GREY WATER FOOTPRINT OF CROPS AND CROP-DERIVED PRODUCTS: ANALYSIS OF WEAKNESSES AND SENSITIVITY IN CALCULATION METHOD

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1 Proceedings of the 13 th International Conference on Environmental Science and Technology Athens, Greece, 5-7 September 2013 GREY WATER FOOTPRINT OF CROPS AND CROP-DERIVED PRODUCTS: ANALYSIS OF WEAKNESSES AND SENSITIVITY IN CALCULATION METHOD C.S. LASPIDOU University of Thessaly, Department of Civil Engineering EXTENDED ABSTRACT The explosive increase in world population, along with the fast socio-economic development, has led to an increased water demand, making water shortage one of the greatest problems of modern society. The impact of global water resource consumption can be mapped with the concept of the water footprint. The water footprint of an entity is defined as the total volume of freshwater used to produce the goods and services consumed by the consumers in that entity. An analog to the ecological footprint that denotes the area (in hectares) needed to sustain a population, the water footprint represents the water volume (in m 3 /year) required and can be defined for a nation, a product, a process, a group of consumers, a business, etc. In order to assess the water footprint of a crop in a country, we use information on the domestic water resources used for crop growth, namely the virtual water of the crop. Virtual water (or embedded water ) is defined as the volume of water used to produce a commodity and is classified as green, blue and dilution water. Water footprint analysis is a geographically and temporally explicit indicator of different water sources. These 'sources' can break down into direct and indirect water use, or into a specific type of water (i.e. blue, green, and grey water). Blue water refers to the volume of fresh ground and surface water consumed along the supply chain of a product; green water quantifies the volume of precipitation stored in the soil as soil moisture; and grey water measures the volume of water required to dilute pollutants associated with a product's production chain to meet ambient water quality standards. The grey water footprint is a different segment of quantifying available water resources; calculations require much different data inputs than the blue and green counterparts. Grey water takes into consideration the degree to which water consumed throughout the process is polluted. When applied to growing an agricultural crop such as wheat or rice, the calculation of the grey component of the total footprint requires that the following quantities be determined: chemical (fertilizer) application rate, crop yield, fertilizer leaching fraction, maximum allowed pollutant concentration in natural water bodies and natural pollutant concentration in the water body in which pollution is rejected. A large part of this data is usually not available; hence, researchers seem to proceed with a series of crude assumptions and report grey water footprint figures. In this article, the assumptions that are commonly used in the literature are reviewed and prominence is given to weaknesses of the calculation method, while the method sensitivity is also assessed. Keywords: Water Footprint, Grey Water Footprint, Nitrogen Pollution, Nutrient Leaching Fraction

2 1. INTRODUCTION World population increase, followed by an increase in food production and energy consumption, has exerted an intense stress on energy and water resources. Furthermore, over the next three decades, projected population and economic growth levels will exert stress on freshwater and energy demand, while water pollution and water quality degradation have become important issues for many regions. People continue flocking in urban areas, creating more pressure on local water resources. Water faces increasing demands due to increased global population, as it is intimately connected to food production, economic growth (Laspidou et al., 2012) and energy production the socalled water-energy nexus. Anthropogenic nitrogen flows are a major component of the earth s biogeochemical cycles and have become a topic of increasing research attention; nitrogen loading to land surface has doubled since the pre-industrial time (Chatzimpiros and Barles, 2010). Such changes in nitrogen flows significantly affect the sustainability of food production and the environment, since excessive nitrogenous fertilizer use causes eutrophication, loss of diversity, water and air pollution and others; therefore, poor management in nitrogen application can seriously affect environmental health. Crop production is by far the single largest cause of human alteration of the global nitrogen cycle, thus raising global sustainability concerns (Liu et al., 2010; Gialis et al., 2012; Laspidou et al., 2011). The area and/or volume of water required for the needs and the productive activities of a given population during a specific period of time represent the so-called water footprint of that specific population. Hence, the water footprint corresponds to the total volume of water used to produce the goods and services consumed by individuals and communities or produced by firms, in a defined area (Hoekstra and Chapagain, 2007); for several countries, it plays an important role in the optimization of water resources management. The water footprint can be divided into three components: the blue, green and grey water footprint. Each component is related to a specific type of water resource and the volumes of that type of water that should be consumed for household or productive purposes. The blue component is related to freshwater used from surface water and ground water, the green to rain water and the grey to polluted water, in the sense that this volume of water is required to dilute pollutants in order to reach water quality standards (Hoekstra and Hung, 2005). Mekonnen and Hoekstra (2010) present a global analysis of the water footprint of crop production for a period of ten years. They found that a large part (around 80%) of the global water used is green, confirming the importance of rain for agriculture, while blue and grey water footprint had a smaller share (about 10% each). The quality and reliability of these results are based on the quality of input data and underlying assumptions for these calculations. Regarding grey water, Mekonnen and Hoekstra (2010) state that their estimate is rather conservative, since it is based on the analysis of the required assimilation volume for leached nitrogen fertilizers only, leaving out relevant pollutants such as phosphorus and pesticides that may be equally important on the quality degradation of the aquatic environment. Naturally, the amount of nitrogen that is leached to the aquatic environment is also a quantity that is difficult to determine and the quality of data reported may be questionable. A discussion of the problems and weaknesses embedded in the calculation of grey water in agriculture is the focus of this article. 2. MATERIALS AND METHODS Out of the three types of water footprints (blue, green and grey) and especially in the sector of agriculture, the grey is the one that has been focused on the least. Mekonnen

3 and Hoekstra (2010) present a list of 17 studies, in which no calculation of the grey water footprint is included. They present calculations of the global water footprint of crop production for the period and find that the share of grey water footprint is relatively small (around 10% of the total). According to Mekonnen and Hoekstra (2011), the grey water footprint is calculated by quantifying the volume of water needed to assimilate the nutrients that reach ground- or surface water. Nutrients leaching from agricultural fields are a main cause of non-point source pollution of surface and subsurface water bodies. In most studies, the grey water footprint is quantified as related to nitrogen use only although recently, Liu et al. (2012) focus on phosphorus as well. The grey component of the water footprint (GWF) is calculated by multiplying the fraction (f) of nitrogen that leaches or runs off by the nitrogen application rate (L N) and dividing this by the difference between the maximum acceptable concentration of nitrogen (c N,max) and the natural concentration of nitrogen (c N,nat) in the receiving water body and by the actual crop yield (Y) (Equation 1). Naturally, to obtain an accurate figure for the grey water footprint of a crop, one needs to have a good estimate of the nitrogen fertilizer application rate for the specific crop, as well as the applied nitrogen fertilizer lost through leaching. (1) A sensitivity analysis on this grey water footprint calculation is performed using the global average water footprint volumes for 14 primary crop categories, as calculated by Mekonnen and Hoekstra (2010) for the 1996 to 2005 period. The authors present green, blue and grey water footprint volumes, in m 3 per ton of crop, while grey water calculations are performed for nitrogen, using the following assumptions: f = 10% c N,max = 10 mg/liter of nitrate-nitrogen (NO 3 -N) c N,nat = 0 Calculations of the same figures of grey water footprint volume with different f values are presented in Table 1, in order to test its sensitivity to this factor that is often assumed arbitrarily to be 10%. Sensitivity analysis is also performed for the natural nitrogen concentration c N,nat. Keeping the f value constant at 10% and c N,max at 10 mg/l NO 3 N, grey water volumes are re-calculated for the same 14 crops increasing c N,nat to 2 mg/l NO 3-N and the sensitivity of grey water numbers to this parameter is assessed. Liu et al. (2010) present a detailed analysis of which c N,nat concentrations are appropriate to use, depending on the nutrient of interest (nitrogen or phosphorus, inorganic or organic, particulate or dissolved, etc.) for each water body. In this paper, a simpler analysis is presented using only a single natural concentration of NO 3-N. The results of this analysis are presented in Table 2, as previously. 3. RESULTS AND DISCUSSION As shown in Table 1, an important parameter in the calculation of the grey water footprint of crops and crop-related products is the fraction of nitrogen that leaches, or runs off to surface waters. The number of 10% is used throughout the literature for all countries and crops (Mekonnen and Hoekstra, 2010; Chapagain et al., 2006). Undoubtedly, this is a crude assumption, since leaching fraction f depends on crop type, crop-specific nitrogen fixation, soil composition and condition, meteorological conditions such as atmospheric

4 Table 1. Global average water footprint of 14 primary crop categories for the 1996 to 2005 period. In the Table below, calculations are based on the following assumptions: c N,max = 10 mg/litre of nitrate-nitrogen (NO 3 -N), c N,nat = 0; L N and Y are the same for each crop category, while f is variable. Water footprint (m 3 /ton) * Water footprint (m 3 /ton) ** Water footprint (m 3 /ton) ** f = 10% f = 20% f = 5% Crop category Grey Total % Total in Grey Total % Total in Grey Grey Total % Total in Grey Grey Sugar % % % Fodder % % % Vegetables % % % Roots & tubers % % % Fruits % % % Cereals % % % Oil crops % % % Tobacco % % % Fibers, veg. origin % % % Pulses % % % Spices % % % Nuts % % % Rubber, gum, waxes % % % Stimulants % % % AVERAGE % % % * Source: Mekonnen and Hoekstra (2010). ** Author calculations

5 precipitation, local climate and area topography and relief, among others. The sensitivity analysis performed herein, which involves the recalculation of grey water footprint for different values of f indicates that grey water is quite sensitive to the f value. In fact, doubling f doubles the grey water footprint, tripling f triples the grey water footprint, etc. As shown in Table 1, in the case of vegetables, for example a crop with a high grey water footprint the part of total footprint that is grey can be as high as 41% once the large f value of 20% is used. This compares to the respective percentage of 26% that results from using the widely assumed value of f=10%. There is evidence in the literature that shows how f may vary for different crops and different locations. Liu et al. (2010) define the nitrogen recovery rate as the ratio of nitrogen uptaken by crops to nitrogen input and found it to be equal to 59% globally. This number, which is directly related to the leaching fraction f is highly variable and ranges from 20% in Asia to 70% in Europe and North and South America. It is related to crop yields, to the extent of leguminous crops that incorporate most of the available nitrogen and to crop type, with rice having the lowest recovery rate due to difficulties in controlling nitrogen losses. On the other hand, humid and temperate climates result in cropland soils that remain wet for most of the year. Such soils, together with high fertilizer application rates can result in a high level of nitrate leaching. Liu et al. (2010) show that the highest nitrogen leaching occurs in Western Europe, where a large amount of mineral fertilizer is applied in temperate agro-ecological zones. Good soil and fertilizer management practices can reduce nitrate leaching to a certain extent; in tile-drained systems, however, nitrate losses are unavoidable, since they remain high even with good management practices. A point to be emphasized here is that it is essential that more research and data gathering is done to define the leaching fraction f better and to record it for different areas in the world and various crops and soil types in order to obtain more accurate grey water footprint numbers. Of critical importance to the grey water footprint calculation are the values of c N,nat and c N,max in the receiving water bodies. Until recently, most published articles that present calculations of the grey water footprint would consistently report the value of zero for c N,nat due to lack of data (Chapagain et al., 2006; Mekonnen and Hoekstra, 2011). For the maximum concentration, c N,max, Mekonnen and Hoekstra (2011) state that the recommended maximum value of nitrate in surface and groundwater by the World Health Organization and the European Union is 50 mg nitrate (NO 3) per liter and the maximum value recommended by US-EPA is 10 mg/l measured as nitrate-nitrogen (NO 3-N); thus, in most studies, the standard of 10 mg/l NO 3-N is used following Chapagain et al. (2006), which is a reasonable assumption. In a recent publication, Liu et al. (2012) offer a different point of view for c N,nat, stating that it is not zero, since all rivers naturally transport some nutrients and that it is not the actual concentration of the river or other water body, but the concentration that would occur, if there were no human influence. Given the recent population increase, water scarcity, pollution problems and thus the amount of stress our water bodies have been under during the last couple of centuries, the concentration that would occur if there were no human influence is obviously hard to determine and may even be irrelevant in some cases. Although the grey water footprint of a product refers to the volume of freshwater that is required to assimilate the load of pollutants based on existing ambient water quality standards, using as c nat the concentration that would occur, should there exist no human influence, does not take into account the existing water quality of the water bodies. For the same crop and nutrient load, we would get the same grey water footprint, no matter what the water quality of the water body that receives the crop runoff is. A major weakness of the method is that although the impact would be different if the runoff would flow in a high water-quality high-flow river, or in a low water-quality low-flow river,

6 the grey water footprint would be the same in both cases. Table 2 shows that increasing the natural nitrogen concentration from 0 to 2 mg/l NO 3-N results in a 25% increase of the average grey water footprint for all crops (from 306 to 383 m 3 /ton). The percentages vary depending on crop and grey water footprint size, but in general, we see that the natural concentration is significant in the calculation of grey water footprint. Liu et al. (2012) reach the same conclusion, although they follow a different calculation procedure. Great care needs to be taken in defining the natural pollutant concentration figures for each water body in order to obtain accurate grey water footprint figures for each crop. Table 2. Global average water footprint of 14 primary crop categories for the 1996 to 2005 period. Calculations are based on the following assumptions: cn,max = 10 mg/l NO3 -N, f = 10%; LN and Y are the same for each crop category. Water footprint (m 3 /ton) * Water footprint (m 3 /ton) ** c N,nat = 0 c N,nat = 2 mg/l NO 3 -N Crop category Grey Total % Total in Grey Grey Total % Total in Grey Sugar % % Fodder % % Vegetables % % Roots & tubers % % Fruits % % Cereals % % Oil crops % % Tobacco % % Fibers, veg. origin % % Pulses % % Spices % % Nuts % % Rubber, gum, waxes % % Stimulants % % AVERAGE % % * Source: Mekonnen and Hoekstra (2010). ** Author calculations An important issue for the calculation of the grey water footprint for crops is that fertilizer application rates per crop per country are not available for most crops. The figures that are available are usually gross estimates based on crude assumptions, or are based on outdated figures that may be irrelevant when used. Terres et al. (2002), in a Joint Research Center European Commission report present an analysis on the agricultural nitrogen quantity for EU river basins based on Nomenclature of Territorial Units for Statistics (NUTS) census level data for the years 1990 to Thus, the quality of estimates in 2002 is already questionable since it is based on data that are over five years old. To deal with the lack of data, Mekonnen and Hoekstra (2010) use rates, which are based on different sources and a number of assumptions. Thus, the same crop in a country is assumed to receive the same fertilizer application rate everywhere in the country. This assumption becomes problematic for countries with large and diverse agricultural areas, since they may have differences in soil structure and fertilization needs. It also becomes problematic for countries with bold topographical relief that may grow crops in quite different agricultural settings. Irrigated crops generally receive more fertilizer than

7 rain-fed ones. Besides, most small subsistence farmers likely use no or less fertilizer. The grey water footprint is estimated based on a simplified approach, which gives a rough estimate; it leaves out local factors that influence the precise leaching and runoff rates, such as rainfall intensity, irrigation patterns, soil property, slopes and the amount of already mineralized nitrogen in the upper soil layer. Systematic comparison of the estimate from such simplified approach with other regression models (Liu et al., 2010) might be required to test the uncertainties and limitation of this approach. A good example here is olive cultivation, which is intensely fragmented, with olive farming being done either in intensified traditional (low-density) or modern (higher-density) plantations making systematic use of fertilizers and pesticides, or in mountainous plantations that are usually exclusively rain-fed with no chemical inputs (Nianoukis and Halvadakis, 2006). Obviously, for olive groves in the same country, it may be difficult to generalize on the rate of fertilizer application. Future research should focus on better defining the fertilizer application rate for each crop and geographical area. The choice of pollutant related to grey water footprint calculation is also important. Although most studies focus on nitrogen, other pollutants could be equally important: pesticides, herbicides such as atrazine, organics usually related to industrial cropprocessing plants and thermal pollution are some of the polluting factors that are usually not given enough attention by researchers. Processing of crop-derived products often results in large pollution loads ending up in water bodies and the simple emphasis on nitrogen pollution for such products results in the underestimation of the grey water footprint of these crops to the environment. Olive-mill waste is a good example, as researchers focus mostly on nitrogen grey water for the olive crop (Salmoral et al., 2010), when high-bod olive processing waste creates big environmental problems in major olive-producing countries that often lack specific regulation regarding the discharge of this waste. In Greece for example, in practice, all the generated olive-mill wastewater results in creeks (58%), or in sea and rivers (11.5%), or in soil (19.5%) (Niaounakis and Halvadakis, 2006). Grey water footprint analysis of crops and crop-derived products should, in a systematic way, be expanded to include all aspects of water pollution and not remain limited in the environmental impact of nutrient leaching. A point to be made regarding current sophisticated water footprint analysis is that it focuses almost exclusively on volume, which is a very good tool to raise awareness, but probably not sufficient to represent the impact on a water resource. Thus, while the volume of water footprint maybe the same for a crop or a product regardless where it is produced, the impact on a given water resource reservoir, lake, river, etc., maybe completely different, given the local water scarcity, pollution-stress and water quality. Vegetable crops grown in water-stressed areas with significant reliance on irrigation will have a multiple impact on water resources than a different crop with the same or even higher volumetric water footprint that is grown in areas with abundant fresh water supplies. The water footprint concept should be expanded from the volumetric analysis towards including the aspect of impact on local water resources, taking into account localized water quality and scarcity/abundance. 4. CONCLUSIONS An analog to the ecological footprint that denotes the area needed to sustain a population, the water footprint represents the water volume required and can be defined for a nation, a product, a process, a group of consumers, a business, etc. It is a new and important tool that has been in use for only about a decade, but has already done a great deal in raising awareness about the critical importance of water use and the hidden water associated with production in modern society. Grey water footprint specifically places the necessary emphasis on water pollution and the limited pollutant-assimilative

8 capacity of our water resources. Lack of data and crude assumptions are some of the biggest problems of these calculations. In this paper, we bring out some of the weaknesses of the grey water footprint calculation method. Nutrient leaching fraction f and natural concentration c nat are two of the factors included in the calculation that are being studied herein and are proved to be important in predicting grey water footprint accurately. The analysis shows that research should be directed towards better defining these quantities since they greatly affect grey water footprint volumes for crops and croprelated products. If water footprint analysis is going to be used as a tool for water resources management, it should probably be expanded to include the localized impact on water resources, taking into account quantity and quality of local water resources. REFERENCES 1. Chapagain, A.K.; Hoekstra, A.Y.; Savenije, H.H.G. and Gautam, R The water footprint of cotton consumption: an assessment of the impact of worldwide consumption of cotton products on the water resources in the cotton producing countries, Ecological Economics, 60(1): Chatzimpiros, P. and Barles, S. Nitrogen, land and water inputs in changing cattle farming systems and Sci Total Environ 408(20):10 (2010), PMID Gialis, S.E.; Loukas, A. and C. Laspidou Regional Development and World Trade: A Review of Virtual Water and Water Footprint Concepts in Agriculture, Proceedings: Protection and Restoration of the Environment XI: Hoekstra, A.Y. and Chapagain, A.K The water footprints of Morocco and the Netherlands: Global water use as a result of domestic consumption of agricultural commodities. Ecological Economics, 64: Hoekstra, A.Y. and Hung, P.Q Globalisation of water resources: international virtual water flows in relation to crop trade. Global Environmental Change Part A, 15(1): Laspidou, C.S., I. Kakoulidis and A. Loukas Ecosystem simulation modeling of nitrogen dynamics in the constructed wetland Carla in Greece Desalination and Water Treatment 33: Laspidou, C.S.; Nydreos-Sakouelos, P. and Kungolos, A Carbon footprint calculation of desalination units in Greece. Fresenius Environmental Bulletin 21(8b), Liu, C., Kroeze, C., Hoekstra, A.Y. and Gerbens-Leenes, W Past and future trends in grey water footprints of anthropogenic nitrogen and phosphorus inputs to major world rivers, Ecological Indicators 18, Liu, J., L. You, M. Amini, M. Obersteiner, M. Herrero, A.J.B. Zehnder and H. Yang A high-resolution assessment on global nitrogen flows in cropland, PNAS 107(17): Mekonnen, M.M. and Hoekstra, A.Y The green, blue and grey water footprint of crops and derived crop products, Value of Water Research Report Series No. 47, UNESCO-IHE, Delft, the Netherlands. 11. Mekonnen, M.M. and Hoekstra, A.Y The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15: Niaounakis M. and Halvadakis, C.P Olive Processing Waste Management: Literature Review and Patent Survey, Waste Management Series 5, 2 nd ed., Elsevier, Oxford. 13. Salmoral, G.; Aldaya, M.M.; Chico, D.; Garrido, A. and Llamas, M.R The water footprint of olive oil in Spain. Fundacion Marcelino Botin, Madrid. 14. Terres J.M., Campling.P., Vandewalle S., VanOrshoven J Calculation of Agricultural Nitrogen Quantity for EU River Basins. Report EUR EN. 87p.