Federal Department of Economic Affairs FDEA
Agroscope Reckenholz-Tänikon Research Station ART
October 2013
Life Cycle Assessment of Agricultural Systems:
Introduction
Thomas Nemecek
Agroscope Reckenholz-Tänikon Research Station ART CH-8046 Zurich, Switzerland
http://www.agroscope.ch [email protected]
2
Oveview
Specific aspects of agriculture
Consequences for agricultural LCA
Defining system boundaries
Defining the functional unit
Impact assessment for biodiversity and soil quality
Variability of agricultural production and use of multivariate
statistics
Examples of application of LCA:
Cropping system analysis
Animal production, meat, milk and cheese
Biofuels
Environmental assessment of farms
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
3
Specific aspects of agricultural systems
Strong reliance on natural resources: land, water, sunlight,
nutrients, soil, biodiversity
Dependence on living organisms
Open systems
Processes are difficult to control: e.g. nutrient leaching,
erosion, N2O emissions
Emissions are difficult to measure, due to the open nature of
the systems
Small-scale structure: numerous farm businesses
Complex systems, only partly understood
Nonlinear nature of many environmental mechanisms
High variability of processes and products, due to soil,
climate, topography, agricultural management, traditions
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
4
Consequences of these specificities of agriculture (1) Environmental models and data need to be developed or
adapted to agriculture
Account for non-linear relationships of environmental
processes
Difficulty to clearly delimit the ecosphere (environmental
system) and the technosphere (economic system): e.g.
agricultural soil, biodiversity in the field
Due to the variability a large number of observations is
needed to get representative data (but often insufficient
resources)
Efficient LCA databases and calculation procedures are
required to manage this large number of observations
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
5
Consequences of these specificities of agriculture (2)
Since measurements at a large scale are not feasible
environmental models are needed, reflecting the main
influencing factors
Need to include specific impact categories, related to the use
of natural resources: land use, land use change, biodiversity,
soil quality, water resources
Need to adapt some impact categories, e.g. impact of
pesticides on ecotoxicity
Collaboration between agronomists, environmental scientists
and local experts is required
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
6
Fossil energy and carbon footprint are not enough for agricultural systems
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
x = agricultural products
Fossil energy use is identified by all
methodologies as the most important
driver of environmental burden of the
majority of the commodities included, with
the main exception of agricultural
Products (x). Huijbreghts et al. (2010)
7
20-60% of environmental impacts in Europe related to the food sector
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Source: EIPRO study (Tukker et al. 2006)
20% of energy use
for food sector
Other impacts
even higher share
8
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Defining system boundaries: Temporal system boundaries
Annual crops:
Starting after harvest of previous crop (including fallow period
or catch crop, if no product)
Ending with harvest of the considered crop
Permanent crops:
Annual basis (1st January to 31st December) or
Multiannual cropping cycle (distinguishing different phases:
planting, young plantation, main yielding phase, eradication)
9
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Single crop or cropping system?
YearMonth 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
Year 4 5 6Month 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
Spring
barleyGrass-clover mixture ...
Fa
llo
w ...
... F
allo
w
1 2 3
... G
ras
s-
clo
ve
r m
ixtu
re
PotatoesG
ree
n m
an
ure
Winter wheat Forage catch crop Grain maize
10
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Methodology: Crop combinations
1 2 3 Year Month 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
Fa
llo
w ...
Oil Seed
Rape Winter Wheat Winter Barley
Fa
llo
w ...
Fa
llo
w ...
Oil
Se
ed
Ra
pe
Crop Rotation
Crop Combination F
all
ow
...
Oil Seed
Rape Winter Wheat
Fall
ow
...
Winter wheat Winter Barley
Fa
llo
w ...
Winter Barley
Fall
ow
...
Oil Seed
Rape 1
2
3
11
Comparison of different crop rotations with (P) and without (S) pea
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0
20
40
60
80
100
120
S1 S2 P1 P2 P3 S1 S2 P1 P2 P3 S1 S2 P1 P2 P3 S1 S2 P1 P2 P3
Beauce_Con Beauce_INT Burgundy Moselle
kg N-eq
/€
kg N
-eq
/ha/
a
mechanisation N fertiliser P/K fertiliser field emissions
irrigation seed pesticides per € gross margin II
Source: Hayer et al. (2011)
12
Relationship between N fertilisation and global warming potential
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
R² = 0.97
R² = 0.92
R² = 0.96
R² = 0.95
2000
2400
2800
3200
3600
75 100 125 150 175
kg C
O2-
eq/h
a/a
N-fertiliser [kg N/ha]Beauce-M Beauce+M Burgundy Moselle
Source: Hayer et al. (2011)
13
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Defining system boundaries: Example of crop production
Products:
Infrastructure:
•Buildings
•Machinery
Field work processes:
•Soil cultivation
•Fertilisation
•Sowing
•Chemical plant protection
•Mechanical treatment
•Harvest
•Transport
Field production
Catch crops
Silage maize
Sugar beets
Fodder beets
Beetroot
Carrots
Cabbage
Wheat
Barley
Rye
Oats
Grain maize
CCM
Faba beans
Soya beans
Protein peas
Sunflowers
Rape seed
Potatoes
Co-Product: Straw
Product treatment:
Grain drying
Potato grading
System boundary
Reso
urc
es
Direct and indirect emissions
Manure storage
Animal excrements
Animal production system
Inputs:
•Seed
•Fertilisers (min. & org.)
•Pesticides
•Energy carriers
•Irrigation water
14
Defining system boundaries: Farm/Animal products
Infrastructure
•Buildings
•Equipment
•Machines
Purchased inputs
•Energy carriers
•Fertilisers
•Seed
•Pesticides
•Feedstuffs, straw
•Animal
•Water
Field operations
•Tillage
•Sowing
•Fertilisation
•Maintenance
•Irrigation
•Harvest
•Transport to farm
Animal production
•Feeding
•Milking
•Manure management
•Grazing
Manure
storage Fodder
conservation Re
so
urc
es
Indirect emissions Direct emissions
System boundary = farm gate
Animal products
•Milk
•Meat
•Eggs
•Wool
Vegetal products,
e.g.
•Wheat
•Maize
•Potatoes
•Vegetables
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Introduction to agricultural LCA
15
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Defining system boundaries: Where to draw the line between animal and plant production?
Animal production (incl. feedstuffs,
buildings, emissions, etc.)
Manure storage
and treatment
Manure application
(incl. machinery use and emissions)
Nutrient use in plant production
?
16
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Multifunctionality of agriculture: Functions and functional units
1. Land management function: ha*year
goal: minimize land use intensity
2. Productive function: physical unit (MJ
gross calorific value) goal: optimise eco-
efficiency (minimal impact per produced
energy unit)
3. Financial function: monetary unit
goal: optimise eco-efficiency
(minimal impact per € gross margin 1)
17
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
SALCA methodology Method for biodiversity - framework
• 11 Indicator species groups were determined considering ecological
and LCA criteria: flora, birds, mammals, amphibians, molluscs, spiders,
carabids, butterflies, wild bees, and grasshoppers.
• Two characteristics: overall species diversity of the indicator species
groups and ecologically demanding species
• Extensive inventory data about agricultural practices: occupation,
emissions, farming intensity indicators (e.g. number of cuts) and
process figures (e.g. herbicide type). Beside typical cultivated fields,
semi-natural habitats were integrated.
• Characterisation based on scoring system was evolved to estimate
every indicator species group’s reaction to agricultural activities
followed by an aggregation step resulting in scores.
• Aggregation and normalisation of scores: biodiversity value and
biodiversity potential
18
LCA Food 2012 Saint Malo | 3 October 2012
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
AA-3
ISG-1
ISG-3
ISG-4
ISG-2
ISG-5
Biodiversity =
Indicator Species
Groups (ISG)
Birds
Butterflies
AA-1
AA-2
AA-4
AA-5
Agricultural
Activities (AA)
Mowing
Wild flower strips
Insecticide application
Estimation of impacts on biodiversity
Impact
Bottom-up approach: Scores based on scientific and expert knowledge
01/11/2013
Spiders
SALCA-Biodiversity
19
LCA Food 2012 Saint Malo | 3 October 2012
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
SALCA-Biodiversity: Aggregation steps
Impact
ISG-1
Rating-1
Rating-2
Habitat-1
Score Habitat-1
for biodiversity
AA-1
AA-2
…
Score Habitat-2
for biodiversity
… …
…
Farm score
for biodiversity
Score Habitat-1
for ISG-1
Score Habitat-1
for ISG-2
20
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
SALCA methodology Method for biodiversity – DOK trial
Biodiversity points D0 D1 D2 O1 O2 C1 C2 M2
Total aggregated 8.7 8.1 8.0 8.0 8.0 7.7 7.6 7.6
Flora arable land 14.8 13.9 13.9 13.8 13.8 12.8 12.6 12.5
Flora grassland 4.9 4.3 4.1 4.2 4.2 4.1 3.9 3.9
Birds 10.3 8.7 8.6 8.5 8.5 8.0 8.0 7.9
Small mammals 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5
Amphibians 2.5 2.1 2.1 2.1 2.1 2.0 2.0 2.0
Molluscs 2.5 2.3 2.3 2.3 2.3 2.3 2.3 2.3
Spiders 13.9 13.2 13.2 13.0 13.0 12.2 12.0 12.1
Carabids 14.7 14.0 14.0 14.0 14.0 13.7 13.5 13.6
Butterflies 9.8 8.8 8.6 8.8 8.6 8.5 8.4 8.5
Wild bees 4.9 4.8 4.8 4.8 4.8 4.8 4.8 4.9
Grasshoppers 11.0 9.8 9.5 9.9 9.6 9.4 9.3 9.3
Amphibians 1.7 1.4 1.3 1.3 1.3 1.3 1.2 1.3
Spiders 13.4 12.7 12.6 12.5 12.4 11.6 11.5 11.6
Carabids 14.7 14.0 14.0 14.0 14.0 13.7 13.6 13.7
Butterflies 9.8 8.8 8.5 8.8 8.5 8.4 8.4 8.5
Grasshoppers 10.9 9.6 9.3 9.6 9.4 9.2 9.1 9.2
Higher values mean higher species richness
favourable
very favourable
Total species richness
Species with high ecological requirements
compared to reference C2
D = Bio-dynamic
O = Organic
C = Conventional
(mixed min./org.
fertilisation)
M = Conventional
(mineral fertilisation)
2 = normal
fertilisation level
1 = 50% fertilisation
level
0 = no fertilisation
Source: Nemecek et al. (2005)
21
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
no relevance for the considered system
SALCA methodology Method for biodiversity – case study 1/2
Results of SALCA-Biodiversity. Biodiversity scores
are given per ha cultivated crop. A, B, C, D are
management systems with main characteristics :
Winter wheat systems:
(A) Conventional production; 5.8t DM/ha
(B) Integrated production – intensive; 5.5t DM/ha
(C) Integrated production – extensive; 4.5t DM/ha
(D) Organic production; 3.5t DM/ha
Grassland systems (hay production):
(A) 5 cuts/year, fertilised with slurry; 11t DM/ha
(B) 4 cuts/year, fertilised with slurry; 9t DM/ha
(C) 3 cuts/year, fertilised with solid manure; 5.6t DM/ha
(D) 1 cut/year, no fertilisation; 2.7t DM/ha
Scores of grassland (A) and winter wheat (B) systems are
set as reference scores. Color codes are given for rough
comparison:
similar to the reference (95%<score<104%)
much better than the reference (score >115%)
better than the reference (105%<score<114%)
Grassland Winter Wheat
(D) (A) (B) (C) (D) (A) (B) (C)
Overall species diversity 6.2 6.4 13.8 21.3 7.7 7.5 8.4 8.7
Grassland flora 3.7 3.9 11.4 18.5
Crop flora 15.2 15.1 16.0 17.3
Birds 6.4 6.7 13.8 22.0 5.3 5.0 6.2 6.4
Mammals 7.3 7.3 11.1 11.1 4.6 4.6 4.6 4.6
Amphibians 2.1 2.1 5.2 9.5 1.7 1.7 1.8 1.8
Molluscs 5.4 5.6 5.8 11.3 2.2 2.2 2.2 2.2
Spiders 9.1 9.3 15.8 22.4 8.2 8.0 10.5 10.7
Carabid Beetles 7.0 7.4 13.6 21.0 10.9 10.6 11.7 11.9
Butterflies 6.8 7.0 20.0 36.0
Wild Bees 7.4 7.6 18.6 23.0 5.2 4.9 5.0 4.8
Grasshoppers 6.9 6.9 19.4 33.1
Amphibians 0.8 0.8 2.9 4.8 1.5 1.4 1.6 1.6
Spiders 8.9 9.0 15.3 21.6 8.0 7.8 10.3 10.5
Carabid Beetles 7.0 7.3 13.4 20.6 10.6 10.1 11.2 11.3
Butterflies 6.7 6.8 19.4 36.0
Grasshoppers 6.8 6.8 19.3 32.9
Biodiversity scores
Production system
Species with high ecological requirements
Source: Jeanneret et al. 2006
23 Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Physical Rooting depth of soil
Macropore volume
Aggregate stability
Chemical Soil organic matter
Inorganic pollutants
Organic pollutants
Biological Earthworm biomass
Microbial biomass
Microbial activity
SALCA methodology Method for soil quality - framework
Spatial system boundary = farm;
Temporal system boundary = crop rotation period (6-8 years)
Management data of all plots of a farm in a single year are
considered as representative for a whole crop rotation
Only influences of agricultural management practices are included,
not immissions
The development trend of soil properties is assessed, not absolute
values
Criteria
According to ISO 14040 and ISO
14042
Depending on the needs of Life
Cycle Assessment
Soil properties
Physical
Chemical
Biological
9 D
ire
ct
Ind
ica
tors
=
me
asu
rab
le s
oil
pro
pe
rtie
s
Source: Oberholzer et al. (2006)
24
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Risk of soil com-
paction by wheeling
Number of applica-
tions per year with
possibly toxic effects
Amount of organic
substances
Humus balance
Impact classes
SALCA methodology Method for soil quality – impact assessment
Pro
cesses
Direct indicators
Soil organic matter
Microbial biomass
Microbial activity
Earthworm biomass
Macropore volume
Aggregate stability
Management data
Slurry
application
Soil texture
Soil moisture
Soil structure
Figure 1: Example of impact assessment of a slurry application
Example: slurry application
25 Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
SALCA methodology Method for soil quality – example DOK
No fer-
tiliser D0
Bio-dyna-
mic D2
Bio-orga-
nic O2
Conventiona
l
K2
Mineral
fertiliser M
Soil tillage ploughing ploughing
Fertiliser type no Liquid
manure,
compost
Liquid
manure,
dung
Org. and
mineral
fertiliser
Mineral
fertiliser
Fertiliser kg N/ha 0 93 86 165 125
Growth regulators and
Fungicides
no Yes
Weed regulation type mechanical herbicides
Weed regulation,
period and frequ.
Spring, 3 applications Spring and autumn, 2
applications
Seeding month October October
Harvest month August August
Crop residues removed removed
Main characteristics of the analysed cultivation systems
26 Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
SALCA methodology Method for soil quality – Results DOK
Results of SALCA-Soil Quality for the five treatments
•Minor differences between the three farming systems because most management practices are
similar or equal regarding soil quality. Some indicators do not show a positive effect in D2 because
of slightly less organic input compared to O2 and K2.
•D0 and M: Impacts on soil quality because of insufficient organic carbon supply without organic
fertilisers and removal of crop residues.
•O2 and K2: Positive effect of crop rotation on macropore volume is not negated by a high
compaction risk.
D0 D2 O2 K2 M
Rooting depth of soil 0 0 0 0 0
Macropore volume 0 0 + + 0
Aggregate stability - + + + -
Corg content -- + + + --
Heavy metal content 0 0 0 0 0
Organic pollutants 0 0 0 0 0
Eathworm biomass 0 0 0 + 0
Microbial biomass - 0 + + -
Microbial activity - 0 + + -
Chem
ical
Bio
logic
al
Physic
al
Direct Indicators for soil quality
27
Example of cropping systems research: Organic and integrated farming / Energy demand in the DOC-trial
0
5
10
15
20
25
30
D0 D1 D2 O1 O2 C1 C2 M2
GJ-e
q/(
ha·a
)
0
0.5
1
1.5
2
2.5
3
MJ-e
q/k
g D
M
P: Seed
P: Pesticides
P: Fertiliser
P: Energy carriers
M: Transport
M: Harvest
M: Maintenance
M: Plant protection
M: Fertilisation
M: Sowing
M: Soil cultivation
per kg DM
Bio-
dynamicBio-
organic
Conv. mixed
fert.
Conv.
min. fert.
P: production means
M: mechanisation
Source: FAL report 58 (2006)
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Introduction to agricultural LCA
28
GWP 100a
0
1000
2000
3000
4000
5000
6000
7000
CH_IP CH_OR DE_IP DE_OR IT_IP IT_OR ES_IP ES_OR FR_IP
kg
CO
2-e
q *
ha
-1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
kg
CO
2 -eq
* kg
ap
ple
-1
Field operations Fertilisation Establishing of orchard Pesticides
Fence Hailnet Direct field emissions per kg DM
Example of horticultural research (EU-project ENDURE): Global warming potential pome-fruit
Source: Frank Hayer (ART)
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Introduction to agricultural LCA
29
Example of animal production research: EU-Project Grain Legumes (GLIP) Effect of replacing soya beans in pig feed
0
1
2
3
4
SO
Y
GLE
U
SO
Y
GLE
U
SA
A
FA
RM
CAT NRW
kg
CO
2-e
q. / kg
pig
(live w
eig
ht)
Land transform. soya BRA
Land transform. soya ARG
Land transform. palm oil MYA
Soya bean meal
Diff. protein rich feeds
Peas
Energy rich feeds
Mineral feeds
Transport of feeds
Feed processing
Piglet production
Housing
Manure management
- 2%
- 5%
- 16% - 6%
Raw
mat. p
roduction
Pig
farm
s
Feedstu
ff p
roduction
Source: Daniel Baumgartner (ART)
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Introduction to agricultural LCA
31
Evaluation of bioenergy production systems: Eutrophication potential
Kg N/(ha*5a): 229 280 95 59 0
eutrophication potential
0
100
200
300
400
500
600
CR reference CR energy Miscanthus Willow Perm.
meadow
kg N
-eq/(
ha*5
a)
0.000
0.002
0.004
0.006
0.008
0.010
0.012
kg N
-eq/k
g o
DM
NO3- (groundwater) NH3 (air) N2O (air)NOx (air) other N-compounds P-compoundsper kg oDM
Source: Thomas Kägi (ART)
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Introduction to agricultural LCA
32
Food LCA: Beef at point of sale
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART Source: Alig et al. (2012)
33
Food LCA: Chicken at point of sale
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART Source: Alig et al. (2012)
34
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Variability and uncertainty: Factors influencing environmental impacts
Crop
management
Pedo-climatic
conditions
Crop yield
Life cycle
inventory
Environmental
impacts
To understand the
variability of
environmental
impacts, we need to
look on the
variability of the
influencing factors
Socio-economic
conditions
35
Variability of environmental impacts: GWP of wheat from literature
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
0
0.2
0.4
0.6
0.8
1
0 2000 4000 6000 8000 10000
kg C
O2e
q/k
g
Yield kg/ha
36
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Potential use of multivariate statistics in LCA to explain variability
To select proxies, we have to identify similar
datasets
Multivariate statistics (like principal component
analysis, PCA) can be used to show similarities
between environmental impacts
It can be also used to group environmental profiles,
e.g. of crops
Analysis based on a set of midpoint LCIA indicators
In the study applied to crop inventories from
SALCA (Switzerland) and ecoinvent (global)
37
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Principal component analysis of SALCA inventories
Eigenvalues of correlation matrix
Active variables only
52.73%
27.63%
6.18% 5.07% 4.51% 2.24%
.94% .70%
-1 0 1 2 3 4 5 6 7 8 9 10
Eigenvalue number
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Eig
en
va
lue
80% of variance explained by first two principal components
38
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Principal component analysis of SALCA inventories
Relationship between impact indicators and factors 1 and 2
Projection of the variables on the factor-plane ( 1 x 2)
Energy
GWP Ozone
Eutro
Acidi
TET_EDIP
AET_EDIP
HTP_CML
-1.0 -0.5 0.0 0.5 1.0
Factor 1 : 52.73%
-1.0
-0.5
0.0
0.5
1.0
Fa
cto
r 2
: 2
7.6
3%
39
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Factor 1: - can group crops - related to the yield
CER
LEG
MAI
OIL
ROOT
VEG-6 -4 -2 0 2 4 6
Factor 1
-4
-3
-2
-1
0
1
2
3
4
5 Data for Swiss crops
from SALCA database:
grouping by crop group
(CER = cereals,
LEG = legumes,
MAI = maize,
OIL = oil crops,
ROOT = root crops,
VEG = vegetables).
Facto
r 2
40
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Factor 2: - related to the farming system and the intensity
Conv
Ipint
Ipext
Org-6 -4 -2 0 2 4 6
Factor 1
-4
-3
-2
-1
0
1
2
3
4
5
Data for Swiss crops from
SALCA database: grouping
by farming system
(Conv=conventional,
IPint = integrated intensive,
IPext = integrated extensive,
Org = organic). Facto
r 2
41
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Principal component analysis of SALCA inventories
Yield is a key factor
Scatterplot (FALSR58_Res 14v*246c)
Factor 1 = -5.9426-2.1271*x
-3.0 -2.8 -2.6 -2.4 -2.2 -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4
LnInvYield
-7
-6
-5
-4
-3
-2
-1
0
1
2
Fa
cto
r 1
LnInvYield:Factor 1: r2 = 0.4561
42
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Principal component analysis of ecoinvent inventories
Cereals in different countries
w heat
barley
rye
CH
FRES
DE
CH
CH
US
CH
FRES
DE
CH
CH
CH
RER
CH
CH
-8 -6 -4 -2 0 2 4 6
Factor 1
-2
-1
0
1
2
3
43
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Potential use of multivariate statistics in LCA to explain variability
Between 76 and 80% of the variability could be explained by the first
two principal components.
Factor 1 crop (group) and yield
Factor 2 farming system (conventional, integrated, extensive,
organic)
More data are needed for more systematic analyses
The analysis helps to
show similarities and differences between environmental profiles
to find suitable proxies
to derive simplified methods for extrapolations and approximations
44
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Methodology example 1: Factor analysis Milk production in 35 farms
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2
Factor 1
Fa
cto
r 2
Terr.ecotox.
Tot.eutr.Terr.eutr.
Aq.Eutr.
Acid.
Aq.ecotox.
GWP100
GWP500
Ozone
Hum.tox.
Energy
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2
Factor 1
Fa
cto
r 3
Terr.ecotox.
Tot.eutr.
Terr.eutr.
Aq.Eutr.
Acid.
Aq.ecotox.
GWP100 GWP500
Ozone
Hum.tox.
Energy
Source: Rossier & Gaillard (2001)
45
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
1 2 3
No. Impact categories
1 Energy use (GJ eq. ha-1
) 0.95 -0.03 0.06
2 Global warming potential for 100 years (t CO2 eq. ha-1
) 0.95 -0.01 0.20
3 Ozone formation (kg C2H4 eq. ha-1
) 0.94 -0.04 -0.01
4 Aquatic ecotoxicity (kg Zn eq. ha-1
) 0.00 0.93 0.07
5 Terrestrial ecotoxicity (kg Zn eq. ha-1
) 0.07 0.93 0.00
6 Aquatic eutrophication (kg PO4 eq. ha-1
) 0.19 0.05 0.98
7 Terrestrial eutrophication (kg PO4 eq. ha-1
) 0.90 0.13 0.16
8 Acidification (kg SO2 eq. ha-1
) 0.94 0.13 0.16
Total variance explained
Initial eigenvalues 4.58 1.76 0.89
Variance explained (% of variance) 57.19 22.00 11.17
N = 445; loadings exceeding 0.8 are in bold print.
Component
Methodology example 2: Principal component analysis (PCA) 445 apple orchards, Switzerland, 1997-2000
Source: Mouron et al. (2006)
46
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Result: The Management triangle
47
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Application of the management triangle to the environmental management of farms Example for 35 milk producers, impacts per kg milk Small area = favourable for the environment
Nutrients
Ressources
Pollutants
31 17 32 30 27 10 35
22 24 9 29 23 26 28
19 14 8 3 16 11 13
34 2 12 4 6 15 5
7 1 18 20 25 21 33
Source: Rossier D. & Gaillard G., 2001. Bilan écologique
de l'exploitation agricole: Méthode et application à 50
entre-prises. Rapport SRVA et FAL, 105 pp. et annexes.
48
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Environmental management of apple orchards Input-impact-map: correlations between selected impacts and input groups 445 apple orchards, Switzerland, 1997-2000; Pearson correlation (r)
0.0
0.2
0.4
0.6
0.8Diesel
Equipment
Buildings
Tractors
Hail protection
Ca- and Mg-fertilizer
N-fertilizer Other plant treatment
products
K-fertilizer
Fungicides
Insecticides
Herbicides
P-fertilizer
Energy use (GJ eq. ha-1)
Aquatic ecotoxicity (kg Zn eq. ha-1)
Aquatic eutrophication (kg PO4 eq. ha-1)
Energy demand
correlated to 8 of 13
inputs.
Aq. ecotox. determined
by insecticides (0.7) and
fungicides (0.5).
Aq. eutrophication
depends on P-fertiliser
(0.8) and N-fertiliser
(0.4).
Source: Mouron et al. (2006)
49
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Conclusions multivariate analysis
Midpoint impact indicators can be grouped by
multivariate statistical methods
Three dimensions were derived for farming systems:
Resource management
Nutrient management
Pollutant management
Related to
Different environmental impacts
Different management options
Different time scales
Enables improved management and communication
50
Global warming potential of dairy farms and amount of milk produced
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
0.0
0.5
1.0
1.5
2.0
2.5
0 100'000 200'000 300'000 400'000 500'000
kg C
O2
-Eq
./kg
milk
Amount of milk produced (kg/year)
51
GWP of milk processed in dairies
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.0E+00 1.0E+07 2.0E+07 3.0E+07 4.0E+07
CO
2-e
q/k
g m
ilk p
roce
sse
d
Milk processed (kg)
52
GWP and dairy size
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
All
0.0
-1.0
Mio
. kg
1.0
-1.5
Mio
. kg
1.5
-2.0
Mio
. kg
2-3
Mio
. kg
3-5
Mio
. kg
5-1
0 M
io.
kg
>1
0 M
io.
kg milk processed
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18kg
CO
2e
q/k
g m
ilk p
roce
sse
d
53
Communication of results to farmers Overview of environmental impacts
Environmental impacts per ha UAA
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
200% ow n farm
model farm
terrestrial ecotoxicity
(Toxp./ha)
aquatic ecotoxicity
(Toxp./ha)
energy demand (MJ-eq./ha)
global w arming
potential (CO2-
eq./ha)
eutrophication (N-
eq./ha)
Environmental impacts per Swiss-Fr. return
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
200%
ow n farm
model farm
terrestrial ecotoxicity
(Toxp./Fr.)
global w arming
potential (CO2-eq./
Fr.)
aquatic ecotoxicity
(Toxp./ Fr.)
energy demand (MJ-eq./ Fr.)
eutrophication
N-eq. / Fr. RL
Environmental impact per MJ digestible energy
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
200%ow n farm
model farm
terrestrial ecotoxicity
(Toxp./MJ DE)
aquatic ecotoxicity
(Toxp./MJ DE)
energy demand (MJ-eq./MJ DE)
global w arming
potential (CO2-
eq./MJ DE)
eutrophication (N-
eq./MJ DE)
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Introduction to agricultural LCA
54
Communication of results to farmers Detailed environmental impacts
Share of the different means of production in the total energy demand
0
200000
400000
600000
800000
1000000
1200000
1400000
ow n farm model farm
MJ-e
q.
Other inputs
Animal husbandry
Animals (purchase)
Pesticides
Machines
Bulidings/facilities
Feedstuff (purchase)
Energy carriers
Fertilisation/Nutrients
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Introduction to agricultural LCA
55
Communication of results to farmers Environmental impacts by product group
Environmental impact cowmilk
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
200%
ow n farm
model farm
energy demand MJ-eq/ha
global warming
potential CO2-eq./ha
eutrophication N-eq./haaquatic ecotoxicity Toxp./ha
terrestrial ecotoxicity
Toxp./ha
Environmental impacts cattle breeding
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
200%
ow n farm
model farm
energy demand MJ-eq./ha
global w arming
potential CO2-
eq./ha
eutrophication
N-eq./ha
aquatic ecotoxicity
Toxp./ha
terrestrial ecotoxicity
Toxp./ha
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Introduction to agricultural LCA
56
Communication of results to farmers Comparison to similar farms
Energy demand: comparison of dairy farms
0
10000
20000
30000
40000
50000
60000
70000
80000
1 2 3 4 5 6 7 8 9 10 11 12 13 14
MJ
-eq
. /h
a
Other inputs
Animal husbandry
Animal (purchase)
Pesticides
Machines
Bulidings/facilities
Feedstuff (purchase)
Energy carriers
Fertilisation/Nutrients
Eutrophication: comparison of dairy farms
0
50
100
150
200
250
300
350
1 3 9 2 12 8 10 5 13 11 4 6 7 14
N-e
q.
/ha
Other inputs
Animal husbandry
Animal (purchase)
Pesticides
Machines
Bulidings/facilities
Feedstuff (purchase)
Energy carriers
Fertilisation/Nutrients
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Introduction to agricultural LCA
57
Milk production Energy demand of specialised milk production farms
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33
MJ-é
q./
ha S
AU
*an
Autres intrants
Animaux (achats)
Fourrages (achats)
Semences (achats)
Produits phytosanitaires
Engrais
Agents énergétiques
Machines
Bâtiments/ équipements
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Introduction to agricultural LCA
58
Milk production Energy demand of specialised milk production farms
44%
0%3%
11%
19%
17%
0%
6%
Agents énergétiques
Semences (achats)
Fourrages (achats)
Animaux (achats)
Bâtiments/ équipements
Machines
Engrais
Produits phytosanitaires
Autres intrants
Infrastructure &
intrants industriels
33%
Consommation directe
44%
Intrants agricoles
23%
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART
Introduction to agricultural LCA
59
Conclusions
The environmental impacts of agriculture and the food sector
are considerable
Agriculture has a number of specific aspects that need to be
considered
LCA provides good insights into the behaviour of the systems
For this a close collaboration between agronomists,
environmental scientists and local experts is required
Introduction to agricultural LCA
Thomas Nemecek | © Agroscope Reckenholz-Tänikon Research Station ART