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Article

Assessment of Green and Blue Water Footprint Components of Agricultural Crops in the Shu–Talas River Basin, Kazakhstan

by
Sayat Alimkulov
1,
Lyazzat Makhmudova
1,*,
Mikhail Tskhay
1,
Elmira Talipova
1,2,*,
Lyazzat Birimbaeva
1,
Tursun Ibrayev
1,
Oirat Alzhanov
1,2 and
Dilnaz Nurlanova
1,2
1
Institute of Geography and Water Security, 050000 Almaty, Kazakhstan
2
Department of Geography and Environmental Science, Al-Farabi Kazakh National University, 050040 Almaty, Kazakhstan
*
Authors to whom correspondence should be addressed.
Water 2026, 18(11), 1344; https://doi.org/10.3390/w18111344
Submission received: 17 April 2026 / Revised: 26 May 2026 / Accepted: 30 May 2026 / Published: 1 June 2026
(This article belongs to the Section Hydrology)
Browse Figures
Versions Notes

Abstract

Amid growing water scarcity, assessing agricultural water consumption and crop water footprint has become increasingly critical. This study aims to assess the water footprint of crops within the Shu–Talas River Basin, disaggregated into green and blue components. Using meteorological data from the 2000–2024 period, reference evapotranspiration (ET0) and actual crop evapotranspiration (ETc) were calculated according to the FAO methodology. The water footprint (WFgreen, WFblue, and WFquant) was determined based on crop evapotranspiration, effective precipitation, and crop yields for maize, sugar beet, sunflower, and potato. It was found that total water consumption during the growing season ranges from 650 to 950 mm, with the blue water share exceeding 80%, reflecting the high dependence of agricultural systems on irrigation. The minimum WFquant values were observed in sugar beet, while the maximum WFquant values were recorded for sunflower. The study identifies crop yield, rather than absolute water consumption, as the key factor in water footprint formation. These findings and established patterns can be utilized to optimize cropping patterns and support sustainable agricultural water management in arid regions.

1. Introduction

Water resources play a pivotal role in ensuring sustainable agricultural development and global food security. According to the Food and Agriculture Organization of the United Nations (FAO), agriculture is the largest consumer of freshwater [1]. Driven by population growth and shifting consumption patterns, global food demand is projected to increase by approximately 50% by 2050, inevitably leading to a further rise in water consumption within the agricultural sector. Concurrently, water scarcity is intensifying. FAO estimates suggest that in the coming years, up to two-thirds of the global population will experience water stress, while approximately 1.8 billion people will live under conditions of absolute water scarcity. Current assessments indicate [1,2] that water shortages already affect more than 3 billion people, with about 60% of irrigated lands operating under high water stress.
Against this background, quantitative assessment of agricultural water consumption and crop water footprint has become increasingly important for sustainable water resource management. One of the most widely adopted approaches is the water footprint concept. This methodology enables the evaluation of crop water consumption volumes, disaggregated into green and blue components, and supports the assessment of irrigation dependence and agricultural water demand [3,4,5].
Current research demonstrates that the water footprint is a comprehensive indicator reflecting the interplay between climatic conditions, agronomic factors, and crop yields. It serves as an effective tool for evidence-based decision-making in water resource management and the optimization of agricultural production patterns [6,7,8]. In this regard, regional studies that account for specific water supply conditions, local climates, and irrigation systems are of paramount importance, as global assessments often fail to capture the localized nuances of water footprint formation [6,7,8,9].
The Shu–Talas River Basin, situated in the southern part of the Republic of Kazakhstan, is characterized by limited water availability and a high reliance of agriculture on irrigation. Given the intensifying climate variability and growing competition for water resources, a comprehensive assessment of crop water consumption and water footprint has become essential.
The scientific problem addressed in this study is the lack of a detailed quantitative assessment of the water footprint of agricultural crops, broken down into green and blue components, under the conditions of arid irrigated agroecosystems in the Shu–Talas River Basin. Despite the region’s high dependence on irrigation and the increasing pressure on water resources, the relationship between evapotranspiration, crop yield, and the water footprint remains insufficiently studied at the regional level.
Four agricultural crops—maize, sugar beet, sunflower, and potato—were selected as the subjects of this study. These crops account for a significant share of the cropland in the Zhambyl Region and are characterized by differences in water consumption and yield levels.
The research hypothesis is that the value of the water footprint is determined not only by the total water consumption of crops but also, to a greater extent, by their yield; consequently, high-yielding crops may exhibit lower water footprint values even with significant evapotranspiration rates. The objective of this study is to assess the water footprint of major agricultural crops within the Shu–Talas River Basin based on the calculation of reference and actual evapotranspiration and the partitioning of crop water consumption into green and blue components.

2. Materials and Methods

2.1. Study Area

The Shu–Talas River Basin encompasses the basins of the Shu, Talas, and Asa rivers and constitutes a transboundary river system situated within the territories of the Kyrgyz Republic and the Republic of Kazakhstan. The upper and middle parts of the basin are associated with the mountainous regions of the Kyrgyz Alatau and Talas Alatau, where the bulk of the river runoff is formed, whereas the lower reaches of the basin are located within the Zhambyl region of the Republic of Kazakhstan [10,11].
The Shu and Talas rivers originate in the Tian Shan Mountains and flow predominantly in a northwesterly direction toward the arid lowlands of southern Kazakhstan. The Shu River dissipates within the inland delta system of the Turan Lowland. The Talas River currently does not reach its terminal reaches, as its runoff is almost entirely consumed for irrigation purposes [11].
According to the conditions of runoff formation, a significant portion of the territory under consideration constitutes a mountainous region with complex topography, consisting primarily of mountain range systems extending predominantly in a latitudinal direction. These mountain formations serve as natural accumulators of atmospheric moisture, which acts as the source of recharge for the well-developed river network in the mountains. At average catchment altitudes (2400–3200 m), the mean runoff modules vary from 3.8 to 13 L/sec km2 [10,11,12,13]. In the foothill and lowland zones, as a result of intensive evaporation and infiltration into the loose deposits of alluvial fans, surface waters dissipate to varying degrees, with a portion contributing to groundwater recharge.
The annual runoff regime of the rivers within the Shu–Talas basin is characterized by marked seasonal variability, stemming from the orographic and climatic features of the territory. The intra-annual runoff distribution is determined by the hypsometric position of the catchments and the recharge type, which depends on the proportional contribution of snow, rain, and glacial meltwater to the overall water balance. The bulk of the runoff is generated during the warm season, coinciding with the intensification of snowmelt, glacier recharge, and precipitation processes (Figure 1).
The region is characterized by arid and semi-arid climatic conditions, high interannual variability of water resources, and a substantial reliance of agriculture on irrigation. Agricultural production in the basin is dominated by irrigated cropping systems. The present study focuses on four major irrigated crops: maize, sugar beet, sunflower, and potato.

2.2. Data Sources

Agricultural data. The study utilized statistical data regarding sown areas, yields, and gross harvests for the primary agricultural crops (maize, sugar beet, sunflower, and potato) covering the 2000–2024 period. These data were retrieved from the official website of the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan [14] under the section “Statistics of agriculture, forestry, hunting and fisheries”.
Meteorological data. The following meteorological data for the 2000–2024 period were employed in the study:
–
air temperature (monthly mean, minimum, and maximum);
–
monthly precipitation totals;
–
relative humidity;
–
wind speed;
–
solar radiation.
Meteorological data were obtained from the official website of the RSE “Kazhydromet” [15].
Agricultural production data used in the study are summarized in Appendix A (Table A1). A representative fragment of the meteorological dataset used for evapotranspiration calculations is presented in Table A2 of Appendix A, while the complete dataset is available from the corresponding author upon reasonable request.

2.3. Research Methodology

The research methodology is based on the integration of agricultural and meteorological data, employing the water footprint concept alongside the FAO methodological approaches developed by Arjen Hoekstra [16] and Richard Allen [17] for assessing crop water consumption. The overall workflow comprises a sequence of analytical steps (Figure 2).
The phased approach aligns with modern methodologies for water footprint assessment [18,19,20].
Estimation of Crop Evapotranspiration. Crop evapotranspiration was calculated using the crop coefficient approach (FAO-56):
E T c = K c × E T 0
where ETc is the crop evapotranspiration (mm); Kc is the crop coefficient representing the plant growth stage (dimensionless); and ET0 is the reference evapotranspiration (mm).
The application of the Kc coefficient accounts for the biological characteristics of crops and their dynamics throughout the growing season. In the framework of water footprint studies, the calculation of crop water consumption represents a fundamental step, as evaporation and transpiration account for the bulk of the water consumed [17,21].
Estimation of Reference Evapotranspiration. Reference evapotranspiration was calculated using the FAO-56 Penman–Monteith equation recommended by FAO [17]:
E T 0 = 0.408 ∆ R n − G + γ 900 T + 273 u 2 ( e s − e a ) ∆ + γ ( 1 + 0.34 u 2 )
where ET0 is the reference evapotranspiration (mm/day); Δ is the slope of the saturation vapor pressure curve (kPa/°C); Rn is the net radiation at the surface (MJ/m2/day); G is the soil heat flux (assumed to be 0 at the monthly time scale) (MJ/m2/day); T is the mean daily air temperature (°C); γ is the psychrometric constant (kPa/°C); es is the saturation vapor pressure (kPa); ea is the actual vapor pressure (kPa); and u2 is the wind speed at 2 m height (m/s).
The Penman–Monteith method serves as the international standard for estimating reference evapotranspiration, accounting for the radiation balance and aerodynamic factors [17,22]. This parameter set ensures a physically based assessment of water consumption and is extensively utilized in hydrological and agrometeorological studies [23].
Water Footprint Calculation of Agricultural Crops. Within the water footprint framework, crop water use is divided into two main components: green (derived from precipitation) and blue (derived from irrigation). The quantitative assessment of these components was carried out by partitioning crop evapotranspiration (ETc) into green and blue fractions:
E T c = E T g r e e n + E T b l u e
where ETc is the crop evapotranspiration over the calculation period (mm); ETgreen is the portion of crop evapotranspiration supplied by effective precipitation (mm); and ETblue is the portion of crop evapotranspiration supplied by irrigation water (mm).
This partitioning enables the quantification of the relative contributions of precipitation and irrigation to total crop water use.
Calculation of effective precipitation. Effective precipitation Peff is defined as the portion of total atmospheric precipitation available for use by agricultural crops through evapotranspiration. In this study, effective precipitation was calculated using the empirical relationship developed by the USDA Soil Conservation Service, recommended by the FAO, and widely used in the CROPWAT model [18]:
P e f f = P × 125 − 0.2 P 125 , P ≤ 250 125 + 0.1 P ,   P > 250
where Peff—effective precipitation, and P—total precipitation for the calculation period, mm.
This approach takes into account that only a portion of the precipitation is utilized by plants, while the remainder is lost through surface runoff and infiltration beyond the root zone.
Partitioning into Green and Blue Components. The separation of crop evapotranspiration into green and blue components was performed based on the relationship between total crop evapotranspiration and effective precipitation:
E T g r e e n = min E T c , P e f f
E T b l u e = max E T c − E T g r e e n , 0
where ETgreen is the green component of evapotranspiration (mm); ETblue is the blue component of evapotranspiration (mm); ETc is the crop evapotranspiration (mm); and Peff is the effective precipitation (mm).
The green component reflects water consumption sourced from atmospheric moisture, whereas the blue component characterizes the requirement for supplemental water supply (irrigation).
Calculation of Crop Water Use (CWU). Crop water use was determined by summing crop evapotranspiration over the growing season and subsequently converting it into volumetric units:
C W U g r e e n = 10 × ∑ E T g r e e n
C W U b l u e = 10 × ∑ E T b l u e
where CWUgreen is the crop water use from green water (m3/ha); CWUblue is the crop water use from blue water (m3/ha); ETgreen is the green component of evapotranspiration (mm); ETblue is the blue component of evapotranspiration (mm); Σ denotes summation over the growing season; and 10 is the conversion factor from water depth (mm) to volumetric units (m3/ha) (1 mm of water over 1 ha corresponds to 10 m3).
Calculation of the water footprint. The water footprint of agricultural products is calculated as the ratio of crop water use to the yield of the corresponding crop:
W F g r e e n = C W U g r e e n Y i e l d
W F b l u e = C W U b l u e Y i e l d
W F q u a n t = W F g r e e n + W F b l u e
where Y is the crop yield (t/ha); WFgreen is the green water footprint (m3/t); WFblue is the blue water footprint (m3/t); WFquant is the quantitative water footprint (m3/t); CWUgreen is the crop water use from precipitation (m3/ha); and CWUblue is the crop water use from irrigation (m3/ha).
In this study, the quantitative water footprint component (WFquant), including the blue and green components, was calculated [24]. The gray water footprint component (WFgrey), associated with water pollution, was not considered due to the lack of the necessary data on pollutant concentrations and allowable pollution loads.
The green component reflects the utilization of atmospheric precipitation, whereas the blue component characterizes the pressure on regulated water resources (rivers, reservoirs, and irrigation systems). Under arid climatic conditions, the contribution of the blue component typically dominates, underscoring the dependence of agriculture on irrigation [16].
Rationale for the methodology. The selected methodology integrates the water footprint concept and the FAO approach to calculating crop water consumption. According to contemporary research, the water footprint of agricultural crops is composed of green and blue water and varies significantly depending on climatic conditions and crop types [16]. The proposed methodological approach allows for the assessment of crop water consumption patterns and the dependence of agricultural production on irrigation water resources.
Uncertainty management and methodology limitations. The assessment of crop water footprint was conducted considering the main sources of uncertainty associated with meteorological data, crop coefficients, effective precipitation estimation, and agricultural statistics. Within this study, uncertainties are regarded as an inherent characteristic of large-scale agrohydrological assessments and were considered during the interpretation of the obtained results. Crop yield and cultivated area data may contain uncertainties related to differences in statistical reporting procedures and data collection methodologies. Meteorological calculations were performed using unified monthly time series, while periods with critical data gaps were excluded from the analysis. The estimation of effective precipitation and the partitioning of evapotranspiration into green and blue components represent simplified approximations commonly applied in FAO-based water footprint studies [16,17,18]. Therefore, the obtained results should be interpreted within the framework of regional-scale comparative assessment and interannual variability analysis rather than as exact field-scale measurements. Despite these limitations, the adopted methodology enables the identification of stable patterns in crop water consumption and irrigation dependence under arid climatic conditions.

3. Results

3.1. Dynamics of Gross Agricultural Production

Based on the collected agricultural statistics, an assessment of gross production dynamics for the primary crops was performed (Figure 3).
The results show that potatoes demonstrate the most consistent growth in production, increasing from 74,000 tons at the beginning of the study period to 260,000–287,000 tons in 2020–2022.
Maize is characterized by stable growth, reaching peak values of over 120,000 tons. Sugar beet exhibits high interannual variability, including sharp production spikes, such as in 2024. Sunflower maintains relatively low but stable production volumes. This structure indicates the dominant role of potatoes in regional agricultural production and the substantial interannual variability of sugar beet cultivation.

3.2. Spatiotemporal Dynamics of ET0 and ETc

The obtained results indicate that reference evapotranspiration ET0 exhibits pronounced seasonality, peaking in the summer (June–August) with values reaching 180–220 mm/month, while minimum values are observed in winter (<30 mm/month). Actual crop evapotranspiration ETc is characterized by an absence of water consumption outside the growing season (October–March), a sharp increase starting in April, a peak in July (for sugar beet and potato), and a gradual decline in September (Figure 4).
When aggregated over the growing season, it was revealed that ETc (maize) reaches 740–860 mm, ETc (sugar beet) is 830–950 mm, ETc (sunflower) is 650–750 mm, ETc (potato) is 800–920 mm. The highest seasonal water demand is characteristic of sugar beet and potato, which is due to their long growing season and high crop coefficients Kc.

3.3. Assessment of the Green and Blue Water Footprint Components

Water footprint calculations show a pronounced dominance of the blue component across all agricultural crops, reflecting the high dependence of irrigated agriculture in the region under consideration (Figure 5). The results obtained are presented in Table 1.
The results demonstrate a consistent pattern in which the blue component of the water footprint significantly exceeds the green component, indicating a high pressure on regional water resources and the limited contribution of precipitation in determining crop water consumption. The values presented in Table 1 are given as ranges, reflecting the interannual variability of the water footprint indicator over the study period (2000–2024).

3.4. Comparison of Crops by WFgreen, WFblue and WFquant

Comparative analysis reveals fundamental differences in the water footprint characteristics of the crops. The minimum water footprint is observed in sugar beet, resulting from high yields and a decrease in WFquant. Potatoes exhibit a moderate level with relatively stable values. Maize shows an elevated water footprint, reflecting substantial dependence on irrigation. The highest water footprint values are observed for sunflower, with extreme values (10,000–16,000 m3/t) resulting from low yields and high evapotranspiration.
Under the conditions of the Shu–Talas Water Management Basin (Zhambyl Region), agriculture is characterized by strong dependence on irrigation, with the share of blue water exceeding 80%, resulting in elevated pressure on regional water resources. Based on the water footprint assessment, sugar beet is the most resilient crop, while sunflower is the crop associated with the highest water-related risk.
The findings confirm the critical dependence of the region’s agrosystems on regulated water supply and highlight the importance of considering crop-specific water demand in regional agricultural planning.

4. Discussion

The results obtained show that the water footprint of agricultural products in the Shu–Talas River Basin is determined by the combined influence of climate aridity, crop yield levels, and the degree of agrosystem dependence on irrigation. In the region under study, the dominance of the blue water footprint component over the green one was identified for all crops considered, indicating the critical role of regulated water supply in sustaining agricultural production. Such a water footprint structure methodologically aligns with the classic Water Footprint Assessment framework, in which the green component is associated with effective precipitation, while the blue component represents the irrigation coverage of the crop’s water consumption deficit [16,19,25,26,27].
The identified predominance of WFblue over WFgreen is consistent with research findings for other arid regions. For instance, studies on Egyptian agricultural crops have shown that the contribution of green water to the total water footprint is significantly lower than that of blue water, and water resource management in arid conditions is primarily linked to the irrigation component [27,28,29]. In this sense, the estimates obtained for the Zhambyl region align well with the general pattern: under limited atmospheric precipitation, even with interannual rainfall variability, the main pressure on the water management system is driven by blue water.
It is important to emphasize that the use of the term “potential risk of water scarcity” requires a detailed sustainability assessment in accordance with the Water Footprint Assessment methodology, including the comparison of the water footprint with available water resources and environmental constraints. Within the framework of the present study, such an analysis was not conducted, and therefore the obtained results do not allow a direct assessment of the sustainability of water use.
The results show that differences between agricultural crops are explained not only by variations in total water consumption but, to an even greater extent, by yield. The lowest WFquant values were obtained for sugar beet and potato, while the highest values were recorded for sunflower, with maize occupying an intermediate position. This hierarchy is consistent with the general conclusion formulated in several studies on crop water footprint: the specific water footprint is determined by the ratio of water consumed to yield. Therefore, even under comparable ETc conditions, crops with higher yields demonstrate lower water footprint values [19,21,25,30]. This exact relationship is emphasized by Tuninetti et al. [25], who demonstrate that the spatial variability of the crop water footprint is largely controlled by yield rather than climate alone [25,31,32].
From this perspective, the results obtained for sunflower are particularly illustrative. The crop is characterized by the highest WFquant values, reaching extreme levels in certain years, which is attributed to low yields despite sustained water consumption throughout the growing season. A similar conclusion is reached in a study by Hossain et al. [33] for Australia: crops with relatively low productivity and high water requirements generate the highest specific water footprint, whereas more productive crops demonstrate substantially lower water footprint values. Although absolute values between regions should be compared with caution due to differences in climate, cropping calendars, and calculation methodologies, the most critical finding remains consistent: high yield is the key factor in reducing WFquant while low yield is the cause of its sharp increase.
The results obtained for sugar beet demonstrate consistently low water footprint values compared to other crops, which aligns well with examples from the Water Footprint Assessment Manual [16,19]. In these works, sugar beet is regarded as a crop capable of generating a relatively low water footprint per unit of product, precisely due to its high yield. In our case, this result is particularly significant, as it shows that under the conditions of irrigated agriculture in southern Kazakhstan, a high-productivity crop can compensate even for substantial seasonal water consumption through more efficient conversion of water into yield.
Potato also proved to be one of the most water-efficient crops in the system under consideration. Its water footprint is lower than that of maize and significantly lower than that of sunflower. The findings are consistent with international assessments [19,30], according to which potatoes are often classified as crops with a moderate or low water footprint per ton of product, thanks to a combination of relatively high yields and a fairly compact active growth period. In a study on Australia, for instance, potatoes were identified among crops with relatively favorable water footprint values compared to more water-intensive crops [33].
The obtained seasonal evapotranspiration estimates are physically consistent. The highest seasonal evapotranspiration values are characteristic of sugar beet and potato, followed by slightly lower values for maize and lower still for sunflower. However, it is the latter crop that demonstrates the maximum water footprint due to its low yield. This confirms that high water consumption by a crop does not, in itself, signify a high water footprint; what is critically important is the volume of production generated per unit of water consumed [25,30,34].
Consequently, in the basin under consideration, the water footprint metric should be interpreted not merely as a characteristic of hydro-climatic pressure, but also as an integral indicator of crop water consumption and irrigation dependency. In a broader context, the results obtained align with contemporary global crop water footprint assessments. Recent datasets and model reconstructions across dozens and hundreds of crops emphasize that the water footprint varies significantly in space and time; depends on the differences between rainfed and irrigated systems; and is sensitive to agronomic parameters—primarily the length of the growing season, soil–water balance parameters, and yield [31,35]. This is particularly important for the interpretation of the results obtained. Interannual changes in WFgreen and WFblue in the Shu–Talas River Basin reflect not only fluctuations in weather conditions but also variations in yield, which may be linked to agronomic practices, crop variety composition, and the quality of water supply.
At the same time, the obtained estimates should be discussed, taking into account methodological limitations. According to contemporary reviews and studies [25,27,30,36], the magnitude of the water footprint can vary significantly depending on the quality of input data, the effective precipitation calculation scheme, the adopted crop coefficients, and the representation of yield. It should be noted that official statistics on water use and agricultural production may contain certain uncertainties related to the specifics of data recording, aggregation, and data collection methods. In particular, statistical data on irrigation water use include both consumptive water use and return flow, which are not accounted for in water footprint calculations.
In some cases, differences between estimates can reach approximately ±30% [22,25]. In this study, the absolute values of WFgreen and WFblue should be interpreted as the best available calculated estimates within the existing database, whereas their comparative analysis between crops and across years is more robust and methodologically reliable.
From a practical standpoint, the research findings have direct implications for water resource management. The dominance of the blue component across all agricultural crops means that any strategy to enhance the sustainability of the region’s agriculture must focus primarily on improving irrigation management practices. At the same time, the crops themselves differ significantly in their water footprint characteristics: sugar beet and potato demonstrate a lower water footprint per unit of product, whereas sunflower exhibits the highest.
Consequently, when developing recommendations for cropping patterns, water allocation regimes, and adaptation to water scarcity, it is necessary to consider not only the absolute water consumption of crops but also their crop-specific water footprint characteristics.
Overall, the results show that the irrigated areas of the Shu–Talas River Basin exhibit the same fundamental patterns as other arid agricultural regions of the world: the dominance of WFblue; the critical role of yield in determining WFquant and substantial inter-crop heterogeneity in water footprint characteristics. At the same time, the region’s specificity is manifested in the exceptionally high dependence of agriculture on regulated water supply, which makes the water footprint not only an indicator of agro-ecological efficiency but also a vital tool for decision support in water resource management.

Limitations and Applicability of the Study Results

The obtained results of the water footprint assessment for agricultural crops are subject to several limitations related to both the quality of the input data and the methodological approaches employed.
First, the estimation of effective precipitation is based on an empirical FAO relationship, which represents a generalized approximation and does not fully account for local soil conditions, infiltration processes, and surface runoff. Second, the use of crop coefficients relies on tabulated values that may differ from the actual cultivation conditions in the study region. Third, uncertainties may also arise from agricultural statistics related to crop yields and cultivated areas.
Despite these limitations, the results obtained have practical relevance and can be used for comparative assessment of crop water footprint, for comparative analysis of agricultural crops, and to support decision-making in regional water resource management. The scope of application of the results includes the development of strategies for sustainable water use, optimization of agricultural production structure, and support for decision-making in the fields of water and food security.

5. Conclusions

The comprehensive analysis of the spatiotemporal dynamics of evapotranspiration, water consumption patterns, and the water footprint of agricultural crops in the Shu–Talas River Basin identified key patterns in the formation of crop water footprint under arid conditions:
–
It was established that the water footprint of the territory under consideration is formed under the combined influence of climate conditions, evapotranspiration characteristics, and crop yield levels.
–
The spatiotemporal dynamics of evapotranspiration are characterized by pronounced seasonality; during the growing season, total values vary within the range of 650–950 mm, depending on the crop.
–
It was shown that the blue water footprint component dominates for all agricultural crops studied, with its share exceeding 80%, indicating a high dependence of the region’s agrosystems on irrigated agriculture.
–
It was established that differences between crops in terms of water footprint magnitude are driven not so much by absolute water consumption as by yield levels.
–
High-yielding crops (sugar beet and potato) are characterized by lower water footprint values, whereas crops with relatively low yields (sunflower) exhibit the highest water footprint values.
–
A pronounced inter-crop differentiation of water footprint characteristics was identified; the water footprint metric confirms its informativeness as an integral indicator of crop water consumption and irrigation dependency.
–
It was established that the interannual variability of WFgreen and WFblue reflects both climatic variability (precipitation and evaporation) and changes in crop productivity, indicating the complex nature of water footprint formation and the necessity of analyzing it within an integrated “climate–agrotechnology–water footprint” framework.
The practical implications of the obtained results are as follows:
–
The obtained estimates demonstrate the applicability of incorporating water footprint metrics when planning agricultural development and irrigation management.
–
In conditions of water scarcity, priority can be given to higher-yielding crops with lower water footprint values while simultaneously improving irrigation management practices.
–
The use of the water footprint metric serves as a decision support tool for regional water resource management and agricultural planning.

Author Contributions

Conceptualization, S.A. and L.M.; methodology, L.M. and M.T.; software, O.A.; validation, E.T., L.B. and T.I.; formal analysis, L.M.; investigation, L.M., E.T. and L.B.; data curation, L.B. and O.A.; writing—original draft preparation, L.M.; writing—review and editing, S.A. and L.M.; visualization, O.A. and D.N.; supervision, L.M.; project administration, S.A.; funding acquisition, S.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan, Grant No. BR287006/0225, “Water security of the Republic of Kazakhstan: the transboundary Shu–Talas basin under climate change and economic activity for the period until 2050.”

Data Availability Statement

The data presented in this study may be obtained on request from the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Source Data Used for Agricultural, Water Use, and Meteorological Calculations

Table A1. Source data on agricultural production used in water footprint calculations.
Table A1. Source data on agricultural production used in water footprint calculations.
RegionYearCropArea, haYield, t/haProduction, t
Zhambyl2000maize17,0003.2755,590
Zhambyl2001maize17,7004.1773,809
Zhambyl2002maize18,4004.6886,112
Zhambyl2003maize15,7005.3784,309
Zhambyl2004maize18,7005.2998,923
Zhambyl2005maize15,4005.3882,852
Zhambyl2006maize13,6005.2871,808
Zhambyl2007maize14,5004.6166,845
Zhambyl2008maize13,4003.9452,796
Zhambyl2009maize13,5615.2070,517
Zhambyl2010maize98005.0149,098
Zhambyl2011maize10,2095.2853,919
Zhambyl2012maize10,2855.4355,848
Zhambyl2013maize13,6785.7178,101
Zhambyl2014maize15,7665.6789,391
Zhambyl2015maize13,8515.8080,333
Zhambyl2016maize14,4606.1989,506
Zhambyl2017maize15,7026.1195,941
Zhambyl2018maize18,1326.16111,695
Zhambyl2019maize18,0296.06109,256
Zhambyl2020maize19,0085.97113,477
Zhambyl2021maize18,6506.25116,563
Zhambyl2022maize18,6306.48120,722
Zhambyl2023maize19,5806.27122,767
Zhambyl2024maize13,2007.1594,380
Zhambyl2000sugar beet451018.784,337
Zhambyl2001sugar beet350016.858,800
Zhambyl2002sugar beet260020.352,832
Zhambyl2003sugar beet500021.1105,450
Zhambyl2004sugar beet430019.282,431
Zhambyl2005sugar beet130016.521,437
Zhambyl2006sugar beet30012.73822
Zhambyl2007sugar beet9509.509025
Zhambyl2008sugar beet47007.8036,660
Zhambyl2009sugar beet190012.724,054
Zhambyl2010sugar beet570013.777,862
Zhambyl2011sugar beet700019.7137,550
Zhambyl2012sugar beet540013.070,057
Zhambyl2013sugar beet100025.625,645
Zhambyl2014sugar beet80027.421,928
Zhambyl2015sugar beet540019.6105,894
Zhambyl2016sugar beet570021.6123,291
Zhambyl2017sugar beet950022.9217,835
Zhambyl2018sugar beet840025.4213,412
Zhambyl2019sugar beet560029.5165,289
Zhambyl2020sugar beet459329.2133,944
Zhambyl2021sugar beet560031.1174,104
Zhambyl2022sugar beet549527.9153,256
Zhambyl2023sugar beet10,79732.6351,871
Zhambyl2024sugar beet11,25857.6647,982
Zhambyl2000sunflower52000.462392
Zhambyl2001sunflower47000.542538
Zhambyl2002sunflower46000.642944
Zhambyl2003sunflower28001.363808
Zhambyl2004sunflower41001.295289
Zhambyl2005sunflower36001.445184
Zhambyl2006sunflower44001.185192
Zhambyl2007sunflower39001.204680
Zhambyl2008sunflower53001.035459
Zhambyl2009sunflower41001.204920
Zhambyl2010sunflower32001.254000
Zhambyl2011sunflower23001.453335
Zhambyl2012sunflower25931.564048
Zhambyl2013sunflower27001.363673
Zhambyl2014sunflower35001.224270
Zhambyl2015sunflower35001.224270
Zhambyl2016sunflower36001.445184
Zhambyl2017sunflower37001.505550
Zhambyl2018sunflower42861.556627
Zhambyl2019sunflower38931.776873
Zhambyl2020sunflower33141.805966
Zhambyl2021sunflower36761.816654
Zhambyl2022sunflower51341.819293
Zhambyl2023sunflower41261.917880
Zhambyl2024sunflower19571.883680
Zhambyl2000potato520014.374,360
Zhambyl2001potato590014.786,907
Zhambyl2002potato580015.992,394
Zhambyl2003potato550016.892,620
Zhambyl2004potato590017.0100,300
Zhambyl2005potato570018.5105,393
Zhambyl2006potato520017.993,132
Zhambyl2007potato580017.9103,704
Zhambyl2008potato580018.2105,618
Zhambyl2009potato580018.6107,764
Zhambyl2010potato600019.4116,400
Zhambyl2011potato750020.3152,475
Zhambyl2012potato816021.3173,598
Zhambyl2013potato740021.1156,388
Zhambyl2014potato860021.3183,008
Zhambyl2015potato860022.2190,920
Zhambyl2016potato880022.9201,784
Zhambyl2017potato901122.7204,640
Zhambyl2018potato972522.8221,719
Zhambyl2019potato10,17023.1234,714
Zhambyl2020potato11,16523.1258,240
Zhambyl2021potato11,44623.4267,715
Zhambyl2022potato11,83624.3287,030
Zhambyl2023potato808825.4205,281
Zhambyl2024potato676426.1176,537
Table A2. Representative fragment of meteorological data used for evapotranspiration calculations.
Table A2. Representative fragment of meteorological data used for evapotranspiration calculations.
StationYearMonthTmeanTmaxTminPRWind
Taraz20001−1.84.2−6.53882.32.0
Taraz20002−1.05.7−6.0674.52.0
Taraz200035.412.6−0.7766.32.4
Taraz2000415.623.09.01962.82.4
Taraz2000519.026.711.53353.42.3
Taraz2000623.731.514.71540.92.6
Taraz2000725.633.517.25542.82.2
Taraz2000824.932.816.6739.42.3
Taraz2000918.527.010.4544.22.4
Taraz2000107.713.43.39073.51.8
Taraz2000111.87.4−2.13179.31.8
Taraz2000121.86.6−1.42680.91.8
Merke20001−2.13.7−6.22080.90.4
Merke20002−1.64.6−6.31375.80.4
Merke200035.112.2−0.51769.50.6
Merke2000415.222.58.52763.30.6
Merke2000518.826.411.64259.00.6
Merke2000623.231.314.61143.20.9
Merke2000725.533.717.33144.30.7
Merke2000824.633.016.41441.60.9
Merke2000918.327.19.91243.70.5
Merke2000107.513.53.08673.20.5
Merke2000111.77.5−2.24080.70.5
Merke2000120.44.6−2.62187.60.6
Tole Bi20001−2.93.7−6.32580.90.5
Tole Bi20002−1.14.6−0.5671.00.6
Tole Bi200036.212.28.51059.40.7
Tole Bi2000417.022.511.61549.51.2
Tole Bi2000520.226.414.63256.41.0
Tole Bi2000625.031.317.3643.21.0
Tole Bi2000726.633.716.41447.90.7
Tole Bi2000825.033.09.9046.90.8
Tole Bi2000919.027.13.0843.11.0
Tole Bi2000107.513.5−2.28271.10.6
Tole Bi2000111.07.5−2.61773.20.6
Tole Bi200012−0.14.6−9.14186.60.4
Kordai20001−4.12.7−7.23772.14.3
Kordai20002−4.65.6−6.22067.74.8
Kordai200031.614.6−0.62069.44.4
Kordai2000412.324.710.51157.64.5
Kordai2000516.928.312.14354.53.4
Kordai2000620.833.616.71642.44.0
Kordai2000723.035.418.14544.33.8
Kordai2000822.933.816.6541.83.7
Kordai2000917.028.411.43841.64.5
Kordai2000104.714.22.110576.23.6
Kordai200011−0.57.4−4.02477.23.7
Kordai200012−0.94.3−3.22079.13.0
Saudakent20001−3.50.2−7.61584.82.9
Saudakent20002−1.4−0.2−8.2579.63.0
Saudakent200035.06.3−2.41562.63.0
Saudakent2000416.618.07.23458.13.0
Saudakent2000519.823.510.91261.13.1
Saudakent2000625.027.614.21937.03.2
Saudakent2000726.729.517.11336.72.8
Saudakent2000826.029.316.9036.83.0
Saudakent2000918.323.311.2040.12.6
Saudakent2000107.08.91.23667.62.8
Saudakent2000110.33.8−3.8978.62.5
Saudakent2000121.12.4−4.01888.32.6
Moinkum20001−5.21.8−7.51763.72.7
Moinkum20002−2.83.9−5.8660.13.1
Moinkum200034.812.6−0.91449.53.1
Moinkum2000415.924.59.41042.43.5
Moinkum2000520.528.811.41341.62.6
Moinkum2000625.133.416.4338.83.2
Moinkum2000726.535.517.61939.23.1
Moinkum2000825.334.917.5039.62.8
Moinkum2000918.628.39.3046.82.9
Moinkum2000106.513.91.53958.52.4
Moinkum200011−0.46.1−4.21262.43.5
Moinkum200012−1.04.2−1.31367.62.5
………………………
Taraz202410.05.0−4.43882.93.3
Taraz20242−2.23.8−6.44483.32.5
Taraz202435.511.80.53477.63.3
Taraz2024412.819.27.16871.92.7
Taraz2024517.424.111.32365.92.3
Taraz2024625.333.416.7339.62.4
Taraz2024725.833.118.42242.72.0
Taraz2024825.333.017.4038.72.1
Taraz2024916.223.88.81344.92.7
Taraz20241011.417.86.04869.31.9
Taraz2024115.511.70.75878.42.4
Taraz202412−2.43.9−7.11584.72.3
Merke20241−0.94.5−6.44083.90.5
Merke20242−2.62.60.62482.20.8
Merke202435.411.97.33975.30.8
Merke2024413.120.411.25767.00.8
Merke2024517.324.317.47163.90.8
Merke2024625.033.018.71742.60.7
Merke2024725.733.318.15343.50.7
Merke2024825.633.89.6940.30.6
Merke2024916.725.26.01846.90.6
Merke20241011.218.40.56868.40.7
Merke2024115.011.5−8.84673.00.5
Merke202412−4.31.81.83476.30.4
Tole Bi20241−2.74.5−7.13680.81.2
Tole Bi20242−4.12.6−8.73577.11.4
Tole Bi202435.711.90.22871.71.6
Tole Bi2024414.120.47.42561.02.2
Tole Bi2024518.824.311.93560.71.5
Tole Bi2024626.133.017.21640.41.7
Tole Bi2024726.133.318.61749.01.9
Tole Bi2024825.833.817.3343.11.3
Tole Bi2024916.725.29.0344.72.1
Tole Bi20241011.218.45.36869.31.3
Tole Bi2024114.411.50.04178.71.4
Tole Bi202412−5.81.8−10.32886.11.0
Kordai20241−3.00.9−6.04875.34.0
Kordai20242−6.1−1.1−9.93374.65.4
Kordai202432.37.5−1.04670.33.7
Kordai202449.915.95.84261.84.7
Kordai2024515.221.311.03658.83.6
Kordai2024622.529.917.02229.53.3
Kordai2024723.530.918.21937.03.0
Kordai2024823.931.318.5732.12.9
Kordai2024914.421.010.1938.96.1
Kordai2024109.715.26.43751.84.1
Kordai2024113.69.10.73556.93.5
Kordai202412−4.5−0.4−7.51561.63.8
Saudakent20241−2.50.9−7.02382.21.9
Saudakent20242−2.1−1.1−6.51575.81.8
Saudakent202435.67.50.04882.21.2
Saudakent2024414.915.98.24267.41.2
Saudakent2024519.321.312.12460.81.3
Saudakent2024626.929.917.6435.80.6
Saudakent2024726.830.918.9445.00.7
Saudakent2024825.431.316.2339.40.5
Saudakent2024916.521.07.5141.61.0
Saudakent20241011.715.25.33366.61.0
Saudakent2024114.29.1−0.92084.81.3
Saudakent202412−4.0−0.4−8.51789.71.0
Moinkum20241−3.32.3−8.43667.41.9
Moinkum20242−5.62.1−11.72462.32.2
Moinkum202434.812.5−2.03261.21.3
Moinkum2024414.222.45.24147.62.1
Moinkum2024519.328.010.52248.11.5
Moinkum2024626.935.815.91632.21.3
Moinkum2024726.334.517.73838.91.3
Moinkum2024825.734.916.1034.61.0
Moinkum2024916.224.77.3138.31.4
Moinkum20241011.318.74.96952.31.4
Moinkum2024113.710.4−1.03161.11.8
Moinkum202412−4.81.3−9.71767.41.7

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Water 18 01344 g001
Figure 1. Overview map of the study area.
Figure 1. Overview map of the study area.
Water 18 01344 g001
Water 18 01344 g002
Figure 2. Workflow of evapotranspiration calculation and water footprint assessment.
Figure 2. Workflow of evapotranspiration calculation and water footprint assessment.
Water 18 01344 g002
Water 18 01344 g003aWater 18 01344 g003b
Figure 3. Distribution of gross production of major agricultural crops in the Shu–Talas River Basin: (a) maize, (b) sugar beet, (c) sunflower, (d) potato.
Figure 3. Distribution of gross production of major agricultural crops in the Shu–Talas River Basin: (a) maize, (b) sugar beet, (c) sunflower, (d) potato.
Water 18 01344 g003aWater 18 01344 g003b
Water 18 01344 g004aWater 18 01344 g004b
Figure 4. Distribution of total evapotranspiration of agricultural crops during the growing season: (a) maize, (b) sugar beet, (c) sunflower, (d) potato.
Figure 4. Distribution of total evapotranspiration of agricultural crops during the growing season: (a) maize, (b) sugar beet, (c) sunflower, (d) potato.
Water 18 01344 g004aWater 18 01344 g004b
Water 18 01344 g005
Figure 5. Distribution of the green and blue water footprint components of agricultural crops: (a) maize, (b) sugar beet, (c) sunflower, (d) potato.
Figure 5. Distribution of the green and blue water footprint components of agricultural crops: (a) maize, (b) sugar beet, (c) sunflower, (d) potato.
Water 18 01344 g005
Table 1. Summary ranges of quantitative water footprint components for major agricultural crops.
Table 1. Summary ranges of quantitative water footprint components for major agricultural crops.
Agricultural Crop W F b l u e , m3/t W F g r e e n , m3/t W F q u a n t , m3/t Key Result
Maize900–1800100–4001100–2500The share of the blue water footprint is approximately 80–90%
Sugar beet200–110020–150250–1300The share of the blue water footprint is approximately 85–95%
Sunflower3000–14,000500–30003500–16,000High variability, with a strong dependence on crop yield
Potato250–60050–150300–650A relatively balanced structure with the dominance of the blue component
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Alimkulov, S.; Makhmudova, L.; Tskhay, M.; Talipova, E.; Birimbaeva, L.; Ibrayev, T.; Alzhanov, O.; Nurlanova, D. Assessment of Green and Blue Water Footprint Components of Agricultural Crops in the Shu–Talas River Basin, Kazakhstan. Water 2026, 18, 1344. https://doi.org/10.3390/w18111344

AMA Style

Alimkulov S, Makhmudova L, Tskhay M, Talipova E, Birimbaeva L, Ibrayev T, Alzhanov O, Nurlanova D. Assessment of Green and Blue Water Footprint Components of Agricultural Crops in the Shu–Talas River Basin, Kazakhstan. Water. 2026; 18(11):1344. https://doi.org/10.3390/w18111344

Chicago/Turabian Style

Alimkulov, Sayat, Lyazzat Makhmudova, Mikhail Tskhay, Elmira Talipova, Lyazzat Birimbaeva, Tursun Ibrayev, Oirat Alzhanov, and Dilnaz Nurlanova. 2026. "Assessment of Green and Blue Water Footprint Components of Agricultural Crops in the Shu–Talas River Basin, Kazakhstan" Water 18, no. 11: 1344. https://doi.org/10.3390/w18111344

APA Style

Alimkulov, S., Makhmudova, L., Tskhay, M., Talipova, E., Birimbaeva, L., Ibrayev, T., Alzhanov, O., & Nurlanova, D. (2026). Assessment of Green and Blue Water Footprint Components of Agricultural Crops in the Shu–Talas River Basin, Kazakhstan. Water, 18(11), 1344. https://doi.org/10.3390/w18111344

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