A Comparative Water Footprint Analysis of Conventional versus Organic Citrus Production: A Case Study in Spain

Abstract

Spain is the leading citrus producer in the European Union, with the Segura River Basin in southeastern Spain playing a crucial role in this industry. However, the impact of local agricultural production on water appropriation has been overlooked. This study assesses the water footprint (WF) of both conventional and organic citrus production using the Water Footprint Network approach, addressing beneficial practices aiming to reduce the water appropriation impact. Focusing on four citrus fields, the evaluation covers green, blue, and grey components of the WF, and secondary impacts from electricity and fossil fuel consumption, which are usually omitted from the WF assessments. The results indicate that the total WF for organic orange and lemon production is over 19% lower than for the conventional system. Notable differences are observed in the blue component, attributed to the use of vegetative mulches, and in the grey component due to the reduced impact of fertilizers in organic practices. The individual and total WF values are lower than those reported in other citrus studies, and are linked to efficient resource management in semi-arid regions that helps overcome water scarcity. Nevertheless, the sustainability analysis reveals major challenges for the citrus sector in the basin, highlighting the strain on resources given the limited water availability. The available water remaining (AWARE) indicator demonstrates extremely high potential water deprivation in the area. Overall, the study underscores the necessity of integrating WF analyses in agricultural planning to manage resource scarcity effectively. Future research should focus on developing precise methodologies and incorporating unconventional farming practices to enhance sustainability. This research provides valuable insights for stakeholders aiming to optimize water use in agriculture under scarce resource conditions.

1. Introduction

The escalating demand for freshwater driven by global population growth, economic expansion, increased food production and climate change is a pressing concern [1,2]. In particular, the intensification of irrigation for food security has led to significantly higher freshwater consumption [3,4]. With the current global population of 8 billion people facing a critical shortage of clean water, the projected population increase to 9.7 billion by 2050 will further aggravate this challenge [5]. Despite technological advances that have improved water use efficiency, only modest reductions in water withdrawals have been achieved [6], posing significant obstacles, especially when considering climate change [7]. Therefore, the agricultural sector must proactively address future water requirements and resource degradation [1,7].

To alleviate the pressure of the water requirements of agriculture, five key strategies have been developed: (i) modernization/pressurization of water supply infrastructures and irrigation systems [6]; (ii) advanced technology for monitoring water, soil, and plants [2]; (iii) sustainable farming management to optimize crop patterns [8]; (iv) the incorporation of non-conventional water resources [9]; and (v) assessment tools for sustainable water management [10,11]. Concerning the latter, the water footprint (WF) assessment pioneered by Hoekstra [12], is a comprehensive tool which quantifies freshwater appropriation for products, encompassing green, blue, and grey water components [11,13]. Particularly in agriculture, green and blue WF pertain to rainfall and crop irrigation, respectively, while grey WF is linked to pollution assimilation by freshwater bodies expressed as a dilution water requirement [1].

According to WoS data (www.webofscience.com, accessed on 16 January 2024), over 11% of WF studies from 2003 to 2023 focused on agricultural water appropriation, with the Water Footprint Network’s guidelines (WFN) being distinguished as a predominant methodology [14,15]. While historical emphasis was on large-scale national studies [16,17,18], recent trends highlight a shift towards field-scale assessments, offering valuable insights into water consumption patterns in agriculture [11,19]. For instance, Mekonnen and Hoekstra [1] used a grid-based dynamic water balance model to estimate the green, blue, and grey WF of various crops based on the global average for each crop. Munro et al. [20] focused on primary citrus production in South Africa, utilizing SAPWAT 3.0 software, and revealed that the blue component comprised approximately 50% of the total WF, emphasizing regional variations. Machin-Ferrero et al. [7] and Novoa et al. [21] applied the WFN method to assess water appropriation in lemon production in the Tucumán region (Argentina) and the Cachapoal River basin (Chile), respectively. Their studies provided valuable insights into the uncertainties and patterns in water consumption influenced by climate and geographical conditions. Recent research trends indicate increased focus on integrating various methodologies to assess the overall sustainability of agri-food production and the need for further research to reduce WF effectively [10,15,22].

In addition to local conditions, emerging cultivation models such as organic production seem promising in reducing water consumption [23], despite posing challenges to WF estimations owing to agronomic uncertainties and productivity variations [24]. Understanding the relationship between such models and WF assessments is crucial for the advancement of sustainable agriculture. However, accurate calculation is challenging due to limited data availability, which can potentially lead to underestimation and inaccuracy in water consumption analysis [13,25]. Therefore, careful selection of methodologies is essential for addressing specific goals and contextual factors [26]. Simplified methods and enhanced data availability are necessary to ensure broader stakeholder engagement.

The case study in southeastern Spain, a significant contributor to the national economy through food production [27], underscores the critical role of water resources in cultivation under an arid climate [28]. Spanish water policy investments in precision technologies and non-conventional water resources seek to achieve water-efficient cultivation [6,9,29].

River basin management planning stages in Spain (2009–2015, 2015–2021, and 2022–2027) have integrated WF assessments since 2008, as mandated by the European Water Framework Directive (2000/60/EC). The latest plans consolidate green and blue WF accounts using input–output tables, although they often overlook socioeconomic and environmental impacts. Additionally, their scope is limited, providing only aggregated WF data for sectors such as agriculture, urban, or industrial activities [30]. This lack of specific crop-level data impedes their utility for informed decision-making in local agriculture, thereby failing to effectively influence resource allocation or water planning criteria [18].

On the other hand, recent Spanish legislation in certain agricultural areas has mandated farmers to create digital field notes for agricultural plots (Royal Decree 1054/2022; Order APA/204/2023), including specific data required for adherence to different regulations (BOE-B-2020-24791; Decree-Law No. 3/2023). This information enables farmers to conduct simple WF assessments, ensuring compliance while identifying and addressing critical stages in their production systems. These actions are crucial for maintaining labor, production, and the region’s high-value agricultural sector, in a scenario of climate variability and evolving environmental conditions. Incorporating small-scale research can enhance the understanding of water appropriation in each region, providing more relevant data for basin-level studies.

In this context, our study evaluated the WF of various commercial citrus plots located in the Segura River Basin (SRB, southeastern Spain), comparing conventional and organic production using the WFN approach [1]. The results demonstrate how assessing the WF of agro-industrial activities can be easily implemented, offering valuable insights in order to reduce water consumption at the field level.

2. Materials and Methods

2.1. Case Study

2.1.1. Fields and Assessment Scope

The SRB contains 261,626 ha of irrigated agricultural fields, of which 27.9% are citrus crops [31]. The climate in the region is semi-arid, characterized by warm, dry summers and mild winters. The average annual reference evapotranspiration (ET₀) is 1100 mm, and the rainfall is 400 mm. Despite periodic water scarcity, irrigation adequately satisfies crop water requirements. This is often facilitated by the accumulation of water in reservoirs during prolonged dry periods, thanks to the allocation of water from the central region of the country via the Tagus–Segura water transfer.

Four distinct citrus orchards were selected for the study, comprising two lemon and two orange plots. Each crop type was represented by one conventionally managed orchard and one managed organically. The plots were chosen for their representativeness of citrus agriculture in the area. The study includes both lemon (Verna) and orange (Lane Late) crops, which are common and economically significant in the region, comparing the WF of two agricultural practices. The selected fields are representative of the tree spacing arrangements for citrus production in the study area. Moreover, the different tree ages between crops (5 and 12 years in lemon and orange plots) allow the study to account for the varying water needs of trees at different stages of growth. This diversity ensures that the study covers a comprehensive range of citrus practices, making the findings more broadly applicable. The WF analysis covered a one-year period from January to December 2022. Figure 1 illustrates the location of the fields, with

Table 1. Main characteristics of the conventional and organic fields selected for the study.

Attribute	Conventional		Organic
Field	A	B	C	D
Crop	Lemon	Orange	Lemon	Orange
Variety	Verna	Lane Late	Verna	Lane Late
Surface (ha)	16.36	5.76	18.61	7.20
Tree spacing (m × m)	6 × 3	6 × 4	6 × 3	6 × 4
Tree age (years)	5	12	5	12
Fertilization	Fertigation	Fertigation	Fertigation	Fertigation
Disease control	Conventional and organic pesticides	Conventional and organic pesticides	Bio protection products	Bio protection products
Weed control	Chemical and mechanical	Chemical and mechanical	Mechanical	Mechanical
Harvesting	Manual	Manual	Manual	Manual
Pruning	Manual and mechanical	Manual and mechanical	Manual and mechanical	Manual and mechanical
Commercial yield (tonne ha−1 year−1) *	25.07	40.59	27.87	34.08
Nearest meterological station	Almoradí, Alicante	Almoradí, Alicante	Pilar de la Horadada, Alicante	LO-11, Lorca, Murcia
Public access	http://riegos.ivia.es/listado-de-estaciones/almoradi (accessed on 7 February 2024)		http://riegos.ivia.es/listado-de-estaciones/pilar-de-la-horadada (accessed on 7 February 2024)	http://siam.imida.es/apex/f?p=101:1:458639124210467 (accessed on 7 February 2024)

* Commercial fruit avoids small and damaged fruits.

presenting their main characteristics.

The study focused on the agriculture stage, comprising all tasks performed in the orchards until the product left the farm. As a result, upstream and downstream processes were excluded from the study, as they typically account for less than 1% of the total water used [7,32]. These also include sampling production in the nursery, tree planting, packaging, or manufacturing. Figure 2 provides an overview of the tasks investigated, covering direct (green, blue, and grey) and secondary (electricity and fossil fuels) WF components.

2.1.2. Fertigation, Pest Control, and Harvesting

Water scarcity is a persistent challenge in the southeastern region of Spain, which leads farmers to adopt precise and localized irrigation methods to minimize evaporation and leaching losses. For each plot studied, the farm’s irrigation system consisted of two drip lines laid on the soil surface, with four emitters of 4 L h⁻¹ per tree. Irrigation doses were determined based on the previous week’s accumulated daily crop evapotranspiration (ET_c; mm), and soil moisture control probes. ET_c values were calculated by multiplying the daily reference evapotranspiration (ET₀) and the specific crop (K_c) and water stress (K_s) coefficients. For each field, crop ET₀ estimation was automatically performed by a reference meteorological station (Table 1), programmed to use the FAO Penman–Monteith methodology, considering factors such as soil type and relative moisture (%) data, average altitude, minimum and maximum temperatures (°C), and wind speed (m s⁻¹), among others. Links to access publicly available data from each meteorological station are given in Table 1. Monthly K_c and K_s coefficients were those commonly used by farmers in the area. Soil moisture control probes installed in each plot at depths of 0.30 m and 0.60 m allowed for continuous monitoring of water content and adjustment of irrigation doses (ET_c), based on soil water saturation at the area of maximum root density (0.4 m deep).

Fertilizers were supplied through irrigation water, with conventional and organic fertilizers and pest control products complying with OJEU-2018-848 (European Union) and Royal Decree 834/2007 (Spain) regulations.

Table 2. Nutrient requirement supplied from January to December 2022 on each farm and list of commercial products used for conventional and organic fertilization.

UF (t)	Field A	Field B	Field C	Field D
N	1.73	1.50	0.71	0.30
P	0.13	0.10	0.05	0.02
K	1.34	1.59	0.47	0.17
Ca	0.26	0.29	–	–
Fertilizer
	Conventional		Organic
Commercial name (N–P–K–Ca)	Manure (2–0–0–0)	Ammonium nitrate (34.5–0–0–0)	Ecomed Brio K (0–0–20.8–0)	Agrimartin Biologico Fe (2–0–5–0)
	Acifort (15–0–0–0)	Monoammonium phosphate (12–26.6–0–0)	Ecomed Actiphos (7–0.9–3.3–0)	GepaBi PeptiBio (8–0–0–0)
	Nutri Liquid (4–1.3–8.3–1.4)	Potassium nitrate (13–0–38.2–0)	Solublack H-87 (0.12–0.4–6.6–0)	RizoBioN Plenus (9–4.8–9.1–0)
	Ammonium nitrate (34.5–0–0–0)	Potash (0–0–49.8–0)	RomBiogan (2.4–0–4–0)	RizoBioLiq Kyayum Plus (0–0–12.5–0)
	Nova MAP (12–26.6–0–0)	Calcium nitrate (14.5–0–0–19.3)
	Nova Calcium (15.5–0–0–18.9)
	Nova N–P (13–0–38.2–0)
	Potassium nitrate (13–0–38.2–0)

summarizes the agrochemicals used for both the conventional and the organic cropping systems. Weed growth was prevented in the orchards, and pruning and vegetative residues were incorporated into the soil between crop rows in organic cultivars to reduce the water vapor exchange with the atmosphere, thus minimizing soil water evaporation [33]. Fruits were harvested manually after ripening and then either sold fresh or processed industrially.

2.2. Components of Water Footprint Calculation

The direct WF assessment followed the methodology outlined in the WFN guidelines [1]. The three components were determined monthly using local data, including information from public meteorological stations and farmer’s fieldnotes, while secondary WF from electricity and fossil fuel consumption was estimated following comprehensive literature suggestions.

2.2.1. Direct Green Water Footprint

The green WF (WF_green, m³ t⁻¹) was calculated as follows:

$${\mathrm{W}\mathrm{F}}_{\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}=\frac{{\mathrm{C}\mathrm{W}\mathrm{U}}_{\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}}{\mathrm{Y}}$$

(1)

where CWU_green is the crop water use from rainfall (m³ ha⁻¹); and Y is the yield (t ha⁻¹).

The CWU_green can be estimated as the minimum value between ET_c and effective precipitation (EP) for a specific period (min(∑_monthET_c, ∑_monthEP)). EP represents the fraction of total precipitation utilized to fulfill the crop’s water needs, excluding deep infiltration, surface overflow, and soil surface evaporation. In this study, EP was calculated using the method developed by the Soil Conservation Service of the United States Department of Agriculture (SCS–USDA), with monthly values obtained as follows:

$$\mathrm{E}\mathrm{P}=\left(1.25247·{\mathrm{P}}^{0.82416}-2.93522\right)·{10}^{0.00095\mathrm{C}\mathrm{U}}·(0.531747+0.011621·{\mathsf{\Delta }}_{\mathrm{I}}-8.9·{10}^{-5}·{{\mathsf{\Delta }}_{\mathrm{I}}}^2+2.3·{10}^{-7}·{{\mathsf{\Delta }}_{\mathrm{I}}}^3)$$

(2)

where P is the precipitation (mm); CU is the average consumptive use of water (mm); Δ_I is the net irrigation dose (mm), with each factor corresponding to a monthly basis.

2.2.2. Direct Blue Water Footprint

The blue WF (WF_blue, m³ t⁻¹) can be evaluated through two approaches: either by considering the crop water requirements (ET_blue) or by using the actual irrigation program implemented in the field. Mekonnen and Hoekstra et al. [1] recommended the latter method as being the most accurate, since it incorporates all the information regarding the irrigation volume supplied to the crop. Software tools such as CROPWAT 8.0^® utilize meteorological, soil, and crop data to calculate daily water balances and distinguish between green and blue water evapotranspiration (ET_green and ET_blue; mm) [7]. However, in southeastern Spain, local farmers typically estimate irrigation supplies based on crop requirements (ET_c), resulting in irrigation calculations that align closely with estimated needs by crop software. Thus, both approaches are likely to yield analogous results. Nevertheless, in this study, the presence of spontaneous vegetation cover may influence the water requirements for each crop. Therefore, WF_blue was calculated using two methods: based on crop irrigation water documented by farmers in their field notes (WF_blue-ir; Equation (3)) and also using CROPWAT 8.0^® software (WF_blue-cw; Equation (4)) as follows:

$${\mathrm{W}\mathrm{F}}_{\mathrm{b}\mathrm{l}\mathrm{u}\mathrm{e}\text{-}\mathrm{i}\mathrm{r}}=\frac{{\mathrm{C}\mathrm{W}\mathrm{U}}_{\mathrm{b}\mathrm{l}\mathrm{u}\mathrm{e}}}{\mathrm{Y}}$$

(3)

$${\mathrm{W}\mathrm{F}}_{\mathrm{b}\mathrm{l}\mathrm{u}\mathrm{e}\text{-}\mathrm{c}\mathrm{w}}=\frac{10·\sum {\mathrm{E}\mathrm{T}}_{\mathrm{b}\mathrm{l}\mathrm{u}\mathrm{e}}}{\mathrm{Y}}$$

(4)

where ET_blue is the daily blue water evapotranspiration (mm); 10 is a unit conversion factor to transform the units into water volume per land surface (mm to m³ ha⁻¹); and CWU_blue is the real volume of water supplied to the crop (m³ ha⁻¹), each factor corresponding to a monthly basis.

2.2.3. Direct Grey Water Footprint

The grey WF (WF_grey, m³ t⁻¹) was assessed following the recommendations of Mekonnen and Hoekstra [1] and Franke et al. [34]:

$${\mathrm{W}\mathrm{F}}_{\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{y}}=\frac{{10}^6·\mathrm{V}·\mathrm{⍺}/({\mathrm{C}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{C}}_{\mathrm{n}\mathrm{a}\mathrm{t}})}{\mathrm{Y}}$$

(5)

where 10⁶ is a unit conversion factor to transform the units into water volume per land surface; V is the application rate of the agrochemical to the field (t ha⁻¹); ⍺ is the leaching-runoff fraction (%); C_max (mg L⁻¹) is the maximum acceptable concentration of the pollutant in the receiving water body specified by local legislation; and C_nat (mg L⁻¹) is the natural concentration in the water body.

In this study, it was assumed that fertilizers and pesticides applied by foliar spraying were retained and absorbed by the trees, and hence did not reach the soil. Consequently, they were not included in the calculations [7]. Spanish legislation specifies maximum values of 50 mg L⁻¹ for total nitrogen and 2.2 mg L⁻¹ for phosphorus (Royal Decree 3/2023), which were individually used as C_max in Equation (5). Thus, the WF_grey selected for total WF was determined by the most restrictive value resulting from the two C_max, since it indicates the minimum water volume required to dilute contaminants. However, different authors have suggested a lower concentration for total nitrogen (10 mg L⁻¹) [7,34]. Therefore, given that nitrogen tends to be the most restrictive component among those used due to its high content in fertilizers, and to assess such a factor in a more comprehensive context, WF_grey was calculated using C_max values of 10, 30, and 50 mg L⁻¹ in Equation (5). C_nat was considered to be zero (0 mg L⁻¹) to align with other studies, as the natural concentration of each element is not accurately known. Leaching-runoff fractions for nitrogen and phosphorus were set at 10% and 3%, respectively, as suggested by Franke et al. [34].

2.2.4. Secondary Water Footprint of Electricity and Fossil Fuel Consumption

The consumption of electricity and fossil fuel in the field was regarded as secondary WF. Currently, the WFN guidelines do not contain a standardized methodology for calculating the WF derived from electricity or fossil fuels. However, considering that water is utilized in the production process of both components, it seems logical to approximate the water volume (n m³) required to produce a specific amount of electricity (n’ GJ) to some extent. For instance, Gerbens-Leenes et al. [35] evaluated the consumptive WF of heat, electricity, and other primary energy carriers, proposing equivalence factors for each source. In our study, an approximate equivalence factor of 0.00134 m³ kg⁻¹ was chosen for fossil fuel (mainly diesel) consumption to calculate their footprint (WF_ff) [36,37]. Moreover, considering the diverse electricity sources in Spain, different equivalence values were employed in proportion to the percentage of electrical energy obtained from each source, including renewable, cogeneration, or nuclear, among others. The equivalence factors utilized in estimating the secondary footprint of electricity (WF_e) are summarized in

Table 3. Equivalence factor for secondary water footprint calculation derived from electricity consumption.

Source	Proportion (%)	Equivalence Factor (m3 GJ−1) *	Derived Equivalence Factor (m3 GJ−1)
Renewable	43.6	22.300	9.723
Cogeneration	11.0	1.058	0.116
Coal	2.0	0.164	0.003
Gas	1.7	0.109	0.002
Nuclear	22.8	0.086	0.020
Natural gas	1.0	0.265	0.003
Others	17.9	0.109	0.020
Total	100.0		9.886

* Values suggested by Gerbens-Leenes et al. [35,36].

.

$$\mathrm{S}\mathrm{e}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{d}\mathrm{a}\mathrm{r}\mathrm{y} \mathrm{W}\mathrm{F} ({\mathrm{m}}^3{\mathrm{t}}^{-1})=\frac{{\mathrm{E}}_{\mathrm{C}}·\mathrm{E}\mathrm{F}+{\mathrm{F}\mathrm{F}}_{\mathrm{C}}·\mathrm{E}\mathrm{F}}{\mathrm{Y}}$$

(6)

where E_c is the electricity consumption (GJ); FF_c is the fossil fuel consumption (kg); and EF is the specific equivalence factor (m³ GJ⁻¹).

2.3. Crop Irrigation and Water Scarcity Footprint

In addition to WF values, the Water Scarcity Footprint (WSF) indicator serves to evaluate the level of scarcity in a region linked to the water demand of a specific activity, thus aiding in identifying areas where water resources are under stress [38]. WSF primarily focuses on the blue WF component and is often computed alongside WF assessments [7]. In this study, the available water remaining (AWARE) consensus model was utilized to evaluate the WSF. This approach examines the relationship between water extraction for human purposes, such as agriculture, and the total water availability in a given region. Monthly characterization factor (CF) values for each specific basin are employed in this method [38], with values ranging from 0.1 to 100. A higher CF, and consequently a higher associated WSF value, indicate greater vulnerability to water consumption in a region. For example, a CF of approximately 1 corresponds to the global average, while a CF of 100 indicates that the available water in the area amounts to 100 times less than the world average [39]. It is important to note that the interpretation of CF depends on activity specifics, local water scarcity conditions, and regional differences in water availability.

The AWARE model incorporates a Google Earth layer and a database, rendering it user-friendly for visualizing the spatial distribution of water scarcity and potential for water deprivation. The WSF (m³ world eq. t⁻¹) indicator can be estimated using the monthly WF_blue calculated in Section 2.2 and the monthly CF values from the AWARE database [38] as follows:

$$\mathrm{W}\mathrm{S}\mathrm{F}=\sum _{\mathrm{p},\mathrm{t}}{\mathrm{W}\mathrm{F}}_{\mathrm{b}\mathrm{l}\mathrm{u}\mathrm{e}\mathrm{p},\mathrm{t}}·{\mathrm{C}\mathrm{F}}_{\mathrm{p},\mathrm{t}}$$

(7)

where p is the region (SRB), and t the period (monthly).

3. Results and Discussion

3.1. Organic and Conventional Water Footprint

Figure 3 displays the total WF per tonne of harvested fruit for the two citrus crops and the two cultivation systems, considering WF_blue-ir, and WF_grey with a C_max of 50 mg L⁻¹ for nitrogen in Equation (5). In the conventional system, the total WF was 183.18 m³ t⁻¹ for lemons and 181.88 m³ t⁻¹ for oranges. Conversely, in organic farms, these values decreased to 140.31 m³ t⁻¹ and 154.64 m³ t⁻¹, respectively. It is important to note the similarity between the farms in both cases (lemon and orange), particularly in tree age and density, which facilitates comparison. Overall, WF_blue constituted the primary contribution to the total WF, ranging from 58.6 to 79.0%, while the WF_green accounted for approximately 29.1% on average. The grey component had the smallest impact, representing less than 7.3% of the total WF. It is noteworthy that the EP depended on the area’s precipitation, which varied considerably among farms. The study region (SRB) experienced very limited periods of precipitation, with frequent torrential rainfall events occurring in April–May and September–October. As a result, the EP often fell significantly below ET_c values, so irrigation is therefore essential to meet crop water requirements.

This variability can substantially impact WF values, reducing the green component and thereby increasing the blue WF, due to the heightened reliance on rainfall water by the crop [20]. However, it should be noted that the combined green and blue WF was higher in the conventional farms compared to the organic plots. Moreover, in both cases, the individual WF_blue-ir and WF_grey of the organic farms were lower than those of the conventional cropping farms. The reduction in WF_blue-ir was positively associated with the use of vegetative mulch, which appeared to effectively decrease soil evaporation and maintain a more consistent moisture [40,41,42]. Additionally, the reduction in WF_grey was linked to the use of organic fertilizers with a natural base and lower concentrations of nitrogen and phosphorus supplied to the crop [23].

In addition to the direct WF, the secondary fraction derived from electricity and fossil fuel consumption accounted for less than 1.7% of the total footprint of each farm (Figure 3). WF_e primarily depends on the electricity used for irrigation pumping systems, which are typically energy-efficient [43]. Moreover, the negligible WF_ff (≤0.01%) was associated with the low fuel consumption attributed to manual labor, particularly in the case of organic farms. Other studies have also reported a relatively low impact from electricity and fossil fuel consumption on WF evaluation compared to other activities such as irrigation and fertilizer supply [44,45].

Concerning water use efficiency, typical benchmarks in citrus production generally range from 4 to 8 kg of fruit per m³ of water used. However, these figures can vary significantly among species, production systems, and regions, posing challenges on the establishment of specific thresholds. For each farm, Figure 4 shows water consumption per hectare derived from each component of the WF. Focusing on the blue fraction, irrigation water usage amounted to 2690 m³ ha⁻¹ in the conventional lemon plot and 2335 m³ ha⁻¹ in the organic plot, reflecting a 13.2% reduction. Similarly, water consumption dropped from 4850 m³ ha⁻¹ to 4160 m³ ha⁻¹ in the conventional and organic orange plots, respectively, marking a reduction of 14.2%, with those trees being in the adult stage. Furthermore, considering the sum of both green and blue fractions, the water used by the crop totaled 4311 m³ ha⁻¹ and 3805 m³ ha⁻¹ in conventional and organic lemon plots, respectively, and 6720 m³ ha⁻¹ and 5137 m³ ha⁻¹ in orange plots, respectively. This resulted in water use efficiency ratios of 5.8 kg m⁻³ and 7.3 kg m⁻³ in conventional and organic lemon plots, respectively, and 6.0 kg m⁻³ and 6.6 kg m⁻³ in orange plots.

In lemon production, the observed reduction in consumption in both plots can be attributed to the young age of the trees (Table 1), although this consumption is expected to gradually increase as the trees mature, while in both organic crops, the overall reduction in water consumption may be associated with mulching cover, which enhanced water-use efficiency despite yielding lower production.

Mulching techniques, such as straw mulching, conserve soil moisture by covering the surface, thereby enabling extended crop production in water-deficient regions and enhancing root development and productivity due to energy and water conservation [33]. The use of multifunctional vegetative covers, integrating usable side crops, presents an innovative approach to sustainable agriculture, offering benefits for soil health and also water conservation [46,47]. Research suggests that intercropping practices improve soil conditions by increasing the nutrient content and preventing erosion, although such crops may compete with the main crop for the available water [47,48]. Legume cover crops, for instance, significantly increased soil nitrogen levels by fixing atmospheric nitrogen, potentially reducing the need for fertilization [49]. Additionally, incorporating usable side crops into intercropping systems offers economic benefits, by diversifying revenue streams in woody and citrus operations. Overall, these options can either help reduce the crop’s WF by diminishing irrigation water volumes, or increase the water use efficiency ratio by incorporating vegetative species that enhance the main crop development.

Moreover, various irrigation strategies, together with soil-covering techniques, are employed in water-scarce areas to reduce water consumption. Deficit irrigation, for example, is effective for citrus and woody crops, enhancing adaptation to future shortages [50]. Studies suggest a minimal productivity impact on various citrus species, enabling irrigation volume reductions of 12% to 50%, without compromising tree productivity [51,52]. Furthermore, research comparing irrigation strategies has indicated that subsurface drip and deficit irrigation are particularly useful for reducing consumptive WF in arid and semi-arid conditions [41], offering significant potential for water consumption reduction within the study area. In our case, deficit irrigation could potentially reduce the total WF from 140–183 m³ t⁻¹ to 100–121 m³ t⁻¹, primarily impacting on the blue fraction. This approach can be particularly beneficial given the yield rate differences between organic and conventional farming practices, which are about a 20% lower in organic cropping. Implementing deficit irrigation techniques to reduce water consumption alongside ecological fertilization methods, while maintaining yield levels, can enhance water use efficiency ratios, thereby minimizing the impact of yield differences [53,54].

These improvements are increasingly crucial in the current context of global climate change [1,32], where water resources are becoming scarce, and efforts to minimize soil and nearby water body pollution are essential for ensuring crop development security [7].

To enhance the sustainability of agricultural systems, it is crucial to integrate tools that streamline crop planning and improve resource-use efficiency. In our study, such effective management and conservation of irrigation water is further supported by comparing water consumption and WF_blue obtained from both the farmer’s irrigation program (WF_blue-ir) and the CROPWAT 8.0 software (WF_blue-cw) for each field (Figure 5). The software accurately determined crop water requirements, demonstrating great similarity with farmers’ estimations. However, it also revealed potential underestimation or overestimation of required water volumes. In conventional plots, both sets of results were comparable, with errors below 5%, whereas in the organic plots, the actual water consumption was 9.2% to 17.9% lower than that estimated. This discrepancy could potentially increase the WF_blue in lemon and orange organic plots from 83.9 m³ t⁻¹ to 98.9 m³ t⁻¹ and from 122.1 m³ t⁻¹ to 133.3 m³ t⁻¹, respectively.

CROPWAT requires a substantial amount of data for precise outcomes. Studies recommend calibration prior to using similar software, particularly adjusting the crop and soil coefficients to fit specific agricultural conditions [55]. Moreover, CROPWAT 8.0 does not account for plant cover or organic and synthetic mulches. The notable differences observed in organic cropping may stem from the incorporation of mulches, which are known to reduce evapotranspiration and consequently irrigation water consumption [42,46]. Organic farming represents a distinct agricultural production model, evidenced by reduced water use, emphasizing the need for tools that facilitate accurate water appropriation analysis and mitigate potential overestimations that could inflate WF impacts.

3.2. Citrus Water Footprint in the Segura Basin vs. Other Studies

Water appropriation can vary significantly depending on the region, primarily influenced by climate conditions (rainfall and evapotranspiration), the irrigation methods applied (surface, sprinkler, or drip), and the fertilizers and pesticides allowed [1]. It also depends on crop characteristics, especially the age of the trees and their productivity, as well as specific irrigation management legislation, thus comparisons between studies should be approached cautiously.

In this regard, the fields analyzed exhibited lower values for green, blue, grey, and total WF compared to other studies. Figure 6 presents the WF data on various citrus crops studied by several authors. Firstly, a lower WF_green is expected in our case due to the semi-arid climate of the SRB compared to the Lower Sundays River Valley [20], and the Tucumán province [7], among others. These conditions have also prompted the development and use of techniques aimed at saving water, to enhance irrigation efficiency [6]. Therefore, considering water shortages, the ability of localized irrigation to provide the necessary water to meet crop requirements, and with the local farmers’ management, the WF_blue-ir of both crops and systems (Figure 5 and Figure 6) was also lower than those reported for lemons [20], oranges [1,20], and other citrus fruits [21,56,57], except in regions where the irrigation needs are met by rainfall [7].

Regarding the WF_grey, research has reported values ranging from 30 to 82 m³ t⁻¹, accounting for up to 23.6% of the total WF (Figure 6). In contrast, our study observed lower figures of 2.4–13.3 m³ t⁻¹, representing up to 7.3% of the total WF. Notably, the maximum nitrogen concentration normally used for WF_grey estimation is 10 mg L⁻¹ [1,34], whilst our farms used 50 mg L⁻¹. The sensitivity analysis conducted revealed an increase in the grey component as the maximum allowed concentration decreased. The most significant difference between conventional and organic fields was evident when calculations were performed using a C_max of 10 mg L⁻¹. Furthermore, the WF_grey in the conventional plots closely aligned with results reported in other studies at this nitrogen concentration (Figure 6). Conversely, such values remained consistently below 16 m³ t⁻¹ in the organic plots (Figure 7), ranging from 68% to 81% lower than in the conventional plots. To the best of our knowledge, no data on WF in organic citrus crops are available. Nonetheless, our results underscore the substantial influence of the cultivation model on the derived impact of fertilization.

Recent research by Kowalczyk and Kuboń [23] found that the WF of organic carrot farming was over five times lower compared to conventional production, owing to the significant impact of fertilization and pesticide products. In our study, the total WF was approximately 30% higher in the conventional plots, with the grey component being three to six times higher in lemon and orange, respectively, compared to the organic production. These findings suggest that figures may vary significantly among species and cropping systems, emphasizing the need for further research on the impact of organic agriculture on the WF.

3.3. Future Management Perspectives on Water Footprint Scarcity

Due to water scarcity, the monthly CF obtained from AWARE for the SRB area showed an annual aggregated CF of 99.7 and reached a monthly value of 100 from April to December. In our study, the WSF values for each field were 7410.9 (C), 9187.7 (A), 10,067.3 (B) and 10,108.5 (D) m³ world eq. t⁻¹. These figures highlight the severe impact of water scarcity associated with citrus orchards in the area, indicating substantial water use relative to local conditions [38]. While comparing data between studies can be challenging due to specific circumstances, results may be comparable when similar calculation methods have been used. In recent research, Machin-Ferrero et al. [7] assessed the WF sustainability of lemon production in the Tucumán region using the WSF indicator and showed a much lower value (102 m³ world eq. t⁻¹). This disparity among regions can be attributed to continuous rainy periods in the Tucumán area, resulting in higher WF_green and lower WF_blue due to reduced irrigation needs in certain months.

The SRB is globally recognized for its agricultural production under semi-arid conditions, facilitated by efficient irrigation techniques and integrated management of different water resources within the basin. Consequently, the WF_blue evaluated is relatively low compared to other studies (Figure 6). Nevertheless, results do suggest that agricultural activities in the region will encounter significant sustainability challenges in the near future, and how the available resources will be managed still remains a key issue. To address these challenges and ensure the continuity of food production, various actions concerning local water policy and use, and irrigation management must be assessed to mitigate water appropriation impacts in the basin.

Overall, irrigation strategies have consistently shown beneficial results, positioning themselves as an interesting option to mitigate resource scarcity [50]. Such strategies include subsurface and deficit irrigation, among others, and which can adjust water doses to unreliable water availability, potentially reducing WF_blue [51,52]. Moreover, utilizing capacitive soil moisture probes enables targeted irrigation practices, offering precise insights into moisture levels and reducing water consumption. These strategies, in conjunction with the cultivation models assessed in this study (organic farming and mulching), should promote future research aimed at enhancing sustainable production systems.

Nowadays, hydrological planning in Spain incorporates WF calculations, although the approach remains somewhat limited, primarily focusing on accounting for water usage across various sectors. However, a more precise methodology based on direct agricultural data is needed so decision-makers can obtain more actionable insights. Consequently, future studies should prioritize determining the direct WF of the primary crops cultivated within the SRB. Such studies should draw upon data directly sourced from farmers, enabling the provision of specific insights into blue, green, and grey WF components. This approach aims to enhance comprehension and facilitate the management of available water resources, delimiting and addressing the downsides faced by the agricultural system in the region.

4. Conclusions

Our study has revealed the effectiveness of water footprint analysis to identify the advantages and drawbacks related to the impact on water resources from citrus production, and demonstrated the ability of organic cropping to reduce both the blue and grey WF. Vegetative mulching helps retain more water in the soil for plant use, while organic fertilization decreases nitrogen and phosphorus inputs from organic natural products. Additionally, secondary components such as electricity and fossil fuel consumption constitute less than 2% of the total WF across both systems, with organic plots showing lower values due to more manual labor practices.

This analysis highlights the primary impacts of specific agricultural systems, thereby facilitating effective solutions. However, it also reveals that the current lack of tools to manage the benefits of organic cropping can lead to significant water consumption and environmental impacts.

Although our study shows that the overall green, blue, and grey components, as well as total WF values, are lower than those reported in previous citrus production studies worldwide, the sustainability assessment indicates a significant impact on agricultural production in the Segura River Basin, mainly due to water appropriation amidst scarce resource availability. Caution is advised when comparing with other studies, as regional conditions such as reduced rainfall and the use of precision irrigation techniques, may influence cultivation’s impact on local water appropriation. Nonetheless, our results demonstrate local agriculture’s capacity to cope with the prevailing resource scarcity conditions in the area, though the need for improved tools and sustainable practices remains urgent.

The presented data can serve as a reference for private and public stakeholders aiming to enhance their impact on current agricultural production in water-scarce regions. This study also emphasizes the need for three key elements to identify and reduce the water impacts of agricultural activities: (i) incorporate extensive WF analysis at regional and local levels to reveal hydrological and agronomic limitations; (ii) develop methodologies and software that facilitate the performance of these analyses; and (iii) integrate the knowledge from unconventional production systems, such as organic farming or precision technologies, into existing tools to prevent potential overestimation of resources.