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Article

Impact of Ground Applied Micronutrients on Root Growth and Fruit Yield of Severely Huanglongbing-Affected Grapefruit Trees

by
Lukas M. Hallman
1,
Davie M. Kadyampakeni
2,
Rhuanito Soranz Ferrarezi
3,
Alan L. Wright
4,
Mark A. Ritenour
1,
Evan G. Johnson
5 and
Lorenzo Rossi
1,*
1
Indian River Research and Education Center, Horticultural Sciences Department, Institute of Food and Agricultural Sciences, University of Florida, Fort Pierce, FL 34945, USA
2
Citrus Research and Education Center, Soil, Water and Ecosystem Sciences Department, Institute of Food and Agricultural Sciences, University of Florida, Lake Alfred, FL 33850, USA
3
Department of Horticulture, University of Georgia, Athens, GA 30602, USA
4
Indian River Research and Education Center, Soil, Water and Ecosystem Sciences Department, Institute of Food and Agricultural Sciences, University of Florida, Fort Pierce, FL 34945, USA
5
Citrus Research and Education Center, Plant Pathology Department, Institute of Food and Agricultural Sciences, University of Florida, Lake Alfred, FL 33850, USA
*
Author to whom correspondence should be addressed.
Horticulturae 2022, 8(9), 763; https://doi.org/10.3390/horticulturae8090763
Submission received: 25 July 2022 / Revised: 17 August 2022 / Accepted: 24 August 2022 / Published: 25 August 2022
(This article belongs to the Section Fruit Production Systems)
Browse Figures
Review Reports Versions Notes

Abstract

:
Citrus greening (huanglongbing, HLB) is a bacterial disease caused by Candidatus Liberibacter asiaticus (CLas) and vectored by the Asian citrus psyllid (Diaphorina citri) (ACP). No cure is yet available, and as a result, increased fertilizer applications continue to be a major management method used to prolong the productive life of affected trees. However, questions remain regarding proper fertilizer rates and in what form these nutrients should be applied to increase tree health and yield. Therefore, the goal of this study was to identify optimum micronutrient application forms and rates to increase root growth, canopy size and fruit yield as well as reduce leaf and root CLas cycle threshold (Ct) value of HLB-affected grapefruit trees (Citrus × paradisi Macfad). A large-scale field study consisting of six-year-old HLB-affected ‘Ruby Red’ grapefruit trees grafted on ‘sour orange’ (Citrus × aurantium) rootstock was conducted in the Florida Indian River District. Eight different treatments consisting of four granular and four liquid fertilizers with 1×, 2×, and 4× the current University of Florida, Institute of Food and Agricultural Sciences recommended rates of boron, zinc, manganese, and iron were applied for three times a year (granular), biweekly (liquid), or weekly (liquid), for three years. Root density, length and diameter, root, and leaf CLas Ct value, canopy volume, visual HLB symptoms, and fruit yield were measured throughout the study. Regardless of treatment, tree health declined over time, and no significant impact on severely HLB-affected grapefruit tree health was detected within the three-year time frame of the study. This was the first study to solely compare the impact of ground applied micronutrient application rates and methods on HLB-affected grapefruit tree health in Florida’s Indian River District. More time would be needed to determine the potential benefits of increased micronutrient performance of HLB-affected grapefruit trees.

1. Introduction

During the past 15 years, citrus greening (huanglongbing, HLB) has continued to hinder Florida’s citrus industry. Vectored by the invasive Asian Citrus Psyllid (Diaphorina citri) (ACP), the phloem-limited Gram-negative bacteria Candidatus Liberibacter asiaticus (CLas), which causes HLB in Florida, has rapidly spread around the citrus growing counties in the state [1,2]. In the year before the first reported case of HLB (2003/2004 crop year), the state produced 13,045,000 tons of citrus. In the 2020/2021 season, Florida only produced 2,592,000 tons of citrus [3]. This roughly 80% overall decrease in production has cost about USD 1 billion a year and a loss of an estimated 5000 jobs in the past five years [4].
The decrease in citrus production is particularly impactful for the Indian River District (IRD). This 320 km-long strip along Florida’s central east coast is renowned for its high-quality grapefruit (Citrus × paradisi Macfad) [5]. The IRD continues to be the epicenter of Florida’s grapefruit industry, producing 80% of the state’s grapefruit during the 2019–2020 season [6]. Due to grapefruit higher sensitivity to HLB compared to other citrus species grapefruit production has declined by roughly 85% from 2003/2004 to 2019/2020 [2,7] [8].
The disease impacts the roots, leaves, and fruit leading to the decline and eventual death of HLB-affected trees. The CLas bacteria first enters the leaf phloem during ACP feeding and quickly moves to the root system. Root dieback occurs soon after infection, compromising the trees’ ability to uptake nutrients [9,10,11]. The phloem of HLB-affected citrus trees becomes clogged because of both bacteria presence and callose deposition [12,13]. This clogging reduces carbohydrate allocation throughout the plant [14,15]. The disruption of carbohydrate transportation leads to starch buildup in the chloroplasts resulting in decreased photosynthesis [16,17]. Over a longer period of time, HLB reduces fruit yield and quality, and eventually leads to the death of the tree [18,19].
With no cure or tolerant rootstock/scion combinations available, management practices that prolong the producing life of affected citrus trees are essential to maintaining operational profitability. Proper nutrient management has been identified as one of these practices. The HLB can lead to a reduction in nutrients within affected trees, particularly manganese (Mn) iron (Fe), boron (B), and zinc (Zn) [20,21,22]. The incidence of higher nutrient deficiencies observed in HLB-affected trees have prompted producers to apply micronutrients at higher rates compared to the current Morgan et al. [23] recommendations from the University of Florida, Institute of Food Agricultural Sciences (UF/IFAS).
Research has produced mixed results on the effectiveness of nutrient overdoses as an HLB management method. For example, a study by Zambon et al. [24] found that applications of 4× UF/IFAS dosage of Mn reduced CLas titer of HLB-affected ‘Vernia’ sweet orange [(Citrus sinensis (L.) Osbeck)] trees on Rough Lemon rootstock (Citrus jambhiri Lush.). Similarly, a study conducted by Kwakye et al. [25] found that 2× the recommended UF/IFAS rate of Mn resulted in higher root growth and biomass of HLB-affected ‘Valencia; sweet orange trees on Kuharske cintrage rootstock (Citrus sinensis (L.) Osbeck × Poncirus trifoliata L. Raf.). Lastly, foliar nutrient application in combination with ground applied nutrients were shown to increase HLB-affected ‘Valencia’ sweet orange yield on Swingle citrumelo rootstcock [C. paradisis × Poncirus trifoliata (L.) Raf.] [26]. However, a study by Phuyal et al. [27] found that supplemental soil and foliar micronutrient applications with 0, 1.5×, 3×, and 6× the recommended UF/IFAS rates of B, Mn, Fe, and Zn did not improve yield or tree growth of HLB-affected ‘Ray Ruby’ grapefruit trees on ‘Kuharske’ rootstock in the IRD. Another study found that ground fertilizer applications of B with 0, 2×, and 4× the current UF/IFAS recommendation of B had no consistent impact on tree canopy for 9-year-old ‘Vernia’ sweet orange trees on Rough Lemon rootstock [28].
In addition to the inconsistencies reported in the current literature regarding the positive impacts of micronutrient overdoses, micronutrient toxicity can be an issue. The difference between the actual amount of micronutrients required by the plant and the amount that is considered toxic is rather small. Improper micronutrient fertilization, particularly B and Mn overdoses, can lead to toxicity effects in HLB-affected trees. In citrus trees, this toxicity can result in reduced biomass and impairment in photosynthesis [25,29,30].
To further complicate nutrient management in the age of HLB, the soils native to the IRD pose significant challenges to grapefruit growers. The poorly drained Alfisols and Spodosols commonly found in the district are low in fertility and organic matter, have low cation exchange capacity (CEC), and low pH [31]. Additionally, trees must be grown on raised beds to avoid water logging of the roots [32]. Due to the unique challenges these soils pose to soil fertility, UF/IFAS nutrient guidelines developed for Florida’s other citrus growing regions may not be appropriate for the IRD in an HLB-endemic world.
Much of the research mentioned above investigated nutrient management of HLB-affected citrus trees. However, past studies combined additional factors such as foliar nutrition sprays, macronutrient rates, and irrigation management. Additionally, many of these studies focus on sweet oranges grown in different geographic locations, not grapefruit trees in the IRD. This has created a research gap regarding the impact of ground applied micronutrients rates and application methods on HLB-affected grapefruit trees. The mixed results obtained from the past nutritional studies, the challenging soil conditions in the IRD, and the lack of long-term field studies create many new research questions. Particularly, how beneficial are increased rates of micronutrients to improve tree performance of severely HLB-affected grapefruit? Can ground applied fertilizers supply the necessary micronutrients for severely HLB-affected grapefruit trees? The goal of this study was to answer these questions by investigating the relationship between micronutrient rates and application methods with root density, length and diameter, root, and leaf CLas Ct value, canopy volume, visual HLB symptoms, and fruit yield.

2. Materials and Methods

2.1. Site Description

This 3-year field study was conducted at the University of Florida, Institute of Food and Agricultural Sciences (UF/IFAS), Indian River Research and Education Center (IRREC) located in Fort Pierce, Florida. Citrus trees used in this research consisted of 6-year-old ‘Ruby Red’ grapefruit trees grafted on ‘sour orange’ (Citrus × aurantium) rootstock. The grove was severely affected by HLB.
The soils in the grove were identified as Pineda sands of the order Alfisol classified as Loamy, siliceous, active, hyperthermic Arenic Glossaqualfs. The average soil pH was 5.8 and CEC was 3.5 cmol kg−1. Trees were grown on raised beds roughly 1 m tall to facilitate drainage. Irrigation was delivered using 39.7 dm3 h−1 microjet sprinklers (Maxijet, Dundee, FL, USA).
A total of eight different granular and liquid fertilizer treatments with varying amounts of B, Fe, Mn, and Zn were tested. The four granular treatments consisted of a control, controlled-release fertilizer (CRF) 1, CRF2, and CRF4. The control was a 100% soluble granular fertilizer and contained the UF/IFAS recommended [23] amount for B, Fe, Zn, and Mn. The CRF1, CRF2, and CRF4 treatments contained 1×, 2×, and 4× the currently recommended application rate for B, Fe, Zn, and Mn [23]. The granular treatments were applied by hand around the dripline of the trees, three times a year.
The liquid treatments consisted of F1, F2, and F4, which used 1×, 2×, and 4× the currently UF/IFAS recommended micronutrient rates and were applied bi-weekly. The FW treatment used the current recommendation for micronutrients but was applied every week in rates half the size as the bi-weekly treatments. All liquid treatments were applied as soil drench applications using a ~1100-L single axle admire mobile spray tank (Chemical Containers, Inc., Lake Wales, Florida, USA.). Each tree received the liquid fertilizers in a volume of 11.36 dm3 which was sprayed in a circle around the tree.

2.2. Canopy Volume

Canopy volume was evaluated every January (winter) and July (summer) between the winter of 2019 and winter of 2021. Eight trees per replication were measured for canopy height, canopy width taken parallel to the row, and canopy width taken perpendicular to the row. These measurements were then used to determine the canopy volume using the equation as reported in Obreza and Rouse [33]:
Canopy   volume   = [ π ( 4 3 ) ( TH 2 ) ( ACR ) 2 ]
where:
  • π = 3.14
  • TH = Canopy height
  • ACR = Average canopy radius.

2.3. Root Growth Parameters

Root growth and development were measured using the minirhizotron technique [34,35]. Eighty-four acrylic transparent tubes 2.54 cm in diameter (CID Bio-Science Inc., Camas, WA, USA) were installed at 45° angle into the central root mass of two trees per experimental plot. Images were taken at two depths per tube (depth 1 = 0–15.7 cm, depth 2 = 15.7–38.6 cm) using the CI-602 narrow gauge root imager (CID Bio-Science Inc., Camas, WA, USA). Root count, average root length, and average root diameter were measured at two points (March 2020 and March 2021) throughout the study. Images were taken at 300 DPI and analyzed using CI-690 RootSnap! software (CID Bio-Science Inc., Camas, WA, USA) for root count, root length, and root diameter following similar methodology described in DoVale and Fritsche-Neto and Varga et al. [36,37].

2.4. Visual HLB Ratings

Disease rating was done using a modified quadrant rating scale on eight trees per replication [38]. Two raters were used with one rating each side of the tree. Each side of the canopy was visually divided into 4 quadrants and rated on a scale of 1 to 5 (Table 1). The scores from both raters were averaged to obtain one score per tree.

2.5. Root Density

Root density was collected from four trees from each replication every January (winter) and July (summer) following methodology described by Atkinson [39] and Johnson et al. [40]. Roots were obtained using a one-piece soil auger (One-Piece Auger model #400.48; AMS, Inc., American Falls, ID, USA) 7 cm in diameter and 10 cm in depth cm in depth. Four soil cores were taken within the irrigated zone in a spatially representative pattern from the same 4 trees in each replication. The cores consisting of both roots and soil from a single tree were mixed in a large bucket. A 1 dm3 subsample containing both roots and soil was taken from the bucket. Roots from the 1 dm3 subsample were separated from the soil using a No. 6 U.S. Standard Sieve (3.34 mm), washed with deionized water, dried at room temperature for three days, and weighed. Root density was then calculated by dividing the dry weight in roots by the volume of soil (1 dm3). A subsample of washed roots was collected for root CLas Ct value analysis.

2.6. Leaf and Root CLas Ct Values

Leaf and root samples were collected every January (winter) and July (summer) for the duration of the experiment. One leaf with HLB symptoms from each of the 4 quadrants of a tree were sampled. The same four trees per replication were sampled for the entire experiment. Once collected, midribs were excised and chopped together (100 mg total). Samples were cooled to −80 °C or with liquid nitrogen immediately prior to bead beating. The DNA was then extracted from freshly ground midrib using the DNeasy Plant Mini kit® (Qiagen, Hilden, Germany) using the kit protocol except eluting with 2 rounds of 0.05 cm3 of EB instead of 2 rounds of 0.10 cm3. Quantification of CLas DNA was done with Applied Biosystems 7500 Fast Real-Time PCR System (Thermo Fisher Scientific, Waltham, MS, USA) using the CQULA primers/probe set Wang et al. [41] with a standard curve of 101 to 106 copies of pLBA2 plasmid [42].
Roots samples were collected using the same protocol and soil probe as root density samples. Four soil cores were taken within the irrigated zone in a spatially representative pattern from the same 4 trees in each plot. Roots were separated from soil using a No. 6 U.S. sieve, washed with DI water, and dried at 70 °C overnight [40]. Once roots were dried, 25 mg of chopped roots were placed in tubes for bead beating. Extraction of DNA was preformed using the Mo Bio PowerSoil DNA isolation kit described in [40]. Once DNA extraction was completed, the extract was used in qPCR assays following the same procedure as leaf DNA extracts.

2.7. Fruit Yield

Fruit harvest was conducted in February 2020 and 2021 from 10 trees plot. Total fruit per tree (kg tree−1) was collected and weighed using a digital field scale (Ohaus Corporation, Parsippany, NJ, USA).

2.8. Experimental Design and Statistical Analysis

A completely randomized experimental design with a split plot arrangement was used to test the 4 granular and 4 liquid fertilizers. Application method (liquid vs. granular) was the whole plot factor while the fertilizer rate was the subplot factor. The eight different treatments were replicated 5 times equating to 40 experimental subplots. Within each of the subplots, eight different trees were sampled.
Data was checked for normality and analyzed using analysis of variance (ANOVA) to determine significant differences between fertilizer rates and linear contrast to determine differences between liquid and granular fertilizers. When data was not normally distributed, a square root transformation was utilized. Additionally, sampling dates were analyzed independently. When differences between treatments were significant (p < 0.05), a Tukey HSD test was used to separate means. The software R (https://www.r-project.org, accessed on 10 August 2022) was used to carry out the analysis [43].

3. Results

3.1. Canopy Volume

No significant differences in canopy volume were observed between treatments at any of the time points (Figure 1A). Canopy volume tended to decrease between summer 2019 and winter 2020 and then stayed the same thereafter. Additionally, no differences were observed between granular treatment and liquid treatments at any of the time points. Canopy volume did not significantly change the first year (Winter 2019–Winter 2020) however, canopy volume among all treatments was greater in the summer of 2020. Canopy volume decreased among all treatments in the winter of 2021.

3.2. Visual HLB Symptoms

No significant differences were observed between any of the treatments in the winter of 2019 and the summer of 2020 and no differences between granular and liquid treatments were seen (Figure 1B). In the summer of 2019, the F2 and the FW treatments had higher ratings compared to the CRF1, CRF2, and CRF4 treatments. Additionally, the F1 and F4 treatments had higher ratings compared to the CRF1, CRF2, and CRF4 treatments. Liquid treatments in total had higher ratings compared to granular treatments. Similarly, in the summer of 2020 liquid treatments had higher ratings compared to granular treatments. During this sampling period, the F1 treatments had higher ratings compared to the control and the CRF1 treatments. Interestingly, this pattern was less pronounced in the winter of 2021. The F1 and F2 treatments were only significantly different from the FW treatment. No other differences were observed, including no differences between liquid and granular treatments.

3.3. Root Count, Length, and Diameter

Minirhizotron images were taken at 2 different depths 2 times (March 2020, and March 2021) through the duration of the study. Generally, higher root counts were observed in the deepest windows of the tubes. No significant differences were observed in root count in the 0–15.7 cm depth in the first sampling (Table 2). In the 2nd sampling (March 2021), FW and F4 treatments had higher root counts compared to the Control (Table 3). Regarding the second depth (15.7–31.4 cm) significant differences were detected during the first sampling (March 2020) were the root count for CRF1 was significantly larger than CRF4 (Table 4). No significant difference was observed during the samplings at the second depth in March 2021 (Table 5).

3.4. Root Density

Differences among treatments were only observed in the winter of 2020. The F1, F2, and F4 treatments had significantly higher root density compared to the CRF2 treatment (Figure 2). Additionally, liquid treatments in general had higher root densities compared to the granular treatments. No differences between individual treatments or between granular and liquid treatments were observed in the summer of 2020 and summer of 2021.

3.5. Root and Leaf CLas Ct Values

All treatments had leaf Ct values between 24 and 26 at the initial sampling indicating high HLB incidence. No differences in leaf CLas Ct values were observed between treatments in the winter of 2019 and summer 2020. In the winter of 2021 the Control, F4, and FW treatments had higher Ct value compared to the F1 and F2 treatments (Figure 3A). No differences between granular and liquid treatments were observed.
Similarly, no differences were observed in root CLas Ct values in the winter of 2019, summer of 2020, and winter of 2021. In the Summer of 2021, the F2 treatments had a higher Ct value compared to the Control treatment (Figure 3B). Interestingly, in both leaf and root analysis, higher Ct values were observed in summer months compared to winter months.

3.6. Fruit Yield

No significant differences in fruit yield were observed in February 2020 and February 2021 (Table 6). Overall, yield greatly decreased from 2020 to 2021 regardless of treatment.

4. Discussion

The rationale behind this study was that HLB-affected trees may require higher amounts of the micronutrients B, Zn, Mn, and Fe. It has been demonstrated for example that plants use more nutrients when affected by diseases [44]. Supplying these nutrients in greater amounts then previously recommended was believed to correct any nutritional deficiencies caused by diversions of nutrients to new root growth and plant defense [45]. Additionally, studies have shown that HLB greatly reduces the fine root mass reducing the uptake of nutrients into the plant [9,46].
Recent research has provided evidence that micronutrient overdoses may reduce HLB symptoms. For example, Zambon et al. [24] found that granular Mn at 4× the recommended dosage reduced HLB bacteria titer in sweet orange trees grown in a greenhouse. Atta et al. [47] found that trees treated with 2× the recommended Mn, Zn, and B in a foliar and ground application mix had improved root lifespan of field grown sweet orange trees. In addition, another study by Atta et al. [48] found that split ground application of 224 kg ha−1 of N, foliar application of N at 9 kg ha−1, and 9 kg ha−1 of ground applied Mn and Zn improved physiology and yield of field grown HLB-affected sweet orange trees.
Overall, our data shows severe tree decline due to HLB regardless of micronutrient rate and application method. The lack of differences observed in root length and diameter in addition to lower root density and Ct values reported at the conclusion of the study indicate an overall root system decline. No differences in canopy volume were observed between any of the treatments at any time. Leaf Ct values at the initial sampling were between 28 and 18 for all treatments and remained below 28 at the conclusion of the study. This range of Ct values is where visual HLB symptoms are typically detected [49]. Additionally, the HLB ratings of all treatments increased throughout the duration of the study. Lastly, the average yield of all treatments in 2020 was 10.04 kg tree−1. In comparison, the average yield per tree for 6–8 years old red grapefruit trees grown in the IRD in 2019/2020 was 70 kg tree−1 [6]. Average fruit yield among all treatments was 4.08 kg tree−1 in 2021, a 60% decrease from 2020, and 94% lower than the average yield for the IRD.
The decline in tree health, growth, and yield observed in this study regardless of increased nutrient application is consistent with research conducted by Gottwald et al. [50] which reported increased nutrient management lead to no health improvements of HLB-affected sweet oranges in a two-year study. Similarly, da Silva et al. [51] found that CLas impaired root growth in sweet oranges regardless of different Cu, Mn, and Zn fertilization schemes. Additionally, Bassanezi et al. [52] found no improvements HLB severity and tree yield when testing five different foliar nutrient treatments on sweet oranges in a three-year field study.
The main goal of this study was to see if increases in B, Fe, Mn, and Zn in different application methods led to increases in plant health, growth, and yield. This was not observed, instead, the trees declined in health even with 2× and 4× the recommended doses of micronutrients. The results of this experiment add to the evidence that rehabilitation of HLB-affected trees using ground applied micronutrient overdoses may be limited by the age of the tree and number of years the tree has been HLB-affected. The grapefruit trees used in this study were planted in 2013 and by the end of the study were 8 years old. Since the HLB disease is endemic in Florida, the trees were most likely affected by HLB within the first year of planting. With research showing the lifespan of HLB-affected trees to be greatly reduced to less than 15 years, the trees approached the end of their lifespan [53]. This observation is also supported by the low yield recorded at the end of our study.
Current research indicates that root dieback occurs before foliar symptoms are observed, and that dieback can be greater than 70% once canopy dieback begins [40]. As enunciated above, the grapefruit trees employed in this research have been in a high incidence HLB environment since planting, likely leading to early infection. It can be hypothesized that the root systems were severely compromised, reducing the trees’ ability to uptake nutrients. Regardless of the amount of nutrients available to the tree in the soil, an impaired root system will not be able to uptake nutrients. This hypothesis is supported by the fact that all treatments, even those receiving up to 4× the amount of Zn, Mn, and Fe, were still either deficient or low in the micronutrient contents in leaves, according to current UF/IFAS guidelines [23]. The severe root dieback could also explain the discrepancies observed in the current literature. Many of the cited studies showed improvements in HLB-affected tree physiology and yield when micronutrients are applied in a combination of foliar applications and ground applications. Foliar applications of certain nutrients may be an effective strategy to deliver nutrients to trees with depleted root systems and needs to be researched further in the complex and severe context of HLB.
This study only investigated the relationship between ground applied micronutrient rates with root density, length and diameter, root and leaf CLas Ct values, canopy volume, visual HLB symptoms, and fruit yield. Although no clear statistically significant differences were observed, the research conducted was necessary for creating more precise and science-based micronutrient management guidelines for a specific Florida grapefruit tree growing region: the IRD.

5. Conclusions

The results described above show that increased rates of B, Fe, Mn, and Zn did not have a significant effect on overall tree health and yield of severely HLB-affected grapefruit trees. Although adequate mineral nutrition is essential for optimal tree growth and yield, overdosing ground applied micronutrients does not appear to be beneficial to the health of severely HLB-affected grapefruit trees in the short-term (three years), and more time would be needed to determine any positive effects of mineral nutrition of HLB-affected grapefruit. Since HLB-affected grapefruit trees present impaired root systems, long-term field studies that combine ground and foliar applied micronutrient overdoses need to be conducted in order to better understand the therapeutical role of these micronutrients on trees physiology and yield.

Author Contributions

Conceptualization, L.R., R.S.F. and D.M.K.; methodology, R.S.F., E.G.J., D.M.K., L.R. and A.L.W.; statistical analysis, L.M.H.; resources, L.R., R.S.F. and D.M.K.; writing—original draft preparation, L.M.H.; writing—review and editing, M.A.R., E.G.J., L.M.H., R.S.F., D.M.K., L.R. and A.L.W.; data visualization, L.R. and L.M.H.; supervision, L.R.; funding acquisition, L.R., R.S.F. and D.M.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Citrus Research and Development Foundation project #18-042C “Development of Root Nutrient and Fertilization Guidelines for Huanglongbing (HLB)-Affected Orange and Grapefruit”. This work was also supported by the U.S. Department of Agriculture National Institute of Food and Agriculture, Hatch project #FLA-IRC-005743.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Raw data will be available by requesting them via email to [email protected].

Acknowledgments

The authors are grateful to Jacob Lange and Felix Palencia for assistance in treatment application and data collection. Lastly, the authors would like to thank H. Thomas James, III and Randy G. Burton for their management of the UF/IFAS Indian River Research and Education Center experimental citrus grove.

Conflicts of Interest

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

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Figure 1. Canopy volume (A) and canopy visual huanglongbing (HLB) symptoms (B) measured from Winter 2019 to Summer 2021. Six-year-old HLB-affected ‘Ruby Red’ grapefruit trees grafted on sour orange rootstock were used. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to determine significant differences between means. Letters indicate statistically significant differences (p ≤ 0.05). Bars represents standard error. Asterisks (*) indicate differences between the means of all granular treatments vs. all liquid treatments determined using a linear contrast analysis.
Figure 1. Canopy volume (A) and canopy visual huanglongbing (HLB) symptoms (B) measured from Winter 2019 to Summer 2021. Six-year-old HLB-affected ‘Ruby Red’ grapefruit trees grafted on sour orange rootstock were used. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to determine significant differences between means. Letters indicate statistically significant differences (p ≤ 0.05). Bars represents standard error. Asterisks (*) indicate differences between the means of all granular treatments vs. all liquid treatments determined using a linear contrast analysis.
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Figure 2. Root density (kg/m3) measured from Winter 2020 to Summer 2021. Six-year-old HLB-affected ‘Ruby Red’ grapefruit trees grafted on sour orange rootstock were used. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to deter-mine significant differences between means. Letters indicate statistically significant differences (p ≤ 0.05). Bars represents standard error. Asterisks (*) indicate differences between the means of all granular treatments vs. all liquid treatments determined using a linear contrast analysis.
Figure 2. Root density (kg/m3) measured from Winter 2020 to Summer 2021. Six-year-old HLB-affected ‘Ruby Red’ grapefruit trees grafted on sour orange rootstock were used. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to deter-mine significant differences between means. Letters indicate statistically significant differences (p ≤ 0.05). Bars represents standard error. Asterisks (*) indicate differences between the means of all granular treatments vs. all liquid treatments determined using a linear contrast analysis.
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Figure 3. Candidatus Liberibacter asiaticus Ct values measured in citrus leaves (A) and roots (B) from Winter 2019 to Summer 2021. Six-year-old HLB-affected ‘Ruby Red’ grapefruit trees grafted on sour orange rootstock were used. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to deter-mine significant differences between means. Letters indicate statistically significant differences (p ≤ 0.05). Bars represents standard error.
Figure 3. Candidatus Liberibacter asiaticus Ct values measured in citrus leaves (A) and roots (B) from Winter 2019 to Summer 2021. Six-year-old HLB-affected ‘Ruby Red’ grapefruit trees grafted on sour orange rootstock were used. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to deter-mine significant differences between means. Letters indicate statistically significant differences (p ≤ 0.05). Bars represents standard error.
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Table
Table 1. Visual huanglongbing (HLB) rating system. Each canopy hemisphere is subdivided into four equal quadrants by two imaginary perpendicular planes (vertical and horizontal at mid canopy height) passing through the axis of the tree trunk. The resulting eight sections are scored individually on a 0–5 scale indicative of the proportion of limbs expressing HLB disease symptoms within each section.
Table 1. Visual huanglongbing (HLB) rating system. Each canopy hemisphere is subdivided into four equal quadrants by two imaginary perpendicular planes (vertical and horizontal at mid canopy height) passing through the axis of the tree trunk. The resulting eight sections are scored individually on a 0–5 scale indicative of the proportion of limbs expressing HLB disease symptoms within each section.
RatingDescription
1No HLB symptoms
2Minor HLB symptoms
3Leaf drop present
4Leaf drop present in most quadrants
5Significant leaf/branch dieback
Table
Table 2. Root growth parameters measured in March 2020 at a depth of 0–15.7 cm. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to determine significant differences between means. Data represents means ± standard error.
Table 2. Root growth parameters measured in March 2020 at a depth of 0–15.7 cm. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to determine significant differences between means. Data represents means ± standard error.
TreatmentRoot CountRoot Length (mm)Root Diameter (mm)
Control1.33 ± 1.332.63 ± 0.010.76 ± 0.76
CRF151.00 ± 44.0015.36 ± 2.111.41 ± 0.36
CRF214.00 ± 7.0920.84 ± 5.441.15 ± 0.14
CRF41.00 ± 1.0010.22 ± 5.400.36 ± 0.35
F17.00 ± 6.029.36 ± 5.400.34 ± 0.33
F23.33 ± 1.859.92 ± 2.400.97 ± 0.07
F46.67 ± 2.0212.53 ± 0.940.52 ± 0.26
FW6.67 ± 3.7514.62 ± 7.440.63 ± 0.31
Table
Table 3. Root growth parameters measured in March 2021 at a depth of 0–15.7 cm. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to determine significant differences between means. Letters indicate statistically significant differences (p ≤ 0.05). Data represents means ± standard error.
Table 3. Root growth parameters measured in March 2021 at a depth of 0–15.7 cm. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to determine significant differences between means. Letters indicate statistically significant differences (p ≤ 0.05). Data represents means ± standard error.
TreatmentRoot CountRoot Length (mm)Root Diameter (mm)
Control0.00 ± 0.00 b0.00 ± 0.000.00 ± 0.00
CRF13.00 ± 1.15 ab19.60 ± 5.951.22 ± 0.06
CRF28.66 ± 5.92 ab9.76 ± 5.170.38 ± 0.38
CRF42.33 ± 1.85 ab9.77 ± 4.880.97 ± 0.09
F11.00 ± 1.00 ab6.64 ± 6.640.41 ± 0.41
F23.00 ± 2.51 ab22.13 ± 12.761.01 ± 0.57
F414.33 ± 4.97 a11.01 ± 2.110.60 ± 0.30
FW14.00 ± 3.21 a15.07 ± 1.050.76 ± 0.07
Table
Table 4. Root growth parameters measured in March 2020 at a depth of 15.7–31.4 cm. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to determine significant differences between means. Letters indicate statistically significant differences (p ≤ 0.05). Data represents means ± standard error.
Table 4. Root growth parameters measured in March 2020 at a depth of 15.7–31.4 cm. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to determine significant differences between means. Letters indicate statistically significant differences (p ≤ 0.05). Data represents means ± standard error.
TreatmentRoot CountRoot Length (mm)Root Diameter (mm)
Control5.66 ± 0.66 ab20.57 ± 0.811.70 ± 0.07
CRF146.33 ± 19.47 a13.04 ± 6.054.49 ± 3.47
CRF215.66 ± 8.95 ab8.31 ± 4.170.59 ± 0.29
CRF42.00 ± 2.00 b8.81 ± 8.800.38 ± 0.38
F15.33 ± 1.85 ab9.54 ± 2.670.58 ± 0.32
F213.33 ± 10.03 ab6.22 ± 4.290.53 ± 0.27
F410.00 ± 4.16 ab14.70 ± 3.480.67 ± 0.05
FW20.00 ± 3.46 ab15.95 ± 1.830.84 ± 0.89
Table
Table 5. Root growth parameters measured in March 2021 at a depth of 15.7–31.4 cm. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to determine significant differences between means. Data represents means ± standard error.
Table 5. Root growth parameters measured in March 2021 at a depth of 15.7–31.4 cm. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to determine significant differences between means. Data represents means ± standard error.
TreatmentRoot CountRoot Length (mm)Root Diameter (mm)
Control1.00 ± 1.006.47 ± 6.470.31 ± 0.31
CRF14.67 ± 3.2810.74 ± 9.686.02 ± 5.48
CRF22.33 ± 2.3311.28 ± 11.270.00 ± 0.00
CRF40.67 ± 0.6610.58 ± 10.580.50 ± 0.49
F14.00 ± 2.5117.03 ± 5.651.09 ± 0.10
F217.00 ± 13.206.80 ± 3.800.39 ± 0.19
F42.67 ± 0.6617.91 ± 2.820.85 ± 0.04
FW9.00 ± 4.9331.63 ± 14.670.90 ± 0.14
Table
Table 6. Fruit weight per tree for 2020 and 2021. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to determine significant differences between means. Data represents means ± standard error.
Table 6. Fruit weight per tree for 2020 and 2021. A one-way analysis of variance (ANOVA) with a Tukey honestly significant difference (HSD) test was used to determine significant differences between means. Data represents means ± standard error.
TreatmentFruit Weight in 2020Fruit Weight in 2021
kg tree−1
Control10.16 ± 1.254.36 ± 0.84
CRF114.41 ± 1.914.37 ± 0.70
CRF29.49 ± 1.263.65 ± 0.56
CRF49.71 ± 1.093.63 ± 0.58
F17.17 ± 0.893.03 ± 0.59
F28.69 ± 1.513.06 ± 0.50
F49.39 ± 1.205.08 ± 0.88
FW11.26 ± 1.295.47 ± 0.80
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Hallman, L.M.; Kadyampakeni, D.M.; Ferrarezi, R.S.; Wright, A.L.; Ritenour, M.A.; Johnson, E.G.; Rossi, L. Impact of Ground Applied Micronutrients on Root Growth and Fruit Yield of Severely Huanglongbing-Affected Grapefruit Trees. Horticulturae 2022, 8, 763. https://doi.org/10.3390/horticulturae8090763

AMA Style

Hallman LM, Kadyampakeni DM, Ferrarezi RS, Wright AL, Ritenour MA, Johnson EG, Rossi L. Impact of Ground Applied Micronutrients on Root Growth and Fruit Yield of Severely Huanglongbing-Affected Grapefruit Trees. Horticulturae. 2022; 8(9):763. https://doi.org/10.3390/horticulturae8090763

Chicago/Turabian Style

Hallman, Lukas M., Davie M. Kadyampakeni, Rhuanito Soranz Ferrarezi, Alan L. Wright, Mark A. Ritenour, Evan G. Johnson, and Lorenzo Rossi. 2022. "Impact of Ground Applied Micronutrients on Root Growth and Fruit Yield of Severely Huanglongbing-Affected Grapefruit Trees" Horticulturae 8, no. 9: 763. https://doi.org/10.3390/horticulturae8090763

APA Style

Hallman, L. M., Kadyampakeni, D. M., Ferrarezi, R. S., Wright, A. L., Ritenour, M. A., Johnson, E. G., & Rossi, L. (2022). Impact of Ground Applied Micronutrients on Root Growth and Fruit Yield of Severely Huanglongbing-Affected Grapefruit Trees. Horticulturae, 8(9), 763. https://doi.org/10.3390/horticulturae8090763

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