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Energy Use in the Food Sector: A data survey

Energy Use in the Food Sector:
A data survey
Annika Carlsson-Kanyama
Environmental Strategies Research Group
Department of Systems Ecology
Stockholm University
Stockholm, Sweden
Mireille Faist
Department of Civil and Environmental Engineering
Swiss Federal Institute of Technology (ETH Zürich)
Zürich, Switzerland
The study presented here is a survey of data for estimating energy requirements in the food
sector. It contains a large number of data about the energy required for crop farming, animal
husbandry, food processing, storage, transportation and food preparation. The survey is based
on published material or information communicated directly to the authors. Recommendations
for further data surveys are made.
This database can be used for estimating the energy use for various food items over their life-
cycle. The applicability of the database is exemplified by estimating the energy requirements
of a hamburger with bread, lettuce, onions, cucumbers and cheese. The possibilities for
lowering the energy use of a hamburger are discussed briefly on the basis of the results.
We thank the following persons who contributed with useful suggestions for how to improve
the manuscript:
Gregor Dürrenberger, Swiss Federal Institute for Environmental Science and Technology
(EAWAG), Göran Finnveden, Environmental Strategies Research Group, Susanne Kytzia,
Swiss Federal Institute for Environmental Science and Technology (EAWAG), Charlotte
Lagerberg, Swedish University of Agricultural Sciences, Berit Mattsson, Swedish Institute for
Food and Biotechnology (SIK), Peter Steen, Environmental Strategies Research Group, Hans-
Erik Uhlin, University of Gävle, and Christine Wallgren, Environmental Strategies Research
This work was financed by the Foundation for Strategic Environmental Research in Sweden
the Swiss Federal Institute for Environmental Science and Technology in Dübendorf,
Switzerland and the Swiss Federal Institute of Technology (ETH) in Zürich, Switzerland
1. Introduction ..................................................................................................................5
1.1 Why this report? ...........................................................................................................5
1.2 Data quality ..................................................................................................................6
1.3 The structure of the report.............................................................................................6
2. Mass flows and energy use for a hamburger with bread and other ingredients ........7
2.1 Bread............................................................................................................................7
2.3 Dressing .......................................................................................................................9
2.4 Lettuce..........................................................................................................................9
2.5 Onions (freeze-dried)..................................................................................................10
2.6 Cucumber, pickled ................................................................................................11
2.7 Cheese........................................................................................................................11
2.8 Total energy use for a hamburger................................................................................12
3. The Data Survey .........................................................................................................13
3.1 Recipes.......................................................................................................................13
3.2 Loss and mass transformation coefficients ..................................................................13
3.3 Crop production..........................................................................................................15
3.4 Animal Husbandry (with some data on aquaculture and fisheries)...............................17
3.5 Food Processing and Food Preparation........................................................................19
3.6 Storage .......................................................................................................................21
3.7 Locations....................................................................................................................23
3.8 Energy: basic data.......................................................................................................24
3.9 Transportation.............................................................................................................25
3.10 Farm inputs...............................................................................................................25
4. Allocation ....................................................................................................................25
5. Conclusions .................................................................................................................27
6. References ...................................................................................................................27
Contents in the appendixes
Appendix 1: Mass flows and energy use of a hamburger with bread and other ingredients
Appendix 2: Recipes
Appendix 3: Data for losses and mass transformation coefficients
Appendix 4: Crop Production
Appendix 5: Animal Husbandry with aquaculture and fisheries
Appendix 6: Food Processing and Food preparation
Appendix 7: Storage
Appendix 8: Energy-basic data
Appendix 9: Transportation
Appendix 10: Farm inputs
1. Introduction
There is an increasing worry about the ecological consequences of consumption and
production patterns adopted by the wealthy nations (Loh et al, 1998, Parikh and Painuly,
1994). This worry is well documented in many international declarations and action plans
(e.g. United Nations, 1993) where more sustainable patterns of production and consumption
are called for.
Food is a basic human need, and therefore it is important to find ways to make food
consumption patterns sustainable. Food consumption affects the environment in numerous
ways. Throughout the life cycle of food, which includes agricultural production, storage,
transportation, processing, preparation and waste disposal, resources are used and emissions
are released to the environment (e.g. Andersson, 1998). The whole chain of food production
and consumption also uses a lot of energy: 1/5 of the total energy use in Sweden (Uhlin,
1.1 Why this report?
In the spirit of Agenda 21, numerous efforts have been made to inform consumers about the
environmental consequences of consumption choices. Such efforts have resulted in the
provision of Green Consumer Guides (e.g. Brower, 1999) or research projects where
consumers have been actively involved in striving for more sustainable consumption patterns
(e.g. the Perspectief project in the Netherlands). Such studies require large amounts of data
often laborious and/or hard to come by.
The idea to produce a survey of data about resource use in the food sector first came to our
minds in spring 1999 when attending a workshop on sustainable household consumption in
the Netherlands. 1 It became apparent that a new scientific research area was being
established: that of household consumption and its related environmental impacts. We heard
about several projects related to food consumption, all based on quantitative data from the
food sector. Unfortunately, the data used for making these estimations sometimes seemed
very hard to come by. In some cases research groups used quantitative estimations made by
others without having access to original data sources, and this, of course, makes a scrutiny of
results presented difficult.
As both of us have for some time been working with data collection about resource use in the
food sector we decided to pool our common knowledge into a report. We thought this useful
A lot of useful data is published in national languages not commonly spoken outside the
home country, something that is especially true for the Swedish material.
A lot of useful data is published in “grey” literature (reports, handbooks, memos) hard to
access through conventional literature databases
1 The second International Symposium on Sustainable Household Consumption, Household Metabolism: from
concept to application. Groningen-Paterswolde, the Netherlands, June 3-4, 1999.
By compiling several examples of e.g. a crop budget for the same crop one can get a
feeling of whether or not existing material indicates large deviations in resource use per
unit of product produced. If so, this may be an indication of that further data collection is
needed. Special care should be taken when estimating the resource use involving that
particular crop.
By systematically organising data it is possible to see in which areas there is already a lot
of information and where there is not.
By using the compiled data for analysing some food items it is possible to show the extent
to which results can vary depending on data divergences. These divergences are an
indication of the scope for lowering energy requirements by using resource efficient
technologies. They are also an indication of the uncertainties in an analysis.
This report should be seen as a first attempt to present an overview of data sources and data
that can be used by those groups or individuals interested in more sustainable patterns of food
consumption and production. Hopefully, the future will bring much more complete data
surveys where use of e. g. water and materials, not covered here, will be included as will more
updated figures for e. g. food processing.
1.2 Data quality
The data presented in this report are of two types:
Data from published material
Data that has been communicated directly to us.
We have not systematically controlled data quality in the sense that we did not trace the origin
of all data or cross-checked that all secondary data were referred to correctly. We would
welcome future data surveys with this ambition.
The general expectation is that processes become more resource efficient with time. Age of
data is therefore often a good indicator of how representative figures may be for analysing
current conditions. We have given information about data age whenever possible and we have
referred to the exact pages in publications so that the data can be accessed quickly.
1.3 The structure of the report
What data is needed to make an estimation of the energy used during the life-cycle of a food
item, here exemplified by a hamburger with bread and other ingredients? We have structured
our report around this question in an attempt to present the data survey in an instructive and
clear way. The analysis of the hamburger is presented in section 2 and Appendix 1. It is
followed by an account of the data survey (section 3) that we have organised in a sequence
logical for an analysis:
Recipes, section 3.1 and Appendix 2
Losses and mass transformation coefficients along the food production chain, section 3.2
and Appendix 3.
Information about the energy and material requirements of the processes needed for
bringing the hamburger to the consumer. That includes data about crop production
(section 3.3 and Appendix 4), animal husbandry (section 3.4 and Appendix 5), food
processing (section 3.5 and Appendix 6), storage (section 3.6 and Appendix 7) and
locations (section 3.7).
Complementary information needed for calculating the energy use of a hamburger during
its life-cycle. This includes data about energy coefficients (section 3.8 and Appendix 8),
energy use for transport (section 3.9 and Appendix 9) and production of farm inputs
(section 3.10 and Appendix 10).
Some guidelines for allocation are discussed in section 4. Section 5 lists some major
conclusions from the study and section 6 contains a list of references.
2. Mass flows and energy use for a hamburger with bread and other
The data presented in this report makes it possible to make a rough estimation of the energy
use during the life-cycle of a hamburger with bread and other ingredients. Our purpose with
this estimation is:
to see if the data-base presented in sections 3 in this report can be used for quickly making
an estimation of energy use over the life-cycle for various foods
to present two different levels of energy use for food that represent possible choices
concerning energy efficiency of appliances and processes during an analysis.
The hamburger ingredients are analysed one. Details about assumptions made are presented in
Appendix 1, Mass flows and energy use of a hamburger and other ingredients. The energy
estimates include conversion losses as well as production and delivery energy. The recipe,
which is the starting point of the analysis, is presented in Table 1.
Table 1: Recipe for a hamburger with bread and other ingredients (Mac Donalds
Sweden 1999, personal communication about a BigMac)
Ingredients kg/hamburger
Bread 0,0740
Hamburger 0,0900
Dressing 0,0200
Lettuce 0,0280
Onions (freeze dried) 0,0017
Cucumber (pickled) 0,0074
Cheese 0,0145
2.1 Bread
The mass flows for bread is presented in Table 2 and the energy use in Table 3. In each
calculation of the energy requirements we estimated a lowest and highest value so as to show
the range of the variations in the data.
We assume that the bread is frozen and put in storage for some time before preparation of the
hamburger. We do not estimate mass flows for ingredients other than wheat flour. From the
recipe of bread presented in Table 1, Appendix 2, it is obvious that wheat flour and water are
the main ingredients in bread while margarine, yeast, sugar and salt are minor inputs.
Table 2: Mass flows of hamburger bread
kg bread 0.074
kg bread to restaurant 0.078
kg bread to storage facility 0.078
kg bread baked 0.097
kg flour needed 0.067
kg wheat milled 0.083
kg wheat cultivated 0.083
Table 3: Energy use for hamburger bread (MJ per 74 grams bread)
Low, MJ High, MJ
Crop production incl.
0.17 0.24
Milling 0.03 0.39
Baking 0.45 1.0
Storage 0.31 1.6
Transportation 0.07 0.09
Total 0.96 3.2
The energy use per kg of hamburger bread becomes 13-44 MJ per kg in our example. Baking
and storage are the most energy consuming stages and transportation the least energy
consuming one. Assumptions about resource use during crop production, storage time and
transportation distances are equal in both examples.
The estimation of the mass flows for the hamburger is more complex than for bread because it
involves accounting for fodder needs of cattle. The mass flows are presented in Table 4 -5 and
the energy use in Table 6.
Table 4: Mass flows of a hamburger
kg meat 0.090
kg meat to frying table 0.093
kg meat to restaurant 0.11
kg meat to storage facility 0.11
kg meat to cutter 0.14
kg animal to slaughter house 0.23
kg of feed consumed 1.45
Table 5: Feed requirements for a hamburger (Appendix 5, Table 7a)
Feed composition kg/hamburger
Cereals 0.68
Protein fodder 0.043
Coarse fodder, DM 0.72
Pasture on arable land, DM 0
Pasture, cutover, DM 0
In our example, we assumed that the meat came from a spring born calf that eats 2’728 kg of
feed before attaining a carcass weight of 265 kg. The feed consumption per kg live weight is
6.4 kg with a dressing yield of 62 %. The feed is supposed to be composed of barley (cereals),
fodder peas (protein fodder) and hey (coarse fodder). We assume that the amount of feed
consumed is equal to the amount of barley, peas and hey produced not considering losses
during feed preparation or farm losses.
Table 6: Energy use for a hamburger (MJ per 90 grams meat)
Low, MJ High, MJ
Crop production, drying, fodder
3.5 5.0
Stable, slaughtering, cutting 0.23 1.4
Grinding, freezing 0.12 0.16
Storage 0.45 2.3
Frying 0.79 1.0
Transportation 0.44 0.59
Total 5.6 10
The energy use per kg of hamburger becomes 62-116 MJ per kg in our example. Crop
production, drying and fodder production are the most energy demanding stages followed by
storage and frying. We have assumed that the hamburger is frozen after processing.
Assumptions about resource use during crop production, storage time and transportation
distances are equal in both examples.
2.3 Dressing
As we did not have any recipe for dressing, we omitted this ingredient from the analysis.
2.4 Lettuce
The mass flows for lettuce (Table 7) are fairly easy to analyse as this ingredient is of
vegetable origin and has not been processed.
Table 7: Mass flows for lettuce
kg lettuce 0.028
kg lettuce to restaurant 0.039
kg lettuce harvested 0.039
The energy use for lettuce (Table 8) show high variations due to the cultivation methods
assumed: open ground or in greenhouse. The energy use per kg of lettuce varies between 3.4-
160 MJ per kg. For lettuce produced in greenhouse, it is the crop production stage that is the
most energy demanding. Assumptions about storage time and transportation distance are the
same in both examples.
Table 8: Energy use for lettuce (MJ per 28 grams lettuce)
Low, MJ High, MJ
0.04 4.27
Storage 0.02 0.05
Transportation 0.04 0.04
Total 0.09 4.36
2.5 Onions (freeze-dried)
The mass flows for freeze-dried onions (Table 9) shows that it takes about 12 kg of fresh
onions to obtain one kg of freeze-fried onions when losses during storage and processing etc.
are accounted for.
Table 9: Mass flows for freeze-dried onions
kg onions 0.0017
kg onions to restaurant 0.0021
kg onions to storage facility 0.0021
kg onions entering processing in freeze-dry plant 0.017
kg onions delivered to freeze-dry plant 0.020
kg onions entering long-term storage 0.021
kg onions harvested 0.021
The energy use for freeze-drying onions has been estimated from data about fabrication of
potato flakes and freezing of foods in general. More accurate data on the freeze-drying
process would be needed for further analysis, especially since freeze-drying seem to be the
most energy consuming stage in the life-cycle of the onions studied. The energy use per kg of
freeze-dried onions varies from 32-62 MJ in our example. Assumptions about crop budget,
storage time and transportation distances were equal in both energy estimates. Energy use for
storage after processing (in room temperature) has not been estimated (Table 10).
Table 10: Energy use of freeze-dried onions (MJ per 1.7 grams freeze-dried onions)
Low, MJ High, MJ
crop production 0.012 0.015
freeze-drying 0.041 0.073
storage 0.0039 0.0093
transportation 0.0085 0.0109
Total 0.057 0.12
2.6 Cucumber, pickled
The mass flows for pickled cucumbers (Table 11) shows that about 2.5 kg of cucumbers are
harvested for every kg pickled cucumber in a hamburger. Data for canning of tomatoes were
used for the processing estimate and losses during storage of cucumbers prior to processing
were assumed to be zero.
Table 11: Mass flows for pickled cucumbers
kg cucumber/Big Mac 0.0074
Kg cucumber to restaurant 0.010
kg cucumber to storage facility 0.010
kg cucumber entering processing
in canning plant
kg cucumber delivered to canning
Kg cucumber harvested 0.019
The energy use for pickled cucumber varies from 6.2-7.6 MJ per kg in our examples (Table
12) where assumptions about crop budget, storage time and transportation distances are the
same. A storage time of 30 days prior to processing is assumed and data on energy use for
pickling is taken from estimates about canning of fruits and vegetables. As with the onion
example, it is the processing stage that is the most energy demanding. This is probably a
characteristic feature of many processed vegetable products. We have assumed that the
cucumbers were cultivated on the open ground.
Table 12: Energy use of pickled cucumber (MJ per 7.4 grams pickled cucumbers)
Low, MJ High, MJ
crop production 0.0074 0.0097
storage 0.0008 0.0074
pickling 0.02 0.032
transportation 0.014 0.0072
Total 0.046 0.056
2.7 Cheese
As with the hamburger, analysing mass flows for cheese includes accounting for fodder needs
of dairy cows. The mass flows for cheese (Table 13-14) shows that about 12 kg of milk are
needed for 1 kg of cheese in a hamburger. In our example we assumed that milk came from a
cow that eat 5’820 kg of feed while milking 7’300 kg of milk during one year. The feed is
supposed to be composed of barley (cereals), fodder peas (protein fodder) and hey (coarse
fodder and pasture). We assume that the amount of feed consumed is equal to the amount of
barley, peas and hey produced not considering losses during feed preparation or farm losses.
No allocation was made to the meat of the cow’s calf.
Table 13: Mass flows for cheese
kg cheese 0.015
kg cheese to restaurant 0.017
kg cheese to storage facility 0.017
kg milk to dairy plant 0.18
kg milk milked from cow 0.18
kg feed consumed 0.14
Table 14: Feed requirements for cheese (Appendix 5, Table 8a)
feed composition kg/hamburger
Cereals 0.037
Protein fodder 0.015
Coarse fodder 0.065
Pasture 0.022
Minerals 0.0005
The energy use per kg of cheese becomes 38-62 MJ per kg in our examples. Crop and fodder
production, milking and making cheese are the most energy demanding stages. Long-term
storage was not supposed to consume energy, as cheese is commonly stored in caves that
naturally hold suitable temperatures. Storage in a refrigerator during 15 days is included and
the transportation distances are the same in both examples (Table 15).
Table 15: Energy use for cheese (MJ per 15 grams cheese)
Low, MJ High, MJ
Crop production, drying, fodder
0.26 0.37
Milking, making cheese 0.16 0.32
Storage 0.01 0.07
Transportation 0.11 0.15
Total 0.54 0.90
2.8 Total energy use for a hamburger
When we summarise the analyses for the various ingredients in a hamburger, the resulting
energy use varies between 7.3-20 MJ (Figure 1). It is the hamburger itself that requires the
most energy followed by lettuce if this crop is cultivated in a greenhouse. The energy use for
the ingredients freeze-dried onions and pickled cucumber are minor when compared to the
total; together they represent only about 1 %.
Figure 1: Energy use for a hamburger (MJ per hamburger with bread, lettuce,
cucumbers, onions and cheese).
The variation in energy use is an indicator of the potential for lowering energy use by using
today’s most efficient technology in lorries, refrigerators and industrial processes. There are,
however, several other options for obtaining even lower energy values. Some of these are:
A higher utilisation level of animal body parts (less body parts for other uses than human
Burgers made of vegetables, chicken or fish
No lettuce from greenhouse
Shorter storage time for frozen ingredients such as bread and meat
3. The Data Survey
3.1 Recipes
Obtaining recipes is a crucial step for estimating resource use from food products and most
often the starting point of an investigation. Recipes are descriptions of the ingredients
necessary for preparing food products composed of several food items. We have not made any
attempt to make an extensive account of recipes here, as they can vary from product to
product. Recipes are relatively easily available from e.g. food packaging, cookbooks or food
industries information desks. Some few examples of recipes are given in Appendix 2 and the
recipe for the hamburger with bread and other ingredients was presented in Table 1.
3.2 Loss and mass transformation coefficients
Loss and mass transformation coefficients give information about the amounts of products
needed to obtain one food item out of another. Examples of mass transformations are the
production of flour out of grain or the production of meat or eggs through the metabolism of
grain or other fodder by animals. There are numerous types of transformations to be
considered for supplying consumers with a Western type of diet. Several of these have
multiple outputs, exemplified by oily crops, such as rape seed being transformed into oil and
Low High
Cucumber pickled
Onions, freeze dried
meal or fodder being transformed into milk and meat. Further, information about the
magnitude of food losses are necessary inputs in a mass balance. Food losses occur at all steps
when handling or storing food: quality deterioration of fruit and vegetables during storage is
one example and losses of food during processing because food cling to equipment is another.
In Appendix 3, Table 1-14, there are losses and mass transformation coefficients for:
Food processing (Table 1a-c)
Food preparation (Table 2)
Food losses (Table 3)
Feed conversion (Tables 4-11)
Dressing shares (Table 12-13)
Animal body parts in percentage of live weight (Table 14)
Examples from food processing are the transformation of milk into cheese, which requires
about 10 litres milk/kg cheese,or the transformation of grain into flour, which requires 1.3 kg
grain/kg flour. Food processing is related to the industry while food preparation happens
mostly in households or restaurants.
Mass transformation coefficients for food preparation have information about weight losses
of food during cooking or frying, in the household or elsewhere. Examples are that it requires
1.28 units of raw chicken to obtain one unit of fried chicken or that it takes 1.25 units of
potatoes to obtain one unit of boiled and peeled potatoes.
Coefficients for food losses are rather scarce and needs to be complemented. On a global
basis, one quarter of the food entering the institutional and household distribution system is
lost. Levels of waste are closely correlated with levels of income, with little end use food
waste at low levels of income, but with 30-60 % of food requirements lost in high income
countries (Bender, 1994). Examples of waste levels from Table 3, Appendix 3, are that 1.2
units of meat is required for every unit of meat eaten and that for every unit of potatoes
“surviving” long-term storage, 1.22 units of potatoes have entered the storage facility.
Transformation coefficients for feed conversion (or feed consumption) is given per animal
for animals mostly used for breeding or feeding and per live weight or carcass weight for
animals normally slaughtered. The carcass weight of an animal is obtained by multiplying the
live weight with the dressing share 2 that varies from animal to animal and with feeding
practices. For example, the dressing share of cattle vary with feed composition as grazing
cattle have a heavier stomach content than cattle fed with grain do.
Feed conversion efficiencies vary: for egg production, between 2.2 and 2.7 kg of feed per kg
of egg may be needed. Generally, fish and broilers are the most efficient feed converters with
1.1-2.6 kg of feed per kg of carcass.3 Sheep are much less efficient with 12 kg of feed per kg
of carcass. Feed composition for different kinds of animals vary substantially as will be
shown in section 3.4, Animal Husbandry and Appendix 5. Table 13 in Appendix 5 have two
2 A definition of dressing percentage or dressing share is ”a measure of the percentage yield from slaughtered
animals derived by comparing the weight of a chilled carcass with its live weight” (Lipton, 1995).
4. For fish the data for this estimation is taken from Table 11, 1 kg of feed per kg of fish and from Table 13,
dressing share for salmon of 0.91 (1/0.91). For broilers the data for the estimation is taken from Table 5, 4.4 kg
of feed for a bird with a live weight of 2.3 kg and from Table 12, dressing share of a broiler 0.73 (4.4/2.3/0.73).
examples of inputs in aquaculture where the feed conversion rate is much less efficient than
the figures presented in Table 11, Appendix 3. Age of data may be one explanation for these
Ways of estimating the dressing share also varies from country to country: the inclusion or
exclusion of fat in the abdomen is one reason for these variations. Dressing shares for broilers
and pigs are higher than for cattle and sheep. One unit of a broiler gives 0.7 unit of carcass
while one unit of cattle gives 0.5-0.6 unit of carcass. Carcass weight is not always the same as
“eatable” meat. When broilers are sold whole, carcass weight and “eatable” amount of meat is
equal with no regards for household waste from skin and bones. For pork and beef, carcass
weight is not the same as eatable meat because blood and inedible parts are removed from the
carcass and never enter the dinner table (at least in Sweden). The taste and culture in human
societies vary however, with consequences for the demand of animal body parts. In Sweden,
intestines are not much in demand and sometimes exported to Africa where they are more
popular. Dog food is another destiny for unpopular animal body parts. When performing an
environmental analysis of food, the specific situation in the country or culture studied should
be considered for determining how resource use during slaughter and the other meat
processing steps should be allocated.
The Table about Animal body parts in percentage of live weight (Table 14, Appendix 3)
gives some basic information about the partitioning of various body parts from some common
livestock. This information can be used as a starting point when investigating the various
allocation options during animal husbandry and slaughtering.
Additional data for analysing mass flows can be found under section 3.3, Crop production and
section 3.4, Animal husbandry with aquaculture. From the former section, mass flows of
agricultural inputs such as fertilisers can be analysed. From the latter section mass flows of
specific fodder components can be established.
3.3 Crop production
Resources such as diesel, gasoline, fertilisers, pesticides and seeds are commonly needed
during crop production. Appendix 4 contains data about resource use and the respective yield
of a large number of crops. 4 The data is organised as follows:
Grains and legumes, with examples from 12 crops of major importance for world food
supply. For most of these crops, there are examples from several countries in the North.
Data for the following crops can be found: barley, corn, dry beans and peas, oat, peanuts,
rape seed, rice, rye, sorghum, soy beans, sunflower seed and wheat.
Fruit and vegetables with examples from 24 of the major vegetables, with several
examples both from cultivation on the open ground and in greenhouses. For many of
these crops there are examples from several countries. Data for the following crops can be
found: apples, bananas, beans green, broccoli, cabbage Chinese, cabbage white, carrots,
cucumbers, cherries, grapes, lettuce, onions, olives, oranges, palm fruits, parsley, peas
green, potatoes, strawberries, sweet pepper, red beets, sugar beets, squash and tomatoes.
Forage crops etc. with examples from fodder beets, hey, corn, silage and pasture.
4 Energy use is given as process energy. “Process energy is the energy input required and consumed by the
considered process to operate within the process phase, excluding production and delivery energy” (Audesley,
1997, p. 28).
With the help of this data it is possible to get a rough estimation of the variations in inputs per
unit of output for several crops. Soil and climate differ from country to country, as do
cultivation methods. Therefore it is natural to find variations in data for crop production.
Some examples of this are given below (Table 16):
Table 16: Litres of diesel per kg of crop during crop production
Litre diesel per kg crop Average Median Min Max
Wheat (9 observations) 0.018 0.017 0.013 0.034
Rape seed (10 observations) 0.043 0.038 0.027 0.063
Potatoes (9 observations) 0.0090 0.0093 0.0047 0.014
There are nine observations for wheat where diesel is the only energy input during crop
production (Table 1.11, Appendix 4). The average, median, minimum and maximum values
for diesel use per unit of wheat harvested, given in Table 16, shows that the maximum value
is almost three times as big as the minimum value. The highest value for wheat was found for
an organically produced crop (Switzerland) and the lowest for conventionally produced winter
wheat (Sweden). This difference is mainly due to the lower yield of organic production.
The 10 observations for rape-seed (Table 1.6, Appendix 4) shows that the average, median,
minimum and maximum values for diesel use per unit of rape-seed harvested (Table 16) are
more than twice as high as for wheat. This is mainly due to lower yields for rape-seeds. Both
the highest and lowest values in Table 16 were found for conventionally produced winter
rape-seeds that were grown in Sweden.
The nine observations for potatoes (Table 2.20, Appendix 4) shows that the average, median,
minimum and maximum values for diesel use per unit of potato harvested (Table 16) are at
least half those of wheat. This despite that diesel use per ha during potato cultivation is higher
but high yields counteract this. There are potato cultivation systems where the diesel use per
unit of harvest is as high as for wheat as well as systems where the diesel consumption is as
low as 0.005 litres/kg of potato.
These examples show that estimations about resource inputs in agriculture are subject to high
variations. Differences in climates and soils as well as cultivation methods influence the
resource use. However, the data collected here don’t allow any general comments about the
magnitude of this influence.
Resource use for cultivation on the open ground or in greenhouses differ substantially as can
be seen from e.g. Table 2.8 and 2.9, Cucumbers, Appendix 4. In Table 17, the inputs of fuels
in the two cultivation systems are compared per unit of output. The result shows that
cucumbers in greenhouses require more than 100 times the fuel needed for cultivation on the
open ground. Comparisons with similar results can be made for lettuce (Table 2.12 and 2.13,
Appendix 4), strawberries (Table 2.21-2.22, Appendix 4) and tomatoes (Table 2.27-2.28,
Appendix 4)
Table 17: Use of fuel for cultivation of cucumbers in the open and in greenhouses.
Cucumbers, open ground Cucumbers, greenhouse
Litres of fuel per m2 0.034 48
Harvest kg per m2 4.5 55
Litres of fuel per kg of crop 0.0076 0.87
Most fruits are produced from plants with a long lifetime (trees) and usually these crops have
to be maintained and cared for during several years before production on-set. Resource inputs
during those unproductive years should, ideally, be allocated to the production period of the
tree. However, data about resource inputs during establishment are not always available. In
Table 2.1, Appendix 4, there are three observations of resource inputs during the lifetime of
an apple orchard and seven observations with resource inputs during one productive year
only. When we examined the resulting levels of resource inputs per kg of output, we found no
systematic differences between these two kinds of observations, however. This indicates that
finding data about resource inputs during the establishment phase of fruit trees may not be
important. However, this conclusion may not be valid if lifetime of fruit trees is shorter than
in our examples.
It is imperative that more data on resource use during crop production becomes available so as
to better understand the magnitude of uncertainties in estimates of resource use for various
3.4 Animal Husbandry (with some data on aquaculture and fisheries)
Feed and water is given to animals and resources such as energy and materials are used for
providing them with a suitable climate and for giving them the necessary care. Resources are
also used for slaughtering, fodder preparation, fishing and aquaculture. In Appendix 5 the data
is organised as follows: 5
energy use for fodder production (Table 1-2)
feeding plans for various animals (Tables 3-9) with feeding plans for laying hens,
broilers, pigs (two types), sheep, bulls (five types), steers, fattening bull, milking cows
(six types) and heifers (six types).
energy use in animal shelters (Table 10)
energy use for slaughtering (Table 11a-d)
energy use for fishing (Table 12)
resource use in aquaculture (Table 13)
In Table 1, Appendix 5, figures on energy use for fodder production show a span of 0.26-
0.40 MJ per kg output for fodder ready for consumption. Two figures for drying of whey, a
by-product from cheese production commonly used as fodder, show relatively large energy
requirements due to the high water content in the fresh whey. Table 2, Appendix 5 contains an
estimation of the energy required to produce fishmeal, given as the energy used per kg of
input (1.09 MJ diesel). World production of fishmeal were 6’293’000 tonnes in 1990 and
during the same year the amounts of landed fish used for other purposes than human
consumption was 27’034’000 tonnes (Tacon, 1993, p. 50). This puts the amount of fish
needed to produce one unit of fishmeal to 4.3. According to a Swedish fishmeal factory, 5 kg
5 Energy use is given as process energy.
of fish is required for every kg of fishmeal (Västkustfisk SVC AB, pers. comm., -00). The
diesel use per kg of fishmeal may be 4.7-5.5 MJ.
Table 3-5, Appendix 5 has information about the feeding plans for hens, broilers and pigs,
Since all these animals are monogastric, their feeding plan is composed of cereals and protein
rich fodder from e.g. beans, peas or fishmeal. In Table 6-7, Appendix 5, feeding plans are
shown for the ruminants’ sheep and cattle. Fodder from grass etc. is a dominant ingredient in
the feeding plan for these animals, but there are some exceptions. The feeding plan for a
fattening bull (Table 7f, Appendix 5) shows that cereals constitute 66 % of the total feed with
coarse fodder accounting only for 7 %. This makeup of feeding plan is quite similar to those
for broilers and pigs. An intensive feeding plan for a bull in Switzerland however consists of
70% of silage; while fatstock fodder, a protein-rich fodder mixture, is only 25 % (Table 7g,
Appendix 5). On the other hand, the feeding plan for sheep is almost entirely composed of
coarse fodder and pasture, with cereals only 10% of the total.
In Tables 8 a-f, Appendix 5, there is information about feeding plans for milking cows in
Sweden and Switzerland. Cereals constitute 22-28 % of the total in the Swedish examples but
a minor share in the Swiss feeding plans where grass and silage dominates. Milk yield is also
different with lower yields in the Swiss examples. The Swiss and Swedish cases are examples
of systems with different intensities.
Tables 9a-f, Appendix 5, show feeding plans for heifers in Sweden and Switzerland. The
dominating ingredients are generally coarse fodder and pasture. Heifers give birth to their first
calf at the age of 24 to 30 months or when they weigh around 500 kg (live weight). In
intensive production systems a milking cow may be kept for 2.5 years before being
slaughtered and in less intensive the production time is about 5 years. When performing an
environmental analysis, fodder requirements for heifers need to be divided by milk produced,
the meat obtained and calves born. However, heifers may also be used as suckling cows or as
meat. The fate of heifers is important for knowing how fodder requirements for heifers should
be allocated in an analysis (se further section 4 for a discussion about allocation).
Data on energy use in shelters are shown in Table 10, Appendix 5. This table lacks data
about energy use for shelters with bulls, steers and broilers. The energy use for shelters with
pigs (two figures) deviate: from 0.41–1.8 MJ electricity per kg carcass produced. 6 It is not
known whether or not such variations are common. The reported electricity use for milking
and cooling equipment (four estimations) indicates a possible use of 0.2-0.7 MJ per kg of
milk. 7 Electricity use for egg production range from 0.72 -1.6 MJ per kg egg.
An energy-consuming phase during slaughtering is cooling the carcasses from +37o C to +4o
C. We found some figures relevant for slaughtering of cattle, baconers and poultry, shown in
Tables 11a-d, Appendix 5. The two observations of energy use for slaughtering of cattle vary
from 0.7 -3 MJ per kg carcass. 8
Efficiency of energy use for fishing in the sea varies from 3.4 -13 kg of fish caught per litre
of fuel spent (Table 12, Appendix 5). Compared to the efficiency during farm production
6 Assuming that the baconer weighs 100 kg when slaughtered and that the dressing yield is 0.71 (Table 12,
Appendix 3)
7 Assuming a milk yield of 7000 kg per year for estimations expressed in MJ per cow, year.
8 Assuming a dressing share of 0.60 for the slaughtering of cattle reported in Heiss (Table 11c, Appendix 5).
fishing appears less efficient with 0.077-0.29 litres of fuel used per kg of fish caught.
However, energy for producing inputs such as fertilisers during crop production is not
accounted for in that comparison.
Table 13, Appendix 5, has two examples of inputs in aquaculture. It is worth noting that the
efficiency of feed conversion rate in these examples 2.1-2.8 kg of feed per kg of fish – is
much less efficient than the figures presented in Table 11, Appendix 3. Age of data may be
one explanation for these differences.
3.5 Food Processing and Food Preparation
Food processing and preparation requires resources such as energy, water and materials.
Appendix 6 has data on energy use for various types of food processing and preparation
organised as follows: 9
1. Food processing
Baby food (Table 1.1)
Bread etc. (Table 1.2)
Breakfast cereals (Table 1.3)
Canning etc. (Table 1.4)
Chips (Table 1.5)
Chocolate (Table 1.6)
Coffee (Table 1.7)
Dairy products (Table 1.8)
Drying, energy per unit of water evaporated (Table 1.9)
Drying, energy per unit of dry crop (Table 1.10)
Freezing and cooling (Table 1.11)
Ice cream (Table 1.12)
Juice (Table 1.13)
Meat (Table 1.14)
Milling and polishing (Table 1.15)
Oil extraction and refining (Table 1.16)
Pasta (Table 1.17)
Peeling (Table 1.18)
Soft drinks and alcohol (Table 1.19)
Sugar and Candy (Table 1.20)
2. Food preparation
Food preparation in households (Table 2.1)
Food preparation in restaurants and industries (Table 2.2)
Food preparation: theoretical values based on producer information (Table 2.3)
The rather large number of observations about energy use for bread making (31) give
possibilities for discussing variations in energy inputs for this process. There are eight
observations of bread making where the only reported energy input is electricity. Energy use
in these examples varies between 1.53-4.56 MJ per kg of bread. Two observations of energy
use for baking bake-off baguettes at a retailer show energy uses between 1.22-1.87 MJ per kg
of bread. To obtain the complete picture of energy use for baking, figures on energy use for
pre-baking those products must also be added, but no such data are presented here. As bake-
9 Energy use is given as process energy.
off products are increasingly becoming popular, collecting such figures should be a priority in
further comparative studies of bread supply systems. A single figure for knäcke-bread shows
high energy requirements with 15 MJ electricity per kg of bread produced. Further data
collection could determine whether or not this level of energy use is representative.
The figures about energy use for breakfast cereals vary largely with figures from Pimentel
(1996, reference from 1977) adding up to 66 MJ per kg cereal and figures from Singh (1986)
of 19 MJ per kg output. It seems necessary to collect more recent data for these processes.
Figures about energy use for producing breakfast cereals are expected to vary with a higher
energy use for baked products than for those that are just mixed from inputs such as dry fruit
and cereal flakes.
According to Table 1.4, Appendix 6, canning of fruit and vegetables (three observations)
requires between 2.1- 3.8 MJ per kg output and canning of meat (three observations) between
5.2 - 25 MJ per kg output.
Three observations on energy use for chips fabrication (Table 1.5, Appendix 6) show little
variation with 11-15 MJ per kg output. All figures are of recent origin. A recent figure on
energy use from a Swedish plant for chocolate production is that 8.6 MJ are used per kg of
chocolate bar (Table 1.6, Appendix 6). One observation from fabrication of instant coffee is
that 50 MJ are needed per kg of coffee (Table 1.7, Appendix 6). Energy use for fabrication of
chocolate and coffee should be further investigated for a more reliable data material.
Dairy products seem, together with bread, to be among the most investigated products (Table
1.8, Appendix 6). For milk, there are seven observations where electricity is the only source
of energy input during milk processing and the use varies from 0.50-2.6 MJ per kg of milk
Drying is also a process for which there are relatively many observations. The theoretical
value for evaporating one kg of water is 2.60 MJ according to Pimentel (1996) who also
writes that the real energy use is 2-6 times higher than that, or 5.2-15.6 MJ. This statement
can be compared to the other data reported on energy use per kg of water evaporated in Table
1.9, Appendix 6. From these data, it seems that the real energy use is 2-3 times the theoretical
value proposed by Pimentel. The energy use per kg of dry crop (Table 1.10, Appendix 6)
depends, of course, on the water content before and after drying. One example is 6.4 MJ per
kg of output for drying beet pulp from 80 % to 10 % moisture content. Another example is
0.47 MJ per kg of output for drying soybeans from 17 % to 11 % moisture content. Five
observations of manufacturing of potato flakes and granules tell that 15-42 MJ per kg of
output may be used for these processes. For every kg of potato flakes, 5.3 kg of potatoes are
needed. 10 Potatoes usually contain 0.75-0.78 kg of water per kg and dried mashed potatoes
about 0.07 kg of water per kg. As 3.6-3.8 kg of water has to be evaporated for every kg of dry
potatoes produced, energy use for drying potatoes only may be in the order 19-20 MJ per kg
potato flakes. 11
10 Information from food packaging in Sweden - 00: one kg of potato powder contains 860 grams of dried and
mashed potatoes. 4.6 kg of potatoes may be needed for producing that amount (Appendix 3, Table 1b).
11 Assuming two times the theoretical energy value for evaporation of water: 2*2.60 MJ/kg of water*kg of water
evaporated. Water evaporated: 5.3 kg of potatoes*0.68-0.71 kg of water per kg.
Energy for freezing (Table 1.11, Appendix 6) are in the order of 0.3 MJ electricity per kg of
product frozen (two observations) while one observation from Pimentel (1996) gives a figure
of 7.6 MJ per kg of output. Ice cream production (Table 1.12, Appendix 6) with two
observations requires 2.2-3.7 MJ per kg output.
Two observations of energy juice for juice fabrication (Table 1.13, Appendix 6) give an
energy use of 1.15 MJ per kg output for juice made from concentrate and 4.6 MJ per kg of
output for juice made from fresh citrus fruits. Four observations of energy use for fabrication
of sausages range from 3.9-36 MJ per kg output because degree of processing for sausages
vary (Table 1.14, Appendix 6).
Milling is yet another process with relatively many observations (Table 1.15, Appendix 6).
Electricity use is between 0.32-2.58 MJ per kg of wheat flour according to 12 observations
where electricity is the only energy use recorded. Energy use for oil extraction (Table 1.16,
Appendix 6) recorded as energy per kg input is in the order of 0.28-1.5 MJ. Generally, two
products are obtained during oil extraction, oil and meal, and various allocation procedures
can be used to partition the energy use between those two outputs.
Pasta fabrication requires about 0.8-2.4 MJ per kg output (Table 1.17, Appendix 6) and
drinks between 2.4-6 MJ per kg output (Table 1.19, Appendix 6). Reported energy use for
sugar extraction (six observations) show a range of 2.3 - 26 MJ per kg output while
fabrication of candy (Table 1.20, Appendix 6) may require around 6 MJ per kg output.
There are several observations of energy use for food preparation in households (Table 2.1,
Appendix 6) where the level of energy use is 3-5 MJ per kg of output. Much lower values are
found for food prepared in microwave oven (four observations) where energy use per kg of
output is lower than 1 MJ. Data on energy use for food preparation in restaurants and
industries (Table 2.2, Appendix 6) show similar levels of energy use when similar food is
In Table 2.3, Appendix 6, where energy use for food preparation is given as theoretical
values for various appliances, it is possible to distinguish some basic characteristics about
equipment. Ovens, gas or electrical, are more energy consuming than plates on stoves and
much more energy consuming than microwave ovens. Wood stoves are the most energy
consuming appliances described in Table 2.3, Appendix 6, but rarely used for food
preparation in the North.
In conclusion, data about energy use for food processing show large variations both in terms
of energy used for different products and in terms of energy used for fabrication of similar
products. Some processes, such as bread baking, milk processing, milling and oil extraction
are relatively well documented here while data for other processes, such as wine making, are
missing. A more complete account of the various steps involved in food processing would be
valuable to get better estimations of the energy use.
3.6 Storage
Energy is used for keeping food at a desirable temperature during storage. Appendix 7 has
data on energy use for cold storage and storage in room temperature organised as follows: 12
12 Energy use is given as process energy.
Cold storage with energy requirements of refrigerator and freezers in households,
restaurants and industries (Tables 1.1-1.4).
Storage in room temperature with some energy use relevant for households, restaurants
and industries (Tables 2.1-2.2).
Table 1.1-1.2, Appendix 7, gives some examples of energy use for storage in refrigerators and
freezers in households. There are two types of sources: estimates from different studies and
producer information. Producer information gives mostly theoretical values about energy
requirements, whereas studies try to find out the real requirements. However, such studies are
often based on producer information with assumptions about e.g. room temperature or load of
Producer information tells e.g. that a ten-year old refrigerator uses 2.7 times as much energy
per litre usable volume as a new A-class one. 13 In an energy analysis of food it is therefore
important to examine the assumption made about equipment during storage. Further, it is
important to examine assumption about levels of utilisation as they can have decisive
influence on results. One example is the energy use for a 10 year old freezer, 0.029 MJ per
litre net volume, day which with only 50 % utilisation becomes 0.058 MJ per litre, day.
Assuming a storage time of 90 days, energy use for storage in the household becomes 5.2 MJ
per litre food. This level of energy use is comparable to those for juice or candy fabrication
(Appendix 6, Tables 1.13 and 1.20). If, on the other hand, energy use during storage is based
on assumptions about a new A-class freezer (0.012 MJ per litre net volume, day) with a 90 %
utilisation, energy use during 90 days is only 1.2 MJ per litre. This is less than a fourth of the
energy used in the first example.
Energy use for storage in refrigerators in restaurants, industries etc. (Tables 1.3-1.4, Appendix
7) has been estimated to 0.0025-0.082 MJ electricity per litre net volume, day. The age and
size of appliances explains such variations as well as the kinds of products stored. Long-term
cold storage of apples may consume between 0.0017-0.0009 MJ electricity per kg, day. This
low level of energy consumption means that even if apples are stored during one year, energy
use does not exceed 0.7 MJ.
Energy consumption in cold racks and other equipment where products are exposed to
consumers is much higher than for any other facility investigated. Cold racks at Swedish
retailers may use 0.12 MJ per litre usable volume, day and if assuming a utilisation rate of 75
% and a storage time of one week the energy use exceed 1 MJ per litre. That is more than for
the long-term storage of apples during one year.
Energy use for storage in freezers varies with freezer size as demonstrated by BELF (1983).
Energy use per litre net volume, day can be 0.0010 MJ when food is stored in rooms of
10’000 m3 while it can be 0.015 MJ when food is stored in rooms of 10m3. The difference is a
factor 15.
Energy use for storage in room temperature is naturally lower than for cold storage as no extra
energy is used for cooling already heated premises. Based on estimates of energy use for
heating Swedish average houses, the energy use for storage of products in room temperature
can be estimated to 0.00064 MJ per litre and day. Storing a litre of flour at home for a year
13 There is a labelling system for energy efficiency of household appliances within the European Union. The A-
label is for the most energy efficient appliances, while B,C and D labels indicate energy efficiency in descending
use 0.23 MJ if only energy for heating is accounted for. It can be discussed whether or not
household storage of food in room temperature should be included in an analysis. Food
occupies a minor share of household space, at least in today’s households.
In summary, assumptions about energy efficiency of equipment and utilisation levels are
important for the outcome of a study as is assumptions about cold or not cold storage. To our
knowledge, little is known about utilisation levels and more information about this would be
3.7 Locations
An important step in an analysis of resource use for food is to determine transportation
distances between consumers or producers etc. and therefore their geographical locations.
When locations have been established it is possible to proceed with estimations about
transportation distances which are the basis for estimates of resource use during
Sometimes it is straightforward to determine locations from producer information and no
further analysis is needed. But as our experience shows, producers often have only vague or
insufficient information about locations further up or further down the food-chain. Therefore,
we present some suggestions for how to determine locations when producer information is
1) In order to determine producer origin a method called the weighted average source point
(the WASP method) can be used. Figure 1 exemplifies how a WASP is calculated.
atitude or x
longitude or y
1 2 3 4 5 6
A; 1,3
B; 4,2
C; 1,1
= (50x1+10x4+40x1)/100
y = (50x3+10x2+40x1)/100 WASP; 1.3, 2.1
Amount of product
from each location
A= 50 units
B=10 units
C= 40 units
Total= 100 units
Figure 2: Tutorial box showing how to calculate the Weighted Average Source Points,
WASP (Carlsson-Kanyama. 1997a). The points A-C are locations where food is
produced and their co-ordinates are multiplied by the amount of food produced at each
location. From the equations indicated in the figure, a new pair of co-ordinates is
obtained that indicate the location of the WASP.
The WASP method can be used for estimating the location of farm production for a certain
crop. For example, the WASP for soy bean production in USA is located at 39o 42’ N and 89o
42’ W, close to Springfield, Illinois and the WASP of German rape seed production is located
at 51o 33’ N and 10o 54’ E, close to Sondershausen (Carlsson-Kanyama, 1998).
2) For determining consumer location the average consumption point in any country may be
used. In Sweden, this point, i.e. the WASP for population origin, is located at 59o 2’N and
15o 11’ E close to Svennevad and is sometimes called the centre of populations mass.
Some Atlases carry information about the centre of population mass and this location is
commonly calculated in many countries.
3.8 Energy: basic data
Information about densities of energy carriers, inherent energy and production and delivery
energy for energy carriers 14 are commonly needed for estimating resource use and emissions
from food. Such information are presented in Appendix 8 where the data is organised as
Densities of fuels (Table 1)
Inherent energy and production and delivery energy for energy carriers (Tables 2-9)
Energy in steam (Table 10)
Conversion efficiencies (Table 11)
Densities of fuels oils (Table 1, Appendix 8) vary between 0.84-0.94 kg per litre and density
of diesel vary from 0.84-0.95 kg per litre. An extended data survey for densities of fuels
could possibly reveal variations in densities for other types of fuels as well.
Estimations of the inherent energy content in energy carriers vary from e.g. from 46.1 to 51.9
MJ per kg for natural gas. Estimations of production and delivery energy for energy carriers,
expressed as parts of the inherent energy content, vary too. For natural gas the fraction that
should be added to the inherent energy content has been estimated to between 2 % and 9.1 %
and for diesel between 6 % and 9.5 %. Inherent energy in steam (Table 10, Appendix 8)
varies with temperature and pressure. Conversions efficiencies 15 reported here are between
0.89-1.04 for heat production and between 0.44-0.58 for power production. Steam production
may have a conversion efficiency of 0.8-0.9. A more elaborated data survey should include
estimations for conversion efficiencies for combined heat and power production as well as
explanations for variations in conversion efficiencies.
Electricity in a country has got various sources of production, e.g. water or atomic power.
When calculating the primary energy one has to take into account the different mix of sources
as they have different conversion efficiencies. Solar power or hydropower e.g. are more
efficient than electricity out of coal. In Frischknecht (1996) the calculated overall efficiency
for the European mix in Switzerland is 32%. In an earlier study of (Habersatter, 1991, p. 31) it
14 Inherent energy content is the extracted energy which remains in the product after its production and delivery
to its site of use (Audesley, 1997, p..28). Production and delivery energy is the energy into the processes which
extract, process, refine and deliver energy or material inputs to a process (Audesley, 1997, p..28)
15 According to Sullivan and Heavner (1981) conversion efficiency is ” the percentage of total thermal energy
that is actually converted into electricity by an electric generating plant”. Here, the term conversion efficiency
means ”the percentage of total energy delivered to a plant for the production of heat, power or steam that is
actually converted into heat, electricity or steam”.
was somewhat higher with 37,8 %. The effect of the choice of the electricity model in LCAs
is discussed in e.g. Ménard (1998).
3.9 Transportation
Appendix 9 has energy use for transportation with various vehicles organised as follows: 16
Vehicle classes for lorries (Table 1)
Energy use for transportation with lorries (Table 2)
Energy use for transportation with trains (Table 3)
Energy use for transportation with ships (Table 4)
Several of the reported data come from the Internet site This site is run by the Swedish organisation NTM
(Network for transportation and the environment). Energy use and emissions for both freight
and passenger transport can be accessed and the data are updated regularly.
In general, the data in Appendix 9 shows that energy use per unit of distance and freight
transported is lowest for large ships and highest for small trucks. Also, estimates of energy
use per tonne-km for the same type of vehicle varies: for diesel fuelled trains this difference is
almost a factor 4.
3.10 Farm inputs
A few examples of energy use for producing fertilisers are given in Appendix 10. Generally, it
is the production of N-fertilisers that are the most energy consuming with 40-63 MJ per kg of
N produced when conversion losses and production and delivery energy is included. The
corresponding values for P-fertilisers range from 10-39 MJ per kg P and for K-fertilisers from
5-12 MJ per kg K. Production of lime are in the range of 1-5 MJ per kg output.
Energy use for production of pesticides may range from 118-400 MJ per kg active ingredient
according to examples from pesticides used in wheat production (Audesley, 1997, p. 34). This
figure includes conversion losses and production and delivery energy.
Estimations of energy use for seeds and plants are not included but should be part of a more
extensive data survey.
4. Allocation
Allocation problems occur when dealing with multifunctional processes– a process that fulfils
more than one function. Examples are a production process with more than one product, a
waste management process dealing with more than one product, a waste management process
dealing with more than one waste flow, or a recycling process providing both waste
management and material production (Ekvall, 1999, Paper VI, p. 2).
When analysing the resource use of food, such problems may occur for combined meat and
milk production, for oil extraction (oil and meal) and for cheese production (cheese and
whey). It is important to remember that the same output may or may not be considered as a
product depending on time, time-scale and location. In situations when fertilisers are scarce,
16 Energy use is given as process energy.
animal manure can be considered as a valuable product, while the opposite situation may be
the case when artificial fertilisers are abundant.
The International Organisation for Standardisation (ISO) has presented a standard for Life-
Cycle Inventories (LCI) which may be of help for those wanting to proceed with estimations
of resource use of foods. This standard–ISO 14041–requires that the following procedure
should be used for allocation in multi-functional processes (from Ekvall, 1999, Paper VI, p.
Allocation should be avoided whenever possible, either through division of the multi-
functional process into sub processes and collection of separate data for each sub-process,
or through expansion of system boundaries until the same functions are delivered by all
systems compared.
Where allocation cannot be avoided, the allocation should reflect the physical
relationships between the environmental burdens and the functions i.e., how the burdens
are changed by quantitative changes in the functions delivered by the system.
Where such physical relationships alone cannot be used as the basis for allocation, the
allocation should reflect other relationships between the environmental burdens and the
Using the example of oil extraction (resulting in oil and meal), the ISO standard could be
interpreted as follows:
The first principle could be applied if resource use of an alternative to oil was known (e.g.
butter). Resource use for butter production could be subtracted from the resource use for oil
production and the resulting level would be relevant for meal.
The second principle hardly seem applicable to oil extraction. The third principle would
include various allocation options such as economic value, energy content in the outputs or by
allocation by mass. Carlsson-Kanyama (1998) allocated emissions for oil and meal based on
the weight of the respective outputs. This method would fall into the last category in the ISO
In practice, allocation between meat and milk in combined meat and milk production has been
carried out according to the principle of “biology” which is based on the casual relationship
between fodder input and outputs of milk and meat. This resulted in 85 % allocation to milk
and 15 % to meat (Cederberg, 1998, p. 13). This method seems to fit well into the description
of the second recommendation in the ISO standard. Moller and Hogaas (1997) showed two
possible ways of allocating meat and milk in combined meat/milk production systems with
very similar outcomes (Table 18).
Table 18: Allocation according to biological need and economic value (Moller and
Hogaas, 1997).
Biological need Economic value
Meat 65 66
Milk 35 34
5. Conclusions
The data presented in this report and its’ appendixes can be used for quick and rough
estimates of the energy use for various food products along the whole production chain.
Estimates such as the ones presented here, can be used to quickly illustrate some major
differences in energy use for foods. Such differences are e.g. animal contra vegetable
products, products cultivated in greenhouses or in the open, or fresh versus canned or
frozen products.
Estimates based on data presented here can be used for a simplified Life-Cycle
Assessment (LCA). A detailed LCA requires, however, system specific data. 17
More data on food losses, storage times, storage energy and food processing would be
particularly welcome for further studies.
Data collections of the kind presented here should be used with care; we strongly
recommend anyone who wants to make their own calculations to consult the original
6. References
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(Ed. ), International Application of Life Cycle Assessment in Agriculture, Food and Non-
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Andersson K. 1998. Life-Cycle Assessment (LCA) of Bread Produced on Different Scales:
Case study. AFR report 214, Swedish Waste Research Council, Swedish Environmental
Protection Agency, Stockholm, Sweden.
Andersson K. 1998. Life Cycle Assessment (LCA) of Food Products and Production Systems.
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Anonymous. 1999. Personal communication from a chips manufacturer in Scandinavia.
Anonymous. 1998. Personal communication from a soft drinks manufacturer in Scandinavia.
Anonymous. 2000. Personal communication from a refrigerator retailer in Sweden.
Arla Sweden. 1999. Personal communication from the information desk.
Audesley E. 1997. Harmonisation of environmental life cycle assessment for agriculture.
Final Report Concerted Action AIR3-CT94-2028. Silsoe Research Institute, Silsoe, UK.
17 According to Christianssen (1997, p. 9) a simplified LCA is the application of the LCA methodology for a
comprehensive screening assessment, .i.e. covering the whole life-cycle but superficial. A detailed LCA is an
application of the LCA methodology for a detailed, quantitative and mostly system specific study.
BAK, Bundesamt für Konjunkturfragen (Ed). 1992. Strom rationell nutzen: umfassendes
Grundlagewissen und praktischer Leitfaden zur rationellen Verwendung von Elektrizität -
RAVEL Handbuch. Zürich, Verlag der Fachvereine.
Balk-Spruit, E. M. and. Spigt, R. M. 1994. Kwantitatieve Informatie voor de Akkerbouw en
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... [Méndez, comunicación personal; Rodríguez, comunicación personal]), (c) para el girasol se consideró solo el peleteado (consumo de electricidad obtenido de Montero [2014]), (d) para el trigo se consideró el secado y la molienda (Carlsson-Kanyama et al., 2000;Donato, 2011). ...
... El rodeo se ordeña 2 veces por día durante 2,5 horas con máquinas de más de 10 años de antigüedad (Lazzarini et al., 2019). Se asumió un consumo eléctrico de 0,18 kWh/kg de leche (Carlsson-Kanyama et al., 2000). ...
Full-text available
Ante el gran desafío que implica alimentar a una población que crece, cambia sus dietas y estilos de vida, al mismo tiempo que se conservan los recursos naturales y protege la biodiversidad, se han propuesto múltiples estrategias para lograr la sustentabilidad de los sistemas alimentarios global y nacional. Sin embargo, las estrategias que dominaron los debates académicos y productivos históricamente han sido dos: (1) cambiar la forma de producir los alimentos en el campo con el objetivo de incrementar la eficiencia de los sistemas productivos, y (2) reducir las pérdidas y desperdicios a lo largo de la cadena agro-alimentaria. Pero a pesar de que se construyan modelos productivos capaces de proveer alimentos en cantidad suficiente y calidad adecuada, las elecciones de los consumidores pueden finalmente determinar la demanda de alimentos y, en consecuencia, el uso de los recursos naturales y el deterioro del ambiente. A esto se suma que las elecciones alimentarias también afectan a la salud humana de manera significativa y, junto con el sedentarismo, el tabaquismo y el consumo excesivo de alcohol son, en gran parte, responsables de la elevada prevalencia actual (y creciente) de las Enfermedades Crónicas No Transmisibles (ECNT). De esta manera, se ha propuesto que la adopción de dietas saludables representa una valiosa herramienta para contribuir a la mitigación de las crisis ambiental y de salud pública que estamos enfrentando. Dado que las dos primeras estrategias han sido contempladas en nuestro país y forman parte de la agenda académica, gubernamental y productiva, ésta Tesis se propone explorar la huella ambiental asociada a la adopción de dietas saludables en la Argentina, utilizando cinco indicadores de impacto ambiental: ocupación de total de la tierra, demanda de tierra de cultivos, emisión de gases de efecto invernadero, consumo de energía fósil y uso agua dulce. Dado el importante rol que tienen los productos animales sobre la huella ambiental de las dietas y los sistema alimentarios, primero se cuantificó la huella ambiental para producir los cinco principales alimentos de origen animal consumidos en Argentina (carne vacuna, porcina y aviar, leche y huevo) (Objetivo 1). Luego, se calculó la huella ambiental de la dieta argentina mediante un modelo del sistema alimentario nacional que conecta el consumo de alimentos en el hogar con la producción en el campo (Objetivo 2). Finalmente, se desarrollaron diferentes escenarios dietarios para analizar el efecto de la adopción de dietas saludables a nivel nacional sobre los indicadores mencionados anteriormente (Objetivo 3). Además, se analizó la calidad y el costo de las dietas a fines de enriquecer el análisis. El patrón alimentario actual en la Argentina es sorprendentemente homogéneo en todos los estratos socio-económicos y se caracteriza por un alto consumo de carnes rojas y procesadas, cereales refinados (particularmente panificados y galletitas), vegetales ricos en almidón, y ultra-procesados (incluyendo bebidas azucaradas), así como por una baja ingesta de frutas, verduras, legumbres, cereales integrales, pescado, frutos secos y semillas. Esto significa que la población argentina está lejos de tener una dieta saludable debido a la gran exposición a factores de riesgo dietarios relacionados con el desarrollo de ECNT. Además, dada la preferencia por los alimentos de origen animal (especialmente carne vacuna) y las particularidades de los sistemas productivos que los proveen, la dieta argentina presenta una muy alta emisión de GEI y ocupación de la tierra (totales y de cultivo), y un muy bajo consumo de energía fósil y agua dulce. La adopción de dietas saludables en la Argentina tiene el potencial de mejorar significativamente la salud de la población y de reducir algunos aspectos de la huella ambiental de los consumidores y del sistema alimentario nacional. En este sentido, la adopción de dietas saludables tiene el potencial de reducir las emisiones de GEI y la ocupación de la tierra hasta un 79% y un 88% respectivamente, principalmente debido a una disminución en el consumo de alimentos de origen animal (particularmente carne vacuna). Sin embargo, debido principalmente a un incremento en la demanda de verduras, frutas y frutos secos, también pueden aumentar el consumo de agua dulce y de energía fósil hasta un 200% y un 100%, respectivamente. Además, las dietas saludables son más costosas que la dieta promedio en Argentina, lo que implica que una importante proporción de la población no puede afrontar los gastos asociados. Pero, a pesar de esta generalidad, existe una diversidad en el impacto a la salud, la huella ambiental y el costo de las dietas saludables que deben ser considerados. Indudablemente, la adopción de dietas saludables representa un gran desafío en la Argentina debido a la mala calidad de la dieta actual y al profundo arraigo cultural asociado a la carne en general, y a la vacuna en particular. Sin embargo, dicha acción tiene el potencial de contribuir a mitigar la crisis sanitaria y ambiental que afronta el país. Aún así, sorprendentemente, incluso con una población dispuesta a adoptar una dieta saludable y un gobierno preparado para acompañar el proceso, el sistema alimentario nacional tiene importantes limitaciones a la hora de proporcionar los alimentos necesarios para toda la población. Por lo tanto, la alineación de las políticas de producción agrícola y ambiental con las de salud humana podría tener importantes beneficios sinérgicos.
... The grinding tests (Table 3), carried out in a pilot-scale mill using a 7.5-Mg sample of high-amylose bread wheat grains at Grandi Molini Italiani SpA (Venice, Italy;; 12 October 2022), involved an electricity consumption of about 1100 kWh, equivalent to 147 Wh/kg of grain milled, such specific consumption yield falling within the range of values detected by Carlsson-Kanyama and Faist [47]. Such energy consumption was included in the HABW flour production step (cf. ...
... (ii) The specific electric and thermal energy consumption yields during fresh pasta production were increased by +100% with respect to the default conditions (i.e., 200 kWh/Mg and 20 kWh/Mg, respectively). (iii) The default cooked pasta waste of 2% of cooked pasta [47] was enhanced by a factor of 10, as detected by Barilla [57]. ...
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To improve glycemic health, a high-amylose bread wheat flour fresh pasta characterized by a low in vitro glycemic index (GI) and improved post-prandial glucose metabolism was previously developed. In this study, well-known life cycle analysis software was used in accordance with the PAS 2050 and mid-and end-point ReCiPe 2016 standard methods to assess, respectively, its carbon footprint and overall environmental profile, as weighted by a hierarchical perspective. Even if both eco-indicators allowed the identification of the same hotspots (i.e., high-amylose bread wheat cultivation and consumer use of fresh pasta), the potential consumer of low-GI foods should be conscious that the novel low-GI fresh pasta had a greater environmental impact than the conventional counterpart made of common wheat flour, their corresponding carbon footprint or overall weighted damage score being 3.88 and 2.51 kg CO2e/kg or 184 and 93 mPt/kg, respectively. This was mainly due to the smaller high-amylose bread wheat yield per hectare. Provided that its crop yield was near to that typical for common wheat in Central Italy, the difference between both eco-indicators would be not greater than 9%. This confirmed the paramount impact of the agricultural phase. Finally, use of smart kitchen appliances would help to relieve further the environmental impact of both fresh pasta products.
... Similarly, the energy consumption of a pasta-producing facility has been reported as 1.1 kWh of thermal energy and 0.18 kWh of electricity per kg of pasta produced [23]. Carlsson-Kanyama and Faist [24] found similar energy consumption values at 0.22-0.67 kWh/kg of pasta produced. ...
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Escalating energy costs in resource-limited cassava growing regions impede industrial exploits, which contribute to high postharvest loss in the cassava value chain. Studies have uncovered potentials for replacing up to 50% of spaghetti wheat flour (WF) with cassava flour (CF) (i.e., cassava-wheat flour spaghetti (CWFS)). Modification of the CWFS scheme is proposed to eliminate the CF drying energy and explore the direct use of dewatered cassava pulp (DCP) and WF to produce spaghetti (i.e., cassava dough-wheat flour spaghetti (CDWFS)). However, uncertainties regarding the energy and product quality impacts of CDWFS compared to conventional wheat flour spaghetti (WFS) are foreseeable constraints to the industrial adoptions. Therefore, the referred impacts were analysed based on established schemes for the feedstock production (i.e., CF, WF, and DCP) and laboratory demonstrations of the spaghetti processing. All three schemes showed comparable product yields (≈0.665-0.671 kg/kg dough). Egg incorporation to augment the protein content in the CWFS and CDWFS also proved strategic for achieving comparable compositions (moisture, crude fiber, and carbohydrate), energy content, and cooking qualities (cohesiveness, adhesiveness, and water absorption) with commercial WFS products. The CWFS and CDWFS schemes could mitigate the process energy by 5.64% and 14.25%, respectively, compared to the WFS. Hence, the CWFS and CDWFS schemes are promising for energy cost reduction and advancing sustainable spaghetti industries in energy-resource-limited cassava growing regions.
... All the available literature presents the energy audit of agro-processing industries for particular locations which cannot be applied everywhere as energy consumption in any field depends on various parameters such as location of the energy user, on the type and quality of the raw agriculture product, the amount of processing needed by a particular product, the effectiveness of the processing machinery used in operation and even the skill of the labour used, etc. (Sunday & Aondover, 2013). Although researches have been carried out in some agro-processing industries for the evaluation of energy consumption (Carlsson-Kanyama & Faist, 2000;Konieczna et al., 2021;Sanusi & Akinoso, 2022), no study has been carried out for the small agro-firms to evaluate its energy consumption keeping in view reduction of its energy and cost incurred in processing. These firms do not have much facilities and face many challenges in energy management. ...
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India being more focused on its agricultural development requires information on energy input requirements for agro-based industries, where most of the energy is consumed by post-harvest operations. This study evaluates energy consumption by both the conventional method and the non-conventional method of agro-processing of maize on a small scale with the objectives of effective management of the limited resources in the agriculture, finding energy and cost-saving solutions for the two most energy- and cost-intensive post-harvest processes, providing energy-saving recommendations with suggestions for minimizing the involved cost of operation by focusing on the use of eco-friendly renewables in the agro-processing sector. Energy auditing with the help of energy tools and experimentally collected data was used for the energy analysis of the processes, and break-even analysis was used to find out the new selling price of the product using alternate energy sources for processing. The net energy and cost savings obtained through solar drying and solar milling were 545.38 GJ/yr, 50,017.50 GJ/yr, and USD 7,146.68, and USD 3,880, respectively. The findings reveal that drying and milling when accomplished by using solar energy offer perfect solution to much of the problem for small farm holders as well as for the environment by reducing 39.8 kg of CO2 emission. This research work will help to understand the use of energy analysis and the ways of its implementation in agro-based industries for the development of sustainable agriculture strategy. Policy directions provided help to improve policy regarding the integration of renewable energy in post-harvest processing operations.
... Research on energy consumption in the American food system was conducted by Hendrickson, who summarized energy data divided into food subsectors and also pointed to potential measures to reduce energy consumption [26]. In Sweden, research on energy consumption in the food sector was carried out by Carlsson-Kanyama and Faist [27]. They analysed food-processing data, taking into account very detailed information on products, processes, and energy sources. ...
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The aim of this study was to assess the energy consumption of production in selected branches of the food industry in Poland and to identify its changes after Poland’s accession to the EU. This issue is particularly important in the period of energy transformation and soaring energy prices. The novelty of this article is the determination of changes in the energy efficiency of various branches of the food industry. The main source of data was mass statistics data and unpublished data from the Central Statistical Office for 2004–2020. Descriptive statistics, comparative analysis, and strategic group mapping were used in the data analysis. The research shows that the production of foodstuffs is one of the most energy-intensive processing sectors. This results, among others, from many active enterprises in this sector and a large variety of industries. Individual food-processing industries are characterized by large differences in the energy consumption of production, which determines the different levels of electricity costs and affects the competitiveness of enterprises. In 2004–2019, the average electricity consumption in the food industry in Poland decreased by 31.5%. A greater increase in the value of sold production compensated for the higher energy consumption. This indicates an improvement in production efficiency and contributes to greater environmental protection. In the food sector, simple comparative advantages disappear in the form of lower production costs. This situation encourages processing companies to look for energy savings. The research results can be useful not only in Poland but also in other countries in shaping economic policy. The energy crisis caused by the war in Ukraine may require different actions to be taken against various sectors of the food industry.
... wholesaling and retailing were done by the same actor), that 10 days would elapse between slaughter and consumer purchase, and that the meat was transported 50 km in a truck with carbon dioxide refrigeration from the distribution point to the retail store. The electricity required for meat storage was set at 11.7 kWh per m 3 ( Carlsson-Kanyama 1999 ). We also assumed that pork would require 2.23 m 3 of space per tonne during transportation and storage ( Baker et al. 2012 ). ...
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Most existing life cycle assessment studies that have compared the sustainability of organic and conventional pork supply chains are environmental assessments. The economic and social sustainability dimensions of pork supply chains are currently under-researched. The study reported here was designed to assess the environmental, economic and social sustainability of conventional and organic pork in Sweden. Life Cycle Sustainability Assessment was undertaken using 20 indicators expressed per unit product (1000 kg pork fork weight) and per unit area (1000 ha of farmland) for the four main subsystems in pork supply chains: (1) farm and feed production, (2) slaughter, (3) wholesaling and retailing, and (4) consumption. The organic pork supply chain out-performed the conventional chain in 11 of the 20 indicators expressed per unit product and 18 of the 20 indicators expressed per unit area. It was therefore the more sustainable of the two chains in nearly all the indicators expressed per unit area. However, the organic supply chain was less sustainable in some of the indicators expressed per unit product because, more feed per kg of pork was required in organic pork production. Pig welfare improvement leads to higher production costs and environmental impacts. Assessment of all three sustainability dimensions – environmental, economic and social – helps to identify trade-offs between these three pillars of sustainability. However, the selection of indicators influences results, and obtaining environmental, economic and social data simultaneously is challenging.
... Carlsson-Kanyama and Faist [4] had reported a survey of data for estimating energy requirements in the food Sector. As per the study, energy consumption for oil extraction was recorded as in the range of 0.28-1.5 MJ per kg output. ...
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The great expansion of industries in Ethiopia has significantly increased the demand for the limited energy source of the country. Hence, effective and efficient way of utilizing the available energy is a crucial issue in all industries. This study investigates the energy consumption performance and possible energy saving potentials of Pasta and Macaroni Factory-a case of Africa PLC which is located in Adama city. It is the biggest pasta and macaroni producing factory in the city and consuming a very large amount of energy. The thermal and electrical energy consumption of the factory has been analyzed based on the actual data measured. The thermal performances of the two boilers (steam generators) are analyzed using an indirect method (i.e., calculating the different losses) and the typical boiler efficiency obtained are 81% and 80% for boiler #1 and #2 respectively. It is found that the major heat loss from the boilers is due to the dry exhaust gas. Similarly, water chiller, air compressor, electrical motors, pumps and lighting system equipment energy utilization have been analyzed. The various energy saving measures are analyzed and it is found that the industry can save 2,301 GJ/year of electrical energy by using high efficient motors instead of the existing normal standard motors, 782.6 GJ/year of heat energy can be recovered from the dry exhaust gas by using air preheater and 1,221 GJ/year of heat energy can be recovered by controlling the air to fuel ratio which is a significant energy saving potential. Finally this study reveals that the total possible annual bill saving potential of the factory is 1,676,871 ETB.
En este documento, en una dimensión sectorial se estudia el consumo energético y su contribución al Valor Agregado (V.A) en Chile. Inicialmente a través del enfoque insumo-producto (IP) y utilizando la metodología de Alcántara y Padilla (2002), la cual consiste en una extensión del cálculo de elasticidades producción/demanda propuesto por Pulido y Fontela (1993) se construye una matriz de energía nacional, año 2014 a 12 sectores y se realiza una caracterización de las actividades productivas, su correlación entre V.A y energía a tres y doce sectores. Mediante el enfoque de elasticidad de consumo energético se caracterizan las actividades productivas según su importancia en el impacto energético sectorial y la implicancia de los sectores claves en la política energética de Chile.
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In the present research article, an indirect type solar cooking system has been developed for indoor cooking. In the proposed cooking system, a cooking pot has been placed at a distance of 5 m from the parabolic dish collector, and the heat has been transmitted from the collector to the cooking pot by means of heat transfer fluid. A gear pump of 40 W and insulated pipes have been used to circulate the fluid. A number of experiments have been performed to analyze the performance of the cooking system. During the investigation, the system achieved the temperature of the heat transfer fluid up to 175°C. The time taken for cooking the rice and the black grams has been observed 21 and 68min, respectively. The average thermal efficiency of the proposed system for the entire day has been achieved at 13.11%.
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Purpose Uncertainty and variability need to be taken into account in life cycle assessment (LCA) studies to make robust decisions. We introduce a novel approach in LCA that allows to decide if either uncertainty or variability is dominating in the results: two-dimensional Monte Carlo simulations (2DMC). We aim to do so in a pedagogical and transparent way, allowing interested readers to fully grasp all technical details for their own potential use in future studies. Methods In 2DMC, an approach from quantitative risk assessment, the model parameters are divided into four categories: deterministic, variable, uncertain, and uncertain ánd variable; and appropriate distributions are selected. These distributions are sampled separately, so they can be assessed separately in the output as well. Firstly, the approach was translated to the LCA context with an illustrative proof of concept model, freely available on our website. Further, two variants of the post-harvest apple chain in Belgium (bulk versus pre-packed) are worked out as a real life comparative LCA case study. This real-life case study is also analyzed in a classical, deterministic way and by performing a more often used one-dimensional Monte Carlo simulation (1DMC), allowing a comparison with the 2DMC results and associated interpretations. Results and discussion Deterministic results do not reflect the complexity of reality. 1DMC results provide an indication on the robustness and conclusiveness of the result of a comparative LCA, but do not provide a way to guide further decisions. 2DMC results do provide this as results typically belong to one out of three possibilities. Firstly, the 2DMC results may confirm the result of the deterministic results. Secondly, the 2DMC curves may show proof that the two products are equivalent when it comes to environmental impact. One may then decide to analyze the variability causes further or that other reflections, like cost, should be considered as well. Thirdly, the 2DMC curves may indicate that more detailed and accurate information is needed to come to conclusive results. Conclusions 1DMC results give a first indication on the need for a 2DMC analysis. If that is the case, 2DMC can be used in a comparative LCA to take uncertainty and variability separately into account. 2DMC results can guide decisions to obtain more conclusive results. We recommend to consider a 2DMC analysis when comparing two products or processes if needed, hereto, our proof of concept model fully documented available online may be a starting point.
Aus den Besprechungen: "Mit Hilfe einer großen Zahl vortrefflich ausgewählter Koautoren ist Prof. Heiss eine umfassende Abhandlung aller lebensmitteltechnologischen Verfahren gelungen. Hierfür gliederte er seine Lebensmitteltechnologie übersichtlich nach Produktgruppen. ... Vervollständigt wird diese Auswahl noch durch kurze Erläuterungen der Verfahren zur Herstellung alkaloidhaltiger Lebensmittel. ... Die einzelnen Verfahrensschritte werden immer kurz und bündig dargestellt. Aus der vorteilhaften Kürze resultiert sicherlich die leichte Lesbarkeit des Buches. Sie ist dazu angetan, neue Leser, die sich in Lebensmitteltechnologie weiterbilden wollen, zu gewinnen ..." #Zuckerindustrie# Für die 6. Auflage wurden sämtliche 50 Beiträge kritisch überprüft und dem Stand der Technik angepasst. Alle zur Anwendung gelangten verfahrenstechnischen Schritte werden beschrieben, ansonsten auf weiterführende Fachliteratur verwiesen.
A new approach to the allocation problem in open-loop recycling is introduced. The approach models the indirect effects of a change in the supply of, or demand for, the recycled material. This approach can be used for system expansion as well as for allocation.
This book presents papers on energy conservation is food processing plants. Topics considered include methods of energy accounting, regression analysis, exergy analysis, the selection of electric motors, the measurement of steam flow, energy consumption in food processing systems, energy management, energy requirements in food irradiation, energy conversion methods, heat recovery, waste heat utilization, cogeneration, energy losses, and the economics of energy use in food processing.