6.3.2. 2008: Mesh- exclusion vs. re
Page 158 and 159: 6.4. Discussion 6.4.1. Mesh-exclusi
Page 162 and 163: component, expressing a total soil
Page 164 and 165: Kucera C.L., Kirkham D.R., 1971. So
Page 166: Acknowledgements This work was poss
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