Mitigating water stress in Medicago sativa L. through irrigation system modification: a field study in a semi-arid region

Vahdat Barkhordari; Hossein Shamsi Mahmodabadi; Esmaeil Moghbeli Dameneh; Gholamreza Zarei

doi:10.1038/s41598-026-40417-6

. 2026 May 3;16:20454. doi: 10.1038/s41598-026-40417-6

Mitigating water stress in Medicago sativa L. through irrigation system modification: a field study in a semi-arid region

Vahdat Barkhordari ¹, Hossein Shamsi Mahmodabadi ^1,^✉, Esmaeil Moghbeli Dameneh ^1,^2,^✉, Gholamreza Zarei ¹

PMCID: PMC13332013 PMID: 42071010

Abstract

In arid and semi-arid regions, severe water scarcity critically threatens agricultural sustainability and food security. Enhancing crop resilience and water productivity is therefore an urgent priority. This study addresses this imperative by evaluating advanced irrigation strategies for alfalfa (Medicago sativa), a vital forage crop, to optimize yield and quality under water-limited conditions. This study evaluated irrigation strategies to enhance water use efficiency (WUE) and resilience in alfalfa. A two-year field experiment in Jiroft, Iran, compared three irrigation systems—Flooding Irrigation (FI), Subsurface Drip-Tape Irrigation (SDTI), and Subsurface Drip Pipe Irrigation (SDPI)—under four irrigation levels (100%, 90%, 80%, and 70% of crop water requirement). Results showed that the SDPI system achieved the highest water use efficiency (WUED and WUEF). However, maximum dry and fresh forage yields (DFY, FFY) were obtained with FI at full irrigation (100%). Under irrigation levels (70%), SDPI improved forage quality by increasing CP, soluble carbohydrates, proline, and antioxidant enzyme activity, while reducing fiber content. Irrigation at 70% generally enhanced WUE and these biochemical indicators across all systems. The findings demonstrate that the SDPI system, particularly under irrigation levels (70% requirement), effectively improves the nutritional value and water productivity of alfalfa, supporting its adoption for sustainable production in water-limited environments.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-40417-6.

Keywords: Alfalfa, Drought, Flooding irrigation, Stress, SDPI, SDTI

Subject terms: Environmental sciences, Plant sciences

Introduction

In semi-arid and arid regions, water stress is the primary environmental factor restricting plant growth, development, and yield, ultimately leading to oxidative stress¹. Thus, optimizing water consumption is a critical priority for achieving sustainable agriculture, especially for crops with high water demands². A central goal of sustainable agriculture is to optimize water management while maintaining acceptable product quality. Agricultural research has clearly demonstrated that both the choice of irrigation system and the level of water application significantly influence crop production and quality^3–5. This is particularly critical for high-water-demand strategic crops—such as sugarcane, alfalfa, potato, and sugar beet—whose cultivation is often severely constrained in arid and semi-arid regions. Consequently, achieving an appropriate balance among crop yield, water use, and product quality is essential for expanding the cultivation of such water-intensive crops in water-limited environments.

Alfalfa (Medicago sativa L.) is one of the most important forage plants, playing a crucial role in animal nutrition due to its production of high-quality forage^6,7. Alfalfa has a high water requirement, yet its extensive and deep root system contributes to its ability to withstand drought stress^8,9. Given the scarcity of natural water resources, farm management must prioritize the optimal use of each unit of water. Alfalfa also offers superior nutritional value compared to many other forage sources¹⁰. These traits—combined with its inherent drought tolerance—make alfalfa an ideal forage crop for production under water-limited conditions in arid and semi-arid regions worldwide.

The aim of limited irrigation management is to maximize WUE in crop production under water-scarce conditions¹¹. It is well established that irrigation method significantly influences WUE⁴. In this context, subsurface drip irrigation (SDI)—including systems such as SDTI and SDPI—holds particular promise. SDI allows direct application of water and fertilizer, minimizes water losses, and offers the highest irrigation efficiency among common methods^12,13,3. Studies report that SDI requires substantially less water for alfalfa than FI or sprinklers¹⁴. Although previous research has shown that alfalfa fresh and dry yields decline as water supply decreases, WUE consistently increases under irrigation levels. Nevertheless, long-term operation of SDI systems can encounter challenges such as emitter clogging by roots, which occurs as roots grow toward water-release points^15,16.

However, long-term operation of subsurface irrigation systems can lead to practical challenges, notably the clogging of emitters by plant roots as they grow toward water-release points¹⁷.

Biochemical changes represent a common adaptive response in plants to abiotic stresses such as water deficit. Key biochemical parameters—including soluble carbohydrates, total protein, proline content, and the activity of antioxidant enzymes like catalase and peroxidase—are often modulated under stress conditions^18–20.

The objectives of this study were to (1) assess the effects of irrigation system and water level on alfalfa productivity and water-use efficiency, and (2) characterize the crop’s biochemical responses to water-deficit stress.

However, the long-term use of such subsurface irrigation system causes problems such as clogging of drippers by plant roots, as a result of the movement of roots towards pipe outlet¹⁷. Changes in the biochemical are general adaptive responses in plants to abiotic stresses, such as water deficit stress. The biochemical parameters analyzed included soluble carbohydrates, total protein, proline content, and the activity of enzymes such as catalase and peroxidase^18–20. The aims of this study were to evaluate the effects of irrigation type and level on alfalfa production performance and to determine the crop’s biochemical responses to water stress.

Materials and methods

Plant collection

The alfalfa plant material was collected (South Kerman Agricultural and Natural Resources Research and Education Center) and all methods were carried out in accordance with relevant institutional, national, and international guidelines. The material was identified by the Seed and Seedling Breeding and Production Research Institute, Iran (Herbarium number: A545789345- Alfalfa) and authenticated by Dr. Ahmad Ahmadpour, a botanist at the South Kerman Agricultural and Natural Resources Research and Education Center, AREEO, Jiroft, Iran.

Site description and planting

The two-year field experiment (2022–2023) was conducted at the Agricultural Research Center of southern Kerman, Jiroft, Iran (28°46′N, 57°44′E; elevation 650 m a.s.l.). A strip-split plot design was used within a randomized complete block arrangement, replicated three times. Treatments comprised three irrigation systems—Flooding Irrigation (FI), SDTI, and SDPI—and four irrigation levels: 100% (I₁₀₀), 90% (I₉₀), 80% (I₈₀), and 70% (I₇₀) of weekly crop water requirement. Plots measured 4 × 2 m (8 m²) and contained eight crop rows spaced 25 cm apart. Seedbed preparation involved deep ploughing in autumn followed by light ploughing in spring of each year. The seedbeds were ready on 1 March 2022 and 1 March 2023 for the first and second experimental years, respectively.

Determination of crop water requirements (ETc)

The actual crop water requirement for alfalfa was determined as crop evapotranspiration (ETc), which was estimated from the reference evapotranspiration (ETo) and the appropriate crop coefficient (Kc) using the standard equation:

Evapotranspiration (ETo): Daily ETo was calculated using the FAO Penman-Monteith method²¹, which is the internationally accepted standard. This calculation was based on daily climate data—including maximum and minimum air temperature, relative humidity, solar radiation, and wind speed—obtained from the nearest synoptic weather station in Kerman, Iran.

Crop Coefficient (Kc): The Kc values for alfalfa were adopted from FAO Irrigation and Drainage Paper No. 56²¹. These standard coefficients vary with the crop’s growth stage (initial, mid-season, and late season). The specific monthly Kc values applied in this study were 0.40 (initial), 1.15 (mid-season), and 0.50 (late season), which were adjusted for the local climatic conditions of the study region as recommended.

Irrigation Water Supply and Measurement: Irrigation water was supplied from wells near the experimental site. A central control station managed the water flow from a main 75 mm tube, which branched into three separate 63 mm lines. Each 63 mm line delivered one of the four designated irrigation levels (100%, 90%, 80%, or 70% of ETc). At the beginning of each 63 mm line, a calibrated volumetric water meter was installed to ensure accurate measurement and precise control of the applied irrigation water.

The chemical properties of the soil and the irrigation water used in the study are presented in Tables 1 and 2, respectively.

Table 1.

Physicochemical properties of soil in this study.

0–30 cm	Soil texture	N (%)	P (ppm)	K (ppm)	EC (ds/m)	pH	Soil particles percentages	Bulk density (g cm⁻³)	Field capacity (%)	permanent wilting point (%)	Available water (%)
0–30 cm	Clay loam	0.03	2.4	230	0.79	8.4	loam - clay	1.3	25–35	10–15	15–20
0–60 cm	Clay loam	0.03	2.0	215	0.89	8.2	loam - clay	1.4	25–35	10–15	15–20

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Table 2.

Physicochemical properties of water in this study.

EC (µs/cm)	pH	Carbonate (meq/L)	Bicarbonate (meq/L)	Na (meq/L)	Ca + Mg (meq/L)	Sodium adsorption ratio
816	7.4	0	3.8	4.13	1.65	3.91

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Yield measurement

Fresh forage yield (FFY) was measured at the 30% flowering stage by harvesting plants from a 10 cm stubble height. The harvested material was weighed immediately using a digital scale (accuracy ± 0.001 kg) and expressed as tons per hectare.

For dry forage yield (DFY), a representative subsample of approximately 500 g of fresh forage was oven-dried at 68 °C for 72 h to constant weight. The dry weight was then used to calculate DFY, also reported in tons per hectare.

WUE measurement

WUEF and WUED was calculated as the ratio of fresh or dry forage yield (in tons per hectare) to the total volume of irrigation water applied (in cubic meters per hectare), resulting in units of tons ha^− 1. The formula used was:

Determination of crude protein (CP), ash

Crude protein (CP) content was determined using the Kjeldahl method as described by Hemke and Sparks (1996). Ash content was measured according to the procedure outlined by Adebooye (1996)²².

Determination of water soluble carbohydrates (WSC), neutral detergent fiber (NDF), acid detergent fiber (ADF) and CF

The concentrations of key forage quality components WSC, NDF, ADF, and CF—were analyzed. Forage samples were first oven-dried at 65 °C to a constant weight and then ground to pass through a 1-mm screen. The measurements were performed using a Near Infrared (NIR) spectrometer (NIRFlex N-500). Spectral data were interpreted, and concentrations were calculated using calibration equations developed according to the standard wet chemistry methods described by Van Soest et al. (1991). All results for WSC, NDF, ADF, and CF are expressed on a dry matter basis as a percentage (%). Each analysis was performed with three technical replications per treatment sample to ensure precision²³.

Determination of proline

Proline content was evaluated using the acid-ninhydrin method as described by Bates et al.²⁴, Proline content was evaluated using the acid-ninhydrin method as described by Bates et al. (1973)²⁴. Briefly, approximately 0.5 g of fresh leaf tissue was homogenized in 10 mL of 3% (w/v) aqueous sulfosalicylic acid. The homogenate was centrifuged, and the supernatant was reacted with acid-ninhydrin reagent. Absorbance of the developed chromophore was measured at 520 nm using a spectrophotometer. Proline concentration was quantified against a standard calibration curve prepared with L-proline. The final proline content is expressed on a fresh weight basis as micromoles per gram (µmol g⁻¹ FW). The analysis was performed with three biological replicates per treatment, each with two technical replicates.

Determination of antioxidant enzymes

The activity of peroxidase (POD) and catalase (CAT) in M. sativa L. leaf tissues were assayed according to Aebi²⁵and Polle et al.²⁶ methods, respectively. The activities of peroxidase (POD; EC 1.11.1.7) and catalase (CAT; EC 1.11.1.6) were assayed. For enzyme extraction, 0.5 g of fresh leaf tissue was homogenized in 5 mL of ice-cold 50 mM potassium phosphate buffer (pH 7.0) containing 1 mM EDTA. The homogenate was centrifuged at 12,000 × g for 20 min at 4 °C. The supernatant was used as the crude enzyme extract. POD activity was determined by monitoring the increase in absorbance at 470 nm due to guaiacol oxidation (extinction coefficient: 26.6 mM⁻¹ cm⁻¹). CAT activity was assayed by tracking the decomposition of H₂O₂ at 240 nm (extinction coefficient: 39.4 mM⁻¹ cm⁻¹). Enzyme activity is expressed as units per milligram of protein (U mg⁻¹ protein) or on a fresh weight basis (µmol min⁻¹ g⁻¹ FW), where one unit is defined as the amount of enzyme required to transform 1 µmol of substrate per minute under the assay conditions.

Statistical analysis

All data were subjected to analysis of variance (ANOVA). A two-way ANOVA was performed to evaluate the effects of irrigation system and irrigation level, as well as their interaction, on all measured traits. Correlation patterns among parameters were further explored using principal component analysis (PCA) in R software (version 4.3.19). Treatment means were compared using the least significant difference (LSD) test at the 1% probability level. All statistical analyses were conducted with SAS software (version 9.2).

Results

Fresh/dry function yield, and WUE for fresh/dry yield

Analysis of variance (mean squares) for the effects of year, irrigation system, and irrigation level on fresh yield (FFY), dry yield (DFY), WUEF, and WUED of M. sativa L. is presented in Table 3. The three-way interaction (year × irrigation system × irrigation level) was statistically significant for DFY and WUED. In addition, the two-way interaction between irrigation system and irrigation level was significant (p < 0.05) for FFY, DFY, and WUED (Table 3).

Table 3.

Analysis of variance (mean squares) for the effects of year (Y), irrigation system (S), irrigation level (L) and their interactions on fresh forage yield (FFY), dry forage yield (DFY), WUEF and WUED in M. sativa L.

SOV	df	Mean squares
SOV	df	Fresh function yield	Dry function yield	WUEF	WUED
Year	1	15.28^**	12.95^**	0.0512^ns	0.007^ns
Block (year)	4	0.0365^**	0.043^ns	0.0077^**	0.00057^**
a	2	109.13^**	10.24^**	0.486^**	0.0125^**
Error Year * Block	4	0.0087	0.0025	0.0076	0.0046
Year*a	2	0.119^**	0.0046^ns	0.00018^ns	0.0000014^ns
r*a(Year)	8	0.0036^ns	0.0232^ns	0.00059^ns	0.00015^**
Error Year * Block * Irrigation system	12	0.0049	0.0009	0.0036	0.0047
b	3	60.272^**	4.83^**	5.1^**	0.208^**
a*b	6	0.636^**	0.209^**	0.0033^ns	0.00017^**
Year*b	3	0.0256^**	0.0433^ns	0.00025^ns	0.0000014^ns
Yearab	6	0.0061^ns	0.0725^**	0.000068^*	0.0000014^ns
Error Year * Irrigation system * Irrigation	36	0.0076	0.0098	0.0188	0.0034

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**,*, ^ns: respectively, significant at the level of 1, 5%, and non-significant. a: irrigation system; b: irrigation levels.

The results showed that increasing irrigation levels significantly reduced both fresh and dry forage yield (FFY and DFY) across all three irrigation systems (Table 4). While the absolute lowest yields were recorded at the 70% irrigation level (I70) in the SDPI system, the relative decline in yield from the full irrigation (I100) level was less pronounced in SDPI compared to the FI and SDTI systems. This indicates that the SDPI system offered greater yield stability under water deficit. Conversely, all irrigation levels treatments resulted in significantly higher WUEF compared to the I100 level within the same system. The highest absolute WUEF was observed in the FI system at I100, while the most efficient use of water (a key irrigation levels goal) was achieved in the SDPI system at I70.

Table 4.

Mean comparison of fresh forage yield (FFY), dry forage yield (DFY), WUEF, and WUED of alfalfa as affected by irrigation system and irrigation level.

Irrigation systems	Irrigation levels (%)	Fresh function yield (tons ha^− 1)	Dry function yield (tons ha^− 1)	WUEF(tons ha^− 1)	WUED(tons ha^− 1)
FI	I₁₀₀	65.35^a	16.41^a	2.40^k	0.57ⁱ
	I₉₀	64.38^b	15.93^ab	2.86^h	0.65^f
	I₈₀	63.34^c	15.68^b−d	3.27^e	0.72^d
	I₇₀	61.36^e	15.25^c−e	3.69^b	0.82^b
SDTI	I₁₀₀	65.05^a	15.91^ab	2.48^j	0.59^h
	I₉₀	64.05^b	15.80^a−c	2.97^g	0.66^f
	I₈₀	63.18^c	15.39^d−e	3.35^d	0.73^d
	I₇₀	61.07^e	15.06^de	3.72^b	0.83^b
SDPI	I₁₀₀	62.35^d	15.36^b−e	2.71ⁱ	0.61^g
	I₉₀	60.24^f	14.95^e	3.16^f	0.68^e
	I₈₀	59.29^g	14.12^f	3.55^c	0.78^c
	I₇₀	57.26^h	13.86^f	3.90^a	0.87^a

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Treatment means with same letters do not differ significantly according to LSD test (p˂0.01). In forage quality analysis, ash content is typically expressed as a percentage of the dry matter (%).

Both fresh and dry forage yield (FFY, DFY) declined significantly with increasing water deficit across all irrigation systems. The highest absolute yields were consistently achieved under full irrigation (I100) with the FI system, while the lowest yields were recorded at the 70% irrigation level (I70) in the SDPI system.

In contrast to yield, water productivity (WP_fresh and WP_dry) increased under irrigation levels. This resulted in a strong negative correlation between yield and WP, as shown in Fig. 1 (R² = 0.80 for FFY and 0.69 for DFY). Both FI and SDI systems showed similar overall trends for the relationship between yield and WP across irrigation levels.

Fig. 1 — Correlation matrix illustrating the relationships among measured parameters in alfalfa under different irrigation systems and irrigation levels. FFY: fresh forage yield; DFY: dry forage yield; WUEF: water use efficiency for fresh yield; WUED: water use efficiency for dry yield; CP: crude protein; WSC: water-soluble carbohydrates; NDF: neutral detergent fiber; ADF: acid detergent fiber; CF: crude fiber; POD: peroxidase; CAT: catalase. Double asterisks (**) indicate correlations significant at the 1% level.

The response pattern of WUED to decreasing irrigation levels was consistent across all three irrigation systems (Table 4). Relative to their respective full irrigation (I₁₀₀) controls, WUED increased by 13.5%, 26.3%, and 43.9% under I₉₀, I₈₀, and I₇₀ in the Flooding Irrigation (FI) system. Comparable increases were observed in the SDPI system (11.9%, 25.0%, and 42.0%) and the SDTI system (10.9%, 26.6%, and 41.0%). Both fresh and dry forage yields (FFY and DFY) exhibited strong negative correlations with WUED (R² = 0.79 and R² = 0.70, respectively; Fig. 1), confirming the trade-off between biomass production and water productivity under irrigation levels.

CP and WSC

The analysis of variance (mean squares) for crude protein (CP) and water-soluble carbohydrates (WSC) in M. sativa L. is presented in Table 5. The three-way interaction (year × irrigation system × irrigation level) was not statistically significant for either CP or WSC. However, the two-way interaction between irrigation system and irrigation level was significant (p < 0.05) for both CP and WSC (Table 5).

Table 5.

Analysis of variance interaction of years, irrigation systems and irrigation levels on CP, WSC, NDF, ADF, crude fiber, and Ash of M. sativa L.

SOV	df	Mean squares
SOV	df	CP (%)	WSC (%)	NDF (%)	ADF (%)	CF (%)	Ash (%)
Year	1	0.0868^ns	0.0715^ns	0.478^ns	0.2926^ns	2.92 ^ns	0.145^ns
Block (year)	4	0.055^**	0.0662^**	0.088^**	0.112^**	1.7 ^ns	0.048^**
Error Year * Block	4	0.0023	0.0072	0.0037	0.0.0058	0.0046	0.0034
a	2	1.255^**	1.406^**	1.550^**	1.29^**	3.03^ns	1.0023^**
Year*a	2	0.00012^ns	0.00029^ns	0.0006^ns	0.0188^ns	1.17^ns	0.00079^ns
r*a(Year)	8	0.0013^ns	0.00064^ns	0.0048^ns	0.016^ns	1.21^ns	0.00078^ns
Error Year * Block * Irrigation system	12	0.0023	0.0312	0.0167	0.0047	0.0071	0.0039
b	3	13.235^**	16.124^**	17.904^**	17.034^**	22.73^**	12.666^**
a*b	6	0.0161^**	0.0292^**	0.0088^**	0.0423^**	1.05^ns	0.0134^**
Year*b	3	0.00036^ns	0.00018^ns	0.0027^ns	0.0253^ns	1.07^ns	0.00029^ns
Yearab	6	0.00095^ns	0.00038^ns	0.0046^ns	0.0238^ns	1.09^ns	0.00132^ns
Error Year * Irrigation system * Irrigation	36	0.00459	0.00269	0.00236	0.01492	1.156	0.00117

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**, ns: respectively, significant at the level of 1%, and non-significant. a: irrigation system; b: irrigation levels.

The concentrations of CP and WSC exhibited a consistent and significant increase under irrigation levels across all three irrigation systems (Table 6). This shared response highlights a conserved biochemical adaptation to water stress, where alfalfa accumulates osmolytes and stress-related proteins. The most pronounced improvements in forage quality were achieved under the most severe water deficit (I₇₀), with the SDPI system consistently yielding the highest absolute values for both CP and WSC. This suggests SDPI is particularly effective at enhancing the nutritional density of alfalfa under water-limited conditions. Statistically, CP and WSC showed a strong negative correlation with fresh and dry forage yields (DFY) (R² = 0.68–0.79), illustrating the classic trade-off between biomass production and the concentration of quality components. Conversely, both parameters were strongly positively correlated with WUEF, WUED (R² = 0.98–0.99) and with each other (R² = 0.98). These relationships underscore that the improvement in water productivity under irrigation levels is intrinsically linked to a metabolic shift toward the accumulation of osmoprotectants (WSC) and proteins (CP), which collectively support cellular tolerance to drought.

Table 6.

Mean comparison of the interaction of three types of irrigation systems FI, SDTI, and SDPI and irrigation levels on CP, WSC, NDF, ADF, crude fiber (CF) and Ash of Alfalfa.

Irrigation systems	Irrigation levels (%)	CP (%)	WSC (%)	NDF (%)	ADF (%)	Crude fiber (%)	Ash (%)
FI	I₁₀₀	15.12^l	10.11^l	45.73^a	36.73^a	21.75^l	5.88^l
	I₉₀	15.93ⁱ	10.76ⁱ	45.11^d	36.02^d	21.05ⁱ	6.69ⁱ
	I₈₀	16.54^f	11.38^f	44.29^g	35.02^g	20.20^f	7.35^f
	I₇₀	17.16^c	12.21^c	43.43^j	34.43^j	19.34^c	7.93^c
SDTI	I₁₀₀	15.37^k	10.34^k	45.46^b	36.42^b	21.43^k	6.17^k
	I₉₀	16.14^h	10.95^h	44.86^e	35.73^e	20.74^h	6.94^h
	I₈₀	16.84^e	11.67^e	44.03^h	35.02^h	18.47^e	7.58^e
	I₇₀	17.37^b	12.49^b	43.17^k	34.17^k	19.12^b	8.10^b
SDPI	I₁₀₀	15.69^j	10.50^j	45.27^c	36.19^c	21.23^j	6.37^j
	I₉₀	16.28^g	11.16^g	44.54^f	35.42^f	20.44^g	7.13^g
	I₈₀	17.01^d	11.87^d	43.73ⁱ	34.74ⁱ	19.63^d	7.77^d
	I₇₀	17.60^a	12.88^a	42.98^l	33.99^l	18.73^a	8.21^a

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Treatment means with same letters do not differ significantly according to LSD test (p˂0.01).

NDF, ADF, CF, and ash

Analysis of variance (Table 6) indicated that the three-way interaction (year × irrigation system × irrigation level) was not significant for neutral detergent fiber (NDF), acid detergent fiber (ADF), crude fiber (CF), or ash content. However, the two-way interaction between irrigation system and level was significant (p < 0.05) for all parameters except CF, which was influenced only by the main effect of irrigation level.

Irrigation levels significantly reduced fiber content across all systems. All reduced irrigation levels (I₉₀, I₈₀, I₇₀) resulted in lower NDF, ADF, and CF compared to their respective full irrigation (I₁₀₀) controls within the same system (Table 6). The greatest reductions in fiber components were observed in the SDTI system.

In contrast, ash content increased progressively with water deficit. The highest ash content was recorded under the most severe deficit (I₇₀) in the SDPI system, whereas the SDPI system overall exhibited the smallest proportional increase in ash content, indicating greater compositional stability under stress.

Ash content exhibited a strong negative correlation with forage yield (FFY, DFY) and fiber components (NDF, ADF, CF) (R² = 0.67–0.99). Conversely, it was strongly positively correlated with WUEF, WUED and the quality parameters water-soluble carbohydrates (WSC) and crude protein (CP) (R² = 0.98–0.99) (Fig. 1).

Proline, peroxidase enzyme and catalase enzyme

Analysis of variance (Table 7) for proline content, peroxidase (POD), and catalase (CAT) activity revealed distinct patterns of significance. The three-way interaction between year, irrigation system, and irrigation level was non-significant (p > 0.05) for all three biochemical parameters, indicating that the combined effect of these factors did not differ across the two experimental years. For proline and POD activity, the two-way interaction between irrigation system and irrigation level was statistically significant (p < 0.01). This indicates that the effect of reducing irrigation water depended on the type of system used. In contrast, for CAT activity, this two-way interaction was non-significant (p > 0.05). Instead, CAT activity was influenced independently by the main effects of both irrigation system and irrigation level (p < 0.01 for both). No other simple or interaction effects significantly altered CAT activity.

Table 7.

Analysis of variance interaction of years, irrigation systems and irrigation levels on proline, peroxidase and catalase of M. sativa L. a: irrigation system; b: irrigation levels.

SOV	df	Mean square
SOV	df	Proline	Peroxidase	Catalase
Year	1	0.099 ^ns	0.109 ^ns	0.187 ^ns
Block (year)	4	0.165^**	0.325^**	0.095^**
Error Year * Block	4	0.0045	0.0024	0.0045
a	2	4.16^**	5.383^**	2.593^**
Year*a	2	0.0006 ^ns	0.002^ns	0.007 ^ns
r*a(Year)	8	0.006 ^ns	0.003^ns	0.009 ^ns
Error Year * Block * Irrigation system	12	0.0031	0.0046	0.0076
b	3	44.046^**	328.79^**	57.67^**
a*b	6	0.038^**	0.084^**	0.018^ns
Year*b	3	0.0000014^ns	0.00035^ns	0.012^ns
Yearab	6	0.00035 ^ns	0.0009^ns	0.018^ns
Error Year * Irrigation system * Irrigation	36	0.00485	0.00477	0.0148

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**, ns: respectively, significant at the level of 1%, and non-significant. a: irrigation system; b: irrigation levels.

As indicated by the significant main and interaction effects in the ANOVA (Table 7), irrigation levels and irrigation system type significantly influenced the accumulation of key stress-response compounds.

Across all three irrigation systems (FI, SDTI, SDPI), proline content and the activities of peroxidase (POD) and catalase (CAT) increased progressively with water deficit (I₉₀, I₈₀, I₇₀) compared to their respective full irrigation (I₁₀₀) controls (Table 8). The highest absolute values for all three parameters were consistently recorded under the most severe deficit (I₇₀) in the SDPI system.

Table 8.

Mean comparison of the interaction of three types of irrigation systems FI, SDTI, and SDPI and irrigation levels on proline, peroxidase and catalase of Alfalfa.

Irrigation systems	Irrigation levels (%)	Proline (µM g^− 1 FW)	Peroxidase (µmol min − 1 mg − 1 protein)	Catalase (µmol min − 1 mg − 1 protein)
FI	I₁₀₀	5.29^l	6.30^l	6.19^l
	I₉₀	6.28ⁱ	7.33ⁱ	7.04ⁱ
	I₈₀	7.34^f	11.45^f	8.28^f
	I₇₀	8.81^c	15.47^c	10.27^c
SDTI	I₁₀₀	5.64^k	6.61^k	6.32^k
	I₉₀	6.77^h	7.84^h	7.38^h
	I₈₀	7.84^e	11.87^e	8.62^e
	I₇₀	9.31^b	16.02^b	10.57^b
SDPI	I₁₀₀	5.98^j	6.92^j	6.79^j
	I₉₀	7.01^g	8.41^g	7.77^g
	I₈₀	8.34^d	12.37^d	8.94^d
	I₇₀	9.72^a	16.64^a	10.90^a

Open in a new tab

Treatment means with same letters do not differ significantly according to LSD test (p˂0.01).

However, the relative increase from the I₁₀₀ baseline was greatest in the SDTI system. This suggests that while the SDPI system fostered the highest overall levels of these adaptive compounds, plants under SDTI experienced a more pronounced relative stress signal at equivalent water deficit levels, potentially indicating differences in root-zone water availability or stress perception between the two subsurface methods.

To better understand the relationships among traits under different irrigation regimes, a principal component analysis (PCA) was performed (Fig. 2). The first two principal components (PC1 and PC2) explained approximately 60.0 and 90.6% of the total variance, respectively. The analysis revealed that irrigation level strongly influenced trait associations. Parameters including crude protein (CP), WUEF, WUED, catalase, peroxidase, and proline were closely correlated with the severe deficit treatment (I₇₀) under Flooding Irrigation (FI) in both experimental years. In contrast, acid detergent fiber (ADF), neutral detergent fiber (NDF), dry forage yield (DFY), fresh forage yield (FFY), and crude fiber (CF) were more closely associated with the moderate deficit (I₉₀) under SDPI (Fig. 2).

Fig. 2 — Principal component analysis (PCA) biplot of alfalfa traits under different irrigation treatments. Included traits are crude protein (CP), water-soluble carbohydrates (WSC), neutral detergent fiber (NDF), acid detergent fiber (ADF), water use efficiency for dry yield (WUED), water use efficiency for fresh yield (WUEF), dry forage yield (DFY), and fresh forage yield (FFY). Labels 1 and 2 denote the first and second experimental years, respectively. Irrigation systems: FI = Flooding Irrigation; SDTI = Subsurface Drip-Tape Irrigation; SDPI = Subsurface Drip Pipe Irrigation. Irrigation levels: I₁₀₀ = 100%, I₉₀ = 90%, I₈₀ = 80%, and I₇₀ = 70% of weekly crop water requirement.

Discussion

Arid and semi-arid regions face harsh environmental conditions—low rainfall, high solar radiation, and elevated evaporation. These areas cover approximately 41% of Earth’s land surface and support more than one-third of the global population²⁷. Iran lies within such a climatic zone, with precipitation well below the global average and a negative water balance. Consequently, effective water management is essential for sustainable agriculture in these regions.

Under water deficit, crop growth is primarily limited by reduced photosynthesis. Stomatal closure restricts CO₂ uptake²⁸, inhibiting photosynthesis and cell expansion²⁹. Therefore, improving WUE is a critical goal, and irrigation levels can be an effective strategy.

In this study, principal component analysis (PCA) and analysis of variance (ANOVA) confirmed that irrigation system and level significantly affected all measured variables. WUE increased as water supply was reduced to 70% of crop demand, although forage yield declined correspondingly. These findings align with previous work showing that irrigation levels enhances WUE in alfalfa, often at the expense of biomass³⁰. For instance, Ismail and Almarshadi³¹observed significantly higher WUE when irrigating at 70% of field capacity. Conversely, large reductions in irrigation amount can sharply reduce alfalfa yield³². However, studies such as Lamm et al.³ have reported that irrigation at 70% of the requirement can increase WUE without significantly affecting yield, emphasizing the importance of system-specific management.

Other research^33,16 also supports our results. Zafari and Ebadi³⁴ and Riasat³⁵ reported a 27% forage loss in alfalfa under water deficit. The higher WUE under limited irrigation may be attributed to alfalfa’s adaptability to drought, supported by genetic, morphological, and physiological mechanisms. Known responses include reduced plant height, chlorophyll a/b ratio, aboveground biomass, leaf water potential, and relative water content, alongside increased root–shoot ratio, osmolyte accumulation, total chlorophyll content, lipid peroxidation, and antioxidant activity levels^30,36.

Irrigation method also significantly influenced WUE. The SDPI system markedly improved WUE, consistent with earlier reports for alfalfa and other forage crops. For example, Behbahani and Saberi³⁷ noted increased alfalfa dry matter production with one-third less water compared to full irrigation. In California’s arid regions, Montazar et al.³⁸ found that subsurface drip irrigation (SDI) enhanced WUE and mitigated the negative effects of irrigation levels, maintaining 80% of the yield achieved under full irrigation. The superiority of SDI over flood irrigation in improving WUE and crop yield is well documented for various crops^39,3,4,40.

SDI systems (including SDTI and SDPI) are advantageous due to direct fertilizer application, minimal water loss, and high irrigation efficiency. In our study, irrigation method and level also affected biochemical traits in alfalfa. Reducing irrigation to 70% under SDPI increased crude protein, water-soluble carbohydrates, proline, and antioxidant enzyme activities while decreasing NDF, ADF, ash, and crude fiber. In general, biochemical quality declined as irrigation increased to 100%.

The use of SDI systems (SDTI and SDPI) has a special place due to the possibility of applying plant irrigation fertilizer directly, the lowest water loss and the highest irrigation efficiency among other irrigation methods. In this study, irrigation method and level affected some biochemical in the alfalfa. The decreased irrigation levels up to 70% and SDPI system led to higher CP, WSC, proline, peroxidase and catalase activities, while it reduced NDF, ADF, ASH and crude fiber content compared to other treatments. In overall, biochemical showed decreasing trend by increasing of irrigation level up to 100%.

It is well established that plants accumulate osmolytes such as proline to cope with abiotic stresses like water deficit²⁰. Proline accumulates in the cytosol, enhancing water uptake, stabilizing proteins and membranes, and activating antioxidant enzymes such as catalase, peroxidase, and superoxide dismutase^{41,18,42–44}. Antioxidant enzymes help maintain membrane integrity under drought-induced oxidative stress. Similarly, accumulation of proteins and soluble carbohydrates aids in osmotic adjustment under water shortage⁴⁵.

Our results are consistent with known biochemical adaptations of alfalfa to drought stress. Accumulation of proline^46,47, water-soluble carbohydrates⁴⁸, proteins^49,50,48, and antioxidant responses^51,52 are key mechanisms by which alfalfa withstands water deficit⁵². The elevated levels of CP, WSC, proline, and antioxidant enzymes observed here likely represent adaptive responses to water limitation.

Conclusions

This study demonstrates a clear trade-off in alfalfa production under different irrigation regimes. While full flood irrigation (FI) maximized biomass yield, it resulted in the lowest WUE. Adopting a irrigation levels strategy at 70% of crop water requirement significantly improved WUE across all systems. The SDPI system under 70% RDI emerged as the optimal strategy, achieving the highest WUE while enhancing forage quality. This treatment increased crude protein, soluble carbohydrates, proline, and antioxidant activity while reducing fiber content—indicating a beneficial stress-adaptive response. In contrast, SDTI system improved WUE but not forage quality, and FI produced high biomass with poor water efficiency and inferior quality under deficit. We recommend SDPI combined with 70% RDI for sustainable alfalfa production in semi-arid regions, as it optimally balances water conservation with high-quality forage output. Future studies should assess the long-term economic and operational viability of this approach.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1^{(21.2KB, xlsx)}

Supplementary Material 2^{(10KB, xlsx)}

Supplementary Material 3^{(13.6KB, docx)}

Abbreviations

FI: Flooding irrigation
SDTI: Subsurface drip-tape irrigation
SDPI: Subsurface drip pipe irrigation
WUED: Water use efficiency dry
WUEF: Water use efficiency fresh
DFY: Dry forage yields
FFY: Fresh forage yields
SDPI: Subsurface drip pipe irrigation
CP: Crude protein
WSC: Water-soluble carbohydrates
NDF: Neutral detergent fiber
ADF: Acid detergent fiber

Author contributions

V. B. performed the experiment; H. S. M. and E. M. D. wrote and revised the manuscript; G. Z. contributed to data and technical analyses. All authors read and approved the final manuscript version.

Data availability

Data is provided within the manuscript or supplementary information files.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Hossein Shamsi Mahmodabadi, Email: Shamsi_35@yahoo.com.

Esmaeil Moghbeli Dameneh, Email: esmaeil804@gmail.com.

References

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Associated Data

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Supplementary Materials

Supplementary Material 1^{(21.2KB, xlsx)}

Supplementary Material 2^{(10KB, xlsx)}

Supplementary Material 3^{(13.6KB, docx)}

Data Availability Statement

Data is provided within the manuscript or supplementary information files.

[CR1] 1.Hura, T., Hura, K. & Ostrowska, A. Drought-stress induced physiological and molecular changes in plants. Int. J. Mol. Sci.23, 4698 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR2] 2.Shabankareh, G. Evaluation of the rainfed and irrigated conditions on biomass and essential oil yield of German chamomile (Matricaria chamomilla) in response to melatonin foliar application. BMC Plant. Biol.25, 131 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR3] 3.Lamm, F. R. & Trooien, T. P. Subsurface drip irrigation for corn production: a review of 10 years of research in Kansas. Irrig. Sci.22, 195–200 (2003). [Google Scholar]

[CR4] 4.Parsa Motlagh, B., Shahdadi, F., Salehi Sardoei, A., Parviz, L. & Ghorbanpour, M. Foliar-applied melatonin alters grain yield and the fatty acid profile of Sesame (Sesamum indicum L.) under drought stress. J. Crop Health. 76, 725–737 (2024). [Google Scholar]

[CR5] 5.Reddy, S. & Nayak, P. Crop production with limited irrigation: A review. Agric. Rev.39, 12–21 (2018). [Google Scholar]

[CR6] 6.Sen, S., Makkar, H. P. & Becker, K. Alfalfa saponins and their implication in animal nutrition. J. Agric. Food Chem.46, 131–140 (1998). [DOI] [PubMed] [Google Scholar]

[CR7] 7.Suwignyo, B., Rini, E. A. & Helmiyati, S. The profile of tropical alfalfa in indonesia: A review. Saudi J. Biol. Sci.30, 103504 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR8] 8.Shewmaker, G. E., Allen, R. G. & Neibling, W. H. Alfalfa irrigation and drought. University of Idaho, College of Agricultural and Life Sciences: Moscow, ID, USA (2011).

[CR9] 9.Diatta, A. A., Min, D. & Jagadish, S. K. Drought stress responses in non-transgenic and Transgenic alfalfa—Current status and future research directions. Adv. Agron.170, 35–100 (2021). [Google Scholar]

[CR10] 10.Hammond, K. et al. Crop water productivity: Within-Field Spatial variation in irrigated alfalfa (Medicago sativa L). Agric. Eng.7, 115 (2025). [Google Scholar]

[CR11] 11.Schneekloth, J. P., Bauder, T. A. & Hansen, N. C. Limited Irrigation Management: Principles and Practices (Colorado State University Extension Fort Collins, CO, 2009).

[CR12] 12.Camp, C., Lamm, F., Evans, R. & Phene, C. in Proc. Fourth Decennial Nat’l Irrigation Symp., Nov. 14–16.

[CR13] 13.Godoy-Avila, C., Perez-Gutierrez, A., Torres-E, C. & Hermosillo, L. Reyes-J, I. Water use, forage production and water relations in alfalfa with subsurface drip irrigation. Agrociencia37, 107–115 (2003). [Google Scholar]

[CR14] 14.Kazumba, S., Gillerman, L., DeMalach, Y. & Oron, G. Sustainable domestic effluent reuse via subsurface drip irrigation (SDI): alfalfa as a perennial model crop. Water Sci. Technol.61, 625–632 (2010). [DOI] [PubMed] [Google Scholar]

[CR15] 15.Lamm, F., Harmoney, K., Aboukheira, A. & Johnson, S. Alfalfa production with subsurface drip irrigation in the central great plains. Trans. ASABE. 55, 1203–1212 (2012). [Google Scholar]

[CR16] 16.Ismail, S. M. & Almarshadi, M. H. Maximizing productivity and water use efficiency of alfalfa under precise subsurface drip irrigation in arid regions. Irrig. Drain.62, 57–66 (2013). [Google Scholar]

[CR17] 17.Pandey, R. & Saxena, C. Drip irrigation with marginal quality land and waters. Sustainable Irrigated Agric.28, 108–132 (2020). [Google Scholar]

[CR18] 18.Hayat, S. et al. Role of proline under changing environments: a review. Plant. Signal. Behav.7, 1456–1466 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR19] 19.Gamalero, E. & Glick, B. R. Recent advances in bacterial amelioration of plant drought and salt stress. Biology11, 437 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR20] 20.Hosseinifard, M. et al. Contribution of exogenous proline to abiotic stresses tolerance in plants: a review. Int. J. Mol. Sci.23, 5186 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR21] 21.Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. Fao Rome. 300, D05109 (1998). [Google Scholar]

[CR22] 22.Adebooye, O. Proximate composition and nutrient analysis of six selected leaf vegetables of Southwest Nigeria. Ife J. Agric.18, 56–62 (1996). [Google Scholar]

[CR23] 23.Van Soest, P., Robertson, J. B. & Lewis, B. A. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy. Sci.74, 3583–3597 (1991). [DOI] [PubMed] [Google Scholar]

[CR24] 24.Bates, L. S., Waldren, R. & Teare, I. Rapid determination of free proline for water-stress studies. Plant. Soil.39, 205–207 (1973). [Google Scholar]

[CR25] 25.Aebi, H. In Catalase in vitro. Methods in Enzymology Vol. 105, 121–126 (Elsevier, 1984). [DOI] [PubMed]

[CR26] 26.Polle, A., Otter, T. & Seifert, F. Apoplastic peroxidases and lignification in needles of Norway Spruce (Picea abies L). Plant. Physiol.106, 53–60 (1994). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR27] 27.Begizew, G. Agricultural production system in arid and semi-arid regions. Int. j. agric. sci. Food technol.67, 234–244 (2021). [Google Scholar]

[CR28] 28.Chaves, M. M. et al. How plants Cope with water stress in the field? Photosynthesis and growth. Ann. Bot.89, 907 (2002). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR29] 29.Munns, R., James, R. A. & Läuchli, A. Approaches to increasing the salt tolerance of wheat and other cereals. J. Exp. Bot.57, 1025–1043 (2006). [DOI] [PubMed] [Google Scholar]

[CR30] 30.Jafarian, S., Chaichi, M. R. & Moghaddam, H. Surfactant and limited irrigation effects on forage and seed production and water use efficiency in alfalfa (Medicago sativa L). J. Agric. Sci.7, 56 (2015). [Google Scholar]

[CR31] 31.Ismail, S. M. & Almarshadi, M. H. Effects of water stress applied with sub-surface drip irrigation on forage productivity and water use efficiency of alfalfa under precise irrigation practice in arid climate. Am. -Eurasian J. Sustain. Agric.5, 97–106 (2011). [Google Scholar]

[CR32] 32.Klocke, N. L., Currie, R. S. & Holman, J. D. Alfalfa response to irrigation from limited water supplies. Trans. ASABE. 56, 1759–1768 (2013). [Google Scholar]

[CR33] 33.Testa, G., Gresta, F. & Cosentino, S. Dry matter and qualitative characteristics of alfalfa as affected by harvest times and soil water content. Eur. J. Agron.34, 144–152 (2011). [Google Scholar]

[CR34] 34.Zafari, M., Ebadi, A. & GODEHKAHRIZ, S. J. Effect of seed inoculation on alfalfa tolerance to water deficit stress. Not Bot. Horti Agrobo Cluj-Napoca. 45, 82–88 (2017). [Google Scholar]

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PERMALINK

Mitigating water stress in Medicago sativa L. through irrigation system modification: a field study in a semi-arid region

Vahdat Barkhordari

Hossein Shamsi Mahmodabadi

Esmaeil Moghbeli Dameneh

Gholamreza Zarei

Abstract

Supplementary Information

Introduction

Materials and methods

Plant collection

Site description and planting

Determination of crop water requirements (ETc)

Table 1.

Table 2.

Yield measurement

WUE measurement

Determination of crude protein (CP), ash

Determination of water soluble carbohydrates (WSC), neutral detergent fiber (NDF), acid detergent fiber (ADF) and CF

Determination of proline

Determination of antioxidant enzymes

Statistical analysis

Results

Fresh/dry function yield, and WUE for fresh/dry yield

Table 3.

Table 4.

Fig. 1.

CP and WSC

Table 5.

Table 6.

NDF, ADF, CF, and ash

Proline, peroxidase enzyme and catalase enzyme

Table 7.

Table 8.

Fig. 2.

Discussion

Conclusions

Supplementary Information

Abbreviations

Author contributions

Data availability

Declarations

Competing interests

Footnotes

Contributor Information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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