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Article

Water Management Strategies and Yield Response in Pecan Orchards: A Comparative Analysis of Irrigation Systems

by
Jorge L. Preciado
1,*,
A. Salim Bawazir
2,
Alexander G. Fernald
1 and
Richard Heerema
3
1
Water Resources Research Institute, New Mexico State University, Las Cruces, NM 88003, USA
2
Department of Civil Engineering, New Mexico State University, Las Cruces, NM 88003, USA
3
Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM 88003, USA
*
Author to whom correspondence should be addressed.
Water 2025, 17(18), 2715; https://doi.org/10.3390/w17182715
Submission received: 23 July 2025 / Revised: 11 September 2025 / Accepted: 12 September 2025 / Published: 13 September 2025
(This article belongs to the Special Issue Methods and Tools for Sustainable Agricultural Water Management)
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Abstract

Although substantial research has been conducted on pecan cultivation, studies that account for variability in farm scale, from small to large commercial operations, are still needed. To address this gap, the primary objective of the present study was to quantify and compare groundwater recharge rates and crop yield in pecan orchards utilizing different irrigation systems. This investigation employed in-orchard water budget measurements combined with analytical water balance models to facilitate comparative analysis between orchard sizes. The study tested the hypothesis that groundwater recharge rates vary significantly with farm scale, with larger commercial orchards exhibiting higher recharge rates than their small-scale counterparts. Analysis of 2021–2023 irrigation data revealed significant variability in deep percolation (DP) as a percentage of total water applied (TWA) across orchard sites, ranging from 0% to 52%, with P1 exhibiting the highest recharge and yield, while P2 had the lowest due to limited irrigation. ANOVA revealed significant differences in DP and yield among sites, with P1 outperforming the others. CWP averaged 0.33 kg/m3 but varied considerably by site (0.12–0.42 kg/m3). The results showed significant variability in DP, highlighting its dependence on management practices. These findings emphasize the critical role of site-specific irrigation strategies in optimizing productivity.

1. Introduction

Water used for irrigation worldwide accounts for approximately 70% of the global freshwater withdrawal from surface and subsurface water systems [1]. In the dry southwestern United States, groundwater withdrawals primarily serve agricultural and municipal purposes. In New Mexico (NM), for instance, the Rio Grande aquifer system allocates 66% of its water to irrigation, underscoring agriculture’s central role in the region’s water budget [2]. Within this context, research on high-value, irrigated crops is critical for developing sustainable water management strategies.
In NM, crop production is led by forage, wheat, corn, and pecans [3]. In the decade 2010–2020, pecan production increased in Doña Ana County making pecans the main crop with 15,599 hectares [3]. These changes in crop production make it a very important crop for the county and the state, with an approximate production value of 189.2 million dollars in the state [3]. This growth, however, comes with a significant hydrological cost. Pecans are a high-water use crop [4] with an extended growing season, requiring substantial irrigation to maximize nut yield and quality [5,6] and prevent yield reduction [7]. Studies in NM report seasonal evapotranspiration (ET) for mature, flood-irrigated orchards ranging from 1095 to 1307 mm [8,9]. A study using a one-propeller eddy covariance reporter similar ET values [10], and research in a similar climate of northwestern Mexico reported ET between 1287 and 1700 mm [11,12]. These findings underscore the major demand pecans place on the region’s limited water supplies.
While irrigation is essential for production, a portion of applied water can percolate past the root zone and contribute to groundwater recharge. Understanding this deep percolation (DP) is crucial for maintaining water sustainability, particularly in regions with limited water resources. By recharging aquifers during wet years, groundwater can be stored for use during dry seasons. Incorporating such information into water planning and adjudication processes can enhance accountability and improve hydrologic sustainability throughout the Southwest [13].
Although groundwater recharge has been studied in other irrigated contexts in NM, such as alfalfa fields using actual evapotranspiration data in the southern [14] and northern [15] parts of the state, comprehensive field-based water budgets for pecan orchards remain scarce. Existing research on pecan water management has focused on irrigation scheduling [5], utilized open-path eddy covariance systems [16,17], applied the Surface Energy Balance Algorithm for Land (SEBAL) [18], employed a system dynamics approach [19], and modeled recharge theoretically [20]. Despite these efforts, a significant knowledge gap persists. Critically, to our knowledge, only one study has provided direct field measurements of recharge beneath a pecan orchard [21]. This scarcity of empirical data fundamentally limits the accuracy of regional water budgets and hinders the development of sustainable groundwater management strategies.
Furthermore, a significant and unaddressed socio-hydrological gap exists. The pecan farming community is socio-economically diverse, encompassing large-scale commercial operations and numerous small-scale, lifestyle-oriented producers. Current water management policies and data are predominantly derived from large-scale operations, creating a risk that they may be ineffective or inequitable if applied to smaller farms. The potential for differential water use and recharge between these distinct farm scales has never been investigated. Therefore, to address the general lack of field-based water budgets for pecans and the complete absence of data comparing farm scales, this study has a dual objective: (1) to quantify groundwater recharge rates and yields in pecan orchards under different irrigation systems and (2) to directly compare these hydrological functions between small-scale and commercial orchards.
The novelty of this research is twofold. First, field-measured data on deep percolation and recharge within pecan orchards, moving beyond theoretical models. Second, and most critically, it introduces a novel socio-hydrological framework by testing the hypothesis that recharge and water use efficiency differ significantly between commercial and small-scale farms. This direct comparison is unprecedented, and its findings are essential for developing equitable and effective water management policies that account for the entire farming community. By implementing detailed water budgets and analytical models across this farm size gradient, this study provides insights that are vital for optimizing irrigation efficiency, assessing recharge, and informing the conjunctive management of groundwater and surface water in the region.

2. Materials and Methods

2.1. Study Area and Climate

The study sites are located in the Mesilla Valley (Figure 1), which is part of the Rio Grande flood plain in southcentral New Mexico. The valley includes about 36,400 ha of bottom valley land where agriculture has been active for centuries [22]. This study was performed on four orchards (Table 1). Two sites are located at Leyendecker Plant Science Center, around 13 km southeast of Las Cruces, and the other two sites are at Stahmann farms, located 10 km southwest of Las Cruces.
The Mesilla Valley features a semi-arid climate, characterized by an average precipitation of 222 mm/year, based on a 108-year climate record [23]. Precipitation is distributed seasonally, with approximately half occurring during winter months and the other half during the monsoon season. From April 1959 to December 2005, the mean annual maximum and minimum air temperatures in Las Cruces were 25.2 °C and 7.8 °C, respectively [24].
The sites at Leyendecker have a total area of 4.03 ha, as is shown in Table 1, with approximately 475 trees, 343 trees in the LD orchard, and 132 trees in LF. The trees were cultivar Pawnee with a square planting pattern of 9.1 m by 9.1 m and an average height of 7 m with a trunk diameter of 0.30 m; this orchard was planted in 2010. The other two sites are located at Stahmann farms and have a total area of 16.59 ha, with approximately 1305 trees, 625 trees in P1 and 680 trees in P2. The trees were a cultivar mix of Bradley and Western with a rectangular planting pattern of 9 m by 18 m and an average height of 13 m, and an average trunk diameter of 0.6 m; the orchards were planted in the 1940s.

2.2. Experiment Set Up

Various parameters in the sites were analyzed to calculate deep percolation (DP) using the water balance approach from the 2021 to 2023 irrigation season. This approach is appropriate to estimate recharge from irrigation events within the growing season. DP is the water that infiltrates into the subsurface and passes the effective root zone, which was considered in this study to be below 1.50 m as discussed in Section 3.3. Data collected during the study were analyzed and returned a groundwater recharge estimate (Figure 2). The study sites were leveled and bordered before planting, and precipitation is not heavy; therefore, RO is neglected in the water balance [25].

2.2.1. Precipitation

The annual precipitation was measured and recorded at Leyendecker III Plant Science Research Center (PSRC) rain gauge, located at geographical coordinates of 32°12′3.33″ N and 106°44′33.79″ W. This gauge is located at about 830 m from Leyendecker sites and at about 4.5 km from Stahmann sites with an elevation of 1176 m above mean sea level. In addition, precipitation was measured at Stahmann’s farms; this rainfall was measured using a TE525MM rain gauge from Campbell Scientific Inc. (CSI) (Logan, UT, USA). The rain bucket has a 24.5 cm funnel and measures rainfall in 0.1 mm increments using an internal siphon and a tipping bucket mechanism. Precipitation data were collected at hourly intervals from the two locations. The hourly measurements were then aggregated to calculate the total daily rainfall, which was subsequently incorporated into the water balance calculation. The precipitation in the study area was predominantly in the form of rainfall. The highest percentage of rainfall in the valley was observed between June and September, coinciding with the North American monsoon season.

2.2.2. Irrigation

The orchards were irrigated mainly from pumped groundwater and a few irrigations with surface water from the Rio Grande. Orchards at Leyendecker were irrigated with groundwater, while the orchards at Stahmann were irrigated with groundwater and surface water when available. This water is managed and supplied to farmers by Elephant Butte Irrigation District (EBID) using a network of irrigation channels. Irrigation water from the well at Leyendecker Flood (LF) was measured with a metered pipe (McCrometer, Inc. Hemet, CA, USA) where the total volume of water was discharged directly from the valves installed in the orchard. The pecan orchard at Leyendecker Drip (LD) was irrigated by a surface drip irrigation system (Figure 3).
The drip irrigation system installed in the LD site comprises 38 rows, with 19 rows located on both the northern and southern sides of the orchard. Each row is equipped with four ½ inch diameter laterals. Emitters are spaced at 60 cm intervals along each lateral, with laterals deployed approximately 60 and 120 cm from the trunk. Irrigation was supplied from a groundwater source. The volume of water applied to the orchard was measured by a flow meter (McCrometer, Inc. Hemet, CA, USA) and regulated by a dedicated control valve installed on each row.
The irrigation water data for Stahmann pecan orchards used from the well were measured with an irrigation Magmeter (G3000 from Seametrics, Kent, WA, USA) installed in the wells for groundwater irrigations at Stahmann orchard P1. This irrigation water was conveyed by a concrete-lined trapezoidal canal to the orchard. The canal was 1 m at the base, 2.5 m at the crest, and 0.85 m deep. For the orchard P2, irrigation from surface water from the Rio Grande diversion channel was conveyed by an unlined irrigation canal. Flow from the canal was measured using an ultrasonic sensor “Bigfoot” from Intermountain Environmental, Inc. Logan, UT, USA installed in the discharge pipes. This device was connected to a CR300 datalogger from Campbell Scientific Inc. (CSI), powered by a battery and solar panels installed on the site. This device measures the velocity and elevation of water, and then the data from this device were converted to volume of water. The data were collected at hourly intervals and then aggregated to calculate the total irrigation. Finally, the volume of water applied to the orchards was measured and then divided by the total irrigated land area to calculate the depth of irrigation water applied. This calculation was performed to determine the amount of irrigation water applied per unit area, expressed in millimeters (mm). Irrigation was mainly from groundwater in all orchards, with a couple of surface water irrigations on the P2 orchard from the Rio Grande.

2.2.3. Volumetric Soil Water Content

For this study, the vertical movement of water within the unsaturated zone was characterized by three distinct hydrological layers: a surface layer (0–150 cm depth), where water dynamics are dominated by evapotranspiration losses from both precipitation and irrigation; a middle layer (150–210 cm depth), which experiences minimal influence from either surface irrigation or capillary rise from deeper horizons; and a deep layer (>210 cm depth), where water content is primarily affected by upward capillary flow.
Change in storage, ΔS, was determined as,
∆ S = ∑ i = 1 n θ 2 − θ 1 i ∆ d i
where n is the number of layers to the depth of the effective root zone, θ1 and θ2 are the volumetric soil water contents on the first and the second time of sampling, respectively, in mm3/mm3, and Δdi is the thickness of each soil layer in mm. Daily volumetric soil water content values were determined as the mean data during the 24 h from midnight to midnight.
CSI 655 Time Domain Reflectometry (TDR) sensors were installed in the soil profile at the four sites. For the LD orchard in Leyendecker, seven sensors were installed in one soil column at each site in the orchards at different depths of 30, 60, 90, 120, 150, 180, and 210 cm, as shown in Figure 4. At the LF orchard, five sensors were installed to measure the change in storage in the soil. Volumetric soil water content at 30, 60, 90, 120, and 150 cm in the root zone was considered water uptake by the plant, as Sorensen and Jones [26] described. These 655 TDR sensors sense a volume of 3600 cm3, ≈7.5 cm radius around each probe rod, and 4.5 cm beyond the end of the rods with an accuracy of ±3%. The accuracy of the TDR probes was evaluated using core samples collected from the orchard. Sensor readings were compared to gravimetric measurements obtained from the core samples to determine the VSWC. The analysis revealed an average accuracy of 3%, which is consistent with the specifications. While soil-specific calibration can achieve higher accuracy, it was not performed in this study due to the complexity of replicating the heterogeneous soil properties below the ground surface in a laboratory.
Volumetric Soil Water Content (VSWC) measurements were collected every 5 min. To facilitate data analysis and storage, hourly averages were calculated and recorded in a CR300 data logger. This data logger was installed with a 5-watt solar panel and a battery for continuous measurements at every site (Figure 4). Soil cores were collected from a contiguous wall of the pit where the sensors were installed. The cores were taken at depths corresponding to the installation depths of the sensors. Soil samples collected during sensor installation were subjected to laboratory analysis to determine their bulk density. This analysis provided essential information on soil physical properties like volumetric soil water content, bulk density, and soil texture.
Similarly, 655 TDR sensors were installed at Stahmann farms. For the P1 orchard, seven sensors were installed below the drip lines to measure changes in VSWC, as shown in Figure 5. On the other orchard P2, the sensors were installed similarly as they were installed on P1. These sensors measured VSWC and continuously acquired data from June 2020 to December 2023 for Stahmann sites, while at Leyendecker, the sensors were installed in January 2020.

2.2.4. Evapotranspiration

The weather data used in this research were taken from a weather station (Leyendecker III). This station measured wind speed and direction, solar radiation, infrared radiation, air temperature and relative humidity, precipitation, soil temperature, and barometric pressure. The average wind speed was 1.32 m/s at 2 m for the study period. Crop water requirement (ETc) was calculated according to FAO guidelines using the Penman-Monteith (PM) equation for short canopy reference evapotranspiration (ETo), and historical maximum crop coefficients (Kc max) derived from published studies conducted within the same geographic area as this research, ensuring the relevance and accuracy of the applied coefficients [8,16,27], with the following equation:
ETc = Kc ETo
where ETc is crop water requirement (mm), ETo is reference evapotranspiration (mm), and Kc is the open canopy crop coefficient. This approach integrates locally relevant crop coefficients with a standardized method for ETo, thereby enhancing the reliability of the ETc estimates for the study area. The open canopy pecan crop coefficient given by Wang et al., (2007) [27] was used as described in the following equation:
Kc = Kc max × 1.33 ECC
where Kc is the crop coefficient for open canopy pecan orchards, Kc max is the maximum crop coefficient, and ECC is the effective canopy coverage measured in the fields. For this study, we analyzed the area shaded by the pecan trees at the four sites, and the ECC estimated was 66% of tree coverage for LD and LF fields, 58% for P1, and 72% for P2 during the study period. ETc used here was a reasonable estimate of the actual crop evapotranspiration by the trees.

2.2.5. Groundwater Level

Observation wells were installed to monitor the groundwater level at the sites. The wells were installed at Leyendecker and Stahmann with a depth of 5 m below the ground surface on each site. Manual measurements were taken with a water level indicator (DGSI Tucker, GA, USA) during field visits every week. In addition, groundwater data from different wells were analyzed in the area from the Elephant Butte Irrigation District (EBID) website [28]. Rio Grande flow data were also collected from this site at the River Gauge 5—Mesilla Cable. The depth of water in the area ranges from 15 to 23 m below the land surface.

2.3. Crop Yield and Statistical Analysis

Crop yield samples were collected from all four experimental sites. The samples were taken after the machinery shook the trees to have all the pecans on the floor ready for the other machine to pass by and pick up all the nuts. A circular area was delineated beneath the tree canopy, and yield samples were collected from opposite sides within this circle. This sampling area represented 6.21% of the total yield for each tree. This approach has been adopted in previous studies [7,29] due to the practical challenges associated with collecting the entire tree yield for laboratory analysis. The main limitation of this technique is that we have only collected a percentage of the tree yield. We collected random samples from 5 different trees at each site; then the data were analyzed in the laboratory.
A three-season dataset were analyzed using specific parameters to characterize the interactions between TWA, DP, and yield. Statistical analysis was conducted using linear regression and ANOVA to determine significant relationships within and across fields. Significant differences in means between the four sites were considered at α = 0.05.

3. Results

3.1. Precipitation and Irrigation

Rainfall and irrigation data were the positive components of the water balance. These components were used to calculate recharge on the four sites. For the study period, yearly rainfall in the study area varied from 106 to 227 mm. 2023 was the driest year with 105 mm, and 2021 was the wettest year with 227 mm of rain. Precipitation was very low in the Mesilla Valley during the first months. Therefore, irrigation is vital during this time to sustain healthy tree growth and to leach out salts that accumulated during the previous irrigation season. Rainfall was not heavy during the study period; consequently, it did not have a significant impact on the water balance.
Leyendecker was irrigated from groundwater in the drip and the flood orchards. At Stahmann Farm, most of the irrigation came from groundwater, with a few surface water irrigations from the river. This irrigation data were added to the water balance to estimate groundwater recharge. Total irrigation for LD was between 514 and 801 mm, and for LF was between 853 and 1839 mm during the study period. The other sites on Stahmann farms were irrigated between 1054 mm and 2137 mm for P1 and between 108 mm and 1042 mm for P2; this orchard was irrigated only once in the 2022 growing season. Table 2 shows the number of irrigations for P1.

3.2. Change in Volumetric Soil Water Content

Soil textures and bulk density were determined from undisturbed core samples collected during the excavation of sensor installation pits. Texture was analyzed using the hydrometer method, and bulk density was measured directly from the core samples. Analysis of physical properties revealed distinct textural profiles with depth. At a depth of 0.30 m, the texture was sandy clay loam at the Leyendecker site and loam at the Stahmann site. This was underlain by a clay loam horizon at 0.60 m at Leyendecker, contrasting with a sandy loam at the equivalent depth at Stahmann. From 0.90 to 2.10 m below ground surface, the texture was uniformly sandy loam at both sites (Table 3).
Volumetric soil water content (VSWC) measured in the sites was analyzed, and it is presented in Figure 6. The change in storage changed with every irrigation, with the sensors installed at 30 and 60 cm being the ones that have a higher change in water content. The sensors installed at 90 and 120 cm were not very susceptible to irrigation, and their water content changed less when compared to the near-surface sensors. The zone beneath the root system is characterized by a zero-flux plane, which hydrologically separates upward movement driven by ET from downward drainage driven by gravity [30]. A depth of 150 cm was established as the lower boundary for calculating changes in soil moisture storage. This depth was selected based on previous research by Miyamoto, S. (1983) [8], which characterized the root system distribution in similar soil profiles. Additionally, site excavations revealed no root presence below this depth, suggesting that ET contribution to soil water flux beneath 150 cm is negligible.
Changes in soil moisture storage were calculated for each site using Equation (1). At the Leyendecker site, a malfunction of the primary sensor at 150 cm depth necessitated the use of data from a secondary sensor installed at an equivalent depth within the same orchard. Deep percolation was quantified by summing up the daily changes in VSWC values over the inter-irrigation periods. The cumulative change in storage over the entire study period ranged from 166 mm to 452 mm at Leyendecker and from 3 mm to 223 mm at Stahmann.

3.3. Evapotranspiration and Recharge

Reference ET data for the study period ranged from 1507 mm to 1552 mm per year. This evapotranspiration was low in 2021, with 1507 mm, and in 2023, it was the highest year for the study period, with 1552 mm per year. The highest ETc act was in 2021 in the P1 field, while the lowest was 609 mm in the P2 field.
For groundwater recharge, the results show the amount of water that was calculated through the water balance. For this research, data were collected and analyzed from 2021 to 2023. The irrigation amount varied among the orchards. Water applied during each irrigation was higher at Stahmann sites compared to Leyendecker. Total water applied (TWA) was higher at the P1 site.
The rise in groundwater elevation and the sensor at 210 cm below the ground coincided with the water being released upstream into the river from the dam at Caballo Reservoir, and releases of water in the Rio Grande (Figure 7). The shallow depth to groundwater at the monitoring sites exhibited a rapid response to river flow, a consequence of their proximal location to the river and ongoing surface water irrigation in adjacent agricultural areas. The release of surface water into the irrigation network prompts a reduction in groundwater extraction for irrigation across the valley. Consequently, the observed increase in groundwater elevation during this period is attributed to a combination of decreased pumping rates, seepage from the river and irrigation canals, and enhanced infiltration from surface water application.
The results for 2021 show that there was 1% of the total water applied (TWA) in the P1 drip/flood orchard and 4% of the TWA in the P2 of water recharge at Stahmann farms. For Leyendecker, there was 5% of the TWA in the LD orchard and 52% water recharge of the TWA in the LF orchard. During 2022, for Stahmann farms, there was 50% of TWA in P1, and there was no percolation in the P2 because this year the orchard was irrigated only once with surface water during the growing season due to the lack of management practices. For Leyendecker, there was no percolation in the orchard LD due to the small amount of water applied on each irrigation in the drip orchard during the season, and 47% of TWA deep percolated in the LF orchard. Finally, Table 2 shows the water balance for the 2023 irrigation season, in which all the water balance components are presented; negative numbers in recharge mean that there was an irrigation deficit. Recharge was 33% of the TWA for P1 and 4% of the TWA for P2. For Leyendecker sites on LD, there was only 1% of TWA, and for the LF orchard, deep percolation was 45% of the TWA.

3.4. Crop Yield and Statistical Analysis

Yield samples were collected at all four sites. The samples were taken after the machinery shook the trees to have all the pecans on the floor ready for the other machine to pass by and pick up all the nuts. We collected random samples from different trees at each site; then the data were analyzed in the laboratory, and the results are shown in Table 4.
A one-way analysis of variance (ANOVA) was performed to evaluate significant differences in deep percolation (DP) and in-shell nut yield among the four sites (Figure 8). The results revealed statistically significant differences in DP (F = 8.10, p < 0.05) and average in-shell nut yield (F = 3.44, p = 0.028) across the sites. Notably, site P1 exhibited the highest mean yield, whereas site P2 demonstrated the lowest. Post hoc analysis indicated that the significant differences in yield were primarily driven by the superior performance of site P1, which outperformed both LF and P2 sites. In contrast, the LD site displayed intermediate yield performance, with no significant differences observed relative to the other sites. Furthermore, ANOVA results for average in-shell weight showed significant differences among the sites (F = 20.50, p = 1.34 × 10−7), underscoring the variability in yield metrics across sites.

4. Discussion

4.1. Change in Volumetric Soil Water Content

Soil texture profiles (Table 3) influenced water retention and movement. The clay loam surface layers at Leyendecker likely reduced infiltration rates compared to the coarser sandy loam at deeper layers in Stahmann, contributing to higher surface runoff potential (neglected in this study) and lateral saturation. However, the manual soil mixing during orchard establishment at both sites created heterogeneous profiles, complicating direct comparisons.
The 150 cm depth consistently functioned as a critical hydrological boundary across sites, as defined in the methods. Shallow sensors (30–120 cm) showed dynamic responses to irrigation, while deeper sensors (150–210 cm) exhibited minimal fluctuations, confirming limited root activity below 150 cm. This observation supports the hypothesis that mature pecan trees, particularly at Stahmann, rely on deeper root systems to access groundwater, thereby reducing dependence on irrigation and mitigating DP. In contrast, younger trees at Leyendecker, with shallower roots, required more frequent irrigation, which increased percolation losses under flood irrigation systems, a finding consistent with studies showing that root distribution is a critical determinant of irrigation efficiency and water loss [31].
The observed changes in VSWC at different depths confirm that surface irrigation practices, such as flood irrigation, primarily affect the upper soil layers (30–60 cm), where root water uptake is the most active. At Leyendecker, significant soil moisture fluctuations were observed up to 150 cm, with minimal changes at greater depths, consistent with the concept of the zero-flux plane in tree crop systems [32]. This indicates that water movement was largely restricted to the root zone, with limited downward flow beyond 150 cm unless influenced by flooding or excessive irrigation.
Deep sensors (150–210 cm) at both Stahmann and Leyendecker sites occasionally detected increased soil moisture at depth during periods of surface water releases from the Rio Grande (Figure 7). This suggests that flood irrigation events can facilitate deeper infiltration, potentially contributing to groundwater recharge. These findings highlight the importance of tailoring irrigation strategies to account for site-specific soil profiles and tree maturity, particularly in regions where groundwater is a management objective [33].
By focusing on the relationship between soil texture, irrigation practices, and root distribution, this study provides new insights into optimizing water use and minimizing percolation losses in pecan orchards. The results underscore the need for site-specific irrigation management to enhance water use efficiency and support sustainable groundwater resources.

4.2. Groundwater Recharge

The data collected from the Leyendecker and Stahmann farms over three growing seasons highlight the variability in DP due to factors such as irrigation type, crop type, soil properties, and precipitation patterns. The methodological approach combined direct soil moisture monitoring with groundwater level tracking, a strategy recommended for robust assessment of recharge dynamics in agricultural settings [34]. Groundwater levels at all sites responded to Rio Grande flow variations (Figure 7 and Figure 9). During surface water releases, groundwater pumping is reduced, and seepage increases from irrigation canals, elevating the water table, particularly at Stahmann near the river. This conjunctive use of surface and groundwater highlights the valley’s hydrologic interconnectedness. However, the rapid groundwater decline post-irrigation underscores the aquifer’s limited recharge capacity in this area. Negative DP values in some years (Table 2) likely reflect upward capillary rise from shallow groundwater during dry periods. These findings emphasize the need for integrated water management to balance aquifer sustainability with agricultural demand.
The stark contrast in DP between irrigation systems was a key finding. At Leyendecker, the flood-irrigated (LF) orchard consistently showed the highest DP values, while the drip-irrigated (LD) system minimized them. Recharge rates at Stahmann P1 orchard, which employed a combination of flood and drip irrigation, showed a more moderate rate of recharge when surface water from the Rio Grande was used sparingly. Notably, P2, which received surface water only once in 2022, showed no deep percolation. This case highlights how infrequent irrigation can lead to a water deficit, and the importance of consistent irrigation to maintain a stable water balance and prevent deficits in recharge. Overall, these results confirm that both irrigation volume and method play a critical role in determining DP rates.
The significant differences in DP between flood and drip irrigated orchards highlight the critical role of irrigation efficiency. Authors like Gutierrez-Jurado et al. [35] reported a recharge range of 31–38% for flood-irrigated alfalfa in northern New Mexico. Grafton et al. [36] reported up to 50% recharge for surface irrigation and up to 10% for drip irrigation. At Leyendecker, LF resulted in DP values as high as 52% of TWA, whereas LD limited DP to ≤5% of TWA. This aligns with studies demonstrating that flood irrigation often exceeds crop water requirement [37], leading to substantial water loss through percolation [38]. The higher DP in flood systems is attributed to the rapid application of large water volumes, which surpass the soil’s infiltration capacity and root zone retention. In contrast, drip irrigation’s controlled, localized water application minimizes excess moisture below the root zone. At Stahmann, the combination of flood and drip methods in older orchards resulted in intermediate DP values (1–50% of TWA), suggesting that a hybrid irrigation strategy may offer a balance between water delivery efficiency and tree health in mature orchards. This hybrid system at Stahmann P1 resulted in intermediate DP values, suggesting this strategy may offer a practical balance between water delivery efficiency and tree health in mature orchards.
The relatively shallow depth of groundwater observed at the study sites, generally between 2.2 and 3.3 m (Figure 9), with fluctuation reflecting the interactions between irrigation practices and groundwater levels. In particular, the fluctuations reinforced the influence of surface water availability on groundwater recharge. Specifically, the rise in groundwater elevation at all sites coincided with periods of water release in the Rio Grande, reinforcing the influence of surface water availability on groundwater recharge. These observations suggest that irrigation practices not only directly influence DP but also have a cascading effect on regional groundwater systems, especially in areas where surface water is intermittently available.
The interplay of irrigation methods, soil properties, tree age, and regional hydrology underscores the complexity of water management in agricultural systems under water-scarce conditions. This study further highlighted the relationship between groundwater level changes and irrigation schedules, with noticeable increases in groundwater levels following the cessation of groundwater pumping and the introduction of surface water. This emphasizes the importance of integrated water management strategies that consider both groundwater and surface water sources for sustainable agricultural practices in arid regions like the Mesilla Valley.

4.3. Crop Yield and Water Productivity

Crop yield data revealed variability across the orchards and seasons, with Stahmann farms generally producing higher yields than Leyendecker. In 2021, Stahmann P1 orchard yielded an average of 26.34 kg/tree, compared to Leyendecker’s 18.93 kg/tree. Interestingly, in the 2022 growing season, when water application was lower at Leyendecker, yield dropped substantially, particularly at the LF orchard, which had lower deep percolation. This suggests that while water application is crucial for sustaining tree growth, other factors, such as water use efficiency and soil characteristics, also contribute significantly to crop productivity [39]. The data also illustrates how water management strategies, particularly irrigation scheduling, can directly impact not only groundwater recharge but also the agricultural output.
Despite higher DP in flood-irrigated orchards, crop yields at the LF site were inconsistent, with notably low yields in 2022 compared to Stahmann P1. This disparity may reflect the advantages of mature trees with extensive root systems at Stahmann, which can buffer water stress and optimize nutrient uptake, a phenomenon supported by studies on root distribution and tree age [32]. Conversely, drip irrigation at the Leyendecker LD site achieved moderate yield with minimal DP, demonstrating the potential for efficient water use in younger orchards. The superior yields at Stahmann P1, despite high DP, suggest that mature orchards prioritize biomass production over water conservation, whereas younger systems require precision irrigation to balance growth and efficiency.
In this study, crop yield was affected by irrigation management practices. LD showed higher yields compared to the LF. Similarly, the orchard at Stahmann farms that received surface water irrigation P2 showed lower yields compared to the orchard that received groundwater irrigation P1. We collected yield samples manually because farmers typically are reluctant to disclose their on-farm yields and often consider requests for such information to be invasions of their privacy. Even when yields are reported, there are numerous reasons why farmers may either under-report or over-report their actual crop yields [13]. Therefore, this study relies on manual yield sampling introduces uncertainties. Yield sampling from limited tree areas may not fully represent orchard productivity.
Finally, the crop water productivity (CWP) was quantified by calculating the ratio of total in-shell weight to total water applied (TWA). The results indicated a range of CWP values, with the lowest observed in 2021 at 0.27 kg/m3 and the highest in 2022 at 0.38 kg/m3. Over the study period, the average CWP was determined to be 0.33 kg/m3. Variability in CWP was also evident, with the highest value of 0.42 kg/m3 recorded in the LD orchard and the lowest value of 0.12 kg/m3 observed in the P1 orchard. Notably, the CWP calculations were based solely on nut production and did not account for organic mass generated from leaves and branches during the growing seasons. The similarity in CWP values between large and small orchards suggests that CWP is independent of orchard size and instead is influenced by site-specific management practices.

4.4. Implications for Sustainability

Different management strategies have been studied to improve the sustainability of pecan orchards, including optimized pruning techniques [40], irrigation scheduling guided by tensiometers [41], and monitoring soil-gas stress responses [42]. Research and extension activities have also been conducted to promote these practices [43]. Furthermore, remote sensing technology is being developed to support more precise irrigation scheduling [44]. Beyond orchard management, broader strategies for the valley have been explored, such as land fallowing to mitigate drought impacts [45] and the introduction of alternative crops to improve economic return per unit of water and enhance long-term sustainability [46]. However, the viability of these strategies is threatened by climate change. While reduced water supply may be manageable in some growing seasons, projected temperature increases across the southwest United States [47], and specifically in New Mexico [48], will directly affect plant growth [49]. Consequently, more frequent and prolonged droughts under regional climate change scenarios will necessitate the development of optimization frameworks to allocate the valley’s water resources efficiently.
In water-scarce regions like the Mesilla Valley, optimizing irrigation practices is essential for both agricultural productivity and aquifer sustainability. This need is amplified by regional climate change projections, which predict reduced Rio Grande flow and increased drought frequency, threatening long-term water security [50]. While drip irrigation minimizes DP, its adoption in mature orchards may require a significant investment for older farms like Stahmann. Conversely, flood irrigation with high DP could be strategically utilized for leaching salts in saline soils, though this must be balanced against groundwater depletion risks. Given anticipated reductions in surface water availability due to climate change, including earlier snowmelt, altered runoff timing, and increased ET, farmers and policymakers must collaborate to accelerate the adoption of efficient irrigation technologies and diversify management strategies. Such efforts should be designed to enhance system-wide resilience, recognizing the interconnectedness of surface water, groundwater, and riparian ecosystems under shifting climatic conditions.
These findings have important implications for water management and agricultural productivity in the Mesilla Valley. By adopting strategic irrigation management practices, such as managed flood irrigation, farmers can help recharge groundwater and maintain crop yields. This is critical as population growth and climate change put pressure on agriculture and make it difficult to maintain food security. According to the Food and Agriculture Organization, crop production must increase by 140% or more by 2050 [51]. Future work could integrate remote sensing for spatially continuous ET and soil moisture data or employ isotopic tracers to quantify groundwater recharge pathways [52]. Furthermore, long-term field monitoring and modeling are critical for anticipating and adapting to the impacts of increased drought frequency and severity, as projected for the southwest U.S. under various climate scenarios.
Climate change is altering the regional hydrological cycle, impacting both the quantity and timing of water availability [53,54]. Projected increases in temperature [55], along with earlier and more rapid snowmelt runoff, are expected to diminish natural groundwater recharge from key sources such as the Rio Grande [56]. This anticipated reduction in recharge necessitates the development of adaptive conjunctive management strategies for surface water and groundwater resources under climate change [57]. Within this framework, the infiltration of applied irrigation water during wet years becomes a critical process for replenishing aquifer storage and sustaining groundwater reserves during subsequent droughts. Future studies should address these challenges by quantifying the role of managed infiltration under different climate change scenarios. For instance, comparing outcomes across Representative Concentration Pathway (RCP) models could identify optimal irrigation management systems under projected water stress, with broader implications for agricultural resilience in arid and semi-arid regions worldwide.
As water scarcity intensifies due to climate change, adopting precision irrigation technologies and adaptive management practices such as hybrid irrigation will be critical to safeguarding pecan productivity and rural livelihoods. Such strategies are not merely operational improvements but are essential for building socio-ecological resilience, contributing to the sustainability of agriculture in the Mesilla Valley while reducing pressure on dwindling water resources and supporting broader efforts to adapt to a changing climate.

5. Conclusions

This study was motivated by a critical gap in water resource planning: the existing literature has not considered the socio-economic diversity of pecan farming communities, leading to an absence of data comparing water use and recharge between small-scale and commercial-scale operations. To address this, our primary objective was to quantify and compare groundwater recharge rates and yield under different irrigation systems across these distinct farm scales. Our findings demonstrate that irrigation methods, orchard age, and soil-hydrologic dynamics collectively govern deep percolation and crop yield, confirming that farm scale is a decisive factor in the local water budget.
The replicable, field-based methodology, combining measurements of soil moisture and ET with analytical water balance models, provides a clear framework that can be applied to other orchards in the Mesilla Valley or transferred to other arid agricultural regions. This approach offers tangible insights for water management committees, farmers, and policymakers, directly informing the development of guidelines for conjunctive use of groundwater and surface water.
Ultimately, this research provides a foundational dataset that moves beyond theory to practical application. The pathways identified, such as transitioning to efficient irrigation in younger orchards and leveraging the resilience of mature trees, are essential for reconciling agricultural productivity with groundwater sustainability. Future work must use these field-validated findings to calibrate models simulating climate change scenarios, ensuring the long-term viability of irrigation strategies. Adopting such adaptive management practices will be crucial for safeguarding pecan production, rural livelihoods, and socio-ecological resilience in the Mesilla Valley and similar arid regions worldwide.

Author Contributions

Conceptualization, J.L.P. and A.G.F.; methodology, J.L.P. and A.S.B.; software, J.L.P.; validation, J.L.P., A.S.B. and R.H.; formal analysis, J.L.P. and A.S.B.; investigation, J.L.P.; resources, A.G.F. and R.H.; data curation, J.L.P. and A.S.B.; writing—original draft preparation, J.L.P. and A.S.B.; writing—review and editing, A.G.F. and R.H.; visualization, J.L.P.; supervision, A.S.B. and R.H.; project administration, A.G.F.; funding acquisition, A.G.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the United States Department of Agriculture (USDA), Agriculture and Food Research Initiative Competitive Grant#2021-69012-35916, and the USDA National Institute of Food and Agriculture, NextGen Program, Award#2023-70440-40158.

Data Availability Statement

The data analysis presented in this study is openly available within the New Mexico Water Resources Research Institute’s Data Set Repository at https://nmwrri.nmsu.edu/.

Acknowledgments

We would like to thank the Mexican Department of Science, Humanities, Technology, and Innovation (SECIHTI) for the support provided to the first writer. The authors also thank Stahmann Farms and Leyendecker Plant Science Research Center for their support and the students involved in this work. Thank you to the reviewers for their invaluable feedback.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Water 17 02715 g001
Figure 1. Geographic location of Leyendecker and Stahmann’s Farms.
Figure 1. Geographic location of Leyendecker and Stahmann’s Farms.
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Figure 2. Methodology with steps and phases of the research.
Figure 2. Methodology with steps and phases of the research.
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Figure 3. Different types of irrigation in the study sites. (A) flood irrigation at Leyendecker (LF). (B) surface drip irrigation at Leyendecker (LD). (C) subsurface drip irrigation at Stahmann (P1).
Figure 3. Different types of irrigation in the study sites. (A) flood irrigation at Leyendecker (LF). (B) surface drip irrigation at Leyendecker (LD). (C) subsurface drip irrigation at Stahmann (P1).
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Figure 4. Seven VSWC sensors were installed at the Stahmann site at each 30 cm below the ground up to 2.10 m below the ground level.
Figure 4. Seven VSWC sensors were installed at the Stahmann site at each 30 cm below the ground up to 2.10 m below the ground level.
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Figure 5. The sensor set up at Leyendecker and Stahmann orchards.
Figure 5. The sensor set up at Leyendecker and Stahmann orchards.
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Figure 6. Change in volumetric soil water content (VSWC) at different depths below land surface (30 cm, 90 cm, 150 cm, 210 cm, the right axis shows irrigation and precipitation in mm at Stahmann P1 orchard for the 2022 growing season.
Figure 6. Change in volumetric soil water content (VSWC) at different depths below land surface (30 cm, 90 cm, 150 cm, 210 cm, the right axis shows irrigation and precipitation in mm at Stahmann P1 orchard for the 2022 growing season.
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Figure 7. Rio Grande Flow in Cubic Meters per Second at Mesilla (right axis) and change in Volumetric Soil Water Content (left axis) at Stahmann site P1 for the sensors installed at 150 cm, 180 cm, and 210 cm in the 2022 irrigation season. The low baseline VSWC values are characteristic of the coarse-textured soils at these depths. The delayed response of deep VSWC to increases in river flow demonstrates aquifer recharge and the hydraulic connection between the river and the orchard’s groundwater source.
Figure 7. Rio Grande Flow in Cubic Meters per Second at Mesilla (right axis) and change in Volumetric Soil Water Content (left axis) at Stahmann site P1 for the sensors installed at 150 cm, 180 cm, and 210 cm in the 2022 irrigation season. The low baseline VSWC values are characteristic of the coarse-textured soils at these depths. The delayed response of deep VSWC to increases in river flow demonstrates aquifer recharge and the hydraulic connection between the river and the orchard’s groundwater source.
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Figure 8. Yield data for the four sites during the study period. Total in-shell weight per tree and in-shell nut weight. The X represents the mean, showing that P1 2021 had the highest variability during the study period.
Figure 8. Yield data for the four sites during the study period. Total in-shell weight per tree and in-shell nut weight. The X represents the mean, showing that P1 2021 had the highest variability during the study period.
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Figure 9. Water level data were measured at Stahmann and Leyendecker sites and near the sites from EBID data. The figure shows the depletion of the water level when farmers irrigate their lands and an increase in the water level when there is water running in the Rio Grande.
Figure 9. Water level data were measured at Stahmann and Leyendecker sites and near the sites from EBID data. The figure shows the depletion of the water level when farmers irrigate their lands and an increase in the water level when there is water running in the Rio Grande.
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Table 1. Location and area of Leyendecker and Stahmann farms, and their respective distance from the Rio Grande.
Table 1. Location and area of Leyendecker and Stahmann farms, and their respective distance from the Rio Grande.
NameLocation
Latitude, Longitude		Elevation AMSL (m)Area (ha)Distance to Rio Grande (m)Irrigation Type
Leyendecker Drip (LD)32°11′35″ N,
106°44′ 15″ W		11492.42110surface drip
Leyendecker Flood (LF)32°11′35″ N,
106°44′12″ W		11491.61115flood
Stahmann Orchard (P1)32°13′09″ N,
106°47′18″ W		115310.52135Flood (1 year)/subsurface drip (2 years)
Stahmann Orchard (P2)32°13′ 01″ N,
106°47′11″ W		11536.07120subsurface drip
Table 2. Water Balance for the 2023 growing season at P1 field.
Table 2. Water Balance for the 2023 growing season at P1 field.
EventIrrigation Date
d/m/y		DaysIrrigation
(mm)		Rainfall
(mm)		ETc
(mm)		Δ Storage
(mm)		Recharge
(mm)
114/3/2023736279223−46
227/3/2023131094161581
34/4/202381140120103
45/4/20231139016276
51/5/202326560737−24
66/5/2023548020523
711/5/2023551023523
819/5/202384323654
925/5/20236401429025
107/6/202313340728−45
1116/6/202391230591153
1229/6/202313670861−19
137/7/20238486534−3
1412/7/20235108134−479
1520/7/20238166252−2118
1625/7/2023597033262
172/8/2023885852832
1814/8/202312115482−339
1917/8/20233181818216
2022/8/202354403770
2128/8/202361213291283
221/9/2023463021−446
2317/9/202316266788−53
2411/10/2023244791064−53
Total 2841823841114174618
Mean 1276346726
Table 3. Soil physical properties, including bulk density and particle size distribution, were characterized for the Leyendecker and Stahmann sites through laboratory analysis of manually collected samples taken at depths corresponding to sensor installation.
Table 3. Soil physical properties, including bulk density and particle size distribution, were characterized for the Leyendecker and Stahmann sites through laboratory analysis of manually collected samples taken at depths corresponding to sensor installation.
OrchardSoil Depth (cm)Bulk Density (g/cm3)Sand (%)Clay (%)Silt (%)Soil Texture
Leyendecker301.42592120sandy clay loam
601.35393130clay loam
901.41571924sandy loam
1201.4275159sandy loam
150-79183sandy loam
180-80172sandy loam
210-80191sandy loam
Stahmann301.28412534loam
601.477176sandy loam
901.4277194sandy loam
1201.4381172sandy loam
1501.5883152sandy loam
180-82171sandy loam
210-83170sandy loam
Table 4. Yield results for the study period.
Table 4. Yield results for the study period.
YearIrrigation TypeSiteAvg In-Shell Nut Yield (kg/Tree)Avg In-Shell Nut Weight (g)Total Yield (Tons/ha)
2021dripLD16.416.842.2
floodLF21.457.29
flood/sub dripP126.344.921.6
subsurface dripP214.875.07
2022dripLD12.457.881.0
floodLF3.937.92
flood/sub dripP139.445.502.1
subsurface dripP213.355.98
2023dripLD42.256.754.5
floodLF34.816.46
flood/sub dripP143.244.862.9
subsurface dripP231.235.15
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Preciado, J.L.; Bawazir, A.S.; Fernald, A.G.; Heerema, R. Water Management Strategies and Yield Response in Pecan Orchards: A Comparative Analysis of Irrigation Systems. Water 2025, 17, 2715. https://doi.org/10.3390/w17182715

AMA Style

Preciado JL, Bawazir AS, Fernald AG, Heerema R. Water Management Strategies and Yield Response in Pecan Orchards: A Comparative Analysis of Irrigation Systems. Water. 2025; 17(18):2715. https://doi.org/10.3390/w17182715

Chicago/Turabian Style

Preciado, Jorge L., A. Salim Bawazir, Alexander G. Fernald, and Richard Heerema. 2025. "Water Management Strategies and Yield Response in Pecan Orchards: A Comparative Analysis of Irrigation Systems" Water 17, no. 18: 2715. https://doi.org/10.3390/w17182715

APA Style

Preciado, J. L., Bawazir, A. S., Fernald, A. G., & Heerema, R. (2025). Water Management Strategies and Yield Response in Pecan Orchards: A Comparative Analysis of Irrigation Systems. Water, 17(18), 2715. https://doi.org/10.3390/w17182715

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