Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.1 

# CHAPTER 11

N 2 

# O EMISSIONS FROM MANAGED SOILS, AND CO 2 

# EMISSIONS FROM LIME AND UREA APPLICATIONVolume 4: Agriculture, Forestry and Other Land Use

11.2 2006 IPCC Guidelines for National Greenhouse Gas Inventories 

### Authors Cecile De Klein (New Zealand), Ra fael S.A. Novoa (Chile), Stephen Ogle (USA), Keith A. Smith (UK), Philippe Rochette (Canada), and Thomas C. Wirth (USA) Brian G. McConkey (Canada), Arvin Mosier (USA), and Kristin Rypdal (Norway)



### Contributing Author s Margaret Walsh (USA) and Stephen A. Williams (USA)Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.3 

# Contents

11 N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

11.1 Introduc tion................................................................................................................... ......................11.5
11.2 N 2 O Emissions from Managed Soils................................................................................................. ..11.5

11.2.1 Direct N 2 O emissions ..................................................................................................................11 .6

11.2.1.1 Choice of method............................................................................................................... ....11.6
11.2.1.2 Choice of emission factors...................................................................................................11 .10
11.2.1.3 Choice of ac tivity data ........................................................................................................ .11.12
11.2.1.4 Uncertainty as sessment........................................................................................................1 1.16
11.2.2 Indirect N 2 O emissions..............................................................................................................11.19

11.2.2.1 Choice of method............................................................................................................... ..11.19
11.2.2.2 Choice of emission, volatilisation and leaching f actors.......................................................11.23
11.2.2.3 Choice of ac tivity data ........................................................................................................ .11.23
11.2.2.4 Uncertainty as sessment........................................................................................................1 1.24
11.2.3 Completeness, Time se ries, QA/QC..........................................................................................11.25
11.3 CO 2 Emissions from Liming ......................................................................................................... ....11.26

11.3.1 Choice of method ............................................................................................................... .......11.27
11.3.2 Choice of emissi on factors ..................................................................................................... ...11.29
11.3.3 Choice of ac tivity data........................................................................................................ .......11.29
11.3.4 Uncertainty assessment ......................................................................................................... ....11.29
11.3.5 Completeness, Time se ries, QA/QC..........................................................................................11.30
11.4 CO 2 Emissions from Urea Fertili zation............................................................................................. 11.32

11.4.1 Choice of method ............................................................................................................... .......11.32
11.4.2 Choice of emis sion f actor

...................................................................................................... ....11.34
11.4.3 Choice of ac tivity data........................................................................................................ .......11.34
11.4.4 Uncertainty assessment ......................................................................................................... ....11.34
11.4.5 Completeness, Time series consistency, QA/QC ......................................................................11.35
Annex 11A.1 References for crop residue data in Tabl e 11.2 .........................................................................11.37
References .................................................................................................................... ...............................11.53Volume 4: Agriculture, Forestry and Other Land Use

11.4 2006 IPCC Guidelines for National Greenhouse Gas Inventories 

# Equations

Equation 11.1 Direct N 2 O emissions from managed soils (Tier 1).............................................................11.7 Equation 11.2 Direct N 2 O emissions from manage d soils (Tier 2)...........................................................11.10 Equation 11.3 N from organic N additi ons applied to soils (Tier 1).........................................................11.1 2 Equation 11.4 N from animal manure applied to soils (Tier 1) ................................................................11. 13 Equation 11.5 N in urine and dung deposited by grazing animals on pasture, range and paddock (Tier 1)................................................................................................11.13 Equation 11.6 N from crop residues and fo rage/pasture renewal (Tier 1) ................................................11.14 Equation 11.7 Dry-weight correc tion of reported crop yields ..................................................................11 .15 Equation 11.7A Alternative approach to estimate F CR (using Table 11.2) ..................................................11.15 Equation 11.8 N mineralised in mineral soils as a result of loss of soil C through change in land use or management (Tiers 1 an d 2)........................................................................ 11.16 Equation 11.9 N 2 O from atmospheric deposition of N volatilis ed from managed soils (Tier 1) ..............11.21 Equation 11.10 N 2 O from N leaching/runoff from managed soils in regions where leaching/runoff occurs (T ier 1) ................................................................................................................ ...11.21 Equation 11.11 N 2 O from atmospheric deposition of N volatilis ed from managed soils (Tier 2) ..............11.22 Equation 11.12 Annual CO 2 emissions from lime applicatio n ...................................................................11.27 Equation 11.13 Annual CO 2 emissions from urea applicatio n....................................................................11.32



# Figures

Figure 11.1 Schematic diagram illustrating the sources and pathways of N that result in direct and indirect N 2 O emissions from soil s and waters. ...............................................11.8 Figure 11.2 Decision tree for direct N 2 O emissions from ma naged soils ...............................................11.9 Figure 11.3 Decision tree for indirect N 2 O emissions from managed soils ..........................................11.20 Figure 11.4 Decision tree for identification of appropriate tier to estimate CO 2 emissions from liming.................................................................................................................... ....11.28 Figure 11.5 Decision tree for identification of appropriate tier to estimate CO 2 emissions from urea fe rtilisa tion ........................................................................................................ 11.33



# Tables

Table 11.1 Default emission f actors to estimate direct N 2 O emissions from managed soils...............11.11
Table 11.2 Default factors for estimation of N added to soils from crop residues ..............................11.17 Table 11.3 Default emission, volatilisation and leaching factors for indirect soil N 2 O emissions ......11.24Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.5 

# 11 N 2 

# O EMISSIONS FROM MANAGED SOILS, AND CO 2 

# EMISSIONS FROM LIME AND UREA APPLICATION

11.1 INTRODUCTION

Chapter 11 provides a description of the generic methodologies to be adopted for the inventory of nitrous oxide (N 2 O) emissions from managed soils, including indirect N 2 O emissions from additions of N to land due to deposition and leaching, and em issions of carbon dioxide (CO 2 ) following additions of liming materials and urea-containing fertiliser. Managed soils 1 are all soils on land, including Forest Land, which is managed. For N 2 O, the basic three-tier approach is the same as used in the IPCC Good Practice Guidance for Land Use, Land-use Change and Forestry ( GPG-LULUCF ) for Grassland and Cropland, and in the IPCC Good Practice Guid ance and Uncertainty Management in National Greenhouse Gas Inventories ( GPG2000 ) for agricultural soils while relevant parts of the GPG-LULUCF methodology have been included for Forest Land. Because the methods are based on pools and fluxes that can occur in all the different land-us e categories and because in most cases, only national aggregate (i.e., non-land use specific) da ta are available, generic information on the methodologies, as applied at the national level is given here, including:

• a general framework for applying the methods, and appropriate equations for the calculations;

• an explanation of the processes governing N 2 O emissions from managed soils (direct and indirect) and CO 2 emissions from liming and urea fertilisation, and the associated uncertainties; and

• choice of methods, emission factors (including de fault values) and activity data, and volatilisation and leaching factors.

• If activity data are available for specific land-use categ ories, the equations provided can be implemented for specific land-use categories. The changes in the 2006 IPCC Guidelines , relative to 1996 IPCC Guidelines , include the following:

• provision of advice on estimating CO 2 emissions associated with th e use of urea as a fertilizer;

• full sectoral coverage of indirect N 2 O emissions;

• extensive literature review leading to revised emission factors for nitrous oxide from agricultural soils; and

• removal of biological nitrogen fixation as a direct source of N 2 O because of the lack of evidence of significant emissions arising from the fixation process. 

# 11.2 N 2 

# O EMISSIONS FROM MANAGED SOILS

This section presents the methods and equations for estimating total national anthropogenic emissions of N 2 O (direct and indirect) from managed soils. The generic equations presented here can also be used for estimating N 2 O within specific land-use categories or by condition-sp ecific variables (e.g., N additions to rice paddies) if the country can disaggregate the activity data to that level (i.e., N use activity within a specific land use). Nitrous oxide is produced naturally in soils through the pr ocesses of nitrification and de nitrification. Nitrification is the aerobic microbial oxidation of ammonium to nitr ate, and denitrification is the anaerobic microbial reduction of nitrate to nitrogen gas (N 2 ). Nitrous oxide is a gaseous intermediate in the reaction sequence of denitrification and a by-product of nitrification that l eaks from microbial cells into the soil and ultimately into the atmosphere. One of the main contro lling factors in this reaction is the av ailability of inorganic N in the soil. This methodology, therefore, estimates N 2 O emissions using human-induced net N additions to soils (e.g., synthetic or organic fertilisers, deposited manure, crop resi dues, sewage sludge), or of mineralisation of N in soil organic matter following drainage/management of organic soils, or cultivation/land-use change on mineral soils (e.g., Forest Land/Grassland/Se ttlements converted to Cropland).

1 Managed land is defined in Chapter 1, Section 1.1.Volume 4: Agriculture, Forestry and Other Land Use

11.6 2006 IPCC Guidelines for National Greenhouse Gas Inventories The emissions of N 2 O that result from anthropogenic N inputs or N mineralisation occur through both a direct pathway (i.e., directly from the soils to which the N is added/released), and through two indirect pathways: (i) following volatilisation of NH 3 and NO x from managed soils and from fossil fuel combustion and biomass burning, and the subsequent redeposition of these gases and their products NH 4 + and NO 3 - to soils and waters; and (ii) after leaching and runoff of N, mainly as NO 3 - , from managed soils. The principal pathways are illustrated in Figure 11.1.
Direct emissions of N 2 O from managed soils are estimated separately from indirect emissions, though using a common set of activity data. The Tier 1 methodologies do not take into account different land cover, soil type, climatic conditions or management practices (other than specified above). Neither do they take account of any lag time for direct emissions from crop residues N, and allocate these emissions to the year in which the residues are returned to the soil. These factor s are not considered for direct or (w here appropriate, indirect) emissions because limited data are available to pr ovide appropriate emission f actors. Countries that ha ve data to show that default factors are inappropriate for their country should utilise Tier 2 equations or Tier 3 approaches and include a full explanation for the values used.



# 11.2.1 Direct N 2 

# O emissions
In most soils, an increase in availabl e N enhances nitrification and denitrif ication rates which then increase the production of N 2 O. Increases in available N can occur through human-induced N additions or change of land-use and/or management practices that mineralise soil organic N. The following N sources are in cluded in the methodolog y for estimating direct N 2 O emissions from managed soils:

• synthetic N fertilisers (F SN );

• organic N applied as fertiliser (e.g., animal manur e, compost, sewage sludge, rendering waste) (F ON );

• urine and dung N deposited on pasture, range and paddock by grazing animals (F PRP );

• N in crop residues (above-ground and below-ground), including from N-fixing crops 2 and from forages during pasture renewal 3 (F CR );

• N mineralisation associated with loss of soil orga nic matter resulting from change of land use or management of mineral soils (F SOM ); and

• drainage/management of organic soils (i.e., Histosols) 4 (F OS ).


## 11.2.1.1 C HOICE OF METHOD
The decision tree in Figure 11.2 provides guidance on which tier method to use.
Tier 1 In its most basic form, direct N 2 O emissions from managed soils are estimated using Equation 11.1 as follows:

2 Biological nitrogen fixation has been removed as a direct source of N 2 O because of the lack of evidence of significant emissions arising from the fixation pro cess itself (Rochette and Janzen, 2005). These authors concluded that the N 2 O emissions induced by the growth of legume crops/forages ma y be estimated solely as a function of the above-ground and below-ground nitrogen inputs from crop/fora ge residue (the nitrogen residue from forages is only accounted for during pasture renewal). Conversely, th e release of N by mineralisation of soil organic matter as a result of change of land use or management is now included as an a dditional source. These are significant ad justments to the me thodology previously described in the 1996 IPCC Guidelines

. 3 The nitrogen residue from perennial forage crops is only acc ounted for during periodic pasture renewal, i.e. not necessarily

on an annual basis as is the case with annual crops.

4 Soils are organic if they satisfy the requirements 1 and 2, or 1 and 3 below (FAO, 1998): 1. Thickness of 10 cm or more. A horizon less than 20 cm thick must have 12 percent or more or ganic carbon when mixed to a depth of 20 cm; 2. If the soil is never saturated with water for more than a few days, and contains more than 20 percent (by weight) organic carbon (about 35 percent organic matter); 3. If the soil is subject to water saturation episode s and has either: (i) at least 12 perce nt (by weight) organic carbon (about 20 percent organic matter) if it has no clay; or (ii) at least 18 percent (by weight) organic carbon (about 30 percent organic matter) if it has 60 percent or more clay; or (iii) an inte rmediate, proportional amount of organic carbon for intermediate amounts of clay (FAO, 1998).Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.7 E QUATION 11.1 D IRECT N 2 O EMISSIONS FROM MANAGED SOILS (T IER 1)

PRP OS inputs N Direct N O N N O N N O N N O N

− +

− +

− = − 2 2 2 2 Where: 

## ( ) 

## [ ] ( ) 

# [ ] ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

• + + + + • + + +

= − FR FR SOM CR ON SN SOM CR ON SN inputs N EF F F F F EF F F F F N O N
1 1 2 

# ( ) ( )

( )( ) ( ) ⎥ ⎥ ⎥ ⎥

⎦ ⎤

⎢ ⎢ ⎢ ⎢

⎣ ⎡

• + • + • + • + •

= −

Trop F Trop F OS NP Temp F NP Temp F OS NR Temp F NR Temp F OS Trop CG Trop CG OS Temp CG Temp CG OS

OS EF F EF F EF F EF F EF F
N O N

, 2 , , , , 2 , , , , , 2 , , , , 2 , , , 2 , ,
2


# ( ) ( ) 

# [ ] SO PRP SO PRP CPP PRP CPP PRP PRP EF F EF F N O N

, 3 , , 3 , 2 • + • = −
Where:
N 2 O Direct –N = annual direct N 2 O–N emissions produced from managed soils, kg N 2 O–N yr -1

N 2 O–N N inputs = annual direct N 2 O–N emissions from N inputs to managed soils, kg N 2 O–N yr -1

N 2 O–N OS = annual direct N 2 O–N emissions from managed organic soils, kg N 2 O–N yr -1

N 2 O–N PRP = annual direct N 2 O–N emissions from urine and dung inputs to grazed soils, kg N 2 O–N yr -1

F SN = annual amount of synthetic fertiliser N applied to soils, kg N yr -1

F ON = annual amount of animal manure, compost, sewage sludge and other organic N additions applied to soils (Note: If including sewage sludge, cross-check with Waste Sector to ensure there is no double counting of N 2 O emissions from the N in sewage sludge), kg N yr -1

F CR = annual amount of N in crop residues (above-ground and below-ground), including N-fixing crops, and from forage/pasture renewal, returned to soils, kg N yr -1

F SOM = annual amount of N in mineral soils that is mineralised, in association with loss of soil C from soil organic matter as a result of changes to land use or management, kg N yr -1

F OS = annual area of managed/drained organic soils, ha (Note: the subscripts CG, F, Temp, Trop, NR and NP refer to Cropland and Grassland, Forest Land, Temperate, Tropical, Nutr ient Rich, and Nutrient Poor, respectively)
F PRP = annual amount of urine and dung N deposited by grazing animals on pasture, range and paddock, kg N yr -1 (Note: the subscripts CPP and SO refer to Cattle, Poultry and Pigs, and Sheep and Other animals, respectively)
EF 1 = emission factor for N 2 O emissions from N inputs, kg N 2 O–N (kg N input) -1 (Table 11.1)
EF 1FR is the emission factor for N 2 O emissions from N inputs to flooded rice, kg N 2 O–N (kg N input) -1 (Table 11.1) 5

EF 2 = emission factor for N 2 O emissions from drained/managed organic soils, kg N 2 O–N ha -1 yr -1 ; (Table 11.1) (Note: the subscripts CG, F, Temp, Trop, NR and NP refer to Cropland and Grassland, Forest Land, Temperate, Tropical, Nutrient Ri ch, and Nutrient Poor, respectively)
EF 3PRP = emission factor for N 2 O emissions from urine and dung N deposited on pasture, range and paddock by grazing animals, kg N 2 O–N (kg N input) -1 ; (Table 11.1) (Note: the subscripts CPP and SO refer to Cattle, Poultry and Pigs, and Sheep and Other animals, respectively)

5 When the total annual quantity of N applied to flooded paddy rice is known, this N input may be multiplied by a lower

default emission factor appl icable to this crop, EF 1FR (Table 11.1) (Akiyama et al. , 2005) or, where a country-specific emission factor has been determined, by that factor instead. Although there is some evidence that intermittent flooding (as described in Chapter 5.5) can increase N 2 O emissions, current scientific data indicate that EF 1FR also applies to intermittent flooding situations.Volume 4: Agriculture, Forestry and Other Land Use

11.8 2006 IPCC Guidelines for National Greenhouse Gas Inventories Figure 11.1 Schematic diagram illustrating the sources and pathways of N that result in direct and indirect N 2 O emissions from soils and waters
Note: Sources of N applied to, or deposited on, soils ar e represented with arrows on the left-hand side of the graphic. Emission pathways are also shown with arrows including the various pathways of volatilisation of NH 3 and NO x from agricultural and non-agricultural sources, deposition of these gases and their products NH 4 + and NO 3 - , and consequent indirect emissions of N 2 O are also illustrated. “Applied Organic N Fertilisers” include animal manure, all compost, sewage sludge, tankage, etc. “Crop Residues” include above- and below-ground residues for all crops (non-N and N fixing) and from perennial forage crops and pastures following renewal. On the lower right-hand side is a cut-away view of a representative sections of managed land; Histosol cultivation is represented here.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.9 Figure 11.2 Decision tree for direct N 2 O emissions from managed soils

Start

Do you have rigorously documented country-specific emission factors for EF 1 , EF 2 , and/or EF 3PRP ? Obtain country-specific data.

Estimate emissions using the Tier 1 default emission factor value and

country-specific activity data. No

Note: 1: N sources include: synthetic N fertiliser, organic N additions, urine and dung deposited during grazing, crop/forage residue , mineralisation of N contained in soil organic matter that accompanies C loss from soils following a change in land use or manag ement and drainage/management of organic soils. Other organic N additions (e.g., compost, sewage sludge, rendering waste) can be included in this calculation if sufficient information is available. The waste input is measured in units of N and added as an additional source sub-term under F ON in Equation 11.1 to be multiplied by EF 1 . 2: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resour ces), for discussion of key categories and use of decision trees. 3: As a rule of thumb, a sub-category would be significant if it accounts for 25-30% of emissions from the source category. Is this

a key category 2 and

is this N source

significant 3 ?

No

Yes

Estimate emissions using Tier 2equation and available countryspecific emission factors, or Tier 3 methods. Yes Yes

Box 2: Tier 1 Box 1: Tier 1 No

Estimate emissions using Tier 1 equations, default emission factors,

FAO activity data for mineral N fertiliser use and livestock populations, and expert opinion on other activity data. For each N source ask: Do you have country-specific activity data 1 ?

Box 3: Tier 2 or 3Volume 4: Agriculture, Forestry and Other Land Use

11.10 2006 IPCC Guidelines for National Greenhouse Gas Inventories Conversion of N 2 O–N emissions to N 2 O emissions for reporting purposes is performed by using the following equation:
N 2 O = N 2 O–N ● 44/28
Tier 2 If more detailed emission factors and corresponding activity da ta are available to a count ry than are presented in Equation 11.1, further disaggregation of the terms in the equation can be undertaken. For example, if emission factors and activity data are available for the application of synthetic fertilisers and organic N (F SN and F ON ) under different conditions i , Equation 11.1 would be expanded to become 6 :

E QUATION 11.2 D IRECT N 2 O EMISSIONS FROM MANAGED SOILS (T IER 2)



## ( ) ( ) ∑

− +

− + • + + • + = − i PRP OS SOM CR i i ON SN Direct N O N N O N EF F F EF F F N O N 2 2 1 1 2

Where:
EF 1 i = emission factors developed for N 2 O emissions from synthetic fertiliser and organic N application under conditions i (kg N 2 O–N (kg N input) -1 ); i = 1, ...n.

Equation 11.2 may be modified in a variety of ways to accommodate any combination of N source-, crop type-, management-, land use-, climate-, soil- or other condition-specific emission factors that a country may be able to obtain for each of the individual N input variables (F SN , F ON , F CR , F SOM , F OS , F PRP ).
Conversion of N 2 O–N emissions to N 2 O emissions for reporting purposes is performed by using the following equation:
N 2 O = N 2 O–N ● 44/28
Tier 3 Tier 3 methods are modelling or measurement approaches. Models are useful because in appropriate forms they can relate the soil and environmental variables responsible for N 2 O emissions to the size of those emissions. These relationships may then be used to predict emissions from whole countries or regions for which experimental measurements are impracticable. Models sh ould only be used after validation by representative experimental measurements. Care should also be taken to ensure that the emission estimates developed through the use of models or measuremen ts account for all anthropogenic N 2 O emissions. 7 Guidance that provides a sound scientific basis for the development of a Tier 3 Model-based Accounting System is given in Chapter 2, Section 2.5.


## 11.2.1.2 C HOICE OF EMISSION FACTORS

Tiers 1 and 2 Three emission factors (EF) are needed to estimate direct N 2 O emissions from managed soils. The default values presented here may be used in the Tier 1 equation or in the Tier 2 equation in combination with country-specific emission factors. The first EF (EF 1 ) refers to the amount of N 2 O emitted from the various synthetic and organic N applications to soils, including crop residue and mineralisation of soil organic carbon in mineral soils due to land-use change or management. The second EF (EF 2 ) refers to the amount of N 2 O emitted from an area of drained/managed organic soils, and the third EF (EF 3PRP ) estimates the amount of N 2 O emitted from urine and dung N deposited by grazing animals on pasture, range and paddock. Default emission factors for the Tier 1 method are summarised in Table 11.1.

6 It is important to note that Equation 11.2 is just one of many possible modifi cations to Equation 11.1 when using the Tier 2

method. The eventual form of Equation 11.2 will depend upon the availability of condition-specific emission factors and the ability to which a country can disaggregate its activity data.

7 Natural N 2 O emissions on managed land ar e assumed to be equal to emissions on unmanaged land. These latter emissions

are very low. Therefore, nearly all em issions on managed land are considered anthropogenic. Estimat es using the IPCC methodology are of the same magnitude as total measured emissions from managed land . The so-called 'background' emissions estimated by Bouwman (1996) (i.e., approx. 1 kg N 2 O–N/ha/yr under zero fertiliser N addition) are not “natural” emissions but are mostly due to cont ributions of N from crop residue. These emissions are anthropogenic and accounted for in the IPCC methodology.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.11 In the light of new evidence, the default value for EF 1 has been set at 1% of the N applied to soils or released through activities that result in mineralisation of organic matter in mineral soils 8 . In many cases, this factor will be adequate, however, there are recent data to suggest th at this emission factor could be disaggregated based on (1) environmental factors (climate, soil organic C content, soil texture, drainage and soil pH); and (2) management-related factors (N application rate per fe rtiliser type, type of crop, with differences between legumes, non-leguminous arable crops, and grass) (Bouwman et al ., 2002; Stehfest and Bouwman, 2006). Countries that are able to disaggregate their activity data from all or some of these factors may choose to use disaggregated emission factors with the Tier 2 approach.

T ABLE 11.1 D EFAULT EMISSION FACTOR S TO ESTIMATE DIRECT N 2 O EMISSIONS FROM MANAGED SOILS Emission factor Default value Uncertainty range

EF 1 for N additions from mineral fertilisers, organic amendments and crop residue s, and N mineralised from mineral soil as a result of loss of soil carbon [kg N 2 O–N (kg N) -1 ] 0.01 0.003 - 0.03

EF 1FR for flooded rice fields [kg N 2 O–N (kg N) -1 ] 0.003 0.000 - 0.006

EF 2 CG, Temp for temperate organic crop and grassland soils (kg N 2 O–N ha -1 ) 8 2 - 24

EF 2 CG, Trop for tropical organic crop and grassland soils (kg N 2 O–N ha -1 ) 16 5 - 48

EF 2F, Temp, Org, R for temperate and boreal organic nutrient rich forest soils (kg N 2 O–N ha -1 ) 0.6 0.16 - 2.4

EF 2F, Temp, Org, P for temperate and boreal organic nutrient poor forest soils (kg N 2 O–N ha -1 ) 0.1 0.02 - 0.3

EF 2F, Trop for tropical organic forest soils (kg N 2 O–N ha -1 ) 8 0 - 24

EF 3PRP, CPP for cattle (dairy, non-dairy and buffalo), poultry and pigs [kg N 2 O–N (kg N) -1 ] 0.02 0.007 - 0.06

EF 3PRP, SO for sheep and ‘other animals’ [kg N 2 O–N (kg N) -1 ] 0.01 0.003 - 0.03

Sources: EF 1 : Bouwman et al. 2002a,b; Stehfest & Bouwma n, 2006; Novoa & Tejeda, 2006 in press; EF 1FR : Akiyama et al. , 2005; EF 2CG, Temp , EF 2CG, Trop , EF 2F,Trop : Klemedtsson et al. , 1999, IPCC Good Practice Guidance, 2000; EF 2F, Temp : Alm et al. , 1999; Laine et al. , 1996; Martikainen et al. , 1995; Minkkinen et al. , 2002: Regina et al. , 1996; Klemedtsson et al. , 2002; EF 3, CPP , EF 3, SO : de Klein, 2004.

The default value for EF 2 is 8 kg N 2 O–N ha -1 yr -1 for temperate climates. Because mineralisation rates are assumed to be about 2 times greater in tropical climat es than in temperate climates, the emission factor EF 2 is 16 kg N 2 O–N ha -1 yr -1 for tropical climates 9 . Climate definitions are given in Chapter 3, Annex 3A.5.
The default value for EF 3PRP is 2% of the N deposited by all animal types except ‘sheep’ and ‘other’ animals. For these latter species, a default emission factor of 1% of the N deposited may be used 10

.

8 The value of EF 1 has been changed from 1.25% to 1%, as compared to the 1996 IPCC Guidelines , as a result of new

analyses of the available experimental data (Bouwman et al. , 2002a,b; Stehfest and Bouwman, 2006; Novoa and Tejeda, 2006 in press). These analyses draw on a much larger number of measurements than were available for the earlier study that gave rise to the previous value used for EF 1 (Bouwman, 1996). The mean value for fertiliser- and manure-induced emissions calculated in these reviews is close to 0.9%; however, it is considered that, given the uncertainties associated with this value and the inclusion in the inventory calculation of other contributions to the nitrogen additions (e.g., from crop residues and the mineralisation of soil organi c matter), the round value of 1% is appropriate.

9 The values of EF 2 , for both temperate and tropical climates, have been changed fr om the values provided in the 1996 IPCC

Guidelines to those contained in the GPG2000

. 10 The addition of a default emission factor for sheep is a change from the 1996 IPCC Guidelines . The default emission factor

value for EF 3PRP has been disaggregated for different animal types based on a recent review on N 2 O emissions from urine and dung depositions (de Klein, 2004). This review indicated that the emission factor for sheep is lower than that for cattle and that a value of 1% of the nitrogen deposite d is more appropriate. Reasons for the lower EF 3PRP for sheep include more even urine distribution (smaller and more frequent urinations), and smaller effects on soil compaction during grazing.Volume 4: Agriculture, Forestry and Other Land Use

11.12 2006 IPCC Guidelines for National Greenhouse Gas Inventories 

## 11.2.1.3 C HOICE OF ACTIVITY DATA

Tiers 1 and 2 This section describes generic methods for estimating the amount of various N inputs to soils (F SN , F ON , F PRP , F CR , F SOM , F OS ) that are needed for the Tier 1 and Tier 2 methodologies (Equations 11.1 and 11.2). Applied synthetic fertiliser (F SN

) The term F SN refers to the annual amount of synthetic N fertiliser applied to soils 11 . It is estimated from the total amount of synthetic fertiliser consumed annually. Annual fertiliser consumption data may be collected from official country statistics, often recorded as fertiliser sa les and/or as domestic production and imports. If countryspecific data are not available, data from the In ternational Fertilizer Industry Association (IFIA) (http://www.fertilizer.org/ifa/statistics.asp) on total fertiliser use by type and by crop, or from the Food and Agriculture Organisation of the United Nations (FAO ): (http://faostat.fao.org/) on synthetic fertiliser consumption, can be used. It may be useful to compare na tional statistics to international databases such as those of the IFIA and FAO. If sufficient data are available, fertiliser use may be disaggregated by fertiliser type, crop type and climatic regime for major crops. These data may be useful in developing revised emission estimates if inventory methods are improved in the future. It should be noted that most data sources (including FAO) might limit reporting to agricultural N uses, although applications may also occur on Forest Land, Settlements, or other lands. This unaccounted N is likely to account for a sma ll proportion of the overall emissions. However, it is recommended that countries seek out this additional information whenever possible. Applied organic N fertilisers (F ON

) The term “applied organic N fertiliser” (F ON ) refers to the amount of organic N inputs applied to soils other than by grazing animals and is calculated us ing Equation 11.3. This includes applied animal manure, sewage sludge applied to soil, compost applied to soils, as well as other organic amendments of regional importance to agriculture (e.g., rendering waste, guano, brewery waste, etc.). Organic N fertiliser (F ON ) is calculated using Equation 11.3:

E QUATION 11.3 N FROM ORGANIC N ADDITIONS APPLIED TO SOILS (T IER 1)

OOA COMP SEW AM ON F F F F F + + + =
Where:
F ON = total annual amount of organic N fertiliser applie d to soils other than by grazing animals, kg N yr -1

F AM = annual amount of animal manure N applied to soils, kg N yr -1

F SEW = annual amount of total sewage N (coordinate with Waste Sector to ensure that sewage N is not double-counted) that is applied to soils, kg N yr -1

F COMP = annual amount of total compost N applied to soils (ensure that manure N in compost is not double-counted), kg N yr -1

There are no or very limited data for N 2 O emission factors of other animal types, and the emission factor for poultry and swine remains at 2% of nitrogen deposited. However, a valu e of 1% of the nitrogen deposited may be used for animals classified as ‘other animals’ which in cludes goats, horses, mules, donkeys, camels, reindeer, and camelids, as these are likely to have nitrogen excretion rates and patterns that are more similar to sheep than to cattle. The review further suggested that a disaggregation of EF 3PRP for dung vs. urine nitrogen could also be considered. However, this is difficult to implement as it is unlikely that countries have the re quired information readily available to assess excretion rates in urine and dung. However, this approach may be considered by countri es that use a higher tier methodology. Finally, the review revealed that current information is insufficient or inconclusive to allow for disaggregation of EF 3PRP based on climate region, soil type or drainage cl ass, and/or grazing intensity.

11 For the Tier 1 approach, the amounts of applied mineral nitrogen fertilisers (F SN ) and of applied organic nitrogen fertilisers

(F ON ) are no longer adjusted for the amounts of NH 3 and NO x volatilisation after application to soil. This is a change from the methodology described in the 1996 IPCC Guidelines . The reason for this change is that field studies that have determined N 2 O emission factors for applied N were not adjusted for volatilisation when they were estimated. In other words, these emission factors were de termined from: fertiliser-induced N 2 O–N emitted / total amount of N applied, and not from: fertiliser-induced N 2 O–N emitted / (total amount of N applied – NH 3 and NO x volatilised). As a result, adjusting the amount of N input for volatilisation before multiplying it with the emission factor would in fact u nderestimate total N 2 O emissions. Countries using Tier 2 or Tier 3 a pproaches should be aware that correction for NH 3 /NO x volatilisation after mineral or organic N application to soil may be required depending on the emission factor and/or the inventory methodology used.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.13 F OOA = annual amount of other organic amendments used as fertiliser (e.g., rendering waste, guano, brewery waste, etc.), kg N yr -1

The term F AM is determined by adjusting the amount of manure N available (N MMS_Avb ; see Equation 10.34 in Chapter 10) for the amount of managed manure used for feed (Frac FEED ), burned for fuel (Frac FUEL ), or used for construction (Frac CNST ) as shown in Equation 11.4. Data for Frac FUEL , Frac FEED , Frac CNST can be obtained from official statistics or a survey of experts. Ho wever, if these data are not available use N MMS_Avb as F AM without adjusting for Frac FUEL , Frac FEED , Frac CNST .
E QUATION 11.4 N FROM ANIMAL MANURE APPLIED TO SOILS (T IER 1) 

# ( ) 

# [ ] CNST FUEL FEED Avb MMS AM Frac Frac Frac N F + +

− • = 1

Where:
F AM = annual amount of animal manure N applied to soils, kg N yr -1

N MMS_Avb = amount of managed manure N available for soil application, feed, fuel or construction, kg N yr -1 (see Equation 10.34 in Chapter 10)
Frac FEED = fraction of managed manure used for feed
Frac FUEL = fraction of managed manure used for fuel
Frac CNST = fraction of managed manure used for construction

Urine and dung from grazing animals (F PRP ) The term F PRP refers to the annual amount of N deposited on pasture, range and paddock soils by grazing animals. It is important to note that the N from managed animal manure applied to soils is included in the F AM term of F ON . The term F PRP is estimated using Equation 11.5 from the numb er of animals in each liv estock species/category T (N (T) ), the annual average amount of N excr eted by each livestock species/category T (Nex (T) ), and the fraction of this N deposited on pasture, range and paddo ck soils by each livestock species/category T (MS (T,PRP) ). The data needed for this equation can be obtained from th e livestock chapter (see Chapter 10, Section 10.5). Equation 11.5 provides an estimate of the amount of N deposited by grazing animals:

E QUATION 11.5 N IN URINE AND DUNG DEPOSITED BY GRAZING ANIMALS ON PASTURE , RANGE AND PADDOCK (T IER 1) 

# ( ) 

# [ ] ∑ • • = T PRP T T T PRP MS Nex N F ) , ( ) ( ) (

Where:
F PRP = annual amount of urine and dung N deposited on pasture, range, paddock and by grazing animals, kg N yr -1

N (T) = number of head of livestock species/category T in the country (see Chapter 10, Section 10.2)
Nex (T) = annual average N excretion per head of species/category T in the country, kg N animal -1 yr -1 (see Chapter 10, Section 10.5)
MS (T,PRP) = fraction of total an nual N excretion for each livestock species/category T that is deposited on pasture, range and paddock 12 (see Chapter 10, Section 10.5) Crop residue N, including N-fixing crops and fo rage/ pasture renewal, returned to soils, (F CR

) The term F CR refers to the amount of N in crop residues (above-ground and below-ground), including N-fixing crops, returned to soils annually 13 . It also includes the N from N-fixing and non-N-fixing forages mineralised

12 In the livestock section, pasture, range and paddock is re ferred to as one of the manure management systems denoted as

“S”.

13 The equation to estimate F CR has been modified from the previous 1996 IPCC Guidelines to account for the contribution of the below-ground nitrogen to the total input of nitrogen from cr op residues, which previously was ignored in the estimate of F CR . As a result, F CR now represents a more accurate estimate of the amount of nitrogen input from crop residue, whichVolume 4: Agriculture, Forestry and Other Land Use

11.14 2006 IPCC Guidelines for National Greenhouse Gas Inventories during forage or pasture renewal 14 . It is estimated from crop yield statis tics and default factors for above-/belowground residue:yield ratios and residue N contents. In ad dition, the method accounts for the effect of residue burning or other removal of residues (direct emissions of N 2 O from residue burning are addressed under Chapter 2, Section 2.4. Because diffe rent crop types vary in residue:yield ratio s, renewal time and N contents, separate calculations should be performed for major crop types and then N values from all crop types are summed up. At a minimum, it is recommended that crops be segregated into: 1) non-N-fixing grai n crops (e.g., maize, rice, wheat, barley); 2) N -fixing grains and pulses (e.g., soybean, dry be ans, chickpea, lentils); 3) root and tuber crops (e.g., potato, sweet potato, cassava); 4) N-fixing forage crops (alfalfa, cl over); and 5) other forages including perennial grasses and grass/clover pastures. Equation 11.6 provides the equation to estimate N from crop residues and forage/pasture renewal, for a Tier 1 approach.

E QUATION 11.6 N FROM CROP RESIDUES AND FORAGE / PASTURE RENEWAL (T IER 1) 

##    

#   

#           

  

    

  


T T BG T BG T T move Re T AG T AG f T T T new Re T CR N R Area Frac N R C burnt Area Area Frac Crop F ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( 1

Where:
F CR = annual amount of N in crop residues (above and below ground), including N-fixing crops, and from forage/pasture renewal, return ed to soils annually, kg N yr -1 Crop ( T ) = harvested annual dry matter yield for crop T , kg d.m. ha -1

Area ( T ) = total annual area harvested of crop T , ha yr -1
Area burnt ( T ) = annual area of crop T burnt, ha yr -1

C f

= combustion factor (dimensionless) (refer to Chapter 2, Table 2.6)
Frac Renew ( T ) = fraction of total area under crop T that is renewed annually 15 . For countries where pastures are renewed on average every X years, Frac Renew = 1/X. For annual crops Frac Renew = 1
R

AG( T ) = ratio of above-ground residues dry matter (AG DM( T ) ) to harvested yield for crop T (Crop ( T ) ), kg d.m. (kg d.m.) -1

, = AG DM( T ) ● 1000 / Crop ( T ) (calculating AG DM( T ) from the information in Table 11.2)
N AG( T ) = N content of above-ground residues for crop T , kg N (kg d.m.) -1 , (Table 11.2)
Frac Remove( T ) = fraction of above-ground residues of crop T removed annually for purposes such as feed, bedding and construction, kg N (kg crop-N) -1 . Survey of experts in country is required to obtain data. If data for Frac Remove are not available, assume no removal.
R

BG( T ) = ratio of below-ground residues to harvested yield for crop T , kg d.m. (kg d.m.) -1 . If alternative data are not available, R BG( T ) may be calculated by multiplying R BG-BIO in Table 11.2 by the ratio of total above-ground biomass to crop yield ( = [(AG DM( T ) ● 1000 + Crop ( T ) ) / Crop ( T ) ], (also calculating AG DM( T ) from the information in Table 11.2).
N BG( T ) = N content of below-ground residues for crop T , kg N (kg d.m.) -1 , (Table 11.2)
T

= crop or forage type Data on crop yield statistics (yields and area harvested, by crop) may be obt ained from national sources. If such data are not available, FAO publishes data on crop production: (h ttp://faostat.f ao.org/). Since yield statistics for many crops are reported as fiel d-dry or fresh weight, a corr ection factor can be applied to estimate dry matter yields (Crop ( T ) ) where appropriate (Equation 11.7). Th e proper correction to be used is dependent on the standards used in yield reporting, whic h may vary between countries. Alternatively, the default values for dry matter content given in Table 11.2 may be used.

14 The inclusion of nitrogen from forage or pasture renewal is a change from previous 1996 IPCC Guidelines

. 15 This term is included in the equation to account for N release and the subsequent increases in N 2 O emissions (e.g., van der Weerden et al. , 1999; Davies et al. , 2001), from renewal/cultivation of grazed grass or grass/clove r pasture and other forage crops.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.15 E QUATION 11.7 D RY - WEIGHT CORRECTION OF REPORTED CROP YIELDS DRY Fresh Yield Crop T T   ) ( ) (

Where: Crop ( T ) = harvested dry matter yield for crop T , kg d.m. ha -1 Yield_Fresh ( T ) = harvested fresh yield for crop T , kg fresh weight ha -1 DRY = dry matter fraction of harvested crop T , kg d.m. (kg fresh weight) -1

The regression equations in Table 11.2 may also be used to calculate the total above-ground residue dry matter, and the other data in the table then permit the calculation in turn of th e N in the above-ground residues, the below-ground dry matter, and the total N in the below- ground residues. The total N addition, F CR , is the sum of the above-and below-ground N contents. With this approach, F CR is given by Equation 11.7A:

E QUATION 11.7A A LTERNATIVE APPROACH TO ESTIMATE F CR ( USING T ABLE 11.2) 

##    

##   

##           

     

     

 





T T BG T BIO BG T T DM T T move Re T AG T DM T T T new Re CR N R Crop AG Area Frac N AG CF burnt Area Area Frac F ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

( ) 1000 ( 1 1000

An improvement on this approach for determining F CR (i.e., Tier 2) would be the use of country-specific data rather than the values provided in Table 11.2, as well as country-specific values for the fraction of above-ground residue burned. Mineralised N resulting from loss of soil organic C stocks in minera l soils through land -use change or management practices (F SOM ) 16 The term F SOM refers to the amount of N mineralised from loss in soil organic C in mineral soils through landuse change or management practices. As explained in Chap ter 2, Section 2.3.3, land-use change and a variety of management practices can have a significant impact on soil organic C storage. Organic C and N are intimately linked in soil organic matter. Where so il C is lost through oxidation as a result of land-use or management change, this loss will be accompanied by a simultaneous mi neralisation of N. Where a loss of soil C occurs, this mineralised N is regarded as an additiona l source of N availabl e for conversion to N 2 O (Smith and Conen, 2004); just as mineral N released from decomposition of cr op residues, for example, becomes a source. The same default emission factor (EF 1 ) is applied to mineralised N from soil or ganic matter loss as is used for direct emissions resulting from fertiliser and organic N inputs to agricultural land. This is because the ammonium and nitrate resulting from soil organic matter mineralisation is of equal value as a substr ate for the microorganisms producing N 2 O by nitrification and denitrifi cation, no matter whether the mine ral N source is soil organic matter loss from land-use or management change, decomposition of crop residues, synthetic fertilisers or organic amendments. (Note: the opposite process to mineralisa tion, whereby inorganic N is sequestered into newly formed SOM, is not taken account of in the calculation of the mineralisati on N source. This is because of the different dynamics of SOM decom position and formation, and also because reduced tillage in some circumstances can increase both SOM and N 2 O emission.) For all situations where soil C losses occur (as calculated in Chapter 2, Equation 2.25) the Tier 1 and 2 methods for calculating the release of N by mineralisation are shown below: Calculation steps for estimating cha nges in N supply from mineralisation Step 1 : Calculate the average annual loss of soil C ( ∆ C Mineral, LU ) for the area, over the inventory period, using Equation 2.25 in Chapter 2. Using the Tier 1 approach, the value for ∆ C Mineral, LU will have a single value for all land-uses and management systems. Using Tier 2, the value for ∆ C Mineral, LU will be disaggregated by individual land-use and/or management systems. Step 2 : Estimate the N mineralised as a co nsequence of this loss of soil C (F SOM ), using Equation 11.8:

16 The inclusion of the term F SOM is a change from the previous 1996 IPCC Guidelines , which did not include the N from

mineralisation associated with a loss of soil organic C.Volume 4: Agriculture, Forestry and Other Land Use

11.16 2006 IPCC Guidelines for National Greenhouse Gas Inventories E QUATION 11.8 N MINERALISED IN MINERAL SOILS AS A RESULT OF LOSS OF SOIL C THROUGH CHANGE IN LAND USE OR MANAGEMENT (T IERS 1 AND 2) 

### ∑ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

• ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

•

Δ = LU LU eral Min SOM R C F 1000 1

,

Where:
F SOM = the net annual amount of N mineralised in mineral soils as a result of loss of soil carbon through change in land use or management, kg N
∆ C Mineral, LU = average annual loss of soil ca rbon for each land-use type ( LU ), tonnes C (Note: for Tier 1, ∆ C mineral, LU will have a single value for all land-uses and management systems. Using Tier 2 the value for Δ C mineral, LU will be disaggregated by individual land-use and/or management systems. R = C:N ratio of the soil organic matter. A default value of 15 (uncertainty range from 10 to 30) for the C:N ratio (R) may be used for situations involving land-use change from Forest Land or Grassland to Cropland, in the absence of more specific data for the area. A default value of 10 (range from 8 to 15) may be used for situations involving management changes on Cropland Remaining Cropland

. C:N ratio can change over time, land use, or management practice 17 . If countries can document changes in C:N ratio, then different values can be us ed over the time series, land use, or management practice.
LU = land-use and/or management system type Step 3 : For Tier 1, the value for F SOM is calculated in a single step. For Tier 2, F SOM is calculated by summing across all land-uses and/or management system types ( LU ). Countries that are not able to estimate gross ch anges of mineral soil C will create a bias in the N 2 O estimate, and it is good practice to acknowledge this limitation in the reporting documentation. It is also good practice to use specific data for the C:N ratios for the disaggregated land areas, if these are availabl e, in conjunction with the data for carbon changes. Area of drained/managed organic soils (F OS

) The term F OS refers to the total annual area (ha) of drained/managed organic soils (see footnote 4 for definition). This definition is applicable for both the Tier 1 and Ti er 2 methods. For all land uses, the areas should be stratified by climate zone (temperate and tropical). In ad dition, for temperate Forest Land the areas should be further stratified by soil fertility (nutrient rich and nutr ient poor). The area of drained/managed organic soils (F OS ) may be collected from official national statistics. Alternatively, total areas of organic soils from each country are available from FAO (http://faostat.fao.org/), and expert advice may be used to estimate areas that are drained/managed. For Forest Land, national data will be available at soil survey organisations and from wetland surveys, e.g., for international conventions. In case no stratification by soil fertility is possible, countries may rely on expert judgment.


## 11.2.1.4 U NCERTAINTY ASSESSMENT

Uncertainties in estimates of direct N 2 O emissions from managed soils are caused by uncertainties related to the emission factors (see Table 11.1 for uncertainty ranges), natural variability, partitioning fractions, activity data, lack of coverage of measurements, spatial aggregation, and lack of information on specific on-farm practices. Additional uncertainty will be introduced in an in ventory when emission measurements that are not representative of all conditions in a country are used. In ge neral, the reliability of activity data will be higher than that of the emission factors. As an example, further uncertainties may be caused by missing information on observance of laws and regulations related to handling and application of fertiliser and manure, and changing management practices in farming. Generally, it is difficul t to obtain information on the actual observance of laws and possible emission reductions achieved as well as information on farming practices. For more detailed guidance on uncertainty assessment refer to Volume 1, Chapter 3.

17 Information on C:N ratios in forest and cropped soils may be found in the following re ferences: Aitkenhead-Peterson et

al. , 2005; Garten et al. , 2000; John et al. , 2005; Lobe et al. , 2001; Snowdon et al. , 2005, and other references cited by these authors.Chapter 11: N2O Emissions from Managed Soils, and CO2Emissions from Lime and Urea Application2006 IPCC Guidelines for National Greenhouse Gas Inventories11.17TABLE11.2DEFAULT FACTORS FORESTIMATION OFNADDED TO SOILS FROM CROP RESIDUESaCropDry matterfraction ofharvestedproduct(DRY)Above-ground residue dry matter AGDM(T)(Mg/ha):AGDM(T)= (Crop(T)/1000)* slope(T)+ intercept(T)N content ofabove-groundresidues(NAG)Ratio of belowgroundresidues toabove-groundbiomass(RBG-BIO)N content ofbelow-groundresidues(NBG)Slope2 s.d. as % ofmeanIntercept2 s.d. as % ofmeanR2adj.Major crop typesGrains0.88 1.092%0.886%0.65 0.0060.22 (16%)0.009Beans & pulsesb0.91 1.1319%0.8556%0.28 0.0080.19 (45%)0.008Tubersc0.22 0.1069%1.0670%0.18 0.0190.20 (50%)0.014Root crops, otherd0.94 1.0719%1.5441%0.63 0.0160.20 (50%)0.014N-fixing forages 0.90 0.350% default0 - - 0.0270.40 (50%)0.022Non-N-fixingforages0.90 0.350% default0 - - 0.0150.54 (50%)0.012Perennial grasses 0.90 0.350% default0 - - 0.0150.80 (50%)l0.012Grass-clovermixtures0.90 0.350% default0 - - 0.0250.80 (50%)l0.016pIndividual cropsMaize0.87 1.033%0.6119%0.76 0.0060.22 (26%)0.007Wheat 0.89 1.513%0.5217%0.68 0.0060.24 (32%)0.009Winter wheat 0.89 1.613%0.4025%0.67 0.0060.23 (41%)0.009Spring wheat 0.89 1.295%0.7526%0.76 0.0060.28 (26%)0.009Rice0.89 0.9519%2.4641%0.47 0.0070.16 (35%)NABarley 0.89 0.988%0.5941%0.68 0.0070.22 (33%)0.014Oats0.89 0.915%0.898%0.45 0.0070.25 (120%)0.008Millet0.90 1.4318%0.14308%0.50 0.007 NA NASorghum0.89 0.8813%1.3327%0.36 0.007 NA 0.006Ryee0.88 1.0950% default0.8850% default- 0.005 NA 0.011Volume 4: Agriculture, Forestry and Other Land Use2006 IPCC Guidelines for National Greenhouse Gas Inventories11.18TABLE11.2(CONTINUED)DEFAULT FACTORS FORESTIMATION OFNADDED TO SOILS FROM CROP RESIDUESaCropDry matterfraction ofharvestedproduct(DRY)Above-ground residue dry matter AGDM(T)(Mg/ha):AGDM(T)= (Crop(T)/1000)* slope(T)+ intercept(T)N content ofabove-groundresidues(NAG)Ratio of belowgroundresidues toabove-groundbiomass(RBG-BIO)N content ofbelow-groundresidues(NBG)Slope2 s.d. as % ofmeanIntercept2 s.d. as % ofmeanR2adj.Soyabeanf0.91 0.9331%1.3549%0.16 0.0080.19 (45%)0.008Dry beang0.90 0.36100%0.6847%0.15 0.01 NA 0.01Potatoh0.22 0.1069%1.0670%0.18 0.0190.20 (50%)m0.014Peanut (w/pod)i0.94 1.0719%1.5441%0.63 0.016 NA NAAlfalfaj0.90 0.29k31%0 - - 0.0270.40 (50%)n0.019Non-legume hayj0.90 0.1850% default0 - - 0.0150.54 (50%)n0.012aSource: Literature review by Stephen A. Williams, Natural Resource Ecology Laboratory, ColoradoState University. (Email: stevewi@warnercnr.colostate.edu) for CASMGS(http://www.casmgs.colostate.edu/). A listof the original references is given in Annex 11A.1.bThe average above-ground residue:grain ratio from all data used was 2.0 and included data for soya bean, dry bean, lentil, cowpea, black gram, and pea.cModelled after potatoes.dModelled after peanuts.eNo data for rye. Slope and intercept values are those for all grain. Default s.d.fThe average above-ground residue:grain ratio from all data used was 1.9.gOrtega, 1988 (see Annex 11A.1). The average above-ground residue:grainratio from this single source was 1.6. default s.d. forroot:AGB.hThe mean value for above-ground residue:tuber ratio in thesources used was 0.27 with a standard error of 0.04.IThe mean value for above-ground residue: pod yield in the sources used was 1.80 with a standard error of 0.10.jSingle source. Default s.d. for root:AGB.kThis is the average above-ground biomass reported as litter orharvest losses. This does not include reported stubble, which averaged 0.165 x Reported Yields. Default s.d.lEstimate of root turnover to above-groundproduction based on the assumption that innatural grass systems below-ground biomassis approximately equal to twice (one tothree times) the above-ground biomass and thatroot turnover in these systems averages about40% (30% to 50%) per year. Default s.d.mThis is an estimate of non-tuber roots based on the root:shoot values found for other crops. If unmarketable tuber yield is returned to the soil then data are derived from Vangessel and Renner,1990 (see Annex 11A.1)(unmarketable yield = 0.08 * marketable yield = 0.29 * above-ground biomass) suggest that the total residues returned might then be on the order of 0.49 * above-ground biomass. Default s.d.nThis is an estimate of root turnover in perennial systems. Default s.d.pIt is assumed here that grass dominates the system by 2 to 1 over legumes.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.19 

# 11.2.2 Indirect N 2 

# O emissions

In addition to the direct emissions of N 2 O from managed soils that occur through a direct pathway (i.e., directly from the soils to which N is applied), emissions of N 2 O also take place through two indirect pathways (as illustrated above in Section 11.2). The first of these pathways is the volatilisation of N as NH 3 and oxides of N (NO x ), and the deposition of these gases and their products NH 4 + and NO 3 - onto soils and the surface of lakes and other waters. The sources of N as NH 3 and NO x are not confined to agricultura l fertilisers and manures, but also include fossil fuel combustion, biomass burning, and processes in the chemical industry (see Volume 1, Chapter 7, Section 7.3). Thus, these processes cause N 2 O emissions in an exactly analogous way to those resulting from deposition of agriculturally derived NH 3 and NO x , following the application of synthetic and organic N fertilisers and /or urine and dung deposition from grazing animals. The second pathway is th e leaching and runoff from la nd of N from synthetic and organic fertiliser additions, crop residues 18 , mineralisation of N associated with loss of soil C in mineral and drained/managed organic soils through land-use change or management practices, and urine and dung deposition from grazing animals. Some of the inorganic N in or on the soil, mainly in the NO 3 - form, may bypass biological retention mechanisms in the soil/vegetation system by tr ansport in overland water fl ow (runoff) and/or flow through soil macropores or pipe drains. Where NO 3 - is present in the soil in excess of biological demand, e.g., under cattle urine patches, the excess leaches through the soil profile. The nitrific ation and denitrification processes described at the beginning of this chapter transform some of the NH 4 + and NO 3 - to N 2 O. This may take place in the groundwater below the land to which the N was applied, or in riparian zones receiving drain or runoff water, or in the ditches, str eams, rivers and estuaries (and their sediments) into which the land drainage water eventually flows. This methodology described in this Chapter addresses the following N sources of indirect N 2 O emissions from managed soils arising from agricultural inputs of N:

 synthetic N fertilisers (F SN );

 organic N applied as fertiliser (e.g., applied animal manure 19 , compost, sewage sludge, rendering waste and other organic amendments) (F ON );

 urine and dung N deposited on pasture, range and paddock by grazing animals (F PRP );

 N in crop residues (above- and below-ground), including N-fixing crops and forage/pasture renewal returned to soils (F CR ) 20 ; and

 N mineralisation associated with loss of soil orga nic matter resulting from change of land use or management on mineral soils (F SOM ) 20

. The generic Tier 1 and Tier 2 methods described below can be used to estimate aggregate total indirect N 2 O emissions from agricultural N additions to managed soil s for an entire country. If a country is estimating its direct N 2 O from managed soils by land-use category, the indirect N 2 O emissions can also be estimated by the same disaggregation of land-use categories using the eq uations presented below with activity data, partitioning fractions, and/or emission factors specific for each land-use category. The methodology for estimating indirect N 2 O emissions from combustion-related and industrial sources is described in Volume 1, Chapter 7, Section 7.3.


## 11.2.2.1 C HOICE OF METHOD
Refer to the decision tree in Figure 11.3 (Indirect N 2 O Emissions) for guidance on which Tier method to use.

18 The inclusion of crop residues as an N input into the leach ing and runoff component is a change from the previous IPCC

Guidelines.

19 Volatilisation and subsequent deposition of nitrogen from the manure in ma nure management systems is covered in the manure management section of this Volume.

20 Nitrogen from these components is only included in the leaching/run-off component of indirect N 2 O emission.Volume 4: Agriculture, Forestry and Other Land Use

11.20 2006 IPCC Guidelines for National Greenhouse Gas Inventories Figure 11.3 Decision tree for indirect N 2 O emissions from managed soils

Start

For each N source, do you have rigorously documented country-specific emission factors (EF 4 or EF 5 ) and as appropriate rigorously documented country-specific partitioning fractions (Frac GASF ,Frac GASM , Frac LEACH ) values? Obtain countryspecific data.

Estimate emissions using the Tier 1 equation with default emission

and partitioning factors and

available activity data.

Note: 1: N sources include: synthetic N fertilizer, organic N additions, urine and dung depositions, crop residue, N mineralization/i mmobilization associated with loss/gain of soil C on mineral soils as a result of land use change or management practices (crop residue and N mineralization/immobilization is only accounted for in the indirect N 2 O emissions from leaching/runoff). Sewage sludge or other organic N additions can be included if sufficient information is available. 2: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resour ces), for discussion of key categories and use of decision trees. 3: As a rule of thumb, a sub-source category would be significant if it accounts for 25-30% of emissions from the source catego ry. Is this a key category 2 and is this N source significant 3 ?

No

Yes

Estimate emissions us ing Tier 2 equation, country-specific activity data and country-specific emission factors and partitioning fractions, or Tier 3 method. Yes Yes

Box 1: Tier 1 Box 2: Tier 2 No

Estimate emissions with Tier 2 equation, using a mix of country-specific and other available data and country-specific emission and partitioning factors. For each agricultural N source 1 , for both volatilization and leaching/runoff, ask: Do you have country-specific activity data?

Box 4: Tier 2 or 3 Do you have rigorously documented country-specific EF values (EF 4 or EF 5 ) and asappropriate rigorously documented countryspecific partitioning fractions (Frac GASF , Frac GASM , Frac LEACH ) values?

Estimate emissions using Tier 1 or Tier 2

equation, country-specific activity data and a mix of county-specific or default emission factors and partitioning fractions. No Yes

Box 3: Tier 1 or 2 NoChapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.21 Tier 1 Volatilisation, N 2 O (ATD) The N 2 O emissions from atmospheric deposition of N volatilised from managed soil are estimated using Equation 11.9:

E QUATION 11.9 N 2 O FROM ATMOSPHERIC DEPOSITION OF N VOLATILISED FROM MANAGED SOILS (T IER 1) 

## ( ) ( ) ( ) 

## [ ] 4 ) ( 2 EF Frac F F Frac F N O N GASM PRP ON GASF SN ATD • • + + • = −

Where:
N 2 O (ATD) –N = annual amount of N 2 O–N produced from atmospheric deposition of N volatilised from managed soils, kg N 2 O–N yr -1

F SN = annual amount of synthetic fertiliser N applied to soils, kg N yr -1

Frac GASF = fraction of synthetic fertiliser N that volatilises as NH 3 and NO x , kg N volatilised (kg of N applied) -1 (Table 11.3)
F ON = annual amount of managed animal manure, compos t, sewage sludge and other organic N additions applied to soils, kg N yr -1

F PRP = annual amount of urine and dung N deposited by grazing animals on pasture, range and paddock, kg N yr -1

Frac GASM = fraction of applied organic N fertiliser materials (F ON ) and of urine and dung N deposited by grazing animals (F PRP ) that volatilises as NH 3 and NO x , kg N volatilised (kg of N applied or deposited) -1 (Table 11.3)
EF 4 = emission factor for N 2 O emissions from atmospheric deposition of N on soils and water surfaces, [kg N–N 2 O (kg NH 3 –N + NO x –N volatilised) -1 ] (Table 11.3)
Conversion of N 2 O (ATD )-N emissions to N 2 O emissions for reporting purposes is performed by using the following equation:
N 2 O (ATD) = N 2 O (ATD) –N • 44/28

Leaching/Runoff, N 2 O (L) The N 2 O emissions from leaching and runoff in regions wh ere leaching and runoff o ccurs are estimated using Equation 11.10:

E QUATION 11.10 N 2 O FROM N LEACHING / RUNOFF FROM MANAGED SOILS IN REGIONS WHERE LEACHING / RUNOFF OCCURS (T IER 1) 

## ( ) 5 ) ( ) ( 2 EF Frac F F F F F N O N H LEACH SOM CR PRP ON SN L • • + + + + = −

−
Where : N 2 O (L) –N = annual amount of N 2 O–N produced from leaching and runo ff of N additions to managed soils in regions where leach ing/runoff occurs, kg N 2 O–N yr -1

F SN = annual amount of synthetic fertiliser N applied to soils in regions where l eaching/runoff occurs, kg N yr -1

F ON = annual amount of managed animal manure, compost, sewage sludge and other organic N additions applied to soils in regions wher e leaching/runoff occurs, kg N yr -1

F PRP = annual amount of urine and dung N deposited by grazing animals in regions where leaching/runoff occurs, kg N yr -1 (from Equation 11.5)
F CR = amount of N in crop residues (above- and below-ground), including N-fixing crops, and from forage/pasture renewal, returned to soils annually in regions where leachi ng/runoff occurs, kg N yr -1

F SOM = annual amount of N mineralised in mineral soils associated with loss of soil C from soil organic matter as a result of changes to land use or manage ment in regions where l eaching/runoff occurs, kg N yr -1 (from Equation 11.8)Volume 4: Agriculture, Forestry and Other Land Use

11.22 2006 IPCC Guidelines for National Greenhouse Gas Inventories Frac LEACH-(H) = fraction of all N added to/mineralised in managed soils in regions where leaching/runoff occurs that is lost through leaching and runoff, kg N (kg of N additions) -1 (Table 11.3)
EF 5 = emission factor for N 2 O emissions from N leaching and runoff, kg N 2 O–N (kg N leached and runoff) -1 (Table 11.3) Note: If a country is able to estimate the quantity of N mineralised from organic soils, then include this as an additional input to Equation 11.10.
Conversion of N 2 O (L) –N emissions to N 2 O emissions for reporting purposes is performed by using the following equation:
N 2 O (L) = N 2 O (L) –N • 44/28
Tier 2 If more detailed emission, volatilisation or leaching factors are available to a country than are presented in Table 11.3, further disaggregation of the terms in the equations can also be undertaken. For example, if specific volatilisation factors are available for th e application of synthetic fertilisers (F SN ) under different conditions i , Equation 11.9 would be expanded to become 21 :

E QUATION 11.11 N 2 O FROM ATMOSPHERIC DEPOSITION OF N VOLATILISED FROM MANAGED SOILS (T IER 2) 

# ( ) ( ) 

## [ ] 4 ) ( 2 EF Frac F F Frac F N O N GASM PRP ON i GASF SN ATD i i • ⎭ ⎬ ⎫ ⎩ ⎨ ⎧

• + + • = − 

### ∑

Where:
N 2 O (ATD) –N = annual amount of N 2 O–N produced from atmospheric deposition of N volatilised from managed soils, kg N 2 O–N yr -1

F SN i = annual amount of synthetic fertiliser N ap plied to soils under different conditions i , kg N yr -1

Frac GASF i = fraction of synthetic fertiliser N that volatilises as NH 3 and NO x under different conditions i , kg N volatilised (kg of N applied) -1

F ON = annual amount of managed animal manure, compos t, sewage sludge and other organic N additions applied to soils, kg N yr -1

F PRP = annual amount of urine and dung N deposited by grazing animals on pasture, range and paddock, kg N yr -1

Frac GASM = fraction of applied organic N fertiliser materials (F ON ) and of urine and dung N deposited by grazing animals (F PRP ) that volatilises as NH 3 and NO x , kg N volatilised (kg of N applied or deposited) -1 (Table 11.3)
EF 4 = emission factor for N 2 O emissions from atmospheric deposition of N on soils and water surfaces, [kg N–N 2 O (kg NH 3 –N + NO x –N volatilised) -1 ] (Table 11.3) Note: If a country is able to estimate the quantity of N mineralised from drainage/management of organic soils then include this as one of the N inputs in to the Tier 2 modification of Equation 11.10.
Conversion of N 2 O (ATD) –N emissions to N 2 O (ATD) emissions for reporting purposes is performed by using the following equation:
N 2 O (ATD) = N 2 O (ATD) –N • 44/28

21 It is important to note that Equation 11.11 is just one of ma ny possible modifications to Equa tion 11.9, and is also meant to

illustrate how Equation 11.10 could be modified, when using th e Tier 2 method. The eventual form of Equation 11.11 will depend upon the availability of land use and/or condition-speci fic partitioning fractions and/or emission factors and the ability to which a country can disaggregate its activity data.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.23 Tier 3 Tier 3 methods are modelling or measurement approaches. Models are useful as they can relate the variables responsible for the emissions to the size of those emissions. These relationships may then be used to predict emissions from whole countries or regions for which experimental measurements are impracticable. For more information refer to Chapter 2, Section 2.5, where guidance is given that provides a sound scientific basis for the development of a Tier 3 Model-based Accounting System.


## 11.2.2.2 C HOICE OF EMISSION 

## , VOLATILISATION AND LEACHING FACTORS
The method for estimating indirect N 2 O emissions includes two emission factors: one associated with volatilised and re-deposited N (EF 4 ), and the second associated with N lost through leaching/runoff (EF 5 ). The method also requires values for the fractions of N that are lost through volatilisation (Frac GASF and Frac GASM ) or leaching/runoff (Frac LEACH-(H) ). The default values of all these factors are presented in Table 11.3. Note that in the Tier 1 method, for humid regions or in dryland regions where irrigation (other than drip irrigation) is used, the default Frac LEACH-(H) is 0.30. For dryland regions, where precipitation is lower than evapotranspiration throughout most of the year a nd leaching is unlikely to occur, the default Frac LEACH is zero. The method of calculating whether Frac LEACH-(H) = 0.30 should be applied is given in Table 11.3. Country-specific values for EF 4 should be used with great caution because of the special complexity of transboundary atmospheric transport. Although invent ory compilers may have specific measurements of N deposition and associated N 2 O flux, in many cases the deposited N may not have originated in their country. Similarly, some of the N that volatilises in their country may be transported to and deposited in another country, where different conditions that affect the fraction emitted as N 2 O may prevail. For these reasons the value of EF 4 is very difficult to determine, and the method presented in Volume 1, Chapter 7, Section 7.3 attributes all indirect N 2 O emissions resulting from inputs to managed soils to the country of origin of the atmospheric NO x and NH 3 , rather than the country to which the atmospheric N may have been transported.


## 11.2.2.3 C HOICE OF ACTIVITY DATA

In order to estimate indirect N 2 O emissions from the various N additions to managed soils, the parameters F SN , F ON , F PRP , F CR , F SOM need to be estimated. Applied synthetic fertiliser (F SN

) The term F SN refers to the annual amount of synthetic fertilis er N applied to soils. Refer to the activity data section on direct N 2 O emissions from managed soils (Section 11.2.1.3) and obtain the value for F SN . Applied organic N fertilisers (F ON

) The term F ON refers to the amount of organic N fertiliser mate rials intentionally applied to soils. Refer to the activity data section on direct N 2 O emissions from managed soils (Section 11.2.1.3) and obtain the value for F ON . Urine and dung from grazing animals (F PRP ) The term F PRP refers to the amount of N deposited on soil by animals grazing on pasture, range and paddock. Refer to the activity data section on direct N 2 O emissions from managed soils (Section 11.2.1.3) and obtain the value for F PRP . Crop residue N, including N from N-fixing crops and forage/pasture renewal, returned to soils (F CR

) The term F CR refers to the amount of N in crop residues (above- and below-ground), including N-fixing crops, returned to soils annually. It also includes the N from N-fixing and non-N-fixing forages mineralised during forage/pasture renewal. Refer to th e activity data section on direct N 2 O emissions from managed soils (Section 11.2.1.3) and obtain the value for F CR . Mineralised N resulting from loss of soil organic C stocks in mineral soils (F SOM ) The term F SOM refers to the amount of N mineralised from th e loss of soil organic C in mineral soils through land-use change or management practices. Refer to the activity data section on direct N 2 O emissions from managed soils (Section 11.2.1.3) and obtain the value for F SOM .Volume 4: Agriculture, Forestry and Other Land Use

11.24 2006 IPCC Guidelines for National Greenhouse Gas Inventories T ABLE 11.3 D EFAULT EMISSION , VOLATILISATION AND LEACHING FACTORS FOR INDIRECT SOIL N 2 O EMISSIONS

Factor Default value Uncertainty range

EF 4 [N volatilisation a nd re-deposition], kg N 2 O–N (kg NH 3 –N + NO X –N volatilised) -1 22 0.010 0.002 - 0.05

EF 5 [leaching/runoff], kg N 2 O–N (kg N leaching/runoff) -1 23 0.0075 0.0005 - 0.025
Frac GASF [Volatilisation from synthetic fertiliser], (kg NH 3 –N + NO x –N) (kg N applied) –1 0.10 0.03 - 0.3

Frac GASM [Volatilisation from all organic N fertilisers applied , and dung and urine deposited by grazing animals], (kg NH 3 –N + NO x –N) (kg N applied or deposited) –1 0.20 0.05 - 0.5

Frac LEACH-(H) [N losses by leaching/runoff for regions where Σ (rain in rainy season) - Σ (PE in same period) > soil water holding capacity, OR where irrigation (except drip irrigation) is employed], kg N (kg N additions or deposition by grazing animals) -1 0.30 0.1 - 0.8

Note: The term Frac LEACH previously used has been modified so that it now only applies to regions where soil water-holding capacity is exceeded, as a result of rainfall and/or irrigation (excluding drip irrigation), a nd leaching/runoff occurs, and redesignated as Frac LEACH-(H) . In the definition of Frac LEACH-(H) above, PE is potential evaporation, and the rainy season(s) can be taken as the period(s) when rainfall > 0.5 * Pan Evaporation. (Explanations of po tential and pan evaporation are available in standard meteorological and agricultural texts). For other regions the default Frac LEACH is taken as zero.



## 11.2.2.4 U NCERTAINTY ASSESSMENT

Uncertainties in estimates of indirect N 2 O emissions from managed soils are caused by uncertainties related to natural variability and to the emission, volatilization and le aching factors (see Table 11.3 for uncertainty ranges), activity data, and lack of measurements. Additional uncertainty will be introduced in an inventory when values for these factors that are not represen tative of all conditions in a country are used. In general, the reliability of activity data will be higher than that of the emission, vo latilisation and leaching factors. As with direct emissions, further uncertainties may be caused by missing information on observance of laws and regulations related to handling and application of fertiliser and manure, and changing management practices in farming. Generally, it is difficult to obtain information on the actual observan ce of laws and possible emission reductions achieved as well as information on farming practices. Uncertainties in emission factors are nevertheless likely to dominate and uncertainty ranges are indicated in the tabulatio ns above. For more detailed guidance on uncertainty assessment refer to Volume 1, Chapter 3.

22 The uncertainty range has been widened, in view of results showing that emissi ons from some environm ents, particularly

deciduous forests receiving high rates of N deposition from the a tmosphere, are substantially hi gher than those previously reported (e.g., Butterbach-Bahl et al. , 1997; Brumme et al. , 1999; Denier van der Gon and Bleeker, 2005)., while there is also clear evidence that EFs can be very low (<< 0.01) in low-deposition environments (e.g., Corre et al. , 1999). The mean value of 0.01 has been retained, because it coincides with the revised EF for direct emission from managed land (see Table 11.1 above), and it is recognised that in ma ny countries a substantial fraction of the indirect emissions w ill in fact originat e from managed land.

23 The overall value for the emission factor for leached N (EF 5 ) has been changed from 0.025 to 0.0075 kg N 2 O–N/kg N

leached/ in runoff water. This emission factor incorporates three components: EF 5g , EF 5r and EF 5e , which are the emission factors for groundwater and surface drainage, rivers, and estuaries, respectively. Recent results indicate that the previously used emission factor for groundwater an d surface drainage (0.015) was too high and should be reduced to 0.0025 kg N 2 O– N/kg mineral N (mainly nitrate) leached (Hiscock et al. , 2002, 2003; Reay et al. , 2004, 2005; Sawamoto et al. , 2005). The emission factor for rivers has al so been reduced from 0.0075 kg N 2 O–N/kg N to the same value, 0.0025 kg N 2 O–N/kg N in the water. This is in recognition that wh ile still lower mean values (of the order of 0.0003 to 0.0005) have been reported by, e.g., Dong et al. , (2004) and Clough et al , (2006) for relatively short river systems, there remains the possibility that higher values than those obtained by these authors apply to longer river systems. The value for estuaries remains at 0.0025 kg N 2 O–N/kg N.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.25 

# 11.2.3 Completeness, Time series, QA/QC

COMPLETENESS Complete coverage of the direct and indirect N 2 O emissions from managed land requires estimation of emissions for all of the anthropogenic inputs and activities (F SN , F ON , F CR , F PRP , F SOM and F OS ), if they occur. Experience has shown that none of these sub-categories are likely to be missed in inventories, although countries may have difficulty obtaining accurate statistics for all sub-categories, particularly the amounts of crop residues (by crop type) that are typically returned to soils, and the area of drained/managed organic soils. Currently, the IPCC method does no t explicitly address activities such as plastic sheeting or greenhouse hydroponic systems that may influence N 2 O emissions. These additional activities can be considered if appropriate, and if national activity data for these activ ities are collected. Some of these activities can be readilyincluded in national inventories based on available information. For the additional commercial and noncommercial organic fertilisers, the default emission factor used for applied N may be used. Further research will be required to develop the flux data that are needed to develop emission factors for use of plastic sheeting and hydroponic systems in horticultural areas.


### DEVELOPING A CONSISTENT TIME SERIES Ideally, the same method is used throughout the entire time series. However, it is likely that the detail and disaggregation of emissions estimates from this source ca tegory will improve over time. In cases where some historical data are missing, it may be necessary to derive th e data using other references or data sets. For example, annual data of areas for drained/managed organic soils may need to be derived by interpolation from a longer time series based upon long-term trends (e.g., from decad al statistics over a 20- or 30-year period). Estimates of the amounts of crop residue incorporated annually may also need to be derived based on expert judgment. Interannual changes in Frac GASF , Frac GASM , Frac LEACH , EF 4 , and EF 5 are not expected unless mitigation measures are undertaken. These factors should be changed only with the proper justification and documentation. If updated defaults for any of these variables become available thr ough future research, inventor y agencies may recalculate their historical emissions. It is important that the methods used reflect the results of action taken to reduce emissions and the methods and results are thoroughly documented. If policy measures ar e implemented such that activity data are affected directly (e.g., increased efficiency of fertiliser use resulting in a decrease in fertiliser consumption), the effect of the policy measures on emissions will be transparent, assuming the activity data are carefully documented. In cases where policy measures have an indirect effect on act ivity data or emission factors (e.g., a change in animal population feed practices to improve animal productivity that results in a change in N excretion per head), inventory input data should reflect these effects. The inventory text should thoroughly explain the effect of the policies on the input data. 

### INVENTORY QUALITY ASSURANCE/QUALITY CONTROL (QA/QC) Tier 1 checks of the emission estimates should be undertaken by the persons preparing the inventory along with expert review by people outside the inventory preparati on process. Additional Tier 2 quality control checks and quality assurance procedures may also be applicable, part icularly if higher tier methods are used to determine direct and indirect N 2 O emissions from this source category. Supplement the general QA/QC related to data processing, handling, and reporting, with source-specific category procedures discussed below. The persons who collect data are responsible for reviewing the data collection methods, checking the data to ensure that they are collected and aggregated or disaggrega ted correctly, and cross-checking the da ta with previous years to ensure that the data are reasonable. The basis for the estimates, whether statistical surveys or ‘desk estimates’, must be reviewed and described as part of the QC effort. Documentation is a crucial component of the review process because it enables reviewers to identif y mistakes and suggest improvements.


### Review of emission factors The inventory compiler should review the default emission factors and document the rationale for selecting specific values. If using country-specific factors, the inventory compile r should compare them to the IPCC default emission factors. Also, if accessible, relate to country-specific emission factors used by other countries with comparable circumstances. Differences between country-specific fact ors and default or other country factors should be explained and documented.Volume 4: Agriculture, Forestry and Other Land Use

11.26 2006 IPCC Guidelines for National Greenhouse Gas Inventories 

### Review of any direct measurements If using factors based on direct measurements, the invent ory compiler should review th e measurements to ensurethat they are representative of the actual range of environmental and soil management conditions, and interannual climatic variability, and were developed acc ording to recognised standards (IAEA, 1992). The QA/QC protocol in effect at the sites should also be reviewed and the resulting estimates compared between sites and with default-based estimates. 

### Activity data check The inventory compiler should compare country-specific da ta on synthetic fertiliser consumption with fertiliser usage data from the IFA and synthetic fertiliser consumption estimates from the FAO. The inventory compiler should ensure that N excretion data are consistent with those used for the manure management systems source category. National crop production statistics should be compared to FAO crop production statistics. The inventory compiler should ensure that the QA/QC has been completed for livestock characterisation, because data are shared with the livestock section. Country-specific values for various parameters should be compared to IPCC defaults and any significant differences explained.


### External review The inventory compiler should conduct expert (peer) review when first adopting or revising the method. Given the complexity and uniqueness of the parameters used in calculating country-specific factors for these categories, specialists in the field should be involved in such reviews.


### REPORTING AND DOCUMENTATION
Direct and Indirect N 2 

### O Emissions Document and archive all information required to produce the national emissions inventory estimates. Direct and indirect N 2 O emissions from managed soils are reported in aggregate or disaggregated by land-use category or other subcategory (e.g., ricelands) und er the IPCC category ‘AFOLU’. Repo rting should be done at the same level of disaggregation as was done when calculating the emissions. In addition to completing the reporting formats, the following additional informatio n is necessary to document the estimate: Activity data : Sources of all activity data used in the calcula tions (i.e., complete citations for the statistical databases from which data were coll ected), and in cases when activity da ta were not available directly from databases, the information and assumptions that were used to derive the activity data. This documentation should include the frequency of data collection and estimation, and estimates of accuracy and precision.
Emission factors : The sources of the emission factors that were us ed (specific IPCC default values or otherwise). In inventories in which country- or region-specific emission factors were used, or in which new methods (other than the default IPCC methods) were used, the scientific basis of these emission factors and methods should be completely described and documented. This includes defining the input parameters and describing the process by which these emission factors and methods are derived, as well as describing sources and magnitudes of uncertainties.
Emission results : Significant fluctuations in emissions between years should be explained. A distinction should be made between changes in activity levels and change s in emission, volatilisation and leaching factors from year to year, and the reason s for these changes documented. If different factors are used for different years, the reasons for this should be explained and documented. 

# 11.3 CO 2 

# EMISSIONS FROM LIMING

Liming is used to reduce soil acidity and improve plant growth in managed systems, particularly agricultural lands and managed forests. Adding carbonates to soils in the form of lime (e.g., calcic limestone (CaCO 3 ), or dolomite (CaMg(CO 3 ) 2 ) leads to CO 2 emissions as the carbonate limes di ssolve and release bicarbonate (2HCO 3 - ), which evolves into CO 2 and water (H 2 O). Inventories can be developed using Tier 1, 2 or 3 approaches, with each successive Tier requiring more detail and resources than the previous one. It is good practice for countries to use higher tiers if CO 2 emissions from liming are a key source category.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.27 

# 11.3.1 Choice of method
A decision tree is provided in Figure 11.4 to assist inventory compilers with selection of the appropriate tier.
Tier 1 CO 2 Emissions from additions of carbonate limes to soils can be estimated with Equation 11.12:

E QUATION 11.12 A NNUAL CO 2 EMISSIONS FROM LIME APPLICATION 

## ( ) ( ) Dolomite Dolomite Limestone Limestone EF M EF M Emission C CO • + • = − 2
Where:
CO 2 –C Emission = annual C emissions from lime application, tonnes C yr -1
M = annual amount of calcic limestone (CaCO 3 ) or dolomite (CaMg(CO 3 ) 2 ), tonnes yr -1 EF = emission factor, tonne of C (tonne of limestone or dolomite) -1

Procedural steps for calculations The steps for estimating CO 2 -C emissions from liming are: Step 1: Estimate the total amount (M) of carbonate containing lime applied annually to soils in the country, differentiating between limestone and dolomite (Note: M should include all lime applied to soils even the proportion applied in mixture with fer tilizers). Note that while carbonate limes are the dominant liming material used in managed systems, oxides (e.g., CaO) and hydroxides of lime are used to a limited extent for soil liming. These materials do not contain inorganic carbon and are not included in calculations for estimating CO 2 emissions from application to soils (CO 2 is produced in their manufacture but not following soil application). Step 2: Apply an overall emission factor (EF) of 0.12 for limestone and 0.13 for dolomite. These are equivalent to carbonate carbon contents of the materials (12% for CaCO 3 , 13% for CaMg(CO 3 ) 2 )). The uncertainty is -50% based on approximations suggesting emissions may be less than half of the maximum value, which is the current factor value (West and McBr ide, 2005) (Note: uncertain ties can not exceed the emission factors because these value represent the absolute maximum emissions associated with liming). Step 3: Multiply the total amounts of limestone and dolomite by their respective emission factors, and sum the two values to obtain the total CO 2 –C emission. Multiply by 44/12 to convert CO 2 –C emissions into CO 2 . Tier 2 Tier 2 inventories also use Equation 11.12 and procedural steps, which were provided in the Tier 1 approach, but incorporate country-specific data to derive emission factors (EF). Overall, the CO 2 emissions from liming are expected to be less than using the Tier 1 approach, which assumes that all C in applied lime is emitted as CO 2 in the year of application. However, emissions are likely to be less than assumed using the Tier 1 approach because the amount of CO 2 emitted after liming will depend on sitespecific influences and transport of dissolved inorganic C through rivers and lakes to the ocean. Tier 2 emission factors could be used to better approximate the emissions.
Tier 3 Tier 3 methods use more sophisticated models or measurement procedures, and the procedural steps will depend on the country-specific estimation system. Such an analysis would likely necessitate modelling carbon fluxes associated with primary and secondar y carbonate mineral formation and dissolution in soils, as well as the leaching and transport of dissolved inor ganic C. Note that increases in soil inorganic C or dissolved inorganic C attributed to liming does not constitute a net removal of CO 2 from the atmosphere. Ra ther, carbonate-C from liming that is not returned to the atmosphere is consider ed a net reduction in the emissions associated with this practice. See the Tier 3 section for soil inorganic C in Chapter 2 for additional discussion (Section 2.3.3.1 on Change in Soil C Stocks).Volume 4: Agriculture, Forestry and Other Land Use

11.28 2006 IPCC Guidelines for National Greenhouse Gas Inventories Figure 11.4 Decision tree for identificati on of appropriate tier to estimate CO 2 emissions from liming.

Start

Are CO 2 emissions from liming a key category 1 ?

Collect data for the

Tier 3 or Tier 2 method. Use country-specific data and emission factor for

the Tier 2 method.

No

Note: 1: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resour ces), for discussion of key categories and use of decision trees. Are country-specific data and emission factor available? Yes

Yes

Use data to derive emission factor for Tier 1 method. No

No Box 2: Tier 2 Box 3: Tier 3 Yes Use the detailed activity data for Tier 3 model-basedand/or direct measurementbased method. Are detailed data on lime application available and information to estimate carbonate mineral formation/dissolution, leaching and

transport of inorganic C (for application of modelsand/or measurementbased approaches)?

Are data to derive emission factor available?

Gather data to allow derivation of

emission factor. Yes No

Box 1: Tier 1Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.29 

# 11.3.2 Choice of emission factors

Tier 1 Default emission factors (EF) are 0.12 for limestone and 0.13 for dolomite.
Tier 2 Derivation of emission factors using country-specific data could entail differentiation of sources with variable compositions of lime; different carbonate liming materials (limestone as well as other sources such as marl and shell deposits) can vary somewhat in their C content and overall purity. Each material would have a unique emission factor based on the C content. Country-specific emission factors could also account for the proportion of carbonate-C from liming that is emitted to the atmosphere as CO 2 (e.g., West and McBride, 2005). Disso lved inorganic C in soils can form secondary minerals and precipitate with the Ca or Mg that was added during liming. Furthermore, dissolved inorganic C (bicarbonate) can be transported with Ca and Mg through the soil to deep groundwater, lakes and eventually to the ocean (Robertson and Grace, 2004). In either case, the net emission of CO 2 to the atmosphere is less than the original amount of C added as lime. Coun try-specific emission factors can be derived if there are sufficient data and understanding of inorganic carbon tr ansformations, in addition to knowledge about transport of aqueous Ca, Mg, and inorganic C. It is good practice to document the source of information and method used for deriving country-specific values in the reporting process.
Tier 3 Tier 3 approaches are based on estimating variable emissions from year to year, which depends on a variety of site specific characteristics and environmental driver s. No emission factors are directly estimated. 

# 11.3.3 Choice of activity data

Tier 1 Optimally, national usage statistics for carbonate lime would be available to determine the amount applied annually to soils (M). These data provi de the most direct inference on application. Alternatively, annual sales of carbonate lime may be used to infer the amount that is applied to soils, under the assumption that all lime sold to farmers, ranchers, foresters, etc. is applied during that year. It is also possible to approximate carbonate lime application based on lime availability on an annual basis. Availability is computed based on the new supply for that year (annual domestic mining and import records) minus exports and usage in industrial processes. In the last approach, it is assumed that all available lime is applied in the year of interest. Usage statistics may be gathered as part of the national census or enterprise records, while banks and the lime industry should have information on sales and dom estic production. Import/export records are typically maintained by customs or similar organizations in the government. It is good practice to average data records over three years (current year and two most recent) if emissions are not computed on an annual basis for reporting purposes.
Tier 2 In addition to the activity data that are described for Tier 1, Tier 2 may incorporate information on the purity of carbonate limes as well as site-level and hydrological ch aracteristics to estimate the proportion of carbonate-C in lime applications that is emitted to the atmosphere.
Tier 3 For Tier 3 model-based and/or direct measurement-based in ventories, it is likely that more detailed activity data are needed, relative to Tier 1 or 2 methods, but the exact requirements will be dependent on the model or measurement design. 

# 11.3.4 Uncertainty assessment
Two sources of uncertainty exist for CO 2 emissions from liming: 1) uncertainties in the amount of carbonate lime applied to soils; and 2) uncertainties in the net amount of carbonate-C from liming applications that is emitted as CO 2 . Activity data uncertainties depend on the accuracy of application statistics, sales, import/export records, mining records, and/or usage data. Usage data have the least uncertainty because sales, import/export and mining records have additional uncertainties due to no direct inference about application. Inventory compilers may use a conservative approach and assume that all lime available for application or purchased is applied to soils. This approach may over- or under-estimate emissions in individual years if the total amount of lime available or purchased is not applied in a particular year. Over the long term, this bias should be negligible,Volume 4: Agriculture, Forestry and Other Land Use

11.30 2006 IPCC Guidelines for National Greenhouse Gas Inventories however, assuming there is no long-term stockpiling of lime. Alternatively, inventory compilers can address uncertainties in both the amount of lime available for application and the amount applied in a particular inventory year.
Uncertainties in the net amount of C added to soils from liming that is emitted as CO 2 are dependent on the Tier. Using the Tier 1 method, it is assumed all C in the lime is emitted as CO 2 to the atmosphere. This is a conservative approach, and the default emission factors are considered certain given this assumption. In practice, however, some of the C in lime is likely to be retained in the soil as inorganic C and not emitted as CO 2 , at least in the year of application. Consequently, default emissi on factors can lead to system atic biases in the emission estimates.
It is good practice , therefore, to develop country-specific emission factors or advanced estimation approaches with Tier 2 or 3 methods, particularly if liming is a key source. While the higher tier approaches are likely to limit bias, there may be additional uncertainties associated with these approaches that will need to be addressed. Those uncertainties can stem from insufficient data on site characteristics, hydrology and other environmental variables, which influences the transport and conversion of inorganic C into CO 2 . There may also be uncertainties due to insufficient knowledge about the processes and/or ability of country-specific emission factors or advanced estimation systems to represent the fate of C added to soils in carbonate limes. 

# 11.3.5 Completeness, Time series, QA/QC

COMPLETENESS
Tier 1 Tier 1 inventories are complete if emissions are computed based on a full accounting of all limestone and dolomite applied to soils. Carbonate lime usage statistics provide the most direct infe rence on applications to soils. However, sales records or mining data combined with import/export and industrial processing records provide sufficient information to approximate the amount of lime applied to soils. If current data are not sufficient due to incomplete records, it is good practice to gather additional data for future inventory reporting, particularly if liming C emissions are a key source category.
Tier 2 Completeness in Tier 2 inventories is dependent on the adequacy of the activity data (see Tier 1), but will also depend on additional country-specific data that were used to refine emission factors. This may include the availability of data about the purity of lime and/or site-level and hydrological data to better specify emission factors relating the amount of CO 2 released per amount of C added to soils in carbonate lime.
Tier 3 Beyond the considerations for Tier 1 and 2, completeness of Tier 3 inventories is also dependent on the data needs and representativeness of the measurement design and/or modelling framework. Inventory compilers should review their approach and determine if the advanced estimation system are adequate to address the net release of CO 2 from carbonate limes applied to soils. If gaps or limitations are identified, it is good practice to gather additional data so that the fate of liming carbonates is fully addressed by the Tier 3 method.


### TIME SERIES CONSISTENCY
Tier 1 The same activity data and emissions factors should be a pplied across the entire time series for consistency. At the Tier 1 level, default emission factors are used so consistency is not an issue for this component. However, the basis for the activity data may change if new data are gathered, such as a statistical survey compiling information on applications to soils versus older activity data relying strictly on mining and import/export records. While it is good practice for the same data protocols and procedures to be used across the entire time series, in some cases this may not be possible, and inventory compilers should determine the influence of changing data sources on the trends. Guidance on recalculation for these circ umstances is presented in Volume 1, Chapter 5.
Tier 2 Consistency in activity data records across the time series is important for Tier 2 inventories (see Tier 1). In addition, new factors that are develope d based on country-specific data should also be applied across the entire time series. In rare cases when this is not possible, inventory compilers should determine the influence of changing emission factors on the tr ends; additional guidance on recalculation for these circumstances can be found in Volume 1, Chapter 5.
Tier 3 Similar to Tier 2, it is good practice to apply the country-specific estimation system throughout the entire time series; inventory agencies should use the same measurement protocols (sampling strategy, method, etc.) and/or model-based system throughout the inventory time period.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.31 

### QUALITY ASSURANCE/QUALITY CONTROL
Tier 1 It is good practice to implement Quality Assurance/Quality Contro ls with internal and independent reviews of inventory data and results, ensuring: 1) activity data have been processed appropriatel y to estimate application to soils; 2) activity data have been properly transcribed in to the worksheets or inventory computation software; and 3) emission factors have been assigned appropriately. Internal reviews should be conducted by the inventory compiler(s), and may involve visual inspection as well as built-in program functions to check data entry and results . Independent reviews are conducted by other agencies, experts or groups who are not directly involved with the compilation. These reviews need to consider the validity of the inventory approach, thoroughness of inventory documentation, methods explanation and overall transparency.
Tier 2 In addition to the Quality Assurance/Quality Control measures under Tier 1, the inventory compiler should review the country-specific emission factors for Tier 2 inventories. If using factors based on direct measurements, the inventory compiler shou ld review the measurements to ensu re that they are representative of the actual range of environmenta l conditions. If accessible, it is good practice to compare the country-specific factors with Tier 2 emission factors used by other countr ies with comparable circumstances, in addition to the IPCC defaults. Given the complexity of inorganic C transfor mation, specialists in the field should be involved in the review process to provide an independent critique of the emission factors.
Tier 3 Country-specific inventory systems will likely need additional Quality Assurance/Quality Control measures, but this will be dependent on the systems that are developed. It is good practice to develop a Quality Assurance/Quality Control protocol that is specific to the country’s advanced es timation system, archive the reports, and include summary resu lts in reporting documentation.


### REPORTING AND DOCUMENTATION
Tier 1 For Tier 1, inventory compilers should document trends and uncertainties in carbon lime applications to soils and relate those patterns to the CO 2 emission trends. Significant fluctuations in annual emissions across the time series should be explained.
It is good practice to archive actual databases, such as mining records or usage statistics from surveys, and procedures used to process the data (e.g., statistical programs). The worksheets or inventory software, which was used to estimate emissions, should be archived along with input/output files that were generated to produce the results. In cases where activity data are not available directly fr om databases or multiple data sets were combined, the information, assumptions and procedures that were used to derive the activity data should be described. This documentation should include the frequency of data collection and estimation, and uncertainty. Use of expert knowledge should be documented and correspondences archived.
Tier 2 In addition to the considerations for Tier 1, inventory compilers should document the underlying basis for country-specific emission factors, as we ll as archive metadata and data sources used to estimate country-specific values. Reporting documentation should include the new factors (i.e., means and uncertainties), and it is good practice to include a discussion in the inventory report about differences between these values and default factors or country-specific factors from regions with similar circumstances to those of the reporting country. When discussing trends in emissions and removals from year to year, a distinction should be made between changes in activity levels and changes in methods, includi ng emission factors, and th e reasons for these changes need to be documented.
Tier 3 Tier 3 inventories need similar documentation about act ivity data and emission/removal trends as lower tier approaches, but additional documentation should be included to explain the underlying basis and framework of country-specific estimation systems. With measurement-based inventories, it is good practice to document the sampling design, laboratory procedures and data analysis techniques. Measurement data should be archived, along with results from data analyses. For Tier 3 approa ches using modelling, it is good practice to document the model version and provide a model description, as well as permanently archive copies of all model input files, source code and executable programs.Volume 4: Agriculture, Forestry and Other Land Use

11.32 2006 IPCC Guidelines for National Greenhouse Gas Inventories 

# 11.4 CO 2 

# EMISSIONS FROM UREA FERTILIZATION

Adding urea to soils during fertilisation leads to a loss of CO 2 that was fixed in the industrial production process. Urea (CO(NH 2 ) 2 ) is converted into ammonium (NH 4 + ), hydroxyl ion (OH - ), and bicarbonate (HCO 3 - ), in the presence of water and urease enzymes. Similar to the soil reaction following addition of lime, bicarbonate that is formed evolves into CO 2 and water. This source category is included because the CO 2 removal from the atmosphere during urea manufacturing is estimated in th e Industrial Processes and Product Use Sector (IPPU Sector). Inventories can be developed using Tier 1, 2 or 3 approaches, with each successive Tier requiring more detail and resources than the previous. It is good practice for countries to use higher tiers if CO 2 emissions from urea are a key source category.



# 11.4.1 Choice of method
A decision tree is provided in Figure 11.5 to assist inventory compilers with selection of the appropriate tier.
Tier 1 CO 2 emissions from urea fertilisation can be estimated with Equation 11.13:

E QUATION 11.13 A NNUAL CO 2 EMISSIONS FROM UREA APPLICATION EF M Emission C CO • = − 2
Where:
CO 2 –C Emission = annual C emissions from urea application, tonnes C yr -1 M = annual amount of urea fertilisation, tonnes urea yr -1 EF = emission factor, t onne of C (tonne of urea) -1

Procedural Steps for Calculations The steps for estimating CO 2 –C emissions from urea applications are: Step 1: Estimate the total amount of urea applied annually to a soil in the country (M). Step 2: Apply an overall emission factor (EF) of 0.20 for urea, which is equivalent to the carbon content of urea on an atomic weight basis (20% for CO(NH 2 ) 2 ). A default -50% uncertainty may be applied (Note: uncertainties can not exceed the default emission f actor because this value represents the absolute maximum emissions associated with urea fertilization). Step 3: Estimate the total CO 2 –C emission based on the product of the amount of urea applied and the emission factor. Multiply by 44/12 to convert CO 2 –C emissions into CO 2 . Urea is often applied in combination with other nitrogenous fertilizers, particularly in solutions, and it will be necessa ry to estimate the pr oportion of urea in the fertilizer solution for M. If the propor tion is not known, it is considered good practice to assume that the entire solution is urea, rather than potentially under-estimating emissions for this sub-category.
Tier 2 Tier 2 inventories also use Equation 11.13 and procedural steps, which were provided in the Tier 1 approach, but incorporate country-specifi c information to estimate emission factors.
Tier 3 CO 2 emissions from urea applications could be estimated with more detailed models or measurements that incorporate the possibility of bicarbonate leaching to deep groundwater, and/or lakes and oceans, and thus not contributing to CO 2 emissions, at least not immediately. Note th at increases in soil inorganic C from urea fertilization do not represent a net removal of CO 2 from the atmosphere. The removal is estimated in the IPPU Sector (Volume 3), and the computations for soils only provide estimates of the amount of emissions associated with this practice. See the Tier 3 section for soil inorga nic C in Chapter 2 for additional discussion (Section 2.3.3 on Change in Soil C Stocks).Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.33 Figure 11.5 Decision tree for identificati on of appropriate tier to estimate CO 2 emissions from urea fertilisation

Start

Are CO 2 emissions from urea

fertilization a key category 1 ?

Collect data for the

Tier 3 or Tier 2 method. Use country-specific data and emission factor for

the Tier 2 method.

No

Note: 1: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resour ces), for discussion of key categories and use of decision trees. Are country-specific data and emission factor available? Yes

Yes

Use data to derive emission factor for Tier 1 method. No

No Box 2: Tier 2 Box 3: Tier 3 Yes Use the detailed activity data for Tier 3 model-basedand/or direct measurementbased method. Are detailed data on urea application available and information to estimate leaching and transport of inorganic C (for application of models and/or measurement-based approaches)?

Are data to derive emission factor available?

Gather data to allow derivation of

emission factor. Yes No

Box 1: Tier 1Volume 4: Agriculture, Forestry and Other Land Use

11.34 2006 IPCC Guidelines for National Greenhouse Gas Inventories 

# 11.4.2 Choice of emission factor

Tier 1 The default emission factor (EF) is 0.20 for carbon emissions from urea applications.
Tier 2 Similar to carbonate limes, all C in urea may not be emitted in the year of application. If sufficient data and understanding of inorganic C transformation are available, country-specific specific emission factor could be derived. It is good practice to document the source of information and method used for deriving country-specific values as part of the reporting process.
Tier 3 Tier 3 approaches are based on estimating variable emissions from year to year, which depends on a variety of site specific characteristics and environmental driv ers. No emission factor is directly estimated. 

# 11.4.3 Choice of activity data

Tier 1 Domestic production records and import/export data on ur ea can be used to obtain an approximate estimate of the amount of urea applied to soils on an annual basis (M). It can be assumed that all urea fertiliser produced or imported annually minus annual exports is applied to soils. However, supplemental data on sales and/or usage of urea can be used to refine the calcu lation, instead of assuming all available urea in a particular year is immediately added to soils. Regardless of the approach , the annual application estimates for urea fertilizers should be consistent between CO 2 emission from urea and N 2 O emissions from soils. Usage statistics may be gathered as part of the national ce nsus or through enterprise records, while banks and the fertilizer industry should have information on sales and domestic production. Import/export records are typically maintained by customs or similar organizations in the government. It is good practice to average data records over three years (current year and two most recent) if emissions are not computed on an annual basis for reporting purposes.
Tier 2 In addition to the activity data described for Tier 1, Tier 2 may incorporate additional information on site-level and hydrological characteristics that were used to estima te the proportion of C in urea that is emitted to the atmosphere.
Tier 3 For application of dynamic models and/or a direct measurement-based inventory in Tier 3, it is likely that more detailed activity data are needed, relative to Tier 1 or 2 methods, but the exact requirements will be dependent on the model or measurement design. 

# 11.4.4 Uncertainty assessment
Two sources of uncertainty exist for CO 2 emissions from urea: 1) uncertainties in the amount of urea applied to soils; and 2) uncertainties in the net amount of urea-C that is emitted as CO 2 . Activity data uncertainties will depend on the accuracy of pr oduction, sales, import/export, and/or usag e data. Usage and sales data are likely to have the least uncertainty; import/export and production data have additional uncertainties due to inferences about application. Inventory compilers may use a conser vative approach and assume that all urea available for application or purchased is applied to soils. This approach may create over- or under-estimates in individual years if the total amount of urea available or purchased is not applied in a particular year. Over the longer-term this bias should be negligible, however, assuming th ere is no long-term stoc kpiling of urea fertiliser. Alternatively, inventory compilers can address uncertainties in both the amount of urea available for application and the amount applied in a particular inventory year.
Uncertainties in the net amount of C added to so ils from urea fertilization that is emitted as CO 2 are dependent on the Tier. Using the Tier 1 method, it is assumed all C in the urea is lost as CO 2 from the atmosphere. This is a conservative approach, and the defau lt emission factors are considered ce rtain (given this assumption). In practice, however, some of the C in urea may be re tained in the soil as inorganic C and not emitted as CO 2 , at least in the year of application. Consequently, default emission factors can lead to systematic biases in the emission estimates.
It is good practice , therefore, to develop country-specific emission factors or advanced estimation approaches with Tier 2 or 3 methods, respectively, particularly if urea-C is a key source. While the higher tier approachesChapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.35 are likely to limit bias, there are additional uncertainties th at will need to be addressed. Those uncertainties can stem from insufficient data on site characteristics, hydrology, and other environmental variables which influences the transport and conversion of inorganic C into CO 2 . There may also be uncertainties due to insufficient knowledge about the processes and/or the ability of country-specific emission factors or estimation systems to represent the fate of urea-C. 

# 11.4.5 Completeness, Time series consistency, QA/QC

COMPLETENESS
Tier 1 Tier 1 inventories are complete if emi ssions are computed based on a full accounting of all urea that is applied to soils. Urea usage statistics or sales provide the most direct inference on applications to soils, but production and import/export records are sufficient for making an approximat e estimate of the amount of urea applied to soils. If current data are not sufficient due to incomplete records, it is good practice to gather additional data for future inventory reporting, particularly if urea-C emissions are a key source category.
Tier 2 Completeness in Tier 2 inventories is also dependent on the adequacy of the activity data (see Tier 1), but will also depend on additional country-specific data that was used to refine emission factors. This may include the availability of data about site-level and hydrological data that are used to better specify emission factors relating the amount of CO 2 released per amount of urea-C added to soils.
Tier 3 Beyond the considerations for Tiers 1 and 2, completeness of Tier 3 inventories is also dependent on the data needs and representativeness of the measurement design and/or modelling framework. Inventory compilers should review their approach and dete rmine if the advanced estimation syst em is adequate to address the net release of CO 2 from urea applied to soils. If gaps or limitations are identified, it is good practice to gather additional data so that the fate of urea-C is fully addressed by the Tier 3 method.


### TIME SERIES CONSISTENCY
Tier 1 The same activity data and emissions factors should be a pplied across the entire time series for consistency. At the Tier 1 level, default emission factors are used so consistency is not an issue for this component. However, the basis for the activity data may change if new data are gathered, such as a statistical survey compiling information on urea applications to soils versus older activity relying strictly on domestic production and import/export data. While it is good practice for the same data protocols and procedures to be used across the entire time series, in some cases this may not be possible, and inventory compilers should determine the influence of changing data sources on the trends. Guidan ce on recalculation for these circumstances is presented in Volume 1, of Chapter 5.
Tier 2 Consistency in activity data records across the time series is important for Tier 2 inventories (see Tier 1). In addition, new factors that are developed based on country-specific data should be applied across the entire time series. In rare cases when this is not possible, invent ory compilers should determine the influence of changing emission factors on the trends; add itional guidance on recalculation for th ese circumstances can be found in Volume 1, Chapter 5.
Tier 3 Similar to Tier 2, it is good practice to apply the country-specific estimation system throughout the entire time series; inventory agencies should use the same measurement protocols (sampling strategy, method, etc.) and/or model-based system throughout the inventory time period. 

### QUALITY ASSURANCE/QUALITY CONTROL
Tier 1 It is good practice to implement Quality Assurance/Quality Contro ls with internal and independent reviews of inventory data and results, ensuring 1) activity data have been processed appropriately to estimate application to soils, 2) activity data have been properly transcribed in to the worksheets or inventory computation software, and 3) emission factors have been assigned appropriately. Internal reviews should be conducted by the inventory compiler(s), and may involve visual inspection as well as built-in program functions to check data entry and results . Independent reviews are conducted by other agencies, experts or groups who are not directly involved with the compilation. These reviews need to consider the validity of the inventory approach, thoroughness of inventory documentation, methods explanation and overall transparency.Volume 4: Agriculture, Forestry and Other Land Use

11.36 2006 IPCC Guidelines for National Greenhouse Gas Inventories Tier 2 In addition to the Quality Assurance/Quality Control measures under Tier 1, the inventory compiler should review the country-specific emission factors for Tier 2 inventories. If using factors based on direct measurements, the inventory compiler shou ld review the measurements to ensu re that they are representative of the actual range of environmenta l conditions. If accessible, it is good practice to compare the country-specific factors with Tier 2 emission factors used by other countr ies with comparable circumstances, in addition to the IPCC defaults. Given the complexity of inorganic C transformations, specialists in the field should be involved in the review process, to provide an in dependent critique of the emission factors.
Tier 3 Country-specific inventory systems will likely need additional Quality Assurance/Quality Control measures, but this will be dependent on the systems that are developed. It is good practice to develop a Quality Assurance/Quality Control protocol that is specific to the country’s advanced es timation system, archive the reports, and include summary resu lts in reporting documentation.


### REPORTING AND DOCUMENTATION
Tier 1 For Tier 1, inventory compilers should document trends an d uncertainties in urea applications to soils and relate those patterns to the CO 2 emission trends. Significant fluctuations in annual emissions across the time series should be explained.
It is good practice to archive databases, such as domestic pro duction, import/export records or usage statistics from surveys, and procedures used to process the data (e.g., statistical programs). The worksheets or inventory software, which was used to estimate emissions, should be archived along with the input/output files that were generated to produce the results. In cases where activity data are not available directly fr om databases or multiple data sets were combined, the information, assumptions and procedures that were used to derive the activity data should be described. This documentation should include the frequency of data collection and estimation, and uncertainty. Use of expert knowledge should be documented and correspondences archived.
Tier 2 In addition to the considerations for Tier 1, inventory compilers should document the underlying basis for country-specific emission factors, as we ll as archive metadata and data sources used to estimate country-specific values. Reporting documentation should include the new factors (i.e., means and uncertainties), and it is good practice to include a discussion in the inventory report about differences between country-specific factors and default values or country-specific factors from regions with similar circumstances to those of the reporting country. When discussing trends in emissions and removals from year to year, a distinction should be made between changes in activity levels and changes in methods, includi ng emission factors, and th e reasons for these changes need to be documented.
Tier 3 Tier 3 inventories need similar documentation about act ivity data and emission/removal trends as lower tier approaches, but additional documentation should be included to explain the underlying basis and framework for country-specific estimation systems. With measurement-based inventories, it is good practice to document the sampling design, laboratory procedures and data analysis techniques. Measurement data should be archived, along with results from data analyses. For Tier 3 approaches using modelling, it is good practice to document the model version and provide a model description, as well as to permanently archive copies of all model input files, source code and executable programs.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.37 

# Annex 11A.1 References for crop residue data in Table 11.2
I. Dry matter fraction of harvested product Lander, C.H., Moffitt, D., and Alt, K. (1998). Nutrients av ailable from livestock manure relative to crop growth requirements. Resource Assessment and Strategic Planning Working paper 98-1. USDA Natural Resource Conservation Service. http://www.nhq.nrcs.usda.gov/land/pubs/. 

### II. Above-ground residue dry matter 1. Maize Ames, J.W., and Simon, R.H. (1924). Soil potassium as affected by fertilizer treatment and cropping. Bulletin 379. Ohio Agricultural Experiment Station, Wooster, Ohio. Anonymous (1924). Forty-third annual report for 1923-24. Ohio Agricultural Experiment Station Bull. 382. Wooster, OH. Anonymous (1926). Forty-fourth annual report for 1924-25. Ohio Agricultural Experiment Station Bull. 392. Wooster, OH. Bustillo, J. J. and Gallaher, R.N. (1989). Dry matter Par titioning in No-tillage Tropical Corn in Florida. p.40-42. In I. D. Teare, E. Brown, and C. A. Trimble (ed.) 1989 Southern Conservation Tillage Conference. SB 89- 1. Tallahassee, FL. 12-13 July, 1989. Univ. of Fla., North Fla. Res. and Educ. Ctr., Quincy, FL 32351. Buyanovsky, G.A. and Wagner, G.H. (1986). Post-harvest residue input to cropland. Plant and Soil 93 :57-65. Donald, C.M. and Hamblin, J. (1976). The biological yield and harvest index of cereals as agronomic and plant breeding criteria. Adv. Agron., 28 : 361-405. Fisher, K.S. and Palmer, A.F.E. (1983). Maize. In: Symposium on Potential Productivity of Field Crops Under Different Environments. International Rice Research Institute, Manila, Philippines. Graybill, J.S., Cox, W.J. and Otis, D.J. (1991). Yield and quality of forage maize as influenced by hybrid, planting date, and plant density. Agronomy Journal , 83 : 559-564. Hutcheson, T.B., Hodgson, E.R., and Wolfe, T.K. (1917). Corn culture. Virginia Agricultural Experiment Station Bull. 214. Virginia Polytechnic Institute, Blacksburg, VA. Jones, J.N. Jr., Moody, J.E., and L illard, J.H. (1969). Effects of tillage, no tillage, and mulch on soil water and plant growth. Agron. J

. 61 :719-721. Jones Jr., J.N., Moody, J.E., Shear, G.M., Moschler, W.W. and Lillard, J. H. (1968). The no-tillage system for corn (Zea mays L.). Agron. J

. 60 :17-20. Noll, C.F., Gardner, F.D. and Irvin, C.J (1930). A field test of different sources of phosphorous. School of Agriculture and Experiment Station Bull . 252. Pennsylvania State College Noll, C.F., Gardner, F.D. a nd Irvin, C.J. (1931). Fiftieth anniversar y of the General Fertilizer Tests 1881-1931. School of Agriculture and Experiment Station Bull . 264. Pennsylvania State College. Ockerby, S.E. and Fukai, S. (2001). The management of rice grown on raised beds with continuous furrow irrigation. Field Crops Res

. 69 :215-226. Peters, S.E., Wander, M.M., Saparito, L.S., Harris, G.H. and Friedman, D.B. (1997). Management impacts on SOM and related soil properties in a long-term farmi ng systems trial in Pennsylvania: 1981-1991. In: Soil Organic Matter in Temperate Agroecos ystems. E.A. Paul. K. Paustian, E. T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Peterson, G.A. and Westfall, D.G. (1997). Management of dryland agroecosystems in the Central Great Plains of Colorado. In: Soil Organic Matter in Temperate Agroecos ystems. E.A. Paul. K. Pa ustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Peterson, G.A., Westfall, D.G. and Wood, W. (1989). Crop and soil management in dryland agroecosystems. Technical Bulletin TB89-3. Dept. of Ag ronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Poss, D. , Larson, K. and Thompson, D.L. (1997). Sustainable dryland agroecosystem management. Technical Bulletin TB97-3. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., McGee, E. and Kolberg, R. (1992). Crop and soil management in dryland agroecosystems. Technical Bulletin TB92-2. Dept. of Agronomy, Colorado State University.Volume 4: Agriculture, Forestry and Other Land Use

11.38 2006 IPCC Guidelines for National Greenhouse Gas Inventories Peterson, G.A., Westfall, D.G., Sherrod, L., McGee, E. and Kolberg, R. (1991). Crop and soil management in dryland agroecosystems. Technical Bulletin TB91-1. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Kolber g, R. and Rouppet, B. (1994). Sustainable dryland agroecosystem management. Technical Bulletin TB94-1. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Kolber g, R. and Rouppet, B. (1993). Sustainable dryland agroecosystem management. Technical Bulletin TB93-4. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Kolberg, R. and Poss, D. (1996). Sustai nable dryland agroecosystem management. Technical Bulletin TB96-1. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Kolberg, R. and Poss, D. (1995). Sustai nable dryland agroecosystem management. Technical Bulletin TB95-1. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Wood, W. and Ross, S. (1988). Crop and soil management in dryland agroecosystems. Technical Bulletin LTB88-6. Dept . of Agronomy, Colorado State University. Peterson, G.A., McGee, E., Westfall, D.G., Wood, C.W. and Sherrod, L. (1990). Crop and soil management in dryland agroecosystems. Technical Bulletin TB90-1. Dept. of Agronomy, Colorado State University. Pierce, F.J. and Fortin, M.C. (1997 ). Long-term tillage and periodic plowing of a no-tilled soil in Michigan: Impacts, yield, and soil organic matter. In: Soil Orga nic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C. V. Cole (eds.). CRC Press, Inc. Russell, W.A. (1991). Genetic improvement of maize yields. Advances in Agronomy 46 :245-298. Sewell, M.C. and Call, L.E. (1925) . Tillage investigations relating to wheat production. Tech. Bull. 18. Agricultural Experiment Station. Kansas State Agricultural College. Shear, G.M. and Moschler, W.W. (1969). Continuous co rn by the no-tillage and conventional tillage methods: A six-year comparison. Agron. J. 61 :524-526. Tapper, D.C. (1983). Changes in physiological traits a ssociated with grain yield improvement in single-cross maize hybrids from 1930 to 1970. Ph.D. Dissertation. Agronomy Department, Iowa State University, Ames, IA. Thorne, C.E. (1924). The maintenance of soil fertility. Bulletin 381. Ohio Agricultural Experiment Station. Throckmorton, R.I. and Duley, F.L. (1935). Twenty years of soil fertility investigations. Kansas. Agricultural Experiment Station Tech. Bull. 40. Kansas State College of Agriculture and Applied Science. Manhattan, KS. Vanotti, M.B., Bundy, L.G. and Peters on, A.E. (1997). Nitrogen fertilizer and legume-cer eal rotation effects on soil productivity and organic matter dynamics in Wisconsin. In Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Wianco, A.T. and Jones, S.C. (1918). The value of manure on Indiana soils. Purdue University Agricultural Experiment Station. Bull. No. 222. Lafayette, IN. Wianco, A.T., Conner, S.D. and Jones, S.C. (1919). The value of legumes on Indiana soils. Purdue University Agricultural Experiment Station. Bull. No. 226. Lafayette, IN.


### 2. Winter wheat Austin, R.B., Bingham, J., Blackwell, R.D., Evans, L.T., Ford, M.A., Morgan, C.L. and Taylor, M. (1980). Genetic improvements in winter wheat yields si nce 1900 and associated physiological changes. J. Agric. Sci ., Camb. 94 :675-689. Barraclough, P.B. and Leigh, R.A. (1984). The growth and activity of winter wheat root s in the field: the effect of sowing date and soil type on root growth of high yielding crops. J. Agric. Sci., Camb

. 103 :59-74. Black, A.L. and Tanaka, D.L. (1997). A conservation tillage-cropping systems study in the Northern Great Plains of the United States. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul, K. Paustian, E.T. Elliott and C.V. Cole (eds.). CRC Press, Inc. Bolinder, M.A., Angers, D.A. and Dubuc, J.P. (1997). Estimating shoot to root ratios and annual carbon inputs in soils for cereal crops. Agriculture, Ecosystems and Environment 63 :61-67. Bruce, R.R. and Langdale, G.W. (19 97). Soil carbon level dependence upon cr op culture variables in a thermicudic region. In: Soil Organic Matter in Temperate Agro ecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.39 Bruckner, P.L. and Morey, D.D. (1988). Nitrogen effects on soft red winter wheat yield, agronomic characteristics, and quality. Crop Sci. 28 :152-157. Buyanovsky, G.A. and Wagner, G.H. (1986). Post-harvest residue input to cropland. Plant and Soil 93 :57-65. Cox, T.S., Shroyer, J.P., Liu, B.-H., Sears, R.G. and Martin, T.J. (1988). Genetic improvements in agronomic traits of hard red winter wheat cultivars from 1919 to 1987. Crop Science 28 :756-760. Donald, C.M. and Hamblin, J. (1976). The biological yield and harvest index of cereals as agronomic and plant breeding criteria. Adv. Agron. , 28 : 361-405. Eck, H.V. (1986). Profile modification and irrigation effects on yield and water use of wheat. Soil Sci. Soc. Am . J. 50 :724-729. Entz, M.H. and Fowler, D.B. (1991). Agronomi c performance of winter versus spring wheat. Agron. J. 83 :527- 532. Gent, M.P.N. and Kiyomoto, R.K. (1989). Assimilation and distribution of photosynthate in winter wheat cultivars differing in harvest index. Crop Sci

. 29 :120-125. Halvorson, A.D., Vigil, M. F., Peterson, G.A. and Elliott, E.T. (1 997). Long-term tillage and crop residue management study at Akron, Colorado. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C. V. Cole (eds.). CRC Press, Inc. Ishaq, M., Ibrahim, M. and Lal, R. (2001). Tillage effect on nutrient uptake by wheat and cotton as influenced by fertilizer rate. Soil and Tillage Res

. 62 :41-53. Jensen, M.E. and Sletten, W.H. (1965). Evapotranspira tion and soil moisture-fertilizer interrelations with irrigated winter wheat in the Southern High Plains . USDA Agricultural Resear ch Service. Conservation Research Report No. 4. Jones, O.R., Stewart, B.A. and Unger, P.W. (1997). Management of dry-farmed Southern Great Plains soils for sustained productivity. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Lafever, H.N. (1976). Ohio performance trials of soft red winter wheats including 1976 results. Agronomy Dept. Series 203. Ohio Agricultural Research and Development Center. Wooster, OH. Lyon, D.J., Monz, C.A., Brown, R.E. and Metherell, A. K. (1997). Soil organic matte r changes over two decades of winter wheat – fallow cropping in western Ne braska. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Miller, C.M. (1939). A physiological study of the winter wh eat plant at different stages of its development. Tech. Bull. 47. Agricultural Experiment Station. Kansas State College of Agriculture and Applied Science. Musick, J.T. and Dusek, D.A. (1980). Planting date and water deficit effects on development and yield of irrigated winter wheat. Agron. J

. 72 : 45-52. Noll, C.F., Gardner, F.D. a nd Irvin, C.J. (1931). Fiftieth anniversar y of the General Fertilizer Tests 1881-1931. Pennsylvania State College School of Agriculture and Experiment Station Bull. 264. Noll, C.F., Gardner, F.D. and Irvin, C.J (1930). A field test of different sources of phosphorous. Pennsylvania State College School of Agriculture and Experiment Station Bull. 252. Peterson, G.A. and Westfall, D.G. (1997). Management of dryland agroecosystems in the Central Great Plains of Colorado. In: Soil Organic Matter in Temperate Agroecos ystems. E.A. Paul, K. Pa ustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Peterson, G.A., Westfall, D.G. and Wood, W. (1989). Crop and soil management in dryland agroecosystems. Technical Bulletin TB89-3. Dept. of Ag ronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Poss, D. , Larson, K. and Thompson, D.L. (1997). Sustainable dryland agroecosystem management. Technical Bulletin TB97-3. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., McGee, E. and Kolberg, R. (1992). Crop and soil management in dryland agroecosystems. Technical Bulletin TB92-2. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., McGee, E. and Kolberg, R. (1991). Crop and soil management in dryland agroecosystems. Technical Bulletin TB91-1. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Kolber g, R. and Rouppet, B. (1994). Sustainable dryland agroecosystem management. Technical Bulletin TB94-1. Dept. of Agronomy, Colorado State University.Volume 4: Agriculture, Forestry and Other Land Use

11.40 2006 IPCC Guidelines for National Greenhouse Gas Inventories Peterson, G.A., Westfall, D.G., Sherrod, L., Kolber g, R. and Rouppet, B. (1993). Sustainable dryland agroecosystem management. Technical Bulletin TB93-4. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Kolberg, R. and Poss, D. (1996). Sustai nable dryland agroecosystem management. Technical Bulletin TB96-1. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Kolberg, R. and Poss, D. (1995). Sustai nable dryland agroecosystem management. Technical Bulletin TB95-1. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Wood, W. and Ross, S. (1988). Crop and soil management in dryland agroecosystems. Technical Bulletin LTB88-6. Dept . of Agronomy, Colorado State University. Peterson, G.A., McGee, E., Westfall, D.G., Wood, C.W. and Sherrod, L. (1990). Crop and soil management in dryland agroecosystems. Technical Bulletin TB90-1. Dept. of Agronomy, Colorado State University. Rao, S.C., Coleman, S.W. and Volesky, J.D. (2000). Yiel d and quality of wheat, triticale, and elytricum forage in the Southern Plains. Crop Sci

. 40 :1308-1312. Rasmussen, P.E. and Parton, W.J. (1994). Long-term effects of residue management in wheat-fallow: I. Inputs, yield, and soil organic matter. Soil Sci. Soc. Am. J. 58 :523-530. Rasmussen, P.E., Smiley, R.W. and Albrecht, S.L. (1996). Long-term residue management experiment: Pendleton, Oregon, USA. IN: Evaluation of Soil Organic Matter Models Using Existing Long-term Datasets. D. S. Powlson, P. Smith, and J. U. Smith (eds.). Springer-Verlag, Germany. Sewell, M.C. and Call, L.E. (1925). Tillage investigations relating to wheat production. Tech. Bull. 18. Sharma, R.C. and Smith, E.L. (1986). Selection for high and low harvest index in three winter wheat populations. Crop Sci

. 26 :1147-1150. Siddique, K.H.M., Belford, R.K., Perry, M.W. and Tennant, D. (1989). Growth, development and light interception of old and modern wheat cultiv ars in a Mediterranean -type environment. Aust. J. Agric. Res

. 40 :473-487. Singh, I.D. and Stoskopf, N.C. (1971). Harvest index in cereals. Agron. J

. 63 :224-226. Skidmore, E.L. and Siddoway, F.H. (1978). Crop residue requirements to control wind erosion. Chapter 2 in Crop Residue Management Systems. Am. Soc. Agron. Spec. Pub. No. 31. Ten Eyck, A. M. and Shoesmith, V. M. (1907). Small grain crops. Agricultural Experiment Station Bulletin 144. Kansas State Agricultural College. Manhattan, KS. Thorne, C.E. (1924). The maintenance of soil fertility. Ohio Agricultural Experiment Station Bull. 381. Throckmorton, R.I. and Duley, F.L. (1935). Twenty years of soil fertility investigations. Kansas. Agricultural Experiment Station Tech. Bull. 40. Kansas State College of Agriculture and Applied Science. Manhattan, KS. Unger, P.W. (1977). Tillage effects on winter wheat production where the irrigated and dryland crops are alternated. Agronomy Journal , 69 : 944 – 950. Weir, A.H. and Barraclough, P.B. (1986). The effect of drought on the root growth of winter wheat and on its uptake from deep loam. Soil Use And Management 2 :91-96. Wianco, A.T. and Jones, S.C. (1918). The value of manure on Indiana soils. Purdue University Agricultural Experiment Station. Bull. No. 222. Lafayette, IN. Wianco, A.T., Conner, S.D. and Jones, S.C. (1919). The value of legumes on Indiana soils. Purdue University Agricultural Experiment Station. Bull. No. 226. Lafayette, IN. 

### 3. Spring wheat Bauer, A. and Zubriski, J.C. (1978). Hard red spring wheat straw yields in relation to grain yields. Soil Sci. Soc. Am. J. 42 :777-781. Black, A.L. and Tanaka, D.L. (1997). A conservation tillage-cropping systems study in the Northern Great Plains of the United States. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Campbell, C.A. and Zentner, R.P. (1993). Soil organic matter as influenced by crop rotations and fertilization. Soil Sci. Soc. Am. J

. 57 :1034-1040.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.41 Campbell, C.A., Davidson, H.R. and Ward er, F.G. (1977). Effects of fertilizer N and soil moisture on yield, yield components, protein content and N accumulation in the above-ground parts of spring wheat. Can. J. Soil Sci

. 57 :311-327. Cassman, K.G., Bryant, D.C., Fulton, A.E. and Jackson, L.F. (1992). Crop ecology, production and management. Crop Sci. 32 :1251-1258. Hucl, P. and Baker, R.J. (1987). A study of ancestral and modern Canadian spring wheats. Can. J. Plant Sci

. 67 :87-97. Juma, N.G., Izaurralde, R.C., Robertson, J.A. and McGill, W.B. (1997). Crop yield and soil organic matter trends over 60 years in a typic cryoboralf at Breton, Alberta. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Loffler, C.M., Rauch, T.L. and Busch, R.H. (1985). Grain and plant protein relationships in hard red spring wheat. Crop Sci

. 25 :521-524. Perry, M.W. and D’Antuono, M.F. (1989). Yield improvem ent and associated characteristics of some Australian spring wheat cultivars introduced between 1860 and 1982. Aust. J. Agric. Res

. 40 :457-472. Skidmore, E.L. and Siddoway, F.H. (1978). Crop residue requirements to control wind erosion. In: Crop Residue Management Systems. Am. Soc. Agron. Spec. Pub. No. 31. van Delden, A. (2001). Yield and growth components of potato and wheat under organic nitrogen management. Agron. J. 93 :1370-1385.


### 4. Rice Bainton, S.J., Plumb, V.E., Juliano, B.O., Perez, C.M., Ro xas, D.B., Kush, G.S., de Jesus, J.C. and Gomez, K.A. (1991). Variation in the nutritional value of rice straw. Animal Feed Science and Technology 34 , 261-277. Cho, Y.S., Choe, Z.R. and Ockerby, S.E. (2001). Managi ng tillage, sowing rate and nitrogen top-dressing level to sustain rice yield in a low-input, direct-sown, rice-vetch cropping system. Australian Journal of Experimental Agriculture , 41 :61-69. Donald, C.M. and Hamblin, J. (1976). The biological yield and harvest index of cereals as agronomic and plant breeding criteria. Adv. Agron ., 28 : 361-405. George, T., Magbanua, R., Roder, W., Van Keer, K., Trebu il, G. and Reoma, V. (2001). Upland rice response to phosphorous fertilization in Asia. Agron. J

. 93 :1362-1370. Kinery, J.R., McCauley, G., Xie, Y. and Arnold, J.G. (2001). Rice parameters describing crop performance of four U. S. cultivars. Agron. J

. 93 :1354-1361. Ockerby, S.E. and Fukai, S. (2001). The management of rice grown on raised beds with continuous furrow irrigation. Field Crops Research

. 69 :215-226. San-oh, Y., Mano, Y., Ookawa, T. and Hirasawa, T. (2004). Comparison of dry matter production and associated characteristics between direct-sown and transplanted rice plants in a submerged paddy field and relationships to planting patterns. Field Crops Res. 87 :43-58. Turner, F.T. and McCauley, G.N. (1983). Rice. In: Crop – Water Relations. I.D.Teare and M.M. Peet (eds.). John Wiley and Sons, New York, pp 308-350. Yang, J., Zhang, J., Wang, Z., Zhu, Q. and Wang, W. ( 2001). Remobilization of carbon reserves in response to water deficit during grain filling of rice. Field Crops Res. 71 :47-55. 

### 5. Barley Alston, A.M. (1980). Respons e of wheat to deep placement of nitrogen and phosphorous fer tilizers on a soil high in phosphorous in the surface layer. Aust. J. Agric. Res

. 31 :13-24. Boukerrou, L. and Rasmussen, D.D. (1990). Breeding for high biomass yield in spring barley. Crop Sci. 30 :31- 35. Chery, J., Lefevre, B., Robin, P. and Salsac, L. (1981). Barley breeding for high protein content. Relationship with nitrate reductase and proteo lytic activities. In: Barley Genetic s IV. Proceedings of the Fourth International Barley Genetics Symposium. Edinburgh Univ. Press. Donald, C.M. and Hamblin, J. (1976). The biological yield and harvest index of cereals as agronomic and plant breeding criteria. Adv. Agron. , 28 : 361-405.Volume 4: Agriculture, Forestry and Other Land Use

11.42 2006 IPCC Guidelines for National Greenhouse Gas Inventories Juma, N.G., Izaurralde, R.C., Robertson, J.A. and McGill, W.B. (1997). Crop yield and soil organic matter trends over 60 years in a typic cryoboralf at Breton, Alberta. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Kirby, E.J.M. (1967). The effect of plant density upon the growth and yield of barley. J. Agric. Sci. , Camb. 68 :317-324. Lekes, J. (1981). Results, main directions in using world collections and genetic resources of spring barley in European breeding. In: Barley Genetics IV. Proceedings of the Fourth International Barley Genetics Symposium. Edinburgh Univ. Press. Mahli, S.S., Grant, C.A., Johnston, A.M. and Gill, K.S. (2 001). Nitrogen fertilization management for no-till cereal production in the Canadi an Great Plains: a review. Soil and Tillage Res

. 60 :101-122. Riggs, T.J., Hanson, P.R., Start, N.D., Miles, D.M., Morg an, C.L. and Ford, M.A. (1981). Comparison of spring barley varieties grown in England and Wales between 1880 and 1980. J. Agric. Sci., Camb

. 97 :599-610.Siddique, K.H.M., Belford, R.K. and Tennant, D. (1990). Root:shoot ratios of old and modern, tall and semidwarf wheats in a mediterranean environment. Plant and Soil 121 :89-98. Siddique, K.H.M., Belford, R.K., Perry, M.W. and Tennant, D. (1989). Growth, development and light interception of old and modern wheat cultiv ars in a Mediterranean -type environment. Aust. J. Agric. Res

. 40 :473-487. Skidmore, E.L. and Siddoway, F.H. (1978). Crop residue requirements to control wind erosion. In: Crop Residue Management Systems. Am. Soc. Agron. Spec. Pub. No. 31. Ten Eyck, A.M., and Shoesmith, V.M. (1907). Small grain crops. Agricultural Experiment Station Bulletin 144. Kansas State Agricultural College. Manhattan, KS. Watson, D.J., Thorne, G.N., and French, S.A.W. (1958). Physiological causes of differences in grain yield between varieties of barley. Annals of Botany 87 :321-352. Wych, R.D. and Rasmussen, D.C. (1983). Genetic improvement in malting barley cultivars since 1920. Crop Sci

. 23 :1037-1040. 

### 6. Oats Anonymous (1923). Forty-second annual report for 1922-23. Ohio Agricultural Experiment Station Bull. 373. Wooster, OH. Anonymous (1926). Forty-fourth annual report for 1924-25. Ohio Agricultural Experiment Station Bull. 392. Wooster, OH. Bolinder, M.A., Angers, D.A. and Dubuc, J.P. (1997). Estimating shoot to root ratios and annual carbon inputs in soils for cereal crops. Agriculture, Ecosystems and Environment 63 :61-67. Brinkman, M.A. and Rho, Y.D. (1984). Response of three oat cultivars to N fertilizer. Crop Science 24:973-977. Donald, C.M. and Hamblin, J. (1976). The biological yield and harvest index of cereals as agronomic and plant breeding criteria. Adv. Agron . 28:361-405. Georgeson, C.C., Burtis, F.C. and Otis, D.H. (1893). Ex periments with oats. Experiment Station Bull. No. 42. Kansas State Agricultural College. Georgeson, C.C., Burtis, F.C. and Otis, D.H. (1896). Ex periments with oats. Experiment Station Bull. No. 63. Kansas State Agricultural College. Georgeson, C.C., Burtis, F.C. and Otis, D.H. (1897). Ex periments with oats. Experiment Station Bull. No. 74. Kansas State Agricultural College. Georgeson, C.C., Cottrell, H.M. and Shelton, W. (1890). Experiments with oats. Experiment Station Bull. No. 13. Kansas State Agricultural College. Juma, N.G., Izaurralde, R.C., Robertson, J.A. and McGill, W.B. (1997). Crop yield and soil organic matter trends over 60 years in a typic cryoboralf at Breton, Alberta. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Lawes, D.A. (1977). Yield improvement in spring oats. J. Agric. Sci. , Camb. 89 :751-757. Meyers, K.B., Simmons, S.R. and Stuthman, D.D. (1985). Agronomic comparison of dwarf and conventional height oat genotypes. Crop Sci

. 25 :964-966.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.43 Noll, C.F., Gardner, F.D. and Irvin, C.J. (1930). A field test of different sources of phosphorous. Pennsylvania State College School of Agriculture and Experiment Station Bull. 252. Noll, C.F., Gardner, F.D. a nd Irvin, C.J. (1931). Fiftieth anniversar y of the General Fertilizer Tests 1881-1931. Pennsylvania State College School of Agriculture and Experiment Station Bull. 264. Rattunde, H.F. and Frey, K.J. (1986). Nitrogen harvest index in oats: It s repeatability and association with adaptation. Crop Sci

. 26 :606-610. Sewell, M.C. and Call, L.E. (1925) . Tillage investigations relating to wheat production. Tech. Bull. 18. Agricultural Experiment Station. Kansas State Agricultural College. Ten Eyck, A.M. and Shoesmith, V.M. (1907). Small grain crops. Agricultural Experiment Station Bulletin 144. Kansas State Agricultural College. Manhattan, KS. Thorne, C.E. (1924). The maintenance of soil fertility. Ohio Agricultural Experiment Station Bull. 381. Wianco, A.T. and Jones, S.C. (1918). The value of manure on Indiana soils. Purdue University Agricultural Experiment Station. Bull. No. 222. Lafayette, IN. Wianco, A.T., Conner, S.D. and Jones, S.C. (1919). The value of legumes on Indiana soils. Purdue University Agricultural Experiment Station. Bull. No. 226. Lafayette, IN.


### 7. Millet Peterson, G.A. and Westfall, D.G. (1997). Management of dryland agroecosystems in the Central Great Plains of Colorado. In: Soil Organic Matter in Temperate Agroecos ystems. E.A. Paul. K. Pa ustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Peterson, G.A., Westfall, D.G. and Wood, W. (1989). Crop and soil management in dryland agroecosystems. Technical Bulletin TB89-3. Dept. of Ag ronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., McGee, E. and Kolberg, R. (1992). Crop and soil management in dryland agroecosystems. Technical Bulletin TB92-2. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., McGee, E. and Kolberg, R. (1991). Crop and soil management in dryland agroecosystems. Technical Bulletin TB91-1. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Kolber g, R. and Rouppet, B. (1994). Sustainable dryland agroecosystem management. Technical Bulletin TB94-1. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Kolber g, R. and Rouppet, B. (1993). Sustainable dryland agroecosystem management. Technical Bulletin TB93-4. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Wood, W. and Ross, S. (1988). Crop and soil management in dryland agroecosystems. Technical Bulletin LTB88-6. Dept . of Agronomy, Colorado State University. Peterson, G.A., McGee, E., Westfall, D.G., Wood, C.W. and Sherrod, L. (1990). Crop and soil management in dryland agroecosystems. Technical Bulletin TB90-1. Dept. of Agronomy, Colorado State University. 

### 8. Sorghum Anonymous (1930). A report of the Tribune Branch Agricultural Experiment Station. Agricultural Experiment Station Bull. 250. Kansas State Agricultural College, Manhattan, KS. Arnon, I. and Blum, A. (1962). Factors responsible for yield superiority of hybrid sorghum. Israel J. Agric. Res

. 12 : 95-105. Arnon, I. and Blum, A. (1964). Response of hybrid and self-pollinated sorghum varieties to moisture regime and intra-row competition. Israel J. Agric. Res. 14 : 45-53. Bond, J.J., Army, T.J. and Lehman, O.R. (1964). Row spacing, plant populations and moisture supply as factors in dryland grain sorghum production. Agron. J

. 56 :3-6. Bruce, R.R. and Langdale, G.W. (19 97). Soil carbon level dependence upon cr op culture variables in a thermicudic region. In: Soil Organic Matter in Temperate Agro ecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Craufurd, P.Q. and Peacock, J.M. (1993). Effect of heat and drought stress on sorghum (Sorghum bicolor). II. Grain yield. Expl. Agric

. 29 :77-86. Donald, C.M. and Hamblin, J. (1976). The biological yield and harvest index of cereals as agronomic and plant breeding criteria. Adv. Agron. , 28 : 361-405.Volume 4: Agriculture, Forestry and Other Land Use

11.44 2006 IPCC Guidelines for National Greenhouse Gas Inventories Eastin, J.D. (1983). Sorghum. In Symposium on Pote ntial Productivity of Field Crops Under Different Environments. International Rice Research Institute, Manila, Philippines. Eck, H.V. and Musick, J.T. (1979). Plant water stress effects on irrigated grain sorghum. I. Effects on yield. Crop Sci. 19 :589-592. Jones, O.R., Stewart, B.A. and Unger, P.W. (1997). Management of dry-farmed Southern Great Plains soils for sustained productivity. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Laryea, K.B. and Unger, P.W. (1995 ). Grassland converted to cropland: Soil conditions and sorghum yield. Soil & Tillage res

. 33 :29-45. Peterson, G.A. and Westfall, D.G. (1997). Management of dryland agroecosystems in the Central Great Plains of Colorado. In: Soil Organic Matter in Temperate Agroecos ystems. E.A. Paul. K. Pa ustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Peterson, G.A., Westfall, D.G. and Wood, W. (1989). Crop and soil management in dryland agroecosystems. Technical Bulletin TB89-3. Dept. of Ag ronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Poss, D. , Larson, K. and Thompson, D.L. (1997). Sustainable dryland agroecosystem management. Technical Bulletin TB97-3. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., McGee, E. and Kolberg, R. (1992). Crop and soil management in dryland agroecosystems. Technical Bulletin TB92-2. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., McGee, E. and Kolberg, R. (1991). Crop and soil management in dryland agroecosystems. Technical Bulletin TB91-1. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Kolber g, R. and Rouppet, B. (1994). Sustainable dryland agroecosystem management. Technical Bulletin TB94-1. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Kolber g, R. and Rouppet, B. (1993). Sustainable dryland agroecosystem management. Technical Bulletin TB93-4. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Sherrod, L., Kolberg, R. and Poss, D. (1995). Sustai nable dryland agroecosystem management. Technical Bulletin TB95-1. Dept. of Agronomy, Colorado State University. Peterson, G.A., Westfall, D.G., Wood, W. and Ross, S. (1988). Crop and soil management in dryland agroecosystems. Technical Bulletin LTB88-6. Dept . of Agronomy, Colorado State University. Peterson, G.A., McGee, E., Westfall, D.G., Wood, C.W. and Sherrod, L. (1990). Crop and soil management in dryland agroecosystems. Technical Bulletin TB90-1. Dept. of Agronomy, Colorado State University. Prihar, S.S. and Stewart, B. A. (1991). Sorghum harvest index in relatio n to plant size, environment, and cultivar. Agron. J

. 83 :603-608. Shinano, T., Osaka, M., Yamada, S. and Tadano, T. (1994). Comparison of root growth and nitrogen absorbing ability between Gramineae and Leguminos ae during the vegetative stage. Soil Sci. Plant Nutr

. 40 :485-495. Steiner, J.L. (1986). Dryland grain sorghum water use, light interception, and growth responses to plant geometry. Agron. J. 78 :720-726. Unger, P.W. and Jones, O.R. (1981). Effect of soil water content and a growing season straw mulch on grain sorghum. Soil Sci. Soc. Am. J. 45 :129-134. Unger, P.W. and Wiese, A.F. (1979). Managing irrigated winter wheat residues for water storage and subsequent dryland grain sorghum production. Soil Sci. Soc. Am. J

. 43 :582-588. von Trebra, R.L. and Wagner, F.A. (1 932). Tillage practices for south-west ern Kansas. Agricultural Experiment Station Bull. 262. Kansas State College of Agriculture and Applied Science. Manhattan, KS. 

### 9. Rye No data on rye available at this time. 

### 10. Soyabean Anderson, L.R., and Vasilas, B.L. (1985). Effects of planting date on two soybean cultivars: Seasonal dry matter accumulation and seed yield. Crop Sci. 25 :999-1004.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.45 Bruce, R.R. and Langdale, G.W. (19 97). Soil carbon level dependence upon cr op culture variables in a thermicudic region. In: Soil Organic Matter in Temperate Agro ecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Buyanovsky, G.A. and Wagner, G.H. (1986). Post-harvest residue input to cropland. Plant and Soil 93 :57-65. Buzzell, R.I. and Buttery, B.R. (1977). Soybean harvest index in hill-plots. Crop Sci

. 17 :968-970. Frederick, J.R., Woolley, J.T., Hesketh, J.D. and Peters, D.B. (1991). Seed yield and agronomic traits of old and modern soybean cultivars under irrigation and soil water-deficit. Field Crops research , 27 : 71-82. Hanway, J.J. and Weber, C.R. (1971 ). Dry matter accumulation in eight so ybean (Glycine max (L.) Merrill) varieties. Agron. J

. 63 :227-230. Hodgson, A.S., Holland, J.F. and Rayner, P. (1989). Effects of field slope and duration of furrow irrigation on growth and yield of six grain-legumes on a waterlogging-prone vertisol. Field Crops research, 22 : 165- 180. Kumudini, S., Hume, D.J., and Chu, G. (2001). Genetic improvement in short season soybeans: I. Dry matter accumulation, partitioning, and leaf area duration. Crop Sci

. 41 :391-398. Laing, D.R., Kretchmer, P.J., Zuluaga, S. and Jones, P.G. (1983). Field Bean. In Symposium on Potential Productivity of Field Crops Under Different Environm ents. International Rice Research Institute, Manila, Philippines. Liu, X., Jin, J., Herbert, S.J., Zhang, Q. and Wang, G. (2005). Yield components, dry matter, LAI and LAD of soybeans in Northeast China. Field Crops Research , 93 : 85-93. Peters, S.E., Edwards, W.M. and McCoy, E.L. (1997) . Continuous application of no-tillage to Ohio soils: Changes in crop yields and organic matter-related soil properties. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, and C.V. Cole (eds.). CRC Press, Inc. Sammons, D.J., Peters, D.B. and Hymowitz, T. (1981). Sc reening soybeans for tolerance to moisture stress: A field procedure. Field Crops Research , 3 : 321-335. Sinclair, T.R., Muchow, R.C. , Ludlow, M.M., Leach, G.J., Lawn, R.J. a nd Foale, M.A. (1987). Field and model analysis of the effect of water deficits on carbon and nitrogen accumulation by soybean, cowpea, and black gram. Field Crops Research , 17 : 121-140. Sivakumar, M.V.K., Taylor, H.M. and Shaw, R.H. (1977). Top and root relations of field-grown soybeans. Agron. J. 69 :470-473. Thatcher, L.E. (1925). The soybean in Ohio. Oh io Agricultural Experiment Station. Bull. 384. Walker, A.K. and Fioritto, R.J. (1984). Effect of cultiv ar and planting pattern on yield and apparent harvest index in soybean. Crop Sci

. 24 :154-155. 

### 11. Dry bean Ortega, P.F. (1988). Morphological characterization of six dry bean genotypes grown under non-irrigated conditions in Colorado. M.S. Thesis. Department of Agronomy, Colorado State University, Fort Collins, Colorado.


### 12. Potato Heard, J. (2004). Nu trient accumulation and partitioning by potato es in Manitoba. Proc. of 47th Annual Manitoba Society of Soil Science Meeting. Online at: http://www.gov.mb.ca/agriculture/msss/ 2004/mss600.pdf. van Delden, A. (2001). Yield and growth components of potato and wheat under organic nitrogen management. Agron. J. 93 :1370-1385. Zvomuya, F., Rosen, C.J., Russelle, M.P. and Gupta, S.C. (2003). Nitr ate leaching and nitrogen recovery following application of polyolefin-coated urea to potato. Journal of Environmental Quality , 32 : 480-489.


### 13. Peanut Bell, M.J., Muchow, R.C. and Wilson, G.L. (1987). The effect of plant population on peanuts (Arachis hypogaea) in a monsoonal tropical environment. Field Crops Research, 17 : 91-107. Ghosh, P.K. (2004). Growth, yiel d, competition and economics of groundnut/cereal fodder intercropping systems in the semi-arid tropics of India. Field Crops Research , 88 : 227-237. ICRISAT (2004). Increasing the effectiv eness of research on agricultural resource management in the semi-arid tropics of southern India by combining cropping systems modeling with farming systems research: AVolume 4: Agriculture, Forestry and Other Land Use

11.46 2006 IPCC Guidelines for National Greenhouse Gas Inventories rewarding experience for Ta mil Nadu farmers. International Crops research Institute for the Semi-Arid Tropics. Online at: h ttp://www.icrisat.org/. Singh, P., Boote, K.J., Rao, A.Y., Iruthayaraj, M.R., Sheikh, A.M., Hundal, S.S., Narang, R.S. and Singh, P. (1994). Evaluation of the groundnut model PNUTGRO for crop response to water availability, sowing dates, and seasons. Field Crops research , 39 : 147-162. Witzenberger, A., Williams, J.H. and Lenz, F. (1985). Yield, components of yiel d and quality responses of groundnut (Arachis Hypogaea L.) as influenced by photoperiod and a growth regulator. Field Crops research , 12 : 347-361. 

### 14. Alfalfa Paustian, K., Andren, O., Clarholm, M., Hansson, A.C., Johansson, G., Lagerl of, J., Lindberg, T., Pettersson, R., and Sohlenius, B. (1990). Carbon and nitrogen budgets of four agro-ecosystems with annual and perennial crops, with and without fertilization. J. Appl. Ecol. 27 :60-84. Walley, F.L., Tomm, G.O., Matus, A., Slinkard, A.E. and van Kessel, C. (1996). Allocation and cycling of nitrogen in an alfa lfa-bromegrass sward. Agronomy Journal , 88 : 834-843. 

### 15. Non-legume hay Paustian, K., Andren, O., Clarholm, M., Hansson, A.C., Johansson, G., Lagerl of, J., Lindberg, T., Pettersson, R., and Sohlenius, B. (1990). Carbon and nitrogen budgets of four agro-ecosystems with annual and perennial crops, with and without fertilization. J. Appl. Ecol. 27 :60-84. 

### III. N content of above-ground residues 1. Maize Burgess, M.S., Mehuys, G.R. and Madramootoo, C.A. (2002). Nitrogen dynamics of decomposing corn residue components under three tillage systems. Soil Sci. Soc. Am. J

. 66 :1350-1358. Eghball, B. and Maranville, J.W. (1993). Root development and nitrogen influx of corn genotypes grown under combined drought and nitrogen stresses. Agron. J

. 85 :147-152. Heard, J. (2004). Nutrient accumula tion and partitioning by grain corn in Manitoba. Proc. of 47th Annual Manitoba Society of Soil Science Meeting. Online at: http://www.gov.mb.ca/agriculture/msss/ 2004/mss601.pdf. Janzen, H.H., Beauchemin, K.A., Bruinsma, Y., Campbell, C.A., Desjardins, R.L., Ellert, B.H. and Smith, E.G. (2003). The fate of nitrogen in agroecosyste ms: An illustration using Canadian estimates. Nutrient Cycling in Agroecosystems, 67 : 85-102. Mgheni, D.M., Ndemanisho, E.E., Hvelplund, T. and Weisbjerg, M.R. (2001). Evaluation of the feeding value of two tropical cereal straws, maize stover, rice straw an d their botanical fractions by nylon and mobile bag technique. African Journal of Science and Technology , 2 :65-71. NRCS (2006). Plant nutrient content database. Natural Resources Conservation Service. U. S. Dept. of Agriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html.Reisinger, K., Haslinger, C., Herger, M., Hofbauer, H. (1996). BIOBIB- a Database for Biofuels, THERMIEConference: Renewable Energy Databases, Harwell, United Kingdom. Online at: http://www.vt.tuwien.ac. at/biobib/oxford.html. Subedi, K.D. and Ma, B.L. (2005). N itrogen uptake and partitioning in st ay-green and leafy-maize hybrids. Crop Sci. 45 :740-747.


### 2. Wheat Austin, R.B., Ford, M.A. and Morgan, C.L. (1989). Genetic improvement in the yield of winter wheat: a further evaluation. J. Agric. Sci ., Camb. 112 :295-301. Campbell, C.A., Davidson, H.R. and Ward er, F.G. (1977). Effects of fertilizer N and soil moisture on yield, yield components, protein content and N accumulation in the above-ground parts of spring wheat. Can. J. Soil Sci

. 57 :311-327. Campbell, C.A. and Zentner, R.P. (1993). Soil organic matter as influenced by crop rotations and fertilization. Soil Sci. Soc. Am. J

. 57 :1034-1040. Cassman, K.G., Bryant, D.C., Fulton, A.E. and Jackson, L.F. (1992). Crop ecology, production and management. Crop Sci

. 32 :1251-1258. NRCS (2006). Plant nutrient content database. Natural Resources Conservation Service. U. S. Dept. of Agriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.47Reisinger, K., Haslinger, C., Herger, M. and Hofbauer, H. (1996). BIOBIB- a Database for Biofuels, THERMIEConference: Renewable Energy Databases, Harwell, United Kingdom. Online at: http://www.vt.tuwien.ac. at/biobib/oxford.html.


### 3. Rice Mgheni, D.M., Ndemanisho, E.E., Hvelplund, T. and Weisbjerg, M.R. (2001). Evaluation of the feeding value of two tropical cereal straws, maize stover, rice straw an d their botanical fractions by nylon and mobile bag technique. African Journal of Science and Technology , 2 :65-71. NRCS (2006). Plant nutrient content database. Natural Resources Conservation Service. U. S. Dept. of Agriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html. Ockerby, S.E. and Fukai, S. (2001). The management of rice grown on raised beds with continuous furrow irrigation. Field Crops Research

. 69 :215-226.Reisinger, K., Haslinger, C., Herger, M. and Hofbauer, H. (1996). BIOBIB- a Database for Biofuels, THERMIEConference: Renewable Energy Databases, Harwell, United Kingdom. Online at: http://www.vt.tuwien.ac. at/biobib/oxford.html. 

### 4. Barley Bulman, P. and Smith, D.L. (1993). Accumulation and redistribution of dry matter and nitrogen by spring barley. Agron. J. 85 :1114-1121. Janzen, H.H., Beauchemin, K.A., Bruinsma, Y., Campbell, C.A., Desjardins, R.L., Ellert, B.H. and Smith, E.G. (2003). The fate of nitrogen in agroecosyste ms: An illustration using Canadian estimates. Nutrient Cycling in Agroecosystems , 67 : 85-102. NRCS (2006). Plant nutrient content database. Natural Resources Conservation Service. U. S. Dept. of Agriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html.Reisinger, K., Haslinger, C., Herger, M. and Hofbauer, H. (1996). BIOBIB- a Database for Biofuels, THERMIEConference: Renewable Energy Databases, Harwell, United Kingdom. Online at: http://www.vt.tuwien.ac. at/biobib/oxford.html.


### 5. Oats Janzen, H.H., Beauchemin, K.A., Bruinsma, Y., Campbell, C.A., Desjardins, R.L., Ellert, B.H. and Smith, E.G. (2003). The fate of nitrogen in agroecosyste ms: An illustration using Canadian estimates. Nutrient Cycling in Agroecosystems, 67 : 85-102. NRCS (2006). Plant nutrient content database. Natural Resources Conservation Service. U. S. Dept. of Agriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html.Reisinger, K., Haslinger, C., Herger, M., Hofbauer, H. (1996). BIOBIB- a Database for Biofuels, THERMIEConference: Renewable Energy Databases, Harwell, United Kingdom. Online at: http://www.vt.tuwien.ac. at/biobib/oxford.html. Wych, R.D. and Stuthman, D.D. (1983). Genetic improveme nt in Minnesota-adapted oat cultivars released since 1923. Crop Sci

. 23 :879-881. 

### 6. Millet NRCS (2006). Plant nutrient content database. Natural Resources Conservation Service. U. S. Dept. of Agriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html. 

### 7. Sorghum NRCS (2006). Plant nutrient content database. Natural Resources Conservation Service. U. S. Dept. of Agriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html. 

### 8. Rye Janzen, H.H., Beauchemin, K.A., Bruinsma, Y., Campbell, C.A., Desjardins, R.L., Ellert, B.H. and Smith, E.G. (2003). The fate of nitrogen in agroecosyste ms: An illustration using Canadian estimates. Nutrient Cycling in Agroecosystems , 67 : 85-102. NRCS (2006). Plant nutrient content database. Natural Resources Conservation Service. U. S. Dept. of Agriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html. 

### 9. Soyabean NRCS (2006). Plant nutrient content database. Natural Resources Conservation Service. U. S. Dept. of Agriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html.Volume 4: Agriculture, Forestry and Other Land Use

11.48 2006 IPCC Guidelines for National Greenhouse Gas Inventories 

### 10. Dry bean Janzen, H.H., Beauchemin, K.A., Bruinsma, Y., Campbell, C.A., Desjardins, R.L., Ellert, B.H. and Smith, E.G. (2003). The fate of nitrogen in agroecosyste ms: An illustration using Canadian estimates. Nutrient Cycling in Agroecosystems , 67 : 85-102.


### 11. Potato Heard, J. (2004). Nu trient accumulation and partitioning by potato es in Manitoba. Proc. of 47th Annual Manitoba Society of Soil Science Meeting. Online at: http://www.gov.mb.ca/agriculture/msss/ 2004/mss600.pdf. Janzen, H.H., Beauchemin, K.A., Bruinsma, Y., Campbell, C.A., Desjardins, R.L., Ellert, B.H. and Smith, E.G. (2003). The fate of nitrogen in agroecosyste ms: An illustration using Canadian estimates. Nutrient Cycling in Agroecosystems , 67 : 85-102. Thatcher, L.E. and Willard, C.J. (1962). Crop rotation and soil productivity. Ohio Agricultural Experiment Station Res. Bull. 907. Ohio State University. Zvomuya, F., Rosen, C.J., Russelle, M.P. and Gupta, S.C. (2003). Nitr ate leaching and nitrogen recovery following application of polyolefin-coated urea to potato. Journal of Environmental Quality , 32 : 480-489.


### 12. Peanut NRCS (2006). Plant nutrient content database. Natural Resources Conservation Service. U. S. Dept. of Agriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html. 

### 13. Alfalfa NRCS (2006). Plant nutrient content database. Natural Resources Conservation Service. U. S. Dept. of Agriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html. Schmidt, W.H., Myers, D.K. and Van Keuren, R.W. (2001).Values of legumes for plowdown nitrogen. Extension Fact Sheet AGF-111-01. Ohio State University Extension. Online at: http://ohioline.osu.edu/agf-fact/0111.html. Walley, F.L., Tomm, G.O., Matus, A., Slinkard, A.E. and van Kessel, C. (1996). Allocation and cycling of nitrogen in an alfa lfa-bromegrass sward. Agronomy Journal, 88 : 834-843. 

### 14. Non-legume hay NRCS (2006). Plant nutrient content database. Natural Resources Conservation Service. U. S. Dept. of Agriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html. 

### IV. Ratio of below-ground residues to above-ground biomass 1. Maize Bray, J.R. (1963). Root production and the estimation of net productivity. Can. J. Bot

. 41 :65-72. Bolinder, M.A., Angers, D.A., Giroux, M. and Laverdiere , M.R. (1999). Estimating C inputs retained as soil organic matter from corn (Zea Mays L.). Plant and Soil 215 :85-91. Buyanovsky, G.A. and Wagner, G.H. (1986). Post-harvest residue input to cropland. Plant and Soil 93 :57-65. Eghball, B. and Maranville, J.W. (1993). Root development and nitrogen influx of corn genotypes grown under combined drought and nitrogen stresses. Agron. J

. 85 :147-152. Follett, R.F., Allmaras, R.R. and Reichman, G.A. (1974 ). Distribution of corn roots in sandy soil with a declining water table. Agron. J

. 66 :288-292. Huggins, D.R., and Fuchs, D.J. (1997). Long-term N management effects on corn yield and soil C of an aquic haplustoll in Minnesota. In Soil Organic Matter In Temperate Agroecosystems. E.A. Paul, K. Paustian, E.T. Elliott, and V. Cole (eds.). CRC Press, Inc. Liang, B.C., Wang, X.L. and Ma, B.L. (2002). Maize root-induced change to soil organic pool. Soil Sci. Soc. Am. J

. 66 :845-847. Qian, J.H., Doran, J.W. and Walters, D.T. (1997). Maize plant contributions to root zone available carbon and microbial transformations of nitrogen. Soil Biol. Biochem. 29 :1451-1462. Shank, D.B. (1943). Top-root ratios of inbred and hybred maize. J. Am. Soc. Agron ., pp. 976-986. Tran, T.S. and Giroux, M. (1998). Fate of 15N-labelled fe rtilizer applied to corn grown on different soil types. Canadian Journal of Soil Science , 78 : 597-605.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.49 Triplett, G.B. Jr. and Mannering, J.V. (1978). Crop residue management in crop rotation and multiple cropping systems. Chapter 11 in Crop Residue Management Systems. Am. Soc. Agron. Spec. Pub . No. 31. Yiridoe, E.K., Voroney, R.P. and Weersink, A. (1997) . Impact of alternative farm management practices on nitrogen pollution of groundwater: Evaluation and application of CENTURY Model. J. Environ. Qual. 26 :1255-1263.


### 2. Wheat Barraclough, P.B. (1984). The growth and activity of winter wheat roots in the field: root growth of high yielding crops in relation to shoot growth. J. Agric. Sci ., Camb. 103 :439-442. Barraclough, P.B. and Leigh, R.A. (1984). The growth and activity of winter wheat root s in the field: the effect of sowing date and soil type on root growth of high yielding crops. J. Agric. Sci. , Camb. 103 :59-74. Bolinder, M.A., Angers, D.A. and Dubuc, J.P. (1997). Estimating shoot to root ratios and annual carbon inputs in soils for cereal crops. Agriculture, Ecosystems and Environment 63 :61-67. Bray, J.R. (1963). Root production and the estimation of net productivity. Can. J. Bot. 41 :65-72. Buyanovsky, G.A. and Wagner, G.H. (1986). Post-harvest residue input to cropland. Plant and Soil 93 :57-65. Campbell, C.A. and de Jong, R. (2001). Root-to-straw ra tios – influence of moisture and rate of N fertilizer. Can. J. Soil Sci ., 81 : 39-43. Campbell, C.A. and Zentner, R.P. (1997). Crop producti on and soil orgaqnic matter in long-term crop rotations in the semi-arid Northern Great Plains of Canada. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, an d C.V. Cole (eds.). CRC Press, Inc. Gregory, P.J., McGowan, M., Biscoe, P.V. and Hunter, B. (1978). Water relations of winter wheat. 1.Growth of the root system. J. Agric. Sci. , Camb. 91 :91-102. Pederson, G.A., Brink, G.E. and Fairbrother, T.E. (2002). Nutrient uptake in plant parts of sixteen forages fertilized with poultry litter: Nitrogen, phosphorous, po tassium, copper, and zinc. Agron. J

. 94 :895-904.Siddique, K.H.M., Belford, R.K. and Tennant, D. (1990). Root:shoot ratios of old and modern, tall and semidwarf wheats in a mediterranean environment. Plant and Soil 121 :89-98. Slobodian, N., Van Rees, K. and Pen nock, D. (2002). Cultivation-induced ef fects on below-ground biomass and organic carbon. Soil Sci. Soc. Am. J

. 66 :924-930. Triplett Jr., G.B. and Mannering, J.V. (1978). Crop residue management in crop rotation and multiple cropping systems. Chapter 11 in Crop Residue Management Systems. Am. Soc. Agron . Spec. Pub. No. 31. Weir, A.H. and Barraclough, P.B. (1986). The effect of drought on the root growth of winter wheat and on its uptake from deep loam. Soil Use And Management 2 :91-96. 

### 3. Rice Cassman, K. G. (personal communication 2002) Agron. Dept, U. NE. Khokhar, M.F.K. and Pandey, H.N. (1976). Biomass, productivity and growth analysis of two varieties of paddy. Trop. Ecol. 17 :125-131. Shinano, T., Osaka, M., Yamada, S. and Tadano, T. (1994). Comparison of root growth and nitrogen absorbing ability between Gramineae and Leguminos ae during the vegetative stage. Soil Sci. Plant Nutr. 40 :485-495. Turner, F.T. and McCauley, G.N. (1983). Rice. In: Crop – Water Relations. I.D.Teare and M.M. Peet (eds.). John Wiley and Sons, New York, pp 308-350. 

### 4. Barley Bolinder, M.A., Angers, D.A. and Dubuc, J.P. (1997). Estimating shoot to root ratios and annual carbon inputs in soils for cereal crops. Agriculture, Ecosystems and Environment 63 :61-67. Bray, J.R. (1963). Root production and the estimation of net productivity. Can. J. Bot. 41 :65-72. Gregory, P.J. and Atwell, B.J. ( 1991). The fate of carbon in pulse-l abeled crops of barley and wheat. Plant and Soil 136 :205-213. Gregory, P.J., Palta, J.A. and Batts, G.R. (1997). Root systems and root:mass ratio – carbon allocation under current and projected atmospheri c conditions in arable crops. Hansson, A., Andren, O., Bostrom, U., Clarholm, M., Lagerlof, J., Lindberg, T., Paustian, K., Pettersson, R. and Sohlenius, B. (1989). Chapter 4. Structure of the agroecosystem. In: Andren O., Lindberg T., Paustian K.,Volume 4: Agriculture, Forestry and Other Land Use

11.50 2006 IPCC Guidelines for National Greenhouse Gas Inventories and Rosswall T. (eds.). Ecology of arable land – organisms, carbon and nitrogen cycling. Ecol. Bull

. (Copenhagen) 40 :41-83. Heen, A. (1981). Root growth, transpiration and leaf-firing during water stress in barley: Breeding implications for drought resistance. In: Barley Genetics IV. Proceedings of the Fourth International Barley Genetics Symposium. Edinburgh Univ. Press. Shank, D.B. (1943). Top-root ratios of inbred and hybred maize. J. Am. Soc. Agron ., pp. 976-986.Siddique, K.H.M., Belford, R.K. and Tennant, D. (1990). Root:shoot ratios of old and modern, tall and semidwarf wheats in a mediterranean environment. Plant and Soil 121 :89-98. Watson, D.J., Thorne, G.N. and French, S.A.W. (1958) . Physiological causes of di fferences in grain yield between varieties of barley. Annals of Botany 87 :321-352. Xu, J.G. and Juma, N.G. (1992). Above- and below-ground net primary production of four barley (Hordeum vulgare L.) cultivars in western Canada. Can. J. Plant Sci. 72 :1131-1140. 

### 5. Oats Bolinder, M.A., Angers, D.A. and Dubuc, J.P. (1997). Estimating shoot to root ratios and annual carbon inputs in soils for cereal crops. Agriculture, Ecosystems and Environment 63 :61-67. Pederson, G.A., Brink, G.E. and Fairbrother, T.E. (2002). Nutrient uptake in plant parts of sixteen forages fertilized with poultry litter: Nitrogen, phosphorous, po tassium, copper, and zinc. Agron. J

. 94 :895-904. 

### 6. Millet No data on millet available at this time. 

### 7. Sorghum No data on sorghum available at this time. 

### 8. Rye No data on rye available at this time. 

### 9. Soyabean Allmaras, R.R., Nelson, W.W. and Voorhees, W.B. (1 975). Soybean and corn rooting in Southwestern Minnesota: II. Root distributions and related water flow. Soil Sci. Soc. Amer. Proc. 39 :771-777. Buyanovsky, G.A. and Wagner, G.H. (1986). Post-harvest residue input to cropland. Plant and Soil 93 :57-65. Heatherly, L.G. (1980). Effect of upper-profile soil water potential on soybean root and shoot relationships. Field Crops Research , 3 :165-171. Mayaki, W.C., Teare, I.D. and Stone, L.R. (1976). Top and root growth of irrigated and nonirrigated soybeans. Crop Sci. 16 :92-94. Shinano, T., Osaki, M., Yamada, S. and Tadano, T. (1994). Comparison of root growth and nitrogen absorbing ability between Gramineae and Leguminos ae during the vegetative stage. Soil Sci. Plant Nutr

. 40 :485-495. Taylor, H.M., Mason, W.K., Bennie, A.T.P. and House, H.R. (1982). Responses of soybeans to two row spacings and two soil water levels. I. An analysis of biomass accumulation, ca nopy development, solar radiation interception and components of seed yield. Field Crops Research , 5 : 1-14. 

### 10. Dry bean No data on dry beans available at this time.


### 11. Potato Vangessel, M.J. and Renner, K.A. (1990). Redroot pigweed (Amaranthus retroflexus) and barnyardgrass (Echinochloa crus-galli) interference in potatoes (Solanum tuberosum). Weed Science 38 :338-343.


### 12. Peanut No data on peanuts available at this time. 

### 13. Alfalfa Paustian, K., Andren, O., Clarholm, M., Hansson, A.C., Johansson, G., Lagerlof, J., Lindberg, T., Pettersson, R. and Sohlenius, B. (1990). Carbon and nitrogen budgets of four agro-ecosystems with annual and perennial crops, with and without fertilization. J. Appl. Ecol

. 27 :60-84.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.51 

### 14. Non-legume hay Paustian, K., Andren, O., Clarholm, M., Hansson, A.C., Johansson, G., Lagerlof, J., Lindberg, T., Pettersson, R. and Sohlenius, B. (1990). Carbon and nitrogen budgets of four agro-ecosystems with annual and perennial crops, with and without fertilization. J. Appl. Ecol. 27 :60-84. 

### V. N content of below-ground residues 1. Maize Sanchez, J.E., Paul, E.A., Willson, T.C., Smeenk, J. and Harwood, R.R. (2002). Corn root effects on the nitrogen-supplying capacity of a conditioned soil. Agron. J. 94 :391-396. Subedi, K.D. and Ma, B.L. (2005). Nitrogen uptake an d partitioning in stay-green and leafy-maize hybrids. Crop Sci

. 45 :740-747. Tran, T.S. and Giroux, M. (1998). Fate of 15N-labelled fe rtilizer applied to corn grown on different soil types. Canadian Journal of Soil Science , 78 : 597-605.


### 2. Wheat Campbell, C.A., Cameron, D.R., Nichol aichuk, W. and Davidson, H.R. (1977). Effects of fertilizer N and soil moisture on growth, N content, and moisture use by spring wheat. Can. J. Soil Sci

. 57 :289-310. Pederson, G.A., Brink, G.E. and Fairbrother, T.E. (2002). Nutrient uptake in plant parts of sixteen forages fertilized with poultry litter: Nitrogen, phosphorous, po tassium, copper, and zinc. Agron. J. 94 :895-904.


### 3. Rice No data on rice available at this time. 

### 4. Barley Dev, G. and Rennie, D.A. (1979). Isotope studies on the comparative efficiency of nitrogenous sources. Aust. J. Soil Res

. 17 : 155-162. Haugen-Kozyra, K., Juma, N.G. and Nyborg, M. (1993). Ni trogen partitioning and cycling in barley-soil systems under conventional and zero tillage in central Alberta. Canadian Journal of Soil Science 73 : 183-196. 

### 5. Oats Pederson, G.A., Brink, G.E. and Fairbrother, T.E. (2002). Nutrient uptake in plant parts of sixteen forages fertilized with poultry litter: Nitrogen, phosphorous, po tassium, copper, and zinc. Agron. J. 94 :895-904. 

### 6. Millet No data on millet available at this time. 

### 7. SorghumCueto-Wong, J.A., Guldan, S.J., Lindemann, W.C. and Remmenga, M.D. (2001). Nitrogen recovery from 15Nlabeled green manures: I. Recovery by forage sorghum and soil one season after green manure incorporation. Journal of Sustainable Agriculture , 17 :27-42. 

### 8. Rye Pederson, G.A., Brink, G.E. and Fairbrother, T.E. (2002). Nutrient uptake in plant parts of sixteen forages fertilized with poultry litter: Nitrogen, phosphorous, po tassium, copper, and zinc. Agron. J. 94 :895-904. 

### 9. Soyabean Thatcher, L.E. (1925). The soybean in Ohio. Oh io Agricultural Experiment Station. Bull. 384. 

### 10. Dry bean Janzen, H.H., Beauchemin, K.A., Bruinsma, Y., Campbell, C.A., Desjardins, R.L., Ellert, B.H. and Smith, E.G. (2003). The fate of nitrogen in agroecosyste ms: An illustration using Canadian estimates. Nutrient Cycling in Agroecosystems , 67 : 85-102.


### 11. Potato Lander, C.H., Moffitt, D. and Alt, K. (1998). Nutrients available from live stock manure relative to crop growth requirements. Resource Assessment and Strategic Planning Working paper 98-1. USDA Natural Resource Conservation Service. http://www.nhq.nrcs.usda.gov/land/pubs/nlweb.html Zvomuya, F., Rosen, C.J., Russelle, M.P. and Gupta, S.C. (2003). Nitr ate leaching and nitrogen recovery following application of polyolefin-coated urea to potato. Journal of Environmental Quality , 32 : 480-489.


### 12. Peanut No data on peanuts available at this time.Volume 4: Agriculture, Forestry and Other Land Use

11.52 2006 IPCC Guidelines for National Greenhouse Gas Inventories 

### 13. Alfalfa Baron, V., Young, D.Y. and Ullmann, C. (2001). Can pasture slow down global warming? Western Forage/Beef Group, 5: 3-6. Online at: http://www1.agric. gov.ab.ca/$department/newslett.nsf/all/wfbg38. Bowren, K.E., Cooke, D.A. and Downey, R.K. (1969). Yield of dry matter and nitrogen from tops and roots of sweetclover, alfalfa, and red clove r at five stages of growth. Canadian Journal of Plant Science , 49 : 61- 69. Pederson, G.A., Brink, G.E. and Fairbrother, T.E. (2002). Nutrient uptake in plant parts of sixteen forages fertilized with poultry litter: Nitrogen, phosphorous, po tassium, copper, and zinc. Agron. J. 94 :895-904. Schmidt, W.H., Myers, D.K. and Van Keuren, R.W. (2001).Values of legumes for plowdown nitrogen. Extension Fact Sheet AGF-111-01. Ohio State University Extension. Online at: http://ohioline.osu.edu/agf-fact/0111.html. Walley, F.L., Tomm, G.O., Matus, A., Slinkard, A.E. and van Kessel, C. (1996). Allocation and cycling of nitrogen in an alfa lfa-bromegrass sward. Agronomy Journal , 88 : 834-843. 

### 14. Non-legume hay Campbell, C.A., Biederbeck, V.O., Zentner, R.P. and Lafo nd, G.P. (1991). Effect of crop rotations and cultural practices on soil organic matter, microbial biomass and respiration in a thin Black Chernozemic soil. Canadian Journal of Soil Science , 71 : 363-376. Christian, J.M. and Wilson, S.D. (1999). Long-term ecosystem impacts of an introduced grass in the Northern Great Plains. Ecology , 80 : 2397-2407. Pederson, G.A., Brink, G.E. and Fairbrother, T.E. (2002). Nutrient uptake in plant parts of sixteen forages fertilized with poultry litter: Nitrogen, phosphorous, po tassium, copper, and zinc. Agron. J. 94 :895-904. Schmidt, W.H., Myers, D.K. and Van Keuren, R.W. (2001).Values of legumes for plowdown nitrogen. Extension Fact Sheet AGF-111-01. Ohio State University Extension. Online at: http://ohioline.osu.edu/agf-fact/0111.html. Walley, F.L., Tomm, G.O., Matus, A., Slinkard, A.E. and van Kessel, C. (1996). Allocation and cycling of nitrogen in an alfa lfa-bromegrass sward. Agronomy Journal , 88 : 834-843.Chapter 11: N 2 O Emissions from Managed Soils, and CO 2 Emissions from Lime and Urea Application

2006 IPCC Guidelines for National Greenhouse Gas Inventories 11.53 

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. 44 , 163-186. Bouwman, A.F. (1996). Direct emissions of nitrous oxide from agricultural soils. Nutr. Cycl. Agroecosyst ., 46 , 53-70. Bouwman, A.F., Boumans, L.J.M. and Batjes, N.H. (2002a). Emissions of N 2 O and NO from fertilised fields: Summary of available measurement data. Glob. Biogeochem. Cycles , 16 (4), art. no. 1058. Bouwman, A.F., Boumans, L.J.M. and Batjes, N.H. (2002b). Modeling global annual N 2 O and NO emissions from fertilised fields. Glob. Biogeochem. Cycles , 16 (4), art. no. 1080. Brumme, R., Borken, W. and Finke, S. (1999). Hierar chical control on nitrous oxide emission in forest ecosystems. Global Biochem. Cycles 13 , 1137–1148. Butterbach-Bahl, K., Gasche, R., Breuer, L. And Papen, H. (1997). Fluxes of NO and N 2 O from temperate forest soils: impact of forest type, N deposition and of liming on the NO and N 2 O emissions. Nutr. Cycl. Agroecosyst

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