In situ, the rhizotron is divided in the middle with an aluminum frame to hold two trees per rhizotron.
Greenhouse acidification layout. Rhizotrons were arranged upright in a metal box at a 90° angle, and rhizotrons were covered in black fabric to avoid light exposure.
Images traced with Adobe Photoshop.
Effect of soil acidification on soil resistance.
Effects of soil pH on soil macronutrient concentration (phosphorus, potassium, calcium, magnesium, sulfur, sodium) on ‘Valencia’ sweet orange trees irrigated with water at different pH levels. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Mean soil macronutrient concentration ± standard error with the same letter is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
Effects of soil pH on soil micronutrient concentration (boron, zinc, manganese, iron, copper) on ‘Valencia’ sweet orange trees with different pH levels of irrigation water. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Mean soil micronutrient concentration ± standard error with the same letter is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
Correlation matrix of relationships among soil chemical properties (n = 40). Correlations with P > 0.01 are considered insignificant. Positive correlations are displayed in blue, and negative in red. Color intensity and the size of the circle are proportional to the correlation coefficients. On the right side of the correlogram, the legend color shows the correlation coefficients and the corresponding colors.
Effects of soil pH on tissue macronutrient concentration (nitrogen, phosphorus, potassium, calcium, magnesium, sulfur) in young, healthy ‘Valencia’ sweet orange trees with different pH levels of irrigation water. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Mean soil macronutrient concentration ± standard error with the same letters is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method. No significant differences in all tissue micronutrient concentrations were observed between treatments at all time points (Fig. 9).
Effects of soil pH on tissue micronutrient concentration (mg·kg−1) (boron, zinc, manganese, iron, copper) in young, ‘Valencia’ sweet orange trees with different pH levels of irrigation water. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Mean soil micronutrient concentration ± standard error with the same letters is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
Correlation matrix of relationships among tissue nutrient contents and soil pH (n = 40). Correlations with P > 0.01 are considered insignificant. Positive correlations are displayed in blue, and negative correlations in red. Color intensity and the size of the circle are proportional to the correlation coefficients. On the right side of the correlogram, the legend color shows the correlation coefficients and the corresponding colors.
Correlation matrix of relationships between soil nutrients and tissue nutrient contents (n = 40). Correlations with P > 0.01 are considered insignificant. Positive correlations are displayed in blue, and negative correlations in red. Color intensity and the size of the circle are proportional to the correlation coefficients. On the right side of the correlogram, the legend color shows the correlation coefficients and the corresponding colors.
Changes in leachate pH over time during the three-month study. Error bars denote standard deviation of 10 replications.
(A) Root tips, (B) total root diameter (cm), and (C) total root length (cm) comparisons grouped in four time points in months (0, 1, 2, 3) for four treatments. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Bars represent the standard error from the mean (n = 40) ± standard error with the same letters are not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
(A) Root density (g/cm3), (B) root biomass (g/m2), and (C) total dry root mass (grams). Comparisons were grouped for four treatments. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Bars represent the standard error from the mean (n = 40) ± standard error with the same letters is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
(A) Root length density (cm3), (B) root surface area (cm2), and (C) root volume (cm3) comparisons grouped in four time points by month (0, 1, 2, 3) for four treatments. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Bars represent the standard error from the mean (n = 40) ± standard error with the same letters is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
Monthly (A) cumulative root growth (cm), (B) cumulative root dieback (cm), and (C) living root length (cumulative root growth – cumulative dieback). Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Bars represent the standard error from the mean (n = 40), and different letters represent significant differences at P ≤ 0.05.
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The production of citrus, a dominant fruit crop globally, is declining due to biotic constraints such as Huanglongbing (HLB) and abiotic stresses such as low or high soil pH. This study aimed to investigate the influence of soil pH on citrus root morphology, nutrient uptake dynamics, and overall root health. Forty ‘Valencia’ sweet orange [Citrus sinensis (L.) Osbeck] trees grafted on Swingle citrumelo rootstock [C. paradisis × Poncirus trifoliata (L.) Raf] were divided into four groups by pH treatment (n = 10). Trees planted in rhizotron boxes were irrigated three days a week with four different water pH levels: 5.5, 6.5, 7.5, and 8.5. Soil acidity and alkalinity were routinely monitored with pH probes. The concentration of essential macronutrients and micronutrients from the soil, plant tissue, and leachates was also analyzed monthly to evaluate nutrient uptake efficiency. Parameters such as root length, root surface area, and root diameter were measured to assess the morphological changes in citrus tree roots under different pH treatments. After irrigation, soil pH on treatment with pH = 5.5 decreased drastically since sandy soils acidify more quickly. Soil pH levels for treatments irrigated with solutions at pH 6.5 and 7.5 consistently maintained near-neutral levels, with the former gradually decreasing soil pH over time and then later increasing the soil pH to alkaline levels. The soil P and S concentrations were high at pH = 5.5, contrary to the Mg and Ca concentrations, which were low at the same pH level. Soil pH showed a significant and negative correlation with S, P, and Fe, indicating a decrease in these soil nutrients as soil pH decreased and a nonsignificant positive correlation with Cu. At pH = 5.5, there was significantly higher root growth, which indicates that acidic soils (∼pH = 5.5) can enhance root growth in citrus trees. Acidic soils stimulate root growth, particularly around a pH of 5.5; citrus roots exhibit remarkable resilience and internal compensation mechanisms in response to pH changes. Optimizing soil pH and nutrient management can mitigate the impacts of HLB and promote the resilience of citrus trees. Trees irrigated at pH of 8.5 showed a trend of fewer living roots and lower cumulative root growth, emphasizing the possibility of root damage due to high soil pH.
Citrus is a dominant fruit crop globally, grown in more than 130 countries, including Brazil, China, and the United States (Ladaniya 2008; Spreen et al. 2020), with a significant presence in fresh and processed juice markets (Cuenca et al. 2018), economic value, and nutritional importance (Liu et al. 2012). Globally, citrus production has grown steadily, reaching more than 105 million metric tons annually in the early 2000s (FAOSTA 2019). However, this growth faced many natural challenges in the past two decades due to biotic constraints such as diseases and abiotic stresses such as nutrient disorders and soil pH (Febres et al. 2011; Luckstead and Devadoss 2021). These challenges can significantly affect fruit yield and quality and reduce productivity and fruit quality (Gong and Liu 2013; Gottwald 2007). Although Florida has suitable conditions for citrus production and sandy, well-drained, fertile soils with low lime content and optimal soil pH (Roussos 2016), production is declining due to the devastating Huanglongbing (HLB), commonly known as citrus greening disease, which is caused by the phloem-limited bacterium Candidatus Liberibacter asiaticus (CLas) (US Department of Agriculture 2022). Management strategies, including regional disease management and cultural practices, have been implemented in some areas to mitigate the impact of the disease.
In Florida, elevated soil pH due to high bicarbonates in irrigation water or the presence of calcareous soils has been a concern (Morgan and Graham 2019). In recent years, lower soil pH or acidification to bring the pH to the desired range has been a critical management practice in sustaining citrus production. Soil acidification is the soil accumulation of hydrogen (H+) ions. Various processes add H+ ions to the soil system, including acid rain, the application of acidifying fertilizers such as elemental sulfur (S), urea, or ammonium (NH4+) salts, the growth of legumes such as clover, nutrient uptake by crops and root exudates, and the mineralization of organic matter (Goulding 2016; Wallace 1994; Weil and Brady 2017). Generally, soil acidification can lead to low soil fertility, elevated toxic heavy metals such as aluminum (Al), lead (Pb), cadmium (Cd), and manganese (Mn), and reduced crop yields (Goulding 2016). It also affects the availability of nutrients in the soil, leading to nutritional imbalances in plants (Adams 1981; Rorison 1980). Studies dating back to the 1950s have explored the positive impacts of sulfur-based soil acidification amendments, such as sulfuric acid, on crop yields and soil properties. Christensen and Lyerly (1954) found that soil acidification can improve physical conditions and yields, particularly in certain crops such as cotton, because sulfuric acid can reduce ammonia volatilization losses (Miyamoto et al. 1975). Acidification of irrigation water can improve soil properties and nutrient availability (El-Hady and Shaaban 2010; Khorsandi 1994) and enhance fruit quality (Metochis 1989).
The positive impacts of soil acidification on citrus trees, particularly those affected by HLB, have been found to alter the pH tolerance of citrus rootstocks (Graham 1990), making acid-preferring rootstocks more sensitive to high pH and neutral to mildly acidic rootstocks more sensitive to low pH (Zekri and Parsons 1992; Zhu et al. 2021). This shift in pH tolerance may be influenced by the rootstock and scion combination, with tolerant scion cultivars leading to lower bacterial numbers and less damage (Albrecht and Bowman 2019). The interaction between different bacterial species can also moderate pH and antibiotic tolerance (Aranda-Díaz et al. 2020). CLas infection has been shown to impact citrus root metabolome and microbiome, with variety-specific effects (Padhi et al. 2019). Overall, acidifying irrigation water and soil amendment with elemental sulfur (S) improve citrus production (Morgan and Graham 2019).
Plant roots are essential for plant survival, nutrient and water acquisition, hormone synthesis, and anchoring plants to the soil (Bertin et al. 2003; Schiefelbein and Benfey 1991). They also release exudates that influence the soil microbial community, plant-microbe interactions, and the chemical and physical properties of the soil (Bais et al. 2006; More et al. 2019). The rhizosphere chemistry, including pH and redox potential, is also affected by root growth and exudates, influencing nutrient availability (Marschner et al. 1987; Neumann and Römheld 2011). Citrus-specific factors that can influence root behavior are crucial variables in exploring the physiological response of citrus tree roots. Acidification of irrigation water and soil can mitigate the decline in root density and improve citrus production (Li et al. 2020; Morgan and Graham 2019).
Thus, this study was conducted under greenhouse conditions to 1) determine the impact of soil pH on nutrient uptake efficiency, 2) analyze the impact of soil pH on the residual concentration of nutrients in the soil and leachate, and 3) characterize and quantify root morphology variations to determine the optimum soil pH range that promotes healthy root development in citrus plants. We hypothesized that 1) soil acidification with irrigation water pH will significantly impact soil pH; 2) nutrient uptake efficiency in citrus trees varies significantly with soil pH; and 3) soil pH significantly influences citrus root morphology, with acidic or alkaline conditions leading to measurable changes in root growth.
This project was carried out in a greenhouse at the University of Florida Institute of Food and Agricultural Sciences (UF/IFAS) Citrus Research and Education Center (CREC) (28°06′11.4″N, 81°42′47.5″W) for 3 months (Apr 2023 to Jul 2023) to determine the short-term influence of soil pH on citrus root morphology and nutrient uptake dynamics. Forty young 2-year-old ‘Valencia’ sweet orange trees [Citrus sinensis (L.) Osbeck] on ‘Swingle’ citrumelo rootstock [C. paradisis × Poncirus trifoliata (L.) Raf], obtained from a disease-free nursery were used. Trees were transplanted into rhizotrons. Rhizotrons (54 cm high, 40 cm wide, 3.0 cm thick) were made with transparent acrylic sheets bolted aluminum frames to allow root observations and sampling. Each rhizotron was divided vertically in half to hold two trees, allowing for different soil environments (Fig. 1). Neoprene rubber seals were used between the acrylic and aluminum frames to prevent soil and water leakage. Each aluminum frame had 33 screw holes on each side and several tiny holes on the bottom for water drainage.
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
Each rhizotron was filled with a layer of black aquarium stones at the bottom to ensure proper drainage and reduce sand loss, followed by Candler fine sand, filled to ∼4 cm from the top. Sand was obtained from a Polk County, FL, USA, grove and autoclaved twice to eliminate Phytophthora spp. and nematodes contamination. Rhizotrons were arranged upright in a metal box and covered in black fabric to avoid light exposure on the roots and suppress algal growth (Fig. 2). Treatments were as follows: Treatment 1, irrigation water with pH = 5.5; Treatment 2, irrigation water with pH = 6.5; Treatment 3, irrigation water with pH = 7.5; and treatment 4, Irrigation water with pH = 8.5. No fertilizer was added to these trees. Rhizotrons were arranged in two blocks using a completely randomized design (CRD); each metal box was used as a block. Each metal box had 10 rhizotrons, with two trees each, making 20 trees per box. After planting, trees were irrigated three times weekly and grown for 2 weeks to establish, acclimate, and produce new root growth before collecting data.
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
Sulfuric acid (H2SO4) was used to acidify irrigation water, pH 5.5 and 6.5, tap water was used for pH of 7.5, and sodium hydroxide (NaOH) for alkaline irrigation water, pH of 8.5). Leachates were collected separately from both sides of all rhizotrons into bottles connected to the bottoms of rhizotrons with tubes and valves (Fig. 2). Each side of the rhizotron was irrigated with 500 mL water every irrigation day for sufficient leachate collection. At the bottom of each tank, drainage tubes were connected to cylinders to collect the surplus drainage water and determine potential nitrate leaching. Extra caution was taken in all processes, and personal protective equipment such as safety glasses, gloves, and laboratory aprons were used throughout the research.
Data were collected from all 40 trees, 10 trees per treatment.
Soil pH was routinely monitored using pH probes (Extech PH110) and tested monthly by collecting soil samples. Approximately 20 g of soil and 1 root (∼5 cm in length and 1 mm in diameter) were collected monthly for analysis and determinations. Rhizotrons were opened monthly on side B (the side we were not tracing root growth) to collect the soil samples. Mehlich III method (Wolf and Baker 1985) was used to evaluate the soil samples and analyzed using the inductively coupled plasma–atomic emission spectroscopy method at Waters Agricultural Laboratories, Camilla, GA, USA. At least 20 fully expanded leaves of ∼4 to 6 months were randomly collected across the four quadrants (northwest, northeast, southeast, and southeast) of the tree. Tissue samples were washed in a nonionic detergent solution, followed by several water rinses, and then dried at 65 °C until constant weight. Dried tissue samples were ground to pass through a 0.3-mm diameter sieve and then shipped to Waters Agricultural Laboratories Inc. (Camilla, GA, USA), where tissue nutrient analyses were conducted.
Root systems were photographed weekly with a Canon EOS Rebel T4i DSLR camera at the same distance and zoom each time. A ruler was placed behind the rhizotron for calibration. Images were imported into Adobe Photoshop (Adobe Systems, San Jose, CA, USA). Existing roots were hand-traced as a separate Photoshop layer on the initial image (Fig. 3). Tracings were labeled with a time stamp and copied into the following images, where new root growth at each time was traced in separate layers. Root dieback was also traced in a separate layer for each image at each time. Roots were classified as dead when they appeared reddish brown to dark brown. Each biweekly tracing of new growth and dieback was exported as a separate JPEG image and quantified using WinRHIZOPro software (Regent Instruments Canada Inc., Quebec City, Canada).
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
The total mass of roots of each half of the rhizotron was measured to the specific soil volume of the rhizotron. Root density measures the total root mass per unit volume of soil. The root density of each tree was calculated using the following formula: [1]
where RD is the root density in grams per unit volume (g/cm3), RM is the total root mass (g), and VS is the volume of soil (cm3) (De Baets et al. 2006).
Forty collection leachate containers were set up under the rhizotron blocks, two leachates per rhizotron representing one leachate for each tree (Fig. 2). Leachates were collected every 7 d, and water nutrient concentration and pH were measured.
Data on soil and tissue analysis, soil pH, and root dynamics (cumulative root growth, dieback, and live root length) were evaluated and log-transformed to meet assumptions of normality and homogeneity of variance before statistical analysis. Analysis of variance (ANOVA) was used to determine whether statistical significance exists between the four treatments. The F statistic was calculated to measure how the group means differ. The F statistic was used to determine whether the observed differences were statistically significant, with P ≤ 0.05. Tukey’s honestly significant difference was used to conduct a post hoc test to determine the specificity of the different group means.
Preliminary soil pH, soil, and leaf nutrient analyses were performed before the application of treatments (Tables 1 and 2). On average, the soil showed a pH of about 6.7.
The pH values of irrigation water sampled over the three months (14 weeks) are given in the summary of treatments. As expected, irrigating the soil with water at varying pH changed the soil acidity and alkalinity. The lowest pH value for irrigation water was 5.5 and the highest was 8.5; the soil pH values generally ranged from 4 to 9 across all treatments in the 3 months (Fig. 4). The average soil pH before irrigation was 6.5 to 6.8 across all treatments. After irrigation, soil pH on treatment pH = 5.5 decreased drastically because sandy soils acidify quickly. Water pH of 6.5 and 7.5 consistently maintained near-neutral levels, with water pH of 6.5 gradually decreasing the soil pH over time and a pH of 7.5 increasing the soil pH to alkaline levels. Soil irrigated at pH of 8.5 maintained alkaline levels throughout the experiment (Fig. 4).
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
No significant differences were observed between macronutrients at the beginning of the study. A significant interaction between soil nutrient concentration, treatment, and their relationship to pH was observed across all soil macronutrients from the first to the third month. High phosphorus (P) concentration at pH = 5.5 was observed in the third month, and a similar pattern was observed in sulfur (S) concentration where pH = 5.5 had significantly higher S concentrations in the second and third months. Conversely, soil calcium (Ca) content at pH = 5.5 was significantly lower from the second to the third month. As the pH level increased, soil magnesium (Mg) concentration significantly increased from the second to the third month. Sodium (Na) levels decreased over time across all treatments, although a higher concentration was seen at pH = 8.5. At the beginning of the study, no significant differences were observed between all treatments and soil micronutrients, as expected (Fig. 5).
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
A significant interaction was observed for zinc (Zn), Mg, and iron (Fe) micronutrients in the second and third months. Low Zn and Mg content was observed at pH = 5.5 in the third month, and the opposite was observed for Fe concentration with a significantly higher Fe concentration (Fig. 6).
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
The soil chemical properties correlation coefficients for pH, macronutrients, and micronutrients were determined (Fig. 7). Soil pH showed a significant positive correlation with Ca, Mn, Zn, Na, and Mg, indicating an increase or decrease and synergism in these soil nutrients as pH increased or decreased and a nonsignificant positive correlation with K, B, and Cu. Conversely, soil pH exhibited a significant negative correlation with S, P, and Fe, indicating their sensitivity to changes in soil pH. S, P, and Fe tend to be higher as pH decreases. S and P exhibited a significant negative correlation with Mg, Mn, Ca, and Zn. Na showed a significant negative correlation with S, Cu, P, and Fe, whereas B exhibited a significant negative correlation with Cu, P, and Fe (Fig. 7).
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
High soil P concentration in soil with a pH of 5.5 can be attributed to various factors (Fig. 5). Phosphorus fixation depends on soil pH. In acidic soils, orthophosphate (H2PO4–) is the preferred form for plant uptake and is more abundant (Zhang et al. 2021), and phosphate adsorbed on Fe- or Al-oxide minerals is higher in acidic soils. This result is consistent with the findings of other studies (Beauchemin et al. 2003), whose analysis indicated that the proportion of phosphate that was adsorbed on Fe- or Al-oxides was higher in acidic soil samples with pH ranging from 5.5 to 6.2, compared with the alkaline soils. The overall effect of soil pH on P availability depends on the balance between P solubility, P sorption/desorption dynamics, and other multivalent cations, such as Al and Ca, that can interact with phosphate. The negative relationship between soil pH and P availability and the positive relationship between soil pH and Ca and Mg availability can be attributed to various factors. As soil pH increases, the solubility of P decreases due to its conversion into insoluble forms such as Ca and Mg phosphates (Barrow and Hartemink 2023). This phenomenon is particularly significant in calcareous soils with high pH, where P fertilizer efficiency is limited, impacting crop productivity.
Additionally, increasing soil pH can affect the degree of P saturation and water-extractable P, influencing environmental risks associated with P loss (Brownrigg et al. 2022). Conversely, the availability of Ca and Mg tends to increase with rising pH, potentially enhancing soil nutrient retention and buffering capacity. Staugaitis and Rutkauskiene (2010) also identified a correlation between Mg concentration and soil pH. Results proved that S is effective in the remediation of alkaline soil and is predominantly used as a soil amendment in high-pH soil because it is known for reducing soil pH (Morgan and Mahmoud 2014). S amendments reduce soil pH and increase P availability in high-pH sandy soils (Morgan and Mahmoud 2014); hence, a positive correlation exists between soil P and S (Fig. 7).
No significant differences in all tissue macronutrient concentrations were observed between treatments in the first and second months (Fig. 8). In the third month, significantly higher Mg content was observed at pH levels of 6.5, 7.5, and 8.5 compared with pH 5.5. Conversely, S was significantly higher in the third month at a pH of 5.5 (Fig. 8).
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
No significant differences in all tissue micronutrient concentrations were observed between treatments at all time points (Fig. 9).
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
Figure 10 shows the correlation matrix analysis for tissue nutrients and soil pH. A nonsignificant correlation was observed between soil pH and all tissue nutrient contents. A significant positive correlation was observed between Cu, Fe, Mn, B, S, Zn, and Ca. Magnesium, Zn, N, P, and K showed a negative correlation.
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
Magnesium uptake by plants is influenced by soil factors such as the amount, concentration, and movement of Mg in the soil and the soil’s capacity to replenish Mg (Martínez et al. 2002). As soil pH increases, soil Mg content increases (Fig. 5), which, in turn, tends to influence higher Mg concentrations in the plant tissues, hence the significant positive correlation between soil Mg concentration and tissue Mg concentration (Fig. 11) (Martínez et al. 2002). High tissue S concentration at pH of 5.5 in soils was a result of increased S availability in acidic soil and uptake by plants in acidic conditions (Figs. 5 and 8) (Wang et al. 2020).
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
The pH values of irrigation waters sampled over the 3 months (14 weeks) are given in the summary of treatments (Fig. 12). All leachates maintained a near-neutral pH on average. The pH 5.5 treatment had the lowest average and demonstrated a pH reduction trend by week 12 (Fig. 12).
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
Calcium, Mg, and Zn concentrations of leachates at a pH of 5.5 were relatively high throughout the experiment (Supplemental Figs. 1 and 2) but very low in Week 3, whereas leachate pH was highly acidic. An opposite pattern was also observed for Na concentration, which had a high concentration at pH 8.5.
In soil, the leachate is a complex, sometimes toxic liquid produced when water drains or “leaches” through the soil and beyond the root zone, picking up dissolved and suspended materials (Youcai 2018). Leachate typically contains high concentrations of organic matter, inorganic ions, and heavy metals. Composition varies depending on factors including the presence and mobility of water in the soil. Analyzing the nutrient concentration and pH provides insights into the efficiency of best management practices, potential nutrient losses, and optimizing irrigation (Kadyampakeni et al. 2021).
In this study, most of the minerals were indetectable in the leachates, suggesting the soil was absorbing or immobilizing those nutrients. However, Ca proved to solubilize and increase in concentration at lower pH, while maintaining a lower concentration at neutral pH. Similar results were shown by Blythe et al. (2006), who found low Ca content associated with neutral pH. Similarly to Ca, Zn had increased concentration and mobility at lower soil pH (Chen et al. 2012; Zheng et al. 2012). There was a difference in leachate pH and nutrients concentration in week 3, which was significantly different from the earlier weeks, whereas no discernable pattern or trend was determined. Contrary to our results, Kelly and Strickland (1987) observed increased NH4+, PO43–, and K concentrations in response to simulated acidic precipitation.
There were significantly more root tips in month 1, but no significant differences were observed across treatments (Fig. 13A). There was a significant difference in tree root length at the beginning of the study compared with other time points, possibly because the roots were still emerging and very young (Fig. 13C). No significant differences were observed in tree root diameter between treatments at each month (Fig. 13B). No significant differences were observed in root density, total dry root mass, and root biomass between all treatments (Fig. 14A–C).
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
No significant differences were observed in root length density, root surface area, and root volume between all treatments (Fig. 15A–C). The overall trends in cumulative root growth showed almost the same average root growth at the beginning for all treatments, then a significant increase in growth at pH of 5.5. At the same time, pH levels of 6.5 and 8.5 maintained limited root growth throughout the experiment (Fig. 16A). Water pH levels 5.5, 6.5, and 8.5 had higher cumulative root dieback, whereas a pH of 7.5 maintained very low root dieback throughout the experiment (Fig. 16B). Living roots at pH 6.5 were approaching the amount of living roots at pH 5.5 in month 3, proposing that this trend could continue if this study were carried out for a long period of time. Overall, living roots were more abundant at a pH of 5.5 throughout the experiment.
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
Citation: HortScience 60, 5; 10.21273/HORTSCI18486-25
The relationship between soil pH and citrus tree roots is complex, especially now in the era of HLB. At pH of 5.5, there was significantly higher root growth, which indicates that acidic soils (pH ∼5.5) can enhance root growth in citrus trees. Research on the acidification of irrigation water to maintain acidic pH levels (Morgan 2019; Morgan and Graham 2019) has shown that soil acidity can increase root growth and nutrient uptake. Lin and Myhre (1990) also found that citrus root growth is influenced by low soil pH, which enhances citrus root density. Trees irrigated at pH of 8.5 showed a trend of fewer living roots and lower cumulative root growth, emphasizing the possibility of root damage due to high soil pH. Ghimire et al. (2020) investigated the impact of irrigation water pH on citrus trees and found that higher pH levels decreased the performance of healthy and HLB-affected trees.
This research demonstrates the intricate relationship between soil pH, nutrient availability, and root dynamics in citrus trees. In as much as there was no fertilizer application in this experiment, the findings highlight the significant influence of soil acidity on nutrient concentrations, with pH levels impacting the solubility and mobility of key soil nutrients essential for tree health. Moreover, the study underscores the interactions among soil nutrients, revealing synergistic relationships and potential limitations in nutrient availability under varying pH conditions. This study on root growth dynamics offers valuable insights into the adaptive responses of citrus trees to soil acidification. At a pH of 5.5, there was significantly higher root growth, which indicates that acidic soils (pH ∼5.5) can enhance root growth of citrus roots. The pH level of 8.5 showed a trend of fewer living roots and lower cumulative root growth, emphasizing the possibility of root damage due to high soil pH. By elucidating the complex interplay between soil pH, root physiology, and citrus health, this research provided a foundation for informed decision-making in citrus production. Strategies aimed at optimizing soil pH and nutrient management can mitigate the impacts of HLB and promote the resilience of citrus trees. Continuing research efforts are needed to deepen our understanding of the underlying mechanisms, such as hormonal signaling driving the physiological responses, and to develop tailored management strategies that effectively support citrus tree health and productivity in endemic HLB environments.
In situ, the rhizotron is divided in the middle with an aluminum frame to hold two trees per rhizotron.
Greenhouse acidification layout. Rhizotrons were arranged upright in a metal box at a 90° angle, and rhizotrons were covered in black fabric to avoid light exposure.
Images traced with Adobe Photoshop.
Effect of soil acidification on soil resistance.
Effects of soil pH on soil macronutrient concentration (phosphorus, potassium, calcium, magnesium, sulfur, sodium) on ‘Valencia’ sweet orange trees irrigated with water at different pH levels. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Mean soil macronutrient concentration ± standard error with the same letter is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
Effects of soil pH on soil micronutrient concentration (boron, zinc, manganese, iron, copper) on ‘Valencia’ sweet orange trees with different pH levels of irrigation water. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Mean soil micronutrient concentration ± standard error with the same letter is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
Correlation matrix of relationships among soil chemical properties (n = 40). Correlations with P > 0.01 are considered insignificant. Positive correlations are displayed in blue, and negative in red. Color intensity and the size of the circle are proportional to the correlation coefficients. On the right side of the correlogram, the legend color shows the correlation coefficients and the corresponding colors.
Effects of soil pH on tissue macronutrient concentration (nitrogen, phosphorus, potassium, calcium, magnesium, sulfur) in young, healthy ‘Valencia’ sweet orange trees with different pH levels of irrigation water. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Mean soil macronutrient concentration ± standard error with the same letters is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method. No significant differences in all tissue micronutrient concentrations were observed between treatments at all time points (Fig. 9).
Effects of soil pH on tissue micronutrient concentration (mg·kg−1) (boron, zinc, manganese, iron, copper) in young, ‘Valencia’ sweet orange trees with different pH levels of irrigation water. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Mean soil micronutrient concentration ± standard error with the same letters is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
Correlation matrix of relationships among tissue nutrient contents and soil pH (n = 40). Correlations with P > 0.01 are considered insignificant. Positive correlations are displayed in blue, and negative correlations in red. Color intensity and the size of the circle are proportional to the correlation coefficients. On the right side of the correlogram, the legend color shows the correlation coefficients and the corresponding colors.
Correlation matrix of relationships between soil nutrients and tissue nutrient contents (n = 40). Correlations with P > 0.01 are considered insignificant. Positive correlations are displayed in blue, and negative correlations in red. Color intensity and the size of the circle are proportional to the correlation coefficients. On the right side of the correlogram, the legend color shows the correlation coefficients and the corresponding colors.
Changes in leachate pH over time during the three-month study. Error bars denote standard deviation of 10 replications.
(A) Root tips, (B) total root diameter (cm), and (C) total root length (cm) comparisons grouped in four time points in months (0, 1, 2, 3) for four treatments. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Bars represent the standard error from the mean (n = 40) ± standard error with the same letters are not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
(A) Root density (g/cm3), (B) root biomass (g/m2), and (C) total dry root mass (grams). Comparisons were grouped for four treatments. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Bars represent the standard error from the mean (n = 40) ± standard error with the same letters is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
(A) Root length density (cm3), (B) root surface area (cm2), and (C) root volume (cm3) comparisons grouped in four time points by month (0, 1, 2, 3) for four treatments. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Bars represent the standard error from the mean (n = 40) ± standard error with the same letters is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
Monthly (A) cumulative root growth (cm), (B) cumulative root dieback (cm), and (C) living root length (cumulative root growth – cumulative dieback). Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Bars represent the standard error from the mean (n = 40), and different letters represent significant differences at P ≤ 0.05.
Contributor Notes
We acknowledge with thanks the funding received from the Citrus Research and Development Foundation (Grant No. P0196897-209-2200) and the US Department of Agriculture Hatch Project (No. 006185). The support of members of the Water and Nutrient Management Laboratory at the Citrus Research and Education Center in Lake Alfred, FL, USA, during data collection and application of treatments is also gratefully acknowledged.
In situ, the rhizotron is divided in the middle with an aluminum frame to hold two trees per rhizotron.
Greenhouse acidification layout. Rhizotrons were arranged upright in a metal box at a 90° angle, and rhizotrons were covered in black fabric to avoid light exposure.
Images traced with Adobe Photoshop.
Effect of soil acidification on soil resistance.
Effects of soil pH on soil macronutrient concentration (phosphorus, potassium, calcium, magnesium, sulfur, sodium) on ‘Valencia’ sweet orange trees irrigated with water at different pH levels. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Mean soil macronutrient concentration ± standard error with the same letter is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
Effects of soil pH on soil micronutrient concentration (boron, zinc, manganese, iron, copper) on ‘Valencia’ sweet orange trees with different pH levels of irrigation water. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Mean soil micronutrient concentration ± standard error with the same letter is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
Correlation matrix of relationships among soil chemical properties (n = 40). Correlations with P > 0.01 are considered insignificant. Positive correlations are displayed in blue, and negative in red. Color intensity and the size of the circle are proportional to the correlation coefficients. On the right side of the correlogram, the legend color shows the correlation coefficients and the corresponding colors.
Effects of soil pH on tissue macronutrient concentration (nitrogen, phosphorus, potassium, calcium, magnesium, sulfur) in young, healthy ‘Valencia’ sweet orange trees with different pH levels of irrigation water. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Mean soil macronutrient concentration ± standard error with the same letters is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method. No significant differences in all tissue micronutrient concentrations were observed between treatments at all time points (Fig. 9).
Effects of soil pH on tissue micronutrient concentration (mg·kg−1) (boron, zinc, manganese, iron, copper) in young, ‘Valencia’ sweet orange trees with different pH levels of irrigation water. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Mean soil micronutrient concentration ± standard error with the same letters is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
Correlation matrix of relationships among tissue nutrient contents and soil pH (n = 40). Correlations with P > 0.01 are considered insignificant. Positive correlations are displayed in blue, and negative correlations in red. Color intensity and the size of the circle are proportional to the correlation coefficients. On the right side of the correlogram, the legend color shows the correlation coefficients and the corresponding colors.
Correlation matrix of relationships between soil nutrients and tissue nutrient contents (n = 40). Correlations with P > 0.01 are considered insignificant. Positive correlations are displayed in blue, and negative correlations in red. Color intensity and the size of the circle are proportional to the correlation coefficients. On the right side of the correlogram, the legend color shows the correlation coefficients and the corresponding colors.
Changes in leachate pH over time during the three-month study. Error bars denote standard deviation of 10 replications.
(A) Root tips, (B) total root diameter (cm), and (C) total root length (cm) comparisons grouped in four time points in months (0, 1, 2, 3) for four treatments. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Bars represent the standard error from the mean (n = 40) ± standard error with the same letters are not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
(A) Root density (g/cm3), (B) root biomass (g/m2), and (C) total dry root mass (grams). Comparisons were grouped for four treatments. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Bars represent the standard error from the mean (n = 40) ± standard error with the same letters is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
(A) Root length density (cm3), (B) root surface area (cm2), and (C) root volume (cm3) comparisons grouped in four time points by month (0, 1, 2, 3) for four treatments. Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Bars represent the standard error from the mean (n = 40) ± standard error with the same letters is not significantly different. Significant differences were calculated at P ≤ 0.05 using Tukey’s honestly significant difference method.
Monthly (A) cumulative root growth (cm), (B) cumulative root dieback (cm), and (C) living root length (cumulative root growth – cumulative dieback). Treatments: T1 (pH = 5.5 irrigation water), T2 (pH = 6.5 irrigation water), T3 (pH = 7.5 irrigation water), and T4 (pH = 8.5 irrigation water). Bars represent the standard error from the mean (n = 40), and different letters represent significant differences at P ≤ 0.05.
