1. Study track: Sustainable Development in Agriculture and Management of Animal Resources
M1: Catania University, M2: Montpellier SupAgro
Student: Mohmed Sarhan, E-mail: mohmedsarhan@gmail.com
Agriculture is today one of the main reasons why
three planetary boundaries (climate change, biodiver-
sity loss and changes in the global nitrogen cycle)
have already been transgressed[1].
The expected increase in global demand for animal
products is predicted to double by 2050 relative to a
year 2000 baseline Due to population growth and
change in consumption patterns[2].
Land is a limited resource, which is already under
pressure. The Competition for land used for produc-
tion of food for direct human consumption, feed for
animals, fuel for energy and transportation, fibers for
fabric as well as conservation of forests (e.g. rainfor-
ests) is likely to increase in the future[3].
Climate change is one of the greatest concerns facing
our society .During recent years, it is more likely that
agriculture is responsible for 30-35% of the global
GHGs. In particular the livestock sector has been re-
vealed as one of the main contributors to climate
change, representing 18% of GHGs[4].
In the same period, large emission cuts of GHG are
required in order to meet the target of keeping the
temperature rise due to global warming to a maximum
2°C. The livestock sector therefore faces significant
challenges. When analyzing how to investigate the po-
tentials (and limitations) of different mitigation
measures most efficiently, a life cycle thinking is re-
quired [1].
The term Life cycle assessment is compilation tool of
the inputs and outputs for evaluating environmental
effects of a product, process, or activity throughout
its life cycle or lifetime, which is known as a ‘from
cradle to grave’ analysis [5]. Gerber [6] has intro-
duced a methodology developed for the assessment of
GHGs from the livestock sector based on the LCA ap-
proach and provide insight on the sector’s contribu-
tion to GHGs.
This paper proposes the utilization of GIS in order to
complement the above-mentioned methodology. GIS
integration provides the necessary tools to fully im-
plement a spatially explicit LCA. Using a process based
approach GHGs estimated for:
Feed production (including cultivation, land use
change, manufacture of fertiliser, processing and
transport),
Manure management,
Enteric fermentation,
Energy use (direct and indirect),
Post-farm emissions to the point of retail.
[6]
 Herd Demography Module
Number of animals; herd
rates; adult and slaughter
weights
Herd structure and size; live
weights; off-take
 Feed Basket Module
Number of animals; feed
area and yield per compo-
nent; mechanization; fer-
tilizer inputs, digestibility
and N content of feed;
concentrate use
Animals’ ration; feed produc-
tion: land use, emissions of
N2O and CO2; feed basket:
one average feed with quality
(digestibility, N content of
feed), land use and emissions
 System Module[11]
Herd structure and size;
live weights; feed basket;
manure management sys-
tem
Energy requirements; feed in-
take; animal emissions: CH4
and N2O from enteric fer-
mentation; feed emissions;
system production
 Allocation Module[12]
System production emis-
sions; land use
Protein production; allocates
emissions and land use to
products; emissions and land
use per unit of product
(Protein and non-edible prod-
ucts)
Results & Conclusion
In particular, the added value of the use of GIS
has proven to be essential in the calculations of:
Pig and chicken production systems:
Starting from GIS layers of rural populations
(GRUMP) and pig and chicken distribution (GLW),
together with the country-based percentage of
the backyard and commercial systems for the two
species, a map for each production system is ob-
tainable[10].
Through the GIS potentialities its calculated for
each country the number of backyard pigs/ chick-
en per rural person . Then Multiplied this coeffi-
cient by GRUMP to obtain the backyard distribu-
tions and by Subtracting this from GLW to obtain
the commercial populations (Figure 1).
References
[7]
[8]
GIS can be defined as a system of hardware, soft-
ware and procedures designed to support the cap-
ture, management, manipulation, analysis, modeling
and display of spatially-referenced data for solv-
ing complex planning and management problems [9].
GIS has permitted the utilization of available geo-
referenced data on animal densities to calculate
the feed balance on a local scale and also the data
on herd demography, feed availability, and land use
are related to climatic and socioeconomic condi-
tions and are not bound to national boundaries.
These data have to be combined with statistical
data that are collected on a national scale[6].
Inputs: each module can have input data either in
tabular format or explicit layers available at the
global level or specific production system and rep-
resenting the different variables included in the
model (e.g. Livestock distributions, climatic varia-
bles, agro-ecological zones, the herd dynamics, ma-
nure management system, fertilizer and pesticide
application and feed characteristics).
The input Data (tabular format or explicit layers)
are then converted to GIS data to be integrated
with the other variables already available in GIS
format.
The LCA is completely implemented in GIS and
consists of 4 modules:
Feed Basket for Ruminants
Assuming that there is a strong relationship be-
tween land-use and feed Basket it is possible to infer
the feed basket composition because feed basket de-
pends on the availability of rangeland, the crops
grown and their respective yields and on the number
of animals in the specific region.
Consequently, throughout GIS variables such as above-
ground net primary productivity (NPP), grassland per-
centage and the production of other roughages (like
crop residues, straw and Stover) it is possible to
model the animals’ diet for those regions where data
are not available[13].
Emissions related to the production of feed are calcu-
lated according to the methods of Thomassen[16].
Methane conversion factor for manure
The GIS approach allowed the calculation of manure
emissions to take into account local physical condi-
tions (e.g. Mean annual temperature). Within large
countries, where the value for the Moment Magnitude
Scale (MMS) is constant, by fine tuning the value of
Magnetic Confinement Fusion (MCF), accordingly to
the actual temperature, without using a unique value
all over the country.[15] The example below for pig in
South America (Figure 2).
Data aggregation and extraction
Use of GIS to store data and compute emissions
permits us to maintain the original resolution and thus
avoiding generalization and averaging of input data
where spatially explicit sources were available (such
as feed production, manure management, tempera-
ture, land cover, and feed availability) [13] . As a re-
sult of combining the methodology with GIS, it has
been obtained a GIS based tool for assessment of
GHGs [9] (Figure 3).
Figure 1.the commercial populations
Figure 2. Industrial pig in South America
Figure 3. Data aggregation and extraction
Agris fOondus LMVERSITA ÿÿ'.I""*P'111''
di CATANIA CTlSAgrO
1
INPUT
Rural Population
COUNTRY China India UK
Backyard (%) 37 39 0
LCA system Boundaries
"
Cradle to Retailer"
Other Inputs Services St Products
External -feed
Land -for feed Distribution
Life cycle assessment framework
Inventory
analysis
Impact
assessment
Intrepretation
Functional unit
System boundaries
mjJ
(J{&±ult± & '
Conclusion
Model of the system
Produktion data
Emissions factors
Emissions converted
and aggregated
Allocation between
products
Life Cycle Assessment
(Cradle-to-farm gate)
Emissions
allocated to
calves and cull
cows sold
Resource inputs
Fuel
Electricity
Fertilizer
Pesticide
Plastic
Machinery
Off-farm heifers
Purchased feed
Imported manure
Feed Production
Animal Production
Manure Handling
Milk sold
Animals sold
Feed sold
Exported manure
GHG EMISSION INTENSITIES FOR COW IVULK
(KgC02e/KgFPCM)
4.5
ÿ1 Post-farm gate
a Direct and embedded
energy
ÿLUC-Soybean
a Feed CO,
ÿ Feed N,0
ÿManure IM
ÿ
O
ÿManure methane
ÿEnteric ferm entation
(ÿÿtCACCi
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Climatology, International Center for Tropical Agriculture, Cali, Colombia.
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versity of Piraeus, Greece.
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