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Effects of Wound-Healing Management on Potato Post-Harvest Storability

A critical step in profitable post-harvest potato storage management is to cure tubers at appropriate temperatures long enough for rapid wound-healing to prevent disease and defect development, but not too long to jeopardize storage quality. A two-year storage study was conducted in macro-storage totes at the University of Wisconsin Hancock storage research facility to evaluate the effects of higher wound-healing temperatures (15.6 ◦C, 18.3 ◦C) imposed for different durations, and compare them to the U.S. potato industry’s standard practice (12.8 ◦C), on weight loss and frying quality of multiple processing potato varieties during long-term storage. It was found that in the experimental setting of this study, warmer wound-healing temperatures resulted in (1) less weight loss, particularly during the early storage season across varieties; (2) ameliorated senescent sweetening of the Snowden variety; (3) improved fry quality of the Russet Burbank variety; (4) and no apparent disease spread during long-term storage if tubers were harvested healthy out of fields. Overall, no significant difference was found between 15.6 ◦C and 18.3 ◦C regarding their treatment effects. Our conclusion is that compared to the current standard practice, higher wound-healing temperatures may have the potential benefits of improving potato storage quality while reducing the economic penalty associated with weight loss for specific varieties, but tubers should be healthy at harvest in order to gain the benefits. Further research is needed to test if those benefits of higher wound-healing temperatures hold true in large-scale commercial storage facilities.

Introduction
Storage is a vital process to ensure year-round supplies for the fresh market and processing potato (Solanum tuberosum L.) industries. A key step of successful potato storage management is appropriate wound healing, the first two to three weeks after harvest during which the potatoes are held at an elevated temperature before final holding temperature is reached [1]. Wound healing can impact potato tuber long-term storability and can be detrimental to end product quality [2,3].
The outmost layer of a potato tuber is the periderm that protects potatoes from weight loss and pathogen attack [4]. The periderm is subject to wounding, which is common during potato harvest and handling. Wounds can be in the form of cuts, punctures, abrasions, broken knobs, shatter-bruised areas, or any area that has a break in the periderm [5]. The purpose of wound healing is for the tubers to quickly repair periderm damage under a warm and high-humidity environment. Rapid development of wound periderm is critical to minimize subsequent weight loss or disease spread [6,7]. Wound periderm formation is initiated when cell division increases and suberin deposition occurs in response to wounding [8][9][10]. The primary role of suberin is to serve as a barrier to both water loss and microbial infection [11]. Suberin can auto-fluoresce under ultra-violet light [12,13]. This trait can Target N, P, and K were 340, 72, and 325 kg ha −1 , respectively. A weather station (Campbell Scientific, Logan, UT) was located within 500 m of experimental plots recording precipitation, maximum and minimum soil temperature, solar radiation, relative humidity, and wind speed on a daily basis. Vine killing was conducted in early September (14 days before harvest) of each year to promote skin set on the tubers.

Storage Practices
At harvest, rocks, soil debris and clods, and foreign materials were hand removed from the tubers. Pulp temperatures of the tubers at harvest ranged between 12.2 °C and 15.6 °C. About 1350 kg of each variety was machine harvested and evenly put into three large plastic storage totes (114 cm (L) × 123 cm (W) × 88 cm (H), Capacity 450 kg, Figure 1) (model 34-S, MacroPlastics Inc., Fairfield, CA), which were placed on top of each other to simulate commercial industry practice. Gaps between the totes were tightly sealed by plastic wraps to ensure consistent airflow within the totes. The totes were then held under each of the three wound-healing treatments. Under each treatment, from top to bottom the order of the totes was Snowden, Hodag and Russet Burbank in 2017 and Snowden, Lamoka, Hodag and Russet Burbank in 2018. In each tote three replications of 4.5 kg of healthy potatoes were randomly selected and placed into polypropylene mesh bags, which were then placed on the top of the storage pile within the tote. Relative humidity was maintained above 95% for all treatments throughout the entire duration of storage via a plenum humida-cell with supplemental spray nozzles as a back-up in the event of the humida-cell failing. The sprout inhibitor, isopropyl N-(3-chlorophenyl)carbamate (CIPC, Decco Chemical, at 78.6 % a.i.), was applied to all totes as a thermal aerosol at a rate of 22 mg Kg −1 approximately 30 days after harvest. Sprouting was completely controlled in the 2017 storage season. In 2018, peeping (<1mm) was observed on tubers starting from the 6th month in storage. Three wound-healing treatments were: Relative humidity was maintained above 95% for all treatments throughout the entire duration of storage via a plenum humida-cell with supplemental spray nozzles as a back-up in the event of the humida-cell failing. The sprout inhibitor, isopropyl N-(3-chlorophenyl)carbamate (CIPC, Decco Chemical, at 78.6% a.i.), was applied to all totes as a thermal aerosol at a rate of 22 mg Kg −1 approximately 30 days after harvest. Sprouting was completely controlled in the 2017 storage Agronomy 2020, 10, 512 4 of 17 season. In 2018, peeping (<1mm) was observed on tubers starting from the 6th month in storage. Three wound-healing treatments were: 1.
wound healing at 12.8 • C for 30 days, applied CIPC, and then ramped down to holding temperature (8.9 • C) at a rate of 0.3 • C/day, and this is the standard practice currently adopted by growers; 2.
wound healing at 15.6 • C for 10 days, lowered down to 12.8 • C at a rate of 0.6 • C/day, applied CIPC, and then ramped down to holding temperature (8.9 • C) at a rate of 0.3 • C/day; 3.
wound healing at 18.3 • C for 5 days, lowered down to 12.8 • C at a rate of 0.6 • C/day, applied CIPC, and then ramped down to holding temperature (8.9 • C) at a rate of 0.3 • C/day.
Post-harvest quality measurements, including percent fresh weight loss, fry color of all varieties, sugar end defect of Russet Burbank, sucrose and glucose content, and disease incidence of bacterial soft rot, Pythium leak and Fusarium dry rot were assessed at 1 month intervals for 8 months starting from harvest. There were 9 sampling points in each storage season. To avoid the confounding effects of planting date-induced differences in tuber size distribution on storability and processing quality, only the 171 to 284 g tubers (marketable size) were retained for use in weight loss, processing quality, and disease evaluation.

Weight Loss
Weight loss data was collected on the three replications of 4.5 kg healthy tubers contained in mesh bags within each tote. Tuber sample fresh weights were recorded prior to placing in storage, and then recorded in each month until late June of the following year. Weight loss was reported on a percent fresh weight basis. Monthly weight loss was calculated as (weight in the previous month-weight in the current month)/weight in the previous month. Total weight loss was summed up across the eight months in each storage season.

Assessment of Fry Color
For Russet Burbank, one fried plank (3.0 cm × 0.8 cm × length of tuber) from each of 10-tuber samples, with three replications from each tote, was used for fry color determination within one week of harvest and then monthly in storage. Planks were fried in canola oil at 191 • C for 3.5 min and blotted dry to remove extra oil. Fry color was determined within the first 3 min after frying using a model 577 Photovolt Reflectometer (model 577, Photovolt Instruments Inc., Minneapolis, MN, USA). As per the manufacturer's recommendation, a green filter was used and calibrated, using a black-cavity standard as 0.0% reflectance and a white plaque as 99.9% reflectance. Measurements were taken on the bud and stem ends of each plank. A relationship between the United States Department of Agriculture (USDA) standard fry color and the photovolt reflectance was previously established [25]. A USDA fry color rating 1 was equal to a 44.0 or greater reflectance reading, a USDA 2 rating was less than 44.0 to 35.0 reflectance reading, a USDA 3 rating was less than 35.0 to 26.0 reflectance reading, and a USDA 4 rating was less than 26.0 reflectance reading [26]. Therefore, higher photovolt reflectance readings are associated with lighter fry color. After fry color determination, the presence or absence of sugar end defect was recorded for each plank. A plank was considered to have a sugar end defect if a predominant color of number 3 or darker, when compared with the USDA Munsell Color Chart for French fried potatoes, was seen on any 2 sides extending 13 mm or more from the end of the fried plank.
For chipping potatoes at each sampling point, tubers were cut in half lengthwise. A 1 mm thick slice through the stolon attachment point was removed from one half tuber using a customized cutter and fried in vegetable oil at 185 • C for 2 min 10 s or until bubbling ceased. Fried chips were crushed for color analysis indicated by Hunter L-values using a D25LT colorimeter (HunterLab, Reston, VA). The Hunter L-values indicate the light vs. dark colors where a low number (0 to 50 corresponds to dark) and a high number (51-100 corresponds to light). Each sample consisted of 30 crushed chips, three chips from each of ten tubers per replication [27], three replications per variety/wound healing treatment. Samples with Hunter L-values of 55 or higher is acceptable to the potato processing industry.

Sugar Extraction and Analysis
Sucrose and glucose content were determined on the same 10-tuber sample used for the fry color determination by the method of Sowokinos et al. [28] with modifications. The terminal 200 g on the bud and stem ends, separately, of each tuber were bulked and juiced with an Acme Juicerator (Acme Equipment, Spring Hill, FL) in 50 mL of sodium-phosphate buffer (pH 7.2). The final volume of the homogenate was brought up to 275 mL with the same buffer. Glucose and sucrose concentrations were measured using a YSI model 2900 Select Analyzer (Yellow Springs Instrument Inc., Yellow Springs, OH) as per manufacturer's recommendations and expressed on milligram per gram of fresh weight. Procedure for chipping potato sugar analysis was similar except that the central 200 g on each tuber was processed.

Storage Disease Evaluation
On each sampling date, three replications of 10 tubers from each tote were randomly selected, washed, and cut open from stem end to bud end for a comprehensive disease incidence evaluation of Fusarium dry rot (Fusarium spp.), bacterial soft rot (Pectobacterium spp.) and Pythium leak (Pythium spp.), which are the three major potato storage diseases in Wisconsin. A tuber is considered to have a disease incidence if decay area on the cut surface caused by any of the three pathogens is larger than 5% [29].

Data Analysis
Due to different sources of potatoes between the two years (donated by commercial fields vs. grown on research plots), analysis of variance (ANOVA) was conducted using the PROC GLM procedure in SAS (version 9.4, SAS Institute Inc., Cary, NC, USA) for each year respectively following a three-factor factorial design, with variety, wound-healing treatment, and storage length as the three main effects, and variation between replications as the error term. Means were separated using Fisher's least significant difference (LSD) at the α = 0.05 level. Figure 2 shows the daily maximum and minimum soil temperatures and precipitation between planting and harvesting in each growing season. It is clear that compared to 2017, 2018 is a much more difficult year with more extreme weather conditions, such as snowy freezing days in late April that delayed planting, very hot days (> 30 • C) in late May that had severe impact on plant emergence, big rain events (> 2.5 cm) in mid-June that caused a high percentage of nitrogen fertilizer applied at tuber initiation in early June to leach out, and a series of high-volume rainfalls (> 2.5cm) in late August that led to saturated unharvestable soils, enlarged lenticels associated with appearance and disease issues, and tuber quality defects causing economic loss.

Total Weight Loss
In 2017 across the treatments, Snowden (12.0%) had the highest total weight loss compared to the other two varieties (RB 6.9% and Hodag 6.6%, p < 0.0001) during the 8 months of storage. Across the three varieties, total weight loss under treatment 1 (10.2%) was significantly higher than under treatment 2 (8%) and 3 (7.3%). Hodag and Russet Burbank showed significantly higher weight loss under treatment 1 than the other two treatments, but the difference was not significant on Snowden. For Snowden, total weight loss over the 8-month storage period was always higher than 11%, no matter what wound-healing conditions the tubers were under (Figure 3a).

Total Weight Loss
In 2017 across the treatments, Snowden (12.0%) had the highest total weight loss compared to the other two varieties (RB 6.9% and Hodag 6.6%, p < 0.0001) during the 8 months of storage. Across the three varieties, total weight loss under treatment 1 (10.2%) was significantly higher than under treatment 2 (8%) and 3 (7.3%). Hodag and Russet Burbank showed significantly higher weight loss under treatment 1 than the other two treatments, but the difference was not significant on Snowden. For Snowden, total weight loss over the 8-month storage period was always higher than 11%, no matter what wound-healing conditions the tubers were under (Figure 3a). Over the treatments in 2018, Snowden (10.3%) still showed the highest total weight loss relative to the other varieties (Hodag 7.4%, Lamoka 8.6%, and RB 4.5%, p < 0.0001) during the entire storage season. Unlike 2017, across the four varieties there was not significant difference between total weight loss under different wound-healing treatments, although treatment 1 (8.8%) is greater than treatment 2 (7.6%) and treatment 3 (7.0%). Again for Snowden, total weight loss was always higher than the values of other varieties under any of the wound-healing treatments, and its total weight loss under treatment 1 was the highest (p < 0.05, Figure 3b). Treatment 3 was associated with the lowest total weight loss for all varieties in both years except Hodag in 2018 (Figure 3b).

Monthly Weight Loss
ANOVA results showed that in both years all the main effects, two-way and three-way interactions except variety × treatment interaction on monthly weight loss were significant at α = 0.05 (Table 1). In 2017 for Hodag, monthly weight loss under treatment 1 had always been higher than weight loss under the other two treatments over the eight-month storage time (Figure 4a). The same trends can be seen on Snowden and Russet Burbank (Figure 4b and 4c). For all three varieties under treatment 1, they lost more than 50% of their weight during the first three months of storage, and these trends under treatment 2 and 3 were not as apparent ( Figure 4). Overall, cumulative weight loss of all varieties under treatment 1 were consistently higher than under treatment 2 and 3 ( Figure  4).  Over the treatments in 2018, Snowden (10.3%) still showed the highest total weight loss relative to the other varieties (Hodag 7.4%, Lamoka 8.6%, and RB 4.5%, p < 0.0001) during the entire storage season. Unlike 2017, across the four varieties there was not significant difference between total weight loss under different wound-healing treatments, although treatment 1 (8.8%) is greater than treatment 2 (7.6%) and treatment 3 (7.0%). Again for Snowden, total weight loss was always higher than the values of other varieties under any of the wound-healing treatments, and its total weight loss under treatment 1 was the highest (p < 0.05, Figure 3b). Treatment 3 was associated with the lowest total weight loss for all varieties in both years except Hodag in 2018 (Figure 3b).

Monthly Weight Loss
ANOVA results showed that in both years all the main effects, two-way and three-way interactions except variety × treatment interaction on monthly weight loss were significant at α = 0.05 (Table 1). In 2017 for Hodag, monthly weight loss under treatment 1 had always been higher than weight loss under the other two treatments over the eight-month storage time (Figure 4a). The same trends can be seen on Snowden and Russet Burbank (Figure 4b,c). For all three varieties under treatment 1, they lost more than 50% of their weight during the first three months of storage, and these trends under treatment 2 and 3 were not as apparent (Figure 4). Overall, cumulative weight loss of all varieties under treatment 1 were consistently higher than under treatment 2 and 3 ( Figure 4).   of increasing monthly weight loss during the last two months of storage ( Figure 5). For each variety in 2018, weight loss under treatment 1 during the first three months accounted for at least 50% of the total weight loss over the entire storage season ( Figure 5). It is observed again that cumulative weight loss of each of the four varieties under treatment 1 was always higher than the other two treatments over the storage season.

Glucose Content of the Chipping Varieties
ANOVA shows that in 2017, all the main effects and their two-way and three-way interactions were highly significant (p < 0.001, Table 2), however in 2018, only the effects of variety, storage length and their two-way interactions were significant (Table 2). In 2017 for Hodag, not much difference can be seen on glucose content between different wound-healing conditions. Hodag is featured with its consistently low glucose levels during long-term post-harvest storage (Figure 6a). For Snowden, no difference can be observed until the seventh month in storage, and tubers under treatment 1 showed significantly higher levels of glucose than under the other treatments, however, under treatment 3 glucose stayed below 0.35 mg/g FW throughout the storage season (Figure 6b). In 2018, significant interaction between variety and storage length was shown in Figure 7. For Snowden in particular, there was a steep jump of glucose during the last two storage months.

Glucose Content of the Chipping Varieties
ANOVA shows that in 2017, all the main effects and their two-way and three-way interactions were highly significant (p < 0.001, Table 2), however in 2018, only the effects of variety, storage length and their two-way interactions were significant (Table 2). In 2017 for Hodag, not much difference can be seen on glucose content between different wound-healing conditions. Hodag is featured with its consistently low glucose levels during long-term post-harvest storage (Figure 6a). For Snowden, no difference can be observed until the seventh month in storage, and tubers under treatment 1 showed significantly higher levels of glucose than under the other treatments, however, under treatment 3 glucose stayed below 0.35 mg/g FW throughout the storage season (Figure 6b). In 2018, significant interaction between variety and storage length was shown in Figure 7. For Snowden in particular, there was a steep jump of glucose during the last two storage months.

Glucose Content of Russet Burbank
In 2017, neither wound-healing treatment nor storage length had significant impacts on the stem-end and bud-end glucose content of Russet Burbank tubers. Whereas in 2018, the two main effects and their interactions were significant on the glucose content of both ends (p < 0.05). A big difference of stem-end glucose between treatment 1 and the other two treatments was observed during the last four months of storage (p < 0.05, Figure 8a), suggesting potential fry quality issues. A significant difference of bud-end glucose between treatment 1 and the other two treatments was noticed in the 2nd and 4th storage month, but the number under treatment 3 was significantly higher than under treatment 1 during the 7th month of storage (p < 0.05, Figure 8b). At the other sampling points, no difference of bud-end glucose was captured between the three wound-healing treatments (Figure 8b).

Glucose Content of Russet Burbank
In 2017, neither wound-healing treatment nor storage length had significant impacts on the stem-end and bud-end glucose content of Russet Burbank tubers. Whereas in 2018, the two main effects and their interactions were significant on the glucose content of both ends (p < 0.05). A big difference of stem-end glucose between treatment 1 and the other two treatments was observed during the last four months of storage (p < 0.05, Figure 8a), suggesting potential fry quality issues. A significant difference of bud-end glucose between treatment 1 and the other two treatments was noticed in the 2nd and 4th storage month, but the number under treatment 3 was significantly higher than under treatment 1 during the 7th month of storage (p < 0.05, Figure 8b). At the other sampling points, no difference of bud-end glucose was captured between the three wound-healing treatments (Figure 8b).

Fry Color of the Chipping Varieties
Consistent with ANOVA results from the glucose content analysis, Hunter L-values, which are indicators of chip fry color and need to be above 55 by industry standards, were significantly affected

Fry Color of the Chipping Varieties
Consistent with ANOVA results from the glucose content analysis, Hunter L-values, which are indicators of chip fry color and need to be above 55 by industry standards, were significantly affected by all of the three main effects, their two-way and three-way interactions in 2017, whereas in 2018 only variety, storage length and their interaction had p-values lower than 0.05 (Table 3).

Fry Color and Sugar End Defect of French Fries Made from Russet Burbank
In both years, stem-end and bud-end fry color was only significantly impacted by the main effect of wound-healing treatment (p < 0.05). Treatment 3 led to significantly higher photovolt readings, suggesting lighter fry color compared to treatment 1 ( Table 4). The incidence of sugar end defect was only significantly influenced by treatment in 2018 (p < 0.05), with numbers under treatment 1 >

Fry Color and Sugar End Defect of French Fries Made from Russet Burbank
In both years, stem-end and bud-end fry color was only significantly impacted by the main effect of wound-healing treatment (p < 0.05). Treatment 3 led to significantly higher photovolt readings, suggesting lighter fry color compared to treatment 1 ( Table 4). The incidence of sugar end defect was only significantly influenced by treatment in 2018 (p < 0.05), with numbers under treatment 1 > treatment 2 > treatment 3. No difference of treatments was observed in 2017 (Table 4). Table 4. Photovolt readings at the stem end and sugar end defect incidence of French fries processed from Russet Burbank potatoes, under different wound healing conditions in each year. (n.s. not significant, numbers followed by different letters are significantly different at α = 0.05).

Disease Evaluation
No difference of disease incidence was captured between the wound-healing treatments for any variety in any given year. Disease decay generally was less than 5% in all totes throughout the 8-month storage season.

Warmer Wound-Healing Temperature Resulted in Less Weight Loss, Particularly during the Early Storage Season
In general, higher temperatures at 15.6 and 18.3 • C favored the wound-healing process by causing less total weight loss in all tested varieties during the 8-month storage. This could be a result of faster suberization under 15.6 and 18.3 • C, as warmer temperatures enhance suberization, whereas cooler temperatures suppress suberin synthesis and the formation of wound periderm [30]. Since relative humidity and airflow were controlled to be the same between different treatments in this study, temperature difference turns out to be the only driver for different weight loss of the three wound-healing treatments, regardless of the variety.
Potato weight loss is primarily caused by tuber transpiration and respiration during storage. Transpiration contributes to more than 90% of total weight loss, and is mostly caused by diffusion of water vapor from the tuber skin to the surrounding air due to the difference in water vapor pressure within the cells of the tuber skin and the air, called water vapor pressure deficit [31,32]. The higher the ambient air temperature, the more water loss from the tuber. Respiration, responsible for converting starch and sugars into carbon dioxide and water in stored potatoes [33], accounts for 5% to 10% of total tuber weight loss [34]. Respiration rate is increased under higher storage temperature [35][36][37]. In this study, although higher temperatures at 15.6 and 18.3 • C could promote both transpiration and respiration during the wound-healing stage, their influences could be offset by higher rates of suberization, and therefore the net result is less tuber weight loss over the entire storage season, particularly under treatment 3.
The findings of this study were consistent with previous research that >50% of the total weight loss occurred during the first 2 months of storage because potatoes in the early part of the storage season have higher transpiration and respiration rates [14,38,39], and the trend is especially clear under treatment 1 (Figures 4 and 5). After this period, weight loss stayed below 2% per month.
Another factor that can cause potato weight loss is sprout growth during storage [32]. Burton [40] estimated that respiration of potatoes increases by 50% if sprouts reach 1% of tuber weight. It has been found that the sprout growth equivalent to 1% increase in the tuber surface area can lead to doubling of the moisture loss from the tubers [41]. In the second year of our study (2018), the start of sprouts growth (peeping) was observed from the sixth month of storage, which might be the reason for slight to medium increase of weight loss of all four varieties during the last stage of storage ( Figure 5).

Snowden Tends to Lose More Weight Compared to Other Varieties during Storage
A clear varietal difference was shown by this two-year study that Snowden loses the most weight among the four tested varieties. Potato tuber weight loss can be influenced by many factors, such as the type of tuber skins (russeting vs. non-russeting), tuber skin maturity at harvest, the rate of transpiration and respiration during storage, the rate of sprouting, and storage conditions. It is not clear what caused Snowden to lose the most weight compared to other varieties. Being on top of the storage pile that exposed the tubers to more airflow could be one reason, and researchers who conducted the storage study in macro totes at HPSRF have indicated similar findings previously [42]. However, Peck [14] found no significant difference of weight loss in commercially stored potatoes placed on top of the pile compared to those buried within the pile. The russeted skin of Russet Burbank could be responsible for its lower weight loss compared to Snowden (Figure 3), due to the thicker periderm and thus a better barrier against moisture loss. Future research is needed to investigate how skin maturity affects Snowden's weight loss, as Butchbaker et al. [33] found that later-maturing varieties showed greater total weight loss than earlier-maturing ones. Respiration rate over the course of the storage season should also be assessed in future studies in order to identify the cause for Snowden's high weight loss potential.

Senescent Sweetening of Snowden was Ameliorated under Higher Wound-Healing Temperature
Potato tubers during the late stage of storage will undergo a developmentally controlled, age-dependent shift in metabolism that results in an accumulation of sugars such as sucrose and glucose, a process that is called senescent sweetening [43]. The most widely accepted hypothesis for the mechanism of senescent sweetening is that tuber tissue senescence after a certain period of time in storage including the breakdown of the cell membrane, can facilitate enzyme access to starch granules and speed up starch conversion to sugars [44], which then results in unacceptable darkening of finished fries in a chemical process called the Maillard reaction [45]. Varieties that are most susceptible to senescent sweetening tend to have shorter dormancy. Growing conditions including early planting and water and heat stresses, as well as storage conditions such as higher storage temperature can all affect the onset of senescent sweetening [44].
In this study, Snowden underwent senescent sweetening during the last two months of storage in both years; particularly in 2017, Snowden under treatment 1 showed the worst senescent sweetening (Figures 6b and 7). This is likely due to Snowden's relatively shorter dormancy compared to the other two chipping varieties and Russet Burbank, its growing conditions in the summer and its maturity when harvested on the commercial field in 2017. The finding that Treatment 1, the lowest wound-healing temperature, resulted in the most severe senescent sweetening in 2017 does not agree with previous research findings [44]. Lower wound-healing temperature should slow down the process of tuber physiological aging, leading to less senescent sweetening. Further research needs to be conducted to explore the reason. Comparatively, both Hodag and Lamoka are characterized by their long dormancy, absence of senescent sweetening in the late storage stage, and premium fry quality for long-term storage (Figures 6a and 7)

Fry Quality of Russet Burbank Can Be Improved by Increased Wound-Healing Temperature
For Russet Burbank, wound healing at warmer temperatures resulted in lower stem-end glucose ( Figure 8) and thus lighter fry color compared to the standard treatment under a cooler wound-healing temperature (Table 4). This finding is consistent with [14] and [46]. Glucose is limiting for the Maillard reaction, and fry color is proportional to glucose concentration in tubers at the time of processing [46].


provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2020.jsonl.gz.gz

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Damage caused by cattle to Eucalyptus benthamii trees in pruned and unpruned silvopastoral systems

The objective of this work was to evaluate the effect of tree pruning on the incidence and intensity of the damages caused by cattle to 26-monthold Eucalyptus benthamii, in a silvopastoral system, in the municipality of Pinhais, in the state Paraná, Brazil. The experiment was performed in a randomized complete block design, with two treatments – livestock and forest with pruned eucalyptus (WP), and livestock and forest with unpruned eucalyptus (UP) – and three replicates, characterizing six types of damages and five damage intensities. There were significant differences between the WP and UP treatments for the different types of damages. Cattle entry in the silvopastoral system at 26 months after the planting of eucalyptus causes lowintensity damages to the trees, in the treatment with pruning, and mediumintensity damages to the trees in the treatment without pruning. Damages of stronger intensities were detected in the UP treatment, in which classtwo damages (d2 = average damage) were verified in approximately 91% of the trees. The silvicultural practice of pruning E. benthamii trees does not affect the diameter at breast height or the total height of trees. Pruning can be recommended, as it does not affect tree development; moreover, this practice enables the production of knot-free, better-quality wood.

Introduction
The interest in silvopastoral and agroforestry systems has increased significantly in Brazil. Practices such as livestock and forestry integration ensued after research and extension programs, which were fostered by the Brazilian federal government, such as the low-carbon agriculture program ("Agricultura de Baixo Carbono", ABC Program), created in 2010, and the "Integra SP" project, designed in 2013 in São Paulo state.
Silvopastoral systems (SPS) integrate livestock and timber production in the same area. The association of adequately managed trees with pastures may provide an extra source of income for the farmer, as well it may improve environmental protection and promote animal welfare, while increasing soil fertility and pasture production (Porfírio-da- Silva et al., 2010). It is a dynamic and complex system, since it integrates elements of different natures (Balbino et al., 2011). These systems are also considered a high-investment forest component, and their results are obtained in the long term (Kichel et al., 2014). Therefore, the selection of species and their genetic material are important points to be considered for the system to become successful.
SPS have proved to be a viable supplier of wood products and provider of additional revenues to farmers. In this sense, when SPS are adopted as a production model, the sustainability of the ecosystem as a whole should be expected, instead of the maximum productivity of singular components (Bernardi et al., 2014).
The establishment of tree seedlings in a pasture area represents a critical phase for SPS. This step of the implementation process requires specific attention as any damage caused to the trees at this stage of their development may compromise the system's success (Porfírio-da-Silva et al., 2010).
Even after the trees are grown sufficiently to support the presence of cattle in a given area, tree bark damages can still be caused by the animals (Medrado et al., 2009;Porfirio-da-Silva et al., 2012;Guerreiro et al., 2015). Tree bark damage depreciates the wood quality and may even lead to tree death.
The factors or causes for the removal of eucalyptus bark by cattle are unknown. The suggested hypotheses to explain this type of animal behavior are related to the animal's nutritional state (deficiencies in certain minerals, protein, and energy), to the concentration and quality of ingested fibers (Malafaia et al., 2011;Villalba et al., 2014), to the presence of endoparasites (Saint-Andrieux et al., 2009;Villalba et al., 2013), and, also, to behavioral aspects of the animals (Berridge & Kringelbach, 2008).
Potential factors that influence cattle seeking different sources of food in tree species, and not in forage, may be linked to the quality and quantity of forage available to the animals, according to Brun et al. (2017). These authors also mention that forage quality and quantity are linked to genetic factors related to the selection of forage species / varieties appropriate for each animal species or breed, and to the quality of the soil, which is the basis for the development and nutritional quality of forages.
Tree pruning is a silvicultural practice commonly recommended for the production of quality logs and for the mitigation of competition for light from treetops. Besides improving wood quality for sawmills, pruning favors animal movement and allows a greater availability and uptake of radiation among tree lines, contributing to the maintenance or even to the increase of pasture productivity in SPS (Fontan, 2007).
Porfírio-da- Silva et al. (2012) recommended pruning to reduce leaf damages, as well as to avoid the breaking of branches and sprouts in silvopastoral systems, since these damages allow of the entrance of pathogenic organisms and the formation of 'loose knots' which depreciate the wood quality.
The objective of this work was to evaluate the effect of tree pruning on the incidence and intensity of damages caused by cattle to 26-month-old Eucalyptus benthamii Maiden et Cambage, in a silvopastoral system within an EPA area, since no specific works were found in the literature for this species of eucalyptus.

Materials and Methods
The experiment is part of the project developed by the center of technological innovation for agriculture ( (Paraná, 1996), in the banks of the Iraí dam. The climate is Cfb, according to the Köppen-Geiger's classification. The average annual precipitation ranges from 1,400 to 1,600 mm, with no defined dry season, and the average annual temperatures range from below 18°C, in colder months, to below 22°C, in warmer months (IAPAR, 2014).
The soils of the area have been classified as Cambissolos (Cambisols), Latossolos (Oxisols) and their associations, with smaller portions of Organossolos (Organosols) and Gleissolos (Gleysols), according to the Brazilian Soil Classification System (Santos et al., 2018).
The experimental area was established and the current research was carried out in 2012, and the following protocol was adopted: conventional soil tillage by deep plowing and a subsequent harrowing in June and July; correction of soil acidity, using 10 Mg ha -1 limestone, plus sewage sludge treated by the N-VIRO process, obtained from the sewage treatment company of the municipality of Curitiba, in the state of Paraná, Brazil, in July and August; sowing of 80 kg ha -1 seeds of the 'IAPAR 61' black oat (Avena strigosa) for soil cover; and fertilization with 450 kg ha -1 single superphosphate on the sowing line.
In January and February 2013, a conventional soil preparation for the sowing of perennial summer pasture was carried out. Megathyrsus maximus ' Aries' was seeded in lines (16 kg ha -1 ), and 350 kg ha -1 single superphosphate plus 800 kg ha -1 ammonium chloride were applied to the line. During the pasture establishment phase, mowings were performed three times to reduce weed competition.
In September 2013, the tree lines were marked, and Eucalyptus benthamii seedlings of seminal origin, produced in tubes, were planted in October 2013. This species was chosen mainly due to its resistance to frost, and to its use as a final product for logs and rolling mills at the end of the 20-year cycle.
Eucalyptus trees were planted in single rows with 14 m spacing between rows and 2 m spacing between plants, with the aim at obtaining an initial density of 357 trees per hectare. The trees were planted in contour lines to conserve soil and water and to control erosion, as well as to improve the animals' thermal comfort, and to mitigate the impact of tracks made by animals walking on terrain slopes (Porfírio-da- Silva et al., 2010).
The sowing line was subsoiled at 40 cm depth. The trees were then planted manually using a hoe. Next, 250 mL of a hydrogel solution (commercial brand Hidrogel HyB, Hidroplan-EB, São Paulo, SP, Brazil) were applied to each seedling, according to the manufacturer's recommendations. Subsequently, 200 g N-P 2 O 5 -K 2 O (08-20-20) containing 1% calcium (Ca) and 3% sulfur (S) were applied in two lateral pits (15 cm away from the seedling and 15 cm deep).
The fertilization procedure was based on the soil analysis. In the first year (2013), fertilizers were applied in two stages: the first application was performed in September, using 450 kg ha -1 single superphosphate and 400 kg ha -1 N-P 2 O 5 -K 2 O of the formulation 25-00-25; and the second application was performed in October, using 200 kg ha -1 urea on the soil's surface.
In 2014, fertilizers were applied in three stages. In the first stage, the fertilization was performed in the second fortnight of May, using 600 kg ha -1 natural phosphate, 200 kg ha -1 potassium chloride, and 200 kg ha -1 urea, after the winter pastures (oats) were planted. In the second stage, in September, 450 kg ha -1 single superphosphate were applied. In the third stage, in November, 450 kg ha -1 urea were applied to the soil's surface.
In 2015, fertilizers were applied in two stages. In the first stage, the application occurred in the second fortnight of May, after the winter pastures (oats) were planted, using 300 kg ha -1 natural phosphate, 100 kg ha -1 potassium chloride, and 300 kg ha -1 urea. In the second stage, 200 kg ha -1 urea were applied to the soil's surface at the end of December.
The use of herbicides is not allowed in EPA areas. Therefore, the weed control was performed manually using hoes and motorized brush cutters, during the first 120 days after seedlings were planted. During the seedling planting process, replanting was necessary for about 20% of the experiment's trees, as some seedling deaths occurred mainly because of plant competition and ant attacks.
Ant control was done by applying the Formifuu paste (Formifuu, Veranópolis, RS, Brazil) to all Pesq. agropec. bras., Brasília, v.55, e01275, 2020 DOI: 10.1590/S1678-3921.pab2020.v55.01275 seedlings after 15 days of planting. In addition, orange peels were distributed weekly on strategic locations, both in the area and on its outskirts, to attract ants and reveal the location of their nests, which were then mechanically destroyed.
In July 2015, the first tree pruning under the WP treatment was performed. The branches up to half of each tree's height were removed, since as the trees had already exceeded 6 cm mean diameter at breast height (DBH), considered as the minimum size necessary to support the presence of cattle without breaking the trunk of the tree (Medrado et al., 2009;Porfírio-da-Silva et al., 2012).
The animals entered the experiment when the trees were at 26 months of age and featuring 10.41 cm mean DBH (December 2015).
The experimental area that was 4.8 ha was divided, using an electric fence, in three paddocks that ranged from 1.3 to 1.8 ha each (Figure 1). The animals had water and mineralized salt freely available in all paddocks.
The experiment was carried out in a randomized complete block design with two treatments, pruned eucalyptus (WP) and unpruned eucalyptus (UP), and six types of damage, with three replicates (paddocks) Pesq. agropec. bras., Brasília, v.55, e01275, 2020 DOI: 10.1590/S1678-3921.pab2020.v55.01275 from 1.3 to 1.8 ha each. The blocks were defined according to differences for the soil-slope types. The animals used in the experiment were of no specific breed; they had 285 kg (± 54 kg) mean live weight and ages ranging from 1 to 3 years. The animals were distributed in the paddocks considering their average weight, in order to keep the animal load proportional to the supporting capacity of the paddocks.
The grazing period started on 11/27/2015 and ended on 04/13/2016, totaling 138 grazing days. It was managed under continuous stocking, with variable animal load (Mott & Lucas, 1953), and the pasture was maintained at 28± 5 cm average height.
A systematic inventory was performed on 10% of the trees, before and after the animals' arrival on the experiment; and DBH and total height were measured at the beginning and at the end of the grazing period. The DBH and height mean of the trees at 26 months of age, in December 2015, were respectively of 10.23 cm and 9.37 m under the UP, and of 10.59 cm and 9.15 m under the WP treatment.
The paddock treatments were distributed by alternating lines of WP and UP trees, and 1,142 trees were evaluated, 594 of which under the WP treatment, and 548 under the UP treatment.
The tree damages caused by cattle were evaluated at the end of the grazing period (April 2016), 30 months after planting. The method described by Porfírio-da- Silva et al. (2012) was adopted to evaluate the damages.
The damages caused by the animals were classified according to the damaged parts (incidence) of the affected plants, as: TB, trunk breakage; TI, trunk injury reaching the wood, with removal of the cambium tissue; BB, branch breakage; BI, bark injury not reaching the cambium tissue; SB, stem breakage and browsing on leaves; and LI, large injury (larger than 5 cm diameter). A weighting and scoring system was established for the different types of damages: TB = 10.0; TI = 4.0; BB = 2.0; BI = 1.5; SB = 1.0; and LI = 1.0. The criterion adopted was that of Porfírio-da- Silva et al. (2012) because it seeks to relate the type of damage and its importance for the future development of the tree.
The final score for each individual is the sum of its damages. TB was the only damage for which the maximum score of 10 was adopted, irrespectively of the occurrence of other types of damage. The occurrence of LI, TI, and BB damages was always associated with other damages.
According to the adopted criterion, five classes of damage intensity (d) were established: d0, null (no damage, score 0); d1, low (scores between 0 and 3); d2, mean (scores from 3 to 6); d3, high (scores from 6 to 10); and d4, extreme (score 10). Data were collected from all trees in the experiment (1,142 plants) to quantitatively describe and qualify the damages identified in the treatments.
All six types of damage (TB, TI, BB, BI, SB, LI), as well as the means of the damage scores (d0, d1, d2, d3, and d4) caused by the animals were compared between and within each treatment (WP and UP), using Tukey's test, at 5% probability.
The variance of the sum of damages was analyzed for homogeneity, using Bartlett's test, to verify the normality of the data for use in assumptions for the analysis of variance, to evaluate the damages within each treatment and among treatments. The Assistat program version 7.7 beta was used for the statistical analysis (Silva & Azevedo, 2009).

Results and Discussion
There were significant differences among the types of damage in Eucalyptus benthamii, under both the WP and UP treatments. Under the WP treatment, the damage with the highest incidence was the bark injury not reaching the cambium (BI), with 78.5% (Figure 2).
Under the UP treatment, the damages with highest incidences were branch breakage and stem breakage and browsing on leaves that were detected in 100% of the trees. There were also a high incidence (86.5%) of bark injury, under the UP treatment, and 68.8% large injuries -larger than 5 cm in diameter (Figure 2).
Eucalyptus susceptibility to bovine damage was previously reported by Medrado et al. (2009), who reported a generalized damage to Eucalyptus grandis at three years of age, in a silvopastoral system with Urochloa brizantha 'Xaraés', in the state of Paraná, Brazil. Damages in 61% of the trees increased to 92% over one year, and reached the xylem in 30% of the trees. Guerreiro et al. (2015) evaluated the susceptibility of 10 species of eucalyptus or their clones to bovine damage, in a silvopastoral system, in the city of Brotas, in the state of São Paulo, Brazil, and observed Pesq. agropec. bras., Brasília, v.55, e01275, 2020 DOI: 10.1590/S1678-3921.pab2020.v55.01275 significant differences for damage level between the various eucalyptus species or clones. They identified that the most affected species was Eucalyptus grandis, on which 58% of the injuries caused to the bark reached the wood.
The current study highlighted that the damage occurred mainly on tree leaves, branches, and bark. Trunk breakage was observed only in trees with DBH smaller than 6 cm, with 2.2% incidence under the WP treatment, and 4.2% under the UP treatment. These findings coincide with the results obtained by Porfírio-da- Silva et al. (2012), who reported breakages by livestock only in trees with DBH smaller than 6 cm.
Almost 100% of stem breakages and browsing on leaves and branch breakage were observed under the UP treatment, while the incidence of these damages was of 9.6 and 6.2% under the WP treatment, respectively ( Figure 2). Stem breakages and browsing on leaves, and branch breakages, in the UP treatment, were detected because some of the trees in the treatment were below the average height, which left branches and leaves exposed to cattle damages.
According to Porfírio-da- Silva et al. (2012), stem breakage and browsing on leaves should be expected in silvopastoral systems, especially when the animals are able to reach the branches. Pruning is suggested to reduce this occurrence, as well as branch breakage. Although the breaking of some leaves does not affect negatively tree development, broken branches may cause lesions that allow the access and potential attack of pathogenic organisms.
There were significant differences for damage intensity between the WP and UP treatments in Eucalyptus benthamii (Figure 3). Under the WP treatment, the damage intensity with higher incidence was d1, with 75.9%, which is considered an intensity of low importance, while under the UP treatment, the damage intensity with higher incidence was d2, detected in 90.9% of the trees, which is considered of medium importance for the future development of the tree.  Paraná, Brazil, in 2016. TB, trunk breakage; TI, trunk injury reaching the wood with removal of the cambium tissue; BB, branch breakage; BI, bark injury not reaching the cambium tissue; SB, stem breakage and browsing on leaves; and LI, large injury (larger than 5 cm in diameter). Means followed by equal letters, lowercase between tretments and uppercase between types of damage for the same treatment, do not differ by Tukey's test, at 5% probability.

Figure 3.
Incidence and intensity of damage caused by cattle (bovine) to Eucalyptus benthamii, with pruning (WP), or unpruned treatments (UP), in a silvopastoral system, in the municipality of Pinhais, in the state of Paraná, Brazil, in 2016. d0, null (d0 = 0); d1, low (0 < d1 ≤ 3); d2, medium (3 < d2 ≤ 6); d3, high (6 < d3 < 10); and d4, extreme (d4 = 10). Means followed by equal letters, lowercase between tretments and uppercase between types of damage for the same treatment, do not differ by Tukey's test, at 5% probability. The tree bark performs a wide range of functions in trees. The innermost layer of the bark plays an important role in transporting and storing photoassimilates, while the outermost layer protects the plant against the ingress of pathogens and protects the adjacent tissues from mechanical injury (Romero, 2006).
The extent of the damage depends on the depth and degree of the damage to the tree bark. When the lesion is restricted to the plant's surface and does not reach the cambium tissue, the damaged part recovers evenly. In the case of a deeper damage, scarring is often only partial, with a portion of the wood becoming apparent, which may serve as a gateway for disease infection and pest attack, which may even cause death to the tree (Medrado et al., 2009). When deep lesions occur in the tree bark, a carbohydrate derived from parenchyma tissues is deposited to close the lesion (Romero, 2006). This may increase the cattle's attraction to the tree (Porfírio-da- Silva et al., 2012).
Only 19.1% of the trees under the WP treatment showed no damage (d0). All other trees underwent some type of damage (Figure 3).
Although all treatments were damaged by cattle, a significant difference was observed of the scores assigned to the different types of damages. The WP treatment showed the lowest damage score average (1.67), which qualifies its damage intensity as low; while the UP treatment, with an average score of 5.03, is classified as of medium damage intensity (Table 1).
According to Pinkard (2002), pruning decreases the photosynthetic area of the trees, and may reduce the carbohydrate supply and growth regulators produced in the canopy, therefore reducing the trees' growth in diameter and height. This effect was reported by Barbosa et al. (2014), when evaluating the impact of sheep on the trees of a silvopastoral system planted with an E. grandis x E. urophylla hybrid at 24 months of age; these authors observed that artificial pruning affected the growth of the trees.
In the present experiment no such difference for growth -DBH and height -of E. benthamii trees was observed between the UP and WP treatments (Table 1). At 30 months of age, the mean DBH for E. benthamii was 11.83 cm under the WP treatment, and of 11.17 cm under the UP treatment. The total height was 10.40 m under the WP treatment, and 9.72 m under the UP treatment. There were no significant differences for DBH and total height between the WP and UP treatments.

Conclusions
1. Cattle entry into the silvopastoral system, at 26 months after planting of Eucalyptus benthamii, causes low-and medium-intensity damages to the trees, mainly on leaves, branches, and barks, in the treatments with and without pruning, respectively.
2. The practice of pruning of Eucalyptus benthamii trees do not affect the diameter at breast height and total height of the trees, and it enables the production of knot-free, better quality wood, contributing to reduce the incidence and intensity of damages caused by cattle in the evaluated silvopastoral system. (1) Means followed by identical letters, in the same column, do not differ by Tukey's test, at 5% probability.


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Soil sampling optimization using spatial analysis in irrigated mango fields under brazilian semi-arid conditions análise espacial em área de mangueira irrigada nas condições semiáridas brasileiras

- Soil sampling is a fundamental procedure in the decision making regarding the management of the soil, thus, a sampling plan should represent as accurately as possible the evaluated crop field. Therefore, the objectives of this study were to suggest a soil sampling approach and soil sampling point allocation using spatial analyses and compare to the classic statistic method in irrigated mango orchards in the Brazilian semi-arid region. The experiment was carried out in three commercial mango orchards located in the region of the São Francisco Valley, Brazil. Soil samples were collected in 0-0.2 m and 0.2-0.4 m depths following regular grids where the number of samples varied from 50 to 56. Soil texture, soil bulk density, soil total porosity, microporosity, macroporosity, pH, Ca, Mg, Na, K, Al, P, potential acidity, and the sum of basis were evaluated. Classical and geostatistical statistics were used to determine the ideal number of soil samples. Fuzzy c-means clustering technique was used to separate the areas into homogeneous zones and to allocate the sampling points. The wide method of 20 individual soil samples proved to be inefficient. On the other hand, the use of geostatistics proved to be efficient and is required for each crop field. The c-means clustering was adequate to separate the areas into homogeneous zones and, thus, to assist the sampling point allocation.

Introduction
The irrigated land clusters of the São Francisco valley, located in the semi-arid region, have emerged as the most important fruit-growing center in Brazil. In addition, it is considered the largest mango producing region in Brazil, accounting for 85% of the total mangoes exported from the country (CARVALHO et al., 2017). Considering the importance of this region, it is necessary to improve mango cropping techniques to ensure sustainability and reduce production costs. Among the possible techniques, soil sampling may be highlighted, as successful cropping depends on the success of fertility management and irrigation in crop fields (OLIVEIRA et al., 2007).
The condition of the agricultural field should be reproduced as accurately as possible using a minimum number of soil samples to estimate the mean value of the soil properties with some precision (LIMA et al., 2010). In this context, there is a limitation to the use of classical statistics, as suggested in the study by Catani et al. (1954) wherein the collection of 20 individual soil samples is recommended to obtain a composite sample in areas of up to 10 ha that are considered homogeneous. This method has been used indiscriminately in Brazilian agricultural fields regardless of the management system (conventional or no-tillage), crops (annual or perennial), and soil variability. However, the presence of spatial dependence, verified in most agricultural variables such as soil properties (LEMOS FILHO et al.. 2017;RODRIGUES et al., 2012, RODRIGUES et al., 2015, does not satisfy the requirement of randomness of errors, which is one of the premises of classical statistics (WANG et al., 2008). Thus, geostatistics has been shown as a feasible alternative as it considers spatial dependence (RODRIGUES et al., 2013) for the optimization of the sample number.
The semivariogram is the main tool used in geostatistics for the description of spatial correlation (SEIDEL; OLIVEIRA, 2016). From this, the range parameter is obtained, which is defined as the distance to where the sample points have spatial dependence (OLIVER; WEBSTER, 2014). Therefore, distances greater than the range value guarantee the spatial independence of the sampling points, and is useful in determining the sampling density (CARVALHO et al., 2002).
Some studies on annual and semi-perennial crops (LIMA et al., 2010;MONTANARI et al., 2012;OLIVEIRA et al., 2015;SÓRIA et al., 2018;SOUZA et al., 2006) have shown the efficiency of the use of geostatistics relative to classical statistics to determine the ideal number of soil samples. However, these studies do not indicate the location of sample collection within the crop field; they only define the final number of samples. Therefore, the use of management zone delineation techniques, such as the fuzzy c-means clustering, allows the division of crop fields into smaller and more homogeneous areas (RODRIGUES JR. et al., 2011;TAGARAKIS et al., 2013;VALENTE et al., 2012), which can be useful for assisting the sampling point allocation within the field such that the mean can be even more representative of the crop field and is estimated more accurately.
Additionally, there is no study defining the ideal number of samples in fruit growing areas, especially in the Brazilian semi-arid region. These conditions differ greatly from those found in annual crops, mainly in relation to the management of fertilization, which is carried out by fertigation concentrated under the canopy region of the plant, and commonly makes use of foliar fertilization in complementing the nutritional needs of the plant.
The hypothesis of this study is that the soil sampling strategy using spatial analysis may represent the soil properties of commercial mango areas in the semi-arid region of Brazil with greater accuracy.
The objectives of this study were to suggest a soil sampling approach and soil sampling point allocation using spatial analyses and compare to the classic statistic method in irrigated mango orchards in the Brazilian semiarid region.

-Site description
The experiment was carried out during 2017 and 2018, in three commercial mango orchards considered homogeneous by the farmers (the same type of soil, slope, plant age and management), in the São Francisco valley in the Brazilian semiarid region. The cultivation was under irrigation with the cultivar Tommy Atkins in distinct soils, focusing on the texture and the textural gradient of the three fields, the main characteristics of which are described in Table 1. According to Köppen's classification, the local climate is BSh, which is semiarid, with an annual precipitation of less than 500 mm concentrated only in three to four months of the year and annual averages of temperature varying between 18.7 and 33.6 °C (ALVARES et al., 2013). -Data collection and soil analyses Soil samples were collected under the canopy region after harvest (before application of any product), at depths  (Figure 1c). The number of samples was defined according to two criteria: 1) obtaining at least 30 pairs of points for calculation of the semivariance in the first lag (ARÉTOUYAP et al., 2016) following Yamamoto and Landim's (2013) recommendation of at least 30 to 40 points, and 2) the geometric shape of each area. The disturbed soil samples were obtained using a Dutch auger. Each soil sample was analyzed for particle size (pipette method), pH (1:2 soil/water mixture), Ca (KCl 1.0 mol L -1 solution extract), Mg (KCl 1.0 mol L -1 solution extract), Na (Mehlich-1 extract), K (Mehlich-1 extract), Al (KCl 1.0 mol L -1 solution extract), P (Mehlich-1 extract), and H + Al (calcium acetate and alkalimetric titration of the extract) content according to Teixeira et al. (2017a). From the analytical determinations, the sum of bases was calculated.
The undisturbed samples were collected using a double-cylinder, hammer-driven core sampler to determine soil bulk density (BD) and total porosity (TP), according to Teixeira et al. (2017b). Macroporosity (Ma) and microporosity (Mi) were estimated by a mathematical model proposed by Stolf et al. (2011) using data from BD and sand content.
-Descriptive statistics and obtaining the number of samples by the classical method Descriptive analyses of the data (mean, minimum, and maximum values, and coefficient of variation) was performed. Data normality was verified by the Shapiro-Wilk's test at 5% probability using R statistic software (version 3.2.2) (R Core Team, 2015). The coefficient of variation (CV) was classified according to Pimentel-Gomes and Garcia (2002) who define CV ≤ 10% as low, 10% < CV ≤ 20% as medium, 20% < CV ≤ 30% as high, and CV > 30% as very high variability.
To calculate the ideal number of individual soil samples that is representative of the area, disregarding the spatial dependence of the samples, the Cline method (1944) obtained from Equation 1 was used, which was the same procedure adopted in the study that defined the conventional 20 individual samples recommendation anthropogenic practices.
-Geostatistical analyses in obtaining the number of samples To calculate the ideal number of individual soil samples representative of the crop field, considering the spatial distribution of the samples, geostatistics was used. Semivariogram models were utilized to estimate the spatial dependence between the samples and to identify whether the variations were systematic or random. The semivariogram is a basic tool that can quantitatively represent the variation in a regionalized phenomenon in space and was estimated by Equation (CATANI et al., 1954), where n is the minimum number of samples, tα is the Student's t-test value α = 0.95, CV is the coefficient of variation, and d is the percentage variation around the mean (5%).
The soil properties were divided into three groups of variables, where each group was created based on two criteria: similarity of CV value and number of samples based on the classical approach. The chemistry group comprised the chemical properties of the soil, the physics group included the physical properties that are easily influenced by management (BD, TP, Ma, and Mi), and the texture group comprised the soil texture fractions (sand and clay content) that are considered stable and more related to soil formation than to Eq. 1 2 (OLIVER; WEBSTER, 2014), where N(h) is the number of experimental pairs of data separated by a distance h, Z (x i ) is the value determined at each sampled point, and Z (x i + h) is the value measured at a point plus a distance h.
For variables that showed a trend, that is, there was no stabilization of the sill values and thus, the intrinsic hypothesis was not satisfied, the trend surface method proposed by Vieira et al. (2010) was performed. This method involves fitting a trend surface by the least squares method and subtracting it from the original data generating a new variable called residuals. The semivariogram is then fitted using the residuals.
After the preparation of the experimental semivariogram, the fitting of the models that represent the spatial behavior of the variable under study was performed. The three most commonly used models for each semivariogram are: spherical, exponential, and Gaussian models, as tested by Zůvala et al. (2016). Details of the mathematical expressions of the models can be found in Oliver and Webster (2014).
After obtaining the parameters of the experimental semivariograms for each variable, the scaled semivariograms were performed by dividing the semivariance by the statistical variance, with the objective of reducing them to the same scale, to make the comparison between results of different variables easier (CASTIONI et al., 2019) and allowing the determination of an ideal number of samples that satisfy all the studied variables simultaneously. Three scaled semivariograms were prepared for each area, related to the three groups of variables: chemistry, physics, and soil texture in the two evaluated layers.
The number of individual samples, considering the spatial dependence, was determined using the range value obtained from the scaled semivariogram.
Although distance values greater than the range value already showed spatial independence, and consequently, randomness of the samples (TREVISAN et al., 2017; Eq. 2 Rev. Bras. Frutic., Jaboticabal, 2020, v. 42, n. 5: (e-173) YAMAMOTO; LANDIM, 2013), the collection of samples was simulated at a distance equivalent to one and a half times the range value as a precaution. In addition, the shape of the crop field should take into account to determine the number of samples in each studied field, thus, the following equation was used to number of samples was higher than the number of zones.

Results and Discussion
The descriptive statistics of the chemical and physical properties of soil in the layers at depths of 0-0.2 and 0.2-0.4 m in the Barreiro de Santa Fé, Mandacaru, and Sempre Verde areas are described in tables 2 and 3, respectively. The highest variability in the soil properties common to all three areas in both layers (Tables 2 and  3), based on the CV was observed for K, P, Ma, and clay content. As for the chemical properties, other studies also observed very high variability for K and P (LIMA et al., 2010;OLIVEIRA et al., 2015;RODRIGUES et al., 2012). These results can be justified, according to Rodrigues et al. (2012), as caused by the continuous applications of fertilizers that increase the variability in the crop field.
Additionally, in the semi-arid region, this variability may be increased even more, owing to the use of microsprinkler irrigation systems, where the soil is irrigated daily and most of the fertilizers are applied by fertigation. Moreover, the soils from the semi-arid region generally have a sandy texture, and its adsorption sites (colloids) are easily saturated, which favors the increase in the mobility and leaching of K, as it is a monovalent cation . Silva et al. (2014) confirmed this hypothesis in a study of soil fertility changes in mango orchards in the São Francisco valley region, demonstrating that the movement of the nutrients is favored by daily irrigation practices and the sandy soil texture, similar to observations in the three areas of the present study, contributing to its high variability.
As for the physical attributes, the Ma is also influenced greatly by agricultural management, and its values can be increased by the addition of organic matter (REGELINK et al., 2015), and at the same time present reduction due to human trampling in manual harvest, and the cultivation itself (irrigation, fertilization, etc.), by inducing the fractionation of larger aggregates in smaller units, with consequent reduction of macropores (CUNHA et al., 2011), justifying this high variability.
The high CV values for clay content can be explained by the large range in variation in the area considering the large amplitude (minimum and maximum values, Tables 2 and 3), and in addition, consistent with the results obtained by Ceddia et al. (2009), owing to the greater error associated with clay suction during the pipette method. Similar results were obtained by Rodrigues et al. (2015) in an Ultisol under guava cultivation, which also presented high variability for the clay content.
Analyzing the maximum and minimum values (Tables 2 and 3), it was observed that there is a considerably large amplitude for many attributes, as seen by the high variability. This can result in an error of recommendation due to the underestimation or overestimation of the real value as the average is considered the best estimator of soil attributes in the sample unit in classical statistics calculate the number of samples (Equation 3), where X is the vertical distance from the field; A is the value of range; and Y is the horizontal distance of the area, all variables in meters. When the first and / or second part of the equation obtained a decimal number, the value was rounded to the next integer.
-Obtaining homogeneous zones and allocating samples After the preparation of the experimental semivariograms and fitting of the theoretical models, the data that presented spatial dependence were interpolated using ordinary kriging. This technique is a generic term for a range of least squares methods applied to obtain the best linear unbiased predictions in terms of the minimum variance. It requires only knowledge of the variogram function and data for its implementation (OLIVER; WEBSTER, 2014).
Aiming for the delineation of areas into homogeneous zones for each of the groups of variables, the fuzzy c-means clustering technique (FRIDGEN et al., 2004) was performed with the interpolation maps using Management Zone Analyst software (MZA, version 1.0.1). As the soil collection was carried out in one location for both layers, each group included the variables of the two layers. The configurations in the MZA were: fuzziness exponent = 1.3, maximum number of iterations = 300, convergence criterion = 0.0001, minimum number of zones = 2, and maximum number of zones = 6. The determination of the ideal number of zones was performed using two indexes proposed by Odeh et al. (1992): NCE (Normalized Classification Entropy) and FPI (Fuzziness Performance Index), in which the lowest number for both indexes was selected.
To confirm that the number of zones suggested by the indexes was adequate, the Student's t-test (p < 0.05) was used to compare the mean values of the variables between the zones. It was considered adequate when at least one of the studied variables showed means considered statistically different between the indicated zones.
For the zone maps, the number of samples per zone was defined, which was one sample per zone when the number of zones was equal to the number of samples recommended based on the scaled semivariogram range value or established according to the percentage of the area that each zone represented when the recommended Eq. 3 Rev. Bras. Frutic., Jaboticabal, 2020, v. 42, n. 5: (e-173)     (FERRAZ et al., 2012). The minimum number of individual samples for estimating soil properties based on classical statistics using the Cline method (1944) with a 5% variation around the mean for the three studied areas can be found in Table 4. For the number of samples to be representative, it is necessary to choose the variable that needs the greatest number of samples, thus guaranteeing the representativeness of all samples. Table 4 The minimum number of single samples to estimate the soil properties based on the Cline's method (1944) with 5% variation around the mean at the layers of 0-0.2 m and 0.2-0.4 m depths cultivated with irrigated mango in the Barreiro de Santa Fé, Mandacaru and Sempre Verde areas, São Francisco valley region, Brazil It can be observed that the attributes which required a greater number of individual samples by classical statistics (Table 4) were the same ones that presented greater variability (Tables 2 and 3), which is expected from Cline's equation (1944), which accounts for the CV value. Thus, in the three studied areas, P was the determinant soil property to represent the chemical group, Ma was the determinant for the physical group, and clay was the determinant for the texture group, as they showed greater variability. In addition, it was verified that all groups in both layers and in all studied areas would require a greater number of individual samples than the current recommendation of 20 individual samples to obtain a composite sample.
The experiment carried out by Catani et al. (1954), where the current recommendation (20 individual samples) was determined, occurred in two areas under Oxisols, one cultivated with intercropped coffee and maize, and the other with a maize monoculture, both areas of up to 6 ha. Those conditions differ greatly from the conditions of the present study in terms of soil texture (mostly sandy), area (up to 10 ha), crop type (fruit crop), and soil management (daily irrigations and fertilization concentrated in the region under the plant canopy).
Thus, studying each crop field to determine the ideal sampling density is necessary as different natural characteristics and management are encountered. Schlindwein and Anghinoni (2000), estimating the horizontal variability of soil fertility and number of soil samples for no-tillage systems demonstrated that the current recommendation is inadequate to represent soil fertility in this system, and useful only for the conventional tillage systems.
It was also observed that lower the clay content (Tables 2 and 3) between the three areas (Mandacaru < Barreiro de Santa Fé < Sempre Verde), greater was the variability of soil properties in the chemical and soil texture groups and, consequently, higher the number of samples in each group. This is due to the lower buffering capacity of cations and anions in low clay content soils, indicating the lower resistance of the soil to changes in the concentration of a given nutrient in solution, contributing to higher leaching and subsequently increased variability (MALUF et al., 2015). However, the physical group showed the opposite behavior; higher the clay content (Sempre Verde > Barreiro de Santa Fé > Mandacaru), greater was the Ma variability, which is the soil property that determined the number of samples for this group.
The CV values indicate the degree of variability in the soil properties, with the lowest values showing lower heterogeneity in the studied area (WANG et al., 2008). Although the statistical measure CV allows the comparison of the variability between samples of variables that present different units, it does not allow the analysis of the spatial pattern of the soil properties (OLIVEIRA et al., 2015), making the use of geostatistics necessary.
In addition, it can be shown that using CV as a parameter to define the number of soil samples results in an impractical number of samples. Some authors have observed that soil sampling may have its efficiency increased with the addition of the spatial component (CASTIONI et al., 2019;LIMA et al., 2010;SOUZA et al., 2006), which can be determined using the range value of the semivariogram. Smaller range values indicate greater variability of the data, and consequently, a greater number of samples are required to represent the area (OLIVEIRA et al., 2015).
It was observed that most of the soil properties showed spatial dependence. Only Na and TP in the 0-0.2 m layer and Na, K, and Mg in the 0.2-0.4 m layer in the Barreiro de Santa Fé area, and Na, BD, clay, and Ma in the 0.2-0.4 m layer in the Mandacaru area showed a pure nugget effect, that is, they did not present spatial dependence. The pure nugget effect may have occurred owing to the high spatial variability of these soil properties and use of insufficient distance in the sample grid to model their spatial dependence. The pH in both layers and Mi in the 0-0.2 m layer in the Barreiro de Santa Fé area, and Mg in the 0.2-0.4 m layer in the Sempre Verde area showed a trend in the data, thus, the semivariogram was fitted to the data residues for these variables.
The calculation of the number of individual samples for each variable as a function of the range of the experimental semivariogram allows verification the reduction in the number of samples when geostatistics was used compared to that of classical statistics, but it does not allow the definition of a single ideal number of representative individual samples for all the variables. This is because the range is inversely proportional to the number of samples and to guarantee the spatial independence of the samples should be collect in distance greater than the range value.
Therefore, if sample numbers were obtained for each variable, theoretically the best choice would be the one with the largest number of samples to be representative of the others. However, this variable would have a lower range value, requiring a smaller sampling distance, which would not guarantee the spatial independence of the variables that showed the need for a smaller number of samples (greater range).
On the other hand, the determination of the sample density as a function of the largest range would guarantee the randomness of all variables, however the minimum representative individual number would be not reached for some variable, since the larger the range, the smaller the number of individual samples. Therefore, the variable with shortest range, which would require a larger number of individual samples, would not be satisfactorily represented.
These results are consistent with the experiments conducted by Souza et al. (2006) who studied a very clayey Oxisol under sugar cane in the São Paulo state and Lima et al. (2010) who evaluated a clayey Ultisol under a native forest (Atlantic Forest) in Espírito Santo state, where the use of the semivariogram range reduced the number of individual samples to be collected in relation to classical statistics for most of the soil chemical properties; but, did not allow the determination of a representative number of individual samples for all variables simultaneously.
In addition, Souza et al. (2006) and Lima et al. (2010) observed that if the classic recommendation of 20 individual samples was used, the minimum number obtained in their studies would not be met for some plant nutrients. It is also possible to verify that the number of individual samples for each variable differed between the two studies, reinforcing the need for the application of this technique to each area owing to the different conditions. Similar statement was made by Yu et al. (2011) who observed that the sample density may varies according to the type of soil, and patterns of land use, thus it is not appropriate to extrapolate results from smaller areas to larger areas, due to the different complexities of the involved factors.
In order to solve the problem of recommending an ideal individual sample number to represent each studied soil property individually, scaled semivariograms were performed (Figures 2, 3, and 4), which allow the fitting of a single model for all the variables of each group simultaneously, and therefore, a single range and a representative number of individual samples for all attributes (CASTIONI et al., 2019;OLIVEIRA et al., 2015;SÓRIA et al., 2018).
Considering that the distance between the individual samples collected should be greater than the range so that the randomness of the samples can be guaranteed (OLIVER; WEBSTER, 2014); a distance of one and a half times the value of the range of the scaled semivariogram was adopted as a precaution. Therefore, in the Barreiro   It can be observed that for all variables groups and studied areas the number of individual samples obtained from the scaled semivariogram range values was much lower than those obtained by classical statistics determined by the Cline (1944) method. Additionally, it was shown that the number of individual samples was lower also in relation to the current recommendation, possibly owing to the addition of the spatial component, reflecting the increase in the spatial dependence range and, consequently, in the reduction of the number of samples.
Similar results were obtained by Oliveira et al. (2015) in an Ultisol soil with a anthropic horizon under natural vegetation (Amazon forest) and pasture in the Amazonas state and Sória et al. (2018) studying an area with Oxisol and Ultisol under sugar cane in São Paulo state, with both studies demonstrating the efficiency of geostatistics in obtaining the sampling density through the range of the scaled semivariogram and change in the sampling intensity as the soil and cultivation conditions changed.
The experiments of Oliveira et al. (2015) and Sória et al. (2018) are consistent with the results of the present study and emphasize the need to specifically study each area to determine sampling density. Since, even in similar conditions of climate, soil type and crop (Table 1), a lot of others variables can be related to soil spatial variability (fertilization management, fertilizer sources, irrigation management, etc.) (TESFAHUNEGN et al., 2011) and different soil sampling density could be found, which makes each crop field unique.
Despite the recommendation of the distances at which the individual samples should be collected, random collection in the area could result in errors in estimation of the mean, as there are several ways to allocate the samples in a field following the determined distances. To solve this problem, it was necessary to separate the area into zones to minimize the error and, consequently, increase the accuracy (Figures 5, 6, and 7).
In addition, the separation of the area into zones showed that areas initially identified as homogeneous by the farmers without considering spatial analysis, were considered heterogeneous. The significant statistical difference in the average values between the zones proved this heterogeneity of the area (BAZZI et al., 2013;MALLARINO;WITTRY, 2004), justifying the need to collect individual samples in each zone so that the errors in the estimation of the mean are minimized, as already mentioned, even if it is not possible to apply site-specific management in each zone.
The ideal number of zones for the Barreiro de Santa Fé area was two, five, and two for the chemical ( Figure  5a), physical (Figure 5b), and texture (Figure 5c) groups, respectively, considering the lowest NCE and FPI. As the number of individual samples required was five for the chemical group and three for the texture group, the number of individual samples for each zone took into account the percentage of the area of the zones. Therefore, four and two individual samples from zone 1 and one individual sample each from zone 2 for chemistry and texture, respectively, should be collected. For the physics group, the number of zones coincided with the number of individual samples required; so, the recommendation is one individual sample per zone.  Therefore, one individual sample should be collected in zone 1 and seven individual samples in zone 2. For the physical group, the number of zones coincided with the number of individual samples needed, so a single sample should be collected per zone. The ideal number of zones for this area considered the lowest NCE and FPI index, except for the texture group.
The appropriate number of individual samples for texture was three samples and the ideal number of zones was four, making the collection impossible. Therefore, it was decided to consider the number of zones as three, and that collection should be done by taking one individual sample per zone. Increasing the number of individual samples to four would not meet the condition of independence of the samples, because the distance between them would be smaller than the range.
The ideal number of zones for the Sempre Verde area was two, six, and two for the chemical (Figure 7a  The adequate number of individual samples for the texture group was two individual samples and the ideal number of zones was four, making it impossible to collect. Therefore, it was decided to consider the number of zones as two, and that collection should be done by taking one individual sample per zone. As in the Mandacaru area, increasing the number of samples to four would not meet the independence condition of the samples because the distance between the samples would be less than the range. -Practical implications The technique suggested in this paper provide to farmers two options for soil management which are: 1) the individual samples may be mixed to obtain a composite sample and therefore obtain an average value for each of the attributes for the total area with more accuracy recommendation or 2) may be individually assessed in each zone for site-specific management.
To demonstrate the differences that may exist between the commonly used sampling technique and the technique recommended in the present study, a sampling and analysis procedure was simulated for the Barreiro de Santa Fé area. Twenty individual samples were collected (simulated) in zigzag to obtain a composite sample, which is based on classical statistics. Also, five individual samples were collected (simulated) spaced 180 m in the zones as suggested by the present study ( Figure 5).
To exemplify the possible differences in fertilizer recommendation the P content was used. The mean value of P content using the 20 individual samples was 29.53 mg dm -3 , thus, the nutrient level was classified as medium (20.1 -30 mg dm -3 ) as suggested by Ribeiro et al. (1999). Therefore, the P recommendation would be 100 g plant -1 . On the other hand, the mean value of P content using the geostatistic approach was 70.65 mg dm -3 and the nutrient level being classified as very good (> 45 mg dm -3 ) (RIBEIRO et al., 1999). In this case, the P recommendation would be 50 g plant -1 , that is, the half of the value obtained in the classic sampling method. Of course, the recommendation could be different in another simulation, but the technique addressed in this paper can guarantee the representativeness of the field heterogeneity whereas the classic method could not.
If the farmer opts by using site-specific management, the zone technique also would be useful. The K content was used to exemplify. The mean value of K content using the 20 individual samples was 0.42 cmol c dm -3 , thus, the nutrient level was classified as very good and the recommendation would be 120 g plant -1 (RIBEIRO et al., 1999). However, if the mean value from each zone would be considerate, the K content mean would be 0.33 cmol c dm -3 for zone 1 and 0.28 cmol c dm -3 for zone 2, and the nutrient level would be classified as good and medium, respectively. In this case, the K recommendation would Rev. Bras. Frutic., Jaboticabal, 2020, v. 42, n. 5: (e-173) be 120 g plant -1 for zone 1 and 240 g plant -1 for zone 2 (RIBEIRO et al., 1999). As a result, the plant could not express its full production potential in approximately 30% of the area. Thus, it is evident the importance of indicating the number of representative individual samples, the distance at which these individual samples should be collected to ensure spatial independence and the place of collection, considering the heterogeneity of the sampling unit, to obtain a representative sampling.
In Brazil, most evaluations of chemical and physical properties of soil are still performed using various laboratory analyses of different levels of complexity. When carried out for a large number of samples, these analyses require more time, financial resources, and chemical reagents. Consequently, they generate large amounts of residues, and thus, environmental impacts (SILVA et al., 2017). Therefore, a significant increase in sampling costs may result with the use of geostatistics in the determination of the ideal number of individual samples, limiting its application, as the use of this technique would initially require analyses of at least 100 samples (50 at each layer) in areas of up to 10 hectares.
An alternative is the use of sensors that estimate soil attributes, increasing the accuracy and speed, and reducing the cost of the analyses, and consequently, enabling the application of this technique. Among these sensors, the portable X-ray fluorescence spectrometer can be highlighted, which is useful for determining the total content of chemical elements in soils, allowing the inference of some soil properties (SILVA et al., 2017;UDEIGWE et al., 2015). Also, Vis-NIR spectroscopy technique can be successfully used to estimate soil properties (KODAIRA; SHIBUSAWA, 2013;NOCITA et al., 2013). Another is the sensor that determines the soil magnetic susceptibility which can be used to plan, map, and estimate soil chemical elements (JAKŠÍK et al., 2016;TEIXEIRA et al., 2017) and the apparent electrical conductivity of soil, which has been used to estimate and map the spatial variability of soil properties in multiple cropping systems (GRUBBS et al., 2019;STADLER et al., 2015), among others sensors.

Conclusions
The number of individual samples needed to satisfactorily represent the mango fields, using the classical statistics, was much higher than the current recommendation, which would possibly make the collection impractical and/or prohibitively expensive. On the other hand, the addition of the spatial component, using geostatistics, allowed a reduction of about 70% in the recommended number (20 individual samples) of soil individual samples to represent the three mango production fields; The fuzzy c-means clustering technique was adequate to separate the areas into homogeneous zones in order to allocate the sampling points; A specific geostatistical analysis is required for each area in order to determine the optimal sample density and allocate the samples, since the climate, soil type, texture, field size, cultivation and management influence soil variability; Critical differences in fertilizer recommendation were observed when comparing the classic sampling method and the allocation method based on soil variability within the field. ALVES, J.C. Amostragem para avaliação da fertilidade do solo em função do instrumento de coleta das amostras e de tipos de preparo do solo. Revista Brasileira de Ciência do Solo, Viçosa, MG, v.31, p.973-983, 2007.


extfieldsofstudy: Biology Medicine

text:
Comprehensive transcriptome analysis of reference genes for fruit development of Euscaphis konishii

Background Quantitativereal-time reverse transcriptase polymerase chain reaction is the common method to quantify relative gene expression. Normalizating using reliable genes is critical in correctly interpreting expression data from qRT-PCR. Euscaphis konishii is a medicinal plant with a long history in China, which has various chemical compounds in fruit. However, there is no report describing the selection of reference genes in fruit development of Euscaphis konishii. Methods We selected eight candidate reference genes based on RNA-seq database analysis, and ranked expression stability using statistical algorithms GeNorm, NormFinder, BestKeeper and ReFinder. Finally, The nine genes related to the anthocyanin synthesis pathway of Euscaphis konishii were used to verify the suitability of reference gene. Results The results showed that the stability of EkUBC23, EkCYP38 and EkGAPDH2 was better, and the low expression reference genes (EkUBC23 and EkCYP38) were favourable for quantifying low expression target genes, while the high expression reference gene (EkGAPDH2) was beneficial for quantifying high expression genes. In this study, we present the suitable reference genes for fruit development of Euscaphis konishii based on transcriptome data, our study will contribute to further studies in molecular biology and gene function on Euscaphis konishii and other closely related species.

Genes with expression levels lower than 5 FPKM in tanscriptomes would make poor qPCR 118 references due to the difficulties in detecting and quantifying their expression. (Kimmy AS,119 Patrick PE& Joshua RP, 2017). After their removal, a total of 75,048 genes in E. konishii were 120 evaluated. We calculated the mean expression value, standard deviation, and coefficients of 121 variation based on the raw RNA-seq data. Base on the requirements CV ≤ 0.2 and FC ≤ 0.2, 122 1,131 genes (Table S1) were obtained by removing overabundant genes with low expression 123 levels. Finally, we selected the eight candidate genes according to genome annotation of each, 124 which was assigned base on the best mach of the alignments using Blast to NR, TrEMBL and 125 KEGG databases. The selecting steps of candidate reference genes were shown in Fig S1. In 126 order to ensure the accuracy of the reference gene predictions, the coding sequences of eight 127 selected genes was used as queries for BLAST orderly through the TAIR database 128 (http://www.arabidopsis.org/). The sequences with the highest homology with Arabidopsis were 129 shown in Table 1.

130
Here we obtained the nucleotide sequences of reference genes from our laboratory. The 131 nucleotide sequences of candidatate reference genes were appended in Table S2. According to 132 the nucleotide sequence of candidate reference genes and the design principle of qRT-PCR, 133 forward and reverse primers of all candidate reference genes were designed using Primer 134 Premier 6.0 software and were synthesized by Jinweizhi Biotechnology Co., LTD (Suzhou, 135 China) and purified by PAGE. The primer sequences, amplicon size, anneal temperature were 136 shown in Table 2.    The instrument calculated the Cq value of each sample based on the qRT-PCR experiment. 159 We can analyse the stability of candidate reference genes using GeNorm, NormFinder, and 160 BestKeeper software. Finally, ReFinder was used to calculate the comprehensive ranking of the 161 stability of candidate reference genes based on analysis results.

Validation of the candidate reference genes 163
Anthocyanin is the key factor affecting fruit colouration as an important plant pigment. In 164 general, fruit colouration is closely related to the content and proportion of anthocyanin. To 165 verify the results of our experiments, the expressions of nine genes related to the anthocyanin 166 synthesis pathway were calculated with the reference genes selected (Fig. S5). Moreover, the 167 EkSS3 gene related to glycometabolism pathway was also used to validate the reliability of 168 reference genes. The details of primer were shown in Table 3. The qRT-PCR experimental 169 method was the same as described above, and the relative expression level was calculated by the 170 2 −ΔΔct method. Experimental data from three biological replicates were analysed using analysis of 171 variance (ANOVA), followed by Student's t-test (P<0.05). According to selecting process (Fig. S1), eight candidate reference genes were chosen to 184 perform the gene normalization studies. The sequences of these candidate reference gene were 185 used to design the qRT-PCR primers. The details of eight reference genes were lised in Table 1. 186 The gene models of eight candidate reference refered to the homologous genes of Arabidopsis, 187 and the details were shown in Figure S2.
188 Candidate reference genes for RT-PCR amplification 189 PCR results of the 8 candidate internal reference genes are shown in Figure S4 with a single 190 band, no primer dimer and nonspecific amplification, which could be used for subsequent qRT-191 PCR analysis. We sequenced the PCR amplification products of eight genes and blast the 192 correctness of the PCR amplified fragments (Table S3).  As shown in Figure S3, the melting curve of the 8 candidate reference genes at different 200 developmental stages only had a single main peak, and the amplification curves between the 201 repeated samples had high repeatability.   Manuscript to be reviewed 238 EkUBC23 was used to normalize the expression due to its greater stability. Relative expression 239 levels were normalized using the low expression reference gene (EkUBC23) and high expression 240 reference gene (EkGAPDH2). The results showed that there were some differences between the 241 expression levels of 9 genes related to the anthocyanin synthesis pathway and transcriptome 242 sequencing (Figure 4). When EkUBC23 was used as the reference gene, the results had no 243 significant difference. When EkGAPDH2 was used for normalizing low expression target genes 244 [c57877.graph_c0( DFR), c59825.graph_c0(CHS) and c69862.graph_c1(UFGT)], the results 245 were different from the expression of transcriptome. However, EkGAPDH2 has better accuracy 246 than EkUBC23 when it was used for normalizing high expression target genes 247 [c72659.graph_c0(CHS) and c72737.graph_c0(CHI)]. Therefore, we suggest that the stability 248 and expression of reference genes should be considered as important selection conditions. 249 To ensure the reliabilty of result, We chosen EkSS3 gene related to glycometabolism pathway 250 as target gene. Relative expression levels were normalized using two most stable reference genes 251 (EkGAPDH2 and EkUBC23). As shown in Figure S6, when normalized using EkGAPDH2 and 252 EkUBC23 as reference gene respectively, the results had no significant difference. Compared 253 with the green stage, EkSS3 expression was clearly downregulated at turning stage and red stage.  Many studies have shown that it was quicker and more efficient to screen appropriate 259 reference genes based on transcriptome data. In addition, primers designed according to the 260 transcriptome sequencing of the material itself are more stable than those using other materials. 261 Selection of the reference gene based on transcriptome data has been done in many plant species, 262 such as Corylus (Yang D et al., 2017), Sedum alfredii (Sang J et al., 2013) and Dendrocalamus 263 sinicus [Guo XJ et al., 2018], and so on. In the present study, we selected 3 stable reference 264 genes (EkUBC23, EkCYP38 and EkGAPDH2) based on transcriptome data of E. konishii 265 pericarp in three different developmental stages. This result validates and complements the 266 results of reference gene screening by Liang. (Liang WX et al., 2018b).

267
The expression of reference genes was related to organ type, developmental stages and 268 external environmental conditions. ACT was commonly used as a reference gene. For example, it