2.8 Life Cycle Impact Assessment (LCIA)
The following impacts categories were considered of high relevance: GWP, PED,
Acidification Potential (AP), Eutrophication Potential (EP) and Water Consumption
(WC). The CML impact assessment methodology was used (Institute of
Environmental Sciences of the University of Leiden framework, CML2001, 2013).
The impact categories identified above are also considered by Cotton Inc. [8] and
Textile Exchange [5].
3 Modelling Approach—Agriculture Model
Various factors like the variety of different locations, large number and diversity of
farms, variety of agricultural management practices applied, lack of a determined
border to the environment, complex and indirect dependence of the output (harvest,
emissions) from the input (fertilizers, location conditions, etc.), variable weather
conditions within and between different years, and variable pest populations
(insects, weeds, disease pathogens, etc.) contribute to the complexity of agricultural
modelling. Due to the inherent complications characterizing an agricultural system,
a non-linear agrarian calculation model is applied displaying plant production
(jointly developed by the LBP of the University of Stuttgart and thinkstep AG).
This software model covers a multitude of input data, emission factors and
parameters. The agricultural model accounts for the nitrogen cycle in agricultural
systems. Specifically, the model includes emissions of nitrate (NO 3
− ) in water and
emissions of nitrous oxide (N 2 O), nitrogen oxide (NO) and ammonia (NH 3 ) into air.
The model ensures that emissions from erosion, the reference system (comparable
non-cultivated land area) and nutrient transfers within crop rotations are modelled
consistently. Carbon-based emissions such as CH 4 , CO, CO 2 are considered in
foreground and background datasets. Background datasets include emissions
resulting from production of fertilizer, pesticides, electricity, and diesel while
foreground datasets contain direct emissions such as CO 2 due to combustion of
fossil fuels by the tractor or irrigation engines and application and decomposition of
urea fertilizers in the soil.
4 Results and Discussion
Table 2 shows the comparative LCIA results on absolute basis for all three farming
practices. It clearly reflects that LCA results of organic cotton is better than BCI
cotton and conventional cotton production for all the environmental impacts categories. There has not been significant difference of yield between conventional and
BCI cotton production in this study. However, if the yield of BCI cotton increases
72
P. Shah et al.
The following impacts categories were considered of high relevance: GWP, PED,
Acidification Potential (AP), Eutrophication Potential (EP) and Water Consumption
(WC). The CML impact assessment methodology was used (Institute of
Environmental Sciences of the University of Leiden framework, CML2001, 2013).
The impact categories identified above are also considered by Cotton Inc. [8] and
Textile Exchange [5].
3 Modelling Approach—Agriculture Model
Various factors like the variety of different locations, large number and diversity of
farms, variety of agricultural management practices applied, lack of a determined
border to the environment, complex and indirect dependence of the output (harvest,
emissions) from the input (fertilizers, location conditions, etc.), variable weather
conditions within and between different years, and variable pest populations
(insects, weeds, disease pathogens, etc.) contribute to the complexity of agricultural
modelling. Due to the inherent complications characterizing an agricultural system,
a non-linear agrarian calculation model is applied displaying plant production
(jointly developed by the LBP of the University of Stuttgart and thinkstep AG).
This software model covers a multitude of input data, emission factors and
parameters. The agricultural model accounts for the nitrogen cycle in agricultural
systems. Specifically, the model includes emissions of nitrate (NO 3
− ) in water and
emissions of nitrous oxide (N 2 O), nitrogen oxide (NO) and ammonia (NH 3 ) into air.
The model ensures that emissions from erosion, the reference system (comparable
non-cultivated land area) and nutrient transfers within crop rotations are modelled
consistently. Carbon-based emissions such as CH 4 , CO, CO 2 are considered in
foreground and background datasets. Background datasets include emissions
resulting from production of fertilizer, pesticides, electricity, and diesel while
foreground datasets contain direct emissions such as CO 2 due to combustion of
fossil fuels by the tractor or irrigation engines and application and decomposition of
urea fertilizers in the soil.
4 Results and Discussion
Table 2 shows the comparative LCIA results on absolute basis for all three farming
practices. It clearly reflects that LCA results of organic cotton is better than BCI
cotton and conventional cotton production for all the environmental impacts categories. There has not been significant difference of yield between conventional and
BCI cotton production in this study. However, if the yield of BCI cotton increases
72
P. Shah et al.