Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

CHAPTER 11

N2O EMISSIONS FROM MANAGED
SOILS, AND CO2 EMISSIONS FROMLIME AND UREA APPLICATION

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

Authors
Cecile De Klein (New Zealand), Rafael 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
Margaret Walsh (USA) and Stephen A. Williams (USA)11.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

Contents
11 N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application
11.1

Introduction ......................................................................................................................................... 11.5

11.2

N2O Emissions from Managed Soils ................................................................................................... 11.5

11.2.111.2.1.1

Choice of method ................................................................................................................... 11.6

11.2.1.2

Choice of emission factors ................................................................................................... 11.10

11.2.1.3

Choice of activity data ......................................................................................................... 11.12

11.2.1.4

Uncertainty assessment ........................................................................................................ 11.16

11.2.2

Indirect N2O emissions .............................................................................................................. 11.19

11.2.2.1

Choice of method ................................................................................................................. 11.19

11.2.2.2

Choice of emission, volatilisation and leaching factors ....................................................... 11.23

11.2.2.3

Choice of activity data ......................................................................................................... 11.23

11.2.2.4

Uncertainty assessment ........................................................................................................ 11.24

11.2.3
11.3

Direct N2O emissions .................................................................................................................. 11.6

Completeness, Time series, QA/QC .......................................................................................... 11.25

CO2 Emissions from Liming ............................................................................................................. 11.26

11.3.1

Choice of method ...................................................................................................................... 11.27

11.3.2

Choice of emission factors ........................................................................................................ 11.29

11.3.3

Choice of activity data ............................................................................................................... 11.29

11.3.4

Uncertainty assessment ............................................................................................................. 11.29

11.3.5Completeness, Time series, QA/QC .......................................................................................... 11.30

11.4

CO2 Emissions from Urea Fertilization ............................................................................................. 11.32

11.4.1

Choice of method ...................................................................................................................... 11.32

11.4.2

Choice of emission factor .......................................................................................................... 11.34

11.4.3

Choice of activity data ............................................................................................................... 11.34

11.4.4

Uncertainty assessment ............................................................................................................. 11.34

11.4.5Completeness, Time series consistency, QA/QC ...................................................................... 11.35

Annex 11A.1 References for crop residue data in Table 11.2 ......................................................................... 11.37
References................................................................................................................................................... 11.53

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Equations
Equation 11.1 Direct N2O emissions from managed soils (Tier 1) ............................................................. 11.7
Equation 11.2 Direct N2O emissions from managed soils (Tier 2) ........................................................... 11.10
Equation 11.3 N from organic N additions applied to soils (Tier 1) ......................................................... 11.12
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 forage/pasture renewal (Tier 1) ................................................ 11.14
Equation 11.7 Dry-weight correction of reported crop yields .................................................................. 11.15
Equation 11.7A Alternative approach to estimate FCR (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 and 2) ........................................................................ 11.16
Equation 11.9 N2O from atmospheric deposition of N volatilised from managed soils (Tier 1) .............. 11.21
Equation 11.10 N2O from N leaching/runoff from managed soils in regions where leaching/runoff
occurs (Tier 1) ................................................................................................................... 11.21
Equation 11.11 N2O from atmospheric deposition of N volatilised from managed soils (Tier 2) .............. 11.22
Equation 11.12 Annual CO2 emissions from lime application ................................................................... 11.27
Equation 11.13 Annual CO2 emissions from urea application .................................................................... 11.32

FiguresFigure 11.1

Schematic diagram illustrating the sources and pathways of N that resultin direct and indirect N2O emissions from soils and waters. ............................................... 11.8

Figure 11.2

Decision tree for direct N2O emissions from managed soils ............................................... 11.9

Figure 11.3

Decision tree for indirect N2O emissions from managed soils .......................................... 11.20

Figure 11.4

Decision tree for identification of appropriate tier to estimate CO2 emissions
from liming. ....................................................................................................................... 11.28

Figure 11.5

Decision tree for identification of appropriate tier to estimate CO2 emissions
from urea fertilisation ........................................................................................................ 11.33

TablesTable 11.1

Default emission factors to estimate direct N2O 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 N2O emissions ...... 11.24

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

11 N 2 O EMISSIONS FROM MANAGED SOILS,AND CO 2 EMISSIONS FROM LIME AND UREAAPPLICATION
11.1

INTRODUCTION

Chapter 11 provides a description of the generic methodologies to be adopted for the inventory of nitrous oxide(N2O) emissions from managed soils, including indirect N2O emissions from additions of N to land due todeposition and leaching, and emissions of carbon dioxide (CO2) following additions of liming materials andurea-containing fertiliser.
Managed soils 1 are all soils on land, including Forest Land, which is managed. For N2O, the basic three-tierapproach 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 Guidance 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-use categories and because in most cases, only national
aggregate (i.e., non-land use specific) data 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 N2O emissions from managed soils (direct and indirect) and CO2
emissions from liming and urea fertilisation, and the associated uncertainties; and•choice of methods, emission factors (including default values) and activity data, and volatilisation andleaching factors.

•

If activity data are available for specific land-use categories, the equations provided can be implemented forspecific land-use categories.

The changes in the 2006 IPCC Guidelines, relative to 1996 IPCC Guidelines, include the following:•

provision of advice on estimating CO2 emissions associated with the use of urea as a fertilizer;•

full sectoral coverage of indirect N2O 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 N2O because of the lack of evidence ofsignificant emissions arising from the fixation process.

11.2

N 2 O EMISSIONS FROM MANAGED SOILSThis section presents the methods and equations for estimating total national anthropogenic emissions of N2O(direct and indirect) from managed soils. The generic equations presented here can also be used for estimating
N2O within specific land-use categories or by condition-specific 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 processes of nitrification and denitrification. Nitrificationis the aerobic microbial oxidation of ammonium to nitrate, and denitrification is the anaerobic microbial
reduction of nitrate to nitrogen gas (N2). Nitrous oxide is a gaseous intermediate in the reaction sequence ofdenitrification and a by-product of nitrification that leaks from microbial cells into the soil and ultimately into
the atmosphere. One of the main controlling factors in this reaction is the availability of inorganic N in the soil.
This methodology, therefore, estimates N2O emissions using human-induced net N additions to soils (e.g.,synthetic or organic fertilisers, deposited manure, crop residues, sewage sludge), or of mineralisation of N in soilorganic matter following drainage/management of organic soils, or cultivation/land-use change on mineral soils(e.g., Forest Land/Grassland/Settlements converted to Cropland).
The emissions of N2O 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)1Managed land is defined in Chapter 1, Section 1.1.

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following volatilisation of NH3 and NOx from managed soils and from fossil fuel combustion and biomassburning, and the subsequent redeposition of these gases and their products NH4+ and NO3- to soils and waters;and (ii) after leaching and runoff of N, mainly as NO3-, from managed soils. The principal pathways areillustrated in Figure 11.1.
Direct emissions of N2O from managed soils are estimated separately from indirect emissions, though using acommon 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 factors are not considered for direct or (where appropriate, indirect) emissions
because limited data are available to provide appropriate emission factors. Countries that have 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 available N enhances nitrification and denitrification rates which then increase theproduction of N2O. 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 included in the methodology for estimating direct N2O emissions from managed
soils:•

synthetic N fertilisers (FSN);•

organic N applied as fertiliser (e.g., animal manure, compost, sewage sludge, rendering waste) (FON);•

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

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

N mineralisation associated with loss of soil organic matter resulting from change of land use or
management of mineral soils (FSOM); and•drainage/management of organic soils (i.e., Histosols) 4 (FOS).

11.2.1.1

C HOICE OF METHOD

The decision tree in Figure 11.2 provides guidance on which tier method to use.
Tie r 1
In its most basic form, direct N2O emissions from managed soils are estimated using Equation 11.1 as follows:2

Biological nitrogen fixation has been removed as a direct source of N2O because of the lack of evidence of significant
emissions arising from the fixation process itself (Rochette and Janzen, 2005). These authors concluded that the N2O
emissions induced by the growth of legume crops/forages may be estimated solely as a function of the above-ground andbelow-ground nitrogen inputs from crop/forage residue (the nitrogen residue from forages is only accounted for during
pasture renewal). Conversely, the release of N by mineralisation of soil organic matter as a result of change of land use or
management is now included as an additional source. These are significant adjustments to the methodology previously
described in the 1996 IPCC Guidelines.

3

The nitrogen residue from perennial forage crops is only accounted 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 organic 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 episodes and has either: (i) at least 12 percent(by weight) organic carbon (about 20 percent organic matter) if it has no clay; or (ii) at least 18 percent (by weight) organiccarbon (about 30 percent organic matter) if it has 60 percent or more clay; or (iii) an intermediate, proportional amount oforganic carbon for intermediate amounts of clay (FAO, 1998).

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

EQUATION 11.1
DIRECT N2O EMISSIONS FROM MANAGED SOILS (TIER 1)
N 2ODirect −N = N 2O −N N inputs + N 2O−N OS + N 2O−N PRP
Where:

[(FSN + FON + FCR + FSOM ) • EF1 ] + 
N 2O−N N inputs = [(FSN + FON + FCR + FSOM )FR • EF1FR ]

(
(
(

) (
) (

)

 FOS , CG ,Temp • EF2CG ,Temp + FOS , CG ,Trop • EF2CG ,Trop +N 2O−N OS =  FOS , F ,Temp , NR • EF2 F ,Temp , NR + FOS , F ,Temp , NP • EF2 F ,Temp , NP +  FOS , F ,Trop • EF2 F ,Trop


)

[

)

]

N 2O−N PRP = (FPRP, CPP • EF3 PRP, CPP ) + (FPRP, SO • EF3 PRP, SO )Where:

N2ODirect –N = annual direct N2O–N emissions produced from managed soils, kg N2O–N yr-1
N2O–NN inputs = annual direct N2O–N emissions from N inputs to managed soils, kg N2O–N yr-1
N2O–NOS = annual direct N2O–N emissions from managed organic soils, kg N2O–N yr-1
N2O–NPRP = annual direct N2O–N emissions from urine and dung inputs to grazed soils, kg N2O–N yr-1
FSN = annual amount of synthetic fertiliser N applied to soils, kg N yr-1
FON = annual amount of animal manure, compost, sewage sludge and other organic N additions applied tosoils (Note: If including sewage sludge, cross-check with Waste Sector to ensure there is no double
counting of N2O emissions from the N in sewage sludge), kg N yr-1
FCR = 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
FSOM = annual amount of N in mineral soils that is mineralised, in association with loss of soil C from soilorganic matter as a result of changes to land use or management, kg N yr-1
FOS = 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, Nutrient Rich, and Nutrient
Poor, respectively)
FPRP = 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)
EF1 = emission factor for N2O emissions from N inputs, kg N2O–N (kg N input)-1(Table 11.1)
EF1FR is the emission factor for N2O emissions from N inputs to flooded rice, kg N2O–N (kg N input)-1(Table 11.1) 5
EF2 = emission factor for N2O emissions from drained/managed organic soils, kg N2O–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 Rich, and Nutrient Poor, respectively)
EF3PRP = emission factor for N2O emissions from urine and dung N deposited on pasture, range andpaddock by grazing animals, kg N2O–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 applicable to this crop, EF1FR (Table 11.1) (Akiyama et al., 2005) or, where a country-specificemission factor has been determined, by that factor instead. Although there is some evidence that intermittent flooding (asdescribed in Chapter 5.5) can increase N2O emissions, current scientific data indicate that EF1FR also applies to intermittent
flooding situations.

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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 are represented with arrows on the left-hand side of thegraphic. Emission pathways are also shown with arrows including the various pathways of volatilisation of NH3
and NOx from agricultural and non-agricultural sources, deposition of these gases and their products NH4+ andNO3-, and consequent indirect emissions of N2O are also illustrated. “Applied Organic N Fertilisers” include
animal manure, all compost, sewage sludge, tankage, etc. “Crop Residues” include above- and below-groundresidues 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 isrepresented here.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

Figure 11.2

Decision tree for direct N 2 O emissions from managed soils
Start

For
each N source ask:
Do you have country-specificactivity data1?

Yes

Is thisa key category2 andis this N source
significant3?

No

Obtaincountry-specific
data.

Yes

Do youhave rigorously
documented country-specificemission factors
for EF1, EF2, and/or
EF3PRP?

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.
No

Box 1: Tier 1

Yes

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

Estimate emissions using the Tier 1
default emission factor value andcountry-specific activity data.
Box 2: Tier 1

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 management anddrainage/management of organic soils. Other organic N additions (e.g., compost, sewage sludge, rendering waste) can be included in thiscalculation if sufficient information is available. The waste input is measured in units of N and added as an additional source sub-term
under FON in Equation 11.1 to be multiplied by EF1.
2: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), 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.

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Conversion of N2O–N emissions to N2O emissions for reporting purposes is performed by using the following
equation:
N2O = N2O–N ● 44/28
Tie r 2
If more detailed emission factors and corresponding activity data are available to a country 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 (FSN and FON)
under different conditions i, Equation 11.1 would be expanded to become 6:
EQUATION 11.2
DIRECT N2O EMISSIONS FROM MANAGED SOILS (TIER 2)
N 2ODirect −N = ∑ (FSN + FON )i • EF1i + (FCR + FSOM ) • EF1 + N 2O−N OS + N 2O−N PRPi

Where:
EF1i = emission factors developed for N2O emissions from synthetic fertiliser and organic N application
under conditions i (kg N2O–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 toobtain for each of the individual N input variables (FSN, FON, FCR, FSOM, FOS, FPRP).
Conversion of N2O–N emissions to N2O emissions for reporting purposes is performed by using the following
equation:
N2O = N2O–N ● 44/28
Tie r 3Tier 3 methods are modelling or measurement approaches. Models are useful because in appropriate forms they
can relate the soil and environmental variables responsible for N2O 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 should only be used after validation by representative
experimental measurements. Care should also be taken to ensure that the emission estimates developed throughthe use of models or measurements account for all anthropogenic N2O emissions. 7 Guidance that provides asound 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

Tie r s 1 a nd 2
Three emission factors (EF) are needed to estimate direct N2O 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-specificemission factors. The first EF (EF1) refers to the amount of N2O emitted from the various synthetic and organic
N applications to soils, including crop residue and mineralisation of soil organic carbon in mineral soils due toland-use change or management. The second EF (EF2) refers to the amount of N2O emitted from an area ofdrained/managed organic soils, and the third EF (EF3PRP) estimates the amount of N2O emitted from urine anddung 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 modifications 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 andthe ability to which a country can disaggregate its activity data.

7

Natural N2O emissions on managed land are assumed to be equal to emissions on unmanaged land. These latter emissions
are very low. Therefore, nearly all emissions on managed land are considered anthropogenic. Estimates 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 N2O–N/ha/yr under zero fertiliser N addition) are not “natural”
emissions but are mostly due to contributions of N from crop residue. These emissions are anthropogenic and accounted
for in the IPCC methodology.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

In the light of new evidence, the default value for EF1 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 that 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 fertiliser 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.
TABLE 11.1
DEFAULT EMISSION FACTORS TO ESTIMATE DIRECT N2O EMISSIONS FROM MANAGED SOILS
Emission factor

Default value

Uncertainty range

EF1 for N additions from mineral fertilisers, organic
amendments and crop residues, and N mineralised frommineral soil as a result of loss of soil carbon [kg N2O–N (kg
N)-1]

0.01

0.003 - 0.03

EF1FR for flooded rice fields [kg N2O–N (kg N)-1]

0.003

0.000 - 0.006

EF2 CG, Temp for temperate organic crop and grassland soils (kg
N2O–N ha-1)

8

2 - 24

EF2 CG, Trop for tropical organic crop and grassland soils (kg
N2O–N ha-1)

16

5 - 48

EF2F, Temp, Org, R for temperate and boreal organic nutrient richforest soils (kg N2O–N ha-1)

0.6

0.16 - 2.4

EF2F, Temp, Org, P for temperate and boreal organic nutrient poor
forest soils (kg N2O–N ha-1)

0.1

0.02 - 0.3

EF2F, Trop for tropical organic forest soils (kg N2O–N ha-1)

8

0 - 24

EF3PRP, CPP for cattle (dairy, non-dairy and buffalo), poultry
and pigs [kg N2O–N (kg N)-1]

0.02

0.007 - 0.06

EF3PRP, SO for sheep and ‘other animals’ [kg N2O–N (kg N)-1]

0.01

0.003 - 0.03

Sources:
EF1: Bouwman et al. 2002a,b; Stehfest & Bouwman, 2006; Novoa & Tejeda, 2006 in press; EF1FR: Akiyama et al., 2005; EF2CG, Temp,
EF2CG, Trop, EF2F,Trop: Klemedtsson et al., 1999, IPCC Good Practice Guidance, 2000; EF2F, 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; EF3, CPP, EF3, SO: de Klein, 2004.

The default value for EF2 is 8 kg N2O–N ha-1 yr-1 for temperate climates. Because mineralisation rates are
assumed to be about 2 times greater in tropical climates than in temperate climates, the emission factor EF2 is 16
kg N2O–N ha-1 yr-1 for tropical climates 9. Climate definitions are given in Chapter 3, Annex 3A.5.
The default value for EF3PRP 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 EF1 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 EF1 (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 associatedwith this value and the inclusion in the inventory calculation of other contributions to the nitrogen additions (e.g., fromcrop residues and the mineralisation of soil organic matter), the round value of 1% is appropriate.

9

The values of EF2, for both temperate and tropical climates, have been changed from 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 EF3PRP has been disaggregated for different animal types based on a recent review on N2O 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 deposited is more appropriate. Reasons for the lower EF3PRP for sheep include moreeven urine distribution (smaller and more frequent urinations), and smaller effects on soil compaction during grazing.
There are no or very limited data for N2O emission factors of other animal types, and the emission factor for poultry and2006 IPCC Guidelines for National Greenhouse Gas Inventories

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11.2.1.3

C HOICE OF ACTIVITY DATA

Tie r s 1 a nd 2
This section describes generic methods for estimating the amount of various N inputs to soils (FSN, FON, FPRP,FCR, FSOM, FOS) that are needed for the Tier 1 and Tier 2 methodologies (Equations 11.1 and 11.2).
Applied synthetic fertiliser (FSN)
The term FSN 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 fromofficial country statistics, often recorded as fertiliser sales and/or as domestic production and imports. If countryspecific data are not available, data from the International Fertilizer Industry Association (IFIA)(http://www.fertilizer.org/ifa/statistics.asp) on total fertiliser use by type and by crop, or from the Food andAgriculture Organisation of the United Nations (FAO): (http://faostat.fao.org/) on synthetic fertiliser
consumption, can be used. It may be useful to compare national statistics to international databases such as thoseof 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 ifinventory 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 otherlands. This unaccounted N is likely to account for a small proportion of the overall emissions. However, it isrecommended that countries seek out this additional information whenever possible.
Applied organic N fertilisers (FON)The term “applied organic N fertiliser” (FON) refers to the amount of organic N inputs applied to soils other thanby grazing animals and is calculated using 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 toagriculture (e.g., rendering waste, guano, brewery waste, etc.). Organic N fertiliser (FON) is calculated using
Equation 11.3:
EQUATION 11.3
N FROM ORGANIC N ADDITIONS APPLIED TO SOILS (TIER 1)
FON = FAM + FSEW + FCOMP + FOOA
Where:
FON = total annual amount of organic N fertiliser applied to soils other than by grazing animals, kg N yr-1
FAM = annual amount of animal manure N applied to soils, kg N yr-1
FSEW = annual amount of total sewage N (coordinate with Waste Sector to ensure that sewage N is notdouble-counted) that is applied to soils, kg N yr-1
FCOMP = annual amount of total compost N applied to soils (ensure that manure N in compost is notdouble-counted), kg N yr-1
FOOA = annual amount of other organic amendments used as fertiliser (e.g., rendering waste, guano,
brewery waste, etc.), kg N yr-1
swine remains at 2% of nitrogen deposited. However, a value of 1% of the nitrogen deposited may be used for animals
classified as ‘other animals’ which includes goats, horses, mules, donkeys, camels, reindeer, and camelids, as these arelikely to have nitrogen excretion rates and patterns that are more similar to sheep than to cattle. The review further
suggested that a disaggregation of EF3PRP for dung vs. urine nitrogen could also be considered. However, this is difficult toimplement as it is unlikely that countries have the required information readily available to assess excretion rates in urine
and dung. However, this approach may be considered by countries that use a higher tier methodology. Finally, the review
revealed that current information is insufficient or inconclusive to allow for disaggregation of EF3PRP based on climate
region, soil type or drainage class, and/or grazing intensity.
11 For the Tier 1 approach, the amounts of applied mineral nitrogen fertilisers (FSN) and of applied organic nitrogen fertilisers(FON) are no longer adjusted for the amounts of NH3 and NOx volatilisation after application to soil. This is a change fromthe methodology described in the 1996 IPCC Guidelines. The reason for this change is that field studies that havedetermined N2O emission factors for applied N were not adjusted for volatilisation when they were estimated. In otherwords, these emission factors were determined from: fertiliser-induced N2O–N emitted / total amount of N applied, and notfrom: fertiliser-induced N2O–N emitted / (total amount of N applied – NH3 and NOx volatilised). As a result, adjusting theamount of N input for volatilisation before multiplying it with the emission factor would in fact underestimate total N2O
emissions. Countries using Tier 2 or Tier 3 approaches should be aware that correction for NH3/NOx volatilisation after
mineral or organic N application to soil may be required depending on the emission factor and/or the inventory
methodology used.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

The term FAM is determined by adjusting the amount of manure N available (NMMS_Avb; see Equation 10.34 in
Chapter 10) for the amount of managed manure used for feed (FracFEED), burned for fuel (FracFUEL), or used forconstruction (FracCNST) as shown in Equation 11.4. Data for FracFUEL, FracFEED, FracCNST can be obtained fromofficial statistics or a survey of experts. However, if these data are not available use NMMS_Avb as FAM without
adjusting for FracFUEL, FracFEED, FracCNST.
EQUATION 11.4
N FROM ANIMAL MANURE APPLIED TO SOILS (TIER 1)[ (FAM = N MMS Avb • 1 − Frac FEED + Frac FUEL + FracCNST)]

Where:
FAM = annual amount of animal manure N applied to soils, kg N yr-1
NMMS_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)
FracFEED = fraction of managed manure used for feed
FracFUEL = fraction of managed manure used for fuel
FracCNST = fraction of managed manure used for construction
Urine and dung from grazing animals (FPRP)The term FPRP 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 FAM term of FON.
The term FPRP is estimated using Equation 11.5 from the number of animals in each livestock species/category T(N(T)), the annual average amount of N excreted by each livestock species/category T (Nex(T)), and the fraction ofthis N deposited on pasture, range and paddock soils by each livestock species/category T (MS(T,PRP)). The dataneeded for this equation can be obtained from the livestock chapter (see Chapter 10, Section 10.5).
Equation 11.5 provides an estimate of the amount of N deposited by grazing animals:
EQUATION 11.5
N IN URINE AND DUNG DEPOSITED BY GRAZING ANIMALS ON PASTURE, RANGE AND PADDOCK(TIER 1)FPRP = ∑ N (T ) • Nex(T ) • MS (T , PRP )
T

[(

)

]

Where:FPRP = 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 (seeChapter 10, Section 10.5)
MS(T,PRP) = fraction of total annual 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 forage/ pasture renewal, returned to soils, (FCR)
The term FCR 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 referred 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 crop residues, which previously was ignored in the estimateof FCR. As a result, FCR now represents a more accurate estimate of the amount of nitrogen input from crop residue, whichmakes it possible to assess the contribution to residue nitrogen arising from the growth of forage legumes such as alfalfa,
where the harvesting of virtually all the above-ground dry matter results in no significant residue except the root system.

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Volume 4: Agriculture, Forestry and Other Land Use

during forage or pasture renewal 14. It is estimated from crop yield statistics and default factors for above/below-ground residue:yield ratios and residue N contents. In addition, the method accounts for the effect ofresidue burning or other removal of residues (direct emissions of N2O from residue burning are addressed under
Chapter 2, Section 2.4. Because different crop types vary in residue:yield ratios, 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 grain crops (e.g.,maize, rice, wheat, barley); 2) N-fixing grains and pulses (e.g., soybean, dry beans, chickpea, lentils); 3) root andtuber crops (e.g., potato, sweet potato, cassava); 4) N-fixing forage crops (alfalfa, clover); and 5) other forages
including perennial grasses and grass/clover pastures. Equation 11.6 provides the equation to estimate N fromcrop residues and forage/pasture renewal, for a Tier 1 approach.
EQUATION 11.6
N FROM CROP RESIDUES AND FORAGE/PASTURE RENEWAL (TIER 1)

FCR
=


( Area(T ) − Area burnt(T ) • C f ) • RAG (T ) • N AG (T ) •



 
Crop
•
∑T  (T )  1 − Frac
Re move (T ) ) + Area(T ) • FracRe new (T ) • RBG (T ) • N BG (T )  (Where:
FCR = annual amount of N in crop residues (above and below ground), including N-fixing crops, and fromforage/pasture renewal, returned 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
Cf = combustion factor (dimensionless) (refer to Chapter 2, Table 2.6)
FracRenew (T) = fraction of total area under crop T that is renewed annually 15. For countries where pastures
are renewed on average every X years, FracRenew = 1/X. For annual crops FracRenew = 1
RAG(T) = ratio of above-ground residues dry matter (AGDM(T)) to harvested yield for crop T (Crop(T)), kg
d.m. (kg d.m.)-1,= AGDM(T) ● 1000 / Crop(T) (calculating AGDM(T) from the information in Table 11.2)
NAG(T) = N content of above-ground residues for crop T, kg N (kg d.m.) -1, (Table 11.2)
FracRemove(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 FracRemove are not available, assume no removal.
RBG(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, RBG(T) may be calculated by multiplying RBG-BIO in Table 11.2 by the ratio of
total above-ground biomass to crop yield ( = [(AGDM(T) ● 1000 + Crop(T)) / Crop(T)], (also calculating
AGDM(T) from the information in Table 11.2).
NBG(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 obtained from national sources. If suchdata are not available, FAO publishes data on crop production: (http://faostat.fao.org/).
Since yield statistics for many crops are reported as field-dry or fresh weight, a correction factor can be applied
to estimate dry matter yields (Crop(T)) where appropriate (Equation 11.7). The proper correction to be used isdependent on the standards used in yield reporting, which 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 O emissions (e.g., van der2

Weerden et al., 1999; Davies et al., 2001), from renewal/cultivation of grazed grass or grass/clover pasture and otherforage crops.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

EQUATION 11.7
DRY-WEIGHT CORRECTION OF REPORTED CROP YIELDS
Crop (T ) = Yield Fresh(T ) • DRY
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 the N in the above-ground residues, thebelow-ground dry matter, and the total N in the below-ground residues. The total N addition, FCR, is the sum of
the above-and below-ground N contents. With this approach, FCR is given by Equation 11.7A:
EQUATION 11.7A
ALTERNATIVE APPROACH TO ESTIMATE FCR (USING TABLE 11.2)

( Area(T ) − Area burnt(T ) • C f ) • AGDM (T ) • 1, 000 • N AG (T ) • (1 − FracRe move (T ) ) + 
FCR = ∑    AGDM (T ) • 1, 000 + Crop(T ) 
T   Area(T ) • FracRe new (T ) •  • RBG − BIO (T ) • N BG (T )  

Crop
 
T
(
)
 


An improvement on this approach for determining FCR (i.e., Tier 2) would be the use of country-specific datarather than the values provided in Table 11.2, as well as country-specific values for the fraction of above-groundresidue burned.
Mineralised N resulting from loss of soil organic C stocks in mineral soils through land-use change ormanagement practices (FSOM) 16
The term FSOM 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 Chapter 2, Section 2.3.3, land-use change and a variety ofmanagement practices can have a significant impact on soil organic C storage. Organic C and N are intimately
linked in soil organic matter. Where soil C is lost through oxidation as a result of land-use or managementchange, this loss will be accompanied by a simultaneous mineralisation of N. Where a loss of soil C occurs, thismineralised N is regarded as an additional source of N available for conversion to N2O (Smith and Conen, 2004);
just as mineral N released from decomposition of crop residues, for example, becomes a source. The samedefault emission factor (EF1) is applied to mineralised N from soil organic matter loss as is used for directemissions resulting from fertiliser and organic N inputs to agricultural land. This is because the ammonium andnitrate resulting from soil organic matter mineralisation is of equal value as a substrate for the microorganisms
producing N2O by nitrification and denitrification, no matter whether the mineral N source is soil organic matterloss from land-use or management change, decomposition of crop residues, synthetic fertilisers or organic
amendments. (Note: the opposite process to mineralisation, whereby inorganic N is sequestered into newly
formed SOM, is not taken account of in the calculation of the mineralisation N source. This is because of thedifferent dynamics of SOM decomposition and formation, and also because reduced tillage in somecircumstances can increase both SOM and N2O 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 changes in N supply from mineralisation
Step 1: Calculate the average annual loss of soil C (∆CMineral, LU) for the area, over the inventory period, using
Equation 2.25 in Chapter 2. Using the Tier 1 approach, the value for ∆CMineral, LU will have a single value for allland-uses and management systems. Using Tier 2, the value for ∆CMineral, LU will be disaggregated by individual
land-use and/or management systems.
Step 2: Estimate the N mineralised as a consequence of this loss of soil C (FSOM), 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 frommineralisation associated with a loss of soil organic C.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

EQUATION 11.8
N MINERALISED IN MINERAL SOILS AS A RESULT OF LOSS OF SOIL C THROUGH CHANGE IN LANDUSE OR MANAGEMENT (TIERS 1 AND 2)1
FSOM = ∑  ∆C Mineral , LU •  • 1000
R

LU 


Where:FSOM = the net annual amount of N mineralised in mineral soils as a result of loss of soil carbon throughchange in land use or management, kg N
∆CMineral, LU = average annual loss of soil carbon for each land-use type (LU ), tonnes C (Note: for
Approach 1, ∆Cmineral, LU will have a single value for all land-uses and management systems. Using
Approaches 2 or 3 the value for ∆Cmineral, 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 to15) 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 documentchanges in C:N ratio, then different values can be used over the time series, land use, or management
practice.
LU = land-use and/or management system type
Step 3: For Approach 1, the value for FSOM is calculated in a single step. For Approaches 2 or 3, FSOM iscalculated by summing across all land-uses and/or management system types (LU).
Countries that are not able to estimate gross changes of mineral soil C will create a bias in the N2O 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 available, in conjunction with thedata for carbon changes.
Area of drained/managed organic soils (FOS)The term FOS 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 Tier 2 methods. For all land uses, the areas should be
stratified by climate zone (temperate and tropical). In addition, for temperate Forest Land the areas should befurther stratified by soil fertility (nutrient rich and nutrient poor). The area of drained/managed organic soils(FOS) may be collected from official national statistics. Alternatively, total areas of organic soils from eachcountry 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 N2O emissions from managed soils are caused by uncertainties related to theemission 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 inventory when emission measurements that are notrepresentative of all conditions in a country are used. In general, the reliability of activity data will be higher thanthat 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 difficult 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 references: Aitkenhead-Peterson etal., 2005; Garten et al., 2000; John et al., 2005; Lobe et al., 2001; Snowdon et al., 2005, and other references cited bythese authors.

11.16

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application
TABLE 11.2
DEFAULT FACTORS FOR ESTIMATION OF N ADDED TO SOILS FROM CROP RESIDUES a

Crop

Dry matter
fraction of
harvested
product(DRY)

Above-ground residue dry matter AGDM(T) (Mg/ha):
AGDM(T) = (Crop(T)/1000)* slope(T) + intercept(T)Ratio of belowgroundresidues toabove-groundbiomass(RBG-BIO)

N content of
below-groundresidues
(NBG)

Slope

± 2 s.d. as % ofmeanIntercept± 2 s.d. as % ofmean

R2 adj.

N content ofabove-groundresidues
(NAG)

0.88
0.91
0.22
0.94
0.90

1.09
1.13
0.10
1.07
0.3

± 2%
± 19%
± 69%
± 19%
± 50% default

0.88
0.85
1.06
1.54
0

± 6%
± 56%
± 70%
± 41%
-

0.65
0.28
0.18
0.63
-

0.006
0.008
0.019
0.016
0.027

0.22 (± 16%)0.19 (± 45%)
0.20 (± 50%)
0.20 (± 50%)
0.40 (± 50%)

0.009
0.008
0.014
0.014
0.022

0.90

0.3

± 50% default

0

-

-

0.015

0.54 (± 50%)

0.012

0.90

0.3

± 50% default

0

-

-

0.015

0.80 (± 50%)l

0.012

0.90

0.3

± 50% default

0

-

-

0.025

0.80 (± 50%)l

0.016p

0.87
0.89
0.89
0.89
0.89
0.89
0.89
0.90
0.89
0.88

1.03
1.51
1.61
1.29
0.95
0.98
0.91
1.43
0.88
1.09

± 3%
± 3%
± 3%
± 5%
±19%
± 8%
± 5%
± 18%
± 13%
± 50% default

0.61
0.52
0.40
0.75
2.46
0.59
0.89
0.14
1.33
0.88

± 19%
± 17%
± 25%
± 26%
± 41%
± 41%
± 8%
± 308%± 27%± 50% default

0.76
0.68
0.67
0.76
0.47
0.68
0.45
0.50
0.36
-

0.006
0.006
0.006
0.006
0.007
0.007
0.007
0.007
0.007
0.005

0.22 (± 26%)
0.24 (± 32%)
0.23 (± 41%)0.28 (± 26%)
0.16 (± 35%)
0.22 (± 33%)
0.25 (± 120%)
NA
NA
NA

0.007
0.009
0.009
0.009
NA
0.014
0.008
NA
0.006
0.011

Major crop types
GrainsBeans & pulsesb
TuberscRoot crops, otherd
N-fixing forages
Non-N-fixing
forages
Perennial grasses
Grass-clover
mixtures
Individual crops
Maize
Wheat
Winter wheat
Spring wheat
Rice
BarleyOats
MilletSorghum
Ryee

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 11.2 (CONTINUED)DEFAULT FACTORS FOR ESTIMATION OF N ADDED TO SOILS FROM CROP RESIDUES a

Crop

Soyabeanf
Dry beang
PotatohPeanut (w/pod)i
AlfalfajNon-legume hayj

Dry matter
fraction of
harvested
product
(DRY)

0.91
0.90
0.22
0.94
0.90
0.90

Above-ground residue dry matter AGDM(T) (Mg/ha):
AGDM(T) = (Crop(T)/1000)* slope(T) + intercept(T)Slope± 2 s.d. as % ofmeanIntercept± 2 s.d. as % ofmean

R2 adj.

N content ofabove-groundresidues
(NAG)

0.93
0.36
0.10
1.07
0.29k
0.18

± 31%
± 100%
± 69%
± 19%
± 31%
± 50% default

1.35
0.68
1.06
1.54
0
0

± 49%
± 47%
± 70%
± 41%
-

0.16
0.15
0.18
0.63
-

0.008
0.01
0.019
0.016
0.027
0.015

Ratio of belowgroundresidues toabove-groundbiomass(RBG-BIO)

0.19 (± 45%)NA
0.20 (± 50%)mNA
0.40 (± 50%)n0.54 (± 50%)n

N content of
below-groundresidues
(NBG)

0.008
0.01
0.014
NA
0.019
0.012

a

Source: Literature review by Stephen A. Williams, Natural Resource Ecology Laboratory, Colorado State University. (Email: stevewi@warnercnr.colostate.edu) for CASMGS (http://www.casmgs.colostate.edu/). A listof the original references is given in Annex 11A.1.

b

The 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.

e

No data for rye. Slope and intercept values are those for all grain. Default s.d.

f

The average above-ground residue:grain ratio from all data used was 1.9.

g

Ortega, 1988 (see Annex 11A.1). The average above-ground residue:grain ratio from this single source was 1.6. default s.d. for root:AGB.

h

The mean value for above-ground residue:tuber ratio in the sources used was 0.27 with a standard error of 0.04.

I

The 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.

k

This is the average above-ground biomass reported as litter or harvest losses. This does not include reported stubble, which averaged 0.165 x Reported Yields. Default s.d.

lEstimate of root turnover to above-ground production based on the assumption that in natural grass systems below-ground biomass is approximately equal to twice (one to three times) the above-ground biomass and thatroot turnover in these systems averages about 40% (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.

p

It is assumed here that grass dominates the system by 2 to 1 over legumes.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

11.2.2

Indirect N 2 O emissions

In addition to the direct emissions of N2O from managed soils that occur through a direct pathway (i.e., directlyfrom the soils to which N is applied), emissions of N2O also take place through two indirect pathways (asillustrated above in Section 11.2).
The first of these pathways is the volatilisation of N as NH3 and oxides of N (NOx), and the deposition of thesegases and their products NH4+ and NO3- onto soils and the surface of lakes and other waters. The sources of N as
NH3 and NOx are not confined to agricultural 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, theseprocesses cause N2O emissions in an exactly analogous way to those resulting from deposition of agriculturally
derived NH3 and NOx, following the application of synthetic and organic N fertilisers and /or urine and dungdeposition from grazing animals. The second pathway is the leaching and runoff from land of N from synthetic
and organic fertiliser additions, crop residues 18, mineralisation of N associated with loss of soil C in mineral anddrained/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 NO3- form, may bypass biologicalretention mechanisms in the soil/vegetation system by transport in overland water flow (runoff) and/or flow
through soil macropores or pipe drains. Where NO3- is present in the soil in excess of biological demand, e.g.,under cattle urine patches, the excess leaches through the soil profile. The nitrification and denitrification
processes described at the beginning of this chapter transform some of the NH4+ and NO3- to N2O. This may takeplace 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, streams, 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 N2O emissions frommanaged soils arising from agricultural inputs of N:•

synthetic N fertilisers (FSN);•

organic N applied as fertiliser (e.g., applied animal manure 19, compost, sewage sludge, rendering waste andother organic amendments) (FON);•

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

N in crop residues (above- and below-ground), including N-fixing crops and forage/pasture renewal returned
to soils (FCR) 20; and•N mineralisation associated with loss of soil organic matter resulting from change of land use or
management on mineral soils (FSOM) 20.

The generic Tier 1 and Tier 2 methods described below can be used to estimate aggregate total indirect N2Oemissions from agricultural N additions to managed soils for an entire country. If a country is estimating its
direct N2O from managed soils by land-use category, the indirect N2O emissions can also be estimated by thesame disaggregation of land-use categories using the equations presented below with activity data, partitioning
fractions, and/or emission factors specific for each land-use category. The methodology for estimating indirect
N2O 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 N2O Emissions) for guidance on which Tier method to use.

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

Guidelines.

19 Volatilisation and subsequent deposition of nitrogen from the manure in manure 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 O emission.
2

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Volume 4: Agriculture, Forestry and Other Land Use

Figure 11.3

Decision tree for indirect N 2 O emissions from managed soils
Start

For each
agricultural N source1, for bothvolatilization and leaching/runoff, ask:
Do you have country-specificactivity data?

Yes

Is this a
key category2
and is this N source
significant3?

No

Obtain countryspecific data.

Yes
No

For
each N source,
do you have rigorously
documented country-specificemission factors (EF4 or EF5) and as appropriate
rigorously documented
country-specific partitioning fractions(FracGASF,FracGASM, FracLEACH)
values?

Do youhave rigorously
documented country-specific
EF values (EF4 or EF5) and as
appropriate rigorously documented countryspecific partitioning fractions(FracGASF, FracGASM, FracLEACH)
values?

Yes

Yes

Estimate emissions using Tier 2 equation,
country-specific activity data andcountry-specific emission factors andpartitioning fractions, or Tier 3 method.

Estimate emissions with Tier 2 equation,using a mix of country-specific and otheravailable data and country-specificemission and partitioning factors.

No

Box 2: Tier 2

Box 4: Tier 2 or 3

Estimate emissions using Tier 1 or Tier 2equation, country-specific activity data
and a mix of county-specific or default
emission factors and partitioning fractions.
Box 3: Tier 1 or 2

No

Estimate emissions using the Tier 1
equation with default emission
and partitioning factors andavailable activity data.
Box 1: Tier 1

Note:
1: N sources include: synthetic N fertilizer, organic N additions, urine and dung depositions, crop residue, N mineralization/immobilization
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 N2O 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 resources), 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 category.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

Tie r 1Volatilisation, N2O(ATD)
The N2O emissions from atmospheric deposition of N volatilised from managed soil are estimated using
Equation 11.9:
EQUATION 11.9
N2O FROM ATMOSPHERIC DEPOSITION OF N VOLATILISED FROM MANAGED SOILS (TIER 1)
N 2O( ATD ) −N = [(FSN • FracGASF ) + ((FON + FPRP ) • FracGASM )] • EF4
Where:
N2O(ATD)–N = annual amount of N2O–N produced from atmospheric deposition of N volatilised frommanaged soils, kg N2O–N yr-1
FSN = annual amount of synthetic fertiliser N applied to soils, kg N yr-1
FracGASF = fraction of synthetic fertiliser N that volatilises as NH3 and NOx, kg N volatilised (kg of N
applied)-1 (Table 11.3)
FON = annual amount of managed animal manure, compost, sewage sludge and other organic N additions
applied to soils, kg N yr-1
FPRP = annual amount of urine and dung N deposited by grazing animals on pasture, range and paddock,
kg N yr-1
FracGASM = fraction of applied organic N fertiliser materials (FON) and of urine and dung N deposited bygrazing animals (FPRP) that volatilises as NH3 and NOx, kg N volatilised (kg of N applied ordeposited)-1 (Table 11.3)
EF4 = emission factor for N2O emissions from atmospheric deposition of N on soils and water surfaces,[kg N–N2O (kg NH3–N + NOx–N volatilised)-1] (Table 11.3)
Conversion of N2O(ATD)-N emissions to N2O emissions for reporting purposes is performed by using thefollowing equation:
N2O(ATD) = N2O(ATD) –N • 44/28
Leaching/Runoff, N2O(L)The N2O emissions from leaching and runoff in regions where leaching and runoff occurs are estimated using
Equation 11.10:
EQUATION 11.10
N2O FROM N LEACHING/RUNOFF FROM MANAGED SOILS IN REGIONS WHERE LEACHING/RUNOFF
OCCURS (TIER 1)
N 2O( L ) −N = (FSN + FON + FPRP + FCR + FSOM ) • FracLEACH − ( H ) • EF5

Where:
N2O(L)–N = annual amount of N2O–N produced from leaching and runoff of N additions to managed
soils in regions where leaching/runoff occurs, kg N2O–N yr-1
FSN = annual amount of synthetic fertiliser N applied to soils in regions where leaching/runoff occurs, kg
N yr-1
FON = annual amount of managed animal manure, compost, sewage sludge and other organic N additions
applied to soils in regions where leaching/runoff occurs, kg N yr-1
FPRP = 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)
FCR = amount of N in crop residues (above- and below-ground), including N-fixing crops, and fromforage/pasture renewal, returned to soils annually in regions where leaching/runoff occurs, kg N yr-1
FSOM = 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 management in regions where leaching/runoff occurs, kg
N yr-1 (from Equation 11.8)

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Volume 4: Agriculture, Forestry and Other Land Use

FracLEACH-(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)
EF5 = emission factor for N2O emissions from N leaching and runoff, kg N2O–N (kg N leached andrunoff)-1 (Table 11.3)
Note: If a country is able to estimate the quantity of N mineralised from organic soils, then include this as anadditional input to Equation 11.10.
Conversion of N2O(L)–N emissions to N2O emissions for reporting purposes is performed by using the following
equation:
N2O(L) = N2O(L)–N • 44/28
Tie r 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 specificvolatilisation factors are available for the application of synthetic fertilisers (FSN) under different conditions i,
Equation 11.9 would be expanded to become 21:
EQUATION 11.11
N2O FROM ATMOSPHERIC DEPOSITION OF N VOLATILISED FROM MANAGED SOILS (TIER 2)

(

)



N 2O( ATD ) −N = ∑ FSN i • FracGASFi + [(FON + FPRP ) • FracGASM ] • EF4
i

Where:
N2O(ATD)–N = annual amount of N2O–N produced from atmospheric deposition of N volatilised frommanaged soils, kg N2O–N yr-1
FSNi = annual amount of synthetic fertiliser N applied to soils under different conditions i, kg N yr-1
FracGASFi = fraction of synthetic fertiliser N that volatilises as NH3 and NOx under different conditions i,
kg N volatilised (kg of N applied)-1
FON = annual amount of managed animal manure, compost, sewage sludge and other organic N additions
applied to soils, kg N yr-1
FPRP = annual amount of urine and dung N deposited by grazing animals on pasture, range and paddock,
kg N yr-1
FracGASM = fraction of applied organic N fertiliser materials (FON) and of urine and dung N deposited bygrazing animals (FPRP) that volatilises as NH3 and NOx, kg N volatilised (kg of N applied ordeposited)-1 (Table 11.3)
EF4 = emission factor for N2O emissions from atmospheric deposition of N on soils and water surfaces,[kg N–N2O (kg NH3–N + NOx–N volatilised)-1] (Table 11.3)
Conversion of N2O(ATD)–N emissions to N2O(ATD) emissions for reporting purposes is performed by using thefollowing equation:
N2O(ATD) = N2O(ATD)–N • 44/28
Tie r 3Tier 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 thedevelopment of a Tier 3 Model-based Accounting System.

21 It is important to note that Equation 11.11 is just one of many possible modifications to Equation 11.9, and is also meant toillustrate how Equation 11.10 could be modified, when using the Tier 2 method. The eventual form of Equation 11.11 will
depend upon the availability of land use and/or condition-specific partitioning fractions and/or emission factors and theability to which a country can disaggregate its activity data.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

11.2.2.2

C HOICE OF EMISSION , VOLATILISATION AND LEACHING
FACTORSThe method for estimating indirect N2O emissions includes two emission factors: one associated with volatilised
and re-deposited N (EF4), and the second associated with N lost through leaching/runoff (EF5). The method also
requires values for the fractions of N that are lost through volatilisation (FracGASF and FracGASM) or
leaching/runoff (FracLEACH-(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 FracLEACH-(H) is 0.30. For dryland regions, where precipitation is lower thanevapotranspiration throughout most of the year and leaching is unlikely to occur, the default FracLEACH is zero.
The method of calculating whether FracLEACH-(H) = 0.30 should be applied is given in Table 11.3.
Country-specific values for EF4 should be used with great caution because of the special complexity oftransboundary atmospheric transport. Although inventory compilers may have specific measurements of N
deposition and associated N2O 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 N2O may prevail. For these reasons the value of EF4
is very difficult to determine, and the method presented in Volume 1, Chapter 7, Section 7.3 attributes all
indirect N2O emissions resulting from inputs to managed soils to the country of origin of the atmospheric NOx
and NH3, 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 N2O emissions from the various N additions to managed soils, the parameters FSN,
FON, FPRP, FCR, FSOM need to be estimated.
Applied synthetic fertiliser (FSN)
The term FSN refers to the annual amount of synthetic fertiliser N applied to soils. Refer to the activity data
section on direct N2O emissions from managed soils (Section 11.2.1.3) and obtain the value for FSN.
Applied organic N fertilisers (FON)The term FON refers to the amount of organic N fertiliser materials intentionally applied to soils. Refer to theactivity data section on direct N2O emissions from managed soils (Section 11.2.1.3) and obtain the value for FON.
Urine and dung from grazing animals (FPRP)The term FPRP 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 N2O emissions from managed soils (Section 11.2.1.3) and obtain thevalue for FPRP.
Crop residue N, including N from N-fixing crops and forage/pasture renewal, returned to soils (FCR)
The term FCR 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 the activity data section on direct N2O emissions from managed soils (Section
11.2.1.3) and obtain the value for FCR.
Mineralised N resulting from loss of soil organic C stocks in mineral soils (FSOM)
The term FSOM refers to the amount of N mineralised from the loss of soil organic C in mineral soils throughland-use change or management practices. Refer to the activity data section on direct N2O emissions frommanaged soils (Section 11.2.1.3) and obtain the value for FSOM.

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 11.3
DEFAULT EMISSION, VOLATILISATION AND LEACHING FACTORS FOR INDIRECT SOIL N2O EMISSIONS
FactorDefault
value

Uncertainty
range

EF4 [N volatilisation and re-deposition], kg N2O–N (kg NH3–N + NOX–N
volatilised)-1 22

0.010

0.002 - 0.05

EF5 [leaching/runoff], kg N2O–N (kg N leaching/runoff) -1 23

0.00750.0005 0.025

FracGASF [Volatilisation from synthetic fertiliser], (kg NH3–N + NOx–N) (kg N
applied) –1

0.10

0.03 - 0.3

FracGASM [Volatilisation from all organic N fertilisers applied , and dung andurine deposited by grazing animals], (kg NH3–N + NOx–N) (kg N applied ordeposited) –1

0.20

0.05 - 0.5

FracLEACH-(H) [N losses by leaching/runoff for regions where Σ(rain in rainyseason) - Σ (PE in same period) > soil water holding capacity, OR where
irrigation (except drip irrigation) is employed], kg N (kg N additions ordeposition by grazing animals)-1

0.30

0.1 - 0.8

Note: The term FracLEACH 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), and leaching/runoff occurs, andredesignated as FracLEACH-(H). In the definition of FracLEACH-(H) above, PE is potential evaporation, and the rainy season(s) canbe taken as the period(s) when rainfall > 0.5 * Pan Evaporation. (Explanations of potential and pan evaporation are available
in standard meteorological and agricultural texts). For other regions the default FracLEACH is taken as zero.

11.2.2.4

U NCERTAINTY ASSESSMENT

Uncertainties in estimates of indirect N2O emissions from managed soils are caused by uncertainties related tonatural variability and to the emission, volatilization and leaching 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 representative of all conditions in a country are used. In general, the reliability ofactivity data will be higher than that of the emission, volatilisation and leaching factors. As with direct emissions,further uncertainties may be caused by missing information on observance of laws and regulations related tohandling and application of fertiliser and manure, and changing management practices in farming. Generally, it
is difficult to obtain information on the actual observance of laws and possible emission reductions achieved aswell as information on farming practices. Uncertainties in emission factors are nevertheless likely to dominate
and uncertainty ranges are indicated in the tabulations 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 emissions from some environments, particularly

deciduous forests receiving high rates of N deposition from the atmosphere, are substantially higher than those previously
reported (e.g., Butterbach-Bahl et al., 1997; Brumme et al., 1999; Denier van der Gon and Bleeker, 2005)., while there isalso clear evidence that EFs can be very low (<< 0.01) in low-deposition environments (e.g., Corre et al., 1999). The meanvalue 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 many countries a substantial fraction of the indirect emissions will in fact originate
from managed land.

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

leached/ in runoff water. This emission factor incorporates three components: EF5g, EF5r and EF5e, 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 and surface drainage (0.015) was too high and should be reduced to 0.0025 kg N2O–
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 also been reduced from 0.0075 kg N2O–N/kg N to the same value, 0.0025 kg N2O–N/kg N in
the water. This is in recognition that while 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
N2O–N/kg N.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

11.2.3Completeness, Time series, QA/QCCOMPLETENESS
Complete coverage of the direct and indirect N2O emissions from managed land requires estimation of emissions
for all of the anthropogenic inputs and activities (FSN, FON, FCR, FPRP, FSOM and FOS), if they occur. Experiencehas shown that none of these sub-categories are likely to be missed in inventories, although countries may havedifficulty obtaining accurate statistics for all sub-categories, particularly the amounts of crop residues (by croptype) that are typically returned to soils, and the area of drained/managed organic soils.
Currently, the IPCC method does not explicitly address activities such as plastic sheeting or greenhousehydroponic systems that may influence N2O emissions. These additional activities can be considered if
appropriate, and if national activity data for these activities are collected. Some of these activities can be readily
included 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 andhydroponic 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 anddisaggregation of emissions estimates from this source category will improve over time. In cases where somehistorical data are missing, it may be necessary to derive the 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 decadal 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 FracGASF, FracGASM, FracLEACH, EF4, and EF5 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 through future research, inventory 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 andresults are thoroughly documented. If policy measures are implemented such that activity data are affected
directly (e.g., increased efficiency of fertiliser use resulting in a decrease in fertiliser consumption), the effect ofthe policy measures on emissions will be transparent, assuming the activity data are carefully documented. In
cases where policy measures have an indirect effect on activity 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 thepolicies 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 withexpert review by people outside the inventory preparation process. Additional Tier 2 quality control checks andquality assurance procedures may also be applicable, particularly if higher tier methods are used to determine
direct and indirect N2O emissions from this source category. Supplement the general QA/QC related to dataprocessing, handling, and reporting, with source-specific category procedures discussed below. The persons whocollect data are responsible for reviewing the data collection methods, checking the data to ensure that they arecollected and aggregated or disaggregated correctly, and cross-checking the data with previous years to ensurethat 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 identify 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 compiler 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 factors and default or other country factors should be
explained and documented.

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

Activity data check
The inventory compiler should compare country-specific data 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 andindirect N2O emissions from managed soils are reported in aggregate or disaggregated by land-use category orother subcategory (e.g., ricelands) under the IPCC category ‘AFOLU’. Reporting should be done at the samelevel of disaggregation as was done when calculating the emissions. In addition to completing the reporting
formats, the following additional information is necessary to document the estimate:
Activity data: Sources of all activity data used in the calculations (i.e., complete citations for the statistical
databases from which data were collected), and in cases when activity data were not available directly fromdatabases, 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 used (specific IPCC default values or otherwise).
In inventories in which country- or region-specific emission factors were used, or in which new methods (otherthan the default IPCC methods) were used, the scientific basis of these emission factors and methods should becompletely described and documented. This includes defining the input parameters and describing the process bywhich 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 changes in emission, volatilisation and leaching factors fromyear to year, and the reasons for these changes documented. If different factors are used for different years, thereasons 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 agriculturallands and managed forests. Adding carbonates to soils in the form of lime (e.g., calcic limestone (CaCO3), or
dolomite (CaMg(CO3)2) leads to CO2 emissions as the carbonate limes dissolve and release bicarbonate (2HCO3), which evolves into CO2 and water (H2O).
Inventories can be developed using Tier 1, 2 or 3 approaches, with each successive Tier requiring more detailand resources than the previous one. It is good practice for countries to use higher tiers if CO2 emissions fromliming are a key source category.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

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.
Tie r 1CO2 Emissions from additions of carbonate limes to soils can be estimated with Equation 11.12:
EQUATION 11.12
ANNUAL CO2 EMISSIONS FROM LIME APPLICATION
CO2 −C Emission = (M Limestone • EFLimestone ) + (M Dolomite • EFDolomite )Where:
CO2–C Emission = annual C emissions from lime application, tonnes C yr-1
M = annual amount of calcic limestone (CaCO3) or dolomite (CaMg(CO3)2), tonnes yr-1
EF = emission factor, tonne of C (tonne of limestone or dolomite) -1
Procedural steps for calculations
The steps for estimating CO2-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 fertilizers). 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 CO2
emissions from application to soils (CO2 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 CaCO3, 13% for CaMg(CO3)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 McBride, 2005) (Note: uncertainties can not exceed the emission factors because thesevalue 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 thetwo values to obtain the total CO2–C emission.
Multiply by 44/12 to convert CO2–C emissions into CO2.
Tie r 2Tier 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 CO2 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 CO2 in the year of application. However, emissions are likely to be lessthan assumed using the Tier 1 approach because the amount of CO2 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.
Tie r 3Tier 3 methods use more sophisticated models or measurement procedures, and the procedural steps will dependon the country-specific estimation system. Such an analysis would likely necessitate modelling carbon fluxes
associated with primary and secondary carbonate mineral formation and dissolution in soils, as well as theleaching and transport of dissolved inorganic C. Note that increases in soil inorganic C or dissolved inorganic C
attributed to liming does not constitute a net removal of CO2 from the atmosphere. Rather, carbonate-C fromliming that is not returned to the atmosphere is considered a net reduction in the emissions associated with thispractice. 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).

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Figure 11.4

Decision tree for identification of appropriate tier to estimate CO 2 emissions
from liming.
Start

Are detailed
data on lime application
available and information to estimate
carbonate mineral formation/dissolution, leaching andtransport of inorganic C (for application of models
and/or measurementbased approaches)?

Use the detailed activity
data for Tier 3 model-based
and/or direct measurementbased method.

Yes

Box 3: Tier 3NoAre
country-specific dataand emission factor
available?

Use country-specific dataand emission factor for
the Tier 2 method.

Yes

Box 2: Tier 2
No

Are CO2emissions from liming a
key category1?

Yes
Collect data for the
Tier 3 or Tier 2
method.

No

Are data
to derive emission factor
available?
Yes

No
Gather data toallow derivation ofemission factor.

Use data to derive
emission factor
for Tier 1 method.
Box 1: Tier 1

Note:
1: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for
discussion of key categories and use of decision trees.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

11.3.2

Choice of emission factors

Tie r 1
Default emission factors (EF) are 0.12 for limestone and 0.13 for dolomite.
Tie r 2Derivation 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 andshell deposits) can vary somewhat in their C content and overall purity. Each material would have a uniqueemission factor based on the C content.
Country-specific emission factors could also account for the proportion of carbonate-C from liming that isemitted to the atmosphere as CO2 (e.g., West and McBride, 2005). Dissolved 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 andeventually to the ocean (Robertson and Grace, 2004). In either case, the net emission of CO2 to the atmosphere isless than the original amount of C added as lime. Country-specific emission factors can be derived if there aresufficient data and understanding of inorganic carbon transformations, in addition to knowledge about transportof 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.
Tie r 3Tier 3 approaches are based on estimating variable emissions from year to year, which depends on a variety ofsite specific characteristics and environmental drivers. No emission factors are directly estimated.

11.3.3

Choice of activity data

Tie r 1
Optimally, national usage statistics for carbonate lime would be available to determine the amount appliedannually to soils (M). These data provide the most direct inference on application. Alternatively, annual sales ofcarbonate lime may be used to infer the amount that is applied to soils, under the assumption that all lime sold tofarmers, 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 forthat year (annual domestic mining and import records) minus exports and usage in industrial processes. In thelast 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 domestic production. Import/export records are typicallymaintained 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 forreporting purposes.
Tie r 2
In addition to the activity data that are described for Tier 1, Tier 2 may incorporate information on the purity ofcarbonate limes as well as site-level and hydrological characteristics to estimate the proportion of carbonate-C inlime applications that is emitted to the atmosphere.
Tie r 3
For Tier 3 model-based and/or direct measurement-based inventories, 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 ormeasurement design.

11.3.4

Uncertainty assessment

Two sources of uncertainty exist for CO2 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 isemitted as CO2. 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 isapplied to soils. This approach may over- or under-estimate emissions in individual years if the total amount oflime available or purchased is not applied in a particular year. Over the long term, this bias should be negligible,

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Volume 4: Agriculture, Forestry and Other Land Use

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 particularinventory year.
Uncertainties in the net amount of C added to soils from liming that is emitted as CO2 are dependent on the Tier.
Using the Tier 1 method, it is assumed all C in the lime is emitted as CO2 to the atmosphere. This is aconservative 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 CO2, at leastin 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 approacheswith Tier 2 or 3 methods, particularly if liming is a key source. While the higher tier approaches are likely tolimit 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 CO2. 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.

11.3.5Completeness, Time series, QA/QCCOMPLETENESS
Tie r 1
Tier 1 inventories are complete if emissions are computed based on a full accounting of all limestone anddolomite applied to soils. Carbonate lime usage statistics provide the most direct inference on applications tosoils. 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 notsufficient 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.
Tie r 2Completeness 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 theavailability of data about the purity of lime and/or site-level and hydrological data to better specify emission
factors relating the amount of CO2 released per amount of C added to soils in carbonate lime.
Tie r 3
Beyond the considerations for Tier 1 and 2, completeness of Tier 3 inventories is also dependent on the dataneeds 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 CO2 from carbonate limes applied to soils. If gaps or limitations are identified, it is good practice togather additional data so that the fate of liming carbonates is fully addressed by the Tier 3 method.

TIME SERIES CONSISTENCY
Tie r 1The same activity data and emissions factors should be applied 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, thebasis 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 isgood 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 circumstances is presented in Volume 1, Chapter 5.
Tie r 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 also be applied across the entire
time series. In rare cases when this is not possible, inventory compilers should determine the influence ofchanging emission factors on the trends; additional guidance on recalculation for these circumstances can befound in Volume 1, Chapter 5.
Tie r 3
Similar to Tier 2, it is good practice to apply the country-specific estimation system throughout the entire timeseries; inventory agencies should use the same measurement protocols (sampling strategy, method, etc.) and/or
model-based system throughout the inventory time period.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea ApplicationQUALITY ASSURANCE/QUALITY CONTROL
Tie r 1
It is good practice to implement Quality Assurance/Quality Controls with internal and independent reviews ofinventory data and results, ensuring: 1) activity data have been processed appropriately to estimate application tosoils; 2) activity data have been properly transcribed into the worksheets or inventory computation software; and3) 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 validityof the inventory approach, thoroughness of inventory documentation, methods explanation and overall
transparency.
Tie r 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 should review the measurements to ensure that they are representative of
the actual range of environmental conditions. If accessible, it is good practice to compare the country-specificfactors with Tier 2 emission factors used by other countries with comparable circumstances, in addition to the
IPCC defaults. Given the complexity of inorganic C transformation, specialists in the field should be involved in
the review process to provide an independent critique of the emission factors.
Tie r 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 estimation system, archive thereports, and include summary results in reporting documentation.

REPORTING AND DOCUMENTATION
Tie r 1
For Tier 1, inventory compilers should document trends and uncertainties in carbon lime applications to soils andrelate those patterns to the CO2 emission trends. Significant fluctuations in annual emissions across the timeseries should be explained.
It is good practice to archive actual databases, such as mining records or usage statistics from surveys, andprocedures used to process the data (e.g., statistical programs). The worksheets or inventory software, which wasused to estimate emissions, should be archived along with input/output files that were generated to produce theresults.
In cases where activity data are not available directly from databases or multiple data sets were combined, theinformation, 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.
Tie r 2
In addition to the considerations for Tier 1, inventory compilers should document the underlying basis forcountry-specific emission factors, as well as archive metadata and data sources used to estimate country-specificvalues. Reporting documentation should include the new factors (i.e., means and uncertainties), and it is goodpractice 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 betweenchanges in activity levels and changes in methods, including emission factors, and the reasons for these changes
need to be documented.
Tie r 3Tier 3 inventories need similar documentation about activity data and emission/removal trends as lower tierapproaches, but additional documentation should be included to explain the underlying basis and framework ofcountry-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 themodel version and provide a model description, as well as permanently archive copies of all model input files,
source code and executable programs.

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Volume 4: Agriculture, Forestry and Other Land Use

11.4

CO 2 EMISSIONS FROM UREAFERTILIZATION

Adding urea to soils during fertilisation leads to a loss of CO2 that was fixed in the industrial production process.
Urea (CO(NH2)2) is converted into ammonium (NH4+), hydroxyl ion (OH-), and bicarbonate (HCO3-), in thepresence of water and urease enzymes. Similar to the soil reaction following addition of lime, bicarbonate that isformed evolves into CO2 and water. This source category is included because the CO2 removal from theatmosphere during urea manufacturing is estimated in the 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 detailand resources than the previous. It is good practice for countries to use higher tiers if CO2 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.
Tie r 1CO2 emissions from urea fertilisation can be estimated with Equation 11.13:
EQUATION 11.13
ANNUAL CO2 EMISSIONS FROM UREA APPLICATION
CO2 −C Emission = M • EF
Where:
CO2–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, tonne of C (tonne of urea)-1
Procedural Steps for Calculations
The steps for estimating CO2–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(NH2)2). A default -50% uncertainty may be applied (Note: uncertainties
can not exceed the default emission factor because this value represents the absolute maximum emissions
associated with urea fertilization).
Step 3: Estimate the total CO2–C emission based on the product of the amount of urea applied and the emission
factor.
Multiply by 44/12 to convert CO2–C emissions into CO2. Urea is often applied in combination with othernitrogenous fertilizers, particularly in solutions, and it will be necessary to estimate the proportion of urea in thefertilizer solution for M. If the proportion 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.
Tie r 2Tier 2 inventories also use Equation 11.13 and procedural steps, which were provided in the Tier 1 approach, but
incorporate country-specific information to estimate emission factors.
Tie r 3CO2 emissions from urea applications could be estimated with more detailed models or measurements thatincorporate the possibility of bicarbonate leaching to deep groundwater, and/or lakes and oceans, and thus notcontributing to CO2 emissions, at least not immediately. Note that increases in soil inorganic C from urea
fertilization do not represent a net removal of CO2 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 associatedwith this practice. See the Tier 3 section for soil inorganic C in Chapter 2 for additional discussion (Section 2.3.3
on Change in Soil C Stocks).

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

Figure 11.5

Decision tree for identification of appropriate tier to estimate CO 2 emissions
from urea fertilisation
Start

Are
detailed dataon urea application available
and information to estimate leaching andtransport of inorganic C (for application ofmodels and/or measurement-based
approaches)?

Use the detailed activity
data for Tier 3 model-based
and/or direct measurementbased method.

Yes

Box 3: Tier 3NoAre
country-specific dataand emission factor
available?

Use country-specific dataand emission factor for
the Tier 2 method.

Yes

Box 2: Tier 2
No
Are CO2emissions from urea
fertilization a key
category1?

No

Are data
to derive emission factor
available?
Yes

Yes
Collect data for the
Tier 3 or Tier 2
method.

No
Gather data toallow derivation ofemission factor.

Use data to derive
emission factor
for Tier 1 method.
Box 1: Tier 1

Note:
1: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for
discussion of key categories and use of decision trees.

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11.4.2

Choice of emission factor

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

11.4.3

Choice of activity data

Tie r 1
Domestic production records and import/export data on urea 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 orimported annually minus annual exports is applied to soils. However, supplemental data on sales and/or usage of
urea can be used to refine the calculation, instead of assuming all available urea in a particular year isimmediately added to soils. Regardless of the approach, the annual application estimates for urea fertilizers
should be consistent between CO2 emission from urea and N2O emissions from soils.
Usage statistics may be gathered as part of the national census or through enterprise records, while banks and thefertilizer industry should have information on sales and domestic production. Import/export records are typicallymaintained 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 forreporting purposes.
Tie r 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 estimate the proportion of C in urea that is emitted to theatmosphere.
Tie r 3For application of dynamic models and/or a direct measurement-based inventory in Tier 3, it is likely that moredetailed 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 CO2 emissions from urea: 1) uncertainties in the amount of urea applied tosoils; and 2) uncertainties in the net amount of urea-C that is emitted as CO2. Activity data uncertainties will
depend on the accuracy of production, sales, import/export, and/or usage data. Usage and sales data are likely tohave the least uncertainty; import/export and production data have additional uncertainties due to inferences
about application. Inventory compilers may use a conservative approach and assume that all urea available forapplication or purchased is applied to soils. This approach may create over- or under-estimates in individualyears 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 there is no long-term stockpiling of urea fertiliser.
Alternatively, inventory compilers can address uncertainties in both the amount of urea available for applicationand the amount applied in a particular inventory year.
Uncertainties in the net amount of C added to soils from urea fertilization that is emitted as CO2 are dependent
on the Tier. Using the Tier 1 method, it is assumed all C in the urea is lost as CO2 from the atmosphere. This is aconservative approach, and the default emission factors are considered certain (given this assumption). In
practice, however, some of the C in urea may be retained in the soil as inorganic C and not emitted as CO2, atleast in the year of application. Consequently, default emission factors can lead to systematic biases in theemission estimates.
It is good practice, therefore, to develop country-specific emission factors or advanced estimation approacheswith Tier 2 or 3 methods, respectively, particularly if urea-C is a key source. While the higher tier approaches

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

are likely to limit bias, there are additional uncertainties that will need to be addressed. Those uncertainties canstem from insufficient data on site characteristics, hydrology, and other environmental variables which
influences the transport and conversion of inorganic C into CO2. There may also be uncertainties due toinsufficient knowledge about the processes and/or the ability of country-specific emission factors or estimation
systems to represent the fate of urea-C.

11.4.5Completeness, Time series consistency, QA/QCCOMPLETENESS
Tie r 1
Tier 1 inventories are complete if emissions are computed based on a full accounting of all urea that is applied tosoils. Urea usage statistics or sales provide the most direct inference on applications to soils, but production andimport/export records are sufficient for making an approximate 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.
Tie r 2Completeness 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 theavailability of data about site-level and hydrological data that are used to better specify emission factors relating
the amount of CO2 released per amount of urea-C added to soils.
Tie r 3
Beyond the considerations for Tiers 1 and 2, completeness of Tier 3 inventories is also dependent on the dataneeds and representativeness of the measurement design and/or modelling framework. Inventory compilers
should review their approach and determine if the advanced estimation system is adequate to address the net
release of CO2 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
Tie r 1The same activity data and emissions factors should be applied 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, thebasis 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, insome 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 circumstances is presented in Volume 1, of Chapter 5.
Tie r 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 timeseries. In rare cases when this is not possible, inventory compilers should determine the influence of changing
emission factors on the trends; additional guidance on recalculation for these circumstances can be found in
Volume 1, Chapter 5.
Tie r 3
Similar to Tier 2, it is good practice to apply the country-specific estimation system throughout the entire timeseries; 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
Tie r 1
It is good practice to implement Quality Assurance/Quality Controls with internal and independent reviews ofinventory data and results, ensuring 1) activity data have been processed appropriately to estimate application tosoils, 2) activity data have been properly transcribed into the worksheets or inventory computation software, and3) 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 validityof the inventory approach, thoroughness of inventory documentation, methods explanation and overall
transparency.

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Volume 4: Agriculture, Forestry and Other Land Use

Tie r 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 should review the measurements to ensure that they are representative of
the actual range of environmental conditions. If accessible, it is good practice to compare the country-specificfactors with Tier 2 emission factors used by other countries with comparable circumstances, in addition to the
IPCC defaults. Given the complexity of inorganic C transformations, specialists in the field should be involvedin the review process, to provide an independent critique of the emission factors.
Tie r 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 estimation system, archive thereports, and include summary results in reporting documentation.

REPORTING AND DOCUMENTATION
Tie r 1
For Tier 1, inventory compilers should document trends and uncertainties in urea applications to soils and relate
those patterns to the CO2 emission trends. Significant fluctuations in annual emissions across the time series
should be explained.
It is good practice to archive databases, such as domestic production, 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 weregenerated to produce the results.
In cases where activity data are not available directly from databases or multiple data sets were combined, theinformation, 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.
Tie r 2
In addition to the considerations for Tier 1, inventory compilers should document the underlying basis forcountry-specific emission factors, as well as archive metadata and data sources used to estimate country-specificvalues. Reporting documentation should include the new factors (i.e., means and uncertainties), and it is goodpractice to include a discussion in the inventory report about differences between country-specific factors anddefault 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 betweenchanges in activity levels and changes in methods, including emission factors, and the reasons for these changes
need to be documented.
Tie r 3Tier 3 inventories need similar documentation about activity data and emission/removal trends as lower tierapproaches, but additional documentation should be included to explain the underlying basis and framework forcountry-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 themodel version and provide a model description, as well as to permanently archive copies of all model input files,
source code and executable programs.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

Annex 11A.1

References for crop residue data in Table 11.2

I. Dry matter fraction of harvested productLander, C.H., Moffitt, D., and Alt, K. (1998). Nutrients available from livestock manure relative to crop growthrequirements. 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 Partitioning 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 891. 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 Lillard, J.H. (1969). Effects of tillage, no tillage, and mulch on soil water andplant 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 forcorn (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. and Irvin, C.J. (1931). Fiftieth anniversary 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 farming systems trial in Pennsylvania: 1981-1991. In: Soil
Organic Matter in Temperate Agroecosystems. 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 ofColorado. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, andC.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 Agronomy, Colorado State University.
Peterson, G.A., Westfall, D.G., Sherrod, L., Poss, D., Larson, K. and Thompson, D.L. (1997). Sustainabledryland 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 indryland agroecosystems. Technical Bulletin TB92-2. Dept. of Agronomy, Colorado State University.

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Volume 4: Agriculture, Forestry and Other Land UsePeterson, G.A., Westfall, D.G., Sherrod, L., McGee, E. and Kolberg, R. (1991). Crop and soil management indryland agroecosystems. Technical Bulletin TB91-1. Dept. of Agronomy, Colorado State University.
Peterson, G.A., Westfall, D.G., Sherrod, L., Kolberg, 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., Kolberg, 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). Sustainable dryland agroecosystemmanagement. Technical Bulletin TB96-1. Dept. of Agronomy, Colorado State University.
Peterson, G.A., Westfall, D.G., Sherrod, L., Kolberg, R. and Poss, D. (1995). Sustainable dryland agroecosystemmanagement. 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 indryland 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 Organic 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 corn 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 associated with grain yield improvement in single-crossmaize 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 Peterson, A.E. (1997). Nitrogen fertilizer and legume-cereal 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 since 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 roots in the field: the effectof 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 insoils for cereal crops. Agriculture, Ecosystems and Environment 63:61-67.
Bruce, R.R. and Langdale, G.W. (1997). Soil carbon level dependence upon crop culture variables in a thermicudic region. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott,and C.V. Cole (eds.). CRC Press, Inc.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

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). Agronomic performance of winter versus spring wheat. Agron. J. 83:527532.
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. (1997). 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 byfertilizer rate. Soil and Tillage Res. 62:41-53.
Jensen, M.E. and Sletten, W.H. (1965). Evapotranspiration and soil moisture-fertilizer interrelations withirrigated winter wheat in the Southern High Plains. USDA Agricultural Research 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 forsustained 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 matter changes over two decadesof winter wheat – fallow cropping in western Nebraska. 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 wheat 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. and Irvin, C.J. (1931). Fiftieth anniversary 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 ofColorado. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul, K. Paustian, E.T. Elliott, andC.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 Agronomy, Colorado State University.
Peterson, G.A., Westfall, D.G., Sherrod, L., Poss, D., Larson, K. and Thompson, D.L. (1997). Sustainabledryland 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 indryland 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 indryland agroecosystems. Technical Bulletin TB91-1. Dept. of Agronomy, Colorado State University.
Peterson, G.A., Westfall, D.G., Sherrod, L., Kolberg, R. and Rouppet, B. (1994). Sustainable dryland
agroecosystem management. Technical Bulletin TB94-1. Dept. of Agronomy, Colorado State University.

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Volume 4: Agriculture, Forestry and Other Land UsePeterson, G.A., Westfall, D.G., Sherrod, L., Kolberg, 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). Sustainable dryland agroecosystemmanagement. Technical Bulletin TB96-1. Dept. of Agronomy, Colorado State University.
Peterson, G.A., Westfall, D.G., Sherrod, L., Kolberg, R. and Poss, D. (1995). Sustainable dryland agroecosystemmanagement. 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 indryland agroecosystems. Technical Bulletin TB90-1. Dept. of Agronomy, Colorado State University.
Rao, S.C., Coleman, S.W. and Volesky, J.D. (2000). Yield 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 lightinterception of old and modern wheat cultivars 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 inCrop 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 arealternated. 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.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

Campbell, C.A., Davidson, H.R. and Warder, F.G. (1977). Effects of fertilizer N and soil moisture on yield, yieldcomponents, 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 improvement and associated characteristics of some Australianspring 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., Roxas, 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). Managing tillage, sowing rate and nitrogen top-dressing levelto 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., Trebuil, G. and Reoma, V. (2001). Upland rice response tophosphorous 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 offour 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 associatedcharacteristics between direct-sown and transplanted rice plants in a submerged paddy field andrelationships 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 towater deficit during grain filling of rice. Field Crops Res. 71:47-55.

5. Barley

Alston, A.M. (1980). Response of wheat to deep placement of nitrogen and phosphorous fertilizers on a soil highin 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. 30:3135.
Chery, J., Lefevre, B., Robin, P. and Salsac, L. (1981). Barley breeding for high protein content. Relationshipwith nitrate reductase and proteolytic activities. In: Barley Genetics IV. Proceedings of the FourthInternational 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.

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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 inEuropean 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. (2001). Nitrogen fertilization management for no-till
cereal production in the Canadian Great Plains: a review. Soil and Tillage Res. 60:101-122.
Riggs, T.J., Hanson, P.R., Start, N.D., Miles, D.M., Morgan, C.L. and Ford, M.A. (1981). Comparison of springbarley 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 lightinterception of old and modern wheat cultivars 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 insoils 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). Experiments with oats. Experiment Station Bull. No. 42.
Kansas State Agricultural College.
Georgeson, C.C., Burtis, F.C. and Otis, D.H. (1896). Experiments with oats. Experiment Station Bull. No. 63.
Kansas State Agricultural College.
Georgeson, C.C., Burtis, F.C. and Otis, D.H. (1897). Experiments 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 conventionalheight oat genotypes. Crop Sci. 25:964-966.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

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. and Irvin, C.J. (1931). Fiftieth anniversary 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: Its repeatability and association withadaptation. 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 ofColorado. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, andC.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 Agronomy, Colorado State University.
Peterson, G.A., Westfall, D.G., Sherrod, L., McGee, E. and Kolberg, R. (1992). Crop and soil management indryland 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 indryland agroecosystems. Technical Bulletin TB91-1. Dept. of Agronomy, Colorado State University.
Peterson, G.A., Westfall, D.G., Sherrod, L., Kolberg, 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., Kolberg, 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 indryland 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 andintra-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. (1997). Soil carbon level dependence upon crop culture variables in a thermicudic region. In: Soil Organic Matter in Temperate Agroecosystems. 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.

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Eastin, J.D. (1983). Sorghum. In Symposium on Potential 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 forsustained 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 ofColorado. In: Soil Organic Matter in Temperate Agroecosystems. E.A. Paul. K. Paustian, E.T. Elliott, andC.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 Agronomy, Colorado State University.
Peterson, G.A., Westfall, D.G., Sherrod, L., Poss, D., Larson, K. and Thompson, D.L. (1997). Sustainabledryland 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 indryland 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 indryland agroecosystems. Technical Bulletin TB91-1. Dept. of Agronomy, Colorado State University.
Peterson, G.A., Westfall, D.G., Sherrod, L., Kolberg, 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., Kolberg, 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). Sustainable dryland agroecosystemmanagement. 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 indryland agroecosystems. Technical Bulletin TB90-1. Dept. of Agronomy, Colorado State University.
Prihar, S.S. and Stewart, B.A. (1991). Sorghum harvest index in relation 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 Leguminosae 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 plantgeometry. 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 grainsorghum. 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 subsequentdryland grain sorghum production. Soil Sci. Soc. Am. J. 43:582-588.
von Trebra, R.L. and Wagner, F.A. (1932). Tillage practices for south-western Kansas. Agricultural Experiment
Station Bull. 262. Kansas State College of Agriculture and Applied Science. Manhattan, KS.

9. RyeNo 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 matteraccumulation and seed yield. Crop Sci. 25:999-1004.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

Bruce, R.R. and Langdale, G.W. (1997). Soil carbon level dependence upon crop culture variables in a thermicudic region. In: Soil Organic Matter in Temperate Agroecosystems. 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 andmodern 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 soybean (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: 165180.
Kumudini, S., Hume, D.J., and Chu, G. (2001). Genetic improvement in short season soybeans: I. Dry matteraccumulation, 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 Environments. 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 ofsoybeans 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). Screening 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. and Foale, M.A. (1987). Field and model
analysis of the effect of water deficits on carbon and nitrogen accumulation by soybean, cowpea, andblack 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. Ohio Agricultural Experiment Station. Bull. 384.
Walker, A.K. and Fioritto, R.J. (1984). Effect of cultivar 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-irrigatedconditions in Colorado. M.S. Thesis. Department of Agronomy, Colorado State University, Fort Collins,
Colorado.

12. Potato

Heard, J. (2004). Nutrient accumulation and partitioning by potatoes 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). Nitrate 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, yield, 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 effectiveness of research on agricultural resource management in the semi-arid
tropics of southern India by combining cropping systems modeling with farming systems research: A

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Volume 4: Agriculture, Forestry and Other Land Use

rewarding experience for Tamil Nadu farmers. International Crops research Institute for the Semi-Arid
Tropics. Online at: http://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 yield and quality responses ofgroundnut (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., Lagerlof, J., Lindberg, T., Pettersson, R.,
and Sohlenius, B. (1990). Carbon and nitrogen budgets of four agro-ecosystems with annual andperennial 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 alfalfa-bromegrass sward. Agronomy Journal, 88: 834-843.

15. 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 andperennial 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 accumulation 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 agroecosystems: 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 and 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. ofAgriculture. 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
EnergyDatabases, Harwell,
UnitedKingdom. Onlineat:
http://www.vt.tuwien.ac.at/biobib/oxford.html.
Subedi, K.D. and Ma, B.L. (2005). Nitrogen uptake and partitioning in stay-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 Warder, F.G. (1977). Effects of fertilizer N and soil moisture on yield, yieldcomponents, 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. ofAgriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

Reisinger, K., Haslinger, C., Herger, M. and Hofbauer, H. (1996). BIOBIB- a Database for Biofuels, THERMIEConference:
Renewable
EnergyDatabases, Harwell,
UnitedKingdom. Onlineat:
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 and 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. ofAgriculture. 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
EnergyDatabases, Harwell,
UnitedKingdom. Onlineat:
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 agroecosystems: 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. ofAgriculture. 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
EnergyDatabases, Harwell,
UnitedKingdom. Onlineat:
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 agroecosystems: 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. ofAgriculture. 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
EnergyDatabases, Harwell,
UnitedKingdom. Onlineat:
http://www.vt.tuwien.ac.at/biobib/oxford.html.
Wych, R.D. and Stuthman, D.D. (1983). Genetic improvement 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. ofAgriculture. 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. ofAgriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html.

8. RyeJanzen, 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 agroecosystems: 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. ofAgriculture. 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. ofAgriculture. Online at: http://www.nrcs.usda.gov/technical/ECS/nutrient/tbb1.html.

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10. Dry beanJanzen, 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 agroecosystems: An illustration using Canadian estimates. Nutrient
Cycling in Agroecosystems, 67: 85-102.

11. Potato

Heard, J. (2004). Nutrient accumulation and partitioning by potatoes 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 agroecosystems: 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). Nitrate 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. ofAgriculture. 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. ofAgriculture. 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 alfalfa-bromegrass sward. Agronomy Journal, 88: 834-843.

14. Non-legume hay

NRCS (2006). Plant nutrient content database. Natural Resources Conservation Service. U. S. Dept. ofAgriculture. 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 soilorganic 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 adeclining 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 andmicrobial 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 fertilizer applied to corn grown on different soil types.
Canadian Journal of Soil Science, 78: 597-605.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

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 highyielding 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 roots in the field: the effectof 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 insoils 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 ratios – influence of moisture and rate of N fertilizer. Can.
J. Soil Sci., 81: 39-43.
Campbell, C.A. and Zentner, R.P. (1997). Crop production and soil orgaqnic matter in long-term crop rotationsin the semi-arid Northern Great Plains of Canada. In: Soil Organic Matter in Temperate Agroecosystems.
E.A. Paul. K. Paustian, E.T. Elliott, and 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, potassium, 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 Pennock, D. (2002). Cultivation-induced effects on below-ground biomass andorganic 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 Leguminosae 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 insoils 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-labeled crops of barley and wheat. Plant andSoil 136:205-213.
Gregory, P.J., Palta, J.A. and Batts, G.R. (1997). Root systems and root:mass ratio – carbon allocation undercurrent and projected atmospheric conditions in arable crops.
Hansson, A., Andren, O., Bostrom, U., Clarholm, M., Lagerlof, J., Lindberg, T., Paustian, K., Pettersson, R. andSohlenius, B. (1989). Chapter 4. Structure of the agroecosystem. In: Andren O., Lindberg T., Paustian K.,

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Volume 4: Agriculture, Forestry and Other Land Useand 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 differences 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 insoils 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, potassium, 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. RyeNo data on rye available at this time.

9. Soyabean

Allmaras, R.R., Nelson, W.W. and Voorhees, W.B. (1975). 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 Leguminosae 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, canopy 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 andperennial crops, with and without fertilization. J. Appl. Ecol. 27:60-84.

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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 andperennial 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 thenitrogen-supplying capacity of a conditioned soil. Agron. J. 94:391-396.
Subedi, K.D. and Ma, B.L. (2005). Nitrogen uptake and partitioning in stay-green and leafy-maize hybrids.
Crop Sci. 45:740-747.
Tran, T.S. and Giroux, M. (1998). Fate of 15N-labelled fertilizer applied to corn grown on different soil types.
Canadian Journal of Soil Science, 78: 597-605.

2. Wheat

Campbell, C.A., Cameron, D.R., Nicholaichuk, 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, potassium, copper, and zinc. Agron. J. 94:895-904.

3. RiceNo 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). Nitrogen 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, potassium, copper, and zinc. Agron. J. 94:895-904.

6. Millet

No data on millet available at this time.

7. Sorghum

Cueto-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. RyePederson, G.A., Brink, G.E. and Fairbrother, T.E. (2002). Nutrient uptake in plant parts of sixteen forages
fertilized with poultry litter: Nitrogen, phosphorous, potassium, copper, and zinc. Agron. J. 94:895-904.

9. Soyabean

Thatcher, L.E. (1925). The soybean in Ohio. Ohio Agricultural Experiment Station. Bull. 384.

10. Dry beanJanzen, 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 agroecosystems: 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 livestock manure relative to crop growthrequirements. 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). Nitrate 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.

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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 ofsweetclover, alfalfa, and red clover at five stages of growth. Canadian Journal of Plant Science, 49: 6169.
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, potassium, 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 alfalfa-bromegrass sward. Agronomy Journal, 88: 834-843.

14. Non-legume hay

Campbell, C.A., Biederbeck, V.O., Zentner, R.P. and Lafond, 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, potassium, 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 alfalfa-bromegrass sward. Agronomy Journal, 88: 834-843.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

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