Global Database of GHG Emissions Related to Feed Crops a Life Cycle Inventory

VERSION 1

Global database of GHG emissions related to feed crops A life cycle inventory

LIVESTOCK ENVIRONMENTAL ASSESSMENT AND http://www.fao.org/partnerships/leap I8275EN/1/12.17 PERFORMANCE PARTNERSHIP

version 1

Global database of GHG emissions related to feed crops A life cycle inventory

FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS Rome, 2017 Recommended Citation FAO. 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.

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FAO information products are available on the FAO website (www.fao.org/publications) and can be purchased through [email protected] 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

iii 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

E mission 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/kgM D *year), 2010 75

iv 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 research 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 analysis and the report. We would like to acknowledge the support of Camillo de Camil- lis, Claudia Ciarlantini and Claudia Nicolai.

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 methodology and datasets. The goal of this assessment is to develop a robust life cycle inventory (LCI) and emission intensity database. Specific objectives were to establish 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 carbon 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 information on the life cycle inventory per crop and the emission intensities associated 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 disaggregated 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 application rates, pesticide use and information on field processes and machinery use.

Global database of GHG emissions related to feed crops - A life cycle inventory 2. Data and data collection methods

2.1 DaTAbase overview The 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 pesticides. 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 processes This section reports, per crop, per production system and practice, and per country, 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 Intensity In 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 representativeness Geographic 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 production and the reference year of the database is taken as 2010. Temporal representativeness is especially important for factors that can potentially vary over time such as:

3 Global database of GHG emissions related to feed crops - A life cycle inventory

Table 1: Tier-based classification of input data Input Tier 1 Tier 2 Tier 3 Average yields at national Yields at subnational level Spatial yield distributions Crop yields level (not production system (spatial information of yields disaggregated by system specific) available) (rainfed and irrigated) Total amount of fertilizer per Total amount by type of Amount by type of fertilizer Synthetic fertilizer nutrient (N, P, K) (not crop fertilizer per nutrient (N, P, K) applied per crop specific, national average) Total amount manure Total amount of manure Amount of N manure applied available for application available for application per crop, information on N Organic fertilizer (not crop specific, national (spatial information of N available to crop during first average) nutrients available) year Total amount of active Total amount of active Active ingredients applied per ingredients used per ha Pesticides ingredients applied per crop crop per production system (not crop specific, national (national average) and practice average) Total amount of lime applied Amount of lime applied per Amount of lime applied per Agricultural lime (not crop specific, national crop per country (national crop per production system average) average and crop specific) and number of applications Total amount of irrigated Total amount of irrigated Total amount of irrigated water utilized by water source Water for irrigation water utilized (not crop water utilised by water source (not crop specific, national specific, national average) per crop average)

• 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 collection The 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 collected/generate: • crop and crop residue yield • fertilizer utilization • pesticide utilization • Water use for irrigation • Lime application

4 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 gaps Data 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 practices Data 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.

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 production process results in a number of outputs including crop products and co-products 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 rates Plant propagation method can comprise of generative propagation i.e. by seed or vegetative using cuttings and tubers. Agricultural seed production requires additional 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 database 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 assess land suitability, potential attainable yields and potential production of crops under specified management and input levels, both for rain-fed and irrigated conditions. 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 disaggregated 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 between the conventional and conservation tillage systems. However, due to the

1 http://www.fao.org/nr/gaez/en/

7 Global database of GHG emissions related to feed crops - A life cycle inventory

Table 2: Dry matter fractions of crops Crop 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

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 fertilizers) 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 materials 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.

8 Global database of GHG emissions related to feed crops - A life cycle inventory

3.4.1 Data and data sources Synthetic Nitrogen Data 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 approach 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 consumption 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 average 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, IFA Statistics and FAOSTAT National No Central & South America, Asia, Africa 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 E. Europe Central & Australia W. Europe incl. Russian N. America Asia Africa S. America Federation 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).

9 Global database of GHG emissions related to feed crops - A life cycle inventory

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

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 Lime The 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 agricultural 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 included in the LEAP database are provided in Table 5. Because lime application on a given field will occur in intermittent years, it is assumed 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 gaps For several countries, information on synthetic fertilizer consumption and use of agricultural lime was not available. Two data sources were thus used to fill data gaps: dataset 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

10 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 lime Emission 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 nitrogen per hectare. Due to the lack of country and crop-specific data on manure production 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 lime

Region 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).

11 Global database of GHG emissions related to feed crops - A life cycle inventory

Table 7: Data sources for estimation of organic nitrogen from manure Data Source Robinson et al (2014) approach discribed in: Livestock distributions http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0096084 Nitrogen excretion rates IPCC, 2006: Tier 1 Manure management systems Global Livestock Environmental Assessment Model – GLEAM per species, country (http://www.fao.org/gleam/en/) Arable land and cultivated pastures FAOSTAT: land resources domain

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 gaps Information 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 information 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 factors

Emission factors for the estimation of N2O emissions during the application of manure are derived from 2006 IPCC guidelines.

3.6 Pesticides 3.6.1 Data and data sources The 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.

12 Global database of GHG emissions related to feed crops - A life cycle inventory

Table 8: Data sources for electric and diesel pumps efficiency Country 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 Hekmat, A. (2002) (Islamic Republic of) 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

3.6.2 Data gaps This 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 abstraction 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 model (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 factors Emission factors estimated for the abstraction of water from ground water sources for irrigation are provided in Annex 4 of this document.

13 Global database of GHG emissions related to feed crops - A life cycle inventory

Table 9: Regional emission conversion factors from electricity and diesel consumption Conversion factor for diesel Conversion factor for electricity Region or country (kg CO2-eq MJ-1) (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.

3.8 Machinery and equipment In crop production, tractors and other machinery are used during the cultivation 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 difference 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 sources To 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 mechanization. • 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

14 Global database of GHG emissions related to feed crops - A life cycle inventory

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 gaps For several countries information on current use of farm machinery was not available. In order to fill these data gaps, the following approach was adopted: • Within the same region: information on farm machinery use and data on frequency, 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.

3.9n E ergy The 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 nutrients, for tillage, harvesting, etc. • Energy use for irrigation of crops • On-farm machinery and equipment Emissions 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 production 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

15 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 regions

Region 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 transportation 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 energy 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 estimated 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 factors Emission factors estimated for direct land use change are provided in Annex 6 of this document.

16 Global database of GHG emissions related to feed crops - A life cycle inventory

Table 11: Data sources for estimating emissions from land use change Data type Source • Perennial and annual crops area harvested (ha) from 1991 to 2010 FAOSTAT, 2015. (http://faostat.fao.org/) • Area of forest, grassland and permanent meadows and pastures (ha) • Forest area (ha) by country Global Forest Resources Assessment 2010 • Above and belowground vegetation carbon From IPCC 2006, Chapter 6, Table 6.4 stocks of forest, grassland, and crops • Default reference (under native vegetation) soil organic C stocks (SOCREF) for mineral soils. From IPCC 2006, Chapter 2, Table 2.3 All values in tonnes C/ha in 0-30 cm depth. • Relative stock change factors (FLU, FMG, and FI) (over 20 years) for different management From IPCC 2006, Chapter 4, Table 5.5 activities on cropland • Country climate types (percentage) World map of climate types provided by the JRC • Country soil types (percentage) (http://eusoils.jrc.ec.europa.eu/projects/RenewableEnergy)

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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 2,500 5,000 km

Robinson projection - WGS84 Barley yield (tonnes per hectare) under rainfed conditions 1 - 2 2 - 4 4 - 6 > 6

0 2,500 5,000 km

Robinson projection - WGS84 Barley yield (tonnes per hectare) under irrigated conditions 1 - 2 2 - 3 3 - 6 > 6

27 Global database of GHG emissions related to feed crops - A life cycle inventory

0 2,500 5,000 km

Robinson projection - WGS84

Maize yield (tonnes per hectare) under irrigated conditions 1 - 4 4 - 8 8 - 12 > 12

0 2,500 5,000 km

Robinson projection - WGS84

Maize yield (tonnes per hectare) under rainfed conditions 1 - 3 3 - 6 6 - 9 > 9

28 Global database of GHG emissions related to feed crops - A life cycle inventory

0 2,500 5,000 km

Robinson projection - WGS84 Soybeans yield (tonnes per hectare) under irrigated conditions 1 1 - 2 2 - 3 > 3

0 2,500 5,000 km

Robinson projection - WGS84 Soybeans yield (tonnes per hectare) under rainfed conditions 1 1 - 2 2 - 4 > 4

29 Global database of GHG emissions related to feed crops - A life cycle inventory

0 2,500 5,000 km

Robinson projection - WGS84 Wheat yield (tonnes per hectare) under irrigated conditions 1 - 3 3 - 6 6 - 9 > 9

0 2,500 5,000 km

Robinson projection - WGS84 Wheat yield (tonnes per hectare) under rainfed conditions 1 - 2 2 - 4 4 - 6 > 6

30 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

31 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

32 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

33 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

34 Global database of GHG emissions related to feed crops - A life cycle inventory

Annex 3 P esticide application rates M- aximum, minimum and average values

Standard Minimum Maximum Average Country 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

35 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

36 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

Global database of GHG emissions related to feed crops - A life cycle inventory

Annex 4 Emission factors for abstraction of ground water (KgCO2/ha)

Groundwater abstraction Groundwater abstraction Country using diesel pumps 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

39 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

40 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

41 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

42 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 - Barley Country Activity Equipment Power usage frequency time MFC Conventional tillage Moldboard plough U.S.A., Canada Ploughing Tractor 2.5 1.6 14.8 or Disc plough Disk harrow and Seedbed prep. Tractor 1 0.9 6.6 Field cultivator Sowing/Planting Drill Tractor 1 0.9 3.5 Organic fertilizer Broadcaster Tractor 1 1.8 14.8 application Synthetic fertilizer Broadcaster Tractor 1.6 0.3 4.2 application 1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide Field sprayer Tractor 3.6 0.31 3 application Field sprayer Weeding (herbicide Tractor 3.2 0.31 3 spraying) Harvesting Combine harvester Self-propelled No tractor used 1 1.4 30.5 No-till and Minimal tillage NA (no-till) NA (no-till) 0 0 0 U.S.A., Canada Ploughing Chisel plow 14.9 Tractor (minimal tillage) 2 (minimal tillage) (time *MFC) Disk harrow and NA (no-till) 0 0 0 Seedbed prep. Field cultivator Tractor (minimal tillage) 1 1.7 22 3.7 Tractor (no-till) 1 Sowing/Planting Drill (time *MFC) Tractor (minimal tillage) 1 0.9 3.5 Organic fertilizer Broadcaster Tractor (no-till and minimal) 1 1.8 14.8 application Seed farrow/ Irrigation water No power 1.6 0 0 Synthetic fertilizer (no-till) application Broadcaster Tractor (minimal tillage) 1.6 0.3 4.2 (minimal tillage) 1.5 Liming Broadcaster Tractor (no-till and minimal) 0.33 (time *MFC) Pesticide Tractor (no-till) 5.5 0.31 3 Field sprayer application Tractor (minimal) 3.8 0.31 3 Field sprayer Tractor (no-till) 5.1 0.31 3 Weeding (herbicide spraying) Tractor (minimal) 3.4 0.31 3 Harvesting Combine harvester Self-propelled No tractor used 1 1.4 30.5 43 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 Moldboard plough U.S.A., Canada Ploughing Tractor 1.3 2.78 14.8 or Disc plough Disk harrow and Seedbed prep. Tractor 1 1.57 22 Field cultivator Sowing/Planting Drill Tractor 1 0.9 3.5 Organic fertilizer Broadcaster Tractor 1 1.8 14.8 application Synthetic fertilizer Broadcaster Tractor 1.8 0.3 4.2 application 1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide Field sprayer Tractor 3.0 0.31 3 application Field sprayer Weeding (herbicide Tractor 2.6 0.31 3 spraying) Harvesting Combine harvester Self-propelled No tractor used 1 5.1 30.5

No-till and Minimal tillage

NA (no-till) NA (no-till) 0 0 0 U.S.A., Canada Ploughing Chisel plow 25.9 Tractor (minimal tillage) 0.9 (minimal tillage) (time *MFC)

Disk harrow and Tractor (no-till) 0 0 0 Seedbed prep. Field cultivator Tractor (minimal) 1 1.57 22 3.7 Tractor (no-till) 1 Sowing/Planting Drill (time *MFC) Tractor (minimal) 1 0.9 3.5 Organic fertilizer Broadcaster Tractor (no-till and minimal) 1 1.8 14.8 application Seed farrow/ Tractor (no-till) 1.8 0 0 Irrigation water Synthetic fertilizer (no-till) application Broadcaster Tractor (minimal) 1.8 0.3 4.2 (minimal tillage) 1.5 Liming Broadcaster Tractor (no-till and minimal) 0.33 (time *MFC)

Pesticide Tractor (no-till) 4.1 0.31 3 Field sprayer application Tractor (minimal) 3.2 0.31 3 Field sprayer (herbicide Tractor (no-till) 3.6 0.31 3 spraying) Weeding Field sprayer (herbicide Tractor (minimal) 3.6 2.31 18.4 spraying) +Hoe Harvesting Combine harvester Self-propelled No tractor used 1 5.1 30.5

Notes: weeding = Field sprayer (herbicide spraying) + Hoe

44 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 Moldboard plough U.S.A., Canada Ploughing Tractor 4.5 1.6 14.8 or Disc plough Disk harrow and Seedbed prep. Tractor 1 1.7 22 Field cultivator Narrow row Sowing/Planting Tractor 1 1 3.5 precision seeder Organic fertilizer Broadcaster Tractor 1 1.8 14.8 application Synthetic fertilizer Broadcaster Tractor 1.1 0.3 4.2 application 1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide Field sprayer Tractor 2.5 0.31 3 application Field sprayer Weeding (herbicide Tractor 3.1 0.31 3 spraying) +Hoe Self-propelled No tractor Harvesting Combine harvester 1 1.4 30.5 used No-till and minimal tillage

NA (no-till) NA (no-till) 0 0 0 U.S.A., Canada Ploughing Chisel plow 14.9 Tractor (minimal tillage) 1.4 (minimal tillage) (time *MFC)

Disk harrow and Tractor (no-till) 0 0 0 Seedbed prep. Field cultivator Tractor (minimal) 1 1.7 22 4.10 Narrow row Tractor (no-till) 1 Sowing/Planting (time *MFC) precision seeder Tractor (minimal) 1 1 3.5 Organic fertilizer Broadcaster Tractor (no-till and minimal) 1 1.8 14.8 application Synthetic fertilizer Broadcaster Tractor (no-till and minimal) 1.1 0.3 4.2 application 1.5 Liming Broadcaster Tractor (no-till and minimal) 0.33 (time *MFC)

Pesticide Tractor (no-till) 2.6 0.31 3 Field sprayer application Tractor (minimal) 2.1 0.31 3 Field sprayer (herbicide Tractor (no-till) 2.4 0.31 3 spraying) Weeding Field sprayer (herbicide Tractor (minimal) 2.8 2.31 18.4 spraying) +Hoe Self-propelled No tractor Harvesting Combine harvester 1 1.4 30.5 used Notes: weeding = Field sprayer (herbicide spraying) + Hoe

45 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 Moldboard plough U.S.A., Canada Ploughing Tractor 2.5 1.6 14.8 or Disc plough Disk harrow and Seedbed prep. Tractor 1 1.7 22 Field cultivator Sowing/Planting Drill Tractor 1 0.9 3.5 Organic fertilizer Broadcaster Tractor 1 1.8 14.8 application Synthetic fertilizer Seed farrow / Tractor 1.6 0 0 application Irrigation water 1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide Field sprayer Tractor 3.6 0.31 3 application Field sprayer Weeding (herbicide Tractor 2.6 0.31 3 spraying) Self-propelled No tractor Harvesting Combine harvester 1 1.4 30.5 used No-till and Minimal tillage NA (no-till) NA (no-till) 0 0 0 U.S.A., Canada Ploughing Chisel plow 14.9 Tractor (minimal tillage) 2.3 (minimal tillage) (time *MFC)

Disk harrow and NA (no-till) 0 0 0 Seedbed prep. Field cultivator Tractor (minimal tillage) 1 1.7 22

1 0.9 3.5 Sowing/Planting Drill Tractor (no-till and minimal) 3.7 1 (time *MFC) Organic fertilizer Broadcaster Tractor (no-till and minimal) 1 1.8 14.8 application Chisel (minimal tillage) Synthetic fertilizer Tractor (no-till and minimal) 1.6 0 0 application Seed farrow/ Irrigation water (no-till) 1.5 Liming Broadcaster Tractor (no-till and minimal) 0.33 (time *MFC)

Pesticide Tractor (no-till) 5.0 0.31 3 Field sprayer application Tractor (minimal tillage) 4.9 0.31 3

Field sprayer Tractor (no-till) 4.0 0.31 3 Weeding (herbicide spraying) Tractor (minimal tillage) 3.4 0.31 3 Self-propelled No tractor Harvesting Combine harvester 1 1.4 30.5 used Note: These tools and implements have been developed for three levels of power usage: manual power (hand tillage), animal traction, mechanized power

46 Global database of GHG emissions related to feed crops - A life cycle inventory

References Crop 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/.

47 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, Moldboard Chile, Colombia, Mexico, Peru, Ploughing Tractor 1 1.6 14.8 plough Uruguay Sowing/Planting Row seeder Tractor 1 0.9 3.5 Organic fertilizer Spreader Tractor 1 6.9 15 application Synthetic fertilizer Broadcaster Tractor 0.5 0.3 4.2 application 1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide Disk Tractor 1 0.31 3 application Self-propelled Combine Harvesting No tractor 1 1.6 14.8 harvester used

Machinery: Central and South America - Cassava Country Activity Equipment Power usage frequency time MFC Conventional tillage Argentina, Brazil, Colombia, Moldboard Ploughing Tractor 1 1.6 14.8 Cuba, Paraguay plough Argentina, Brazil, Colombia, Tractor-drawn Seedbed prep. Tractor 1 1.6 14.8 Costa Rica rolling drum Argentina, Brazil, Colombia, Organic fertilizer 1.5 Costa Rica, Cuba, Ecuador, Broadcaster Tractor 0.33 application (time *MFC) Paraguay, Peru, Venezuela Synthetic fertilizer 1.5 Broadcaster Tractor 0.33 application (time *MFC) 1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide Tractor Tractor 1 0.31 3 application Self-propelled Combine Argentina, Brazil, Costa Rica Harvesting No tractor 1 1.6 14.8 harvester used Note: The following activities are manually/oxen performed: Sowing/Planting, Organic fertilizer application, Synthetic fertilizer application, Weeding, Harvesting. Argentina

48 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, Moldboard Ploughing Tractor 1 2.78 14.8 Cuba plough Ecuador, Paraguay, Bolivia, Chile, Colombia, Costa Rica, Guatemala, Moldboard Ploughing Tractor 0.33 2.78 14.8 Honduras, El Salvador, Venezuela, plough Uruguay Panama, Nicaragua Mexico Seedbed prep. Disk harrow Tractor 2 1.7 22 Brazil, Argentina, Peru, Mexico, Sowing/Planting Row seeder Tractor 1 0.9 3.5 Cuba Ecuador, Bolivia, Costa Rica, Venezuela, Uruguay Panama, Sowing/Planting Row seeder Tractor 0.5 0.9 3.5 Nicaragua, Guatemala Brazil, Argentina, Peru, Mexico, Organic fertilizer Spreader Tractor 1 6.9 15 Cuba, Ecuador, Guatemala application Synthetic fertilizer Broadcaster Tractor 1 0.3 4.2 application Brazil, Argentina, Peru, Mexico, Cuba, Ecuador, Paraguay, Bolivia, Chile, Colombia, Costa 1.5 Liming Broadcaster Tractor 0.33 Rica, Guatemala, Honduras, El (time *MFC) Salvador, Venezuela, Uruguay Panama, Nicaragua Brazil, Argentina, Peru, Mexico, Pesticide Disk Tractor 1 0.31 3 Cuba, Ecuador, Guatemala application Mexico Weeding Field sprayer Tractor 1 0.31 3 El Salvador, Ecuador, Bolivia, Costa Rica, Venezuela, Uruguay Weeding Field sprayer Tractor 0.3 0.31 3 Panama, Nicaragua Self-propelled Brazil, Argentina, Mexico, Peru, Combine Harvesting No tractor 1 1.6 14.8 Guatemala harvester used El Salvador, Ecuador, Bolivia, Self-propelled Combine Costa Rica, Venezuela, Uruguay, Harvesting No tractor 0.5 1.6 14.8 harvester Panama, Nicaragua, Guatemala used

49 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 Moldboard Argentina Ploughing Tractor 2 1.6 14.8 plough Brazil, Paraguay, Colombia, Moldboard Ecuador, Guatemala, Mexico, Ploughing Tractor 1 1.6 14.8 plough Uruguay, Venezuela Spring tine Brazil Seedbed prep. Tractor 2.8 0.9 6.6 harrow Spring tine Argentina Seedbed prep. Tractor 1 0.9 6.6 harrow Brazil, Argentina, Paraguay, Colombia, Ecuador, Guatemala, Sowing/Planting Row seeder Tractor 1 0.9 3.5 Mexico, Uruguay, Venezuela Organic fertilizer Spreader Tractor 1 6.9 15 application Brazil, Paraguay, Colombia, Synthetic fertilizer Ecuador, Guatemala, Mexico, Broadcaster Tractor 1 0.3 4.2 application Uruguay, Venezuela Brazil, Argentina, Paraguay, 1.5 Colombia, Ecuador, Guatemala, Liming Broadcaster Tractor 0.33 (time *MFC) Mexico, Uruguay, Venezuela Pesticide Argentina Field sprayer Tractor 3 0.31 3 application Pesticide Brazil, Paraguay Field sprayer Tractor 2.4 0.31 3 application Chisel/ Argentina Weeding Tractor 1 0.31 3 cultivator Self-propelled Brazil, Argentina, Peru, Combine Harvesting No tractor 1 1.4 30.5 Paraguay. Uruguay, Venezuela harvester used No-till

Brazil, Argentina, Paraguay Sowing/Planting 1 0.9 3.5 Organic fertilizer Spreader Tractor 1 6.9 15 application Synthetic fertilizer broadcaster Tractor 1 0.3 4.2 application 1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide Field sprayer Tractor 6 0.31 3 application Self-propelled Combine Harvesting No tractor 1 1.4 30.5 harvester used

50 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, Moldboard Ploughing Tractor 1 1.6 14.8 Cuba, Mexico, Peru Uruguay plough Chile, Mexico Disc harrow Seedbed prep. Tractor 1 1.7 22 Chile, Uruguay Field cultivator Brazil, Argentina, Mexico Sowing/Planting Row seeder Tractor 1 0.9 3.5 Narrow-rowed Chile Sowing/Planting Tractor 1 1 3.5 precision seeder Organic fertilizer Peru, Chile Spreader Tractor 1 6.9 15 application

Brazil, Argentina Synthetic fertilizer 0.5 Broadcaster Tractor 0.3 4.2 Mexico application 1 Brazil, Argentina, Chile, 1.5 Liming Broadcaster Tractor 0.33 Cuba, Mexico, Peru, Uruguay (time *MFC) Chile, Mexico, Peru Uruguay Pesticide application Field sprayer Tractor 1 0.31 3

Mexico Weeding Field sprayer Tractor 1 0.31 3 Combine Brazil, Argentina, Mexico Harvesting Self-propelled 1 1.6 14.8 harvester Chile Harvesting Loader wagon Tractor 1 1 6.3

51 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 methods 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.

52 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 Rotary harrow and Seedbed prep. Tractor 2 2 26.6 Field cultivator Seedbed combined Netherlands Seedbed prep. machine and Field Tractor 2 2 18.9 cultivator 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 1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide application Field sprayer Tractor 3 0.31 3 Field sprayer Weeding (herbicide spraying) Tractor 1 0.31 3 +Hoe Self-propelled Harvesting Combine harvester 1 1.4 30.5 No tractor used No-till and Minimal tillage NA (no-till) NA (no-till) 0 0 0 Ploughing Chisel plow Tractor 1 1 1.3 (minimal tillage) (minimal tillage) NA (no-till) 0 0 0 Rotary harrow and Seedbed prep. Tractor Field cultivator 1 2 26.6 (minimal tillage) Tractor (no-till) 1 1.2 Sowing/Planting Drill Tractor 1 0.9 3.5 (minimal tillage) Organic fertilizer application Broadcaster Tractor 1 1.8 14.8 Tractor (no-till) Synthetic fertilizer application Disks 1.5 0.3 4.2 Tractor (minimal tillage) Tractor (no-till 1.5 Liming Broadcaster 0.33 and minimal) (time *MFC) Tractor 4.7 (no-till) Pesticide application Field sprayer 0.31 3 Tractor 3.8 (minimal tillage) Tractor 4.1 (no-till) Weeding Field sprayer 0.31 3 Tractor 2.8 (minimal tillage) Self-propelled Harvesting Combine harvester 1 1.4 30.5 No tractor used

53 Global database of GHG emissions related to feed crops - A life cycle inventory

Machinery: Europe - Maize Country Activity Equipment Power usage frequency time MFC Conventional tillage Ploughing Moldboard plough Tractor 1 2.8 14.8 Turning plough and Netherlands Ploughing Tractor 1 2.3 14.8 Cambridge role Rotary harrow and Seedbed prep. Tractor 2 1.87 26.6 Field cultivator 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

1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide application Field sprayer Tractor 6 0.31 3 Switzerland Pesticide application Field sprayer Tractor 1.7 0.31 3 Field sprayer (herbicide Weeding Tractor 2 2.31 18.4 spraying) +Hoe Self-propelled Harvesting Combine harvester No tractor 1 5.1 30.5 used

Machinery: Europe - Soybean Country/region Activity Equipment Power usage frequency time MFC Conventional tillage Czechia, France, Hungary, Italy, Moldboard Republic of Moldova, Serbia, Ploughing Tractor 1 1.6 14.8 plough Romania, Ukraine Rotary harrow Seedbed prep. and Field Tractor 1 2 26.6 cultivator Sowing/ Row seeder Tractor 1 0.9 3.5 Planting Organic fertilizer Broadcaster Tractor 1 1.8 14.8 application Synthetic fertilizer Disks Tractor 1 0.3 4.2 application 1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide Field sprayer Tractor 1 0.31 3 application Weeding Field sprayer Tractor 2 0.31 3

Self-propelled Combine Harvesting No tractor 1 1.4 30.5 harvester used

54 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 Rotary harrow and Seedbed prep. Tractor 2 2 26.6 Field cultivator 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 1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide application Field sprayer Tractor 3 0.31 3 Field sprayer Weeding Tractor 1 0.31 3 (herbicide spraying) Self-propelled Harvesting Combine harvester 1 1.4 30.5 No tractor used No-till and Minimal tillage NA (no-till) NA (no-till) 0 0 0 Ploughing Chisel plow Tractor 1 1 1.3 (minimal tillage) (minimal tillage) NA (no-till) 0 0 0 Rotary harrow and Seedbed prep. Tractor Field cultivator 1 2 26.6 (minimal tillage) Tractor (no-till) 1 1.2 3.5 Sowing/Planting Drill Tractor 1 0.9 3.5 (minimal tillage) Organic fertilizer application Broadcaster Tractor 1 1.8 14.8 Tractor (no-till) Synthetic fertilizer application Disks 1.5 0.3 4.2 Tractor (minimal tillage) Tractor (no-till 1.5 Liming Broadcaster 0.33 and minimal) (time *MFC) Tractor 4.7 (no-till) Pesticide application Field sprayer 0.31 3 Tractor 3.8 (minimal tillage) Tractor 4.1 (no-till) Weeding Field sprayer 0.31 3 Tractor 2.8 (minimal tillage) Self-propelled Harvesting Combine harvester 1 1.4 30.5 No tractor used

55 Global database of GHG emissions related to feed crops - A life cycle inventory

References DAAS 2009. Danish Agricultural Advisory Service. Budgetkalkuler. 2009-2010. www.landbrugsinfo.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 cultivation 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 greenhouse gas emissions of feed production and utilization, Machinery use for cultivation. Wageningen University and Research Centre and Blonk Consultants, Lelystad/Gouda, The Netherlands.

56 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, Moldboard Ploughing Tractor 1 1.6 14.8 Tajikistan, Tunisia, Iraq, Syrian Arab plough Republic Morocco Seedbed prep. Disk harrow Tractor 2 0.9 6.6 Grain Algeria Sowing/ drill Tractor 1 0.9 3.5 Morocco, Armenia, Azerbaijan, Libya, Planting Seeder Tajikistan, Tunisia Organic Algeria fertilizer Broadcaster Tractor 1 1.8 14.8 application Morocco, Algeria Armenia Azerbaijan, Synthetic Libya, Tajikistan, Tunisia, Iraq, Syrian fertilizer Broadcaster Tractor 1 0.3 4.2 Arab Republic application Pesticide Field Morocco Tractor 1 0.31 3 application sprayer Field Egypt Weeding Tractor 1 0.31 3 sprayer Self-propelled Algeria, Morocco, Turkey, Armenia Combine Harvesting No tractor 1 1.4 30.5 Azerbaijan Libya Tajikistan, Tunisia harvester used

Machinery: West Asia and Northern Africa - Maize Country Activity Equipment Power usage frequency time MFC Conventional tillage Egypt, Azerbaijan, Iraq, Kazakhstan, 25.9 Kyrgyzstan, Libya, Syria, Tajikistan, Ploughing Chisel plow Tractor 1 (time *MFC) Turkey Seedbed prep. Disk harrow Tractor 1 0.9 6.6 Sowing/ Seeder Tractor 0.9 3.5 0.9 Planting Organic fertilizer Broadcaster Tractor 1 1.8 14.8 application Synthetic fertilizer Spreader Tractor 1 0.3 4.2 application Pesticide Field Tractor 1 0.31 3 application sprayer Weeding Hoe Tractor 2 2 15.4 Self-propelled Combine Harvesting No tractor 1 1.4 30.5 harvester used

57 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, Moldboard Kazakhstan, Armenia, Israel, Jordan, Ploughing Tractor 1 1.6 14.8 plough Iraq Egypt, Saudi Arabia, Uzbekistan, Kyrgyzstan, Azerbaijan, Tajikistan, Ploughing Chisel plow Tractor 1 1.6 14.8 Turkmenistan 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, Sowing/ Kazakhstan, Uzbekistan, Kyrgyzstan, Seeder Tractor 1 0.9 3.5 Planting Azerbaijan, Tajikistan,Turkmenistan Organic Algeria, , Israel, Jordan, Kazakhstan, fertilizer Broadcaster Tractor 1 1.8 14.8 application Synthetic Algeria, Morocco, Israel, Jordan, fertilizer Spreader Tractor 1 0.3 4.2 Kazakhstan, Iraq application Pesticide Field Morocco Tractor 1 0.31 3 application sprayer Field Weeding Tractor 1 0.31 3 sprayer Egypt Weeding Hoe Tractor 2 2 15.4 Self-propelled Algeria, Egypt, Uzbekistan, Morocco, Combine Harvesting No tractor 1 1.4 30.5 Iraq harvester used

58 Global database of GHG emissions related to feed crops - A life cycle inventory

References Akar, 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 conditions. 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 marginal 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

59 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 Moldboard plough South Africa Ploughing Tractor 1 1.6 14.8 and chisel plough 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 Tractor Tractor 1 0.3 4.2 application 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 Synthetic fertilizer South Africa tractor-drawn planters Tractor 1 0.3 4.2 application 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.

60 Global database of GHG emissions related to feed crops - A life cycle inventory

References Agenbag, 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 production 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.

61 Global database of GHG emissions related to feed crops - A life cycle inventory

South Asia - Barley Country 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 Seed cum fertilizer Sowing/Planting Tractor 0.5 3.5 0.9 drill Synthetic fertilizer Seed cum fertilizer Tractor 1 0.3 4.2 application drill 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 - Maize Country 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 - Soybean Country Activity Equipment Power usage frequency time MFC Conventional tillage India, Ploughing Moldboard plough Tractor 0.5 15.9 14.8 Iran (Islamic Republic of) 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 - Wheat Country Activity Equipment Power usage frequency time MFC Conventional tillage Afghanistan Ploughing Moldboard plough Tractor 1 15.9 14.8 India, Pakistan, Ploughing Moldboard plough Tractor 0.5 15.9 14.8 Iran (Islamic Republic of) Pakistan, Ploughing Moldboard plough Tractor 0.3 15.9 14.8 Iran (Islamic Republic of) 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 - Cassava Tillage 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.

62 Global database of GHG emissions related to feed crops - A life cycle inventory

References Afreen, N., Haque, M. S. 2014. Cost benefits analysis of cassava production in Sherpur district 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.

63 Global database of GHG emissions related to feed crops - A life cycle inventory

E ast Asia - Barley The following activities are manually/oxen performed: Ploughing, Seedbed Prepa- ration, Sowing/Planting, Organic fertilizer application, Synthetic fertilizer application, Pesticide application, Weeding, Harvesting.

East Asia - Cassava Country Activity Equipment Power usage frequency time MFC Conventional tillage Moldboard Thailand Ploughing Tractor 1 1.6 14.8 plough Disk plow 13.5 Viet Nam Ploughing Tractor 0.3 (3disks) (time *MFC) Disk harrow Thailand Seedbed prep. Tractor 1 0.9 6.6 (7disks) Sowing/ Row seeder Tractor 1 0.9 3.5 Planting 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 - Maize Country Activity Equipment Power usage frequency time MFC Conventional tillage China, Viet Nam, Thailand, Moldboard Ploughing Tractor 1 2.78 14.8 Republic of Korea plough Cambodia, Lao People’s Democratic Moldboard Ploughing Tractor 0.5 2.78 14.8 Republic, Philippines plough Moldboard Indonesia, Myanmar Ploughing Tractor 0.25 2.78 14.8 plough Seedbed combined China, Republic of Korea Seedbed prep. machine Tractor 1 1.87 26.6 and Field cultivator Thailand, Lao People’s Democratic Field Seedbed prep. Tractor 0.5 1.87 26.6 Republic, Philippines cultivator China, Republic of Korea, Sowing/ Row seeder Tractor 1 0.9 3.5 Dem People’s Rep of Korea, Thailand Planting Lao People’s Democratic Republic, Sowing/ Row seeder Tractor 0.5 0.9 3.5 Vietnam Planting Organic China, Republic of Korea fertilizer Broadcaster Tractor 1 1.8 14.8 application Synthetic China, Republic of Korea, Thailand fertilizer Broadcaster Tractor 1 0.3 4.2 application Pesticide Field Republic of Korea Tractor 3 0.31 3 application sprayer Field sprayer Weeding Tractor 1 0.31 3 (herbicide spraying) Combine Self-propelled Harvesting 1 1.4 30.5 harvester No tractor used

64 Global database of GHG emissions related to feed crops - A life cycle inventory

East Asia - Soybean Country Activity Equipment Power usage frequency time MFC Conventional tillage Japan, Republic of Korea, Lao Moldboard Ploughing Tractor 1 1.6 14.8 People’s Democratic Republic plough China, Indonesia, Myanmar, Moldboard Vietnam, Thailand, Dem People’s Ploughing Tractor 0.5 1.6 14.8 plough Rep of Korea Rotary harrow Japan, Republic of Korea Seedbed prep. Tractor 1 2 26.6 and Field cultivator Japan, Republic of Korea, China, Lao People’s Democratic Republic, Sowing/ Row seeder Tractor 1 0.9 3.5 Dem People’s Rep of Korea, Planting Myanmar Sowing/ Vietnam, Thailand Row seeder Tractor 0.5 0.9 3.5 Planting Organic Japan, Republic of Korea fertilizer Broadcaster Tractor 1 1.8 14.8 application Organic Dem People’s Rep of Korea fertilizer Broadcaster Tractor 0.5 1.8 14.8 application Synthetic Japan, Republic of Korea fertilizer Disks Tractor 1 0.3 4.2 application Pesticide Field Tractor 1 0.31 3 application sprayer Field Weeding Tractor 2 0.31 3 sprayer Combine Self-propelled Harvesting 1 1.4 30.5 harvester No tractor used 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 Moldboard Japan Ploughing Tractor 1 1.6 14.8 plough China, Dem People’s Rep of Korea, Moldboard Ploughing Tractor 0.5 1.6 14.8 Mongolia plough Rotary harrow Japan Seedbed prep. Tractor 2 2 26.6 and Field cultivator Sowing/Planting Row seeder Tractor 1 0.9 3.5 China, Dem People’s Rep of Korea, Sowing/Planting Row seeder Tractor 0.3 0.9 3.5 Mongolia Organic fertilizer Japan Broadcaster Tractor 1 1.8 14.8 application Organic fertilizer Mongolia Broadcaster Tractor 0.5 1.8 14.8 application Synthetic Japan fertilizer Disks Tractor 1 0.3 4.2 application Pesticide Field Tractor 3 0.31 3 application sprayer Field sprayer Weeding Tractor 1 0.31 3 (herbicide spraying) Self-propelled Combine Harvesting No tractor 1 1.4 30.5 harvester used Self-propelled Combine Mongolia Harvesting No tractor 0.5 1.4 30.5 harvester used

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References Centro 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 cassava 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 Disk harrow and Seedbed prep. Tractor 2 2 26.6 Field cultivator Sowing/Planting Row seeder Tractor 1 0.9 3.5 Organic fertilizer Broadcaster Tractor 1 1.8 14.8 application Synthetic fertilizer Broadcaster Tractor 1 0.3 4.2 application 1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide application Field sprayer Tractor 3 0.31 3

Weeding Field sprayer Tractor 3 0.31 3 Self-propelled Harvesting Combine harvester No tractor 1 1.4 30.5 used No till and Minimal Tractor NA (no-till) 0 0 0 (no-till) Australia Ploughing Tractor 14.9 Chisel 1 (minimal) (time *MFC) Tractor NA (no-till) 0 0 0 (no-till) Seedbed prep. Tractor Field cultivator 1 0.8 (minimal) Narrow-rowed Tractor 4.1 1 precision seeder (no-till) (time *MFC) Sowing/Planting Tractor Row seeder 1 0.9 11.2 (minimal) Tractor Organic fertilizer Broadcaster (no-till) 1 1.8 14.8 application (minimal) Tractor Synthetic fertilizer Broadcaster (no-till) 1 0.3 3.5 application (minimal) Tractor Pesticide application Field sprayer (no-till) 3 0.3 3.5 (minimal) Tractor Weeding Field sprayer (minimal) 3 0.3 3.5 (no-till) Tractor 1.5 Liming Broadcaster 0.33 (minimal) (time *MFC) Self-propelled Harvesting Combine harvester No tractor 1 1.4 3 used

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Oceania - Maize Country Activity Equipment Power usage frequency time MFC Conventional tillage

Australia, N. Zealand Ploughing Moldboard plough Tractor 1 Disk harrow and Seedbed prep. Tractor 1 Field cultivator Sowing/Planting Row seeder Tractor 1 Organic fertilizer Broadcaster Tractor 1 1.8 14.8 application Synthetic fertilizer Broadcaster Tractor application 1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide application Field sprayer Tractor Weeding Field sprayer Tractor

Irrigation Self-propelled Harvesting Combine harvester No tractor 1 5.1 30.5 used

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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 Disk harrow and Seedbed prep. Tractor 2 2 26.6 Field cultivator Sowing/Planting Row seeder Tractor 1 0.9 3.5 Organic fertilizer Broadcaster Tractor 1 1.8 14.8 application Synthetic fertilizer Broadcaster Tractor 1 0.3 4.2 application 1.5 Liming Broadcaster Tractor 0.33 (time *MFC) Pesticide application Field sprayer Tractor 3 0.31 3

Weeding Field sprayer Tractor 3 0.31 3 Self-propelled Harvesting Combine harvester No tractor 1 1.4 30.5 used No till and Minimal tillage Tractor NA (no-till) 0 0 0 (no-till) Australia Ploughing Tractor 14.9 Chisel 1 (minimal) (time *MFC) Tractor NA (no-till) 0 0 0 (no-till) Seedbed prep. Tractor Field cultivator 1 0.8 15.4 (minimal) Narrow-rowed Tractor 4.1 1 precision seeder (no-till) (time *MFC) Sowing/Planting Tractor Row seeder 1 0.9 3.5 (minimal) Organic fertilizer Australia, N. Zealand Broadcaster Tractor 1 1.8 14.8 application Tractor Synthetic fertilizer (no-till) Australia Broadcaster 1 0.3 4.2 application Tractor (minimal) 1.5 (time Liming Broadcaster Tractor 0.33 *MFC) Tractor (no-till) Pesticide application Field sprayer 3 0.31 3 Tractor (minimal) Tractor (no-till) Weeding Field sprayer 3 0.31 3 Tractor (minimal) Self-propelled Harvesting Combine harvester No tractor 1 1.4 30.5 used

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References ABS (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 presented 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.

Global database of GHG emissions related to feed crops - A life cycle inventory

Annex 6 Emissions factors for land use change (tons CO2eq/kg DM*year), 2010

Under conventional tillage production practice Weighted Country Crop Normal average Worst case average 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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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

79 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

80 Global database of GHG emissions related to feed crops - A life cycle inventory

Under minimal tillage production practice Country 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

81 Global database of GHG emissions related to feed crops - A life cycle inventory

Under no tillage production practice Country 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

VERSION 1

Global database of GHG emissions related to feed crops A life cycle inventory

LIVESTOCK ENVIRONMENTAL ASSESSMENT AND http://www.fao.org/partnerships/leap I8275EN/1/12.17 PERFORMANCE PARTNERSHIP