LCA Food 2012 in Saint Malo, France! - Manifestations et colloques ...

PARALLEL SESSION 4C: CROP PRODUCTION SYSTEMS 8 th Int. Conference on LCA in the
Agri-Food Sector, 1-4 Oct 2012
3. Results
3.1. Main contributors for the environmental impact of out-of-season tomato in Benin
Most of the variability (90%) observed between individual fields was explained by the 3 first dimensions
of the PCA. In this space, the individual fields P38 is located near the intersection of the dimension. By
definition from the PCA method, this individual field can be considered representative fields for the environmental
impacts of the population. For the following analysis, P38 which had the smallest distance to the
origin of the PCA map (d=1.98) is used to analyse the environmental impact of one hectare of out-of-season
tomato grown in the region. The contribution analysis of P38 (Table 2) identified energy used for irrigation
as the main contributor for 7 among 14 impact categories. Then Nitrogen reactive emissions were the main
contributor for 2 impact categories (TA and ME) and also contributed to 2 other categories (GWP and POF).
Crop protection (insecticides and fungicides) showed high contribution for TET, FET and MD.
Table 2. LCIA results and contribution analysis for P38 and main correlations between environmental impacts
and the first 3 dimensions of the PCA
Impact categories P38 LCIA P38 contribution Correlation
Global Warming Potential- GWP
Ozone Depletion- OD
1.17E+04 kg CO2 eq
1.22E-03 kg CFC-11 eq
Irr. machinery (85%), N emissions (15%)
Irrigation (Irr.) machinery (100%)
Dim 1 (0.99)
Dim 1 (0.96)
Human Toxicity- HT 6.07E+06 kg 1,4-DB eq Irrigation machinery (100%) Dim 1 (0.94)
Photo Oxidant Formation - POF 2.11E+01 kg NMVOC Irr. machinery (95%), N emissions (5%) Dim 1 (0.97)
Terrestrial Acidity- TA
Freshwater Eutrophication- FE
2.41E+02 kg SO2 eq
9.54E-01 kg P eq
N emissions (92%), Irr. machinery (8%)
P emissions (59%), Irr. machinery (36%), Insecticides
(4%)
Dim 2 (0.81)
Dim 3 (-0.83)
Marine Eutrophication- ME 3.99E+01 kg N eq N emissions (99%), Irr. machinery (1%) Dim 2 (0.85)
Terrestrial Eco Toxicity- TET 1.97E+02 kg 1,4-DB eq Insecticides (98%), Irr. machinery (2%) Dim 2 (0.90)
Freshwater Eco Toxicity- FET 1.22E+02 kg 1,4-DB eq
P emissions (51%), Insecticides (39%), Irr. machinery
(10%)
Dim 2 (0.92)
Marine Eco Toxicity - MET 8.60E+02 kg 1,4-DB eq Irr. machinery (99%), Insecticides (1%) Dim 1 (0.95)
Agricultural
ALO
Land Occupation-
1.57E+03 m 2 a Field Area (99%), Irr. machinery (1%) -
Water Depletion- WD 3.81E+03 m 3
Irrigation water (100%) -
Metal Depletion- MD 1.09E+02 kg Fe eq
Irr. machinery (75%), Fungicides (24%), Insecticides
(1%)
-
Fossil Depletion- FD 3.49E+03 kg oil eq Irrigation machinery (100%) Dim 1 (0.96)
3.2. Correlations between major impact categories and cropping system characteristics
Table 2 presents the correlations between the 3 first dimensions and the environmental categories, and
between LCIA results and cropping system components for the field P38. Eleven out of 14 impacts categories
were correlated to one of the three dimensions. These categories are of major concern for the variability
of the environmental impact for the studied system.
Since GWP, POF, FD, OD, MET and HT (see Table 2 for acronyms) were correlated to dimension 1, this
dimension was a good synthetic descriptor for the variability of the dataset (50%). All these impact categories
were primarily affected by the energy used for irrigation. GWP and POF were secondarily affected by N
emissions, while MET was affected by pesticides emissions. Dimension 2 represented 26% of the overall
variability, and was mainly correlated to FET, TET, ME, and TA. ME and TA were mainly impacted by
nitrogen emissions, while FET was impacted by both phosphorus emissions and insecticides. Finally TET
was mainly impacted by insecticides.
The Kuiper’s test showed that the distribution of certain additional variables was consistent with the dimensions
of the PCA. Soil type is correlated to dimension one, with sandy soils showing higher values (v-test
=2.28) and silty soils showing lower values than the population average (v-test =-1.97). The energy use for
irrigation (through the number of pumping hours) was also correlated to dimension 1, with manual systems
showing lower values than the population average (v-test =-2.05). Unexpectedly, the volume of water supplied
did not correlate with this dimension suggesting that a greater energy use for pumping water did not
necessarily result in a greater volume applied due to important discrepancies in the technologies’ efficiency.
The geographical location (urban or peri-urban), was correlated to dimension 2 with the fields of Cotonou
showing greater values (v-test=2.13) than the overall population. The number of pesticides applications at
the field stage and the number of fertiliser applications were also correlated to dimension 2. Finally the rate
of mineral nitrogen was negatively correlated to dimension 3, highlighting a dichotomy between the impact
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