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WSU-TFREC/Postharvest Information Network/Bitter Pit Prediction in Apples and the Commercial Use of Fruit Magnesium Infiltration



Bitter Pit Prediction in Apples and the Commercial Use of Fruit Magnesium Infiltration



Introduction

In a 1992 survey of Chilean apple growers and exporters, bitter pit was the problem most frequently mentioned as affecting the quality of fruit produced in this and other apple-producing countries. Bitter pit appears mainly postharvest as brown spots on the surface of the fruit. For many years, it has been known that bitter pit is linked with the calcium (Ca) nutrition of the fruit; however, the problem still has wide impact on the industry.

Most research has concentrated on the control of bitter pit through preharvest applications of Ca; different compounds have shown to effectively reduce the incidence of the disorder. The degree of control could be improved if the magnitude of its incidence could reliably be predicted or assessed beforehand; however, for Chilean conditions, the predictive method currently used elsewhere (fruit mineral analysis) has had low predictive capacity of the disorder. Since 1991 our research team has studied bitter pit prediction through fruit infiltration with magnesium (Mg) under vacuum, which indirectly measures the Ca concentration in the fruit. With this method, evidence of Ca deficiency (bitter pit-like symptoms) appears 7 to 12 days after infiltration and is inversely related to the Ca concentration of the fruit (Burmeister and Dilley, 1991). Massive use of the system was initiated in the most important Chilean apple producing regions in the 1997 harvest season and has expanded since.

Our aim in this paper is to present the fundamental aspects of bitter pit, its control and the different methods used for its prediction, as well as the characteristics, requirements, and massive use of fruit Mg infiltration as a method to predict bitter pit in apples.


Physiology of Bitter Pit

Bitter pit usually appears postharvest as depressed brown lesions in the skin of the fruit, located mainly on the calyx end of the fruit (Ferguson and Watkins, 1989). Research done in the last 50 years has shown that the incidence of the disorder is inversely related to the Ca concentration of the fruit and, in general, is directly related to Mg, potassium (K), phosphorous (P), and nitrogen (N) levels in fruit tissues (Garman and Mathis, 1956; Fallahi et al., 1997).

Different pieces of evidence (e.g., effect of summer and root pruning, application of growth regulators, fruit thinning, Ca fertilization, effect of localized Ca application) have shown that the incidence of bitter pit relates better to the Ca distribution within the plant than to the total Ca supply from the soil. Because Ca moves mainly through the transpiration stream and because vegetative tissues have less resistance to transpiration, Ca absorbed from the soil will tend to move toward vegetative tissues and away from the fruit (Jones and Higgs, 1982). This understanding of the physiology of bitter pit is important for focusing the control strategies of the disorder.


Bitter Pit Control

Because incidence of bitter pit is linked to the Ca concentration of fruit tissues, control has been directed mainly toward supplementing the Ca supply to the fruit via preharvest foliar applications. Different compounds, application rates, and spray programs have been studied by many researchers around the world (Faust, 1989; Ferguson and Watkins, 1989). Even though variable results have been obtained for different seasons, varieties, growing areas, chemicals, and conditions during and after spraying, a schedule of 6 to10 foliar sprays during the season is commonly used by growers to increase fruit Ca concentrations and, consequently, reduce bitter pit incidence.

Because bitter pit depends not only on soil Ca supply, but on its distribution among different plant organs (Van der Boon, 1980), research has shown that the degree of control can be greatly increased if a comprehensive or integral control that combines preharvest Ca sprays and vegetative control practices (e.g., water and nutrition management, thinning, pruning, scoring), are done concurrently during the season (Faust, 1989; Terblanche, 1981).


Bitter Pit Prediction

Bitter pit prediction has important implications not only for the grower, but also for packinghouses, produce storage companies and the different actors of the marketing chain.

Considering that the price of the commodity (in this case apples) will depend both on its quality and the time of the year that the fruit is marketed, the fruit grower would like to have an early assessment of the storage potential of the fruit. As the general economical theory indicates, when supply is large (i.e., at harvest time) prices are low; thus, to obtain a better price, those in charge of marketing the fruit would like to segregate the fruit from different orchards according to its postharvest life potential. Bitter pit is linked to the Ca concentration of the fruit, which is related to its postharvest life (Ferguson and Watkins, 1989). By having a reliable assessment of the future incidence of bitter pit, segregation of apples according to their bitter pit potential would also aid in separating fruit based on its postharvest life.

Methods for Bitter Pit Prediction

In general, methods for bitter pit prediction are based on the physiology of bitter pit incidence. Considering this, three groups of methods can be distinguished: (1) those linked to the nutritional status of the fruit (Method 1), (2) prediction related to accelerating fruit maturity (Method 2), and (3) those based on the relationship between bitter pit and vegetative growth (Method 3). A comparison of these methods is shown in Table 1.

Table 1. Predictive capacity (r2) of different methods used to predict bitter pit in apples.
(Sources: Retamales and Valdes, 1996; Van der Boon, 1980.)
Prediction Method
Predictive Capacity (r2)
Description
Mg Infiltration
0.67-0.87
Method 1
Maturity hastening
0.60-0.84
Method 2
Terminal shoot growth
0.38-0.50
Method 3
Fruit mineral analysis: K+Mg/Ca
0.12-0.40
Method 1
Fruit mineral analyis: Ca
0.19-0.43
Method 1
r2: measures the proportion of bitter pit incidence in postharvest (after 90 days at 2 °C + 10 days at 18 °C), which is explained by each method.

    Method 1: Nutritional Status Methods

With regard to the first group of methods, because the presence of bitter pit is related to the nutritional status of the fruit, the logical approach has been to measure the concentrations of several elements linked to bitter pit incidence (i.e., Ca, K, Mg, N, and P). However, this method has not always provided reliable results. The difficulty is probably related to the lack of knowledge regarding the tissue directly linked to bitter pit incidence; this drawback is augmented by the large variability in nutritional concentrations within and between fruit (Ferguson and Triggs, 1990). This implies that there is lack of consensus regarding fruit sampling; consequently, between and within the various apple-producing regions, different laboratories sample a wide variety of tissues to measure their nutritional status and have diverse standards to determine the potential for bitter pit incidence (Marcelle, 1990).

Measuring Ca concentration indirectly by Mg infiltration can also be included within this first group. The basis of this method is the antagonism at a cellular level between Ca and Mg (Cooper and Bangerth, 1976). If an apple is immersed in a solution rich in Mg and vacuum is applied, Mg will replace the Ca within the apple and symptoms of Ca deficiency (bitter pit) will appear in the fruit a few days later (Burmeister and Dilley, 1994; Retamales and Valdes, 1996). The possibility of using such a method to predict bitter pit relates to the fact that the exchange of Ca for Mg is inversely related to the Ca concentration of the fruit (i.e., the lower the Ca concentration in the fruit, the higher the intensity of the exchange and the more symptoms that appear). This method represents an indirect procedure for measuring fruit Ca concentration, but differs from the mineral analysis method because it integrates all fruit tissues that are involved in the incidence of the disorder (Hopfinger at al., 1984). Given these conditions, the Mg infiltration method has shown higher capacity to predict bitter pit. This method will be examined in further detail in the "Characteristics of Fruit Magnesium Infiltration" section.

    Method 2: Maturity Acceleration Methods

A second group of bitter pit prediction methods relates to the finding that most bitter pit appears postharvest. Thus, enhancing the maturity of the fruit (with the ethylene releasing compound Ethephon) will trigger earlier appearance of bitter pit symptoms and should serve as a predictive method (Ferguson and Watkins, 1989). However, the results are obtained too late in the season, since it has been found that fruit is only responsive to ethylene when treated with Ethephon very close to harvest. Results have been erratic, most likely because the expression of the symptoms is related to both the nutritional condition of the fruit and its maturity.

    Method 3: Vegetative Growth Methods

A last group of bitter pit predictive methods is associated with the interaction between vegetative growth, Ca distribution among vegetative (shoots and leaves) and reproductive (fruit) tissues, and bitter pit incidence. An intense vegetative growth (expressed as shoot growth) would indicate that Ca is been diverted to shoots instead of fruit tissues; in this situation higher bitter pit incidence would occur. For conditions found in Chile, the predictive capacity of this method is similar to mineral analysis (Method 1); however, because its cost is very low, the results are almost instantaneous, and the assessment is done early in the season, it can be a useful method for the grower as an early warning of potential bitter pit incidence in a given orchard.

Requirements of a Predictive Method

For a method to be useful to predict bitter pit incidence, it should satisfy the following minimum requirements: (1) reliability: throughout different seasons, the method should provide reliable information of the bitter pit potential of orchards of different varieties, management conditions, and growing areas; (2) cost/benefit: the cost has to be affordable, and should relate to its capacity to predict the disorder; and (3) opportunity: the method should provide information before the fruit enters the marketing chain; this would allow fruit segregation according to its storage potential. If possible, results should be available several days before harvest to allow some control of the disorder (Retamales and Valdes, 1996).

Characteristics of Fruit Magnesium Infiltration

This method of bitter pit prediction requires the consecutive stages outlined in the sections that follow.

    Fruit Sampling

To be effective, any bitter pit predictive method should aim at reducing variability. The largest source of variability in bitter pit prediction is the selection of trees and fruit in the orchard (Ferguson and Triggs, 1990). Our experience during the development of the method for the last 9 years has convinced us of the need to specialize personnel for this task. Because the aim should be to predict bitter pit for "most" apples in the orchard, the person in charge of sampling should walk around a given orchard (of uniform soil, management, rootstock, and age) and establish the most frequent canopy size/crop load combination for a particular site. Once this is done and 40 representative trees are selected, fruit (one per tree) should be collected from a specific portion of the tree 60, 40, or 20 days before harvest. In Chile, we recommend to collect fruits growing at chest height in a central position within limbs located in the south-west orientation.

    Infiltration

Within 24 hours after collection, apples are immersed in a 9-L desiccator containing a solution of 0.05 M magnesium chloride, 0.01% Tween-20 (surfactant that favors exchange between fruit and the external solution), and 0.4 M Sorbitol (to equilibrate internal and external ionic concentrations). Vacuum levels for infiltration generated by a vacuum or mechanical pump are maintained for two minutes and vary according to the cultivar sampled: 100 mm of mercury for Granny Smith, Gala, and Jonagold; 500 mm for Red Delicious spur strains, Braeburn, and Fuji (Retamales and Valdes, 1996).

    Symptoms Development

Once infiltrated, fruit are removed from the desiccator, dried with cloth or paper towels and left at room temperature (18 to 20 °C). Bitter pit-like symptoms will start to appear on fruit surface after 7 to 10 days, and will be completed after three weeks. However, pitting is evaluated after 16 to 18 days because the number of fruit affected and the number of pits/fruit at this time are well correlated with the final counts (Retamales and Valdes, 1996). Some training is required to differentiate bitter pit

    Statistical Analysis

Research done by our team in the last six years has established mathematical relationships or equations (for each combination of variety, location of the orchard, and sampling date) between the number of pits obtained after infiltration in Mg and the level of bitter pit that the fruit would show after 3 months of regular storage (2 °C; 90 to 95% relative humidity) plus 10 days at room temperature (18 °C; marketing period). The statistical analysis predicts both incidence (percentage of fruit presenting the disorder) and intensity (number of pits per fruit) of bitter pit in the sample.


Results of Massive Use of Magnesium Infiltration

Considering the interest of the Chilean apple exporting industry for a reliable method to predict bitter pit, and the encouraging results obtained by the research team in controlled experiments in previous seasons, some exporting companies approached the senior author in 1997 to evaluate the performance of the Mg infiltration at a commercial scale. This new development required an important amount of time from the research team in instructing technicians of the exporting companies about the different stages of this bitter pit prediction procedure.

In the first season (1997), a total of 375 samples from 140 orchards were collected 40 days before harvest (each sample representing 10 to 15 ha), according to the guidelines indicated in Method 3. A portion of these samples (22.4%) were collected, infiltrated, and evaluated for symptoms by the exporting companies, while the rest were collected and brought to the Postharvest Laboratory of the Pome Research Center of the Universidad de Talca. Fruit from a randomly selected group of orchards (56), were stored in regular cold storage for 90 days, followed by 10 days at room temperature to evaluate the predictive capacity of the Mg infiltration method at a commercial level (Table 2). Despite the large increase in variability due to the greater number of people collecting, infiltrating, and evaluating symptoms in the fruit, the initial results were encouraging; the predictive capacity of the Mg infiltration method in a commercial setting was reduced only by 10% compared to the research setting.

Table 2. Predictive capacity (r2) of bitter pit* for the Mg infiltration method in commercial orchards of several apple cultivars in two locations of South-Central Chile, 1997 season.
Cultivar
Location=Curico
[r2 (no. of samples)]
Location=San Javier
[r2 (no. of samples)]
Royal Gala
0.95 (5)a
0.74 (9)b
Granny Smith
0.53 (5)c
0.99 (3)b
Scarlet Spur Delicious
0.55 (6)c
0.97 (4)a
Red Chief Delicious
0.37 (5)c
0.42 (5)c
Braeburn
0.79 (4)c
0.70(7)b
Fuji
0.99 (3)b
na
*r2 : measures the proportion of bitter pit incidence in postharvest (after 90 days at 2 °C + 10 days at 18 °C), that is explained by the bitter pit
ar2 significant at P=0.01
br2 significant at P=0.05
cr2 not significant

The number of samples for bitter pit prediction increased dramatically in the 1998 season (1200 samples). The proportion of samples infiltrated and evaluated for symptoms by the exporters also increased dramatically (75%). In 1999 the number of samples processed by the exporting companies remained constant (1175); however, their share of the total rose to 78.5%.

Reports of the bitter pit prediction service are given to the growers/exporters as percent incidence of bitter pit in five categories: 0% (absent), 1 to 3% (very low), 4 to 6% (low), 7 to 9% (medium), 10 to 12% (high) and > 13% (very high). Absolute accuracy in prediction varied between 40 and 70% of the samples, depending on variety; however, when one or two adjacent categories were included, accuracy of bitter bit prediction reached 82 to 100% (Figure 1).

Figure 1. Proportion of samples in which there was coincidence (absolute, or +/-1 or 2 bitter pit levels) among predicted (18 days after Mg infiltration) and "real" (3 months at 2 °C + 10 days at 20 °C) bitter pit levels for five apple varieties. Calculations based on 872 samples, 1998 season.

Depending on the variety, Mg infiltration would either underestimate or overestimate the "real" incidence of bitter pit. Thus, in Royal Gala and Braeburn, underestimation predominated (82 and 75% of the inaccurate predictions, respectively); while for Red Delicious spur strains and Granny Smith, overestimation was more common (75% of the inaccurate predictions).

Even though, in general terms, the massive implementation of bitter pit prediction through Mg infiltration has been successful, there have been several difficulties associated with its use. In decreasing degree of importance these difficulties include variable fruit size, uneven fruit number, inaccurate date of collection, and bruised fruit. The variability in fruit size is important because the incidence of bitter pit will vary according to fruit size (larger fruit generally presenting higher bitter pit incidence due to higher dilution of their Ca content), a sample containing fruit of different sizes will have an "average" incidence of bitter pit that will not be representative of the most predominant fruit size in the orchard. Adequate training and specialization of the person in charge of sampling should greatly reduce this source of variability and help increase the predictive capacity of the Mg infiltration method.


Conclusions

Bitter pit, related to the Ca nutrition of the fruit, is the most important physiological disorder in apples. For its prediction and control, Ca levels in the fruit must be adequately assessed; however, because Ca levels are low and variable within and among fruits, is difficult to measure this mineral directly. Different methods (maturity enhancement; shoot growth; fruit Ca, Mg, and K levels; and fruit Mg infiltration) have been studied to establish the propensity of the fruit to present bitter pit in storage.

Research done since 1991 at the Pome Center of the Universidad de Talca has shown that the Mg infiltration method can adequately predict the incidence of bitter pit after storage. Through these years, the sampling scheme, vacuum levels, concentrations, time for appearance of symptoms, and equations to relate number of bitter pit-like lesions to the incidence of bitter pit in after storage for different varieties, sampling dates, and locations have been established.

Commercial use of the Mg infiltration prediction method for the last three seasons has shown a slightly reduced capacity to predict bitter pit as compared to controlled experiments. Both the total number of samples and the proportion of those done by fruit exporters has increased in these years; it is estimated that about one-third of the total apple acreage in Chile is assessing the probable incidence of bitter pit through fruit Mg infiltration.

The most frequent problems associated with the commercial use of Mg infiltration are: nonuniform fruit size, uneven fruit number, inadequate sampling date, and bruising of samples. Specialization of personnel for sampling and processing the samples is strongly encouraged to reduce variability and increase the capacity to adequately of Mg infiltration to predict bitter pit.

Mg infiltration can help the different actors of the marketing chain by announcing in advance the propensity of different batches of fruit to express bitter pit in storage; this can aid both in segregating fruit according to their potential to show bitter pit and also in increasing fruit Ca levels, which will extend the quality and life of fruit in storage.


Literature Cited

Burmeister, D. M. and D. R. Dilley. 1991. Induction of bitter pit-like symptoms on apples by infiltration with Mg is attenuated by Ca. Postharvest Biol. and Techn., 1: 11-17.

Burmeister, D. M. and D. R. Dilley. 1994. Correlation of bitter pit on Northern Spy apples with bitter pit-like symptoms induced by Mg+2 salt infiltration. Postharvest Biol. and Techn., 4: 301-307.

Cooper T. and F. Bangerth. 1976. The effect of Ca and Mg treatments on the physiology, chemical composition and bitter pit development of Cox's Orange Pippin apples. Sci. Hort. 5: 49-57.

Fallahi, E., W. S. Conway, K. D. Hickey and C. E. Sams. 1997. The role of calcium and nitrogen in postharvest quality and disease resistance of apples. HortSci., 32: 831-835.

Faust, M. 1989. Physiology of temperate zone fruit trees. John Wiley and sons. New York. 337 p.

Ferguson, I. B. and C. M. Triggs. 1990. Sampling factors affecting the use of mineral analysis of apple fruits for the prediction of bitter pit. N. Z. J. of Crop and Hort. Sci. 18: 147-152.

Ferguson, I. B. and C. B. Watkins. 1989. Bitter pit in apple fruit. Hort. Reviews 2: 289-355.

Garman, P. and W. T. Mathis. 1956. Studies of mineral balance as related to ocurrence of Baldwin spot in Connecticut. Connecticut Experiment Station Bulletin 601.

Hopfinger, J. A., B. W. Poovaiah and M. E. Patterson. 1984. Calcium and magnesiun interactions in browning of Golden Delicous apples with bitter pit. Sci. Hort. 23: 345-351.

Jones, H. and K. Higgs. 1982. Surface conductance and water balance of developing apple (Malus pumila Mill.) fruit. J. Exp. Bot. 33: 67-77.

Marcelle, R. D. 1990. Predicting storage quality from preharvest fruit mineral analysis, a review. Acta Hort. 274: 305-313.

Retamales, J. B. and C. Valdes. 1996. Avances en la predicción de bitter pit en manzanos (Advances in the prediction of bitter pit in apples). Rev. Fruticola 17: 93-97.

Terblanche, J. H. 1981. An integrated approach to orchard nutrition and bitter pit control. The Deciduous Fruit Grower. 31(12): 501-513.

Van der Boon, J. 1980. Prediction and control of bitter pit in apples. II Control by summer pruning, fruit thinning, delayed harvesting and soil calcium dressings. J. Amer. Soc. Hort. Sci. 55: 313-321.


Acknowledgements

Research partially funded by Fondecyt-Chile Project N° 1980045

Jorge B. Retamales and Claudio A. Valdes

Centro de Pamaceas, Universidad de Talca, Casilla 747-Talca, Chile
jretamal@pehuenche.utalca.cl


September 2000

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