Global database of GHG emissions related to feed crops
A life cycle inventory
VERSION 1
http://www.fao.org/partnerships/leap I8275EN/1/12.17
LIVESTOCK ENVIRONMENTAL ASSESSMENT AND PERFORMANCE PARTNERSHIP
version 1
Global database of GHGemissions related to feed crops
A life cycle inventory
FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS Rome, 2017
Recommended CitationFAO. 2017. Global database of GHG emissions related to feed crops: A life cycle inventory. Version 1. Livestock Environmental Assessment and Performance Partnership. FAO, Rome, Italy.
The designations employed and the presentation of material in this informationproduct do not imply the expression of any opinion whatsoever on the part of theFood and Agriculture Organization of the United Nations (FAO) concerning the legalor development status of any country, territory, city or area or of its authorities, orconcerning the delimitation of its frontiers or boundaries. The mention of specificcompanies or products of manufacturers, whether or not these have been patented,does not imply that these have been endorsed or recommended by FAO in preferenceto others of a similar nature that are not mentioned.
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ISBN 978-92-5-130078-7
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Contents
Acknowledgements 1
1. INTRODUCTION 3
1.1. Aims and objectives 3
1.2. Scope of the study 3
1.3. Outline of the report 3
2. DATA AND DATA COLLECTION METHODS 5
2.1. Database overview 5
2.2. Data representativeness 6
2.3. Principles for data collection 6
2.4. Dealing with data gaps 7
3. LIFE CYCLE INVENTORY 9
3.1. Seed and seeding rates 9
3.2. Crop yields 10
3.3. Crop residues 10
3.4. Synthetic fertilizer and agricultural lime 11
3.4.1. Data and data sources 11
3.3.2. Data gaps 12
3.3.3. Emission factors for production of synthetic fertilizer and agricultural lime 13
3.5. Organic fertilizer (manure) 13
3.5.1. Data and data sources 14
3.5.2. Data gaps 14
3.5.3. Emission factors 14
3.6. Pesticides 14
3.6.1. Data and data sources 14
3.6.2. Data gaps 15
3.7. Water use for irrigation 15
3.7.1. Data and data sources 15
3.7.2. Emission factors 15
3.8. Machinery and equipment 16
3.8.1. Data and data sources 16
3.8.2. Data gaps 17
3.9. Energy 17
3.10. Land use change 18
3.10.1. Data and data sources 18
3.10.2. Emission factors 18
REFERENCES 21
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Annex 1
YIELD DISTRIbUTION MApS FOR MAIzE, wHEAT, bARLEY AND SOYbEAN 29
Annex 2
N applicatioN rates from maNure (kg N/ ha) 33
Annex 3
pesticide applicatioN rates – maximum, miNimum aNd average values 37
Annex4
emissioN factors for abstractioN of grouNd water (kgco2/ha) 41
Annex 5
MACHINERY AND EqUIpMENT USE, FREqUENCY, OpERATION TIME aNd meaN fuel coNsumptioN 45
Annex 6
emissioNs factors for laNd use chaNge (toNs co2eq/kg dm*year), 2010 75
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Global database of GHG emissions related to feed crops - A life cycle inventory
Acknowledgements
The draft database and accompanying documents is a product of the Livestock En-vironmental Assessment and Performance (LEAP) Partnership. The database has been developed based on the LEAP Feed Guidelines: Environmental performance of animal feeds supply chains: Guidelines for quantification. The LEAP Secretariat coordinated and led the work of this assessment, and ensured coherence between LEAP Guidelines and the analysis.
This work was led by Carolyn Opio (Livestock Policy Officer, FAO). The re-search team included Alessandra Falcucci, Monica Rulli, Ellen Huls, Renato Cu-mani, and Theun Vellinga (modelling and data management). Supporting analysis was carried out by research partners, including Rich Conant and Tom Hiliniski from Colorado State University.
The database development has been carried out by: Luca Pepi and Donatella Mori from FAO and Dick Stamens from Wageningen University.
Appreciation also goes to those who have provided valuable comments, views and information on this first draft version of the database which enriched the analy-sis and the report. We would like to acknowledge the support of Camillo de Camil-lis, Claudia Ciarlantini and Claudia Nicolai.
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Global database of GHG emissions related to feed crops - A life cycle inventory
1. introduction
Life Cycle Inventories (LCIs) are necessary for performing life cycle assessments (LCAs) however the availability of such data is often the greatest barrier for con-ducting LCA. The LEAP Partnership recognizes this challenge and has put great effort in gathering and compiling the LCI data presented in this report. The LCI phase involves the collection and quantification of inputs and outputs throughout the life cycle stages covered by the system boundary of the individual study. This document is part of background documentation for the LEAP global database on GHG emissions from feed crops and describes life cycle inventory data related to the cultivation of 5 main crops used for feed.
1.1 AIMS AND ObjECTIVES The wider context for this study is to ensure that benchmarking of livestock supply chains is based on recognized internationally recognized and harmonized meth-odology and datasets. The goal of this assessment is to develop a robust life cycle inventory (LCI) and emission intensity database. Specific objectives were to estab-lish a global database of GHG emissions and emission intensities for major feed crops disaggregated by crop, production practices, and country and provide a con-solidated database of life cycle inventories to support continued benchmarking in livestock supply chains.
1.2 SCOpE OF THE STUDY The main focus is on the quantification of greenhouse gas emissions arising from the cultivation phase in crop production. The study focuses on 5 main crops: maize, wheat, barley, cassava and soybean and covers the major GHG emissions: CO2, N2O and CH4 from all major processes from raw material production through to the production of crop to the field-gate. In addition, the analysis incorporates car-bon stock changes associated with land-use change. Changes in soil carbon from constant and management will be incorporated in the database.
Results from this analysis are provided in a database. This database provides in-formation on the life cycle inventory per crop and the emission intensities associ-ated with the cultivation of the crop. Users are able to query the database to access aggregate information on emission intensities associated with the studied crops dis-aggregated by production practice and country.
1.3 OUTLINE OF THE REpORT This report presents the life cycle inventories of the five studied crops. Section 2 of the document presents the type of data that can be sourced from the database, the data collection methods, and highlights the data gaps.
Section three presents the life cycle inventory providing information on the data and data sources, assumptions and data gaps and how these were addressed. The Annexes provide information on crop yield distributions, data on nitrogen applica-tion rates, pesticide use and information on field processes and machinery use.
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Global database of GHG emissions related to feed crops - A life cycle inventory
2. data and data collection methods
2.1 DATAbASE OVERVIEwThe database has 2010 as its reference year and it is organized in 3 main sections:
•Life Cycle Inventory (LCI);•Field work processes; and•Emission Intensity.
Life Cycle Inventory (LCI)This section reports the main inputs used in the production of each crop such as: seed, organic fertilizer, synthetic N fertilizer, lime, phosphorus, potassium, and pes-ticides. These values are expressed in kg of input per hectare of harvested area. Ad-ditional inventory data provided include data on crop yields and crop residues per crop. For each variable, the minimum, the maximum, the average and the standard deviation values are provided.
Field work processesThis section reports, per crop, per production system and practice, and per coun-try, the main cultivation activities such as: ploughing, seedbed preparation, sowing, fertilization (lime, organic and synthetic fertilizer application), pesticide spraying, weed control, irrigation and harvesting. For each of these cropping activities, the frequency (number of times the activity is performed) and the type of traction (i.e. mechanical, animal, manual) are reported. The type of traction is expressed as the fraction of the process performed per type of traction, summing up to 1.
Emission IntensityIn this section, the emission intensities, related to the different on-farm activities, such as seed, organic fertilizer, synthetic fertilizer, crop protection, energy use in land work, land use and land use change, are presented, per crop, per production system and practice, and per country. For each, the minimum, the maximum, the average and the standard deviation values are reported in kg of CO2-eq. per kg of dry matter. The four statistical values are calculated to give the user an overview of the variability across each country, and are dependent on the spatial heterogeneity of the crop yields which is in turn determined by the different environmental and management conditions.
2.2 DATA REpRESENTATIVENESSGeographic coverage: The LEAP global database of GHG emissions related to feed crops aims to cover production activities for 5 major crops. The database includes all countries producing the five selected crops; these countries were identified through data taken from FAOSTAT and data collection was undertaken to cover 90% of countries contributing to global production of each crop.
Time: The data is representative of the current average practices for crop produc-tion and the reference year of the database is taken as 2010. Temporal representative-ness is especially important for factors that can potentially vary over time such as:
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Global database of GHG emissions related to feed crops - A life cycle inventory
Table 1: Tier-based classification of input dataInput Tier 1 Tier 2 Tier 3
Crop yieldsAverage yields at national level (not production system specific)
Yields at subnational level (spatial information of yields available)
Spatial yield distributions disaggregated by system (rainfed and irrigated)
Synthetic fertilizerTotal amount of fertilizer per nutrient (N, P, K) (not crop specific, national average)
Total amount by type of fertilizer per nutrient (N, P, K)
Amount by type of fertilizer applied per crop
Organic fertilizer
Total amount manure available for application (not crop specific, national average)
Total amount of manure available for application (spatial information of N nutrients available)
Amount of N manure applied per crop, information on N available to crop during first year
Pesticides
Total amount of active ingredients used per ha (not crop specific, national average)
Total amount of active ingredients applied per crop (national average)
Active ingredients applied per crop per production system and practice
Agricultural limeTotal amount of lime applied (not crop specific, national average)
Amount of lime applied per crop per country (national average and crop specific)
Amount of lime applied per crop per production system and number of applications
Water for irrigationTotal amount of irrigated water utilized (not crop specific, national average)
Total amount of irrigated water utilized by water source (not crop specific, national average)
Total amount of irrigated water utilised by water source per crop
•Crop yields•Application of fertilizers and other agro-chemicals •Land use change •Irrigation use and practices As a general rule, data was averaged over three-years: 2009-2011. Exceptions are
documented in the subsequent sections. Crop rotations and multiple cropping systems not taken into account due to lack
of sufficient data on a global scale.
2.3 pRINCIpLES FOR DATA COLLECTIONThe following criteria were used as guiding principles for the collection and selec-tion of data:
• the type of data (primary or secondary data);• the representativeness of the average practices;•consistency with other existing datasets, in case more than one source was
available; and•whether data was supported by expert knowledge.A tier approach (Table 1) was adopted where data was classified from tier 1 (low)
to tier 3 (high). The grey boxes in the table indicate the tier level corresponding to the input data that was utilized for the estimation of emission intensity.
To calculate the GHG emissions per crop, the following input data was col-lected/generate:
•crop and crop residue yield• fertilizer utilization•pesticide utilization•Water use for irrigation•Lime application
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Global database of GHG emissions related to feed crops - A life cycle inventory
•Machinery use •Information on energy use Data was obtained from various sources including national and global databases,
published literature. In some cases the data e.g. crop yields, crop residue yield, and manure application rates were obtained from modelling.
2.4 dealiNg with data gapsData availability is a significant issue when estimating material carbon footprint or Life Cycle Assessments (LCA). Filling data gaps can be done by a variety of methods, including using data from similar production systems, using surrogate data from related or similar processes. Other options include input-output model-ing, using statistical information for similar products. The following data gaps were identified in this assessment:
• incomplete description of crop production systems and cropping practices • limited information on input utilization or incomplete data sets • lack of time series datasets • lack of recent data representative of current production systems and practicesData gaps were found to be more pronounced for developing and emerging
countries. Subsequent sections below describe the data gaps for each input and how these gaps were addressed.
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Global database of GHG emissions related to feed crops - A life cycle inventory
3. Life cycle inventory
A number of inputs are used for crop production such as seed, plant nutrition or protection products, water, energy, machinery, etc. At the same time, the produc-tion process results in a number of outputs including crop products and co-prod-ucts e.g. grain and straw, and emissions to the environment. This section presents the life cycle inventory included in the LEAP database including inputs used in the production of the five crops. For outputs, only emission intensities are presented because the scope of the database is on GHG emissions.
3.1 seed aNd seediNg ratesPlant propagation method can comprise of generative propagation i.e. by seed or vegetative using cuttings and tubers. Agricultural seed production requires addi-tional processes over and above those of commercial production. The activities in the production of seed for planting in many cases is quite similar to crop production and usually comprises of production, harvesting, drying, transport, packaging and storage. In many situations however, seed for crop production is produced on-farm and therefore the inputs and outputs will be identical to crop production. Due to the difficulty in obtaining sufficient data on a global scale, the LCI and associated emission intensities are currently not incorporated in the database.
Seeding rates are derived from FAOSTAT and data presented in the database represent average values at country level (Tier 1).
3.2 CROp YIELDS Accurate data about crop yields is fundamental to the quantification of emission intensities of crops, since it directly impacts on the functional unit. Crop yields vary due to a number of conditions such as weather, soil, location, input intensity, irrigation, and tillage and seed variety. Crop yield data presented in the LEAP da-tabase and used in the assessment of GHG emissions are derived from the Global Agro-Ecological Zones (GAEZ)1 dataset of FAO-IIASA. The GAEZ modelling framework for crop assessment uses detailed agronomic-based information to as-sess land suitability, potential attainable yields and potential production of crops under specified management and input levels, both for rain-fed and irrigated condi-tions. Crop yields are provided in fresh matter and the following coefficients were used to convert fresh yields into dry matter (DM) (Table 2).
The LEAP feed-crop database provides spatial country specific yield data dis-aggregated by rainfed and irrigated production practices (Tier 3). The database provides crop yield data (the mean, minimum and maximum values) in rainfed and irrigated cropping systems. The yield is averaged over the period 2009-2011 per country and crop. For those crops that are cultivated under both rainfed and irrigated conditions, on average, irrigated crop yields are higher than those from rainfed crop production. A few studies have shown that crop yields can vary be-tween the conventional and conservation tillage systems. However, due to the
1 http://www.fao.org/nr/gaez/en/
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Global database of GHG emissions related to feed crops - A life cycle inventory
lack of sufficient information on this variance, the yields in the LEAP database are representative of conventional production systems. Maps in Annex 1 illustrate yield values and distribution for 5 crops assessed.
3.3 CROp RESIDUES Crop residues constitute an important source of biomass for animal feed and fuel in many parts of the world but are also a potential source of emissions. When residues are left on, or incorporated into the soil following crop harvest, nitrogen is released from the plant material. Mineralization of organic nitrogen in residues is a source for N2O. The LEAP database provides data on crop residue yield per crop (kg DM per ha) and the amount of crop residues is calculated using the IPCC 2006 method equation 11.6 and the regression equations in Table 11.2 to calculate the total above-ground residue dry matter (IPCC, 2006).
3.4 syNthetic fertilizer aNd agricultural lime The use of certain fertilizer products (especially nitrogenous and phosphate fertil-izers) are a source of pollutants emitted from agricultural fields to the air and water-ways. The fertilizer industry deals with primarily with the supply of nitrogen (N), phosphorus (P) and potassium (K). The GHG emissions from fertilizer production are closely linked to energy consumption and vary with aspects of plant design and efficiency, emissions control technologies, and raw material inputs. Three raw ma-terials are particularly important:
•Ammonia: CO2 is emitted from the consumption of hydrocarbons (primarily natural gas) as a hydrocarbon feedstock (to supply H) and as an energy source.
•Nitric acid (HNO3): Nitric acid production is the largest industrial source of N2O (IPCC, 2006) and is emitted as a by-product of the catalytic oxidation of ammonia to nitric acid.
•Phosphoric acid: Produced from reacting phosphate rock with sulphuric acid. The resultant emissions are mainly of CO2, from fuel use and from the C compounds contained in the rock.
To a large degree, the GHGs embedded in a fertilizer product will reflect the relative amounts of these ingredients. The database provides application rates at country level for these three primary nutrients plus agricultural lime. It presents data for synthetic fertilizers by the mass of N, P2O5, and K2O applied per kg ha and kg CaCO3/kg ha.
Table 2: Dry matter fractions of cropsCrop dry matter fraction
Maize 0.87
Barley 0.89
Wheat 0.89
Soybean 0.91
Cassava 0.38
Source: Global Livestock Environmental Assessment Model (GLEAM) and Feedipedia
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Global database of GHG emissions related to feed crops - A life cycle inventory
3.4.1 Data and data sources Synthetic NitrogenData are presented as application rates expressed as kg of synthetic N per ha. Due to the difficulty in obtaining crop specific information, the average application rates (kg/ha) reported in the database were obtained by taking the total consumption in each country and dividing that by the arable area. While data on fertilizer use are available at the country level around the world, crop specific information, and the geographic distribution within countries is not known. Using this top-down ap-proach to obtain country specific application rates guarantees data consistent with the total fertilizer consumption of each country. Review of existing data revealed that USA is the only country with information on crop-specific information on synthetic fertilizer consumption at state level. For consistency, a similar approach i.e. top-down approach to obtaining the application rate was applied to the USA. The inventory on synthetic N application rates included in the LEAP database is averaged over the period 2009-2011. The data on total synthetic N fertilizer con-sumption was derived from the following sources listed in Table 3 below.
There are distinct regional differences in the type of synthetic N fertilizer used; emissions will therefore vary considerably with the form (e.g. ammonium nitrates, urea, NPK, CAN, etc) in which it is applied. The database provides disaggregated information on the share of different N-fertilizers in total N-fertilizer use as aver-age in different regions (Table 4)
Table 3: Data sources for nitrogen consumption data region/country Data source Data crop specific
USA USDA-NASS1 State and national Yes
Canada Canadian Statistics2 Province and national No
Europe IFA3 and Eurostat4 National No
Russian Federation, Oceania, Central & South America, Asia, Africa IFA Statistics and FAOSTAT National No
1 http://www.nass.usda.gov; 2 http://www5.statcan.gc.ca; 3 http://ifadata.fertilizer.org; 4 http://epp.eurostat.ec.europa.eu
Table 4: Regional share of N-fertilizer use by fertilizer type
Australia w. EuropeE. Europe
incl. Russian federation
Central & S. America N. America Asia Africa
AN 0.05 0.18 0.56 0.09 0.04 0.01 0.09
CAN 0.00 0.24 0.01 0.01 0.01 0 0.12
AP 0.20 0.02 0.05 0.14 0.07 0.02 0.02
AS 0.03 0.03 0.04 0.12 0.03 0.11 0.05
urea 0.21 0.19 0.18 0.52 0.23 0.78 0.31
NS 0.00 0.14 0.05 0.05 0.24 0 0.14
NPK 0.50 0.19 0.11 0.07 0.1 0.08 0.12
Ammonia 0.01 0.01 0 0 0.28 0 0.12
Note: AN: Ammonium nitrate; CAN: calcium ammonium nitrate; AP: ammonium phosphate; AS: am9monium sulphate; NS: nitrate solution; ammonia: anhydrous ammonia
Source: Kool et al. (2012).
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Global database of GHG emissions related to feed crops - A life cycle inventory
Phosphorus (P) and Potassium (K)Data are presented as application rates expressed as Kg P2O5 and K2O per ha. A similar approach as described above was used to obtain the average application rates (kg P and K/ha) reported in the database. The data on total consumption P2O5 and K2O fertilizer use was derived from the same sources presented in Table 3.
Agricultural LimeThe GHG impact of liming (used to increase soil PH) was included in the calcula-tions of emissions for specific crops in selected countries where data was available. The data associated with agricultural liming are reported as kg CaCO3 per hectare. Estimation of the quantities of lime, applied to neutralize acid production in agri-cultural soils that results from a range of sources including published studies and databases as illustrated in Table 5. Countries for which data was available and in-cluded in the LEAP database are provided in Table 5.
Because lime application on a given field will occur in intermittent years, it is as-sumed here that these data provide a broad estimate of the total application for the total planted area in a given year i.e. that the lime application rate is allocated by the number of years between applications. However, most studies do not provide clear explanation on whether their application rate is an average rate over several years.
3.4.2 Data gapsFor several countries, information on synthetic fertilizer consumption and use of agri-cultural lime was not available. Two data sources were thus used to fill data gaps: data-set from international Fertilizer Association (IFA) and FAO dataset from FAOSTAT Database. Where country specific data was lacking, the primary dataset used was taken from the IFA database and where IFA data was lacking, data gaps were subsequently
Table 5: Country and crop specific lime application rates Country Crop Source kgCaCO3/ha/year
Argentina Soybean Agri-Footprint (2014) 400
Belgium Barley Agri-Footprint (2014) 353.7
Brazil Soybean Castanheira and Freire, (2014) 375.0
Maize Agri-Footprint (2014) 399.5
France Barley Agri-Footprint (2014) 386.47
Maize Agri-Footprint (2014) 382.29
Germany Wheat Agri-Footprint (2014) 326.67
Barley Agri-Footprint (2014) 328.78
Maize Agri-Footprint (2014) 339.73
Hungary Maize Agri-Footprint (2014) 371.57
UK Barley Agri-Footprint (2014) 398.77
Wheat Agri-Footprint (2014) 397.31
New Zealand Wheat Barber etal., (2011) 432
Maize Barber etal., (2011) 305
USA Soybeans Agri-Footprint (2014) 335.0
Maize Agri-Footprint (2014) 23.0
Denmark Wheat Elsgaard (2010) 275
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Global database of GHG emissions related to feed crops - A life cycle inventory
filled with data from FAOSTAT database. For lime, no attempt was made to fill data gaps because this would introduce uncertainty into emission estimates.
3.4.3 Emission factors for production of synthetic fertilizer and agricultural limeEmission factors used to estimate emissions from the production and transport of synthetic fertilizer and lime are presented in Table 6 below. The emission factors are taken from Kool et al., (2012) who conducted an extensive literature review to establish life cycle inventory for synthetic fertilizer production and use. The au-thors provide a comprehensive set of data per region including mean, minimum and maximum emission factor values. Values for New Zealand were taken from Barber et al. (2011) and are based on a farm survey data. Africa is the only continent of which specific emission factor data was lacking and as a result the calculated global average emission factors where applied in this case.
3.5 orgaNic fertilizer (maNure)The database contains average N application rates from manure expressed in kg nitro-gen per hectare. Due to the lack of country and crop-specific data on manure produc-tion and application, data on manure available for application was modelled from a number of datasets using the approach adopted in the GLEAM model (Table 7). Manure N application to crops was calculated based on global livestock densities for all animal types, their specific nitrogen excretion rates, proportion of manure managed in manure management systems by animal type and region, and the N losses during storage. It is assumed that manure deposited directly onto pastures and range is not collected and therefore is unavailable for application on crops. In addition, of the manure that is deposited in confinement, some of it is used for fuel, and therefore unavailable for crop application. The calculation of the N available also takes into account the nitrogen losses during manure storage. The remaining N in manure is assumed to be available for application on crops. Since not all available manure is applied to crops, but may be discharged into the environment, adopting a similar approach to Conant et al., (2012) and Smil (), we assumed that only 90% of the available manure was applied to arable and cultivated pastureland.
At a global scale, crop-specific data on the application of manure to crops is generally unavailable. For consistency with the approach used in application of synthetic fertilizer which is also available at country level, manure application was
Table 6: Average emission factors from production and transport of N, P2O5, K2O and limeRegion Nitrogen p2O5 k2O Lime
(kg CO2.eq/kg product)
Global Average 5.66 1.36 1.36 0.074
W. Europe 5.62 1.47 1.45
E. Europe Inclu. Russian Federation 6.87 1.57 0.61
central & S. America 3.53 0.54 1.02
Asia 4.00 1.29 1.47
Australia 6.92 1.66 1.63
New Zealand 3.06 1.14 0.74
Source: Kool et al. (2012); Data on New Zealand taken from Barber et al. (2011).
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Global database of GHG emissions related to feed crops - A life cycle inventory
averaged over arable and cultivated pasture land. The difference between the data n synthetic N and organic N from manure is that the latter is a spatially explicit dataset because it is based on spatially explicit information on livestock production systems and animal distributions. Data on N manure application rates (average and standard deviation) are provided in Annex 2 of this document.
3.5.1 Data and data sources Data used to calculate the N available for application to crops was taken from the following sources (Table 7):
3.5.2 Data gapsInformation on the use of manure, quantities applied and to what crops on a global scale is lacking. The approach used in this study was therefore to generate the infor-mation through modelling. It is assumed that all the nitrogen in manure is available in the application year. In reality, only part of the N is available in the first year and the rest in subsequent years. Therefore the application rate provided may be on the high side. There is a gap in the information on the actual use of manure i.e. how much is applied and to what crops. For consistency with synthetic fertilizer, an average application rate is calculated.
3.5.3 Emission factorsEmission factors for the estimation of N2O emissions during the application of manure are derived from 2006 IPCC guidelines.
3.6 pESTICIDES3.6.1 Data and data sourcesThe pesticide inventory data included in the LEAP Feedcrop database is reported in kg active ingredient (a.i.) per hectare. With the exception of USA, Australia and New Zealand, data on pesticide consumption was taken from FAOSTAT database and is an average rate over three years (Annex 3) and is categorized as Tier 1. In this study, data on pesticide consumption includes herbicides, insecticides and fungicides.
For consistent with the application of synthetic and organic fertilizer which is also available at country level, pesticide application was averaged over arable land and permanent crops but not to grassland. This is a simplification as pesticide use varies widely between countries and crops.
Table 7: Data sources for estimation of organic nitrogen from manureData Source
Livestock distributions Robinson et al (2014) approach discribed in:http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0096084
Nitrogen excretion rates IPCC, 2006: Tier 1
Manure management systems per species, country
Global Livestock Environmental Assessment Model – GLEAM (http://www.fao.org/gleam/en/)
Arable land and cultivated pastures FAOSTAT: land resources domain
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Global database of GHG emissions related to feed crops - A life cycle inventory
3.6.2 Data gapsThis study found that there is limited information on a global scale regarding:
• the proportion of the active ingredient in each product,•which crops receive pesticides,•application quantities, and•current pesticide manufacture processes.No attempts were made to fill any of these gaps and future research is required
to fill these gaps.
3.7 water use for irrigatioN 3.7.1 Data and data sources We estimated the energy use and associated GHG emissions from groundwater ab-straction for irrigation at global scale. The methods and data sources are described in detail below. For this purpose, we applied a physical relationship, which pre-scribes the energy required to lift 1 m3 of water (with a density 1000 kg m3) up 1 m at 100% efficiency is 0.0027 kWh (see equation (1), Rothausen and Conway 2011).
The country average lift values were based on the groundwater table depth mod-el (WTD) (Fan and Miguez-Macho, 2013) and the Global Map of Irrigated Ar-eas (GMIA) (2013). In particular, the averaging process has taken into account, for each country, only the cells of the WTD that are irrigated areas in the GMIA. The amount of groundwater water use for irrigation (m3/year) on national basis was derived from Siebert et al. (2010). The electric and diesel pumps efficiency values are extrapolated accordingly to available literature provided in Table 8.
From equation 1, we estimated the total amount of energy consumed for groundwater pumping for irrigation per country and on yearly base. Using the area equipped for irrigation taken from Siebert et al. (2010) and the conversion factors for diesel and electricity (Table 9), we obtained the average GHG emission rate (kg CO2-eq per ha of irrigated areas).
3.7.2 Emission factorsEmission factors estimated for the abstraction of water from ground water sources for irrigation are provided in Annex 4 of this document.
Table 8: Data sources for electric and diesel pumps efficiencyCountry Sources
Australia NSW Department of Primary Industries Farm Energy Innovation Program - Energy & Irrigation
China Shah et al. (2002)
India Mukherji, A.; Shah, T. (2002)
Iran (Islamic Republic of)
Hekmat, A. (2002)
Mexico Scott, C., Shah, T., Buechler, S. (2002)
Other countries Phocaides, A. (2000)
Pakistan Reinemann D.J. et al. (1993)
Spain Rodríguez-Díaz JA et al. (2011)
Sub-Saharan Africa Sugden, C. (2010)
USA North Carolina State University Dept. of Biological and Agricultural Engineering
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Global database of GHG emissions related to feed crops - A life cycle inventory
3.8 MACHINERY AND EqUIpMENTIn crop production, tractors and other machinery are used during the cultiva-tion process for activities such as ploughing, seedbed preparation, weed control, fertilization and harvesting. However, in some parts of the world, traction is still performed either using animal power or human labor. For each crop a list of all on-farm activities was defined from literature sources and databases, including the frequency of the activity and the type of machine used. In a number of situations, the machines are self-propelling and do not need an external source of power, such as tractors. This is often the case with harvesting equipment and in some cases with equipment for spraying of pesticides or application of fertilizer. In all other cases, tractors are required to pull the machine and to provide power. Because of the dif-ference in implements used as well as the frequency of their usage, a distinction was made between the three tillage systems: conventional, reduced and no-till.
3.8.1 Data and data sourcesTo facilitate the performance of LCAs, the LEAP database provides information on the type and number of field operations performed for each crop per cropping system. This information is provided in the form of frequency and level of mecha-nization.
•Frequency defines the number of times an activity is undertaken per crop in a cropping cycle.
•Level of mechanization (traction): three types of mechanization are defined in the database.
Country specific databases and published literature sources were used to gather information on the type of machinery and equipment used the frequency of use, and the power source for the machinery and equipment. For North America, data were mainly obtained from the USDA database (2014). For European countries, data were taken from the Ecoinvent v3.0 (2013) and the MEBOT database (Schreuder et al., 2008) and published literature. Data for Oceania derive mainly from the Aus-tralian Bureau of Statistics (ABS, 2014). For all other global regions (i.e. South Asia, East and South-East Asia, West-Asia and Northern Africa, sub-Saharan Africa, and
Table 9: Regional emission conversion factors from electricity and diesel consumption
Region or country Conversion factor for diesel(kg CO2-eq Mj-1)
Conversion factor for electricity (kg CO2-eq Mj-1)
Africa 0.32 0.171
Asia (excl. China & Japan) 0.32 0.201
China 0.32 0.214
Japan 0.32 0.123
Europe 0.32 0.081
Oceania (excl. Australia & N. Zealand) 0.32 0.142
Australia 0.32 0.238
New Zealand 0.32 0.043
Latin America 0.32 0.052
North America 0.32 0.131
Russian Federation 0.32 0.116
Source: UK Department of Environment, Food and Rural Affairs/Department of Energy and Climate Change; IEA, CO2 emissions from fuel combustion. Highlights. 2013 Edition.
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Central and South America), data were mainly found in the IPNIS database (FAO, 2010) and a wide variety of published literature sources. For all countries, irrigation data were obtained from Aquastat (FAO, 2014). Annex 5 of this document provides additional information on the data used (frequency of the field operations and a characterization of the machinery used in crop production at regional and country level) and the data sources.
3.8.2 Data gapsFor several countries information on current use of farm machinery was not avail-able. In order to fill these data gaps, the following approach was adopted:
•Within the same region: information on farm machinery use and data on fre-quency, mean fuel consumption (MFC) and operation time was extrapolated for countries with similar production systems.
Although we recognize the fact that operation time and MFC can vary between countries, due to lack of country specific data it is assumed in this study that when the same machinery and equipment are used, the same operation time and MFC are required. Where country specific operation time and MFC data are available, these data are applied.
3.9 eNergyThe life cycle inventory of field work processes in crop production is closely linked to the crop and the production system. In this study, field processes where defined for each crop and production system. Field processes covered include plowing/tillage (primary and secondary), fertilization, sowing/planting, plant protection, weeding, irrigation, and harvesting.
The emissions from field work processes are largely energy related. The energy required to grow a crop and the associated emissions can be calculated by account-ing for the energy associated with the inputs required for the production. Energy and GHG emissions from agricultural inputs can be divided into primary (e.g. fuel for on-farm machinery operations), secondary (e.g. production and transportation of inputs) and tertiary (e.g. raw materials to produce farm machinery, equipment and buildings) sources.The following inputs have been included in the inventories:
•Energy use consumed during field processes: i.e. for the application of nutri-ents, for tillage, harvesting, etc.
•Energy use for irrigation of crops•On-farm machinery and equipmentEmissions considered from these processes:•Emissions from the combustion of fuels during the use of farm machinery •Emissions from stationary operations i.e. irrigation •Secondary sources of emissions from the production and maintenance of farm
machinery equipment. The amount of energy used will vary by crop type and cropping system as well
as the frequency of the field work process and level of mechanization. Energy sources: Fossil fuel inputs and electricity are major inputs in crop pro-
duction processes. The production and use of transportation fuels include a wide range of activities that contribute to greenhouse gas (GHG) emissions over their life cycle. Greenhouse gas emissions arise primarily from the combustion of these
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Global database of GHG emissions related to feed crops - A life cycle inventory
Table 10: Emission factors for electricity production and heat generation in selected countries and regionsRegion g CO2/mJ
Europe 81
North America 131
Australia 238
New Zealand 43
Japan 123
Other Pacific 142
Russian Federation 116
Latin America 52
Asia (excluding China) 201
China 214
Africa 171
Source: IEA, 2015
fuels, where CO2 emissions are emitted. In addition to emissions from combustion, GHG emissions (including CH4 and N2O) occur during the production and trans-portation of these fuels, the production of infrastructure and capital goods.
Emissions from electricity production: Electricity is generated from fossil fuel energy sources and other energy sources such as nuclear, hydropower. Emission factors for energy use will therefore depend on the mix of energy sources whether renewable or otherwise. For example, in Latin America and New Zealand most of the energy comes from renewable resources such as hydroelectricity and other forms of biomass and thus the emission factor is very low (Table 10). Data on en-ergy mix is obtained from the International Energy Agency (IEA, 2015).
3.10 laNd use chaNge Emission factors for direct land use change were estimated based on the PAS2050 methodology as recommended in LEAP Feed Guidelines. Emission factors are esti-mated for each country and crop cultivated under conventional, minimal and no-till tillage practices (Annex 6).
3.10.1 Data and data sources The following data sources (Table 11) were used to estimate the emission factors for direct land use change.
3.10.2 Emission factorsEmission factors estimated for direct land use change are provided in Annex 6 of this document.
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Table 11: Data sources for estimating emissions from land use changeData type Source
• Perennial and annual crops area harvested (ha) from 1991 to 2010
• Area of forest, grassland and permanent meadows and pastures (ha)
FAOSTAT, 2015. (http://faostat.fao.org/)
• Forest area (ha) by country Global Forest Resources Assessment 2010
• Above and belowground vegetation carbon stocks of forest, grassland, and crops From IPCC 2006, Chapter 6, Table 6.4
• Default reference (under native vegetation) soil organic C stocks (SOCREF) for mineral soils. All values in tonnes C/ha in 0-30 cm depth.
From IPCC 2006, Chapter 2, Table 2.3
• Relative stock change factors (FLU, FMG, and FI) (over 20 years) for different management activities on cropland
From IPCC 2006, Chapter 4, Table 5.5
• Country climate types (percentage)• Country soil types (percentage)
World map of climate types provided by the JRC (http://eusoils.jrc.ec.europa.eu/projects/RenewableEnergy)
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Wang Jinxia, Rothausen, S.G.S.A., Conway, D., Lijuan Z., Wei X., Holman, I.P. and Yu-min L. 2012 China’s water-energy nexus: greenhouse-gas emissions from groundwater.
Watananonta, W., S. Tangsakul, S. Katong, P. Phetprapai, J. Jantawat, N. Samuthong and R.H. Howeler. 2006. Effect of land preparation on the yield of four cassava va-rieties in Thailand. Proc. 2nd Intern. Symposium on Sweetpotato and Cassava, held in Kuala Lumpur, Malaysia. June 14-17, 2005.
West, T.O. & Marland, (2002). Net carbon flux from agriculture: Carbon emissions, car-bon sequestration, crop yield, and land-use change. Biogeochemistry 63: 73–83. G. Bio-geochemistry (2002) 63: 73. https://doi.org/10.1023/A:1023394024790
Zeghouane, O. and Benbelkacem, A. 2012. Current state and trends of wheat production in Algeria. http://www.slideshare.net/CIMMYT/04-omar zeghouanecurrentstateandtrendsofwheatproductioninalgeria?next_slideshow=2
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Global database of GHG emissions related to feed crops - A life cycle inventory
Annex 1 yield distribution maps for maize, wheat, barley and soybean
0 5,0002,500 km
Robinson projection - WGS84
Barley yield (tonnes per hectare) under rainfed conditions
1 - 2 2 - 4 4 - 6 > 6
0 5,0002,500 km
Robinson projection - WGS84
Barley yield (tonnes per hectare) under irrigated conditions
1 - 2 2 - 3 3 - 6 > 6
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Global database of GHG emissions related to feed crops - A life cycle inventory
0 5,0002,500 km
Robinson projection - WGS84
Maize yield (tonnes per hectare) under irrigated conditions
1 - 4 4 - 8 8 - 12 > 12
0 5,0002,500 km
Robinson projection - WGS84
Maize yield (tonnes per hectare) under rainfed conditions
1 - 3 3 - 6 6 - 9 > 9
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Global database of GHG emissions related to feed crops - A life cycle inventory
0 5,0002,500 km
Robinson projection - WGS84
Soybeans yield (tonnes per hectare) under irrigated conditions
1 1 - 2 2 - 3 > 3
0 5,0002,500 km
Robinson projection - WGS84
Soybeans yield (tonnes per hectare) under rainfed conditions
1 1 - 2 2 - 4 > 4
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Global database of GHG emissions related to feed crops - A life cycle inventory
0 5,0002,500 km
Robinson projection - WGS84
Wheat yield (tonnes per hectare) under irrigated conditions
1 - 3 3 - 6 6 - 9 > 9
0 5,0002,500 km
Robinson projection - WGS84
Wheat yield (tonnes per hectare) under rainfed conditions
1 - 2 2 - 4 4 - 6 > 6
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Global database of GHG emissions related to feed crops - A life cycle inventory
Annex 2
N application rates from manure (kg N/ ha)
Country Average standard deviation
Canada 11.9 75.1
Russian Federation 2.8 8.3
United States of America 7.5 30.8
Norway 39.7 52.5
Finland 26.8 31.0
Sweden 15.8 27.7
Iceland 8.4 36.5
U.K. of Great Britain and Northern Ireland 16.5 10.3
Estonia 6.9 3.4
Latvia 4.6 1.5
Denmark 53.8 16.8
Lithuania 6.7 2.0
Belarus 14.6 4.4
Ireland 21.2 8.0
Kazakhstan 39.0 115.8
Germany 42.6 33.8
Poland 16.2 7.9
China 40.9 129.4
Netherlands 162.5 80.4
Ukraine 7.7 5.2
Mongolia 49.2 194.4
Belgium 64.2 42.9
France 19.8 14.9
Czechia 14.1 6.2
Luxembourg 50.3 0.0
Slovakia 10.8 2.8
Austria 36.7 21.2
Hungary 9.2 2.8
Republic of Moldova, 9.2 1.8
Romania 13.2 5.3
Switzerland 40.7 25.9
Italy 19.7 28.0
Slovenia 20.1 8.9
Croatia 10.1 6.2
Serbia 12.1 4.4
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Global database of GHG emissions related to feed crops - A life cycle inventory
Uzbekistan 80.3 106.3
Japan 44.9 60.1
Bosnia and Herzegovina 12.5 3.8
Bulgaria 6.0 2.6
Spain 20.5 19.0
Georgia 10.7 4.6
Kyrgyzstan 5.6 9.9
Dem People’s Rep of Korea 19.2 10.2
Turkmenistan 160.4 307.8
Azerbaijan 14.4 6.2
Montenegro 9.2 3.2
The former Yugoslav Republic of Macedonia 9.8 5.2
Portugal 13.5 13.5
Turkey 6.8 6.5
Albania 19.7 5.3
Greece 12.0 5.8
Armenia 5.4 1.4
Tajikistan 10.0 12.4
Iran (Islamic Republic of) 14.8 27.1
Republic of Korea 88.1 26.0
Afghanistan 15.1 55.7
Tunisia 104.7 177.5
Iraq 18.4 24.4
Syrian Arab Republic 16.9 13.7
Algeria 88.1 152.0
Pakistan 71.5 169.6
Morocco 56.2 181.2
Jordan 24.0 25.1
Israel 455.5 347.6
India 29.4 24.4
Libya 54.8 135.9
Mexico 5.0 4.8
Saudi Arabia 12.5 43.0
Egypt 82.8 215.6
Nepal 20.6 11.5
Kuwait 54.9 28.4
Myanmar 33.4 27.2
Bhutan 10.8 10.2
Western Sahara 0.3 0.3
Mauritania 13.4 62.8
Bangladesh 101.1 50.8
Oman 216.9 301.3
Qatar 36.8 18.4
United Arab Emirates 178.6 267.2
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Global database of GHG emissions related to feed crops - A life cycle inventory
Mali 65.5 206.0
Niger 44.2 165.5
Chad 18.3 40.4
Viet Nam 26.6 17.1
Cuba 5.5 2.4
Sudan 110.2 193.2
Lao People’s Democratic Republic 24.9 14.8
Philippines 22.8 17.2
Thailand 8.5 5.2
Haiti 14.4 4.9
Dominican Republic 22.4 11.2
Yemen 177.9 265.4
Jamaica 8.2 0.0
Belize 3.8 3.0
Eritrea 1.8 0.1
Guatemala 14.2 15.5
Honduras 9.6 5.7
Senegal 4.3 2.5
Nicaragua 8.7 4.7
Cambodia 10.3 6.4
El Salvador 14.7 5.2
Nigeria 3.8 2.8
Ethiopia 43.2 50.6
Colombia 8.0 9.3
Cameroon 5.7 8.6
Guinea-Bissau 13.0 8.8
Guinea 3.5 2.3
Benin 9.0 0.6
Venezuela 4.2 7.6
Somalia 4.2 2.9
Trinidad and Tobago 135.9 0.0
Costa Rica 11.4 4.3
Ghana 2.3 2.0
Togo 8.0 3.8
Central African Republic 19.1 19.6
Cote d’Ivoire 1.4 2.6
Sierra Leone 2.8 1.9
Sri Lanka 8.8 2.3
Panama 5.5 3.9
Guyana 3.2 3.8
Liberia 2.1 1.4
Malaysia 7.9 8.7
Indonesia 9.4 27.4
French Guiana 49.4 41.5
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Global database of GHG emissions related to feed crops - A life cycle inventory
Democratic Republic of the Congo 1.4 3.3
Brazil 5.7 8.0
Brunei Darussalam 21.2 16.1
Kenya 37.9 55.6
Uganda 27.7 18.9
Equatorial Guinea 0.7 0.2
Congo 0.6 0.8
Gabon 1.9 2.6
Ecuador 11.3 11.9
Singapore 103.2 0.0
Peru 11.3 63.5
United Republic of Tanzania 7.7 9.6
Burundi 5.7 0.0
Angola 0.2 0.3
Timor-Leste 10.3 2.6
Zambia 2.3 4.7
Australia 0.6 4.4
Malawi 20.8 0.0
Bolivia 7.6 44.6
Mozambique 1.0 0.8
Madagascar 2.5 1.9
Zimbabwe 3.0 4.3
Namibia 18.2 62.7
Chile 10.2 42.9
Botswana 0.2 0.2
Paraguay 4.0 1.2
Argentina 1.8 4.4
South Africa 3.9 3.2
New Zealand 0.5 0.4
Uruguay 5.9 0.7
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Global database of GHG emissions related to feed crops - A life cycle inventory
Annex 3
pesticide application rates - maximum, minimum and average values
Country Minimum maximum Average Standard deviation
Kg a.i./kg dm
Algeria 0.4 0.6 0.5 0.1
Antigua and Barbuda 0.0 7.9 3.1 4.2
Argentina 4.3 7.3 6.2 1.6
Armenia 0.0 0.5 0.3 0.3
Austria 0.0 2.4 1.6 1.3
Azerbaijan 0.1 0.2 0.2 0.0
Bahrain 2.0 2.9 2.4 0.5
Bangladesh 0.0 1.6 1.0 0.9
Belgium 5.1 6.4 5.9 0.7
Belize 4.3 5.7 5.0 0.7
Bhutan 0.1 0.2 0.1 0.1
Bolivia (Plurinational State of) 6.7 7.9 7.4 0.6
Brazil 3.9 4.0 3.9 0.1
Brunei Darussalam 0.4 1.5 0.9 0.6
Bulgaria 0.0 2.0 1.3 1.1
Burkina Faso 0.1 0.2 0.1 0.1
Burundi 0.0 0.2 0.1 0.1
Cameroon 0.8 1.4 1.2 0.3
Canada 0.0 1.2 0.4 0.7
Chile 10.3 11.5 10.8 0.6
China, Hong Kong SAR 8.7 13.4 10.6 2.5
Taiwan Province of China 9.4 10.3 9.8 0.5
Colombia 14.4 16.0 15.2 0.8
Cook Islands 0.0 1.2 0.4 0.7
Costa Rica 20.4 24.6 22.4 2.1
Croatia 0.0 1.9 1.1 1.0
Denmark 1.0 1.7 1.4 0.3
Dominican Republic 0.0 4.6 2.9 2.5
Ecuador 3.7 12.2 7.6 4.3
Egypt 2.5 3.5 3.1 0.5
El Salvador 3.4 3.8 3.6 0.2
Estonia 0.6 0.8 0.7 0.1
Ethiopia 0.0 0.3 0.2 0.1
Fiji 7.0 7.5 7.2 0.3
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Global database of GHG emissions related to feed crops - A life cycle inventory
Finland 0.7 0.7 0.7 0.0
France 0.0 2.9 1.9 1.6
French Polynesia 1.0 1.8 1.5 0.4
Germany 2.2 2.4 2.3 0.1
Ghana 0.0 2.0 0.7 1.2
Greece 0.0 1.8 1.1 1.0
Guatemala 5.4 6.1 5.8 0.4
Guinea 0.0 0.1 0.0 0.1
Guyana 0.5 0.9 0.7 0.2
Honduras 2.6 3.5 3.2 0.5
Hungary 1.5 2.0 1.8 0.2
Iceland 0.0 0.0 0.0 0.0
India 0.0 0.2 0.1 0.1
Iran (Islamic Republic of) 0.0 0.4 0.3 0.2
Ireland 2.0 3.3 2.5 0.7
Israel 0.0 16.8 11.2 9.7
Italy 6.6 6.9 6.7 0.1
Japan 11.2 13.2 12.1 1.0
Jordan 2.8 3.9 3.5 0.6
Kyrgyzstan 0.2 0.3 0.2 0.0
Latvia 0.6 0.8 0.7 0.1
Lesotho 0.2 0.5 0.3 0.2
Libya 0.0 2.0 0.9 1.0
Lithuania 0.7 1.0 0.8 0.2
Madagascar 0.0 0.1 0.1 0.0
Malawi 0.1 0.2 0.1 0.0
Malaysia 0.6 8.3 5.4 4.2
Mauritania 0.0 0.2 0.1 0.1
Mauritius 27.3 28.6 27.9 0.7
Mexico 4.2 4.5 4.4 0.2
Montenegro 0.0 0.4 0.3 0.2
Mozambique 0.1 0.2 0.2 0.0
Myanmar 0.1 0.4 0.2 0.1
Nepal 0.1 0.1 0.1 0.0
Netherlands 7.1 7.7 7.4 0.3
New Caledonia 2.3 2.8 2.5 0.2
New Zealand 0.0 8.2 2.7 4.7
Nicaragua 0.0 6.5 3.8 3.4
Norway 0.6 0.9 0.8 0.2
Oman 0.0 6.6 3.6 3.3
Panama 0.0 2.6 0.9 1.5
Peru 2.0 2.7 2.4 0.4
Poland 1.4 1.7 1.5 0.1
Portugal 6.2 6.7 6.4 0.2
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Global database of GHG emissions related to feed crops - A life cycle inventory
Republic of Korea 10.0 12.2 11.0 1.1
Republic of Moldova 1.1 1.1 1.1 0.0
Romania 0.7 0.8 0.7 0.0
Rwanda 0.1 0.8 0.6 0.4
Saint Kitts and Nevis 3.2 3.3 3.2 0.1
Slovakia 0.9 1.2 1.1 0.2
Slovenia 5.6 5.8 5.7 0.1
Spain 1.9 2.1 2.0 0.1
Sri Lanka 0.5 0.7 0.6 0.1
Sudan 0.0 0.1 0.0 0.1
Suriname 6.7 14.7 9.6 4.5
Sweden 0.4 0.4 0.4 0.0
Switzerland 4.2 4.4 4.3 0.1
Tajikistan 0.2 0.2 0.2 0.0
Thailand 3.3 4.2 3.6 0.5
The former Yugoslav Republic of Macedonia 0.0 0.2 0.1 0.1
Togo 0.0 0.1 0.1 0.0
Tunisia 0.0 0.4 0.1 0.2
Turkey 1.3 1.5 1.4 0.1
Ukraine 6.0 13.1 9.9 3.6
United Kingdom 2.2 2.9 2.5 0.4
Uruguay 6.8 9.2 7.9 1.2
Yemen 0.1 0.1 0.1 0.0
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Global database of GHG emissions related to feed crops - A life cycle inventory
Annex 4 emission factors for abstraction of ground water (kgco2/ha)
Country groundwater abstraction using diesel pumps
groundwater abstraction using electric pumps
Afghanistan 188.44 159.68
Albania 2.77 1.14
Algeria 565.84 0.00
Angola 48.23 0.00
Argentina 48.34 10.60
Armenia 111.24 94.26
Australia 76.45 82.43
Austria 37.51 15.37
Azerbaijan 37.62 31.88
Bahrain 565.24 478.99
Bangladesh 57.96 49.11
Barbados 74.10 16.25
Belarus 1.24 0.51
Belgium 1.55 0.64
Belize 23.46 5.14
Benin 19.58 0.00
Bolivia 101.38 22.23
Bosnia and Herzegovina 103.75 42.52
Brazil 80.15 17.57
Bulgaria 39.88 16.34
Burkina Faso 33.65 0.00
Cameroon 8.82 0.00
Canada 8.01 5.14
Chad 49.89 0.00
Chile 45.89 10.06
China 144.23 130.13
Colombia 15.30 3.35
Comoros 2.36 0.00
Costa Rica 102.60 22.49
Croatia 30.49 12.49
Cuba 13.38 2.93
Cyprus 366.39 150.15
Czechia 2.25 0.92
Dem People’s Rep of Korea 23.17 19.64
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Global database of GHG emissions related to feed crops - A life cycle inventory
Denmark 2.36 0.97
Djibouti 367.54 0.00
Dominican Republic 133.84 29.34
Ecuador 80.40 17.63
Egypt 107.53 0.00
El Salvador 55.60 12.19
Eritrea 156.87 0.00
Ethiopia 4.77 0.00
Fiji 0.22 0.04
Finland 0.47 0.19
France 43.39 17.78
French Guiana 1.31 0.29
Gambia 0.84 0.00
Germany 16.64 6.82
Ghana 25.87 0.00
Greece 294.23 120.57
Guatemala 166.94 36.60
Guinea 0.73 0.00
Guinea-Bissau 22.66 0.00
Haiti 77.35 16.96
Honduras 84.37 18.50
Hungary 6.46 2.65
India 156.59 265.39
Indonesia 2.86 2.42
Iran (Islamic Republic of) 961.66 814.93
Iraq 18.23 15.45
Ireland 4.58 1.88
Israel 462.86 392.24
Italy 78.43 32.14
Jamaica 344.82 75.59
Japan 10.44 5.41
Jordan 1375.79 1165.86
Kazakhstan 12.78 10.83
Kenya 3.84 0.00
Kuwait 538.37 456.22
Kyrgyzstan 5.15 4.37
Lao People’s Democratic Republic 0.60 0.51
Lebanon 952.49 807.15
Lesotho 15.64 0.00
Liberia 0.19 0.00
Libya 481.26 0.00
Lithuania 8.93 3.66
Luxembourg 39.16 16.05
Malawi 0.18 0.00
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Global database of GHG emissions related to feed crops - A life cycle inventory
Malaysia 14.32 12.14
Mali 0.52 0.00
Malta 343.38 140.72
Mauritania 81.55 0.00
Mexico 399.35 87.55
Mongolia 130.40 110.50
Montenegro 293.24 120.17
Morocco 377.99 0.00
Mozambique 0.35 0.00
Myanmar 20.91 17.72
Namibia 240.71 0.00
Nepal 290.05 245.80
Netherlands 0.38 0.16
New Zealand 55.42 10.80
Nicaragua 387.99 85.06
Niger 11.83 0.00
Nigeria 29.39 0.00
Norway 0.77 0.32
Oman 4433.56 3757.06
Pakistan 423.34 244.72
Panama 6.74 1.48
Paraguay 14.08 3.09
Peru 464.25 101.78
Philippines 43.73 37.06
Poland 2.83 1.16
Portugal 108.51 44.47
Qatar 368.51 312.28
Republic of Korea 11.52 9.77
Serbia 20.43 8.37
Romania 3.14 1.29
Russian Federation 19.71 6.73
Rwanda 3.21 0.00
Saint Kitts and Nevis 6.52 1.43
Saudi Arabia 1294.65 1097.10
Senegal 11.87 0.00
Sierra Leone 0.69 0.00
Slovakia 5.84 2.39
Slovenia 0.80 0.33
Somalia 29.96 0.00
South Africa 79.58 0.00
Spain 277.35 113.66
Sri Lanka 4.52 3.83
Sudan 5.76 0.00
Swaziland 13.01 0.00
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Global database of GHG emissions related to feed crops - A life cycle inventory
Sweden 5.44 2.23
Switzerland 2.20 0.90
Syrian Arab Republic 622.46 527.48
Tajikistan 142.88 121.08
Thailand 41.07 34.80
The former Yugoslav Republic of Macedonia 28.15 11.54
Timor-Leste 3.03 2.56
Togo 1.06 0.00
Trinidad and Tobago 21.73 4.76
Tunisia 366.70 0.00
Turkey 451.56 382.66
Turkmenistan 14.76 12.51
U.K. of Great Britain and Northern Ireland 11.70 4.79
Uganda 1.46 0.00
United Arab Emirates 3431.34 2907.76
United Republic of Tanzania 33.28 0.00
United States of America 276.02 177.03
Uruguay 11.18 2.45
Uzbekistan 36.97 31.33
Venezuela 5.70 1.25
Viet Nam 3.03 2.57
Yemen 2138.17 1811.92
Zambia 14.32 0.00
Zimbabwe 50.02 0.00
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Global database of GHG emissions related to feed crops - A life cycle inventory
Annex 5
machinery and equipment use, frequency, operation time and mean fuel consumption
Machinery: Northern America - BarleyCountry activity Equipment power usage frequency time MFC
Conventional tillage
U.S.A., Canada Ploughing Moldboard plough or Disc plough Tractor 2.5 1.6 14.8
Seedbed prep. Disk harrow and Field cultivator Tractor 1 0.9 6.6
Sowing/Planting Drill Tractor 1 0.9 3.5
Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application Broadcaster Tractor 1.6 0.3 4.2
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Field sprayer Tractor 3.6 0.31 3
WeedingField sprayer (herbicide spraying)
Tractor 3.2 0.31 3
Harvesting Combine harvester Self-propelled No tractor used 1 1.4 30.5
No-till and Minimal tillage
U.S.A., Canada PloughingNA (no-till) NA (no-till) 0 0 0
Chisel plow (minimal tillage) Tractor (minimal tillage) 2 14.9
(time *MFC)
Seedbed prep. Disk harrow and Field cultivator
NA (no-till) 0 0 0
Tractor (minimal tillage) 1 1.7 22
Sowing/Planting DrillTractor (no-till) 1 3.7
(time *MFC)
Tractor (minimal tillage) 1 0.9 3.5
Organic fertilizer application Broadcaster Tractor (no-till and minimal) 1 1.8 14.8
Synthetic fertilizer application
Seed farrow/Irrigation water (no-till)
No power 1.6 0 0
Broadcaster (minimal tillage) Tractor (minimal tillage) 1.6 0.3 4.2
Liming Broadcaster Tractor (no-till and minimal) 0.33 1.5 (time *MFC)
Pesticide application Field sprayer
Tractor (no-till) 5.5 0.31 3
Tractor (minimal) 3.8 0.31 3
WeedingField sprayer (herbicide spraying)
Tractor (no-till) 5.1 0.31 3
Tractor (minimal) 3.4 0.31 3
Harvesting Combine harvester Self-propelled No tractor used 1 1.4 30.5
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Global database of GHG emissions related to feed crops - A life cycle inventory
Machinery: Northern America – Maize
Country activity Equipment power usage frequency time MFC
Conventional tillage
U.S.A., Canada Ploughing Moldboard plough or Disc plough Tractor 1.3 2.78 14.8
Seedbed prep. Disk harrow and Field cultivator Tractor 1 1.57 22
Sowing/Planting Drill Tractor 1 0.9 3.5
Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application Broadcaster Tractor 1.8 0.3 4.2
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Field sprayer Tractor 3.0 0.31 3
WeedingField sprayer (herbicide spraying)
Tractor 2.6 0.31 3
Harvesting Combine harvester Self-propelled No tractor used 1 5.1 30.5
No-till and Minimal tillage
U.S.A., Canada PloughingNA (no-till)Chisel plow (minimal tillage)
NA (no-till) 0 0 0
Tractor (minimal tillage) 0.9 25.9 (time *MFC)
Seedbed prep. Disk harrow and Field cultivator
Tractor (no-till) 0 0 0
Tractor (minimal) 1 1.57 22
Sowing/Planting DrillTractor (no-till) 1 3.7
(time *MFC)
Tractor (minimal) 1 0.9 3.5
Organic fertilizer application Broadcaster Tractor (no-till and minimal) 1 1.8 14.8
Synthetic fertilizer application
Seed farrow/Irrigation water (no-till)Broadcaster (minimal tillage)
Tractor (no-till) 1.8 0 0
Tractor (minimal) 1.8 0.3 4.2
Liming Broadcaster Tractor (no-till and minimal) 0.33 1.5 (time *MFC)
Pesticide application Field sprayer
Tractor (no-till) 4.1 0.31 3
Tractor (minimal) 3.2 0.31 3
Weeding
Field sprayer (herbicide spraying)
Tractor (no-till) 3.6 0.31 3
Field sprayer (herbicide spraying) +Hoe
Tractor (minimal) 3.6 2.31 18.4
Harvesting Combine harvester Self-propelled No tractor used 1 5.1 30.5
Notes: weeding = Field sprayer (herbicide spraying) + Hoe
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Global database of GHG emissions related to feed crops - A life cycle inventory
Machinery: Northern America - Soybean
Country activity Equipment power usage frequency time MFC
Conventional tillage
U.S.A., Canada Ploughing Moldboard plough or Disc plough Tractor 4.5 1.6 14.8
Seedbed prep. Disk harrow and Field cultivator Tractor 1 1.7 22
Sowing/Planting Narrow row precision seeder Tractor 1 1 3.5
Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application Broadcaster Tractor 1.1 0.3 4.2
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Field sprayer Tractor 2.5 0.31 3
WeedingField sprayer (herbicide spraying) +Hoe
Tractor 3.1 0.31 3
Harvesting Combine harvester Self-propelled No tractor used 1 1.4 30.5
No-till and minimal tillage
U.S.A., Canada PloughingNA (no-till) NA (no-till) 0 0 0
Chisel plow (minimal tillage) Tractor (minimal tillage) 1.4 14.9
(time *MFC)
Seedbed prep. Disk harrow and Field cultivator
Tractor (no-till) 0 0 0
Tractor (minimal) 1 1.7 22
Sowing/Planting Narrow row precision seeder
Tractor (no-till) 1 4.10 (time *MFC)
Tractor (minimal) 1 1 3.5
Organic fertilizer application Broadcaster Tractor (no-till and minimal) 1 1.8 14.8
Synthetic fertilizer application Broadcaster Tractor (no-till and minimal) 1.1 0.3 4.2
Liming Broadcaster Tractor (no-till and minimal) 0.33 1.5 (time *MFC)
Pesticide application Field sprayer
Tractor (no-till) 2.6 0.31 3
Tractor (minimal) 2.1 0.31 3
Weeding
Field sprayer (herbicide spraying)
Tractor (no-till) 2.4 0.31 3
Field sprayer (herbicide spraying) +Hoe
Tractor (minimal) 2.8 2.31 18.4
Harvesting Combine harvester Self-propelled No tractor used 1 1.4 30.5
Notes: weeding = Field sprayer (herbicide spraying) + Hoe
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Global database of GHG emissions related to feed crops - A life cycle inventory
Machinery: Northern America - Wheat
Country activity Equipment power usage frequency time MFC
Conventional tillage
U.S.A., Canada Ploughing Moldboard plough or Disc plough Tractor 2.5 1.6 14.8
Seedbed prep. Disk harrow and Field cultivator Tractor 1 1.7 22
Sowing/Planting Drill Tractor 1 0.9 3.5
Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application
Seed farrow / Irrigation water Tractor 1.6 0 0
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Field sprayer Tractor 3.6 0.31 3
WeedingField sprayer (herbicide spraying)
Tractor 2.6 0.31 3
Harvesting Combine harvester Self-propelled No tractor used 1 1.4 30.5
No-till and Minimal tillage
U.S.A., Canada PloughingNA (no-till) NA (no-till) 0 0 0
Chisel plow (minimal tillage) Tractor (minimal tillage) 2.3 14.9
(time *MFC)
Seedbed prep. Disk harrow and Field cultivator
NA (no-till) 0 0 0
Tractor (minimal tillage) 1 1.7 22
Sowing/Planting Drill Tractor (no-till and minimal)1 0.9 3.5
1 3.7 (time *MFC)
Organic fertilizer application Broadcaster Tractor (no-till and minimal) 1 1.8 14.8
Synthetic fertilizer application
Chisel (minimal tillage)
Tractor (no-till and minimal) 1.6 0 0Seed farrow/Irrigation water (no-till)
Liming Broadcaster Tractor (no-till and minimal) 0.33 1.5 (time *MFC)
Pesticide application Field sprayer
Tractor (no-till) 5.0 0.31 3
Tractor (minimal tillage) 4.9 0.31 3
WeedingField sprayer (herbicide spraying)
Tractor (no-till) 4.0 0.31 3
Tractor (minimal tillage) 3.4 0.31 3
Harvesting Combine harvester Self-propelled No tractor used 1 1.4 30.5
Note: These tools and implements have been developed for three levels of power usage: manual power (hand tillage), animal traction, mechanized power
47
Global database of GHG emissions related to feed crops - A life cycle inventory
REFERENCESCrop Profile for Winter Wheat in Canada, 2010 Pesticide Risk Reduction Program Pest
Management Centre Agriculture and Agri-Food Canada. Dalgaard, T., Halberg, N. and Jørgensen, M.H., 2004. Status for energiinput og –output I
økologisk jordbrug samt muligheder for energibesparelser. In: Jørgensen, U., Dalgaard, T. (eds.) Energi i økologisk jordbrug – reduktion af fossilt energiforbrug og produktion af vedvarende energi, pp. 25-45. Forskningscenter for Økologisk Jordbrug.
Ketterings, Q., Stockin, K., Beckman, J. and Miller, J., 2006. Lime recommendations for field crops. Cornell University Cooperative Extension, Department of Crop and Soil Sci-ences, USA.
Schreuder, R., W Van Dijk, W., Asperen, P., De Boer, J., Van Der Schoot, J.R., 2008. Me-bot 1.01 beschrijving van milieu- en bedrijfsmodel voor open teelten (mebot 1.01 model N-TOOLBOX D1.4 [32] documentation of an environmental and farm model for field crops). Praktijkonderzoek Plant & Omgeving (PPO no. 373), Wageningen, The Nether-lands.
USDA (2014). National Agricultural Statistics Service. United States Department of Agricul-ture. http://www.nass.usda.gov/Statistics_by_Subject/.
48
Global database of GHG emissions related to feed crops - A life cycle inventory
Machinery: Central and South America - Barley
Country activity Equipment power usage frequency time MFC
Conventional tillage
Argentina, Bolivia, Brazil, Chile, Colombia, Mexico, Peru, Uruguay
Ploughing Moldboard plough Tractor 1 1.6 14.8
Sowing/Planting Row seeder Tractor 1 0.9 3.5
Organic fertilizer application Spreader Tractor 1 6.9 15
Synthetic fertilizer application Broadcaster Tractor 0.5 0.3 4.2
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Disk Tractor 1 0.31 3
Harvesting Combine harvester
Self-propelled No tractor
used1 1.6 14.8
Machinery: Central and South America - Cassava
Country activity Equipment power usage frequency time MFC
Conventional tillage
Argentina, Brazil, Colombia, Cuba, Paraguay Ploughing Moldboard
plough Tractor 1 1.6 14.8
Argentina, Brazil, Colombia, Costa Rica Seedbed prep. Tractor-drawn
rolling drum Tractor 1 1.6 14.8
Argentina, Brazil, Colombia, Costa Rica, Cuba, Ecuador, Paraguay, Peru, Venezuela
Organic fertilizer application Broadcaster Tractor 0.33 1.5
(time *MFC)
Synthetic fertilizer application Broadcaster Tractor 0.33 1.5
(time *MFC)
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Tractor Tractor 1 0.31 3
Argentina, Brazil, Costa Rica Harvesting Combine harvester
Self-propelled No tractor
used1 1.6 14.8
Note: The following activities are manually/oxen performed: Sowing/Planting, Organic fertilizer application, Synthetic fertilizer application, Weeding, Harvesting. Argentina
49
Global database of GHG emissions related to feed crops - A life cycle inventory
Machinery: Central and South America - Maize
Country activity Equipment power usage frequency time MFC
Conventional tillage
Brazil, Argentina, Peru, Mexico, Cuba Ploughing Moldboard
plough Tractor 1 2.78 14.8
Ecuador, Paraguay, Bolivia, Chile, Colombia, Costa Rica, Guatemala, Honduras, El Salvador, Venezuela, Uruguay Panama, Nicaragua
Ploughing Moldboard plough Tractor 0.33 2.78 14.8
Mexico Seedbed prep. Disk harrow Tractor 2 1.7 22
Brazil, Argentina, Peru, Mexico, Cuba Sowing/Planting Row seeder Tractor 1 0.9 3.5
Ecuador, Bolivia, Costa Rica, Venezuela, Uruguay Panama, Nicaragua, Guatemala
Sowing/Planting Row seeder Tractor 0.5 0.9 3.5
Brazil, Argentina, Peru, Mexico, Cuba, Ecuador, Guatemala
Organic fertilizer application Spreader Tractor 1 6.9 15
Synthetic fertilizer application Broadcaster Tractor 1 0.3 4.2
Brazil, Argentina, Peru, Mexico, Cuba, Ecuador, Paraguay, Bolivia, Chile, Colombia, Costa Rica, Guatemala, Honduras, El Salvador, Venezuela, Uruguay Panama, Nicaragua
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Brazil, Argentina, Peru, Mexico, Cuba, Ecuador, Guatemala
Pesticide application Disk Tractor 1 0.31 3
Mexico Weeding Field sprayer Tractor 1 0.31 3
El Salvador, Ecuador, Bolivia, Costa Rica, Venezuela, Uruguay Panama, Nicaragua
Weeding Field sprayer Tractor 0.3 0.31 3
Brazil, Argentina, Mexico, Peru, Guatemala Harvesting Combine
harvester
Self-propelled No tractor
used1 1.6 14.8
El Salvador, Ecuador, Bolivia, Costa Rica, Venezuela, Uruguay, Panama, Nicaragua, Guatemala
Harvesting Combine harvester
Self-propelled No tractor
used0.5 1.6 14.8
50
Global database of GHG emissions related to feed crops - A life cycle inventory
Machinery: Central and South America - Soybean
Country activity Equipment power usage frequency time MFC
Conventional tillage
Argentina Ploughing Moldboard plough Tractor 2 1.6 14.8
Brazil, Paraguay, Colombia, Ecuador, Guatemala, Mexico, Uruguay, Venezuela
Ploughing Moldboard plough Tractor 1 1.6 14.8
Brazil Seedbed prep. Spring tine harrow Tractor 2.8 0.9 6.6
Argentina Seedbed prep. Spring tine harrow Tractor 1 0.9 6.6
Brazil, Argentina, Paraguay, Colombia, Ecuador, Guatemala, Mexico, Uruguay, Venezuela
Sowing/Planting Row seeder Tractor 1 0.9 3.5
Organic fertilizer application Spreader Tractor 1 6.9 15
Brazil, Paraguay, Colombia, Ecuador, Guatemala, Mexico, Uruguay, Venezuela
Synthetic fertilizer application Broadcaster Tractor 1 0.3 4.2
Brazil, Argentina, Paraguay, Colombia, Ecuador, Guatemala, Mexico, Uruguay, Venezuela
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Argentina Pesticide application Field sprayer Tractor 3 0.31 3
Brazil, Paraguay Pesticide application Field sprayer Tractor 2.4 0.31 3
Argentina Weeding Chisel/cultivator Tractor 1 0.31 3
Brazil, Argentina, Peru, Paraguay. Uruguay, Venezuela Harvesting Combine
harvester
Self-propelled No tractor
used1 1.4 30.5
No-till
Brazil, Argentina, Paraguay Sowing/Planting 1 0.9 3.5
Organic fertilizer application Spreader Tractor 1 6.9 15
Synthetic fertilizer application broadcaster Tractor 1 0.3 4.2
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Field sprayer Tractor 6 0.31 3
Harvesting Combine harvester
Self-propelled No tractor
used1 1.4 30.5
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Global database of GHG emissions related to feed crops - A life cycle inventory
Machinery: Central and South America – Wheat
Country activity Equipment power usage frequency time MFC
Conventional tillage
Brazil, Argentina, Chile, Cuba, Mexico, Peru Uruguay Ploughing Moldboard
plough Tractor 1 1.6 14.8
Chile, MexicoSeedbed prep.
Disc harrowTractor 1 1.7 22
Chile, Uruguay Field cultivator
Brazil, Argentina, Mexico Sowing/Planting Row seeder Tractor 1 0.9 3.5
Chile Sowing/Planting Narrow-rowed precision seeder Tractor 1 1 3.5
Peru, Chile Organic fertilizer application Spreader Tractor 1 6.9 15
Brazil, Argentina Synthetic fertilizer application Broadcaster Tractor
0.50.3 4.2
Mexico 1
Brazil, Argentina, Chile, Cuba, Mexico, Peru, Uruguay Liming Broadcaster Tractor 0.33 1.5
(time *MFC)
Chile, Mexico, Peru Uruguay Pesticide application Field sprayer Tractor 1 0.31 3
Mexico Weeding Field sprayer Tractor 1 0.31 3
Brazil, Argentina, Mexico Harvesting Combine harvester Self-propelled 1 1.6 14.8
Chile Harvesting Loader wagon Tractor 1 1 6.3
52
Global database of GHG emissions related to feed crops - A life cycle inventory
REFERENCES Bolliger A., Magid, J. Carneiro Amado, T. J., Skora Neto, F., Dos Santos Ribeiro, M.,
Calegari, A., Ralisch, R. Neergaard, A. 2006. Taking stock of the Brazilian “Zero till revolution”: a review of landmark research and farmers’ practice.
Catacora-Vargas, G., Galeano, P., Agapito-Tenfen, S. Z., Aranda, D., Palau, T. and No-dari, R. O. 2012. Soybean production in the Southern Cone of the Americas: Update on land and pesticide use.
Dalgaard, R., Schmidt, J., Halberg, N., Christensen, P., Thrane, M., Pengue, W.A. 2008. LCA of Soybean Meal. The International Journal of Life Cycle Assessment, 13 (3) 240–254.; Panichelli, L., Dauriat, A., Gnansounou, E. 2009. Life cycle assessment of soybean-based biodiesel in Argentina for export. Int J Life Cycle Assess 14:144-159. Doi: 10.1007/s11367-008-0050-8.Ecoinvent, 2013. Ecoinvent data v3.0. Swiss Centre for Life Cycle Inventories, Duebendorf. http://www.ecoinvent.org/database;
Huerta, J. H., Alvear, E. M. and Navarro, R. M. 2012. Evaluation of two production meth-ods of Chilean wheat by life cycle assessment (LCA). IDESIA (Chile), 30 (2), 101-110.; Starkey, P. 2010. Livestock for traction: world trends, key issues and policy implications. AGA working paper series. Rome, FAO.
IPNIS, Integrated Plant Nutrition Information System. http://www.fao.org/ag/agp/ipnis/Meisner, C.A., E. Acevedo, D. Flores, K. Sayre, I. Ortiz-Monasterio, and D. Byerlee, 1992.
Wheat Production and Grower Practices in the Yaqui Valley, Sonora, Mexico. Wheat Spe-cial Report No.6. CIMMYT, Mexico, D.F., Mexico.
Morrla, M.L., with M. Alv.rez and M.A. E.plnoza. 1990. The maize Subsector In Paraguay: A Diagnostic Overview. CIMMYT Economici Working Papar 90105. Me.lco, O.F.: CIM-MYT.
Nadal, Alejandro. 1999. Maize in Mexico: Some Environmental Implication of the North American Free Trade Agreement. (NAFTA) in Assessing Environmental Effects of the north American Free Trade Agreement (NAFTA): An Analytic Framework (Phase II) and Issue Studies. Communications and Public Outreach Department of the CEC Secretariat.
Trigo, E.; Cap, E.; Malach, V.; Villarreal, F. 2009. The case of zero-tillage technology in Argentina IFPRI Discussion Paper 915.; FAO 2013. Save and Grow: Cassava. A guide to sustainable production intensification. Rome.
53
Global database of GHG emissions related to feed crops - A life cycle inventory
Machinery: Europe - Barley
Country activity Equipment power usage frequency time MFC
Conventional tillage
Ploughing Moldboard plough Tractor 1 1.6 14.8
Seedbed prep. Rotary harrow and Field cultivator Tractor 2 2 26.6
Netherlands Seedbed prep.Seedbed combined machine and Field cultivator
Tractor 2 2 18.9
Sowing/Planting Row seeder Tractor 1 0.9 3.5
Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application Disks Tractor 2 0.3 4.2
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Field sprayer Tractor 3 0.31 3
WeedingField sprayer (herbicide spraying) +Hoe
Tractor 1 0.31 3
Harvesting Combine harvester Self-propelled No tractor used 1 1.4 30.5
No-till and Minimal tillage
PloughingNA (no-till) NA (no-till) 0 0 0
Chisel plow (minimal tillage)
Tractor (minimal tillage) 1 1 1.3
Seedbed prep. Rotary harrow and Field cultivator
NA (no-till) 0 0 0
Tractor (minimal tillage) 1 2 26.6
Sowing/Planting DrillTractor (no-till) 1 1.2
Tractor (minimal tillage) 1 0.9 3.5
Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application Disks
Tractor (no-till)
1.5 0.3 4.2Tractor
(minimal tillage)
Liming Broadcaster Tractor (no-till and minimal) 0.33 1.5
(time *MFC)
Pesticide application Field sprayer
Tractor (no-till) 4.7
0.31 3Tractor
(minimal tillage) 3.8
Weeding Field sprayer
Tractor (no-till) 4.1
0.31 3Tractor
(minimal tillage) 2.8
Harvesting Combine harvester Self-propelled No tractor used 1 1.4 30.5
54
Global database of GHG emissions related to feed crops - A life cycle inventory
Machinery: Europe - MaizeCountry activity Equipment power usage frequency time MFC
Conventional tillage
Ploughing Moldboard plough Tractor 1 2.8 14.8
Netherlands Ploughing Turning plough and Cambridge role Tractor 1 2.3 14.8
Seedbed prep. Rotary harrow and Field cultivator Tractor 2 1.87 26.6
Switzerland Seedbed prep. Spring tine harrow Tractor 2 1.2 12.2
Sowing/Planting Row seeder Tractor 1 0.9 3.5
Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application Disks Tractor 2 0.3 4.2
Switzerland Synthetic fertilizer application Broadcaster Tractor 3 0.3 4.2
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Field sprayer Tractor 6 0.31 3
Switzerland Pesticide application Field sprayer Tractor 1.7 0.31 3
Weeding Field sprayer (herbicide spraying) +Hoe Tractor 2 2.31 18.4
Harvesting Combine harvesterSelf-propelled
No tractor used
1 5.1 30.5
Machinery: Europe - Soybean
country/region activity Equipment power usage frequency time MFC
Conventional tillage
Czechia, France, Hungary, Italy, Republic of Moldova, Serbia, Romania, Ukraine
Ploughing Moldboard plough Tractor 1 1.6 14.8
Seedbed prep.Rotary harrow and Field cultivator
Tractor 1 2 26.6
Sowing/ Planting Row seeder Tractor 1 0.9 3.5
Organic fertilizer application
Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application
Disks Tractor 1 0.3 4.2
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Field sprayer Tractor 1 0.31 3
Weeding Field sprayer Tractor 2 0.31 3
Harvesting Combine harvester
Self-propelled No tractor
used1 1.4 30.5
55
Global database of GHG emissions related to feed crops - A life cycle inventory
Machinery: Europe - Wheat
Country activity Equipment power usage frequency time MFC
Conventional tillage
Ploughing Moldboard plough Tractor 1 1.6 14.8
Seedbed prep. Rotary harrow and Field cultivator Tractor 2 2 26.6
Sowing/Planting Row seeder Tractor 1 0.9 3.5
Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application Disks Tractor 1 0.3 4.2
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Field sprayer Tractor 3 0.31 3
Weeding Field sprayer (herbicide spraying) Tractor 1 0.31 3
Harvesting Combine harvester Self-propelled No tractor used 1 1.4 30.5
No-till and Minimal tillage
PloughingNA (no-till) NA (no-till) 0 0 0
Chisel plow (minimal tillage)
Tractor (minimal tillage) 1 1 1.3
Seedbed prep. Rotary harrow and Field cultivator
NA (no-till) 0 0 0
Tractor (minimal tillage) 1 2 26.6
Sowing/Planting DrillTractor (no-till) 1 1.2 3.5
Tractor (minimal tillage) 1 0.9 3.5
Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application Disks
Tractor (no-till)
1.5 0.3 4.2Tractor
(minimal tillage)
Liming Broadcaster Tractor (no-till and minimal) 0.33 1.5
(time *MFC)
Pesticide application Field sprayer
Tractor (no-till) 4.7
0.31 3Tractor
(minimal tillage) 3.8
Weeding Field sprayer
Tractor (no-till) 4.1
0.31 3Tractor
(minimal tillage) 2.8
Harvesting Combine harvester Self-propelled No tractor used 1 1.4 30.5
56
Global database of GHG emissions related to feed crops - A life cycle inventory
REFERENCESDAAS 2009. Danish Agricultural Advisory Service. Budgetkalkuler. 2009-2010. www.land-
brugsinfo.dk/Diverse/KA/Filer/Budgetkalkuler_2010_salg.pdf.Ecoinvent, 2013. Ecoinvent data v3.0. Swiss Centre for Life Cycle Inventories, Duebendorf.
http://www.ecoinvent.org/database/ Elsgaard, L., Olesen, J. E. and Hermansen, J. E. 2010. Greenhouse gas emissions from cul-
tivation of winter wheat and winter rapeseed for biofuels – According to the Directive 2009/28/EC of the European Parliament on the promotion of the use of energy from renewable sources . Revised version 31/08/2010. Department of Agroecology and Envi-ronment, Aarhus University, Denmark. Accessed November 2012, available at: http://ec.europa.eu/energy/renewables/biofuels/emissions_en.htm
Rudelsheim P. L. J., G. Smets, 2012. Baseline information on agricultural practices in the EU Soybean (Glycine max (L.) Merr.). Perseus BVBA, pp. 42 http://www.europabio.org/baseline-information-agricultural-practices-eu-soybean-glycine-max-l-merr [Accessed 8 May 2014].
Schreuder, R., W Van Dijk, W., Asperen, P., De Boer, J., Van Der Schoot, J.R. 2008. Me-bot 1.01 beschrijving van milieu- en bedrijfsmodel voor open teelten (mebot 1.01 model N-TOOLBOX D1.4 [32] documentation of an environmental and farm model for field crops). Praktijkonderzoek Plant & Omgeving (PPO no. 373), Wageningen, The Nether-lands.
Vellinga, T. and De Boer, J. 2012. LCI data for the calculation tool Feedprint for green-house gas emissions of feed production and utilization, Machinery use for cultivation. Wageningen University and Research Centre and Blonk Consultants, Lelystad/Gouda, The Netherlands.
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Global database of GHG emissions related to feed crops - A life cycle inventory
West Asia and Northern Africa - Barley
Country activity Equipment power usage frequency time MFC
Conventional tillage
Algeria, Morocco, Kazakhstan, Egypt Algeria Armenia Azerbaijan, Libya, Tajikistan, Tunisia, Iraq, Syrian Arab Republic
Ploughing Moldboard plough Tractor 1 1.6 14.8
Morocco Seedbed prep. Disk harrow Tractor 2 0.9 6.6
AlgeriaSowing/Planting
Grain drill
Tractor 1 0.9 3.5Morocco, Armenia, Azerbaijan, Libya, Tajikistan, Tunisia Seeder
AlgeriaOrganic fertilizer application
Broadcaster Tractor 1 1.8 14.8
Morocco, Algeria Armenia Azerbaijan, Libya, Tajikistan, Tunisia, Iraq, Syrian Arab Republic
Synthetic fertilizer application
Broadcaster Tractor 1 0.3 4.2
Morocco Pesticide application
Field sprayer Tractor 1 0.31 3
Egypt Weeding Field sprayer Tractor 1 0.31 3
Algeria, Morocco, Turkey, Armenia Azerbaijan Libya Tajikistan, Tunisia Harvesting Combine
harvester
Self-propelled No tractor
used1 1.4 30.5
Machinery: West Asia and Northern Africa - Maize
Country activity Equipment power usage frequency time MFC
Conventional tillage
Egypt, Azerbaijan, Iraq, Kazakhstan, Kyrgyzstan, Libya, Syria, Tajikistan, Turkey
Ploughing Chisel plow Tractor 1 25.9 (time *MFC)
Seedbed prep. Disk harrow Tractor 1 0.9 6.6
Sowing/Planting Seeder Tractor 0.9 3.5 0.9
Organic fertilizer application
Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application
Spreader Tractor 1 0.3 4.2
Pesticide application
Field sprayer Tractor 1 0.31 3
Weeding Hoe Tractor 2 2 15.4
Harvesting Combine harvester
Self-propelled No tractor
used1 1.4 30.5
58
Global database of GHG emissions related to feed crops - A life cycle inventory
Machinery: West Asia and Northern Africa - Wheat
Country activity Equipment power usage frequency time MFC
Conventional tillage
Algeria, Uzbekistan, Morocco, Kazakhstan, Armenia, Israel, Jordan, Iraq
Ploughing Moldboard plough Tractor 1 1.6 14.8
Egypt, Saudi Arabia, Uzbekistan, Kyrgyzstan, Azerbaijan, Tajikistan, Turkmenistan
Ploughing Chisel plow Tractor 1 1.6 14.8
Egypt, Algeria Seedbed prep. Disk harrow Tractor 1 0.9 6.6
Morocco Seedbed prep. Disk harrow Tractor 2 0.9 6.6
Algeria, Egypt, Israel, Jordan, Kazakhstan, Uzbekistan, Kyrgyzstan, Azerbaijan, Tajikistan,Turkmenistan
Sowing/Planting Seeder Tractor 1 0.9 3.5
Algeria, , Israel, Jordan, Kazakhstan,Organic fertilizer application
Broadcaster Tractor 1 1.8 14.8
Algeria, Morocco, Israel, Jordan, Kazakhstan, Iraq
Synthetic fertilizer application
Spreader Tractor 1 0.3 4.2
Morocco Pesticide application
Field sprayer Tractor 1 0.31 3
Weeding Field sprayer Tractor 1 0.31 3
Egypt Weeding Hoe Tractor 2 2 15.4
Algeria, Egypt, Uzbekistan, Morocco, Iraq Harvesting Combine
harvester
Self-propelled No tractor
used1 1.4 30.5
59
Global database of GHG emissions related to feed crops - A life cycle inventory
REFERENCESAkar, T., Avci, M. and Dusunceli, F. 2004. Barley: Post-harvest operations. The Central
Research Institute for Field Crops, Ankara, Turkey.Fitch, J. B. 1983. Maize production practices and problems in Egypt: Results of three farmer
surveys. Centro Internacional de Mejoramiento de Maíz y Trigo.Halilat, M.T. 2004. Effect of potash and nitrogen fertilization on wheat under saharan condi-
tions. Proceedings of the IPI Regional Workshop on Potassium and Fertigation Develop-ment in West Asia and North Africa, November, 24-28, 2004, Rabat, Morocco, pp: 16-16.
ICARDA 2002. ICARDA in Central Asia and the Caucasus. Ties that Bind, No. 12 (revised). ICARDA, Aleppo, Syria, 36 pp. En.
Mahdi, L., Fletcher, D. 2012, Demonstration of New Agricultural Technologies for Boosting Ce-real and Legume Grain Production and Productivity in Anbar Province.
Morris, M.L., Belaid, A. and Byerlee D. 1991. Wheat and barley production in rainfed mar-ginal environments of the developing world. Part I of 1990-91 CIMMYT World Wheat Facts and Trends: Wheat and Barley Production in Rainfed Marginal Environments of the Developing Wor/d. Mexico, D.F.: CIMMYT.
Ramah, M. and Baali, H. 2013. Energy Balance of Wheat and Barley under Moroccan Con-ditions. Journal of Energy Technologies and Policy, 3(10), 20-27.
Schreuder, R., W Van Dijk, W., Asperen, P., De Boer, J., Van Der Schoot, J.R. 2008. Me-bot 1.01 beschrijving van milieu- en bedrijfsmodel voor open teelten (mebot 1.01 model N-TOOLBOX D1.4 [32] documentation of an environmental and farm model for field crops). Praktijkonderzoek Plant & Omgeving (PPO no. 373), Wageningen, The Nether-lands;
Starkey, P. 2010. Livestock for traction: world trends, key issues and policy implications. AGA working paper series. Rome, FAO.
Ulrich, S. E. 2011. Barley: Production, Improvement and Uses. Wiley-Blackwell.; USDA - Foreign Agricultural Service 2010. Analysis Kazakhstan Agricultural Overview,
http://www.pecad.fas.usda.gov/highlights/2010/01/kaz_19jan2010/.Zeghouane, O. and Benbelkacem, A. 2012. Current state and trends of wheat production
in Algeria. http://www.slideshare.net/CIMMYT/04-omar-zeghouanecurrentstatean-dtrendsofwheatproductioninalgeria
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Global database of GHG emissions related to feed crops - A life cycle inventory
Sub-Saharan Africa - Wheat
Country activity Equipment power usage frequency time MFC
Conventional tillage
Tanzania Ploughing Moldboard plough Tractor 0.3 1.6 14.8
Kenya Ploughing Moldboard plough Tractor 0.5 1.6 14.8
South Africa Ploughing Moldboard plough and chisel plough Tractor 1 1.6 14.8
Kenya Seedbed prep. Disk harrow Tractor 1.4 0.9 6.6
South Africa Seedbed prep. Field cultivator Tractor 1 0.9 6.6
Kenya, South Africa Sowing/Planting Seeder Tractor 0.5 3.5 0.9
Synthetic fertilizer application Tractor Tractor 1 0.3 4.2
South Africa Weeding Field sprayer Tractor 0.5 0.3 4.2
Kenya, South Africa Harvesting Combine harvested Tractor 1 1.4 30.5
Note: The following activities are manually/oxen performed: Organic fertilizer application, Pesticide application
Sub-Saharan Africa - Maize
Country activity Equipment power usage frequency time MFC
Conventional tillage
South Africa Ploughing Moldboard plough Tractor 1 2.78 14.8
Tanzania, Kenya Ploughing Moldboard plough Tractor 0.3 2.78 14.8
South Africa Seedbed prep. Field cultivator Tractor 1 0.9 6.6
Kenya Seedbed prep. Field cultivator Tractor 0.5 0.9 6.6
South Africa Sowing/Planting tractor-drawn planters Tractor 1 3.5 0.9
Kenya Sowing/Planting tractor-drawn planters Tractor 0.15 3.5 0.9
South Africa Synthetic fertilizer application tractor-drawn planters Tractor 1 0.3 4.2
Weeding Field sprayer Tractor 1 0.3 4.2
Harvesting Combine harvested Tractor 1 1.4 30.5
Note: The following activities are manually/oxen performed: Organic fertilizer application, Pesticide application.
Sub-Saharan Africa - Cassava
Country activity Equipment power usage frequency time MFC
Conventional tillage
Tanzania Ploughing Moldboard plough Tractor 0.3 1.6 14.8
Note: The following activities are manually/oxen performed: Seedbed Preparation, Sowing/Planting, Organic fertilizer application, Synthetic fertilizer application, Pesticide application, Weeding, Harvesting.
61
Global database of GHG emissions related to feed crops - A life cycle inventory
REFERENCESAgenbag, G.A. 2012. Growth, yield and grain protein content of wheat (Triticum aestivum
L.) in Response to nitrogen fertiliser rates, crop rotation and soil tillage, South African Journal of Plant and Soil, 29:2, 73-79.
Coulibaly, O., Arinloye, A.D., Faye, M.D., Abdoulaye, T. 2014. Regional cassava value chains analysis in West Africa: Case study of Nigeria. Working paper. West and Central African Council for Agricultural Research and Development (CORAF/WECARD).
Klatt., A.R. ed. 1988. Wheat Production Constraints in Tropical Environments. Mexico. D.F.: CIMMYT.
Fanadzo, M., Chiduza, C., Mnkeni,P.N.S., Van der Stoep, I., Stevens, J. 2010. Crop pro-duction management practices as a cause for low water productivity at Zanyokwe Irriga-tion Scheme.
Gianessi, L. 2014. Importance of Herbicides for Zero-Till Wheat and Rice on the Indo-Gan-getic Plains. International Pesticide Benefits Case Study No. 105.
Hassan, R.M., Mwangi, W., Karanja, D. 1993. Wheat Supply in Kenya: Production Tech-nologies, Sources of Inefficiency, and Potential for Productivity Growth. CIMMYT Eco-nomics Working Paper No. 93-02. Mexico, D.F.: CIMMYT.
IPNIS, Integrated Plant Nutrition Information System. http://www.fao.org/ag/agp/ipnis/Nakhone, L., Kabuta, Ch. 1998. A review of gender disaggregated data on maize and wheat
cropping systems in Ethiopia, Kenya, Tanzania and Uganda. 66 p. Addis Ababa (Ethio-pia). CIMMYT. CIDA.
Negassa, A., Shiferaw, B., Jawoo, K., Sonder,K. Smale,M. Braun, H.J. Gbegbelegbe, S., Zhe Guo, Hodson, D. Wood, S. Payne, T. Abeyo, B. 2013. The Potential for Wheat Pro-duction in Africa: Analysis of Biophysical Suitability and Economic Profitability. Mexico, D.F. CIMMYT.
Negatu, W., Mwangl, W., Tessema, T. 1994. Cultural Practices and Varietal Preferences for Durum Wheat by Farmers of ‘\da, Lume and Gimbichu Weredas of Ethiopia. Research Report Series No.1. AUA, DZARC, Debre Zetit.
Nweke F. 2004. New Challenges in the Cassava transformation in Nigeria and Ghana. In-ternational Food Policy Research Institute.
Onyenwoke, C. A., Simonyan, K.J. 2014. Cassava post-harvest processing and storage in Nigeria: A review in African Journal of Agricultural Research, Vol. 9(53). pp. 3853-3863.
Starkey, P. 2010. Livestock for traction: world trends, key issues and policy implications. AGA working paper series. Rome, FAO.
Schreuder, R., W Van Dijk, W., Asperen, P., De Boer, J., Van Der Schoot, J.R. 2008. Me-bot 1.01 beschrijving van milieu- en bedrijfsmodel voor open teelten (mebot 1.01 model N-TOOLBOX D1.4 [32] documentation of an environmental and farm model for field crops). Praktijkonderzoek Plant & Omgeving (PPO no. 373), Wageningen, The Nether-lands.
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South Asia - BarleyCountry activity Equipment power usage frequency time MFC
Conventional tillage
Iran (Islamic Republic of) Ploughing Moldboard plough Tractor 1 15.9 14.8
India Ploughing Moldboard plough Tractor 0.5 15.9 14.8
Sowing/Planting Seed cum fertilizer drill Tractor 0.5 3.5 0.9
Synthetic fertilizer application
Seed cum fertilizer drill Tractor 1 0.3 4.2
Iran (Islamic Republic of) Harvesting Tractor Tractor 1 15.9 14.8
Note: The following activities are manually/oxen performed: Seedbed Preparation, Organic fertilizer application, Pesticide application, Weeding.
South Asia - MaizeCountry activity Equipment power usage frequency time MFC
Conventional tillage
Iran (Islamic Republic of) Ploughing Moldboard plough Tractor 1 15.9 14.8
India, Pakistan, Sri Lanka Ploughing Moldboard plough Tractor 0.5 15.9 14.8
Bangladesh Ploughing Moldboard plough Tractor 0.3 15.9 14.8
Note: The following activities are manually/oxen performed: Seedbed Preparation, Sowing/Planting, Organic fertilizer application, Synthetic fertilizer application, Pesticide application, Weeding, Harvesting.
South Asia - SoybeanCountry activity Equipment power usage frequency time MFC
Conventional tillage
India, Iran (Islamic Republic of) Ploughing Moldboard plough Tractor 0.5 15.9 14.8
Bangladesh Ploughing Moldboard plough Tractor 0.3 15.9 14.8
Note: The following activities are manually/oxen performed: Seedbed Preparation, Sowing/Planting, Organic fertilizer application, Synthetic fertilizer application, Pesticide application, Weeding, Harvesting.
South Asia - WheatCountry activity Equipment power usage frequency time MFC
Conventional tillage
Afghanistan Ploughing Moldboard plough Tractor 1 15.9 14.8
India, Pakistan, Iran (Islamic Republic of) Ploughing Moldboard plough Tractor 0.5 15.9 14.8
Pakistan, Iran (Islamic Republic of) Ploughing Moldboard plough Tractor 0.3 15.9 14.8
India Sowing/Planting Row seeder Tractor 0.5 3.5 0.9
Pakistan Harvesting Combine harvester Tractor 0.5 15.9 14.8
Iran (Islamic Republic of) Harvesting Combine harvester Tractor 0.3 15.9 14.8
Note: The following activities are manually/oxen performed: Seedbed Preparation, Organic fertilizer application, Synthetic fertilizer application, Pesticide application, Weeding.
South Asia - CassavaTillage system activity Equipment power usage frequency time MFC
Conventional tillage
Note: The following activities are manually/oxen performed: Ploughing, Seedbed Preparation, Sowing/Planting, Organic fertilizer application, Synthetic fertilizer application, Pesticide application, Weeding, Harvesting.
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REFERENCESAfreen, N., Haque, M. S. 2014. Cost benefits analysis of cassava production in Sherpur dis-
trict of Bangladesh in Journal of the Bangladesh Agricultural University. Akar, T., Avci, M. and Dusunceli, F. 2004. Barley: Post-harvest operations. The Central
Research Institute for Field Crops, Ankara, Turkey.Badar, H., Din, O.M. 2005. Wheat Production and Marketing: A Comparative Study of
Traditional and Progressive Farmers in Faisalabad (Pakistan). Journal of Agriculture and Social Sciences 1, 16-19.
Bokanga, M. 2000. Cassava: Post-harvest operations. Information network on post-harvest operations, 1-26.
Conklin, A.R., Stilwell, T. 2007. World Food: Production and Use.Encyclopædia Iranica 2015. http://www.iranicaonline.org/.; Baloch, U. K. 1999. Wheat:
Post-harvest operations. Pakistan Agricultural Research Council, 1-21.Hobbs, P.R., Hettel, G.P, Singh, R.K, Singh, R.P., Harrington, L.W., V.P. Singh, Pillai,
K.G. 1992. Rice-Wheat Cropping Systems in Faizabad District of Uttar Pradesh, India: Exploratory Surveys of Farmers’ Practices and Problems and Needs for Further Research. Mexico, D.F.: CIMMYT.
Howeler, R. H. and Tan, S. L. 2001. Cassava’s Potential in Asia in the 21st Century: Pres-ent Situation and Future Research and Development Needs. In Proceeding 6th Regional Workshop, held in Ho Chi Minh city. February (pp. 21-25).
Joshi, P.K., N.P. Singh, N.N. Singh, R.V. Gerpacio, P.L. Pingali. 2005. Maize in India: Pro-duction Systems, Constraints, and Research Priorities. Mexico, D.F. CIMMYT.
IPNIS, Integrated Plant Nutrition Information System. http://www.fao.org/ag/agp/ipnis/Islas-Rubio and Higuera-Ciapara 2002. Soybeans: Post-harvest operations. UAGST/FAO,
Rome, Italy.Mejía, D. 2005. Maize: Post-Harvest Operation. UAGST/FAO, Rome, Italy.Onyenwoke, C. A., Simonyan, K.J. 2014; Howeler, R.H., C.H. Hershey. 2002. Cassava in
Asia: Research and development to increase its potential use in food, feed and industry – A Thai example in Research and Development of Cassava Production to increase its Potential for Processing, Animal Feed and Ethanol. Proc. of a Seminar, organized by DOA in Bang-kok, Thailand. Jan 16, 2002. pp. 1-56.
Schreuder, R., W Van Dijk, W., Asperen, P., De Boer, J., Van Der Schoot, J.R. 2008. Me-bot 1.01 beschrijving van milieu- en bedrijfsmodel voor open teelten (mebot 1.01 model N-TOOLBOX D1.4 [32] documentation of an environmental and farm model for field crops). Praktijkonderzoek Plant & Omgeving (PPO no. 373), Wageningen, The Nether-lands.
Srivastava, N. S. L. 2006. Farm power sources their availability and future requirement to sustain agricultural production status of farm mechanization in India, IASRI, ICAR, PUSA, New Delhi: 57-58.
Starkey, P. 2010. Livestock for traction: world trends, key issues and policy implications. AGA working paper series. Rome, FAO.
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east asia - barleyThe following activities are manually/oxen performed: Ploughing, Seedbed Prepa-ration, Sowing/Planting, Organic fertilizer application, Synthetic fertilizer applica-tion, Pesticide application, Weeding, Harvesting.
East Asia - CassavaCountry activity Equipment power usage frequency time MFC
Conventional tillage
Thailand Ploughing Moldboard plough Tractor 1 1.6 14.8
Viet Nam Ploughing Disk plow (3disks) Tractor 0.3 13.5
(time *MFC)
Thailand Seedbed prep. Disk harrow (7disks) Tractor 1 0.9 6.6
Sowing/Planting Row seeder Tractor 1 0.9 3.5
Weeding Hoe Tractor 0.5 2 15.4
Harvesting Tractor Tractor 0.5 0.3 15.9
Note: The following activities are manually/oxen performed: Organic fertilizer application, Synthetic fertilizer application, Pesticide application.
East Asia - MaizeCountry activity Equipment power usage frequency time MFC
Conventional tillage
China, Viet Nam, Thailand, Republic of Korea Ploughing Moldboard
plough Tractor 1 2.78 14.8
Cambodia, Lao People’s Democratic Republic, Philippines Ploughing Moldboard
plough Tractor 0.5 2.78 14.8
Indonesia, Myanmar Ploughing Moldboard plough Tractor 0.25 2.78 14.8
China, Republic of Korea Seedbed prep.
Seedbed combined machine and Field cultivator
Tractor 1 1.87 26.6
Thailand, Lao People’s Democratic Republic, Philippines Seedbed prep. Field
cultivator Tractor 0.5 1.87 26.6
China, Republic of Korea, Dem People’s Rep of Korea, Thailand
Sowing/Planting Row seeder Tractor 1 0.9 3.5
Lao People’s Democratic Republic, Vietnam
Sowing/Planting Row seeder Tractor 0.5 0.9 3.5
China, Republic of KoreaOrganic fertilizer application
Broadcaster Tractor 1 1.8 14.8
China, Republic of Korea, ThailandSynthetic fertilizer application
Broadcaster Tractor 1 0.3 4.2
Republic of Korea Pesticide application
Field sprayer Tractor 3 0.31 3
Weeding
Field sprayer (herbicide spraying)
Tractor 1 0.31 3
Harvesting Combine harvester
Self-propelled No tractor used 1 1.4 30.5
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East Asia - Soybean
Country activity Equipment power usage frequency time MFC
Conventional tillage
Japan, Republic of Korea, Lao People’s Democratic Republic Ploughing Moldboard
plough Tractor 1 1.6 14.8
China, Indonesia, Myanmar, Vietnam, Thailand, Dem People’s Rep of Korea
Ploughing Moldboard plough Tractor 0.5 1.6 14.8
Japan, Republic of Korea Seedbed prep.
Rotary harrow and Field cultivator
Tractor 1 2 26.6
Japan, Republic of Korea, China, Lao People’s Democratic Republic, Dem People’s Rep of Korea, Myanmar
Sowing/Planting Row seeder Tractor 1 0.9 3.5
Vietnam, Thailand Sowing/Planting Row seeder Tractor 0.5 0.9 3.5
Japan, Republic of KoreaOrganic fertilizer application
Broadcaster Tractor 1 1.8 14.8
Dem People’s Rep of KoreaOrganic fertilizer application
Broadcaster Tractor 0.5 1.8 14.8
Japan, Republic of KoreaSynthetic fertilizer application
Disks Tractor 1 0.3 4.2
Pesticide application
Field sprayer Tractor 1 0.31 3
Weeding Field sprayer Tractor 2 0.31 3
Harvesting Combine harvester
Self-propelled No tractor used 1 1.4 30.5
Laos Weeding Hoe Tractor 1 2 15.4
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East Asia - Wheat
Country activity Equipment power usage frequency time MFC
Conventional tillage
Japan Ploughing Moldboard plough Tractor 1 1.6 14.8
China, Dem People’s Rep of Korea, Mongolia Ploughing Moldboard
plough Tractor 0.5 1.6 14.8
Japan Seedbed prep.
Rotary harrow and Field cultivator
Tractor 2 2 26.6
Sowing/Planting Row seeder Tractor 1 0.9 3.5
China, Dem People’s Rep of Korea, Mongolia Sowing/Planting Row seeder Tractor 0.3 0.9 3.5
Japan Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Mongolia Organic fertilizer application Broadcaster Tractor 0.5 1.8 14.8
JapanSynthetic fertilizer application
Disks Tractor 1 0.3 4.2
Pesticide application
Field sprayer Tractor 3 0.31 3
Weeding
Field sprayer (herbicide spraying)
Tractor 1 0.31 3
Harvesting Combine harvester
Self-propelled No tractor
used1 1.4 30.5
Mongolia Harvesting Combine harvester
Self-propelled No tractor
used0.5 1.4 30.5
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REFERENCESCentro Internacional de Agricultura Tropical (CIAT). 2007. Cassava Research and Devel-
opment in Asia: Exploring New Opportunities for an Ancient Crop. Proceeding of the Sev-enth Regional Workshop held in Bangkok, Thailand. Oct 28-Nov 1, 2001.
Ekasingh, B., P. Gypmantasiri, K. Thong-ngam, and P. Grudloyma. 2004. Maize in Thai-land: Production Systems, Constraints, and Research Priorities. Mexico, D.F., CIMMYT.
FAO 2013. Save and Grow: Cassava. A guide to sustainable production intensification. Rome.Gerpacio, R. V., Labios, J. D., Labios, R. V. and Diangkinay, E. I. 2004. Maize in the Phil-
ippines: Production systems, constraints, and research priorities, Mexico City: CIMMYT.Gerpacio, R. V. and Pingali, P. L. 2007. Tropical and subtropical maize in Asia: Production
systems, constraints and research priorities, Mexico, D.F.: CIMMYT, IFAD.Howeler, R. H. and Tan, S. L. 2001. Cassava’s Potential in Asia in the 21st Century: Present Situation and Future Research and Development Needs. In Proceeding 6th Regional Workshop, held in Ho Chi Minh city. February (pp. 21-25).
Howeler, R.H., C.H. Hershey. 2002. Cassava in Asia: Research and development to increase its potential use in food, feed and industry – A Thai example in Research and Develop-ment of Cassava Production to increase its Potential for Processing, Animal Feed and Eth-anol. Proc. of a Seminar, organized by DOA in Bangkok, Thailand. Jan 16, 2002. pp. 1-56.
IPNIS, Integrated Plant Nutrition Information System. http://www.fao.org/ag/agp/ipnis/Katong, S., Phetprapi, P., Jantawat, S., Samuthong, N., Howeler, R. H., Watananonta,
W. and Tangakul, S. 2005. Effect of methods of land preparation on the yields of four cas-sava cultivars in Thailand. In II International Symposium on Sweetpotato and Cassava: Innovative Technologies for Commercialization 703 (pp. 225-232).
Meng, E.C.H., Ruifa Hu, Xiaohua Shi, and Shihuang Zhang. 2006. Maize in China: Pro-duction Systems, Constraints, and Research Priorities. Mexico, D.F., CIMMYT. Onyen-woke, C. A., Simonyan, K.J. 2014; Howeler, R.H., C.H. Hershey. 2002. Cassava in Asia: Research and development to increase its potential use in food, feed and industry – A Thai example in Research and Development of Cassava Production to increase its Poten-tial for Processing, Animal Feed and Ethanol. Proc. of a Seminar, organized by DOA in Bangkok, Thailand. Jan 16, 2002. pp. 1-56.
Schreuder, R., W Van Dijk, W., Asperen, P., De Boer, J., Van Der Schoot, J.R. 2008. Me-bot 1.01 beschrijving van milieu- en bedrijfsmodel voor open teelten (mebot 1.01 model N-TOOLBOX D1.4 [32] documentation of an environmental and farm model for field crops). Praktijkonderzoek Plant & Omgeving (PPO no. 373), Wageningen, The Nether-lands.
Starkey, P. 2010. Livestock for traction: world trends, key issues and policy implications. AGA working paper series. Rome, FAO.
Swastika, D.K.S., Kasim, F.; Sudana, W.; Hendayana, R.; Suhariyanto, K.; Gerpacio, R.V.; Pingali, P.L. 2004, Maize in Indonesia: Production systems, constraints and research priorities. viii, 40 pags. Mexico, D.F., CIMMYT.
Thanh Ha, D., T. Dinh Thao, N. Tri Khiem, M. Xuan Trieu, R.V. Gerpacio, and P.L. Pin-gali. 2004. Maize in Vietnam: Production Systems, Constraints, and Research Priorities. Mexico, D.F., CIMMYT.
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Oceania - Barley
Country activity Equipment power usage frequency time MFC
Conventional
Australia, N.Zealand Ploughing Moldboard plough Tractor 1 1.6 14.8
Seedbed prep. Disk harrow and Field cultivator Tractor 2 2 26.6
Sowing/Planting Row seeder Tractor 1 0.9 3.5
Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application Broadcaster Tractor 1 0.3 4.2
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Field sprayer Tractor 3 0.31 3
Weeding Field sprayer Tractor 3 0.31 3
Harvesting Combine harvesterSelf-propelled
No tractor used
1 1.4 30.5
No till and Minimal
Australia PloughingNA (no-till) Tractor
(no-till) 0 0 0
Chisel Tractor (minimal) 1 14.9
(time *MFC)
Seedbed prep.NA (no-till) Tractor
(no-till) 0 0 0
Field cultivator Tractor (minimal) 1 0.8
Sowing/Planting
Narrow-rowed precision seeder
Tractor (no-till) 1 4.1
(time *MFC)
Row seeder Tractor (minimal) 1 0.9 11.2
Organic fertilizer application Broadcaster
Tractor (no-till)
(minimal)1 1.8 14.8
Synthetic fertilizer application Broadcaster
Tractor (no-till)
(minimal)1 0.3 3.5
Pesticide application Field sprayerTractor (no-till)
(minimal)3 0.3 3.5
Weeding Field sprayerTractor
(minimal) (no-till)
3 0.3 3.5
Liming Broadcaster Tractor (minimal) 0.33 1.5
(time *MFC)
Harvesting Combine harvesterSelf-propelled
No tractor used
1 1.4 3
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Oceania - Maize
Country activity Equipment power usage frequency time MFC
Conventional tillage
Australia, N. Zealand Ploughing Moldboard plough Tractor 1
Seedbed prep. Disk harrow and Field cultivator Tractor 1
Sowing/Planting Row seeder Tractor 1
Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application Broadcaster Tractor
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Field sprayer Tractor
Weeding Field sprayer Tractor
Irrigation
Harvesting Combine harvesterSelf-propelled
No tractor used
1 5.1 30.5
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Oceania - Wheat
Country activity Equipment power usage frequency time MFC
Conventional tillage
Australia, N. Zealand Ploughing Moldboard plough Tractor 1 1.6 14.8
Seedbed prep. Disk harrow and Field cultivator Tractor 2 2 26.6
Sowing/Planting Row seeder Tractor 1 0.9 3.5
Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Synthetic fertilizer application Broadcaster Tractor 1 0.3 4.2
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Field sprayer Tractor 3 0.31 3
Weeding Field sprayer Tractor 3 0.31 3
Harvesting Combine harvesterSelf-propelled
No tractor used
1 1.4 30.5
No till and Minimal tillage
Australia PloughingNA (no-till) Tractor
(no-till) 0 0 0
Chisel Tractor (minimal) 1 14.9
(time *MFC)
Seedbed prep.NA (no-till) Tractor
(no-till) 0 0 0
Field cultivator Tractor (minimal) 1 0.8 15.4
Sowing/Planting
Narrow-rowed precision seeder
Tractor (no-till) 1 4.1
(time *MFC)
Row seeder Tractor (minimal) 1 0.9 3.5
Australia, N. Zealand Organic fertilizer application Broadcaster Tractor 1 1.8 14.8
Australia Synthetic fertilizer application Broadcaster
Tractor (no-till)
1 0.3 4.2Tractor
(minimal)
Liming Broadcaster Tractor 0.33 1.5 (time *MFC)
Pesticide application Field sprayer
Tractor (no-till)
3 0.31 3Tractor
(minimal)
Weeding Field sprayer
Tractor (no-till)
3 0.31 3Tractor
(minimal)
Harvesting Combine harvesterSelf-propelled
No tractor used
1 1.4 30.5
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REFERENCESABS (Australian Bureau of Statistics) 2011/2012a. Agricultural Resource Management
Practices.ABS (Australian Bureau of Statistics) 2011/2012b. Land Management and Farming in Aus-
tralia.Altham W. and Narayanaswamy V. 2004. Grains Environmental Data Tool, Draft Techni-
cal Report for Grains Research and Development Corporation. Perth, Western Australia, Curtin University of Technology: pps 53.
Booker J.W. 2009. Production, distribution and utilisation of maize in New Zealand. Masters thesis, Lincoln University, New Zealand.
CECP (Centre of Excellence in Cleaner Production) 2005. Paddock Data (Inputs) collected through data sheets by Centre of Excellence in Cleaner Production. Curtin University of Technology, Perth, WA;
Dalgaard, T., Halberg, N. and Jørgensen, M.H. 2004. Status for energiinput og –output I økologisk jordbrug samt muligheder for energibesparelser. In: Jørgensen, U., Dalgaard, T. (eds.) Energi i økologisk jordbrug – reduktion af fossilt energiforbrug og produktion af vedvarende energi, pp. 25-45. Forskningscenter for Økologisk Jordbrug.
Ghatohra, A. S. 2012. Effect of method of tillage on loss of carbon from soils: a thesis present-ed in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, Palmerston North, New Zealand (Doctoral dissertation).
Ketterings, Q., Stockin, K., Beckman, J. and Miller, J. 2006. Lime recommendations for field crops. Cornell University Cooperative Extension, Department of Crop and Soil Sci-ences, USA. NSW Department of Primary Industries – Agriculture. Website http://www.dpi.nsw.gov.au/agriculture.
Ugalde, D., Brungs, A., Kaebernick, M., McGregor, A. and Slattery, B. 2007. Implica-tions of climate change for tillage practice in Australia. Soil and Tillage Research, 97(2), 318-330.
IPNIS, Integrated Plant Nutrition Information System. http://www.fao.org/ag/agp/ipnis/Schreuder, R., W Van Dijk, W., Asperen, P., De Boer, J., Van Der Schoot, J.R. 2008.
Mebot 1.01 beschrijving van milieu- en bedrijfsmodel voor open teelten (mebot 1.01 model N-TOOLBOX D1.4 [32] documentation of an environmental and farm model for field crops). Praktijkonderzoek Plant & Omgeving (PPO no. 373), Wageningen, The Netherlands.
Starkey, P. 2010. Livestock for traction: world trends, key issues and policy implications. AGA working paper series. Rome, FAO.
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Annex 6
emissions factors for land use change (tons CO2eq/kg dm*year), 2010
Under conventional tillage production practice
Country Crop weighted average Normal average worst case
Afghanistan Barley 0.0000 0.0486 0.0486
Argentina Barley 3.5795 2.1844 3.5795
Australia Barley 0.7915 2.0624 2.0624
Azerbaijan Barley 0.3558 1.2193 1.2193
Bosnia and Herzegovina Barley 0.0000 0.0000 0.0000
Croatia Barley 0.1243 0.9170 0.9170
Democratic Republic of the Congo Barley 1.7083 4.4442 4.4442
Egypt Barley 0.0057 1.8713 1.8713
Eritrea Barley 0.0000 0.7304 0.7304
Ethiopia Barley 0.6715 0.4796 0.6715
Guatemala Barley 3.5976 3.1851 3.5976
Kuwait Barley 0.0044 1.4258 1.4258
Lebanon Barley 0.2518 0.5573 0.5573
Mauritania Barley 0.0000 18.0918 18.0918
Peru Barley 0.4048 0.2547 0.4048
Republic of Moldova Barley 7.1421 4.2283 7.1421
Slovenia Barley 0.0613 0.2042 0.2042
Tajikistan Barley 0.1601 1.6158 1.6158
Thailand Barley 0.0060 0.7279 0.7279
Ukraine Barley 3.0567 3.7805 3.7805
United Republic of Tanzania Barley 0.0746 1.0280 1.0280
Western Sahara Barley 6.6793 4.0287 6.6793
Zambia Barley 0.0000 1.4530 1.4530
Zimbabwe Barley 2.2995 1.3911 2.2995
American Samoa Cassava 1.1617 0.5966 1.1617
Angola Cassava 0.2794 0.1381 0.2794
Antigua and Barbuda Cassava 0.4594 0.4571 0.4594
Argentina Cassava 0.1336 0.0814 0.1336
Benin Cassava 0.2276 0.1392 0.2276
Brunei Darussalam Cassava 0.3503 0.2552 0.3503
Burkina Faso Cassava 2.6797 1.4171 2.6797
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Cambodia Cassava 0.2451 0.1718 0.2451
Cameroon Cassava 0.5561 0.2700 0.5561
Central African Republic Cassava 0.8342 0.4525 0.8342
China Cassava 0.0000 0.0270 0.0270
Colombia Cassava 0.0065 0.0379 0.0379
Comoros Cassava 0.3247 0.1756 0.3247
Congo Cassava 0.3913 0.1989 0.3913
Costa Rica Cassava 0.0433 0.1683 0.1683
Côte d’Ivoire Cassava 0.0196 0.2521 0.2521
Cuba Cassava 0.0027 0.0226 0.0226
Dominica Cassava 0.2168 0.2752 0.2752
Dominican Republic Cassava 0.0124 0.0708 0.0708
Equatorial Guinea Cassava 0.3385 0.3385 0.3385
Fiji Cassava 0.0101 0.1578 0.1578
French Guiana Cassava 1.0143 1.0796 1.0796
Gabon Cassava 0.0780 0.1785 0.1785
Gambia Cassava 0.0465 0.0930 0.0930
Ghana Cassava 0.2472 0.1446 0.2472
Grenada Cassava 0.0265 0.0467 0.0467
Guadeloupe Cassava 0.0210 0.0335 0.0335
Guatemala Cassava 0.2194 0.1941 0.2194
Guinea Cassava 0.4295 0.2460 0.4295
Guinea-Bissau Cassava 0.3809 0.2595 0.3809
Haiti Cassava 0.4061 0.4240 0.4240
Honduras Cassava 0.7485 0.5593 0.0000
Kenya Cassava 0.1003 0.0909 0.1003
Lao People’s Democratic Republic Cassava 0.2365 0.1517 0.2365
Liberia Cassava 0.2498 0.1319 0.2498
Madagascar Cassava 0.4010 0.2208 0.4010
Malawi Cassava 0.1859 0.1180 0.1859
Mali Cassava 0.2020 0.1266 0.2020
Mauritius Cassava 0.0009 0.1361 0.1361
Mexico Cassava 0.0054 0.1632 0.1632
Mozambique Cassava 0.2036 0.1204 0.2036
Myanmar Cassava 0.3820 0.2656 0.3820
Nicaragua Cassava 0.5752 0.3502 0.5752
Nigeria Cassava 0.1616 0.1306 0.1616
Peru Cassava 0.5409 0.3203 0.5409
Philippines Cassava 0.0004 0.0206 0.0206
Rwanda Cassava 0.0586 0.1367 0.1367
Saint Lucia Cassava 0.7132 0.7829 0.7829
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Saint Vincent and the Grenadines Cassava 0.3834 0.4728 0.0000
Samoa Cassava 0.0295 0.2226 0.2226
Sao Tome and Principe Cassava 0.1555 0.4308 0.4308
Senegal Cassava 0.1919 0.1224 0.1919
Sierra Leone Cassava 1.1290 0.7072 1.1290
Solomon Islands Cassava 0.2650 0.1543 0.2650
Somalia Cassava 0.3833 0.2002 0.3833
Sudan Cassava 0.5472 0.3871 0.5472
Togo Cassava 0.6539 0.3977 0.6539
Trinidad and Tobago Cassava 0.0812 0.1781 0.1781
Uganda Cassava 0.0287 0.0212 0.0287
United Republic of Tanzania Cassava 0.3871 0.2336 0.3871
Viet Nam Cassava 0.0016 0.1199 0.1199
Zambia Cassava 0.4628 0.2801 0.4628
Zimbabwe Cassava 0.4275 0.3134 0.4275
Antigua and Barbuda Maize 12.1933 6.0351 12.1933
Argentina Maize 1.6867 1.6782 1.6867
Armenia Maize 0.8701 0.5316 0.8701
Australia Maize 0.3387 1.0621 1.0621
Austria Maize 0.1342 0.3523 0.3523
Azerbaijan Maize 0.0032 0.0719 0.0719
Bangladesh Maize 0.3248 1.1078 1.1078
Belarus Maize 1.1948 1.8565 1.8565
Belgium Maize 0.0129 2.2803 2.2803
Belize Maize 0.0276 0.4896 0.4896
Benin Maize 3.5615 2.4106 3.5615
Bolivia (Plurinational State of) Maize 5.4542 3.3362 5.4542
Bosnia and Herzegovina Maize 1.5704 0.9565 1.5704
Botswana Maize 0.0019 0.3320 0.3320
Brazil Maize 0.0435 9.2155 9.2155
Burkina Faso Maize 0.2442 0.1741 0.2442
Burundi Maize 6.0110 3.1796 6.0110
Cambodia Maize 0.0260 0.0433 0.0433
Cameroon Maize 3.5973 2.5214 3.5973
Canada Maize 9.8865 4.7985 9.8865
Central African Republic Maize 0.0000 0.1305 0.1305
Chad Maize 7.0394 3.8219 7.0394
Chile Maize 10.3262 5.7518 10.3262
China Maize 0.0031 0.1135 0.1135
Comoros Maize 0.0000 0.3693 0.3693
Cuba Maize 3.7107 2.0094 3.7107
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Czechia Maize 0.3140 2.4880 2.4880
Democratic Republic of the Congo Maize 0.0274 0.9990 0.9990
Djibouti Maize 0.6326 1.6426 1.6426
Dominica Maize 0.0000 0.9452 0.9452
Egypt Maize 0.6509 0.8284 0.8284
Ethiopia Maize 0.0000 0.1200 0.1200
French Guiana Maize 0.7272 0.5210 0.7272
Gabon Maize 8.6292 9.1835 9.1835
Gambia Maize 1.5196 3.4703 3.4703
Germany Maize 2.4562 4.9515 4.9515
Ghana Maize 0.0349 0.5657 0.5657
Grenada Maize 4.2617 2.4901 4.2617
Guam Maize 1.4482 2.5757 2.5757
Guatemala Maize 1.5587 1.5587 1.5587
Guinea-Bissau Maize 1.7856 1.5794 1.7856
Guyana Maize 12.8225 7.3504 12.8225
Haiti Maize 3.9363 2.6784 3.9363
Honduras Maize 0.0457 2.6910 2.6910
Hungary Maize 4.8139 5.0246 5.0246
India Maize 1.3965 1.0441 1.3965
Indonesia Maize 0.0067 0.0526 0.0526
Iran (Islamic Republic of) Maize 0.2107 0.8796 0.8796
Iraq Maize 1.1356 0.8241 1.1356
Italy Maize 0.0478 0.5790 0.5790
Jordan Maize 0.0222 0.5994 0.5994
Kazakhstan Maize 0.0196 0.1429 0.1429
Kenya Maize 0.0279 0.1659 0.1659
Kuwait Maize 0.0734 0.2239 0.2239
Kyrgyzstan Maize 2.6704 2.4210 2.6704
Lao People’s Democratic Republic Maize 0.0004 0.2569 0.2569
Lesotho Maize 0.0129 0.5291 0.5291
Libya Maize 2.4610 1.5781 2.4610
Luxembourg Maize 0.0000 0.6000 0.6000
Madagascar Maize 0.2811 1.2983 1.2983
Malawi Maize 0.1749 0.5613 0.5613
Maldives Maize 7.3852 4.0742 7.3852
Mali Maize 1.2301 0.7809 1.2301
Mauritania Maize 1.5007 2.3164 2.3164
Micronesia (Federated States of) Maize 2.2416 1.4053 2.2416
Montserrat Maize 9.3859 5.9260 9.3859
Mozambique Maize 0.0366 0.1588 0.1588
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Myanmar Maize 0.3135 0.1914 0.3135
Namibia Maize 4.8228 2.8620 4.8228
Nepal Maize 2.4772 1.7222 2.4772
Netherlands Maize 0.6808 0.4857 0.6808
New Caledonia Maize 1.5134 0.8851 1.5134
New Zealand Maize 0.0394 0.5593 0.5593
Nicaragua Maize 0.5236 2.2458 2.2458
Niger Maize 0.0079 0.0551 0.0551
Pakistan Maize 6.5962 4.0120 6.5962
Papua New Guinea Maize 6.2767 3.5851 6.2767
Paraguay Maize 0.6992 0.3995 0.6992
Peru Maize 1.8684 1.0920 1.8684
Poland Maize 4.6462 2.5475 4.6462
Qatar Maize 2.7550 1.6311 2.7550
Republic of Moldova Maize 0.0029 1.3868 1.3868
Russian Federation Maize 0.0347 0.1667 0.1667
Rwanda Maize 0.3236 1.0822 1.0822
Saudi Arabia Maize 0.0168 1.1366 1.1366
Senegal Maize 1.0487 2.4510 2.4510
Sierra Leone Maize 0.0000 0.6452 0.6452
Slovakia Maize 1.9831 1.2665 1.9831
Somalia Maize 10.6955 6.6999 10.6955
Sri Lanka Maize 0.0313 0.3981 0.3981
Tajikistan Maize 4.5060 2.3497 4.5060
Togo Maize 1.0751 1.3754 1.3754
Trinidad and Tobago Maize 0.0008 0.0981 0.0981
Turkey Maize 6.8378 4.1606 6.8378
Uganda Maize 0.3470 0.7616 0.7616
Ukraine Maize 0.0048 0.1427 0.1427
United Republic of Tanzania Maize 2.7190 1.9939 2.7190
United States of America Maize 0.0866 1.2055 1.2055
Uruguay Maize 5.1743 3.1164 5.1743
Vanuatu Maize 0.0018 0.1575 0.1575
Venezuela (Bolivarian Republic of) Maize 0.2366 0.6475 0.6475
Viet Nam Maize 0.0000 1.2412 1.2412
Zambia Maize 0.7165 1.1812 1.1812
Zimbabwe Maize 0.0189 1.4000 1.4000
Argentina Soybeans 5.0272 3.0683 5.0272
Austria Soybeans 0.0388 0.9158 0.9158
Belize Soybeans 7.9363 5.3684 7.9363
Bolivia (Plurinational State of) Soybeans 24.2717 14.8344 24.2717
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Bosnia and Herzegovina Soybeans 6.5839 4.0082 6.5839
Brazil Soybeans 0.0000 0.2370 0.2370
Burkina Faso Soybeans 3.3837 2.4335 3.3837
Cambodia Soybeans 9.2957 4.9172 9.2957
Canada Soybeans 8.3049 13.3653 13.3653
China Soybeans 7.3016 5.1189 7.3016
Croatia Soybeans 17.6824 8.5883 17.6824
Czechia Soybeans 0.0066 2.6602 2.6602
Democratic Republic of the Congo Soybeans 0.0000 0.4014 0.4014
El Salvador Soybeans 0.3037 2.2340 2.2340
Ethiopia Soybeans 0.1340 4.9883 4.9883
Gabon Soybeans 3.2867 8.5455 8.5455
India Soybeans 0.5952 2.2479 2.2479
Iran (Islamic Republic of) Soybeans 3.2314 2.3148 3.2314
Kazakhstan Soybeans 2.2386 5.0991 5.0991
Lao People’s Democratic Republic Soybeans 1.0440 4.3371 4.3371
Mali Soybeans 0.0310 0.3726 0.3726
Myanmar Soybeans 1.1807 3.6259 3.6259
Nepal Soybeans 4.1518 2.6598 4.1518
Paraguay Soybeans 35.2110 18.5642 35.2110
Peru Soybeans 14.3240 7.8921 14.3240
Republic of Moldova Soybeans 8.8441 5.5447 8.8441
Russian Federation Soybeans 7.1534 4.9711 7.1534
Rwanda Soybeans 5.2447 3.0708 5.2447
Slovakia Soybeans 3.6969 16.3144 16.3144
Slovenia Soybeans 7.1194 3.9021 7.1194
South Africa Soybeans 8.7979 5.2118 8.7979
Sri Lanka Soybeans 1.3394 4.4792 4.4792
The former Yugoslav Republic of Macedonia Soybeans 0.0497 3.3144 3.3144
Uganda Soybeans 2.9235 6.8290 6.8290
Ukraine Soybeans 0.3936 4.9298 4.9298
United Republic of Tanzania Soybeans 0.3284 3.3198 3.3198
United States of America Soybeans 0.0144 2.8758 2.8758
Uruguay Soybeans 0.5043 0.6477 0.6477
Venezuela (Bolivarian Republic of) Soybeans 0.4515 3.6765 3.6765
Viet Nam Soybeans 6.0776 4.4531 6.0776
Zambia Soybeans 0.4093 5.7399 5.7399
Zimbabwe Soybeans 2.9589 1.7867 2.9589
Afghanistan Wheat 0.0148 1.0759 1.0759
Algeria Wheat 0.0058 0.2665 0.2665
Angola Wheat 2.2981 1.1363 2.2981
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Global database of GHG emissions related to feed crops - A life cycle inventory
Armenia Wheat 0.3025 0.9445 0.9445
Australia Wheat 1.0038 2.6141 2.6141
Austria Wheat 0.0135 0.2941 0.2941
Azerbaijan Wheat 0.3907 1.3292 1.3292
Belarus Wheat 0.0165 3.1453 3.1453
Belgium Wheat 0.0030 0.0530 0.0530
Bolivia (Plurinational State of) Wheat 5.7103 3.4750 5.7103
Bulgaria Wheat 0.0025 0.0150 0.0150
Chad Wheat 11.6058 5.6335 11.6058
Cyprus Wheat 3.4053 1.8975 3.4053
Denmark Wheat 0.3297 0.8462 0.8462
Egypt Wheat 0.0025 0.5838 0.5838
Eritrea Wheat 0.0014 0.5571 0.5571
Estonia Wheat 0.0000 0.0716 0.0716
Ethiopia Wheat 0.0718 2.3241 2.3241
Finland Wheat 2.2748 1.6284 2.2748
France Wheat 0.0023 1.1445 1.1445
Germany Wheat 0.0192 0.1601 0.1601
India Wheat 0.0229 0.3715 0.3715
Iran (Islamic Republic of) Wheat 16.3496 12.2120 16.3496
Iraq Wheat 0.0917 0.3760 0.3760
Ireland Wheat 0.0182 0.2235 0.2235
Kazakhstan Wheat 0.0046 0.0688 0.0688
Kuwait Wheat 0.0010 0.2056 0.2056
Latvia Wheat 0.3725 1.1474 1.1474
Lebanon Wheat 0.0038 2.2816 2.2816
Lithuania Wheat 0.2766 2.0837 2.0837
Luxembourg Wheat 0.3384 0.7585 0.7585
Madagascar Wheat 0.0515 1.3134 1.3134
Mali Wheat 0.1568 0.5043 0.5043
Malta Wheat 4.0475 2.2301 4.0475
Mauritania Wheat 2.2279 1.3943 2.2279
Morocco Wheat 0.1365 1.4018 1.4018
Mozambique Wheat 2.8814 1.8203 2.8814
Namibia Wheat 0.0092 0.4881 0.4881
Nepal Wheat 7.3239 4.3433 7.3239
Netherlands Wheat 0.8391 0.5974 0.8391
New Zealand Wheat 2.2414 1.3103 2.2414
Niger Wheat 0.0144 0.2035 0.2035
Nigeria Wheat 0.0700 0.4898 0.4898
Norway Wheat 1.6786 0.9592 1.6786
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Global database of GHG emissions related to feed crops - A life cycle inventory
Oman Wheat 2.9637 2.3977 2.9637
Pakistan Wheat 0.0383 0.9641 0.9641
Paraguay Wheat 0.0279 0.2155 0.2155
Peru Wheat 0.8798 0.5046 0.8798
Republic of Korea Wheat 6.5096 3.5688 6.5096
Republic of Moldova Wheat 7.3697 4.3638 7.3697
Romania Wheat 0.1102 3.2678 3.2678
Russian Federation Wheat 0.1597 0.5398 0.5398
Rwanda Wheat 0.0212 0.2004 0.2004
Somalia Wheat 0.0000 0.0246 0.0246
Swaziland Wheat 1.9779 4.6234 4.6234
Sweden Wheat 9.5171 4.9785 9.5171
Syrian Arab Republic Wheat 0.0054 0.4238 0.4238
Tajikistan Wheat 0.0063 0.6163 0.6163
Thailand Wheat 0.0190 0.3224 0.3224
Turkmenistan Wheat 0.0096 1.0507 1.0507
Uganda Wheat 1.4922 1.8438 1.8438
Ukraine Wheat 0.3893 1.1875 1.1875
United Republic of Tanzania Wheat 4.5315 3.3175 4.5315
Uruguay Wheat 0.0088 0.1433 0.1433
Uzbekistan Wheat 9.7610 5.8859 9.7610
Yemen Wheat 1.0122 2.7741 2.7741
Zambia Wheat 0.0245 0.9722 0.9722
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Under minimal tillage production practiceCountry Crop weighted average Normal average worst case
Argentina Barley 3.1240 1.7263 3.1240
Australia Barley 0.4888 1.7645 1.7645
Croatia Barley 0.0000 0.4738 0.4738
Slovenia Barley 0.0000 0.8155 0.8155
Argentina Maize 0.7590 0.4192 0.7590
Australia Maize 0.0839 0.3004 0.3004
Austria Maize 0.0000 0.0359 0.0359
Belgium Maize 0.0000 0.2735 0.2735
Bosnia and Herzegovina Maize 0.0000 0.1679 0.1679
Brazil Maize 0.2081 0.1401 0.2081
Canada Maize 0.0000 0.0696 0.0696
Germany Maize 0.0000 0.2900 0.2900
Italy Maize 0.0000 0.0925 0.0925
Luxembourg Maize 0.0000 0.3356 0.3356
Netherlands Maize 0.0000 0.2617 0.2617
United States of America Maize 0.0000 0.0904 0.0904
Argentina Soybeans 4.3854 2.4227 4.3854
Austria Soybeans 0.0000 0.4595 0.4595
Bosnia and Herzegovina Soybeans 0.0000 0.1208 0.1208
Brazil Soybeans 2.8851 1.9349 2.8851
Canada Soybeans 0.0000 1.4230 1.4230
Croatia Soybeans 0.0000 1.1519 1.1519
Slovenia Soybeans 0.0000 1.6780 1.6780
The former Yugoslav Republic of Macedonia Soybeans 0.0000 2.3518 2.3518
United States of America Soybeans 0.0000 0.4989 0.4989
Australia Wheat 0.6217 2.2370 2.2370
Austria Wheat 0.0000 0.1471 0.1471
Belgium Wheat 0.0000 0.0300 0.0300
Denmark Wheat 0.0000 0.2767 0.2767
Estonia Wheat 0.0000 1.1870 1.1870
Finland Wheat 0.0000 0.5113 0.5113
France Wheat 0.0000 0.0807 0.0807
Germany Wheat 0.0000 0.1906 0.1906
Ireland Wheat 0.0000 0.1043 0.1043
Latvia Wheat 0.0000 1.1011 1.1011
Lithuania Wheat 0.0000 0.6493 0.6493
Luxembourg Wheat 0.0000 0.3017 0.3017
Malta Wheat 0.0000 1.2168 1.2168
Netherlands Wheat 0.0000 0.0956 0.0956
Norway Wheat 0.0000 0.4708 0.4708
Sweden Wheat 0.0000 0.2788 0.2788
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Under no tillage production practiceCountry Crop weighted average Normal average worst case
Argentina Barley 2.9971 1.5995 2.9971
Australia Barley 0.4190 1.6946 1.6946
Croatia Barley 0.0000 0.3917 0.3917
Slovenia Barley 0.0000 0.6705 0.6705
Argentina Maize 0.7286 0.3888 0.7286
Australia Maize 0.0717 0.2882 0.2882
Austria Maize 0.0000 0.0296 0.0296
Belgium Maize 0.0000 0.2343 0.2343
Bosnia and Herzegovina Maize 0.0000 0.1390 0.1390
Brazil Maize 0.2017 0.1338 0.2017
Canada Maize 0.0000 0.0590 0.0590
Germany Maize 0.0000 0.2372 0.2372
Italy Maize 0.0000 0.0813 0.0813
Luxembourg Maize 0.0000 0.2942 0.2942
Netherlands Maize 0.0000 0.2082 0.2082
United States of America Maize 0.0000 0.0761 0.0761
Argentina Soybeans 4.2073 2.2483 4.2073
Austria Soybeans 0.0000 0.3754 0.3754
Bosnia and Herzegovina Soybeans 0.0000 0.0976 0.0976
Brazil Soybeans 2.8099 1.8596 2.8099
Canada Soybeans 0.0000 1.1974 1.1974
Croatia Soybeans 0.0000 0.9564 0.9564
Slovenia Soybeans 0.0000 1.3792 1.3792
The former Yugoslav Republic of Macedonia Soybeans 0.0000 2.0640 2.0640
United States of America Soybeans 0.0000 0.4173 0.4173
Australia Wheat 0.5350 2.1504 2.1504
Austria Wheat 0.0000 0.1200 0.1200
Belgium Wheat 0.0000 0.0260 0.0260
Denmark Wheat 0.0000 0.2211 0.2211
Estonia Wheat 0.0000 0.9809 0.9809
Finland Wheat 0.0000 0.3964 0.3964
France Wheat 0.0000 0.0666 0.0666
Germany Wheat 0.0000 0.1556 0.1556
Ireland Wheat 0.0000 0.0862 0.0862
Latvia Wheat 0.0000 0.9220 0.9220
Lithuania Wheat 0.0000 0.5293 0.5293
Luxembourg Wheat 0.0000 0.2647 0.2647
Malta Wheat 0.0000 1.1736 1.1736
Netherlands Wheat 0.0000 0.0760 0.0760
Norway Wheat 0.0000 0.3829 0.3829
Sweden Wheat 0.0000 0.2170 0.2170
Global database of GHG emissions related to feed crops
A life cycle inventory
VERSION 1
http://www.fao.org/partnerships/leap I8275EN/1/12.17
LIVESTOCK ENVIRONMENTAL ASSESSMENT AND PERFORMANCE PARTNERSHIP