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Article

Influence of Climatic Factors on the Water Footprint of Dairy Cattle Production in Hungary—A Case Study †

by
István Waltner
,
Attila Ribács
,
Borbála Gémes
and
András Székács
*
Institute of Environmental Sciences, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1, H-2100 Gödöllő, Hungary
*
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 2023, 15(23), 4181; https://doi.org/10.3390/w15234181
Submission received: 27 October 2023 / Revised: 30 November 2023 / Accepted: 1 December 2023 / Published: 4 December 2023
(This article belongs to the Special Issue Assessment of Water Use in Livestock Production Systems and Supply Chains)
Browse Figures
Versions Notes

Abstract

:
Our study aims to provide a look at how the production of dairy cattle is affecting water resources in Hungary. Utilizing the AquaCrop model and field data from a selected field in Hungary, we focused on the evapotranspiration (ET) and water footprint (WF) of maize (the dominant component of silage mixes), while for other feed crops, we obtained data from scientific literature sources. We also considered drinking and servicing water consumption of dairy cattle, utilizing observations from a specific farm, as well as estimating potential heat stress at the country level. Our findings indicated increasing trends of crop ET as well as biomass production for maize, without significant correlations between the two parameters. Spatiotemporal analysis revealed a significant rise in the number of days with potential heat stress based on temperature-humidity indices, manifesting in practically the entire area of Hungary. Thus, while crop ET rates and corresponding crop water use values (4989–5342 m3/ha) did not show substantial changes, maize WF in silage cultivation rose from 261.9 m3/t dry biomass in 2002 to 378.0 m3/t dry biomass in 2020. Feed and water intake was subsequently recorded on a cattle farm and assessed as green and blue water use. Drinking (blue) water uptake, ranging between 74.7 and 101.9 L/dairy cow/day, moderately correlated with temperature-humidity indices as heat stress indicators (r2 = 0.700–0.767, p < 0.05). Servicing water was not recorded daily, but was calculated as a daily average (18 L/dairy cow/day), and was also considered in blue water usage. In contrast, feed consumption at the cattle farm corresponded to 13,352 ± 4724 L green water/dairy cow/day. Our results indicate that while the WF of animal feed remains a dominant factor in the total water use of dairy cattle farms, drinking water consumption and related costs of adaptive measures (such as adaptive breeding, modified housing, and technological measures) are expected to increase due to potential heat stress, particularly in selected regions where farmers should focus more on housing and technological solutions, as well as selecting for thermotolerance.

1. Introduction

The official slogan of FAO’s World Food Day has been “Water is life, water is food” [1], emphasizing the heavy impact of water scarcity on food production and the potential mitigating effect of the economic use of water in food production. It also refers to the fact that agriculture, including livestock production, is responsible for nearly three-quarters of our overall freshwater consumption [2]. Worldwide agricultural production, based on the FAO’s Gross Production Index, increased by 280% between 1961 and 2021, with an average annual increase of 2.3 ± 1.5% (ranging between −2.8% and 5.6%) [3]. In parallel, agricultural production in Europe increased by 45% with an average annual increase of 0.7 ± 3.8% (ranging between −16.7% and 9.1%). Within the same period, worldwide cattle production in livestock units increased by 55%, with an average annual increase of 0.7 ± 0.8% (ranging between −1.6% and 2.9%), but it tremendously dropped in Europe by −36%, with an average annual decrease of 0.7 ± 2.4% (ranging between −11.9% and 4.4%) [3].
The production of food of animal origin, particularly cattle, is known to have the largest water footprint (WF) in agriculture [4,5,6], and 43–98% or even a higher proportion of the total WF of animal products corresponds to the water required to grow feed crops [4,7,8,9]. This water use is the highest in feedlot systems, somewhat lower in fertilized irrigated pasture systems, and substantially lower in extensive unmodified pasture systems and semi-intensive silvopastoral systems [10]. To improve insight into the transparency, consistency, reproducibility, and credibility of assessing the water demand of livestock products, the Livestock Environmental Assessment and Performance Partnership (LEAP) was created in 2012 as an FAO multi-stakeholder initiative that seeks to improve the environmental sustainability of the livestock sector through harmonized methods, metrics, and data [11]. LEAP published its guidelines for water footprinting for livestock supply chains in 2019 [12], providing methodologies for data quality assurance, water use inventory, as well as for water scarcity impact and water productivity assessment; and published further recommendations for quantification of green and blue water consumed [13].
The WF of dairy livestock production has been assessed internationally [4,6,7,9,10,14,15,16,17] and in case studies from the US [18,19], Australia [20], New Zealand [21], China [22,23], Brazil [24], Ireland [25], South Africa [8,26], Tunisia [27], and other countries of the world. Studies have been published evaluating the issue in Hungary as well [28,29,30]. Dairy cattle can be heavily affected by heat stress [31]. Among the multiple potential effects of heat stress, water intake will typically increase, and milk production could decrease [32]. Heat stress can be monitored through both ambient temperature-humidity indices (THIs) or body temperature [33]. Multiple measures have been shown to reduce the effects of heat stress, including proper housing, shading, and the application of different cooling systems [34].
While the abovementioned previous studies have focused on aspects of heats stress or water footprint separately, we have found that there were no studies linking the two aspects together, directly addressing the effects of climatic changes on the water use of dairy cattle, especially for Hungary. The current study, therefore, aims to assess the potential effects of climatic factors (rainfall, evapotranspiration, temperature, humidity) on the water footprint of dairy cattle production (considering both feed and drinking water) by answering the questions: (a) What is the proportion of green water incorporated in animal feed?; (b) How do cattle respond to heat stress, and what are the spatial/temporal trends of heat stress days in Hungary?

2. Materials and Methods

2.1. Crop Cultivation

The commercial maize (Zea mays) cultivar of variety DK-440 was grown in two regions in Hungary, namely the Nagykovácsi region (Pest County) in 2002 and 2020, and the Zsámbék region (Pest County) in 2007, under field conditions, in crop densities of 60,000, 75,000, and 120,000 plants/ha for commodity, silage, and green silage, respectively. The experimental sites were selected for their moderate climatic conditions, not affected by frequent extreme meteorological events. The sites were located in the vicinity of the nature conservation area of Pilisi Parkerdő Zrt. (Visegrád, Hungary), owned by the Hungarian state, near Nagykovácsi, covering an area of 18.4 hectares, and a similar area with comparable characteristics located about 12 km away, near Zsámbék. Both sites showed common soil characteristics typical for Hungary. The topography of the experimental sites was flat, with a uniform surface, belonging to the chernozem brown forest soil type (according to the traditional Hungarian classification system), with predominantly silt texture throughout. Each experimental plot was set to a size of 5 × 30 m, where experiments on maize cultivation for grain, silage, and green silage were conducted (crop densities of 6–12 plant/m2). The experimental plots were arranged in a randomized block design. The typical practice of non-irrigated cultivation, commonly used in the region to reduce cultivation costs, was applied. Maize phenological stages were followed according to Ritchie et al. [35]. Maize plants cultivated for commodity, silage, and green silage purposes were sampled at harvest times in the R6, VT, and V12 phenological stages, respectively. Wet biomass data at the time of harvest were collected and measured by organs. At the time of sampling, four entire plants were collected and dissected, and organs (including, when appropriate: leaves, stem, root, anther, pollen, and grain) were separated. Plant samples were immediately processed upon sampling for fresh and dry weight measurements.

2.2. Water Footprint Calculation for Maize

Crop evapotranspiration (ET) of the cultivated commodities considered was estimated using AquaCrop software version 7.1 (Food and Agriculture Organization of the United Nations, Rome, Italy) [36]. AquaCrop software provides regional and annual prediction of above-ground biomass (as dry biomass/ha), yield, and crop ET. A direct calibration of the actual crop cultivation data and those predicted by AquaCrop could be established only as a formal correlation, because the two systems differed in several parameters: maize variety (DK-440 for Hungary, and multiple varieties suitable for Davis, NC, USA [37]), cultivation mode (silage and commodity maize), and reported biomass data (wet and dry biomass/ha). Climate data were obtained from the national gridded dataset from the Data Repository of the Hungarian Meteorological Service (Budapest, Hungary) [38]. The point-based estimation of ET provided an approximation of Green Water footprint. AquaCrop simulations were run for the 52-year period of 1971–2022, and maize crop ET rates obtained for the years of 2002, 2007, and 2020 were used for further calculations. Crop water use (CWU) values calculated from these total ET rates (of crop cultivation within the cropping period) were attributed to the overall above-ground maize biomass produced. CWU values were partitioned for different tissues of the main crop based on the ratios of the wet biomass data obtained in the given year (as seen in Section 2.1).

2.3. Cattle Production

Lactating Hungarian Simmental cows were raised on the Southern Great Plain (Békés County) [30]. Feed and drinking water intake were determined daily within the period of 1 July–31 in 2019 and 2020. Daily feed included 8 kg maize silage, 3 kg grass hay, 3 kg alfalfa hay, and 6.1 kg concentrate (containing 49% maize, 33% barley, 16% soybean meal, 2% feed supplement with dextrose and calcium carbonate). Thus, feed was of moderate dry matter content (13.3 kg), a high proportion of fodder concentrate (about 50% of the net energy for lactation), and crude fiber content (18% of the dry matter content) adequate to heat stress. Drinking water of 12–14 °C temperature was provided ad libitum via automatic drinkers equipped with flow meters. Water quality complied with livestock production quality requirements, and 24 hr drinking water consumption was registered daily at 6 PM. Animals were milked twice daily at 7 AM and 6 PM. External body temperature was measured individually, once daily at 3 PM (at the end of the warmest period) using non-contact thermometers (Medisana TM-750). Dry bulb air temperature (Td) and relative humidity (RH) were determined simultaneously, daily at 3 PM using a digital measuring instrument. Wet bulb air temperature (Tw) was calculated online using a h-x calculator to obtain temperature-humidity indices (THIs).

2.4. Spatio-Temporal Assessment of Temperature-Humidity Indices

THIs have been developed to express the level of heat stress exerted on affected livestock. THIs are defined based on Td and RH, typically expressed as Tw [39]. There are a number of THIs (THI1 through THI7, in which Td and Tw-values are expressed in degrees Celsius, and RH is a percentage), from which THI1 and THI2 [40,41], most typically applied in Hungary [42], were calculated using the formulae below [40,41]:
T H I 1 = 0.15 × T d + 0.85 × T w × 1.8 + 32
T H I 2 = 0.35 × T d + 0.65 × T w × 1.8 + 32
Spatial and temporal variation of THI1 and THI2 was carried out for the years 1971–2022, utilizing mean daily temperature and mean daily RH data for the entire area of Hungary, obtained from the gridded dataset of the Data Repository of the Hungarian Meteorological Service [38]. The spatial resolution of the dataset was 0.5° × 0.5°.
In order to assess the presence and direction of potential trends in the occurrence of above critical THI values, we applied the Mann–Kendall Test [43], which focuses on the detection of monotonic trends, with a null hypothesis of no trend being present [44]. All calculations and parts of the statistical analysis have been carried out using the R software environment version 4.3.1 (The R Foundation, Wien, Austria) [45]. Spatial processing was carried out using the free open-source software SAGA GIS (System for Automated Geoscientific Analyses; SAGA User Group Association, Hamburg, Germany) [46], and QGIS version 3.28 (Open Source Geospatial Foundation, Beaverton, OR, USA) [47], while further statistical evaluation was done in MS Excel (Microsoft Corp., Seattle, WA, USA).

2.5. Quantification of Water Footprint of Dairy Cattle

As our study focuses on the effects of climatic factors, e.g., THIs, on dairy cattle (and not specifically on milk production), the WF has been approached not as a value related to unit product mass but quantified as WF per animal. Thus, the WF of livestock production was calculated as the sum of indirect water consumption (through feed production) and direct water use (drinking and servicing water) per animal:
W F a n i m a l = W F f e e d + W F d r i n k + W F s e r v
where WFfeed, WFdrink, and WFserv refer to the partial WFs related to the cultivation of feed crops, as well as the consumption of drinking water and service water, respectively. Since no irrigation (blue water) needs to be involved in silage maize production, the crop utilized natural precipitation (green water) and used it as its constituent (moisture content) and for ET. CWU values obtained from the estimated total ET rates of crop cultivation were calculated only for the duration of the cropping period. Therefore, the green WF (GWF) of the crop, calculated from the green water CWU divided by the crop yield, indicates the green water amount required by a unit mass of the crop. Consequently, WFfeed, the amount of green water corresponding to the amount of the feed consumed by the animal, can be calculated from the amount of feed consumed by the animals. In this study, WFfeed was not recorded individually daily but calculated based on green water content needed for the cultivation of the daily amount of feed the animals consumed. In contrast, WFdrink and WFserv were measured daily at the dairy farm. Thus, WFanimal in the present study is expressed as m3 water per day per animal. The reason for not using annual values to express WF is that dairy farm animals were not studied throughout the entire year but only for the one-month duration of the study periods.
The feed of the dairy cows consisted altogether of 54.7% (mass/mass) maize (if maize silage and maize content in the concentrate are considered). Other crop components were grass hay, alfalfa hay, barley, soybean meal, and a minor amount of feed supplements (see Section 2.3). Moreover, certain feed components (e.g., soybean meal and the concentrate including it) were imported products; therefore, their WF could not be related to local weather conditions in Hungary. Therefore, while WF for maize was determined from local conditions, WFs of the other feed components were obtained from the scientific literature [48]. WFs for alfalfa hay (Medicago sativa) and barley (Hordeum vulgare) were calculated from reported yield and ET data for these crops cultivated in Hungary in 2017 at Gödöllő (Pest County) [48], in agreement with worldwide water productivity data reported for alfalfa cultivation [49]. Thus, WFs were 1363.6 and 877.7 m3 green water/t dry biomass for alfalfa hay and barley, respectively. WF values for grass hay were reported [19], and an average WF value of nine different grasses was calculated to be 521.8 ± 83.7 m3 green water/t wet biomass, corresponding to approximately 715 m3 green water/t dry biomass. The GWF of soybean has been reported to be 2037 m3 green water/t dry biomass [50] with high variability by worldwide location and cultivation type [50,51].

3. Results and Discussion

3.1. Crop Cultivation and Its Water Footprint

Biomass (above-ground mass/ha) values estimated by the AquaCrop model had a mean error of 4%, with a maximum error of ±30% when compared to field data. Maize wet biomass production data were analyzed for selected years between 2001 and 2021 in the Nagykovácsi and Zsámbék regions (Pest County) in Hungary. Phenological stages were followed according to Ritchie et al. [35]. Wet biomass data at the time of harvest, measured by organs, are listed for three representative cases in Table 1. Over the studied period (2001–2021), the average total wet biomass production was found to be 32.2 ± 19.2, 29.1 ± 7.6, and 43.1 ± 14.8 t wet biomass/ha for maize cultivated for commodity, silage, and green silage, respectively. The moisture content of the crop varied between 35.2% and 43.1%.
In order to assess model performance, wet biomass data obtained experimentally in field cultivation at Nagykovácsi and Zsámbék, Hungary in 2002, 2007, and 2020 were correlated with corresponding predicted wet biomass data by AquaCrop (calculated from dry biomass with a ~15% moisture content at harvest [52]). The experimental data showed +2.4%, +21.4%, and −34.0% deviation from the predicted values. These divergences can be attributed to differences in maize varieties and cultivation modes. While field experiments were carried out with maize variety DK-440, maize biomass predictions in AquaCrop are based on multiple varieties suitable for Davis, CA. In addition, above-ground biomasses in field experiments corresponded to maize silage cultivation (75,000 plants/ha, harvested in R6, phenological stage), while AquaCrop predictions refer to maize commodity cultivation (60,000 plants/ha, harvested in VT phenological stage). Moreover, the actual moisture content in the maize crop commodity may substantially differ from the 15% assumed for the correlation, which further increases variability. Such deviations are not uncommon in the scientific literature [37,53,54,55,56].
Crop biomass production and total ET during maize silage cultivation in Hungary between 1971 and 2022 were modeled using the AquaCrop software v.7.1 [36]. Although the two cultivation descriptors show similar increasing trends visually in temporal plots (Figure 1), direct numerical correlation analysis between these two parameters indicated a poor linear correlation (r2 = 0.399; p < 0.05). However, it is observable that the biomass provides larger variation than the ET values. As biomass and ET (i.e., green water) are both used in calculating WF (in this case GWF), the increasing trend was not observable in case of GWF, with the exception of extremes.
As specific crop parameters were not available for different varieties of maize, AquaCrop’s general crop coefficients have been applied for the estimation of ET, and differences in yield and tissue biomass ratios have been applied to estimate differences in the WF of maize silage biomass. As crop yield values in the scientific literature typically do not focus on feed crops (maize silage in this case) but rather on commodity cultivations, estimations based on field measurements for different parts of the total above-ground silage crop biomass have been used in the calculations (Table 1). These results indicate that while total ET within the cropping period presented little variation for the selected three years, total above-ground biomass has significantly varied. This could indicate the effect of alternative factors, such as differences in soil water storage (assumed to be at field capacity at the start of simulations), nutrient availability, or diseases and pests. Proportions between different crop tissues have remained fairly consistent for the observed years. These findings are in line with the observations regarding the differences in the variations of biomass and ET observed in Section 3.1.
Based on the average total wet biomass production (29.1 ± 7.6 t wet biomass/ha) and the corresponding CWU values (4989.0–5342.0 m3 green water/ha; average: 5214.3 ± 195.7 m3 green water/ha) for maize cultivated for silage, WF values of the raw crop biomass were 159.5, 158.2, and 244.8 m3 green water/t wet biomass for cultivation years 2002, 2007, and 2020, respectively (Table 1). The corresponding average WF was, therefore, 187.5 ± 49.6 m3 green water/t wet biomass. Based on the moisture content of the crop at harvest, this corresponded to WFs related to dry biomass of 261.9, 278.1, and 378.0 m3 green water/t dry biomass for cultivation years 2002, 2007, and 2020, respectively (average WF: 306.0 ± 62.9 m3 green water/t dry biomass). These maize WF values were used to calculate the green water demand of the maize component used as animal feed in cattle production (see Section 2.3). The WF of the dairy cows’ feed was calculated based on the feed composition using locally determined WFs where possible and WFs obtained from the scientific literature (see Section 2.5), where necessary. A cumulative value of these partial WFs weighed by the composition of the feed (m/m) added up to 664.3 m3 green water/t dry biomass of the feed commodity used for feeding the cows at the cattle farm (see Section 3.3). Therefore, even though more than half (54.7%) of the feed biomass was maize, the WF of this maize silage corresponded only to one-quarter (25.2%) of the overall WF of the feed.

3.2. Spatial and Temporal Variations in Temperature-Humidity Index Calculations

Evaporation in humid air is known to be less effective than in dry air for the cattle to lower their body temperature [57]. Therefore, wet and dry air temperatures need to be considered in parallel to assess body temperature control by the cows. Consequently, numerous THIs have been defined of which THI1 or THI2 are recommended in Hungary [42] as the ratio of Tw to Td appears to be the highest for THI1 and THI2 among all THIs.
Results of the THI1 calculation provide a spatial and temporal look at the meteorological heat stress potentially affecting dairy cattle. Based on the time series of calculated THI1 and THI2 data, we can clearly conclude that there is an upward trend in the number of days with potential heat stress for the animals (Figure 2). Thus, the average number of days with heat stress in the Nagykovácsi region in Hungary during each decade from 1970 to 2020 based on THI1 increased from 4.00 ± 4.85 (range: 0–13) in 1971–1980 to 18.60 ± 7.69 (range: 9–31) in 2011–2020, showing a good correlation over the decades (r2 = 0.922; p ≤ 0.05). A similar trend was observed based on THI2 with an increase from 5.30 ± 5.29 (range: 0–14) in 1971–1980 to 21.60 ± 8.41 (range: 14–35) in 2011–2020 (correlation r2 = 0.934; p ≤ 0.05). The same trend illustrated by other parameters: the number of years with less than 5 days with heat stress during the decades 1971–1980, 1981–1990, 1991–2000, 2000–2010, and 2011–2020 dropped as 7, 4, 7, 1, 0 based on THI1, and 7, 3, 1, 0, 0 based on THI2, respectively.
The trends presented above were not exclusive to the Nagykovácsi region but were present throughout the country. In order to assess the spatial distribution of potential trends, we utilized the Mann–Kendall test for both THIs, as seen in Figure 3. The tau value of the test represents the presence of a trend, from −1 (strong negative trend) to 1 (strong positive trend). The test also provides a significance level (p-value), with the null hypothesis that there is no trend. It is clear that based on the 52-year dataset, similar, upward trends are visible throughout the country, with stronger trends present in the southern and western regions for THI1 and in the central regions for THI2. In general, it can be observed that areas with higher elevations had less expressive trends. All upward trends have proved to be significant (with p-values less than 0.01), with the notable exception of two cells for THI1, both located in the highest mountains in the country, which are not suitable for cattle and thus not relevant as exceptions within the scope of our study (Figure 3b).

3.3. Potential Impact of Temperature-Humidity on the Water Footprint of Dairy Cattle Production

Td in the stables ranged from 22.9 to 33.5 °C, while RH ranged from 31 to 64%. The dry and wet air temperatures followed a similar trend but with, in some cases, a slight delay in the onset of changes in the wet air temperature (Figure 4). The two air temperatures exhibited poor correlation with each other (r2 = 0.55; p ≤ 0.05). Drinking water consumption of the cows varied between 74.7 and 101.9 L/cow/day during the study period. It weakly correlated with the dry air temperature (r2 = 0.76; p ≤ 0.05), and, to an even lesser extent, with the wet air temperature (r2 = 0.64; p ≤ 0.05), as the wet air temperature is highly dependent on RH. A physiological explanation for the better correlation with the dry air temperature could be that the mucous membranes of the cows’ mouths dry out sooner in dry air, causing higher stimuli for thirst. In contrast, water intake showed no correlation with the detected RH (r2 = 0.01; p < 0.1).
THI values, specifically THI1 and THI2, recommended for use in Hungary [42] during the 31-day study period at the dairy farm, are depicted in Figure 5. As expected, THI2 is monotonously 2–4 units higher than THI1 and shows a very similar temporal pattern. This is consistent with the definition of these two THI parameters. THI1 was originally derived from the discomfort index developed by climatologist Earl C. Thom [58], specified to monitor discomfort due to temperature and humidity in humans [59], which could obviously be extended to other mammalians. THI2 has been empirically determined in cattle exposed to heat stress conditions in climatic chambers [40,41]. Thus, THI1 and THI2 strongly correlate with each other (r2 = 0.966; p < 0.05), which is expected as they both depend on temperature. The correlation with drinking water intake indicates that an increase in days with heat stress will likely result in an increase in water intake. Considering the results of Section 3.2, we can generally expect such effects in Hungary.
Water and feed intake were recorded at the cattle farm and are depicted in Figure 6. Feed was provided to the animals in constant amounts daily, but feed consumption was not registered on a daily basis; it was only observed that the previously given portion had been consumed by the time of daily feeding. Therefore, feed consumption is presented as corresponding (green) water use and as a daily average value with minimal and maximal levels. Drinking (blue) water consumption was registered daily; therefore, it is represented as variable daily rates. The amount of servicing (blue) water was not recorded daily but was calculated as a daily average value (18 L/dairy cow/day). The moderate correlation between heat stress and the drinking water intake by dairy cows (see above) is not surprising and indicates that THI1 and THI2 are indeed suitable parameters to reveal heat discomfort in dairy cows.
This interpretation also sheds light on the proportions of green and blue water usage. The feed used for feeding the cows at the cattle farm represented a cumulative WF of 664.3 m3 green water/t dry biomass of the feed commodity (see Section 3.2). Considering the 20.1 kg feed/day/dairy cow feed consumption at the cattle farm, this corresponds to 13,352 ± 4724 L green water/dairy cow/day, adding up to 413,900 ± 146,400 L green water/dairy cow during the 31-day study period. The drinking water intake, recorded daily at the cattle farm, ranged between 74.7 and 101.9 L blue water/dairy cow/day, with a daily average value of 83.4 ± 6.5 L blue water/dairy cow/day, and total consumption during the 31-day study period was 2586 ± 202 L blue water/dairy cow. Service water use is estimated to be 18 L blue water/dairy cow/day and 558 L blue water/dairy cow during the study period.
Taking the above water use values into consideration, green, blue, and combined water use during the 31-day cattle study periods totaled 413,900 ± 146,400, 3100 ± 200, and 417,600 ± 147,700 L water/dairy cow, respectively. This means that in the current case, approximately 99% of the overall (green and blue) water use is represented by the green water demand of the feed used to forage the dairy cows. Limitations to this value are that this proportion does not consider blue water used for irrigation (no maize irrigation was applied in the current case), related water demands of the cultivation technology (fertilization, etc.), and does not include grey water. Nonetheless, it is in good agreement with corresponding data from the scientific literature [7,8,9,10].

4. Conclusions

Main conclusions drawn from the findings in the present study revealed information regarding the overall amount and the composition of water consumption by the dairy farms, as well as on certain factors affecting water use. The composition of the water use included 13.4 ± 4.7 m3 green water/dairy cow/day as water content in the feed, as well as 0.08 ± 0.007 m3 blue water/dairy cow/day as drinking water, and 0.018 m3 blue water/dairy cow/day for service water needs. Thus, green water demand represented by the feed corresponded to 99.1% of the overall WF. This did not include gray water or blue water use associated with feed production, as corn production in Hungary (for animal feed) typically only relies on rainwater. Limitations, yet at the same time specific features of these calculations, are that biomass production data corresponded (a) to maize silage cultivation (while data in the scientific literature mostly refer to maize commodity cultivation), (b) to no irrigation maize cultivation, and (c) to a single country, Hungary. In turn, maize silage content in the feed represented 54.7% (m/m), but that corresponded only to 25.2% of the overall WF of the feed. In the herd and under the conditions studied, the intake of drinking water was dependent on meteorological factors, showed no direct correlation with the RH, weakly correlated with Td and Tw, but a better correlation occurred with two types of THIs (THI1 and THI2). Water consumption was less related to the external body temperature of the livestock than to ambient temperature.
Spatiotemporal analysis of THI1 and THI2 indicated an increasing trend in potential heat stress, in line with previous research [42]. This will likely lead to increased drinking water consumption and/or other management issues for farmers, such as increased energy consumption or relevance of water quality issues. It is also clear that while the upward trend in the occurrence of days with heat stress is clearly present in the whole area of Hungary, there is a clear spatial pattern indicating that farmers in certain regions (generally in the South, West, and Central regions) are more likely to be affected. Farmers in such regions should consider that such effects might increase in the future and therefore implement appropriate measures to control ambient temperatures affecting the animals. Alternatively, selection for thermotolerance might also be an option [32].
In general, in can be concluded that while increasing trends in ET (green water) and biomass lead to a reasonably consistent mean GWF of animal feed, its year-to year variation seems to increase, leading to higher uncertainties, with water used for the production of feedstocks remaining the dominant portion of total water use. Similarly, the increase in the occurrence of heat stress in dairy cows is expected to lead to increasing water use and force farmers to apply adaptive measures, such as the selection of dairy cow breeds with better thermotolerance, and modifications in animal housing and technological steps.

Author Contributions

Conceptualization, I.W. and A.S.; methodology, I.W., A.R. and A.S.; validation, I.W. and A.S.; formal analysis, I.W., B.G., A.R. and A.S.; investigation, I.W., A.R., B.G. and A.S.; writing—original draft preparation, I.W. and A.S.; writing—review and editing, A.R. and B.G.; visualization, I.W. and A.S.; supervision, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Hungarian National Research, Development, and Innovation Office, grant number TKP2021-NVA-22.

Data Availability Statement

Data are contained within the article.

Acknowledgments

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 conflict of interest.

References

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Water 15 04181 g001
Figure 1. Crop biomass production (green circles) and total evapotranspiration (blue circles) in maize cultivation in Hungary between 1971 and 2022 modeled by the AquaCrop software v.7.1 [36]. Trendlines obtained by linear regression (dashed lines) and by moving average (dotted lines) display pattern similarities, but the linear correlation between the two datasets is poor (r2 = 0.399).
Figure 1. Crop biomass production (green circles) and total evapotranspiration (blue circles) in maize cultivation in Hungary between 1971 and 2022 modeled by the AquaCrop software v.7.1 [36]. Trendlines obtained by linear regression (dashed lines) and by moving average (dotted lines) display pattern similarities, but the linear correlation between the two datasets is poor (r2 = 0.399).
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Water 15 04181 g002aWater 15 04181 g002b
Figure 2. Number of days with heat stress based on temperature-humidity indices (THIs) THI1 (a) and THI2 (b) from 1971 to 2022 (blue circles), with a 5-year moving average (dotted lines) for the area of Nagykovácsi (Pest County, Hungary). The corresponding THI threshold values are THI1 ≥ 68 and THI2 ≥ 69.
Figure 2. Number of days with heat stress based on temperature-humidity indices (THIs) THI1 (a) and THI2 (b) from 1971 to 2022 (blue circles), with a 5-year moving average (dotted lines) for the area of Nagykovácsi (Pest County, Hungary). The corresponding THI threshold values are THI1 ≥ 68 and THI2 ≥ 69.
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Figure 3. Spatial distribution of tau (a,c) and p (b,d) values of the Mann–Kendall test for THI1 (a,b) and THI2 (c,d). (Coordinate Reference System: WGS84).
Figure 3. Spatial distribution of tau (a,c) and p (b,d) values of the Mann–Kendall test for THI1 (a,b) and THI2 (c,d). (Coordinate Reference System: WGS84).
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Figure 4. Dry and wet bulb air temperature in the stables of the dairy cow farm during the study period in July 2019: dry bulb temperature (orange line) and wet bulb temperature (blue line).
Figure 4. Dry and wet bulb air temperature in the stables of the dairy cow farm during the study period in July 2019: dry bulb temperature (orange line) and wet bulb temperature (blue line).
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Figure 5. Temperature-humidity indices (THI) in the stables of the dairy cow farm during the study period in July 2019: THI1 (orange dotted line) and THI2 (orange dashed line). The corresponding threshold values for THI1 (68) and THI2 (69) are indicated as thin dotted and dashed horizontal lines.
Figure 5. Temperature-humidity indices (THI) in the stables of the dairy cow farm during the study period in July 2019: THI1 (orange dotted line) and THI2 (orange dashed line). The corresponding threshold values for THI1 (68) and THI2 (69) are indicated as thin dotted and dashed horizontal lines.
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Figure 6. Water intake of the dairy cow farm during the study period in July 2019. Drinking water intake (blue solid line) was recorded daily. Green water intake corresponding to the amount of feed was calculated and considered constant during the study period and is depicted as an average (green slashed line) and minimal and maximal values (green dotted lines). The amount of servicing (blue) water was 0.018 m3/dairy cow/day on an average (not shown in the graph).
Figure 6. Water intake of the dairy cow farm during the study period in July 2019. Drinking water intake (blue solid line) was recorded daily. Green water intake corresponding to the amount of feed was calculated and considered constant during the study period and is depicted as an average (green slashed line) and minimal and maximal values (green dotted lines). The amount of servicing (blue) water was 0.018 m3/dairy cow/day on an average (not shown in the graph).
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Table 1. Biomass production and water footprint (WF) of maize cultivation by crop tissue. Total evapotranspiration (ET), crop water use (CWU), and green WF were calculated by the AquaCrop crop-water productivity model [36].
Table 1. Biomass production and water footprint (WF) of maize cultivation by crop tissue. Total evapotranspiration (ET), crop water use (CWU), and green WF were calculated by the AquaCrop crop-water productivity model [36].
Year2002 12007 12020 1
BiomassCWUBiomassCWUBiomassCWU
(t wet mass/ha)(m3/ha)(t wet mass/ha)(m3/ha)(t wet mass/ha)(m3/ha)
Crop tissue
Root7.1–9.4–3.1–
Stem18.52943.219.53087.911.52805.1
Leaf14.52309.113.22081.88.62107.8
Tassel0.689.70.9142.40.376.0
Total above surface biomass33.55342.033.65312.020.44989.0
Total ET (mm)534.2531.2498.9
CWU use (m3/ha)5342.05312.04989.0
Green WF
(m3/t wet biomass)159.5158.2244.8
(m3/t dry biomass)261.9278.1378.0
Note: 1 Cultivation parameters: silage maize production; maize variety: DK-440, crop density: 75,000 plants/ha, location: Nagykovácsi, Pest County, Hungary in 2002 and 2020; Zsámbék, Pest County, Hungary in 2007.
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Waltner, I.; Ribács, A.; Gémes, B.; Székács, A. Influence of Climatic Factors on the Water Footprint of Dairy Cattle Production in Hungary—A Case Study. Water 2023, 15, 4181. https://doi.org/10.3390/w15234181

AMA Style

Waltner I, Ribács A, Gémes B, Székács A. Influence of Climatic Factors on the Water Footprint of Dairy Cattle Production in Hungary—A Case Study. Water. 2023; 15(23):4181. https://doi.org/10.3390/w15234181

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Waltner, István, Attila Ribács, Borbála Gémes, and András Székács. 2023. "Influence of Climatic Factors on the Water Footprint of Dairy Cattle Production in Hungary—A Case Study" Water 15, no. 23: 4181. https://doi.org/10.3390/w15234181

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