Modular Extrapolation Approach for Crop LCA ...

Modular Extrapolation Approach for Crop LCA MEXALCA: Global Warming Potential of Different Crops and its R elationship to the Y ield Thomas Nemecek , Karin Weiler , Katharina Plassmann , Julian Schnetzer, Gérard Gaillard , Donna Jefferies , Tirma García–Suárez , Henry King and Llorenç Milà i Canals Abstract MEXALCA (Modular EXtrapolation of Agricultural LCA) extrapolates crop inventory data and impacts from an original country inventory to all producing countries worldwide. This allows estimates of worldwide means weighted by production volumes and of the environmental impact distribution. In this paper, the relationship between the yield and the environmental impacts is analysed in order to test whether the yield alone can be used as an extrapolation criterion. The results show that the global warming potential (GWP) per kg decreases with increasing yields for the means of the 27 studied crops. When comparing the production of a crop in different countries, the relationship between GWP per kg and yield exists only for those crops where the contribution from basic cropping operations and tillage to the GWP is significant. Considering the yield alone therefore generally allows only a poor approximation of the GWP.

1

Introduction

Businesses wishing to analyse the environmental impacts of their products using life cycle assessment (LCA) methods increasingly require large amounts of very detailed data which are rarely readily available. This is particularly true for companies operating global and rapidly changing supply chains with a large range of products and ingredients originating from all over the world. Thus, there is an urgent need for data that are sufficiently reliable while being supplied without extensive, time consuming and costly collection. Furthermore, the variability of environmental impacts of agricultural and food products can be considerable [1]. Therefore, alternative cost-effective approaches are required to estimate the ranges T. Nemecek () • K. Weiler • J. Schnetzer • G. Gaillard Agroscope Reckenholz-Tänikon Research Station (ART), Zurich, Switzerland e-mail: thomas.nemecek@art.admin.ch K. Plassmann Agroscope Reckenholz-Tänikon Research Station (ART), Zurich, Switzerland Johann Heinrich von Thünen-Institute, Braunschweig, Germany D. Jefferies • T. García–Suárez • H. King • L. Milà i Canals Unilever, Sharnbrook, Bedford, United Kingdom M. Finkbeiner (ed.), Towards Life Cycle Sustainability Management, DOI 10.1007/978-94-007-1899-9_30, © Springer Science+Business Media B.V. 2011

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of impacts of different crops. Milà I Canals et al. [2] presented two different approaches to filling data gaps: use of proxy data and extrapolation. Proxies are data describing an alternative product, considered to have similar impacts as the product under study. Milà I Canals et al. [2] distinguished scaled proxies, direct proxies and averaged proxies. Using proxy data means that the original values are used without transformation, beyond statistical calculation like averaging. Extrapolation means on the contrary that the original values are transformed in some way. For example, key parameters like the yield for crop production or the feed conversion ratio are used to adapt the original dataset. Another possibility is to take into account a number of influencing factors. For both approaches - using proxies and extrapolation - we need to understand the most critical factors influencing the environmental impacts of a crop product: pedoclimatic conditions, affecting the direct field emissions and also the use of inputs like fertilisers and irrigation water, farming practice, prevalence of pest and diseases, etc. All these factors also influence the crop yield [1]. The latter is shown to be a major factor influencing the environmental impacts per product unit. In this paper we use the extrapolation method MEXALCA (Modular EXtrapolation of Agricultural LCA, [3]) aiming at extrapolating life cycle inventories and impacts between different geographies in order to analyse the relationship between the yield and the environmental impacts and to show in which cases the yield can be a sufficient criterion for selecting proxies and extrapolation.

2

Extrapolation methodology MEXALCA

Detailed data are required for at least one typical production system that is then used as the baseline for the extrapolation to all other (target) countries in the world producing the same crop [3]. Ideally this should be a representative dataset for a large producing country and extreme situations should be avoided. This base country life cycle inventory is split into nine modules corresponding to the main farming operations and inputs known to dominate the environmental impacts of crop cultivation (Table 1). With the exception of the basic cropping operations, which are assumed to be constant worldwide, each module is varied as a function of the agricultural intensity index and of the yield (except soil tillage, since it is assumed that no-till agriculture does not significantly change the yield [4]). Mathematical functions relating global statistics of crop yields and agricultural production intensity (mainly FAOSTAT) in the baseline and target countries, respectively, are applied for the geographical extrapolation for each modular farming input. The extrapolation provides estimates of environmental impacts for

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each producing country; the variability within a country due to regional differences, farming systems, etc. is not considered. The worldwide distribution is obtained by weighting the single country values by the respective production volumes. The first version of the extrapolation methodology and validation using data from the ecoinvent database is described in [3]. The method has been further applied to a number of other crops [5]. Moreover, the method has been adapted by using the same estimator for irrigation as for tillage, variable machinery use, fertiliser and pesticide use. Tab. 1: Overview of the modules of MEXALCA

Category Machinery

Module Basic cropping operations Tillage

Machinery

Variable operations Per ha (area unit)

General

N fertilisation, Fertilisation including Nemissions P fertilisation, Fertilisation including Pemissions Fertilisation K fertilisation Plant protection

Pesticide application

Irrigation

Irrigation

Product drying

Product drying

Impacts

Input parameter

Constant per area unit

-

Per ha (area unit)

% of no-till area Mechanisation index

Per kg N applied (input mass unit)

kg N applied

Per kg P2O5 applied (input mass unit)

kg P2O5 applied

Per kg K2O applied (input mass unit) Per kg pesticide (active ingredient) applied (input mass unit) Per m3 of irrigation water supplied (input volume unit) Per kg of evaporated water (mass unit)

kg K2O applied kg active ingredient m3 water used kg water evaporated

To include the effect of land use change, potential emissions from deforested land were included by applying an approximation model, where the total above ground biomass of cleared forests is calculated as an emission of CO2, while changes in soil organic matter are not considered. The emissions of deforestation are allocated to 100% to the agricultural area (arable land + permanent crops + pastures), other driving factors for deforestation (like timber production) are neglected. The emissions in a given country were distributed to the country's total agricultural area, which means that each ha of land occupied in that country carries the same burden from deforested land, not taking into account the fact that


some crops may be grown more on deforested land than others. The result therefore depends on the countries where the considered crop is grown. They indicate potential risks, but it is a rough model and the results must thus be interpreted with caution. Carbon fixation by crops was not considered, although this may arguably be relevant for plantations. The reliability of the extrapolation results mainly depends on numerous factors like modelling assumptions, factors not considered, and the quality of the underlying data (production inventories, choice of original countries, yield statistics, statistics of agricultural input use) [3].

3

Contribution of modules to the global warming potential

The method was applied to 27 crops [5]. MEXALCA results were validated by using data from the ecoinvent database [6] and using literature data for nonrenewable energy demand and global warming potential (GWP). The validation results are reported in [5]. The method performs reasonably well for the impact categories non-renewable energy demand, GWP and ozone formation potential as well as land occupation. 14000 12000

kgCO2eq/ha

10000

Deforestation Drying

8000

Irrigation Pesticides

6000

Kfertilisation Pfertilisation Nfertilisation

4000

Var.Mach. Tillage

2000

Basic

Wheat Barley Rye Maize Rice Oilpalm Rapeseed Linseed Peanuts Cotton Soybeans Pea Potato Sugarbeet Sugarcane Spinach Tomatoes Pumpkin Carrot Onions Bellpepper Apples Bananas Oranges Peach Almonds Hazelnuts

0

Fig. 1:

Worldwide means of the global warming potential per ha and growing season of 27 crops weighted by production volumes, showing the contribution of the modules and the potential effects of deforestation.

The contribution of the nine MEXALCA modules as well as of deforestation to the GWP is shown in Figure 2 per ha and growing season (in case of permanent crops it is one year) and in Figure 3 per kg product (fresh matter). The highest


GWP per ha was found in rice (mainly due to the methane emissions from rice fields that are included in the basic cropping operations), followed by bananas, oil palm, bell peppers and tomatoes. Low GWP values per ha were found for pea, rapeseed, linseed and cereals. Soybean would have the lowest value of all crops without consideration of deforestation. On a per kg basis the pattern was different due to the very different yields. Rice had still a very high impact, but peanuts were at a similar level (mainly due to low yields). The lowest GWP values were found for the sugar crops (sugar cane and sugar beet). We have to consider that the results are given per kg of fresh mass in FAOSTAT (and this is also the unit in which the traded commodities are expressed) and that the dry matter content differs considerably between crops. 3

2.5 Deforestation

kgCO2eq/kg

2

Drying Irrigation

1.5

Pesticides Kfertilisation Pfertilisation

1

Nfertilisation Var.Mach.

0.5

Tillage Basic

Wheat Barley Rye Maize Rice Oilpalm Rapeseed Linseed Peanuts Cotton Soybeans Pea Potato Sugarbeet Sugarcane Spinach Tomatoes Pumpkin Carrot Onions Bellpepper Apples Bananas Oranges Peach Almonds Hazelnuts

0

Fig. 2:

Worldwide means of the global warming potential per kg product of 27 crops weighted by production volumes, showing the contribution of the modules and the potential effects of deforestation.

The contributions from deforestation varied considerably between the crops. The relative contributions from deforestation to the GWP (Figure 4) were highest for oil palm (mainly Indonesia and Malaysia) and soybeans (mainly Brazil), two crops much discussed with respect to land use change issues. Sugar cane could also have a considerable contribution, which is due to the fact that 32% of the production is located in Brazil. For oranges, the main contributions come from Brazil and Indonesia. For peanuts, Indonesia, Nigeria and Myanmar contributed most to this impact. The deforestation impact for onions was dominated by Indonesia, Myanmar and Brazil; for bell peppers Indonesia played the major role. For pumpkin this was due to Indonesia, Cameroon and Philippines; for maize to Brazil


and Indonesia. Without the inclusion of land use change the N fertilisers (and related emissions) irrigation and basic cropping operations had the highest impact for most crops.

4

Relationships between yield and environmental impacts 4.1 Comparison between crops

The effects of the yield were investigated first for the worldwide means of the GWP of 27 crops weighted by production volumes (as presented in Fig. 1 and Fig. 2). All results presented in this subchapter are excluding the deforestation effects, since the latter depends only on the producing countries and by definition has no relationship with the yield. The GWP per ha (weighted worldwide mean) was not dependent solely on the yield (Fig. 3). There was a high variability of GWP of crops with low yields on the one hand; high yielding crops on the other hand did not necessarily have high GWP impacts. Per kg there was a clear dependence on the yield: The higher the yield of a crop on average, the lower the GWP per kg. The correlation coefficient between the GWP per kg and the inverse of the yield (ha/kg) was r2=0.39, i.e. the yield explained almost 40% of the variance. The weighted means were less variable per ha (coefficient of variation, CV=71%) than per kg (CV=107%).

10

tCO2eq/ha

3

tCO2eq/ha kgCO2eq/kg

8

2.5 2

6

1.5

4

1

2

0.5

0

0 0

Fig. 3:

20

40 yieldt/ha

60

kgCO2eq/kg

12

80

Worldwide means of GWPs of 27 crops per ha weighted by production volumes and per kg as a function of the yield.


4.2 Comparison between geographies

12

1.4

10

1.2

8

kgCO2eq/kg

tCO2eq/ha

In a second step the yield effects were investigated for all country values of the selected crops. Only countries with a contribution of at least 0.1% to the worldwide production were considered, since the yield data from countries with marginal productions appeared to be unreliable. For all investigated crops, the GWP per ha increased with the yield. A higher yield generally correlates with a more intensive production and higher use of inputs like fertilisers or pesticides. Wheat is such an example of a crop, where we can observe the increase of GWP per ha, while there was a slightly increasing trend for the GWP per kg with increasing yield (Fig. 4).

0.8

6

0.6

4

0.4

2

0.2 0

0 0

Fig. 4:

1

5 Yieldt/ha

10

0

5 Yieldt/ha

10

Global warming potential of wheat per ha (left) and per kg (right) as a function of the yield for all producing countries with a share >0.1% of the worldwide production volume.

A different type of relationship where GHG is inversely correlated with the yield per ha was found for a second group of crops where the basic cropping operations had a major contribution. This case is illustrated by pea (Fig. 5). The basic cropping operations are assumed constant worldwide, which - together with a low yield - results in a high impact per kg. In general the GWP was more variable per ha than per kg (variability as expressed by the coefficients of variation, weighted worldwide standard deviation divided by the weighted worldwide mean).This means that higher GWP per ha of more intensively managed crops are partly compensated by the higher yields, when calculated per kg. The results presented in this paper are model outputs and therefore dependent on the model assumptions. Indeed, the same kind of analysis results in similar findings, when applied to values derived from the literature.

1.6

2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

1.4 1.2

kgCO2eq/kg

tCO2eq/ha


5

0.8 0.6 0.4 0.2 0

0

Fig. 5:

1

1

2 3 Yieldt/ha

4

5

0

1

2 3 Yieldt/ha

4

5

Global warming potential of pea per ha (left) and per kg (right) as a function of the yield for all producing countries with a share >0.1% of the worldwide production volume.

Discussion and conclusions

The systematic application of MEXALCA to a range of crops has allowed us to generate efficiently impact values representing estimates for production around the world. With these estimates, relationships between impacts and production parameters may be established which may shed light in e.g. guiding the choice of proxy data or extrapolating datasets in order to bridge data gaps. When we analyse the relationships between yield and GWP we need to distinguish the difference between crops and within crops on the one hand and the considered functional unit on the other hand, namely ha and kg. The analysis of the weighted means of different crops showed no trend per ha, but a decreasing trend per kg. This implies that when we use GWP impacts from a similar crop as a surrogate for a crop for which data are missing, the yield should be included in the extrapolation model for the environmental impacts per kg of product. However, 60% of the variance was due to other factors than the yield. Comparing GWP of the same crop produced in different countries, we found an increase of the GWP per ha with increasing yield per ha, which is generally related to higher farming intensity. For the GWP per kg, no relationship with the yield was observed for most crops. Other factors than the yield seem to be more relevant: the ratio between inputs or emissions on the one hand and the yield on the other hand is determining the impacts together with pedoclimatic conditions, the farming system, etc. For crops, where the GWP is mainly caused by the basic cropping operations and tillage, the GWP per kg decreases with increasing yield.


For other impact categories, the situation might be different: land use related impacts for example are generally related to the inverse of the yield. Extrapolation with the yield alone is likely to provide good estimates only in a few situations, namely when the contribution from basic cropping operations and tillage to the GWP is significant. In fact, when the impacts from a crop are to be used as a proxy for another crop, the yield should be considered. For the extrapolation between producing countries of a given crop, the GWP decreased for those crops where the area-related impacts dominate, while for the other crops either no trend or a slight increase was observed. A case by case analysis is therefore required. Low yields do not necessarily lead to high environmental impacts per kg.

References [1]

[2]

[3] [4] [5]

[6]

Nemecek T, Kägi T (2008) Ecoinvent-based extrapolation of crop life cycle inventories to new geographical areas. In: Proceedings of the 6th International Conference on LCA in the Agri-Food Sector – Towards a sustainable management of the Food chain, Zurich Milà i Canals L, Azapagic A, Doka G, Jefferies D, King H, Mutel C, Nemecek T, Roches A, Sim S, Stichnothe H, Thoma G, Williams A (2011) Approaches for addressing Life Cycle Assessment data gaps for bio-based products. J Ind Ecol.In press. Roches A, Nemecek T, Gaillard G, Plassmann K, Sim S, King H, Canals LMi (2010) MEXALCA: a modular method for the extrapolation of crop LCA. Int J Life Cycle Assess 15:842-854. Chervet A, Ramseier L, Sturny W, Tschannen S (2005) Direktsaat und Pflug im 10-jährigen Systemvergleich. AgrarForschung 12(5):184-189. Nemecek T, Weiler K, Plassmann K, Schnetzer J, Gaillard G, Jefferies D, García–Suárez T, King H, Milà i Canals L (2011) Globally averaged GWP and its Variability for a Multitude of Crops as estimated with a Modular Extrapolation Approach (MEXALCA).In prep. Ecoinvent Centre (2007)Ecoinvent Data - The Life Cycle Inventory Data. Swiss Centre for Life Cycle Inventories, Dübendorf