Assessing the efficacy of different nano-iron sources for alleviating alkaline soil challenges in goji berry trees (Lycium barbarum L.)

Abstract

Alkalinity is a significant environmental factor affecting crop production, which is exacerbated by the current climate change scenario. In alkaline soils, iron availability is severely reduced due to its low solubility at high pH levels and bicarbonate concentrations, which hinders plant iron absorption by rendering it inactive. In modern agriculture, green-synthesized nanoparticles have attracted considerable attention due to their environmental compatibility, cost-effectiveness, and enhanced potential for foliar uptake. This study explores the effects of various iron sources and concentrations, including FeSO₄.7H₂O, Fe-EDDHA, Nano-Fe, and green-synthesized nano-Fe, at three concentrations (0, 0.25, and 0.5 g L^− 1) on the growth, physiological, biochemical parameters, and nutrient uptake of goji berry. The evaluated parameters included leaf area, fresh and dry weight of leaves and fruits, chlorophyll a, b, and a/b ratio, carotenoids, total soluble sugar in leaves and fruits, catalase, guaiacol peroxidase, ascorbate peroxidase enzymes, and the concentrations of nutrient elements (N, P, K, Ca, Mg, Cu, Mn, Zn, and Fe). Results demonstrated that increasing iron concentrations led to enhanced fresh and dry weights of leaves and fruits, with the highest values recorded at 0.5 g L⁻¹ of all iron sources. Nano-Fe significantly boosted fresh and dry weight of leaves, resulting in a 4.95 to 4.84-fold increase compared to the control. The highest fresh (1.267 g) and dry (0.815 g) fruit weights were observed at 0.5 g L⁻¹ of green-synthesized nano-Fe. Regarding photosynthetic pigments, the chlorophyll a/b ratio peaked at 1.62 mg g⁻¹ FW under the 0.5 g L⁻¹ green-synthesized nano-Fe treatment, while the control exhibited the lowest ratio (1.31 mg g⁻¹ FW). A similar trend was observed in nutrient uptake, with the highest leaf iron content (0.189 mg g⁻¹ DW) recorded in the 0.5 g L⁻¹ nano-Fe treatment, and the lowest (0.116 mg g⁻¹ DW) in the control. Although iron concentration positively influenced most traits, it led to a decline in zinc and manganese levels. Overall, these results highlight the potential of green-synthesized nano-Fe as an efficient, cost-effective iron source for improving vegetative growth, photosynthetic pigment levels, and nutrient uptake in goji berries grown in alkaline soils.

Introduction

Lycium barbarum L., commonly referred to as the goji berry or wolfberry, is a perennial deciduous shrub in the Solanaceae family, naturally found in northwestern China, Mongolia’s highlands, and Tibet [1,2,3]. This fruit is a significant source of phenolic compounds like phenolic acids and flavonoids, along with carotenoids, organic acids, sugars (such as fructose and glucose), and notably, ascorbic acid [4]. Goji berries are predominantly cultivated across Asia, contributing 71.2% to the global production, followed by Africa (15.8%), the Americas (8.3%), Oceania (3.4%), and Europe (1.3%) [5]. Currently, climate change exacerbates various environmental stresses on plants, including more frequent droughts, flooding, and rising temperatures, which pose a substantial threat to global food security. Conditions of high temperature and extended drought periods promote soil salinization and alkalinity, creating significant agricultural challenges [6]. Such adverse environmental conditions often lead to osmotic, ionic, and oxidative stress in plants, and are anticipated to intensify with the progression of climate change. Globally, more than 800 million hectares of land are affected by salinity, with alkaline conditions impacting over half of this land [7]. Alkaline soils, which are marked by high pH levels, severely restrict nutrient absorption by plant roots [8]. Specifically, the presence of carbonates and elevated pH reduce nutrient bioavailability and impair photosynthetic processes, frequently resulting in oxidative stress [9].

Iron (Fe) deficiency is particularly problematic in alkaline soils, as high soil pH reduces iron solubility, often making it unavailable for plant uptake [10, 11]. Bicarbonate presence in these soils maintains high pH levels (7.5–8), limiting iron solubility and hindering the activity of root ferric reductase, an enzyme vital for iron uptake, which operates optimally at a pH around 5 [12]. Plants adopt various strategies to address this issue, such as proton release or phytosiderophore secretion from roots [13]. Plants primarily absorb Fe in its ferrous form (Fe²⁺); however, in alkaline soils, the dominance of ferric iron (Fe³⁺) further restricts Fe availability [14]. Consequently, iron-deficient soils yield iron-deficient plants, which manifest stunted growth, young leaf chlorosis, and diminished overall vigor [15]. Iron is essential for multiple biological functions, including chlorophyll synthesis, photosynthesis, chloroplast development, and cellular respiration [16]. It is also crucial in RNA synthesis, the Calvin cycle, and the operation of respiratory enzymes [17]. Chloroplasts harbor a large portion of a plant’s iron, which is necessary for Rubisco activity and stomatal function [18, 19].

Insufficient iron availability results in chlorosis, negatively impacting plant health and yield [10]. Conventional iron fertilizers are generally classified into three types: inorganic iron compounds like ferrous sulfate (FeSO₄), synthetic iron chelates such as ethylene diamine-di-o-hydroxy phenyl acetic acid (EDDHA), and natural iron complexes like amino acids and humates [20, 21]. Application methods of ferrous sulfate include mixing with topsoil, foliar spraying, or trunk injection, which are among the initial approaches to address iron deficiency [22]. Synthetic chelate fertilizers are vital for supplying iron in alkaline and calcareous soils [23]. Among these, Fe-EDDHA is most effective in providing iron to plants in alkaline soils, although its high cost and limited iron content restrict its use to high-value crops [24, 25]. Furthermore, its potential toxicity to certain species may impose additional application limitations. Nanotechnology has garnered interest in agriculture for its ability to enhance nutrient delivery and mitigate environmental impacts [26].

Nanofertilizers provide controlled, gradual nutrient release, improving nutrient use efficiency while reducing leaching and runoff [27]. Their small particle size and large surface area facilitate enhanced nutrient uptake by plant roots and leaves [28]. Typically requiring lower application rates, nanofertilizers are environmentally favorable compared to traditional chemical fertilizers [29]. Compared to conventional fertilizers, nanofertilizers yield numerous advantages, including higher crop yields, improved nutrient uptake, and reduced environmental pollution [30, 31]. Iron nanoparticles possess unique properties, such as a high surface area-to-volume ratio, biocompatibility, and catalytic activity, making them suitable for applications in agriculture, environmental management, and medicine [32,33,34,35]. Numerous studies have documented the benefits of iron nanoparticles in promoting plant growth and stress tolerance. For example, iron nanocomplexes have been shown to enhance growth, photosynthetic pigments, and essential oil production in Calendula officinalis L [36], while green-synthesized iron nanoparticles improved leaf area, decreased oxidative stress, and elevated chlorophyll content in almond trees under bicarbonate stress [37]. In Vitis vinifera L., iron nanoparticles increased antioxidant activity and decreased hydrogen peroxide (H₂O₂) levels, thus protecting against drought-induced oxidative damage [38]. In canola plants, iron nanoparticles supported growth and chlorophyll synthesis under drought stress while also increasing antioxidant enzyme activity [39].

Although chemically and physically synthesized nanoparticles provide benefits, their production processes are often costly, energy-intensive, and involve hazardous chemicals, posing environmental concerns [34]. In contrast, green synthesis offers an eco-friendly alternative, utilizing plant extracts, algae, microorganisms, or enzymes for nanoparticle production [40]. This approach is more economically viable and avoids toxic chemicals, making it ideal for agricultural applications [41, 42]. Green-synthesized nanoparticles serve as cost-effective biofertilizers that enhance physiological and biochemical plant responses [43, 44]. One of the major challenges of iron deficiency in alkaline soils is the formation of ferrous hydroxide, which limits iron bioavailability and uptake. This study aims to synthesize iron nanoparticles using Lycium barbarum L. leaves as a green source and evaluate their effectiveness in comparison to traditional iron fertilizers (Fe-EDDHA, Nano-Fe, and FeSO₄.7H₂O) in enhancing physiological and biochemical traits of goji berry plants under alkaline soil conditions. This research explores the potential of green-synthesized iron nanoparticles to improve plant growth, photosynthetic pigment levels, osmolyte accumulation, antioxidant enzyme activities, and nutrient uptake.

Materials and methods

Plant materials and growth conditions

This study was carried out in the research greenhouse of the Faculty of Agriculture, Urmia University, West Azerbaijan Province, Iran. The experiment followed a completely randomized design (CRD) with four iron sources green-synthesized nano-Fe, FeSO₄.7H₂O, Fe-EDDHA, and Nano-Fe—applied at three concentrations (0, 0.25, and 0.50 g L⁻¹), with three replications. Three-year-old goji berry (Lycium barbarum) saplings were obtained from a commercial nursery in Urmia and kept outdoors for three months to fulfill their chilling requirement. In late March 2022, at the onset of the growing season, saplings were transferred to 40 cm diameter and 50 cm height plastic pots filled with soil (Table 1). Pots were arranged in a greenhouse with a light cycle of 16 h light and 8 h dark, relative humidity between 40% and 60%, and an average temperature range of 16 ± 2 °C (minimum) to 28 ± 2 °C (maximum). Treatments were applied at the beginning of flowering in June 2022. Iron treatments, administered as foliar sprays, included FeSO₄.7H₂O, Fe-EDDHA, Nano-Fe, and green-synthesized nano-Fe at 0, 0.25, and 0.50 g L⁻¹, applied weekly until fruit harvest.

Table 1 Analysis of soil sample used in potting medium

Synthesis of green iron nanoparticles

To synthesize green iron nanoparticles, fresh goji berry leaves were collected, washed, and prepared. A total of 20 g of leaves were weighed and mixed with 300 mL of distilled water, then heated at 80 °C for 1 h. After cooling to room temperature, the extract was filtered using a 0.45 μm vacuum filter. The extract was mixed with 0.1 M iron chloride solution in a 4:1 ratio (extract to iron chloride) and stirred for 1 h using an ultrasonic device (Elmasonic E 30 H, Germany). The production of nanoparticles was confirmed by the appearance of a dark color [45], with the average nanoparticle size being 28.9 nm (Fig. 1).

Fig. 1

SEM image of iron nanoparticles synthesized via green synthesis using goji berry extract

Plant growth parameters

Following fruit harvest, morphological parameters including fresh and dry weights of fruits and leaves (mean of 10 samples) were measured using a digital balance (METTLER, PJ300, accuracy: 0.001 g). For dry weight determination, fruits were shade-dried and leaves were oven-dried at 72 °C for 24 h. Leaf area was determined using 10 mature leaves per pot, randomly selected from the middle of the plant, and measured with a Leaf Area Meter (AM 200). A graphic abstract outlines the stages of treatment application and measurement of relevant parameters (Fig. 2).

Fig. 2

Stages of treatment application and measurement of relevant indices in goji berry leaves and fruits

Measurement of chlorophyll a, b, a/b, and carotenoids

Photosynthetic pigments were measured according to the method of Lichtenthaler and Buschmann [46], with slight modifications. Fully developed leaves (0.1 g) were homogenized in 2.5 mL of 80% acetone. The extract was diluted to 10 mL with acetone and centrifuged at 4000 rpm for 15 min. The absorbance of the supernatant was recorded at 663 nm (chlorophyll a), 645 nm (chlorophyll b), and 470 nm (carotenoids) using a spectrophotometer (HALO DB-20, Dynamica). The amounts of chlorophyll a, chlorophyll b, and carotenoids were calculated in mg g^-1FW (fresh weight) using the following formulas:

$$\text { Chlorophyll } \mathrm{a}=((12.7 \times \text { A663 })-(2.69 \times \text { A645 })) / 1000 \mathrm{~W} \times V$$

$$\text { Chlorophyll b }=((22.9 \times A 645)-(4.68 \times A 663)) / 1000 W \times V$$

$$\text { Carotenoid }=(100(\text { A470 })-3.27(\mathrm{mg} \mathrm{Chl} \mathrm{a})-104(\mathrm{mg} \mathrm{Chl} \mathrm{~b})) / 227$$

V: the volume of filtered solution, A: absorption of the sample at wavelengths of 663, 645 and 470 nm and W: fresh weight of the leaf sample.

Measurement of total soluble sugars in leaves and fruits

Total soluble sugars were measured using 0.1 g of fresh tissue ground with 2.5 mL of 95% ethanol. The supernatant was separated, and the residue was washed with 5 ml of 70% ethanol. The supernatants were combined and centrifuged at 4000 rpm for 15 min. The supernatant was stored at 4 °C until sugar analysis. For the sugar assay, 20 µL of extract and 1.5 ml of freshly prepared anthrone reagent were added to test tubes and placed in a boiling water bath for 10 min to form a colored compound. Absorbance was measured at 625 nm using a spectrophotometer [47].

Preparation of plant extracts for antioxidant enzyme activity

Plant extracts were prepared following a modified method of Kang and Saltveit [48]. Samples (0.1 g) were homogenized in 2 mL of Tris buffer (pH 7.5) containing 0.05 M HCl, 3 mM MgCl₂, and 1 mM EDTA. Extracts were centrifuged at 13,000 rpm for 20 min at 4 °C, and the supernatant was used for enzyme assays.

Catalase activity

Catalase activity was measured by monitoring the decrease in absorbance at 240 nm [49]. The reaction mixture contained 2.5 mL of 50 mM potassium phosphate buffer (pH 7.5), 20 µL of 3% H₂O₂, and 20 µL of extract. Catalase activity was calculated based on the decomposition of H₂O₂ using the extinction coefficient of 43.6 mM⁻¹cm⁻¹.

Guaiacol peroxidase activity

Guaiacol peroxidase (GPX) activity was determined by the increase in absorbance at 420 nm [50]. The reaction mixture included 2.5 mL of 50 mM phosphate buffer (pH 7.5), 1 mL of 1% guaiacol, 0.1 mL of extract, and 1 mL of 1% H₂O₂. GPX activity was calculated using an extinction coefficient of 26.6 mM⁻¹ cm⁻¹.

Ascorbate peroxidase activity

Ascorbate peroxidase (APX) activity was measured by the decrease in absorbance at 290 nm, as described by Nakano and Asada [51]. The reaction mixture included 2 mL of 50 mM phosphate buffer (pH 7), 0.1 mM EDTA, 1 mM sodium ascorbate, 0.2 mL of 1% H₂O₂, and 0.1 mL of extract. The extinction coefficient used for calculations was 2.8 mM⁻¹ cm⁻¹.

Mineral nutrient analysis

At the end of the experiment, mature leaves located at the midpoint of the stem were harvested and dried in an oven at 70 °C for 48 h. The dried tissues were then analyzed for the concentrations of nitrogen (N), phosphorus (P), potassium (K⁺), calcium (Ca²⁺), magnesium (Mg²⁺), iron (Fe²⁺), copper (Cu²⁺), zinc (Zn²⁺), and manganese (Mn²⁺). Dry samples weighing 0.3–0.5 g were individually weighed for each replicate and ground into a fine powder using a mortar and pestle. The powder was subsequently transferred to porcelain crucibles and ashed at 550 °C for 4 to 5 h to convert organic materials to white ash. The resulting white ash was dissolved in 10 ml of 10 M hydrochloric acid to solubilize the mineral components. Nitrogen content was determined using the Kjeldahl method [52]. Phosphorus concentration was estimated using the molybdenum vanadate colorimetric method with a spectrophotometer at 470 nm [52]. Potassium concentration in the leaves was assessed using flame emission spectrophotometry with a flame photometer (CORNING model) [53]. Calcium and magnesium content was estimated by titration with 0.01 N EDTA [54]. The concentrations of Fe²⁺, Zn²⁺, Cu²⁺, and Mn²⁺ were determined using atomic absorption spectrophotometry (Shimadzu AA-6300, Japan) [54].

Statistical analysis

The present experiment was conducted as a completely randomized design (CRD) with three replications. The experimental treatments included various iron sources (Nano-Fe, green-synthesized nano-Fe, Fe-EDDHA, and FeSO₄.7H₂O) and different iron concentrations (0, 0.25, and 0.50 g L^-1). In total, there were 9 experimental treatments with three replications. The data were statistically analyzed using Response Surface Methodology (RSM) and Pearson’s correlation coefficient calculated with RStudio (R). A heatmap was created to identify relationships between traits using CIMMiner software. Multivariate analysis was performed using XLSTAT, a statistical software program.

Results

Plant growth parameters

An increase in iron concentration resulted in a significant expansion in leaf area (p < 0.001). The control treatment (without iron) exhibited the smallest leaf area (327.2 mm²), whereas the 0.5 g L⁻¹ Nano-Fe treatment achieved the largest (527 mm²), marking a 61.1% enhancement over the control (Fig. 3A). Fresh and dry weights of plants are vital morphological markers for evaluating biomass and growth. Both fresh and dry weights of leaves and fruits were significantly influenced by the different iron treatments (p < 0.001). As iron concentration increased, the fresh and dry weights of leaves followed an upward trend, with the maximum values observed at 0.5 g L⁻¹ iron concentration. Among the iron sources, nano-Fe was particularly effective in increasing leaf fresh and dry weights (Fig. 3B, C). Consequently, both the concentration and source of iron influenced the fresh and dry weights. The control group exhibited the lowest leaf fresh and dry weights, while nano-Fe induced the most substantial increases, with FeSO₄.7H₂O having the least effect. Similarly, the fresh and dry weights of fruits also increased with higher iron concentrations, reaching the highest values (1.267 g fresh weight and 0.815 g dry weight) at 0.5 g L⁻¹ nano green-Fe (Fig. 3D, E). The nano-Fe and nano green-Fe treatments produced the most significant effects on fruit biomass, with the fresh weight in the nano green-Fe treatment being 62.8% higher than the control. The lowest fruit fresh (0.778 g) and dry weights (0.351 g) were recorded in the control.

Fig. 3

Impact of different iron concentrations and sources on leaf area (A), Leaf Fresh weight (B), Leaf dry weight (C), Fruit fresh weight (D), and Fruit dry weight (E) in goji berry. Treatments: T₁: Control, T₂: Nano-Fe (0.25 g L^− 1), T₃: Nano-Fe (0.5 g L^− 1), T₄: Nano green Fe (0.25 g L^− 1), T₅: Nano green Fe (0.5 g L^− 1), T₆: FeSO₄.7H₂O (0.25 g L^− 1), T₇: FeSO₄.7H₂O (0.5 g L^− 1), T₈: Fe-EDDHA (0.25 g L^− 1) and T₉: Fe-EDDHA (0.5 g L^− 1). Error bars represent standard errors of the mean

Chlorophyll a, b, chlorophyll a/b ratio, and carotenoids

Chlorophyll, a key pigment for photosynthesis, was significantly affected by foliar iron application. Different iron treatments had a significant impact on chlorophyll a content (p < 0.001), with increasing iron concentrations leading to higher chlorophyll a levels compared to the control (Fig. 4A). The highest chlorophyll a concentration (2.65 mg g⁻¹ FW) was observed in the 0.5 g L⁻¹ nano green-Fe treatment, representing a 54.1% increase compared to the control. Among iron concentrations, 0.5 g L⁻¹ had a more pronounced effect, with the nano green-Fe treatment resulting in a 1.44-fold increase compared to the 0.25 g L⁻¹ treatment. Chlorophyll b and carotenoid levels also rose with higher iron concentrations across all treatments, with statistically significant effects (p < 0.001). The pattern of changes in chlorophyll b mirrored those in chlorophyll a. The 0.5 g L⁻¹ nano green-Fe treatment had the highest chlorophyll b content (1.63 mg g⁻¹ FW), while the control had the lowest (1.27 mg g⁻¹ FW) (Fig. 4B). The highest carotenoid content (0.896 mg g⁻¹ FW) was recorded in the 0.5 g L⁻¹ nano green-Fe treatment, while the control exhibited the lowest content (0.222 mg g⁻¹ FW) (Fig. 4C). The chlorophyll a/b ratio was most significantly influenced by the nano green-Fe treatment at 0.5 g L⁻¹, which also had the greatest effect on carotenoid content (Fig. 4D).

Fig. 4

Impact of different iron concentrations and sources on Chlorophyll a (A), Chlorophyll b (B), Carotenoids (C), and Chlorophyll a/b ratio (D) in goji berry leaves. Treatments: T₁: Control, T₂: Nano-Fe (0.25 g L^− 1), T₃: Nano-Fe (0.5 g L^− 1), T₄: Nano green Fe (0.25 g L^− 1), T₅: Nano green Fe (0.5 g L^− 1), T₆: FeSO₄.7H₂O (0.25 g L^− 1), T₇: FeSO₄.7H₂O (0.5 g L^− 1), T₈: Fe-EDDHA (0.25 g L^− 1) and T₉: Fe-EDDHA (0.5 g L^− 1). Error bars represent standard errors of the mean

Total soluble sugars in leaves and fruits

Iron treatments significantly influenced total soluble sugar content in leaves and fruits (p < 0.001). As iron concentration increased, total soluble sugars in both tissues followed a rising trend. The 0.5 g L⁻¹ nano green-Fe treatment produced the highest sugar levels in leaves (71.06 mg g⁻¹ FW) and fruits (170.86 mg g⁻¹ FW). This highlights the beneficial effect of nano green-Fe on sugar accumulation. In contrast, the control treatment had the lowest total soluble sugar levels in leaves (39.95 mg g⁻¹ FW) and fruits (45.53 mg g⁻¹ FW) (Fig. 5A, B).

Fig. 5

Impact of different iron concentrations and sources on total soluble sugars in leaves (A), Total soluble sugars in fruits (B), Catalase enzyme activity (C), Guaiacol peroxidase enzyme activity (D), and Ascorbate peroxidase enzyme activity (E) in goji berry. Treatments: T₁: Control, T₂: Nano-Fe (0.25 g L^− 1), T₃: Nano-Fe (0.5 g L^− 1), T₄: Nano green Fe (0.25 g L^− 1), T₅: Nano green Fe (0.5 g L^− 1), T₆: FeSO₄.7H₂O (0.25 g L^− 1), T₇: FeSO₄.7H₂O (0.5 g L^− 1), T₈: Fe-EDDHA (0.25 g L^− 1) and T₉: Fe-EDDHA (0.5 g L^− 1). Error bars represent standard errors of the mean

Antioxidant enzymes (catalase, ascorbate peroxidase, guaiacol peroxidase)

The activity of antioxidant enzymes, including catalase, ascorbate peroxidase, and guaiacol peroxidase, was significantly affected by iron treatments (p < 0.001). As iron concentration increased, the activities of these enzymes also rose. Catalase activity was highest (19.38 µmol H₂O₂ min⁻¹ g⁻¹ FW) in the 0.5 g L⁻¹ nano green-Fe treatment, compared to the control (4.32 µmol H₂O₂ min⁻¹ g⁻¹ FW) (Fig. 5C). Similar trends were observed for guaiacol peroxidase activity, which peaked at 1.401 µmol guaiacol min⁻¹ g⁻¹ FW in the 0.5 g L⁻¹ nano green-Fe treatment (Fig. 5D). Nano-Fe and nano green-Fe treatments consistently yielded better results than other iron sources. The highest (0.124 µmol ASA min^-1g FW) and lowest (0.059 µmol ASA min^-1g FW) activities of ascorbate peroxidase enzyme were observed in the 0.5 gl^-1 nano-Fe treatment and the control, respectively. The application of nano-Fe showed a 2.1-fold increase in ascorbate peroxidase enzyme activity compared to the control (Fig. 5E).

Mineral concentration

Nitrogen, phosphorus, potassium, calcium and magnesium concentration

Iron treatments significantly influenced nitrogen, phosphorus, potassium, calcium, and magnesium levels in leaves (p < 0.001). As iron concentration increased, so did the nitrogen content, with the 0.5 g L⁻¹ nano green-Fe treatment yielding the highest nitrogen content (18.29 mg g⁻¹ DW), a 96% increase over the control (9.33 mg g⁻¹ DW) (Fig. 6A). Different iron sources positively influenced leaf phosphorus content, and with increasing iron concentration, leaf phosphorus content increased compared to the control (Fig. 6B). The highest phosphorus content (5.44 mg g^-1 DW) was observed in the 0.5 g L^-1 nano green-Fe treatment, while the lowest (2.70 mg g^-1 DW) was in the control. The nano green-Fe treatment resulted in a 101% increase in phosphorus compared to the control, highlighting the positive effect of iron on phosphorus uptake. Leaf potassium content also increased with rising iron concentration, with the highest potassium content (35.82 mg g^-1 DW) observed at 0.5 g L^-1 nano -Fe treatment (Fig. 6C). Therefore, both iron concentration and iron source type affected leaf potassium content. The lowest potassium content (19.07 mg g^-1 DW) was observed in the control treatment. Nano -Fe treatment increased potassium content by 87.83% compared to the control (Fig. 7). Calcium and magnesium levels showed similar trends, with the 0.5 g L⁻¹ nano green-Fe treatment yielding the highest contents (13.9 mg g⁻¹ DW for calcium and 2.62 mg g⁻¹ DW for magnesium) (Fig. 6D, E).

Fig. 6

Impact of different iron concentrations and sources on leaf nitrogen (A), phosphorus (B), potassium (C), calcium (D), magnesium (E), iron (F), copper (G), zinc (H), and manganese (I) contents in goji berry leaves. Treatments: T₁: Control, T₂: Nano-Fe (0.25 g L^− 1), T₃: Nano-Fe (0.5 g L^− 1), T₄: Nano green Fe (0.25 g L^− 1), T₅: Nano green Fe (0.5 g L^− 1), T₆: FeSO₄.7H₂O (0.25 g L^− 1), T₇: FeSO₄.7H₂O (0.5 g L^− 1), T₈: Fe-EDDHA (0.25 g L^− 1) and T₉: Fe-EDDHA (0.5 g L^− 1). Error bars represent standard errors of the mean

Fig. 7

Pearson correlation heatmap for goji berry responses to different iron sources and concentrations under alkaline soil conditions. The heatmap shows Leaf Area (LA), Leaf Fresh Weight (LFW), Leaf Dry Weight (LDW), Fruit Fresh Weight (FFW), Fruit Dry Weight (FDW), Chlorophyll a, Chlorophyll b, Chlorophyll a/b Ratio, Carotenoids (CARs), Leaf Soluble Sugars (LSS), Fruit Soluble Sugars (FSS), Catalase Activity (CAT), Guaiacol Peroxidase Activity (GPX), Ascorbate Peroxidase Activity (APX), Nitrogen (N), Phosphorus (P), Potassium (K), Calcium (Ca), Magnesium (Mg), Manganese (Mn), Zinc (Zn), Iron (Fe), and Copper (Cu)

Iron, copper, zinc and manganese concentration

Iron and copper concentrations in leaves increased significantly with iron treatments (p < 0.001). The highest iron content (0.189 mg g⁻¹ DW) was observed in the 0.5 g L⁻¹ nano-Fe treatment (Fig. 6F). The highest (0.129 mg g^-1 DW) and lowest (0.058 mg g^-1 DW) copper contents were found in the 0.5 g L^-1 nano green-Fe and control treatments, respectively (Fig. 6G). The amount of copper in nano green-Fe treatment increased by 2.22 times compared to the control. Among different iron concentrations, 0.5 g L^-1 had a greater effect on increasing leaf copper content. The effects of different iron treatments on zinc and manganese contents were different from other elements (Fig. 6H, I). With increasing iron concentration, zinc and manganese contents significantly (p < 0.001) decreased. Zinc content increased up to 0.25 g L^-1 iron but decreased with higher iron concentrations, showing a negative impact of high iron on zinc content. Among different treatments, the highest zinc content (0.096 mg g^-1 DW) was observed in the 0.25 g L^-1 nano-Fe treatment, while the lowest (0.026 mg g^-1 DW) was in the 0.5 g L^-1 nano green-Fe treatment (Fig. 6H). Similarly, the highest manganese content (0.110 mg g^-1 DW) was found in the 0.25 g L^-1 nano-Fe treatment, while the lowest (0.005 mg g^-1 DW) was in the 0.5 g L^-1 nano green-Fe treatment (Fig. 6I).

Correlation analysis

The correlation coefficient analysis between the measured indices under different iron sources and concentrations in goji berry indicated a positive correlation among all evaluated indices (Fig. 7). The Pearson correlation heatmap showed high correlation coefficients between indices, such as fresh and dry leaf weights with a correlation coefficient of 0.95, indicating that an increase in fresh leaf weight corresponds with an increase in dry leaf weight. This trend was similar for other indices as well.

Heatmap analysis

The heatmap results showed a positive relationship among measured indices, including leaf area, fresh and dry leaf and fruit weights, chlorophyll a, b, and their ratio, carotenoids, soluble sugars in leaves and fruits, catalase, guaiacol peroxidase, ascorbate peroxidase, nitrogen, phosphorus, potassium, calcium, magnesium, iron, and copper (Fig. 8). In other words, with an increase in the concentration of different iron sources, all measured indices, except zinc and manganese, increased. The heatmap analysis categorized the treatments based on iron concentration into two groups. The first group included the control and 0.25 g L^-1 iron, while the second group included 0.5 g L^-1 iron. The heatmap created different clusters, with zinc and manganese in one cluster and all other indices in another.

Fig. 8

Heatmap, biplot of measured indices, and heatmap analysis of principal components of evaluated indices in goji berry in response to different iron sources and concentrations in alkaline soil conditions. The heatmap illustrates Leaf Area (LA), Leaf Fresh Weight (LFW), Leaf Dry Weight (LDW), Fruit Fresh Weight (FFW), Fruit Dry Weight (FDW), Chlorophyll a, Chlorophyll b, Chlorophyll a/b Ratio, Carotenoids (CARs), Leaf Soluble Sugars (LSS), Fruit Soluble Sugars (FSS), Catalase Activity, Guaiacol Peroxidase Activity (GPX), Ascorbate Peroxidase Activity (APX), Nitrogen (N), Phosphorus (P), Potassium (K), Calcium (Ca), Magnesium (Mg), Manganese (Mn), Zinc (Zn), Iron (Fe), and Copper (Cu)

PCA chart

The results of the Principal Component Analysis (PCA) were used to examine the physiological, biochemical, and performance components of goji berry plants (Table 2). Data analysis revealed that PC1 explained 73.29% of the total variance, and PC2 accounted for 9.61% of the variance. This indicates that PC1 captured the most variation in the data, followed by PC2. Overall, PC1 and PC2 together explained 82.9% of the total variance (Fig. 9).

Table 2 Eigenvalues, variance percentage, and factor loadings generated by PCA

Fig. 9

Principal component analysis (PCA) to understand parameter and treatment variability in goji berry plants in response to different sources and concentrations of iron in alkaline soil conditions. The heatmap shows Leaf Area (LA), Leaf Fresh Weight (LFW), Leaf Dry Weight (LDW), Fruit Fresh Weight (FFW), Fruit Dry Weight (FDW), Chlorophyll a, Chlorophyll b, Chlorophyll a/b Ratio, Carotenoids, Leaf Soluble Sugars (LSS), Fruit Soluble Sugars (FSS), Catalase Activity, Guaiacol Peroxidase Activity (GPX), Ascorbate Peroxidase Activity (APX), Nitrogen (N), Phosphorus (P), Potassium (K), Calcium (Ca), Magnesium (Mg), Manganese (Mn), Zinc (Zn), Iron (Fe), and Copper (Cu)

Discussion

High concentrations of bicarbonate in soil cause iron deficiency in fruit trees. In the apple rootstock Malus hupehensis, alkaline stress significantly reduced biomass compared to the control [55]. Similar growth cessation due to bicarbonate-induced stress, high pH, and iron deficiency has been observed in other plant species (56; 57; 58). Iron deficiency often leads to oxidative stress in cells, increasing ROS production, disrupting metabolic activities, and impairing photosynthesis and sugar production [59]. These conditions lead to reduced sugar allocation to roots, decreased root volume, length, and dry matter, and negatively impact the solubility and availability of mineral elements by the plant roots [60]. To our knowledge, no studies have yet examined the potential application of various exogenous iron sources to improve alkaline soil tolerance in goji berry plants. Our findings demonstrate that alkaline soil significantly hampers goji berry growth. It is interpreted that the reduced biomass is associated with the direct and indirect effects of alkaline soil on mechanisms involved in biomass production. Alkaline conditions also disrupt enzyme structures, slow cell division, reduce photosynthesis rates, and cause nutritional imbalances, collectively inhibiting plant development [61, 62, 63]. Different iron sources exhibited varying effects on goji berry vegetative growth, with nano-Fe and nano green-Fe proving effective in mitigating the negative impacts of alkaline soil (Fig. 3A-E). This enhancement may be attributed to the critical role of iron in photosynthesis. Nano-Fe and nano green-Fe significantly increased biomass (fresh and dry weight) and counteracted the detrimental effects of alkaline conditions, likely due to their rapid absorption by plants. Nano-iron compounds have been shown to boost Fe content and promote vegetative growth in hydroponically grown lettuce [20]. Similarly, Fe₃O₄ nanoparticles coated with organic materials improved shoot iron content and growth in soil-grown tomatoes [64]. In this study, the application of iron nanoparticles likely enhanced leaf area and fresh and dry leaf and fruit weights in goji berries through several mechanisms: (1) iron is vital for chlorophyll synthesis and the photosynthetic electron transport chain [65], (2) it supports cell division and expansion [66], and (3) it improves nutrient absorption, including nitrogen, phosphorus, and potassium [20].

Iron deficiency leads to chlorophyll degradation due to the accumulation of ROS in chloroplasts, impairing chlorophyll synthesis [63, 67]. Iron is necessary for δ-aminolevulinic acid and protochlorophyllide synthesis, both precursors for chlorophyll biosynthesis [68]. Deficiency disrupts iron-dependent processes such as photosynthetic electron transport and sulfur and nitrogen metabolism, inhibiting growth [69]. The results of this study demonstrated that the application of green nano-iron significantly increased the photosynthetic pigment content in goji berries (Fig. 4A-D). Various enzymatic mechanisms that enhance chlorophyll activity require iron, which can be boosted using iron nanoparticles. Application of γ-Fe₂O₃ nanoparticles in Citrus maxima resulted in increased chlorophyll content [70]. Previous studies have shown that iron foliar application can enhance carotenoid synthesis [71]. Similar results were obtained in this study, where iron fertilizer application increased carotenoid content compared to the control, with nano-iron showing a higher impact due to its unique properties (Fig. 4C). Iron nanoparticles increase chlorophyll and carotenoid levels through several key mechanisms: (1) iron’s involvement in chlorophyll biosynthesis enzymes (2), its role in photosynthetic efficiency (3), Iron is a key component of various proteins in the photosynthetic electron transport chain, including cytochromes and ferredoxin (4) Iron is involved in the activity of enzymes responsible for carotenoid biosynthesis, such as phytoene synthase and phytoene desaturase. These enzymes convert precursors into carotenoids [71, 72, 73].

Increased nano green-Fe concentrations significantly boosted total soluble sugars in goji berries. Shi et al. [74] reported that increased Fe-EDHHA fertilizer improved reducing sugars in grapes. Iron plays a crucial role in carbohydrate metabolism and fruit quality. Given that polysaccharides are a major component of goji berries, iron application can effectively increase fruit sugar levels. Increasing Nano green-Fe concentration from 0.25 to 0.5 g L^− 1 resulted in a 1.5-fold increase in total soluble sugars of fruits. Our results align with previous studies, showing that higher iron levels in all treatments increased total soluble sugar content in leaves and fruits (Fig. 5A, B).

Nano-Fe and nano green-Fe treatments significantly enhanced antioxidant enzyme activities in goji berries, indicating the importance of iron sources and their specific effects on different enzymes (Fig. 5C-E). Accumulation of ROS potential may be due to disruptions in photosynthetic electron transport as a result of iron deficiency [75]. Many antioxidant enzymes and catalysts involved in electron transfer reactions require iron as a cofactor [76]. Iron acts as a crucial cofactor in antioxidant enzymes such as CAT, POD, and APX, and reduced iron content leads to decreased enzyme activity [77]. Heme proteins (e.g., CAT and GPX enzymes) and iron-sulfur proteins (e.g., SOD isoenzymes) are two main groups of iron-containing proteins that can explain the high activity of antioxidant enzymes [78]. Sharma et al. [79] stated that iron plays a vital role in plant metabolism, including the stimulation of catalase enzymes. Ascorbate peroxidase is another enzyme that requires iron as a cofactor for its activity. Iron is located at the enzyme’s active site, where it facilitates the catalytic process. In Arabidopsis, the expression of stromal ascorbate peroxidase (s APX) and iron superoxide dismutase (Fe SOD) genes in chloroplasts decreased during iron deficiency, while the expression of other genes, such as catalase (CAT), was not significantly affected by iron deficiency [80, 81]. The results suggest that both nano-Fe and nano green-Fe significantly enhance the activity of antioxidant enzymes, likely by improving iron availability and supporting the plant’s oxidative stress defense mechanisms more effectively.

In current study, applying nano-green Fe significantly increased nitrogen concentration (Fig. 6A). This positive effect of iron application on nitrogen absorption in plants is likely due to iron’s essential role in nitrogen metabolism. Iron functions as a metal cofactor for key enzymes involved in nitrogen metabolism, including nitrate reductase, nitrite reductase, and glutamate synthase. When iron is deficient, the activity of these enzymes decreases, impairing nitrogen metabolism and consequently reducing nitrogen uptake [82]. Previous studies have shown that applying iron fertilizers can enhance several plant metabolic processes, including auxin biosynthesis, chlorophyll production, and the activities of critical enzymes such as phosphoenolpyruvate carboxylase and ribulose bisphosphate carboxylase, which contribute to the improved uptake efficiencies of nitrogen and phosphorus [83]. Additionally, iron availability affects soil microbial activity related to nitrogen fixation by free living N₂ fixers and regulates gene expression for nitrogen uptake and assimilation pathways, including nitrate transporters and nitrogen-assimilating enzymes [84,85,86]. As a result, higher iron availability is associated with enhanced nitrogen uptake (Fig. 6A), highlighting iron’s role in facilitating nitrogen uptake and assimilation.

In this study, various iron sources application, particularly through iron nanoparticles, not only boosted nitrogen content but also elevated phosphorus and potassium content in the leaves (Fig. 6B, C). Nitrogen, phosphorus, and potassium are closely interlinked in plant metabolic pathways, where nitrogen plays a central role in driving the demand and utilization of phosphorus and potassium [78]. The phosphorus results align with those reported by Zahra et al. [87], who found higher phosphorus content in the aerial parts than in the roots of lettuce plants treated with Fe₃O₄ nanoparticles. In this study, leaf potassium content also increased with rising iron concentration (Fig. 6C). The positive effects of iron nanoparticles on potassium accumulation could be due to iron-dependent activation of NADPH oxidases, as these enzyme activities are essential for controlling intracellular K⁺ homeostasis via ROS-gated ion channels [88]. Thus, the observed increases in potassium uptake in this study may be partially attributed to enhanced nitrogen assimilation driven by iron application. This interconnected effect demonstrates how iron not only directly facilitates nitrogen metabolism, but also indirectly promotes a balanced uptake of phosphorus and potassium, which are essential for optimal growth [78, 89].

Iron is an essential component of Fe oxygen reduction protein in plants, which is involved in plant photosynthesis, nitrate reduction and biological nitrogen fixation [90]. Alkalinity, due to high pH and bicarbonate uptake, reduces available iron concentration in soil, iron mobility in plant roots, increases intracellular pH, and reduces physiological iron activity [91]. Bicarbonate causes iron precipitation within the plant tissue, rendering it inactive in the root due to tissue alkalization [91], In line with the present study’s results, Valipour et al. [92] observed that iron deficiency and bicarbonate treatment significantly reduced the leaf iron concentration in quince (Cydonia oblonga) seedlings compared to the control. Additionally, when hawthorn (Crataegus sp.) and quince rootstocks were exposed to bicarbonate, the leaf iron concentration decreased despite the presence of 50 µM Fe (III)-EDTA in the nutrient solution. Similarly, when grape cultivars were treated with 10 mM sodium bicarbonate, the total iron concentration in the leaves significantly decreased [93]. Our findings demonstrate that nano-Fe treatment leads to elevated iron concentrations in plant leaves (Fig. 6F), likely due to improved translocation and bioavailability of iron within leaf tissues following foliar application of nano-Fe. Nano-Fe, due to its small size and large surface area, directly reaches the leaves upon foliar application, enhancing leaf iron concentration effectively. This direct delivery bypasses soil-related limitations, ensuring that the iron remains available and active within the leaf tissues, ultimately boosting iron levels in the foliage. These findings are consistent with those of El-Nasr et al. [94], who found increased leaf iron content in pear seedlings due to foliar application of magnetite (Fe₃O₄) nanoparticles compared to control plants. In line with the present study’s results, Shakoor et al. [95] observed that the application of Fe₃O₄ nanoparticles significantly increased the leaf iron concentration in cherry radish (Raphanus sativus L.) compared to the control. Iron treatments led to a significant increase in copper concentration in the leaves (Fig. 6G). Iron can enhance copper uptake by stimulating specific enzymes involved in copper transport or increasing the expression of copper transport proteins. High levels of iron can also affect copper storage in plants through the formation of iron-copper complexes [96]. In rice (Oryza sativa L.), the application of Fe₂O₃ nanoparticles increased root copper content under cadmium toxicity conditions [97]. In this study, zinc and manganese contents decreased with increasing iron concentration (Fig. 6H, I). Iron and manganese exhibit antagonism; like iron, manganese is absorbable at pH 6 or lower and competes with iron for uptake by the roots [98]. Iron and zinc interact due to the chemical similarities between their divalent cations and their primary transporter proteins [99]. An antagonistic relationship between iron and zinc has been reported in many plants [100]. In soybean roots and leaves, numerous genes involved in zinc uptake and homeostasis are upregulated under iron deficiency conditions [101]. Iron and zinc are regulated by complex, interconnected homeostatic mechanisms that operate through multiple stage [102]. Iron can reduce zinc uptake in plants through various mechanisms: (1) Iron and zinc compete for uptake by root cells. When iron levels are high, they can inhibit zinc uptake by competing for similar transport pathways and binding sites in the root cell membrane (2) Iron can affect the expression of transport proteins responsible for zinc uptake. For instance, iron may regulate or compete with transporters like ZIP (Zinc-regulated, Iron-regulated Transporters) and other related proteins. Changes in expression or competition for these transport proteins can reduce zinc uptake efficiency [57, 103].

Conclusion

The study highlights the significant impact of different iron sources and concentrations on the physiological, biochemical, and growth indices of goji berry plants in alkaline soil conditions. Iron treatments, particularly nano green-Fe at 0.5 g L^-1, markedly improved various parameters, including leaf chlorophyll content, total soluble sugars in leaves and fruits, and antioxidant enzyme activities (catalase, guaiacol peroxidase, and ascorbate peroxidase). Furthermore, the application of nano green-Fe significantly enhanced the uptake of essential nutrients such as nitrogen, phosphorus, potassium, calcium, magnesium, iron, and copper, while having a complex influence on zinc and manganese levels. Principal Component Analysis (PCA) and heatmap analysis confirmed the positive correlation between iron treatments and the evaluated indices, demonstrating the potential of nano green-Fe as an effective iron source for optimizing goji berry growth and productivity in challenging soil conditions. These findings underscore the importance of selecting appropriate iron sources and concentrations to enhance the nutritional and biochemical quality of goji berries, contributing to better crop management practices and improved agricultural outcomes.