IFA AGRICULTURAL CONFERENCE ON MANAGING PLANT NUTRITION 29 June - 2 July 1999, Barcelona, Spain

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1 IFA AGRICULTURAL CONFERENCE ON MANAGING PLANT NUTRITION 29 June - 2 July 1999, Barcelona, Spain LIFE CYCLE ANALYSIS OF DIFFERENT FERTILIZING STRATEGIES IN WINTER WHEAT PRODUCTION J. Kuesters and F. Brentrup Hydro Agri Europe, Germany

2 LIFE CYCLE ANALYSIS OF DIFFERENT FERTILIZING STRATEGIES IN WINTER WHEAT PRODUCTION 1 J. Kuesters and F. Brentrup Hydro Agri Europe, Germany Juergen.Kuesters@hydro.com SUMMARY The paper describes the results of a Life Cycle Analysis, examining different fertilization strategies (optimum vs. reduced fertilization) in winter wheat production in the north-western part of Germany, and also the impacts of using different types of fertilizers (solids, liquids and manure). The analysis takes account of the environmental impacts from the excavation of raw materials, the production and transportation processes, and the agricultural activities on farmers field. Different LCA methodologies are used to assess the environmental impacts (e.g. global warming, eutrophication, acidification, depletion of fossil fuels) of the wheat production systems, taking account of the potential of each impact to harm the environment. The results show reasonable differences dependent on the methodology used. Using the original Eco-indicator 95 methodology and a modified version of this procedure the impact scores per tonne of grain were highest for the winter wheat systems fertilized with urea and cattle slurry/. In contrast, the use of the Swedish EPS procedure resulted in the lowest impact score for the cattle slurry/ system. Agriculture is carrying the highest environmental burden (73-93% of the Eco-indicator values). Production is responsible for 6-26% of the Eco-indicators, and this analysis shows that packaging and transport have very little negative impact on the environment compared to agriculture. On a per hectare basis, the total Eco-indicator score of the wheat production at optimum N fertilizer rate was 35 % higher compared to the extensive system. However, relating the impacts to one tonne of grain, there was only a difference of 1% in the Eco-indicator values of the different fertilizing systems. 1. Introduction The use of mineral fertilizers is a reason for a continuous debate on the environmental aspects of agriculture. However, the environmental impact of fertilizer production, transport and use has to be seen in a Life Cycle perspective to get a right ranking of the environmental impacts. Life Cycle Analysis (LCA) should be done according to accepted SETAC guidelines. In a first step, the original Eco-indicator 95 methodology (GOEDKOOP, 1995) was used for an integral environmental impact assessment of different farming practices in winter wheat production. To compare the results gained from these calculations a modified Eco-indicator approach (BRAUNSCHWEIG et al., 1996) and the Swedish EPS system (STEEN and RYDING, 1992) were used as alternative impact assessment methodologies. The paper describes the results of such Life Cycle Analysis, examining the impacts of the use of different types and amounts of fertilizers (solids, liquids and manure) to produce winter wheat in the north-western part of Germany. The analysis takes account of the environmental impacts from the excavation of raw materials, the production processes, the transportation of raw materials and products, and the agricultural activities on farmers field. 1 Paper presented at the IFA Agricultural Conference on Managing Plant Nutrition, 29 June-2 July 1999, Barcelona, Spain

3 2. Material and methods 2.1. Methodology of the Life Cycle Analysis (LCA) Three LCA approaches are used to evaluate the environmental impacts of winter wheat production. The Ecoindicator 95 (GOEDKOOP, 1995) and a modified version of this method (BRAUNSCHWEIG et al., 1996) are procedures based on the LCA framework developed by CML (HEIJUNGS et al., 1992). The EPS (Environmental Priority Strategy) method (STEEN and RYDING, 1992) shows great methodological differences compared to the Eco-indicator method. The first step of a LCA, the inventory is a listing of all emissions released and resources used in a production system. This step is mandatory for all LCA methods. The second step in the Eco-indicator method is called classification /characterization. This step is to group and aggregate the emissions according to their environmental effects using equivalence factors, which characterize their contribution to the respective effect (Figure 1). The higher the equivalence factor, the higher is the contribution of an emission to the respective effect. Figure 1 - Aggregation of environmental interventions in the Eco-indicator method Listing of interventions Equivalence factor Effects CO2 N2O CH4 NO3 Ntot Ptot NH3 NOX SO2 VOC Cd Global warming [CO2 equiv.] Eutrophication [PO4 equiv.] Acidification [SO2 equiv.] Summer Smog [C2H4 equiv.] Heavy metals [Pb equiv.] Pesticides 1 Pesticides (kg active ingredients) Energy use Depletion of fossil fuels (GJ) In the third step, the normalization, the contribution of each effect score to the value of the respective effect in Europe is examined. This is done by dividing each effect score by the effect score produced by one average European person during one year (Table 1). The result is a normalized dimension less score for each effect. However, the normalized effect scores say little about the potential of the different effect categories to harm the environment. Therefore, in the fourth step called evaluation, the normalized effect scores are multiplied by a weighting or evaluation factor (Table 2). In the evaluation the distance-to-target principle is used to establish weighting factors for the environmental effects. Distance-to-target means the distance between the current level and a target level of an effect. The target level of an effect represents an acceptable maximum level of impairment to ecosystems and human health according to scientific knowledge. Table 2 gives the weighting factors of each effect for the Eco-indicator methods. The result of this evaluation procedure is an Eco-indicator score for each effect category. As these scores are dimension less they can be summed up and then present the total Eco-indicator score for a system.

4 Table 1 - Normalization values for Europe* (per person and per year) Unit Normalization value Uncertainty Global warming kg CO 2 equ Small Acidification kg SO 2 equ. 111 Small Eutrophication kg PO 4 equ. 38 Moderate Heavy metals kg Cd equ..5 Large Summer Smog kg C 2 H 4 equ. 17 Moderate Pesticides kg active ingredients.966 Large Fossil fuel depletion GJ 248 Small * without former USSR Table 2 - Weighting factors for environmental effects (original and modified Eco-indicator methodology) Environmental effect Weighting factor Criterion original modified Global warming C rise every 1 years Acidification 1 5 exceedance of critical acid loads Eutrophication 5 2,5 Rivers and lakes, degradation of aquatic ecosystems Summer smog Occurrence of smog periods, health complaints, prevention of agricultural damage Heavy metals 5 5 Cadmium content in rivers Pesticides 25 1 exceedance of EU norm for groundwater Fossil fuel depletion* Energy use covered solely by use of renewable resources * own development based on data from RIVM (1992) In the EPS method (EPS = Environmental Priority Strategy, STEEN and RYDING, 1992) environmental interventions (emissions, resource use, land use) are evaluated according to their impact on five safeguard subjects, which are biodiversity, human health, bio-productivity, resources, and aesthetic values. First of all these safeguard subjects are valued on a monetary basis (BRAUNSCHWEIG et al., 1994). After that environmental interventions are classified according to their potential to harm these safeguard subjects. Additional factors like the area or number of persons concerned by an intervention, its intensity and frequency, its extension over time, and its total flow are also accounted for in the evaluation of each intervention (BRAUNSCHWEIG et al., 1994; GOEDKOOP, 1995). This complex calculations yield in socalled Environmental Load Units (ELU) per intervention (Table 3).

5 Table 3 - Weighting factors for emissions and resources (EPS method) Environmental intervention Weighting factor (ELU / intervention) emissions (kg) CO N 2 O 7.21 CH Ntot (incl. NO 3 ).8 Ptot.2 NH 3 - NO x.2167 SO particles.75 VOC - Cd to soil/water - pesticides - Resource use Oil (kg).4 Fossil gas (kg).4 arable land (m 2 *yr).32 The life cycle inventory data (e.g. x kg CO 2 ) can be multiplied by the Environmental Load Units (e.g..889 ELU/kg CO 2 ) to get weighted scores for each intervention. As all these single scores are expressed in ELUs they can be summed up to one total eco-score for the product or process under investigation Goal, scope and system definition Different LCA approaches are used in this study to quantify and compare the environmental impacts of alternative fertilizing strategies in winter wheat production. As functional units one tonne of grain was chosen. The straw remained on the field and was therefore not taken into account in the calculations. Figure 2 shows the system boundary and the environmental inventory (emissions to air and water, resource consumption) considered in the analysis.

6 Figure 2 - Sub-systems and boundary of the winter wheat system Input fossil fuels and resources System boundary Production of fertilizers Exploration of raw materials: fossil fuels process gas minerals Fertilizer production Manufacture of: plant protection substances seeds machines and tractors Packaging: fertilizers, seeds etc. Transport: ocean or inland ship truck train aeroplane Agriculture: soil preparation fertilizer application plant protection harvest, drying Output emissions to air or water - CO2 - N2O - CH4 - Ntot - Ptot - NH3 - NOx - SO2 - VOC - Cd - pesticides The winter wheat systems differ mainly in the form and amount of N fertilizer used (Table 4). There are four mineral N fertilizer systems (calcium ammonium nitrate (), urea and NPK 16:16:16 are used as solid fertilizers, urea ammonium nitrate (UAN) as a liquid form) and one combined cattle slurry/ system. Each of the systems received an optimum N fertilizer rate of 17 kg N/ha. For the cattle slurry/ system it was assumed that 55% of the N amount applied as slurry is directly available for the wheat. Therefore a total of 127 kg N/ha (7 kg NH 4 -N) in the form of the slurry were applied together with 1 kg -N/ha to ensure a mineral N supply of 17 kg/ha. As a sixth system, a system with a reduced N input (1 kg/ha) is included in the analysis to assess its environmental impacts versus the system with optimum N input. This comparison is made on a per tonne of grain and per hectare basis. Information related to P and K fertilization and grain yield is also shown in Table 4. Figures about energy use and emissions associated with fertilizer production are given in Table 5 (production of cattle slurry not considered). Fertilization Table 4 - Important parameters of the fertilizing systems UAN Urea NPK/ optimum 1 17 kg N/ha 7 kg P 2 O 5 /ha () 7 kg K 2 O/ha () Cattle slurry/ Optimum 1 kg N/ha () 127 kg N/ha (slurry) 2 64 kg P 2 O 5 /ha (slurry) 175 kg K 2 O/ha (slurry) extensive 1 kg N/ha () 7 kg P 2 O 5 /ha () 7 kg K 2 O/ha () Yield 8.2 tonnes / ha 6. tonnes/ha 1 NPK / system: 7 kg N in the form of NPK , 1 kg N as

7 2 7 kg N/ha as NH4-N Table 5 - Energy use and important emissions for fertiliser production UAN Urea NPK Cattle slurry per tonne N produced GJ kg CO kg N 2 O kg NH Energy use and emissions for different on-farm activities involved in crop production (soil preparation, seeding, application of fertilizers and plant protection substances, harvesting) were calculated using the following data from literature: ϖ Energy use for the use of farm machinery (tractor and implements) was calculated as 1 MJ per hour and per ton of machinery use, assuming energy use in production of machinery is 4 MJ per kg (Grosse, 1984), and a working life of machinery of 4 hours in 1 years. ϖ Energy use for the repair of farm machinery was assumed to be 188 MJ per ha and year (Haas and Köpke, 1994). ϖ Energy used in the production of plant protection substances was 114 MJ per kg (Oheimb et al., 1987), which was a mean value for herbicides, fungicides, insecticides, and plant growth regulators. ϖ Fuel consumption in field operations (e.g. soil preparation, fertilization, seeding, etc.) was calculated according to data of KTBL (1994) and Hydro Agri (1993). ϖ Energy use involved in the drying of cereal grain was calculated according to data from Hydro Agri (1993). 5% of the harvested grain was assumed to require drying from 16% to 14% moisture content. ϖ NH 3, N 2 O and NO 3 losses on field due to mineral fertilizer and slurry application were calculated based on estimation methods derived from literature (Table 6).Fuel consumption for transport, energy content of diesel and emissions involved in the combustion of diesel fuel were taken from the ETH-Zürich. Table 6 - N emissions on field for the wheat production systems UAN Urea NPK per ha Cattle slurry Volatilization, kg NH Denitrification, kg N 2 O Leaching, kg NO 3 -N NH 3 volatilization: MARSCHNER and HORLACHER (199), ECETOC (1994) N 2O emissions: BOUWMAN, A.F. (1995) NO 3-N leaching: DBG (1992) 3. Results and discussion 3.1. Life cycle inventory (LCI) Figure 3 shows important environmental interventions caused by the total wheat production systems (production, packaging, transport and agricultural activities). CO 2 emissions are highest for the UAN and urea systems, but these fertilizer systems show the lowest N 2 O scores. Compared to UAN and urea all other systems have lower CO 2 but higher N 2 O emissions.

8 Highest NH 3 volatilization is observed for systems, in which urea containing fertilizers (UAN, urea and cattle slurry) were applied. For UAN and urea the NO 3 leaching rate from the applied N is low, because after harvest most of the applied N is lost as ammonia and nitrous oxide emissions or taken up by the crop. Differences in fossil fuel consumption, shown as the related energy consumption in GJ, are mainly due to differences in consumption during N fertilizer production. Figure 3 - Important emissions [kg] and fossil fuel consumption [GJ] for the five winter wheat systems at N fertilizer rates of 17 kg/ha score / t wheat UAN 4 Urea 4 NPK Cattle slurry 2 kg CO2 kg N2O kg NH3 kg NO3-N GJ Generally, to properly interprete these results it is necessary to consider the potential of each emission to contribute to an effect. For instance, 1 kg of N 2 O has a higher effect on global warming than 1 kg of CO 2. Therefore in the Eco-indicator method equivalency factors (Figure 1) are used to translate the emission scores into scores for effects like global warming or acidification. This classification/characterization step is part of the Life Cycle Impact Assessment Life Cycle Impact Assessment (LCIA) The effect scores per tonne of grain for the winter wheat systems are shown in Figure 4. The global warming score is highest for all systems. The scores for summer smog, heavy metals and pesticides are very low compared to the other effects. However, these results do not provide a clear picture about the relative preference of one system. For example, the urea system shows the lowest score for the greenhouse effect, but a high score for acidification compared to the other mineral fertilizer systems. Therefore a further evaluation of the results is necessary. Figure 4 - Effect scores for the five fertilizing systems effect score / t wheat kg CO 2 Global warming kg SO2 kg PO 4 kg C2H4 Acidification Eutrophication Summer Smog kg Pb Heavy metals kg active ingredients Pesticides 4 UAN 4 Urea 4 NPK Cattle slurry GJ Fossil fuel depletion

9 In the next step, the normalization, the contribution of each effect score to the value of the respective score in Europe is examined. This is done by dividing each score of the systems by the score produced by one person in Europe per year (Table 1). Figure 5 shows the normalized effect scores for the different fertilizing systems as percentage values. The values clearly show that the contribution of the wheat production systems to global warming, acidification and eutrophication in Europe is much higher than their contribution to the depletion of fossil fuels, the formation of summer smog and the flow of heavy metals and pesticides to surface and groundwater. However, the normalized effect scores say little about the potential of the different impact categories to harm the environment. Therefore, in the next step called valuation, the normalized scores are multiplied by a weighting factor to obtain so-called Eco-indicator 95 values for each impact (Table 2). Figure 5 - Contribution of the fertilizing systems (effects per tonne of grain) to the environmental effects in Europe (effects caused by one average European person) contribution [%] kg CO 2 Global warming kg SO 2 kg PO4 kg C2H4 Acidification Eutrophication Summer Smog kg Pb Heavy metals kg active ingredients Pesticides 4 UAN 4 Urea 4 NPK Cattle slurry GJ Fossil fuel depletion The resulting Eco-indicator scores for each effect category are dimensionless and can be summed up to present the total Eco-indicator score for a system. The higher the Eco-indicator value, the greater the potential to harm the environment. Figure 6 shows the highest Eco-indicator for the Urea and cattle slurry/ system, which was mainly due to their higher eutrophication and acidification potential. The Eco-indicator values of the other effects were only slightly different between the systems. Figure 6 - Eco-indicator scores for the winter wheat production systems calculated with the original Eco-indicator 95 method 1,4 Eco-indicator / t wheat 1,2 1,8,6,4,2 pesticides heavy metals summer smog eutrophication acidification global warming UAN Urea NPK Cattle slurry

10 Today numerous methods showing marginal differences are available for LCA purposes. The result of an LCA is always influenced by the methodology which was chosen and should therefore be carefully interpreted. To illustrate this, in the following two more LCA procedures (a modified version of the Ecoindicator 95 and the EPS method) are used for an assessment of the environmental impact of the wheat production systems. BRAUNSCHWEIG et al. (1996) suggested a new set of weighting factors for the effects considered in the Eco-indicator 95 approach. Furthermore from own investigations the depletion of fossil fuels was included in this modified Eco-indicator method. As a totally different approach from the Ecoindicator methods the Swedish EPS system (STEEN and RYDING, 1992) is also used to analyze the wheat production systems. Figure 7 shows the relative Eco-indicator scores for three wheat production systems using the original and modified Eco-indicator method. Both methods resulted in highest scores for the Urea and cattle slurry system. However, the modified Eco-indicator values are lower compared to the original Eco-indicator scores due to the reduction in the weighting factors for acidification and eutrophication (Table 2). In contrast to that, the EPS procedure focuses very much on the depletion of natural resources (oil, gas, minerals) and greenhouse gas emissions (e.g. CO 2, N 2 O), but excludes important emissions relevant to agriculture (e.g. NH 3 ) (Figure 8). Therefore the EPS method clearly favours less energy intensive processes. As the mineral fertilizer production is a very energy consuming process and the production of cattle slurry is not considered at all, the EPS score for the cattle slurry/ system is 25% lower than the scores for the and Urea system. This result is totally different from that derived by the Eco-indicator methods. To make a Life Cycle Analysis as transparent as possible it is therefore very important to show the results of different weighting methods as well as the steps before the weighting procedure, i.e. the inventory and/or the effect categories. Figure 7 - Relative Eco-indicator scores for the, Urea and cattle slurry/ system calculated by using the original and the modified Eco-indicator method. The total score for the system is set to 1 % in both methods 35 3 fossil fuel depletion relative score / t wheat, [%] orig. modif. pesticides heavy metals summer smog eutrophication acidification global warming 5 Urea Urea Cattle Cattle slurry slurry

11 Figure 8 - EPS score for the, Urea and cattle slurry/ system compared to the modified Ecoindicator score. system = 1% relative score / t wheat, [%] EPS mod. Eco-indicator Eco-indicator Series12 EPS land use natural gas oil particles SO2 Nox Ptot Ntot CH4 N2O CO2 Urea Urea Cattle Cattle slurry slurry Another interesting aspect of this study is the contribution of the different sub-systems (production, packaging, transport and agricultural activities) to the environmental burden of the complete systems. The share of the agricultural activities on the total modified Eco-indicator value is 73-93% dependent on the wheat production system (Figure 9). Production is responsible for the remaining 6-26% of the Eco-indicators, and this analysis shows that packaging and transport have very little negative impact on the environment compared to agriculture. Finally the environmental impact of the // system with optimum N fertilizer input (17 kg N/ha) was compared to the impact of the system with reduced N fertilizer input (1 kg N/ha) (Figure 1). For this comparison the modified Eco-indicator approach and two functional units were chosen: 1) the product related functional unit, i.e. one tonne of grain and 2) one hectare as an area related functional unit. On a per hectare basis the total environmental impact of the system with a N input of 17 kg/ha was 35% higher compared to the extensive system. Taking account of the different amount of grain produced on one hectare (8.2 tonnes for the optimum and 6. tonnes for the extensive system), i.e. relating the impacts to one tonne of grain, there was only little difference in the modified Eco-indicator value of both fertilizing systems. Figure 9 - Share of the four sub-systems (production, packaging, transport and agriculture) on the total modified Eco-indicator. = 1% % relative score / t wheat, [%] % 26% 73% 93% 93% Packaging Transport Production Agriculture 2 Urea Cattle slurry

12 Figure 1 - Relative modified Eco-indicator scores per ha and per tonne wheat at optimum (17 kg N) and reduced (1 kg N) fertilizer rates. The system supplied with 17 kg N/ha is set to 1% 11 1 relative score / t wheat, [%] per ha 17 per t fossil fuel depletion pesticides heavy metals summer smog eutrophication acidification global warming References Bouwman, A.F. (1995): Compilation of a global inventory of emissions of nitrous oxide, Ph.D. thesis. Landbouwuniversiteit Wageningen, NL. Braunschweig, A. et al. (1994): Evaluation und Weiterentwicklung von Bewertungsmethoden für Ökobilanzen - Erste Ergebnisse. IWÖ-Diskussionsbeitrag Nr. 19, Universität St. Gallen, Schweiz. Braunschweig, A. et al. (1996): Developments in LCA Valuation. IWÖ-Diskussionsbeitrag Nr. 32, Universität St. Gallen, Schweiz. DBG (1992): Strategien zur Reduzierung standort- und nutzungsbedingter Belastungen des Grundwassers mit Nitrat. Deutsche Bodenkundliche Gesellschaft (DBG), Giessen, Germany. ECETOC (1994): Ammonia Emissions to Air in Western Europe. Technical report No. 62. European Chemical Industry Ecology & Toxicology Centre, Brussels, Belgium. Goedkoop, M. (1995): The Eco-indicator 95, final report, NOH report Pre consultants, Amersfoort, Netherlands. Grosse, W. (1984): Zum energetischen Herstellungsaufwand von Landmaschinen. Agrartechnik 34, Haas und Koepke (1994): Enquete-Komission "Schutz der Erdatmosphaere" des Deutschen Bundestages, Economica Verlag, Bonn. Heijungs, R. et al. (1992): Environmental life cycle assessment of products, Guide LCA October CML, Leiden NL. Horlacher and Marschner (199): Schätzrahmen zur Beurteilung von Ammoniakverlusten nach Ausbringung von Rinderflüssigmist. Zeitschrift für Pflanzenernährung und Bodenkunde 153, Hydro Agri (1993): Faustzahlen. Landwirtschaftsverlag Münster-Hiltrup, Germany. IPPC (1995): Climate Change. Impacts, adaptions and mitigation of climate change. Scientific-Technical analyses, Cambridge University Press, UK.

13 Oheimb von, R. (1987): Indirekter Energieeinsatz im agrarischen Erzeugerbereich in der Bundesrepublik Deutschland. KTBL Schrift 32, Landwirtschaftsverlag Muenster-Hiltrup, Germany. RIVM (1992): The environment in Europe: a global perspective. RIVM report no RIVM, Bilthoven, NL. Steen, B. and Ryding, S.-O. (1992): The EPS Enviro-Accounting Method. IVL Report B18, Swedish Environmental Research Institute, Göteborg.