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Article

Water Use in Livestock Agri-Food Systems and Its Contribution to Local Water Scarcity: A Spatially Distributed Global Analysis †

by
Dominik Wisser
1,*,
Danielle S. Grogan
2,
Lydia Lanzoni
1,
Giuseppe Tempio
1,
Giuseppina Cinardi
1,
Alex Prusevich
2 and
Stanley Glidden
2
1
Animal Production and Health Division (NSA), Food and Agriculture Organization of the United Nations (FAO), 00153 Rome, Italy
2
Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
*
Author to whom correspondence should be addressed.
†
OECD disclaimer: The opinions expressed and arguments employed in this publication are the sole responsibility of the authors and do not necessarily reflect those of the OECD or of the governments of its member countries.
Water 2024, 16(12), 1681; https://doi.org/10.3390/w16121681
Submission received: 6 May 2024 / Revised: 30 May 2024 / Accepted: 30 May 2024 / Published: 13 June 2024
(This article belongs to the Special Issue Assessment of Water Use in Livestock Production Systems and Supply Chains)
Browse Figures
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Abstract

:
There is a growing concern about limited water supply and water scarcity in many river basins across the world. The agricultural sector is the largest user of freshwater on the planet, with a growing amount of water extracted for livestock systems. Here, we use data from the GLEAM model to advance previous studies that estimated livestock water footprints by quantifying water use for feed production, animal drinking water, and animal service water. We additionally account for the role of trade in accounting for feed water allocations to different animals in different countries and make use of a hydrologic model to estimate feed irrigation water requirements for individual crops at a high spatial resolution. Lastly, we estimate the contribution of livestock water abstractions to water stress at a small river basin scale for the entire globe. We find that feed production water accounts for the majority (>90%) of global livestock water withdrawals, though there is regional variation. Similarly, we find large regional variation in the water consumption per head by livestock species. Despite consuming >200 km3 of water per year, we find that reducing water use in the livestock system alone will rarely reduce water stress in high-stress basins. This study highlights the need for quantifying locally relevant water use and water stress metrics for individual livestock systems.

1. Introduction

The agriculture sector as a whole is responsible for about 70% of all water abstractions globally and 90% of total water consumption globally [1], mostly for irrigating crops, a significant fraction of which is used to feed animals. With a growing population and an expected increase in the demand for animal products of about 20 percent [2], there is a growing concern about limited water supply and water scarcity in many river basins.
Effective strategies to increase water use efficiency are therefore needed to inform policies, and these should rely on systematic and consistent assessments of the different types of water use along the value chain of livestock agri-food systems. Such assessments should trace the impacts of water consumption to the place of production of feed crops and assess the impact of water abstractions at the river basin level at which water scarcity assessments are most meaningfully carried out.
The appropriation of water for animal products has gained considerable attention during the last decade, partly in relation to debates around environmental impacts of different diets. Many of the studies have used the water footprint concept [3], which measures consumptive water uses for feed crops and direct water use. This approach is adopted as an indicator of the sectors’ impact on water resources, related to meat, milk, and eggs (e.g., [4,5,6,7]). Most of these studies have focused on assessing the water footprints at the country, species, and production system level while overlooking the role of animal feed trade. However, FAOSTAT data suggest an increasing growth in the trade of feed crops in recent years. This gap in previous analyses disconnects the impacts of feed on water resources from where feed crops are grown.
Despite the wide attention these studies have gained, a global water footprint for a given animal product itself is very limited for informing policies regarding national water use or trade [8,9]. These global metrics, due to the lack of spatial granularity in data reporting, fail to adequately differentiate between the distinct water uses for animal feed production and direct water use by animals. Further, they typically report consumptive water use, leaving out information on water withdrawals, which can be up to three times as high as consumption locally [10]. Instead, it is necessary to analyze highly local water withdrawals at each stage along the livestock product production chain and put them in the context of local water budgets, following the recommendations of life cycle assessment (LCA) methods [11]. Water abstraction metrics are also more closely related to indicators defined to measure progress toward achieving the sustainable development goals (SGDs), such as the level of water stress (SDG 6.4.2 indicator), which relates freshwater withdrawal to available resources.
Water use in livestock can be assessed using different methods and system boundaries. Here, in alignment with the Technical Advisory Group for Water Use Assessments of the Livestock Environmental Assessment and Performance Partnership (LEAP) at the Food and Agriculture Organization of the United Nations (FAO) guidelines [12], we estimate livestock’s (blue) water footprint and take a life cycle assessment approach to water scarcity.
We present a spatially varying global assessment of water consumption and withdrawal from livestock agri-food systems. We disaggregate these results by animal species, production systems, and world regions, and separately estimate different water withdrawals along different steps in the production chain. Lastly, we put these withdrawals in the context of local water resources by evaluating how they contribute to the blue water stress index of small (50–500 km2) basins.

2. Materials and Methods

2.1. Overview

We combined global data and modeling methods to estimate total livestock water withdrawals and how livestock water use impacts basin water stress at regional basin scales (50–500 km2 basin areas). First, we calculated the direct livestock water use, which is the water that animals drink, as well as water used for washing and other on-farm applications (Section 2.2). We then calculated the indirect water use, which is the water required to grow feed for livestock (Section 2.3). Lastly, we calculated both the basin blue water stress index (BWSI, and what the BWSI would be without livestock water withdrawals based on the water available and all water extractions within each basin (Section 2.4). Data used for analysis and modeling are described in Table S3.
We largely followed the recommendations of the Technical Advisory Group for Water Use Assessments of the Livestock Environmental Assessment and Performance Partnership (LEAP) at the Food and Agriculture Organization of the United Nations (FAO). These guidelines are aimed at harmonizing scientific yet practical methods to apply life cycle assessment (LCA) for water use along the production chain from cradle to primary processing stages [13]. Three categories of water use are distinguished: direct water used for drinking water for the animals, direct water used for service water for animals during the on-farm production process (e.g., chilling and cleaning), and indirect water that was used to produce animal feed (Figure 1).

2.2. Direct Water Use

Direct water use was estimated using the Global Livestock Environmental Assessment Model (GLEAM v 3.0; [2]), which was originally developed to calculate greenhouse gas emissions along livestock supply chains but has recently been extended to assess interactions of livestock with water and nutrient cycles. In GLEAM, livestock–environment interactions are assessed globally at the pixel level, currently at a spatial resolution of 5 arc minutes (around 10 km at the equator).
In GLEAM, production systems are first classified according to management practices. Ruminants are classified as mixed or grassland, pigs as backyard, intermediate, or industrial, and chickens as backyard, layers, or broilers. Then, within farming systems, herds are classified according to productive orientation purposes as dairy or beef for ruminants, meat only for pigs, and meat or eggs for chickens.
A key data source for GLEAM is the gridded livestock of the world [14], a high-resolution spatial representation of the number of animals for six species (cattle, buffalo, goats, sheep, pigs, and chickens). Animal numbers from GLW were rescaled in each country to match the total stocks reported in FAOSTAT [15]. Based on total stocks, GLEAM estimates the structure of the herd and estimates the number of male and female animals in different cohorts, each having different body weights, as well as feed and water intake requirements. For ruminant systems, three cohorts are distinguished for adult animals, replacement animals, and meat animals. Replacements typically refer to young females or males that are intended to replace older, less productive, or unhealthy reproductive animals in the stock to maintain the herd size and productivity.
For each of these categories, we calculate water abstractions from rivers, groundwater, and fossil groundwater sources, as well as the consumptive water use by species and production systems and the consumptive part of the water that is no longer available for other uses in the hydrological cycle because of evaporation or other losses.
Drinking water requirements were sourced from LEAP guidelines [13] and were assumed to be mostly dependent on body weight, physiological state (lactating vs. non-lactating), and herd orientation (beef vs. dairy animals) for ruminants and pigs. Conversely, for chickens, the drinking water requirements were considered as mostly dependent on ambient temperature. However, it should be noted that several other factors, not accounted for in this study for the sake of simplicity, can influence the actual water consumption by livestock. These include ambient temperature, which also affects ruminants and pigs, as well as water quality, the level of feed intake, and the composition of the diet [16,17]. Service water requirements are highly variable and depend on the type of production system, management practices, and local practices and conditions. In general, they are higher for housed animals than for grazing animals.
We assumed that 5% of service water and 80% of drinking water withdrawals were consumptive. Table 1 summarizes the cohort-weighted daily requirements for drinking and service water for different species and production systems.
The results of these GLEAM model simulations are spatially distributed, animal-specific estimates of service water and drinking water consumptive and non-consumptive withdrawals.

2.3. Indirect Water Use

Indirect water use for livestock is defined here as all water required to grow crops for animal feed (Figure 1). Note that, here, we do not include any water used in the industrial processing of crops post-harvest, or any used in fertilizer production. Many crops are grown as both food (for human consumption) and feed (for animal consumption), and, in some cases, the same crop is divided into portions for food and portions for feed. For example, maize can be grown for either direct human consumption or as feed. Olives can be pressed into olive oil for human consumption, and the residual olive cake can be used as animal feed. Additionally, many crops are traded internationally, and so accounting for the water required to feed a given animal must also consider where the animal’s feed came from. Therefore, our estimation method used three steps: (1) modeling to estimate irrigation water requirements for all irrigated crops (both food and feed) in every grid cell globally that contains an irrigated harvested area; (2) allocating the water extractions in each grid cell to food vs. feed uses; and (3) accounting for the virtual water embedded in traded feed crops.

2.3.1. Modeling Irrigation Water Requirements for All Crops

We used the open-source global hydrologic model WBM ([18,19,20]) to simulate daily irrigation water requirements globally at a 5-arcminute spatial resolution, circa year 2015. This model simulates daily hydrologic stocks and fluxes based on weather data (precipitation and temperature), soil properties, river and reservoir locations, and land cover characteristics, including cropland and irrigated land identification. Here, WBM simulations used the crop physical land areas, crop types, and crop calendars for both irrigated and rainfed crops from the Global Agro-Ecological Zones (GAEZ + 2015) database [21,22], which includes 26 different crop types. Data on irrigated pasture areas not covered in GAEZ were collected from US States [23], a survey of farms in Australia [24], to estimate the share of irrigated pastures (0.05%) out of the total pasture area of 2.6 billion ha. Additional crop water modeling parameters are from [25]. A full description of all WBM model functions, including crop water use and irrigation water withdrawals, is provided in [18]. The model output includes net irrigation water required for each irrigated crop in each grid cell, gross irrigation water withdrawals required for each irrigated crop in each grid cell, evapotranspiration for each crop, water withdrawals for rice paddy flooding, the source of water withdrawals (surface water, sustainable groundwater, or unsustainable groundwater), and return flow volumes from the inefficient portion of irrigation withdrawals. Since the source of water withdrawals for each individual crop is reported, we were able to estimate the source of water for different feed crops in each grid cell and therefore the sources of indirect water associated with different animal products.
WBM uses daily temperature and precipitation inputs to estimate crop potential evapotranspiration, as well as other key hydrologic fluxes like runoff and direct evaporation from rivers and reservoirs. Climate reanalysis products like the one used here [26] are the best available data to use in global gridded modeling, but are still known to have substantial uncertainty in precipitation. To avoid overemphasizing an unusually wet or dry year in our analysis, we simulated irrigation water requirements for weather years 2012–2018, and here report the average annual results from these 7 weather years. The crop inputs remained constant through the simulation.

2.3.2. Allocating Irrigation Water to Feed Crops

To allocate the calculated water requirement for irrigated crops for feed use, we used FAOSTAT’s supply utilization accounts [27], which report different uses (feed, food, industrial use, losses, and other uses) for crop commodities in each country. To allocate different crops and derived feed commodities, we used the feed rations from GLEAM. The feed rations in GLEAM are based on literature studies, expert opinion, and national census data and do not necessarily align with the feed use for different commodities reported in FAOSTAT supply utilization accounts. We assigned the 26 GAEZ crops to GLEAM feed items (see Supplementary Materials Table S1) and used the relative share of GLEAM feed items to distribute irrigated water consumption calculated by WBM. Thereby, we maintained the feed water consumption calculated by WBM and used the relative shares for feed crop intake estimated by the GLEAM model to allocate water use to different species, herd orientation, and production systems. An overview of the workflow and the different data sets is given in Figure 2.

2.3.3. Accounting for International Trade of Feed Crops

A considerable amount of feed crops are traded; we used FAOSTAT’s bilateral trade data [28] to trace water use from producing countries to consuming countries, taking into account re-exports and converting all products in the trade data (e.g., soybean cake) into their primary equivalents (e.g., soybeans) so that water use can be traced, using a method developed by [29]. To smooth out variability, we used a three-year average centered around 2015 for all FAOSTAT data. No bilateral trade and no other use than feed use was considered for pasture and fodder crops.

2.4. Blue Water Stress Index

The blue water stress index (BWSI) [30] is commonly used to identify basins where water is over-extracted by humans [31,32]. It is the ratio of water extracted to water available, where the extracted water is used for domestic, industrial, and agricultural purposes and is the indicator to measure the level of water stress for the SDG target 6.4Available water is the sum of all surface water flows, including reservoir water and baseflow from connected aquifers, minus the environmental flow requirement. Water withdrawals, not water consumption, are used to calculate the BWSI since return flows of non-consumed water do not immediately flow back to surface water systems.
We used the global hydrologic model WBM [18] to simulate both daily water availability and daily water withdrawals at a 5-arcminute resolution. We then aggregated grid cells into basins of size 50–500 km2 based on the river network [33] to provide local, basin-level estimates. These basins are smaller than full river basins of the world’s largest rivers but larger than grid cells, providing a local metric relevant at watershed scales. Full documentation of WBM methods for simulating water availability and water withdrawals for domestic, industrial, and agricultural use is in [18]. Water withdrawals can exceed water availability in any grid cell, resulting in unsustainable groundwater withdrawals and a BWSI > 1.
The blue water stress index is calculated as follows:
BWSI = water withdrawals/(AW − EFR)
where water withdrawals is the sum of water extractions for domestic use, industrial use, irrigation (for both food and feed), and direct livestock water in the basin. The direct livestock water use is taken from the GLEAM results. AW is all water and is the basin-wide sum of runoff plus the river inflows if the basin is downstream of another.
EFR is the environmental flow requirement, and is calculated as follows:
EFR = EFRp × NF
where EFRp is the proportion of natural flows required to maintain the current aquatic ecosystem [34,35] and NF is the natural flow. Natural flows were estimated by simulating natural conditions with the hydrologic model WBM. Under natural conditions, there are no water withdrawals, no cropland, and no reservoirs or other hydro-infrastructure. A basin has no stress if BWSI < 0.1; low stress if 0.1 < = BWSI < 0.2; moderate stress for 0.2 < = BWSI < 0.4; high stress for 0.4 < = BWSI.

3. Results

3.1. Livestock Water Withdrawals

3.1.1. Global Total Withdrawals by Category

Globally, about 43% percent of the global crop production quantity is used for feed, while, for fodder crops and pasture, no other uses were assumed. Out of the 3670 km3 of water that is withdrawn each year for irrigation of crops and pasture, 513 km3 (14%) is allocated to feed items. A significantly smaller amount is withdrawn for drinking water (40 km3) and service water (13 km3). Therefore, as shown in Figure 3, more than 90% of water withdrawals for livestock production are attributed to indirect uses, while a small fraction is used directly (as drinking water and service water).
Within indirect withdrawals, the diverse contributions of the feed items are a function of their respective irrigation needs and the degree of total consumption by livestock. Specifically, WBM estimates that most of the water is coming from maize, crop not elsewhere specified (NES), cotton, rice, and wheat, cumulatively accounting for over three-quarters of the livestock indirect water use. The “crop NES” category is an aggregate of many crops, which individually have small global harvested areas but collectively are the third largest harvested area category in the world.
On a global scale, ruminants are responsible for 316 km3 of the water withdrawals in the livestock sector, which corresponds to 56% of the total. Cattle farming alone represents 34% of the total, with a predominant 164 km3 coming from indirect sources, such as the production of crop NES, fodder crops, and maize, as shown in Figure 3. Buffalo husbandry follows at 14%, especially due to indirect water use (44 km3), mostly employed to produce feed cotton and crop NES. A lesser extent of water withdrawal is associated with small ruminants, which make up 8% of the total.
Monogastric animals, including pigs and poultry, contribute to 250 km3 of the withdrawals, stemming chiefly from indirect usage for feed, particularly for feed maize production, which represents 46% and 57% of the indirect water use for pigs and poultry, respectively. In terms of the commodities produced, over half of the water withdrawals are linked to meat production (364 km3), followed by milk (156 km3) and eggs (46 km3).
As can be seen in Table 2, water withdrawal and their use as direct or indirect water is highly variable across world regions, reflecting the differences in livestock population and farming system distribution.

3.1.2. Regional Livestock Water Withdrawals

Eastern and Southern Asia collectively constitute nearly 30% of the global water withdrawals, predominantly sourced from indirect uses. In Southern Asia, this substantial volume is largely due to cattle and buffalo feed provision, primarily by water-demanding feed items such as crop NES and cotton. In Eastern Asia, water withdrawals are largely for maize that is used as feed for pigs and poultry (Table 3 and Figure 4).
North America ranks as the third-highest region for water withdrawals in the livestock sector, accounting for 66 km3, which is 11% of the total. This is primarily attributed to water used for growing fodder crops for cattle and maize cultivated for feeding monogastric animals.
South America ranks second in dry matter intake yet has a modest water withdrawal of 21 km3. This is largely due to the low percentage of feed water (57%) compared to other regions. The region relies more on rainfed sources like grass and leaves, reflecting the high prevalence of grassland-based production systems.
Conversely, Middle Africa and Northern Europe exhibit among the lowest volumes of total water withdrawal, with each displaying a substantial proportion of direct water use—surpassing 80% of their respective totals. This pattern suggests a minimal reliance on irrigated feed within these regions.

3.2. Consumptive Water Use

No water use is entirely efficient, as water withdrawals are lost to both evaporation and leakage from canals, inefficient irrigation systems, over-application of water, and runoff. Efficiencies can vary spatially and by crop and animal production system. Here, we report the consumptive portion of water withdrawals, which will be less in all cases than the total withdrawals described above.
Globally, 228 km3 of water is consumed by livestock agri-food systems. Of these, 251 km3 (88%) is in the form of evapotranspiration from feed crops, 32 km3 is for drinking water, and 0.636 km3 is used as service water (Table 3).
The feed trade data suggest that about 330 million tons out of the 1525 million tons of feed items (22%) are not consumed in the country where they are produced. The amount of water ‘embodied’ in the traded feed items is equivalent to 13.7 km3, representing 4.8% of the water consumed in relation to livestock, with significantly larger shares in individual countries and for specific crops. The distribution of consumptive water use by feed items corresponds closely to the proportions of water withdrawal. More than two-thirds of this “virtual water” is related to the trade of irrigated maize and wheat.
Regional variations evidenced in water withdrawal are also evidenced in consumptive water use. In Asia, specifically Southern and Eastern regions, consumptive water use totals 174 km3, representing over 60% of global blue water utilization. Within this, a substantial 90% is allocated to domestic feed production. North America and the different European FAO regions use 40 km3 and 30 km3, respectively. North America sources 88% of its indirect feed water domestically. In Europe, the sourcing of feed water varies: Northern and Eastern Europe use 95% from domestic feed, Western Europe, 87%, and Southern Europe, 60%. South America reports 11.3 km3 in consumptive use, predominantly as direct water for livestock drinking (55%). Africa’s FAO regions collectively contribute 11 km3 to consumptive water use, with a smaller portion derived from feed production (70%), predominantly domestic.

Water Consumption per Livestock Species

Relating water consumption to animal herd sizes (Figure 4 and Figure 5) reveals large variability in daily water consumption per head by species, reflecting the different feed rations, body size, feed intake, physiological conditions, livestock farming system, and composition of the herd. The global average numbers mask regional and local differences but generally reveal that the large ruminants (i.e., buffalo and cattle) consume an order of magnitude more water per head per day than other livestock species.

3.3. Basin Blue Water Scarcity

Of the 2007 global basins of 50–500 km2 in size, we find that 393 (20%) basins are highly stressed (BWSI > 0.4), 144 (7%) are moderately stressed (0.2 < = BWSI < 0.4), and 136 (7%) have low stress (0.1 < = BWSI < 0.2). As shown in Figure 6, most highly stressed basins are in the arid to semi-arid regions of eastern Asia, Central Asia, South Asia, the Middle East, North Africa, Central America, and southwestern North America, with a few highly stressed basins in Australia, southeastern Asia, and both coastlines of South America.
The BWSI integrates all water withdrawals; here, we are interested in the contribution of livestock water use to basin water stress. As can be seen in Figure 6 and Figure 7, there is no spatial correspondence between highly stressed basins and basins where a large proportion of water withdrawals are for direct livestock water use (service water and drinking water). Of the 393 highly stressed basins, only 18 have direct water use withdrawals accounting for >20% of all basin water demand. Of the 144 moderately stressed basins, only 6 have direct water use withdrawals accounting for >20% of all basin water demand.
Not surprisingly, since indirect water use is much larger than direct, there is more spatial correspondence between stressed basins and indirect water withdrawals (Figure 6 and Figure 8). Of the 393 highly stressed basins, 140 (36%) have indirect water withdrawals accounting for >20% of all basin water demand, and, in 23 basins, indirect water accounts for >50% of demand.
The proportion of total demand does not, however, indicate how much livestock water use contributes to basin stress, as some basins may have sufficient water to meet demands without stress. We calculated what the BWSI of each basin would be if all livestock water withdrawals (both direct and indirect) were set to 0. Of the 673 basins with low, moderate, or high stress, removing livestock water withdrawals could reduce the stress category of 135 (20%) of them. Figure 8 shows the new stress category for all basins that would reduce their stress without livestock water withdrawals. The starting stress category (with livestock water withdrawals) for these basins is evenly distributed, with 46 (47, 42) of these basins having a high (moderate, low) stress level. As shown in Figure 9, these basins are distributed across the world, with clusters in central North America, Europe, and East Asia. The regions of the world with large collections of highly stressed basins—central Asia, South Asia, the Middle East and North Africa, and Central America—see very little reduction in the BWSI category for eliminating livestock water withdrawals. This suggests that, despite regions like South and East Asia accounting for a large portion of the world’s indirect livestock water use, other water uses (domestic, industrial, and irrigating food crops) would still need to be reduced to alleviate basin blue water stress.

4. Discussion

4.1. Comparison to Other Studies

This study used a combination of GLEAM-based livestock simulations and WBM-based hydrologic simulations to resolve livestock direct and indirect water use at a gridded and small-basin scales across the globe. The data sources employed here differ from those utilized for previous livestock water use estimates, and so it is useful to compare results. We find that the total consumptive blue water use by the livestock systems is consistent with previous studies. Our estimate of 200–280 km3 blue water consumed by livestock per year aligns with findings by [36], which estimated 263 km3 per year. These results are slightly higher than those of [37,38], who reported 151 and 150 km3 per year, though lower than [39], who reported feed water use alone at 370 km3 per year. The study of [37] also estimated the direct water use (drinking and service) for livestock at 27 km3 per year, which is very similar to our finding of 34 km3 per year.
Given that the estimates by [37] are representative of livestock systems around the year 2000 while our data are updated to 2015, we expected an increase for both indirect and direct water use due to an increase in animal population globally. However, the differing data sources and methodologies prevent us from attributing the observed variation between our findings and previous estimates to an actual increase.
Our results confirm the findings of [36] and demonstrate that water use for livestock can differ significantly, by two to three orders of magnitude, across different countries, even for the same animal species. Eastern and Southern Asia were reported to have the greatest water withdrawals, primarily driven by indirect water. Indirect water withdrawals are the predominant source in all regions, except for Middle Africa and Northern Europe, where direct water withdrawals are prevalent.

4.2. Relevance to Dietary Changes

Global dietary patterns are crucial for ensuring food security and the sustainable management of natural resources. Alterations to these patterns—specifically, a reduction in animal-derived proteins to 50%, 20%, 12.5%, and 0%—could lead to a decrease in global blue water consumption by 4%, 6%, 9%, and 14%, respectively [40]. Similar findings were also confirmed by [41].
Even without reducing the consumption of animal-derived proteins, adhering to national dietary guidelines may also contribute to reducing the water footprint, though to a lower extent [5,40,41]. However, this potential change varies across countries. For example, in lower-income countries, increasing animal-sourced food intake may be necessary to meet guidelines’ requirements, which could, in turn, increase the water footprint associated with diets [41]. The promotion of sustainable diets must therefore carefully account for regional disparities and the local availability of natural resources, especially where demand is on the rise and climate change poses risks to supply. Water basin stress metrics and how they relate to livestock water withdrawals, as presented here, can contribute to the discussion of how dietary changes may impact water resources. As climate change alters precipitation patterns, irrigation water requirements, and livestock drinking water requirements, basin water stress metrics can be re-estimated and the livestock water contribution to stress can be re-evaluated to provide locally based recommendations on the role of livestock and dietary changes on water resources.

4.3. International Trade of Feed and Food

The role of international food trade is critical in future research as it influences dietary water footprint calculations and might serve as a mechanism to alleviate water stress in regions facing scarcity [41]. The trade of food items has been explored for the alleviation of both short-term water stress [42] and long-term groundwater sustainability impacts on global food prices and undernourishment [43]. However, the trade of animal feed and livestock products is less often overlooked in an adaptation framework. As reported above, we find that 22% of feed items are exported. The amount of water ‘embodied’ in the traded feed items is equivalent to 13.7 km3, representing 4.8% of the water consumed in relation to livestock, with significantly larger shares in individual countries and for specific crops. Regionally, this imported ‘virtual water’ accounts for 1–15% of indirect blue water consumption by the livestock sector. Although this represents a small fraction of total water consumption, the growing globalization of agricultural markets and the growing demand for animal protein has already significantly influenced patterns of land use and commodity trade since the 1970s [44,45]. The trade of “virtual water” used to produce all agricultural commodities is expected to double or even triple by the end of the century [46], and so the methods employed here for indirect water accounting that includes trade will continue to be an important part of understanding livestock water use.

4.4. Policy Implications

Given growing concerns over water scarcity, policies aimed at reducing water use from agriculture can use the highly local information provided here. Importantly, the finding that indirect water use for producing feed is the largest user of water for livestock production has implications for the geographic distribution of impacts on water scarcity and therefore on policies that are aimed at minimizing such impacts. In most cases (though not all, based on regional variation), the greatest reduction in water withdrawals and consumption for the livestock sector will come from reducing the water used to produce feed. This can be achieved in many ways, and likely will not be the same solution everywhere. As other studies have demonstrated (e.g., [36]), large variations exist in the efficiency (expressed as water used per unit of animal protein generated) for different species, regions, and production systems. These variations can potentially point to options for increasing water use efficiency along livestock agri-food systems. Clearly, in most circumstances, the largest potential for improving water use efficiency is related to the efficiency in crop production and in improving the efficiency of irrigation.
However, improving the impact of livestock water use on water scarcity cannot be seen in isolation from other sustainability goals for which important synergies and tradeoffs exist. For example, reducing water use by changing the animals’ diet toward feed items that require less water, such as rainfed pasture, might be associated with higher methane emissions for ruminants [2] and potentially a much larger land footprint (e.g., [47]). Future work will therefore analyze these synergies toward a more holistic picture of the sector’s environmental impacts. Our results demonstrate that such an analysis should be conducted at small scales in order to be relevant for informing policies. The large dependency on feed water use and the large variations in water demand per crop highlight the need to analyze these impacts for different production systems for which different feed rations exist.

4.5. Limitations

Our analysis provides a partial life cycle assessment of water use from livestock agri-food systems. While we consider indirect and direct water use, there are other water uses in livestock agri-food production that we did not include. Omitted here are the post-harvest processing of livestock products and embedded water (virtual water) in non-irrigation water components of feed production. Post-harvest processing steps that likely have water uses not considered here include slaughterhouses, tanneries, and the packaging of animal products. Non-irrigation feed production items not considered here include the production of farm equipment, fertilizer, pesticides, herbicides, produce washing, and irrigation water used for non-crop production activities like salinity management.
Other studies provide us with some insights into the magnitude of these omitted water uses. For instance, a study conducted on the Spanish pork industry revealed that 24% of the blue water use was derived from use at the slaughterhouse [48]. Another study performed in Bangladesh on the leather production chain showed that 76% of the total water footprint (including green, blue, and grey water) was derived from post-farming activities [49]. However, most of the water for post-farm processing is typically not consumed but discharged [6]. Further, on the consumer side, there are options to reduce water use by reducing the waste of animal products. Another limitation of this study is that indirect water use was only allocated to main crops and not to crop residues. Residues may constitute a significant part of the animal’s diet, in particular ruminants in low-income economies [2]. Uncertainty in allocation factors for crops used for feed and food significantly impacts the results and total water allocation to the livestock sector. For example, a significant fraction of the feed intake for ruminants, in particular in low-income economies, is supplied by crop residues to which no water use is allocated, assuming that the crop was irrigated for the main product (the grain) and not the residues.
Lastly, we note that all values reported here are annual averages based on multi-year representations of local weather and climate. These results therefore do not describe any seasonal variations in irrigation required to produce feed, livestock drinking or service water, or basin water stress. Basin water stress can vary largely on a seasonal basis [50] and from short-term droughts or other climatological variations like the El Niño–Southern Oscillation (ENSO) [51].

5. Conclusions

Here, we have updated previous estimates of global average livestock water consumption to the year 2015 and further shown how widely varying both water consumption and water withdrawals are by animal type, world region, and even local river basin. The main findings are that (1) the vast majority (>90%) of both water consumption and water withdrawals for livestock production are from the indirect water used to produce feed; (2) by animal type, ruminants are the most water-intensive, and (3) by region, Eastern and Southern Asia have the greatest water withdrawals for livestock. These findings are updated to represent water use in the year 2015 and are in agreement with previous studies. In addition to these global updates, we demonstrate here the importance of identifying regional variations in water withdrawals, water consumption, and the share of direct and indirect water use, highlighting the importance of local assessments. The large variability of water withdrawals and the very different impacts on water scarcity depending on river basin location highlights the need for high granularity in the analysis; global average values for water footprints for different products (e.g., per kg of milk, meat…etc.) are not as informative for local policies. Another key finding is the importance of accounting for assessing indirect water use from livestock systems, which is estimated to account for 5% of livestock water consumption, as it is among the drivers of global land use patterns.
In conclusion, effective interventions to minimize livestock water use will depend on the local conditions and production systems and will vary for each individual farm and value chain. Further studies should focus on identifying regional effective strategies to maximize water use efficiency. Clearly, opportunities exist at all stages of production and range from the more efficient application of irrigation water to more reduced water use on the farm and in processing.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16121681/s1, Table S1: Mapping of GLEAM feed items to animal groups and FAOSTAT item codes; Table S2: Dry matter intake by GLEAM item, species, and region; Table S3: WBM input datasets. References [52,53,54,55,56,57,58] are cited in the Supplementary Materials.

Author Contributions

Conceptualization, D.W. and G.T.; methodology, D.W., D.S.G., G.T. and A.P.; software, A.P. and S.G.; formal analysis, D.W. and D.S.G.; data curation, D.W., L.L., A.P. and S.G.; writing—original draft preparation, D.W., D.S.G., L.L. and G.T.; writing—review and editing, D.W., D.S.G., L.L. and G.T.; visualization, D.W., D.S.G., G.C. and L.L.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The results of GLEAM-Water are available online at the FAO GLEAM dashboard (https://www.fao.org/gleam/dashboard/en/).

Acknowledgments

We thank Thomas Kastner for support with the application of the tracing algorithm to the traded feed crops. This work was presented at the workshop “Assessment of Water Use in Livestock Production Systems and Supply Chains”, which took place in Potsdam, Germany on 14–16 December 2022. The workshop was sponsored by the OECD Co-operative Research Programme: Sustainable Agricultural and Food Systems, whose financial support made it possible for some of the invited speakers to participate in the workshop.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Shiklomanov, I.A.; Rodda, J.C. World Water Resources at the Beginning of the Twenty-First Century; Cambridge University Press: Cambridge, UK, 2003; ISBN 0-521-61722-7. [Google Scholar]
  2. FAO. Pathways towards Lower Emissions—A Global Assessment of the Greenhouse Gas Emissions and Mitigation Options from Livestock Agrifood Systems; Food and Agriculture Organization: Rome, Italy, 2023; ISBN 978-92-5-138448-0. [Google Scholar]
  3. Hoekstra, A.Y. The Water Footprint Assessment Manual: Setting the Global Standard, 1st ed.; Routledge: London, UK, 2012; ISBN 978-1-84977-552-6. [Google Scholar]
  4. Gerbens-Leenes, P.W.; Mekonnen, M.M.; Hoekstra, A.Y. The Water Footprint of Poultry, Pork and Beef: A Comparative Study in Different Countries and Production Systems. Water Resour. Ind. 2013, 1–2, 25–36. [Google Scholar] [CrossRef]
  5. Vanham, D.; Mekonnen, M.M.; Hoekstra, A.Y. The Water Footprint of the EU for Different Diets. Ecol. Indic. 2013, 32, 1–8. [Google Scholar] [CrossRef]
  6. Sharma, H.; Singh, P.K.; Kaur, I.; Singh, R. Water Footprints of Dairy Milk Processing Industry: A Case Study of Punjab (India). Water 2024, 16, 435. [Google Scholar] [CrossRef]
  7. Grossi, G.; Bernabucci, U.; Rossi, C.; Cesarini, F.; Lacetera, N.; Evangelista, C.; Turriziani, G.; Vitali, A. Water Footprint of Italian Buffalo Mozzarella Cheese. J. Agric. Food Res. 2024, 16, 101150. [Google Scholar] [CrossRef]
  8. Fereres, E.; Villalobos, F.J.; Orgaz, F.; Minguez, M.I.; van Halsema, G.; Perry, C.J. Commentary: On the Water Footprint as an Indicator of Water Use in Food Production. Irrig. Sci. 2017, 35, 83–85. [Google Scholar] [CrossRef]
  9. Perry, C. Water Footprints: Path to Enlightenment, or False Trail? Agric. Water Manag. 2014, 134, 119–125. [Google Scholar] [CrossRef]
  10. Döll, P.; Siebert, S. Global Modeling of Irrigation Water Requirements. Water Resour. Res. 2002, 38, 8-1–8-10. [Google Scholar] [CrossRef]
  11. Pfister, S.; Boulay, A.-M.; Berger, M.; Hadjikakou, M.; Motoshita, M.; Hess, T.; Ridoutt, B.; Weinzettel, J.; Scherer, L.; Döll, P.; et al. Understanding the LCA and ISO Water Footprint: A Response to Hoekstra (2016) “A Critique on the Water-Scarcity Weighted Water Footprint in LCA”. Ecol. Indic. 2017, 72, 352–359. [Google Scholar] [CrossRef]
  12. Boulay, A.-M.; Drastig, K.; Amanullah; Chapagain, A.; Charlon, V.; Civit, B.; DeCamillis, C.; De Souza, M.; Hess, T.; Hoekstra, A.Y.; et al. Building Consensus on Water Use Assessment of Livestock Production Systems and Supply Chains: Outcome and Recommendations from the FAO LEAP Partnership. Ecol. Indic. 2021, 124, 107391. [Google Scholar] [CrossRef]
  13. FAO. Water Use in Livestock Production Systems and Supply Chains: Guidelines for Assessment, Version 1; Food and Agriculture Organization of the United Nations: Rome, Italy, 2019; ISBN 978-92-5-131713-6. [Google Scholar]
  14. Gilbert, M.; Nicolas, G.; Cinardi, G.; Van Boeckel, T.P.; Vanwambeke, S.O.; Wint, G.R.W.; Robinson, T.P. Global Distribution Data for Cattle, Buffaloes, Horses, Sheep, Goats, Pigs, Chickens and Ducks in 2010. Sci Data 2018, 5, 180227. [Google Scholar] [CrossRef]
  15. FAOSTAT Statistical Database. Available online: https://www.fao.org/faostat/en/#data/QCL (accessed on 21 August 2020).
  16. Shaw, M.I.; Beaulieu, A.D.; Patience, J.F. Effect of Diet Composition on Water Consumption in Growing Pigs. J. Anim. Sci. 2006, 84, 3123–3132. [Google Scholar] [CrossRef] [PubMed]
  17. Wagner, J.J.; Engle, T.E. Invited Review: Water Consumption, and Drinking Behavior of Beef Cattle, and Effects of Water Quality. Appl. Anim. Sci. 2021, 37, 418–435. [Google Scholar] [CrossRef]
  18. Grogan, D.; Zuidema, S.; Prusevich, A.; Wollheim, W.M.; Glidden, S.; Lammers, R.B. Water Balance Model (WBM) v.1.0.0: A Scalable Gridded Global Hydrologic Model with Water-Tracking Functionality. Geosci. Model. Dev. 2022, 15, 7287–7323. [Google Scholar] [CrossRef]
  19. Wisser, D.; Fekete, B.M.; Vorosmarty, C.J.; Schumann, A.H. Reconstructing 20th Century Global Hydrography: A Contribution to the Global Terrestrial Network-Hydrology (GTN-H). Hydrol. Earth Syst. Sci. 2010, 14, 1–24. [Google Scholar] [CrossRef]
  20. Grogan, D.; Zuidema, S. Wsag/WBM: V1.0.0. Available online: https://doi.org/10.5281/zenodo.6263097 (accessed on 20 May 2024).
  21. Grogan, D.; Frolking, S.; Wisser, D.; Prusevich, A.; Glidden, S. Global Gridded Crop Harvested Area, Production, Yield, and Monthly Physical Area Data circa 2015. Sci. Data 2022, 9, 15. [Google Scholar] [CrossRef] [PubMed]
  22. Frolking, S.; Wisser, D.; Grogan, D.; Proussevitch, A.; Glidden, S. GAEZ+_2015 Crop Harvest Area. Available online: https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/KAGRFI (accessed on 21 August 2020).
  23. USDA. Agricultural Statistics 2013; USDA: Washington, DC, USA, 2013.
  24. Australian Bureau of Statistics Water Use on Australian Farms 2020–2021. Available online: https://www.abs.gov.au/statistics/industry/agriculture/water-use-australian-farms/latest-release (accessed on 3 March 2024).
  25. Siebert, S.; Döll, P. Quantifying Blue and Green Virtual Water Contents in Global Crop Production as Well as Potential Production Losses without Irrigation. J. Hydrol. 2010, 384, 198–217. [Google Scholar] [CrossRef]
  26. Gelaro, R.; McCarty, W.; Suárez, M.J.; Todling, R.; Molod, A.; Takacs, L.; Randles, C.A.; Darmenov, A.; Bosilovich, M.G.; Reichle, R.; et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 2017, 30, 5419–5454. [Google Scholar] [CrossRef] [PubMed]
  27. FAO. FAO: Supply Utilization Accounts (2010–). Available online: https://www.fao.org/faostat/en/#data/FBS (accessed on 21 August 2020).
  28. FAO. Detailed Trade Matrix. Available online: https://www.fao.org/faostat/en/#data/TM (accessed on 21 August 2020).
  29. Kastner, T.; Kastner, M.; Nonhebel, S. Tracing Distant Environmental Impacts of Agricultural Products from a Consumer Perspective. Ecol. Econ. 2011, 70, 1032–1040. [Google Scholar] [CrossRef]
  30. Falkenmark, M.; Berntell, A.; Jägerskog, A.; Lundqvist, J.; Matz, M.; Tropp, H. On the Verge of a New Water Scarcity: A Call for Good Governance and Human Ingenuity; Stockholm International Water Institute (SIWI): Stockholm, Sweden, 2007. [Google Scholar]
  31. Sun, G.; McNulty, S.G.; Moore Myers, J.A.; Cohen, E.C. Impacts of Multiple Stresses on Water Demand and Supply Across the Southeastern United States. J. Am. Water Resour. Assoc. 2008, 44, 1441–1457. [Google Scholar] [CrossRef]
  32. Wada, Y.; van Beek, L.P.H.; Bierkens, M.F.P. Modelling Global Water Stress of the Recent Past: On the Relative Importance of Trends in Water Demand and Climate Variability. Hydrol. Earth Syst. Sci. 2011, 15, 3785–3808. [Google Scholar] [CrossRef]
  33. Lehner, B.; Verdin, K.; Jarvis, A. New Global Hydrography Derived from Spaceborne Elevation Data. Eos Trans. AGU 2008, 89, 93. [Google Scholar] [CrossRef]
  34. Smakhtin, V.U.; Eriyagama, N. Developing a Software Package for Global Desktop Assessment of Environmental Flows. Environ. Model. Softw. 2008, 23, 1396–1406. [Google Scholar] [CrossRef]
  35. Sood, A.; Smakhtin, V.; Eriyagama, N.; Villholth, K.G.; Liyanage, N.; Wada, Y.; Ebrahim, G.; Dickens, C. Global Environmental Flow Information for the Sustainable Development Goals; International Water Management Institute (IWMI): Stockholm, Sweden, 2017. [Google Scholar]
  36. Heinke, J.; Lannerstad, M.; Gerten, D.; Havlík, P.; Herrero, M.; Notenbaert, A.M.O.; Hoff, H.; Müller, C. Water Use in Global Livestock Production—Opportunities and Constraints for Increasing Water Productivity. Water Resour. Res. 2020, 56, e2019WR026995. [Google Scholar] [CrossRef]
  37. Mekonnen, M.M.; Hoekstra, A.Y. A Global Assessment of the Water Footprint of Farm Animal Products. Ecosystems 2012, 15, 401–415. [Google Scholar] [CrossRef]
  38. Mekonnen, M.; Hoekstra, A. The Green, Blue and Grey Water Footprint of Farm Animals and Animal Products; Value of Water; Daugherty Water for Food Global Institute: Lincoln, NE, USA, 2010. [Google Scholar]
  39. Weindl, I.; Bodirsky, B.L.; Rolinski, S.; Biewald, A.; Lotze-Campen, H.; Müller, C.; Dietrich, J.P.; Humpenöder, F.; Stevanović, M.; Schaphoff, S.; et al. Livestock Production and the Water Challenge of Future Food Supply: Implications of Agricultural Management and Dietary Choices. Glob. Environ. Chang. 2017, 47, 121–132. [Google Scholar] [CrossRef]
  40. Jalava, M.; Kummu, M.; Porkka, M.; Siebert, S.; Varis, O. Diet Change—A Solution to Reduce Water Use? Environ. Res. Lett. 2014, 9, 074016. [Google Scholar] [CrossRef]
  41. Harris, F.; Moss, C.; Joy, E.J.M.; Quinn, R.; Scheelbeek, P.F.D.; Dangour, A.D.; Green, R. The Water Footprint of Diets: A Global Systematic Review and Meta-Analysis. Adv. Nutr. 2020, 11, 375–386. [Google Scholar] [CrossRef]
  42. Haqiqi, I.; Grogan, D.S.; Horeh, M.B.; Liu, J.; Baldos, U.L.C.; Lammers, R.; Hertel, T.W. Local, Regional, and Global Adaptations to a Compound Pandemic-Weather Stress Event. Environ. Res. Lett. 2023, 18, 035005. [Google Scholar] [CrossRef]
  43. Liu, J.; Hertel, T.W.; Lammers, R.B.; Prusevich, A.; Baldos, U.L.C.; Grogan, D.S.; Frolking, S. Achieving Sustainable Irrigation Water Withdrawals: Global Impacts on Food Security and Land Use. Environ. Res. Lett. 2017, 12, 104009. [Google Scholar] [CrossRef]
  44. Hertel, T.W.; Baldos, U.L.C. Livestock and Processed Foods. In Global Change and the Challenges of Sustainably Feeding a Growing Planet; Springer International Publishing: Cham, Switzerland, 2016; pp. 115–124. ISBN 978-3-319-22661-3. [Google Scholar]
  45. Golub, A.; Hertel, T.W. Global Economic Integration and Land Use Change. J. Econ. Integr. 2008, 23, 463–488. [Google Scholar] [CrossRef]
  46. Graham, N.T.; Hejazi, M.I.; Kim, S.H.; Davies, E.G.R.; Edmonds, J.A.; Miralles-Wilhelm, F. Future Changes in the Trading of Virtual Water. Nat. Commun. 2020, 11, 3632. [Google Scholar] [CrossRef] [PubMed]
  47. Broom, D.M. Land and Water Usage in Beef Production Systems. Animals 2019, 9, 286. [Google Scholar] [CrossRef] [PubMed]
  48. De Miguel, Á.; Hoekstra, A.Y.; García-Calvo, E. Sustainability of the Water Footprint of the Spanish Pork Industry. Ecol. Indic. 2015, 57, 465–474. [Google Scholar] [CrossRef]
  49. Humayra, S.; Hossain, L.; Hasan, S.R.; Khan, M.S. Water Footprint Calculation, Effluent Characteristics and Pollution Impact Assessment of Leather Industry in Bangladesh. Water 2023, 15, 378. [Google Scholar] [CrossRef]
  50. Van Beek, L.P.H.; Wada, Y.; Bierkens, M.F.P. Global Monthly Water Stress: 1. Water Balance and Water Availability. Water Resour. Res. 2011, 47, 2010WR009791. [Google Scholar] [CrossRef]
  51. Veldkamp, T.I.E.; Eisner, S.; Wada, Y.; Aerts, J.C.J.H.; Ward, P.J. Sensitivity of Water Scarcity Events to ENSO-Driven Climate Variability at the Global Scale. Hydrol. Earth Syst. Sci. 2015, 19, 4081–4098. [Google Scholar] [CrossRef]
  52. Fischer, G.; Nachtergaele, F.; Prieler, S.; van Velthuizen, H.T.; Verelst, L.; Wiberg, D. Global Agro-Ecological Zones Assessment for Agriculture (GAEZ 2008); IIASA: Laxenburg, Austria; FAO: Rome, Italy, 2008. [Google Scholar]
  53. FAO/UNESCO 2003 FAO/UNESCO Soil Map of the World. Available online: https://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/ (accessed on 10 October 2014).
  54. Yang, Y.; Donohue, R.J.; McVicar, T.R. Global estimation of effective plant rooting depth: Implications for hydrological modeling: Global hydrological effective rooting depth. Water Resour. Res. 2016, 52, 8260–8276. [Google Scholar] [CrossRef]
  55. Wisser, D.; Frolking, S.; Douglas, E.M.; Fekete, B.M.; Vörösmarty, C.J.; Schumann, A.H. Global Irrigation Water Demand: Variability and Uncertainties Arising from Agricultural and Climate Data Sets. Geophys. Res. Lett. 2008, 35, L24408. [Google Scholar] [CrossRef]
  56. Center for International Earth Science Information Network-CIESIN-Columbia University. Gridded Population of the World, Version 4 (GPWv4): Population Density Adjusted to Match 2015 Revision UN WPP Country Totals, Revision 11; NASA Socioeconomic Data and Applications Center (SEDAC): Palisades, NY, USA, 2018. [CrossRef]
  57. Lehner, B.; Liermann, C.R.; Revenga, C.; Vörösmarty, C.; Fekete, B.; Crouzet, P.; Döll, P.; Endejan, M.; Frenken, K.; Magome, J.; et al. High-resolution Mapping of the World’s Reservoirs and Dams for Sustainable River-flow Management. Front. Ecol. Environ. 2011, 9, 494–502. [Google Scholar] [CrossRef]
  58. FAO AQUASTAT Dissemination System. Available online: https://data.apps.fao.org/aquastat/?lang=en (accessed on 3 June 2024).
Water 16 01681 g001
Figure 1. Overview of blue water use in livestock systems along the value chain of livestock agri-food systems. Note that downstream water use for processing of animal products is not considered here.
Figure 1. Overview of blue water use in livestock systems along the value chain of livestock agri-food systems. Note that downstream water use for processing of animal products is not considered here.
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Figure 2. Overview of the different data sets and the workflow used in the analysis. Following a water footprint approach, we also calculated consumptive water use from irrigated crops, calculated as evapotranspiration by WBM and as the consumptive share of direct water use, and related these to meat, milk, eggs, and total animal protein.
Figure 2. Overview of the different data sets and the workflow used in the analysis. Following a water footprint approach, we also calculated consumptive water use from irrigated crops, calculated as evapotranspiration by WBM and as the consumptive share of direct water use, and related these to meat, milk, eggs, and total animal protein.
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Figure 3. Sankey flow diagram of water withdrawal for livestock agri-food systems and allocation to animal products. Crops with a contribution of less than 1% are not shown.
Figure 3. Sankey flow diagram of water withdrawal for livestock agri-food systems and allocation to animal products. Crops with a contribution of less than 1% are not shown.
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Figure 4. Water withdrawal for different feed items and species by FAO regions, calculated by GLEAM. The size of the pie chart is proportional to the total amount of water withdrawal for feed production. Background colors in the map represent FAO regions.
Figure 4. Water withdrawal for different feed items and species by FAO regions, calculated by GLEAM. The size of the pie chart is proportional to the total amount of water withdrawal for feed production. Background colors in the map represent FAO regions.
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Figure 5. Global comparison of daily water consumption per head by livestock species (from feed and direct water use). Each black point represents data from a distinct country, while the red circles indicate the global average values by species (buffaloes 440 L/day/head, cattle 193, chicken 6, goats 21, pigs 15, and sheep 32).
Figure 5. Global comparison of daily water consumption per head by livestock species (from feed and direct water use). Each black point represents data from a distinct country, while the red circles indicate the global average values by species (buffaloes 440 L/day/head, cattle 193, chicken 6, goats 21, pigs 15, and sheep 32).
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Figure 6. Blue water stress index (BWSI).
Figure 6. Blue water stress index (BWSI).
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Figure 7. The fraction of basin total water demand from direct livestock water withdrawals.
Figure 7. The fraction of basin total water demand from direct livestock water withdrawals.
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Figure 8. The fraction of basin total water demand from indirect water withdrawals for livestock feed.
Figure 8. The fraction of basin total water demand from indirect water withdrawals for livestock feed.
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Figure 9. The blue water stress index for basins that would improve their stress category if livestock water withdrawals were eliminated. Only basins for which a stress index category improvement would occur with the removal of all livestock water are colored. Grey basins either had no stress to begin with or had no change in stress category when livestock water was removed from the calculation.
Figure 9. The blue water stress index for basins that would improve their stress category if livestock water withdrawals were eliminated. Only basins for which a stress index category improvement would occur with the removal of all livestock water are colored. Grey basins either had no stress to begin with or had no change in stress category when livestock water was removed from the calculation.
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Table 1. Number of animals and global average daily requirements for service water and drinking per head (rounded to 2 significant figures). Animal numbers taken from GLW [14], drinking water and service water from several sources (see Supplementary Materials Table S2 for details).
Table 1. Number of animals and global average daily requirements for service water and drinking per head (rounded to 2 significant figures). Animal numbers taken from GLW [14], drinking water and service water from several sources (see Supplementary Materials Table S2 for details).
SpeciesHerd Size
[Million Heads]		Service Water [L/Day/Head]Drinking Water
[L/Day/Head]
Buffalo200560
Cattle1460450
Chicken21,9000.050.4
Goats101043
Pigs9922010
Sheep119045
Table 2. Water withdrawn for different GAEZ crops (calculated by WBM), withdrawal allocated to GLEAM feed crops, and percentage of water withdrawal allocated to feed. See Supplementary Materials Table S3 for GLEAM data on feed intake. The feed use of fodder crops is 100 percent but not all fodder could be allocated to animal feed intake because of spatial discrepancies between GAEZ and GLW data.
Table 2. Water withdrawn for different GAEZ crops (calculated by WBM), withdrawal allocated to GLEAM feed crops, and percentage of water withdrawal allocated to feed. See Supplementary Materials Table S3 for GLEAM data on feed intake. The feed use of fodder crops is 100 percent but not all fodder could be allocated to animal feed intake because of spatial discrepancies between GAEZ and GLW data.
GAEZ CropWithdrawal, WBM [km3]Withdrawal, Allocated to GLEAM Feed Use [km3]Allocated to GLEAM Feed [%]
Banana9.330.475
Barley19.98.744
Cotton20971.734
Crops not elsewhere specified (NES)36853.715
Fodder crops53.84584
Groundnut174.4526
Maize33616750
Millet7.340.638.5
Olives1.0200
Other Cereals2.281.9485
Pasture11.611.6100
Potato/Sweet Potato32.55.7718
Pulses58.55.499.4
Rapeseed5.33.5266
Rice144053.83.7
Sorghum24.36.4827
Soybean20.412.762
Stimulants2.5300
Sugar beet7.430.456
Sugarcane2564.241.7
Sunflower11.23.9235
Tobacco1.710
Vegetables1294.813.7
Wheat65346.57.1
Yams and other roots0.7700.22
Total367051314
Table 3. Water withdrawal for all crops from WBM and percentage used for feed after the allocation.
Table 3. Water withdrawal for all crops from WBM and percentage used for feed after the allocation.
Direct Water WithdrawalFeed Water Withdrawal Total
FAO Region[km3]Percent of Total [%][km3]Percent of Total [%][km3]
Australia and New Zealand1.18156.74857.92
Caribbean0.259201.02801.27
Central America1.411012.29013.6
Central Asia0.6832.724.49725.1
Eastern Africa2.88512.72495.6
Eastern Asia9.175.416195170
Eastern Europe1.777.422.19323.9
Middle Africa0.886830.178171.06
Northern Africa1.771311.58713.3
Northern America4.166.361.69465.8
Northern Europe0.968820.218181.19
South America8.894311.75720.6
South-Eastern Asia2.517.929.19231.6
Southern Africa0.519271.38731.89
Southern Asia10.37.512792137
Southern Europe1.196.5179318.2
Western Africa1.99631.17373.16
Western Asia1.025.517.49418.4
Western Europe1.66255.11756.77
Total53.29.451391566.2
Note(s): regions with a contribution to total water withdrawal of less than 0% were excluded from the table, namely Melanesia (0.034 km3) and Micronesia (0.0000003 km3).
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Wisser, D.; Grogan, D.S.; Lanzoni, L.; Tempio, G.; Cinardi, G.; Prusevich, A.; Glidden, S. Water Use in Livestock Agri-Food Systems and Its Contribution to Local Water Scarcity: A Spatially Distributed Global Analysis. Water 2024, 16, 1681. https://doi.org/10.3390/w16121681

AMA Style

Wisser D, Grogan DS, Lanzoni L, Tempio G, Cinardi G, Prusevich A, Glidden S. Water Use in Livestock Agri-Food Systems and Its Contribution to Local Water Scarcity: A Spatially Distributed Global Analysis. Water. 2024; 16(12):1681. https://doi.org/10.3390/w16121681

Chicago/Turabian Style

Wisser, Dominik, Danielle S. Grogan, Lydia Lanzoni, Giuseppe Tempio, Giuseppina Cinardi, Alex Prusevich, and Stanley Glidden. 2024. "Water Use in Livestock Agri-Food Systems and Its Contribution to Local Water Scarcity: A Spatially Distributed Global Analysis" Water 16, no. 12: 1681. https://doi.org/10.3390/w16121681

APA Style

Wisser, D., Grogan, D. S., Lanzoni, L., Tempio, G., Cinardi, G., Prusevich, A., & Glidden, S. (2024). Water Use in Livestock Agri-Food Systems and Its Contribution to Local Water Scarcity: A Spatially Distributed Global Analysis. Water, 16(12), 1681. https://doi.org/10.3390/w16121681

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