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Article

Determinants of Postharvest Quality in ‘Gala Schniga® SchniCo Red(s)’ Apples: The Role of Harvest Date, Storage Duration, and 1-MCP Application

by
Maria Małachowska
* and
Kazimierz Tomala
Department of Pomology and Horticulture Economics, Institute of Horticultural Sciences, Warsaw University of Life Sciences (SGGW-WULS), ul. Nowoursynowska 159 C, 02-787 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(22), 2363; https://doi.org/10.3390/agriculture15222363
Submission received: 25 September 2025 / Revised: 4 November 2025 / Accepted: 12 November 2025 / Published: 14 November 2025
(This article belongs to the Special Issue Adapting Horticultural Plant Cultivation Technology and Storage to Changing Conditions)
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Abstract

Poland, as a leading apple producer in the EU, must maintain high fruit quality during prolonged storage and distribution, which is crucial for exports to distant markets. Therefore, it is essential to clearly identify which factors most strongly affect quality and the magnitude of their effects in order to make informed choices about pre- and postharvest practices, storage technology, and logistics. The objective of this study was to assess the effect of selected factors on the quality of apples of the ‘Gala Schniga® SchniCo Red(s)’ cultivar after long-term storage. The study analyzed the effects of harvest date (optimal and delayed), three variants of 1-methylcyclopropene application (control-0 µL·L−1 1-MCP, Harvista™, SmartFresh™, and Harvista™ + SmartFresh™), storage period (5, 7, and 9 months), simulated trading period (0 or 7 days at 20 °C) and storage technology (ULO: 1.2% CO2: 1.2% O2; DCA: 0.6% CO2: 0.6% O2) in two consecutive seasons (2022/2023 and 2023/2024). Five quality parameters were evaluated: flesh firmness (F), soluble solid content (SSC), titratable acidity (TA), SSC/TA ratio, and the concentration of 1-aminocyclopropane-1-carboxylic acid (ACC). Backward-elimination stepwise regression and partial eta squared (η2) calculations were used to analyze the data to determine the factors with the greatest impact. The post-harvest application of 1-MCP had the strongest effect in terms of maintaining firmness (η2 = 70.4%) and acidity (η2 = 38.0%) and reducing ACC content (η2 = 21.3%). Harvista™ preparation had a weaker or negligible effect on ACC content, but reduced SSC (η2 = 22.7%). Harvest date, storage duration, and shelf life significantly influenced all traits, with controlled-atmosphere regime further modulating outcomes. By integrating preharvest maturity with treatment timing and CA storage, we disentangled the relative contributions of harvest timing, treatment, and storage. The results provide actionable inputs for a decision-support tool to help producers maintain target quality—firmness, SSC, TA, SSC/TA, and ACC—through optimized practice, storage technology choice, and logistics.

1. Introduction

Poland is the largest apple producer in the European Union and one of the main exporters of apples worldwide [1,2]. The recent years have seen a gradual increase in crop acreage and apple production with a simultaneous decrease in domestic consumption of this fruit [3]. As a result, the ability to maintain the high quality of apples for a long time is gaining in importance, which is particularly relevant in the context of exports to distant markets, which require many months of storage and transport in refrigerated conditions. Poland exports apples primarily to EU countries—especially Germany and Romania—and to third-country markets, chiefly Egypt; other notable destinations include Kazakhstan and India [4]. Firmness is one of the key quality attributes that determine the acceptance of fruit by consumers [3]. This parameter, strongly associated with freshness and crunchiness, is the basic criterion for assessing the sensory quality of apples [5,6]. These traits are particularly important when it comes to hard-flesh dessert cultivars, such as ‘Gala’ apples, which are characterized by high visual attractiveness, a sweet taste, and a compact flesh structure, which significantly determine their popularity among consumers [7,8,9].
At the same time, apples of this cultivar, despite their high initial quality, are susceptible to loss of firmness during storage and trade. A reduction in this parameter below 55 N often results in a negative consumer assessment, as the fruit is perceived as overripe and less fresh, with an undesirable mealy flesh texture [10]. Due to the growing demands of consumers, it is necessary not only to ensure the attractive appearance of apples, such as intense skin color and no mechanical damage, but also to maintain the high internal quality of the fruit. The following parameters are of key relevance in this context: the firmness of the fruit, the right SSC, and a balanced taste resulting from the proportion of sugars and organic acids [11].
Apples of this cultivar, despite their high initial quality, are particularly susceptible to firmness loss during storage and commercial handling. Once firmness declines below ~55 N, consumers frequently judge the fruit as overripe and less fresh, with a mealy texture [10]. Because firmness strongly conditions acceptance at destination, mitigating its decline is central to the research problem addressed here—namely, how pre- and postharvest interventions can slow softening across extended storage and distribution.
In parallel, maintaining internal quality is essential to overall eating experience. Beyond external appearance, key parameters include soluble solids content (SSC) and titratable acidity, whose balance determines perceived sweetness, freshness, and flavor complexity [11]. In this study, we therefore examine how the same interventions that target firmness also influence SSC and acidity, clarifying potential synergies or trade-offs that matter for long-distance export scenarios.
Apples are classified as a climacteric fruit whose ripening is regulated by ethylene, a phytohormone that plays a key role in the growth and aging of tissues [12,13,14,15]. After harvest maturity is reached on-tree, ethylene biosynthesis intensifies and tissue sensitivity increases, triggering physiological and biochemical transitions. Off-tree, the fruit maintains elevated ethylene production and high sensitivity, driving ripening during storage and shelf life; the rate can be modulated by CA/ULO conditions and 1-MCP application. The most important ones include the degradation of pectins and hemicelluloses in cell walls, a decrease in acidity, loss of firmness, and the development of an undesirable, mealy flesh texture [16,17]. These phenomena, although natural, are unfavorable for fruit intended for long-term storage and subsequent sale, as they lead to a significant reduction in its commercial and sensory value [18,19,20].
To slow down ripening and extend the shelf life of apples, 1-methylcyclopropene (1-MCP) is used—a compound that binds strongly to ethylene receptors and blocks them, thereby inhibiting ethylene-driven ripening. In practice, 1-MCP is applied both on-tree (preharvest)—Harvista™—and off-tree (postharvest)—SmartFresh™. The effectiveness of both applications is well documented, and the outcome depends on multiple factors: cultivar, application timing, fruit maturity at harvest, temperature, and the storage technology used [21,22,23,24,25,26,27,28,29]. Because each approach used alone has limitations (preharvest application does not secure very long storage; postharvest application cannot compensate for fruit already advanced in ripening on-tree), a combined application provides complementary control of the ethylene response and increases the likelihood of meeting consumer acceptance thresholds at the destination. In recent years, particular importance has been attached to combining 1-MCP with modern solutions, such as ultra-low oxygen atmosphere (ULO) or dynamically controlled atmosphere (DCA), which allow a further extension of the storage period while maintaining the high quality of the fruit [18,19,30,31,32]. The observed benefits align with the complementary mechanisms of 1-MCP (receptor blockade) and ULO/DCA (metabolic suppression), which together reduce firmness loss and the incidence of physiological disorders during long-term storage. This approach is particularly relevant for ‘Gala’ apples destined for distant export markets, where months-long storage and transport are required.
Modern storage technologies play a key role in reducing the negative physiological changes that occur in fruit during long-term storage. By significantly reducing oxygen concentrations and increasing carbon dioxide concentrations in the storage chamber, these technologies make it possible to slow the intensity of cellular respiration, ethylene biosynthesis, and the associated processes of tissue maturation and aging [18]. Numerous studies have shown the high effectiveness of these methods in maintaining firmness, acidity, appropriate SSC, and overall sensory quality in many apple cultivars, including ‘Royal Gala’, ‘Golden Delicious’, ‘Elstar’, and ‘Reinette Simirenko’ [33,34,35,36]. Furthermore, it has been shown that the efficiency of ultra-low oxygen (ULO) and dynamically controlled atmosphere (DCA) can be significantly increased by combining them with post-harvest application of 1-MCP (SmartFresh™). This indicates a potential synergistic effect—an ethylene inhibitor reduces the perception of the hormonal signal, while an atmosphere with low oxygen levels reduces its biosynthesis [18,19,30,31,32,37,38,39,40,41]. Although 1-MCP blocks ethylene receptors, it also has drawbacks—most notably a reduction in volatile (aroma) emissions, and its effect may wane over longer time horizons; this can be partially mitigated by applying DCA, which slows metabolism and ethylene biosynthesis. This integrated approach, which combines physicochemical and biochemical strategies, is one of the most effective solutions for the long-term storage of apples.
Despite numerous studies on individual factors that affect apple quality, such as storage time and conditions, the use of 1-MCP, or harvest date, these variables are still relatively rarely analyzed in a comprehensive and quantitative way. In particular, there are no studies that would take into account the interactions of individual factors and the relative strength of their impact on the quality of fruit in the context of complex trade processes, including long transport and storage under conditions of simulated trade. To address this gap, we jointly examine harvest date, postharvest 1-MCP, and storage time/conditions. The quality of apples in commercial supply chains arises from interactions, not merely the additive effects of single variables. Harvest date sets the physiological starting point and the potential responsiveness to ethylene inhibitors; 1-MCP modifies ethylene sensitivity and biosynthesis, stabilizing firmness and limiting acid degradation; and storage time and atmosphere (together with shelf life exposure) determine the rate of cumulative quality changes. Only a joint analysis allows us to estimate the relative effect sizes (η2) and to identify true synergies and trade-offs (e.g., delaying softening without excessive flavor loss) under conditions that mimic long transport and commercial handling. In fruit production and export, this knowledge helps select appropriate treatments and methods, enabling better planning of storage and logistics for specific apple cultivars.
A more complete understanding of the mechanisms that shape the quality of fruit requires the use of statistical tools that allow the identification and hierarchization of predictors. In this context, stepwise regression allows for the selection of variables with the greatest impact on the dependent variable while eliminating predictors of low statistical significance. The analysis is complemented by the use of partial eta squared (η2), which allows for determination of the percentage share of a given variable in explaining the total variance of the trait studied, with control of other factors. Unlike classic significance tests, η2 allows for comparison of the strength of an effect between variables that differ in scope, scale, or nature (continuous or categorical), making it a particularly useful tool in multivariate analyses.
Therefore, the objective of this study was to assess the impact of selected pre- and post-harvest factors, i.e., harvest date, 1-MCP treatments, storage technology used, storage length, shelf life, and selected atmospheric factors, on the quality of ‘Gala Schniga® SchniCo Red(s)’ apples in two consecutive storage seasons. The qualitative traits analyzed included firmness (F), titratable acidity (TA), soluble solid content (SSC), SSC/TA ratio, and ACC content. In this study, multiple stepwise regression and effect size analysis (partial eta squared) were applied to determine each factor’s contribution to the variance in the results, enabling clear identification of those that most strongly shaped the quality parameters.

2. Materials and Methods

2.1. Plant Material and Experimental Design

The tests were carried out on ‘Gala Schniga® SchniCo Red(s)’ apples during the storage seasons 2022/2023 and 2023/2024. The fruit came from trees grown at the Warsaw University of Life Sciences (52°14′ N, 21°1′′ E); the trees were grafted on M.9 rootstock and planted in 2014 at a spacing of 3.0 × 1.0 m. The orchard had no drip irrigation, and trees were trained in a slender spindle system. The experiment followed a previously validated methodology [21], adapted to the conditions of two consecutive seasons. The weather data on the plot were gathered using the Davis Vantage Pro weather station installed in the experimental plot.
In each season, the fruit was harvested at two maturity stages determined on the basis of the Streif index [21,22]. In the 2022/2023 season, the optimal harvest date (OHD) was 13 September 2022—122 days after full bloom (DAFB), while the delayed harvest (DH–136 DAFB) was carried out on 27 September 2022. In the 2023/2024 season, the relevant dates were 12 September 2023 (OHD; 121 DAFB) and 26 September 2023 (DH; 135 DAFB). Seven days before the optimal harvest date (OHD−7), 100 trees were sprayed with 1-MCP (Harvista™, AgroFresh Solutions Inc., Philadelphia, PA, USA; 150 g·ha−1). At OHD, fruit were harvested and held for 7 d in air at 1 °C; then the samples were subjected to postharvest 1-MCP (SmartFresh™ ProTabs, AgroFresh Solutions Inc., Philadelphia, PA, USA; 0.65 µL·L−1).
Four variants of fruit treatment with preparations containing 1-MCP were used:
  • Control—0 µL·L−1 1-MCP (no 1-MCP treatment),
  • Harvista™ (1-MCP treatment before harvest),
  • SmartFresh™ (1-MCP treatment after harvest),
  • Harvista™ + SmartFresh™ (1-MCP treatment both before and after harvest).
The fruit was stored at a temperature of 1 °C and a relative humidity of approximately 95% under controlled conditions using two storage technologies: ULO (Ultra Low Oxygen; 1.2% CO2 and 1.2% O2) and DCA (Dynamic Controlled Atmosphere; 0.6% CO2 and 0.6% O2). The storage periods were 5, 7, or 9 months at a temperature of 1 °C and RH-95%, followed by simulated trading for 0 or 7 days at 20 °C. Normal (air) atmosphere (NA) storage was not included because, for Polish ‘Gala’ apples, the commercial practice is predominantly export to distant markets, for which NA does not sustain apple quality long enough to meet shipping and retail windows. Growers typically rely on ULO/DCA regimes following ethylene-management treatments to maintain firmness, color, and disorder control during prolonged distribution. Our experimental design therefore mirrors current industry practice and the intended use case of the proposed decision framework.
In each experimental treatment, the quality of apples was evaluated in four replicates, with ten fruits in each. The following factors were taken into account in the experiment:
−
Use of 1-MCP treatment before harvest (Harvista™: used/not used);
−
Harvest date (optimal/delayed);
−
Use of 1-MCP treatment after harvest (SmartFresh™: used/not used);
−
Storage technology (ULO or DCA);
−
Storage period (5, 7, or 9 months);
−
The length of the simulated trading (shelf life) (0 or 7 days).
All possible combinations of these factors resulted in a total of 96 experimental groups in each study season, which translated into a total of 192 groups throughout the two-year experimental cycle.
Although the layout and the course of the experiment—including the composition of the atmosphere, the doses of preparations, and the storage method—remained consistent with the previously described methodology [21,22], the aim of this study was to identify the key factors influencing the quality of apples during storage using step regression and to assess the strength of their influence using the partial eta squared method (η2).

2.2. Selection of Variables for the Regression Model

In order to identify the most important factors influencing the selected quality parameters of the ‘Gala Schniga® SchniCo Red(s)’ apples, a stepwise regression method was used taking into account the backward elimination procedure. This method allows for the selection of independent variables by removing from the model those predictors that show the least statistical influence on the variable under analysis.
The initial regression model considered all potential independent variables: the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, the number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, the date of harvest, 1-MCP treatment after harvest, storage technology, storage period in months, and shelf life. The selection of weather parameters was based on studies conducted by Lachapelle et al. [42] These variables were numerically encoded to enable the use of regression analysis and the calculation of the effect size. Depending on the nature of a given feature, binary coding (e.g., 1-MCP treatment/no 1-MCP treatment) or categorial coding (e.g., storage period: 5, 7, or 9 months) was used. A detailed description of the independent variables, their variants, and the assigned numerical codes is presented in Table 1.
In the subsequent steps of the analysis, the least significant variables were removed based on the p-values until a model covering only the relevant predictors was obtained (with a significance level of α = 0.05). The analysis was performed separately for each of the five dependent variables: flesh firmness (Instron 5542, Instron, Norwood, MA, USA), soluble solids content (Atago, Palette PR-32, Atago, Co., Ltd., Tokyo, Japan), titration acidity (TitroLine 5000, Xylem Analytics Germany GmbH, Weilheim, Germany), soluble solids-to-titration acidity ratio (SSC/TA), and 1-aminocyclopropane-1-carboxylic acid (ACC) content (HP 5890, Hewlett Packard, Palo Alto, CA, USA). Detailed procedures for the determination of these parameters, except ACC content, have been described in two publications by Małachowska and Tomala [21,22]. ACC content was determined according to the procedures described by Buffer [43] and Lizada and Yang [44], taking into account the modifications proposed by Jobling et al. [45], wherein gas samples for C2H4 analysis were collected after 15 min, and ACC was calculated based on the percent recovery of ACC from spiked samples.
The analysis was complemented by an assessment of the effect size of individual independent variables using partial eta squared (η2). This indicator determines the percentage of variance of the dependent variable that can be assigned to a particular independent variable, while controlling the impact of the other variables included in the model. The value of the η2 coefficient is in the range of 0 to 1, with values closer to 1 indicating a stronger influence of the analyzed factor. In the article, the values of η2 are presented as a percentage (%), which allows for a direct comparison of the relative strength of the influence of individual variables on the evaluated quality characteristics of apples [46].

2.3. Statistical Analysis

A multiple stepwise regression analysis was performed to assess the influence of selected factors on the dependent variables. The model included the following predictors: the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, the number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, the date of harvest, 1-MCP treatment after harvest, storage technology, months of storage, and shelf life. Both unstandardized and standardized β coefficients (unstandardized: change in the dependent variable per one-unit change in the predictor; standardized: effect size in standard-deviation units enabling comparison across predictors), with corresponding p-values, were reported. Statistical significance was set at p < 0.05. Additionally, partial eta squared (η2) values were calculated to quantify effect sizes. All statistical analyses were carried out using Statistica version 13.3 (StatSoft Inc., Tulsa, OK, USA) and IBM SPSS version 26 (IBM Corp., Armonk, NY, USA).

3. Results

3.1. Weather Conditions During the Growing Seasons of 2022 and 2023

Figure 1 and Figure 2 show the average monthly air temperatures and precipitation totals recorded at the orchard meteorological station in Wilanów (N 52°9′36.1″, E 21°5′58.2″) from April to September in 2022 and 2023. Data are presented in the form of climatodiagrams developed according to the method of Walter and Lieth [47] consisting of a graphical presentation of average monthly temperatures and precipitation totals at a 1 °C: 4.5 mm proportion. This allowed for the identification of wet periods (when the precipitation line runs above the temperature line—blue fields) and dry periods (when the temperature line is above the precipitation line—yellow fields).
In 2022 (Figure 1), the growing season was characterized by a gradual increase in average temperature—from about 7 °C in April to a maximum of about 21 °C in August—before it dropped to about 13 °C in September. The highest monthly precipitation occurred in June (more than 120 mm), which resulted in a periodic excess of precipitation in relation to temperature (marked in blue on the graph). On the other hand, from July to September, total precipitation remained below average temperatures, which means that there was a precipitation shortage in this period (drought period—marked in yellow). Therefore, the distribution of rainfall was uneven—an excess in June and a shortage in the subsequent part of the season—which could lead to limited water availability in the soil, especially during the key phases of fruit growth during the summer months. From April to September, 22 days with precipitation exceeding 6 mm were recorded, which could have had a positive effect on the availability of water in the soil during fruit growth. In the case when apples were harvested at the optimal date, there were 13 days with an average temperature below 15 °C within the last 30 days before the harvest, while in the case of delayed harvest, there were as many as 25 such days within 44 days before the harvest. Lower temperature during this period may have had a positive effect on the quality of apples, favoring slower and more uniform ripening of the fruit.
In 2023 (Figure 2), the course of the temperatures in the growing season was similar to the previous year’s—from about 9 °C in April to a maximum of approximately 21 °C in August, followed by a slight decrease to approximately 17 °C in September. The distribution of precipitation was characterized by different dynamics—an excess of precipitation in relation to temperature (marked in blue on the graph) was recorded in April only, while a precipitation shortage (areas marked in yellow) prevailed from May to September, despite the persistence of high temperatures.
From April to September 2023, 15 days with precipitation exceeding 6 mm were recorded in the case of harvest at the optimal date, and 17 days in the case of delayed harvest. Only 1 day with an average temperature below 15 °C was recorded during 30 days preceding the harvest at the optimal date, while there were 3 such days in the case of delayed harvest (that is, during 44 days before harvest). High temperatures before harvest may have accelerated fruit ripening, while limited rainfall in most of the season may have increased the risk of water stress, especially between May and June and at the end of the season.
By comparing the two seasons, 2023 was generally warmer and drier than 2022, especially in the second half of the growing season. The observed differences could have significantly affected the growth rate of the fruit, the level of water stress, and the quality of the apples during storage.
Table 2 presents descriptive statistics for the quality parameters of the ‘Gala Schniga® SchniCo Red(s)’ apples from the 2022/2023 and 2023/2024 storage seasons. The greatest variability was observed for firmness, which ranged from 33.0 to 83.8 N (mean = 59.5 N; SD = 12.4). The high value of the coefficient of determination (R2 = 0.92; R2 corrected = 0.85) indicates a very good fit of the regression model to this parameter.
The titration acidity ranged from 0.14 to 0.36% (mean = 0.26%; SD = 0.04), and the resulting model was characterized by a good fit (R2 = 0.77; R2 adjusted = 0.60). SSC ranged from 11.5 to 16.2 °Bx (mean = 13.3 °Bx; SD = 0.6). Despite the slight variability, the regression model showed a limited ability to explain this parameter (R2 = 0.61; R2 adjusted = 0.36), which may indicate a significant influence of environmental or cultivar-related factors.
The SSC/TA ratio ranged from 39.0 to 94.0 (average = 51.5; SD = 8.0), with a moderate regression model fit (R2 = 0.73; R2 adjusted = 0.53). The obtained ACC content range was wide (0.001–1.12 nmol·kg−1), with an average of 0.18 nmol·kg−1 and a standard deviation of 0.16. The regression model for this parameter was characterized by good predictive accuracy (R2 = 0.61; R2 adjusted = 0.37), indicating a significant influence of experimental factors on the content of this acid.

3.2. Firmness (F)

The results of the stepwise multiple regression analysis (Table 3) indicate that all variables included in the final model had a significant effect on the firmness of the ‘Gala Schniga® SchniCo Red(s)’ apples (p < 0.001). Storage technology was eliminated during the stepwise procedure due to a lack of significance. The final regression equation allows us to determine the direction and strength of the influence of individual predictors on flesh firmness.
The application of 1-MCP after harvest had the strongest positive effect on F (β = 0.594), with the application of 1-MCP before harvest showing a weaker effect (β = 0.168). The number of days with precipitation above 6 mm (β = 0.286) and the number of days with the average air temperature below 15 °C within the last 30 (or 44) days before harvest (β = 0.284) also had a significant positive effect. These results suggest that the weather conditions in the final phase of the growing season may have been conducive to maintaining higher flesh firmness.
On the other hand, the harvest date (β = −0.368), storage (β = −0.085), and shelf life after storage (β = −0.211) had a negative effect on fruit firmness. Particularly important was the influence of a later harvest date and a longer storage period, which were associated with a decrease in flesh firmness.
The resulting regression equation is as follows:
F = (0.37 × X1) + (1.15 × X2) + (4.16 × X3) + (−9.14 × X4) + (14.7 × X5) + (−0.65 × X7) + (−5.24 × X8) + 37.9
where X1—number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest; X2—number of days when cumulative precipitation was above 6 mm; X3—1-MCP treatment before harvest; X4—date of harvest; X5—1-MCP treatment after harvest; X7—storage period; X8—shelf life.
A complementary analysis of the eta squared coefficient (η2, Table 4) confirmed the significance of most of the variables included in the model that describes the firmness of ‘Gala Schniga® SchniCo Red(s)’ apples. The application of 1-MCP after harvest had the greatest explanatory strength for flesh firmness variability (η2 = 70.4%), which clearly indicates the dominant role of the use of this preparation in maintaining fruit firmness after storage.
The harvest date (η2 = 47.8%) and the number of days with precipitation exceeding 6 mm (η2 = 36.2%) also showed a significant impact on firmness, probably due to the weather conditions in the final phase of the growing season and the degree of ripeness of apples. The duration of simulated trade (shelf life) accounted for 23.1% of the firmness variation, while the use of 1-MCP before harvest had a moderate but significant effect (η2 = 15.9%).
The number of days with the average air temperature below 15 °C within the last 30 (or 44) days before harvest accounted for 9.1% of the firmness variation. Storage duration showed a marginal but significant effect (η2 = 4.7%).
The analysis confirmed that the application of 1-MCP after harvest is the most effective single factor determining the maintenance of firmness of ‘Gala Schniga® SchniCo Red(s)’ apples. The harvest date, the weather conditions during the last 30 (or 44) days before harvest, the storage period, and trade duration (shelf life) have also shown a significant impact, highlighting the importance of a comprehensive approach to fruit quality management in the post-harvest period.
The effect of treatment, harvest date, length of storage, and storage conditions separately for both years of the study on firmness was analyzed by analysis of variance and is presented in the Supplementary Materials [Tables S1–S8].

3.3. Titratable Acidity (TA)

The stepwise multiple regression analysis (Table 5) showed a significant effect of six independent variables on the TA of the ‘Gala Schniga® SchniCo Red(s)’ apples (p < 0.001), with atmospheric conditions rejected at the modeling stage due to lack of significance. The use of 1-MCP after harvest (β = 0.496), followed by storage technology (β = 0.113) and the use of 1-MCP before harvest (β = 0.089) were found to have the strongest effects on maintaining acidity, which confirms that the ethylene inhibitor effectively slows the ripening process, allowing for the preservation of higher acidity of apples. The following factors had a negative impact on TA: storage period (β = −0.383), harvest date (β = −0.381) and shelf life after storage (β = −0.203). These results indicate that delayed harvest, extended shelf life, and additional exposure under shelf life conditions led to a decrease in apple acidity, which is consistent with the typical dynamics of organic acid consumption in the respiration process. All of the factors related to post-harvest treatment, such as the use of 1-MCP and storage technology, and the reduction in storage period and trade duration, play a key role in maintaining the taste quality of apples by stabilizing their acidity.
The resulting regression equation is as follows.
TA = (0.01 × X3) + (−0.03 × X4) + (0.04 × X5) + (0.01 × X6) + (−0.01 × X7) + (−0.02 × X8) + 0.32
where X3—1-MCP treatment before harvest; X4—date of harvest; X5—1-MCP treatment after harvest; X6—storage technology; X7—storage period; X8—shelf life.
Partial eta squared analysis (Table 6) showed that the most relevant factor shaping the TA of the ‘Gala Schniga® SchniCo Red(s)’ apples was the post-harvest use of 1-MCP, which accounted for 38.0% of the variability explained, highlighting its key role in slowing the ripening and maintaining higher levels of organic acids during storage. Storage period (26.8%) and harvest date (26.6%) also had a significant impact on TA, confirming the observation that a longer storage period and later harvest lead to greater loss of acidity. Trade duration after storage (9.3%) affected TA moderately but significantly, while 1-MCP treatment before harvest (2.0%) and the storage technology used (3.1%) contributed to a relatively small extent to the variability of TA, although their effects were statistically proven (p < 0.001). Atmospheric factors, including the number of days with an average temperature below 15 °C within the last 30–44 days before harvest and the number of days with precipitation above 6 mm, did not show a significant effect, suggesting that the variability of weather conditions during the final stage of the growing season was less significant for TA than the factors related to fruit harvesting and storage.

3.4. Soluble Solids Content (SSC)

The stepwise multiple regression analysis (Table 7) showed that SSC in apples was primarily determined by the application of 1-MCP, the harvest date, the storage period, and the trade period, while the number of days with an average temperature below 15 °C, the number of days with precipitation above 6 mm, and storage technology were eliminated from the model due to lack of significance. The application of 1-MCP before harvest (β = −0.387), a delayed harvest date (β = −0.229) and a longer shelf life (β = −0.212) had the strongest negative effect on SSC, while a positive effect was observed for the use of 1-MCP after harvest (β = 0.314) and trade period (shelf life, β = 0.130), suggesting that the dynamics of SSC changes can lead to either an increase or a decrease in SSC depending on the combination of these factors, reflecting the typical carbohydrate metabolism and sugar concentration during the ripening and storage of fruit.
The resulting regression equation is as follows:
SSC = (−0.48 × X3) + (−0.29 × X4) + (0.39 × X5) + (−0.08 × X7) + (0.16 × X8) + 13.9
where X3—1-MCP treatment before harvest; X4—date of harvest; X5—1-MCP treatment after harvest; X7—storage period; X8—shelf life.
The partial eta squared (η2) analysis (Table 8) showed that the influence of the investigated variables on SSC was significantly lower than on firmness or TA. The use of 1-MCP before harvest (η2 = 22.7%), which limits the accumulation of sugars in fruit, contributed most to explaining SSC variation, while 1-MCP after harvest also showed a significant effect (η2 = 16.2%), acting in the opposite way and supporting the maintenance of SSC. Storage (η2 = 8.5%) and harvest period (η2 = 9.3%) had a moderate but statistically proven effect, indicating that the storage period and the stage of ripeness at harvest shape the final SSC in the fruit’s flesh. The shelf life effect was marginal but significant (η2 = 3.2% (p < 0.001), which could have been due to transpiration and concentration of juice during trade, while storage technology (η2 = 0.5%; p = 0.061) and weather conditions did not have a significant impact and were not included in the model.

3.5. SSC/TA Ratio

The SSC/TA ratio, illustrating the balance between the sweetness and the acidity of apples, was shaped by seven significant variables included in the final stepwise multiple regression model (Table 9). According to the literature, the optimal range for the ‘Gala’ cultivar should fall between 20 and 60 [48]. All factors showed a very significant effect (p < 0.001), except for the number of days with a temperature below 15 °C within the last 30 or 44 days before harvest, whose effect was weaker but also significant (p = 0.034). Harvest date (β = 0.332), storage time (β = 0.345), and shelf life (β = 0.258) were found to have the strongest positive effect on SSC/TA, indicating that fruit harvested later, stored longer, and displayed longer under marketing conditions was characterized by a higher predominance of sweetness over acidity. On the other hand, 1-MCP applied after harvest lowered the SSC/TA value the most (β = −0.427), which was associated with a lower consumption of organic acids during respiration. The same, although to a lesser degree, was found for 1-MCP before harvest (β = −0.233) and storage technology (β = −0.097).
The resulting regression equation is as follows:
SSC/TA = (−0.05 × X1) + (−3.70 × X3) + (5.28 × X4) + (−6.79 × X5) + (−1.54 × X6) + (1.68 × X7) + (4.10 × X8) + 41.6
where X1—number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest; X3—1-MCP treatment before harvest; X4—date of harvest; X5—1-MCP treatment after harvest; X6—storage technology; X7—storage period; X8—shelf life.
The importance of individual predictors for the formation of the SSC/TA ratio was confirmed by the effect size analysis (Table 10). The highest value of the η2 coefficient was obtained for the use of 1-MCP after harvest (η2 = 35.4%), indicating the dominant effect of this treatment on the SSC/TA ratio. Other important factors were the storage period (η2 = 26.4%) and the harvest date (η2 = 22.5%), the influence of which can be associated with the progressive ripening of the fruit in the orchard and under storage conditions, resulting in a decrease in acidity and/or an increase in SSC, and thus in an increase in SSC/TA. The shelf life (η2 = 16.7%) and the use of 1-MCP before harvest (η2 = 14.0%), which—unlike the post-harvest treatment—reduced the SSC in the fruit, also played a significant role. The number of days with temperatures below 15 °C before harvest showed a smaller but significant effect (η2 = 4.4%; p < 0.001), indicating a possible influence of atmospheric conditions in the final phase of apple growth on their flavor profile. Storage technology was the least important predictor (η2 = 2.7%), which, despite its statistical significance, played a relatively minor role in the model.

3.6. 1-Aminocyclopropane-1-Carboxylic Acid (ACC)

Eight independent variables were considered in a model describing the content of 1-aminocyclopropane-1-carboxylic acid (ACC), a precursor to ethylene in fruit, six of which showed a significant effect on ACC in ‘Gala Schniga® SchniCo Red(s)’ apples (Table 11). The strongest stimulating effects were reported for storage length (β = 0.326), shelf life (β = 0.204), and storage technology (β = 0.181), which indicates that longer storage and simulated trade promote higher ACC content, reflecting the activation of ethylene synthesis during fruit ripening and aging. The only limiting factor for ACC production was the post-harvest use of 1-MCP (β = −0.413), confirming its effectiveness in inhibiting ethylene synthesis and slowing down the ripening process. Harvest date (β = 0.144) also showed a positive effect, suggesting higher ACC content in fruit harvested later, which may be due to more advanced physiological maturity. The number of days with temperatures below 15 °C within the last 30 or 44 days before harvest (β = −0.078; p = 0.012) had a small but significant effect, indicating lower metabolic activity of apples grown in cooler conditions. The number of days with precipitation above 6 mm and 1-MCP before harvest were eliminated from the model as statistically insignificant.
The resulting regression equation is as follows:
ACC = (−0.001 × X1) + (0.03 × X4) + (−0.14 × X5) + (0.06 × X6) + (0.03 × X7) + (0.07 × X8) − 0.05
where X1—number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest; X4—date of harvest; X5—1-MCP treatment after harvest; X6—storage technology; X7—storage period; X8—shelf life.
The interpretation of the regression model is complemented by the effect size analysis (Table 12), which unequivocally confirms the dominant role of post-harvest factors in shaping the ACC content in the ‘Gala Schniga® SchniCo Red(s)’ apples. The use of 1-MCP after harvest (η2 = 21.3%) contributed most to explaining the variation in ACC content, confirming its efficacy in blocking ethylene biosynthesis and delaying the ripening process, thus making it the most important factor limiting ACC accumulation. Another important predictor was storage period (η2 = 14.4%), indicating a gradual increase in ACC with storage time, probably as a result of increasing metabolic activity of the fruit. Shelf life also affected the growth of ACC content (η2 = 6.2%) and so did storage technology, albeit to a lesser extent—its impact was significant (η2 = 4.9%), suggesting the influence of storage conditions on the ACC accumulation rate. Harvest date (η2 = 2.1%) and the number of days with temperatures below 15 °C within the last 30 or 44 days before harvest (η2 = 1.9%) had a limited but significant effect, indicating the role of orchard conditions in shaping the initial ACC content. At the same time, the application of ACC before harvest did not significantly affect the ACC content (η2 = 0.0%; p = 0.726), which means that its effects did not limit ACC biosynthesis during storage. In conclusion, the η2 analysis showed that the main factors determining the ACC content were the storage period, the shelf life, and post-harvest 1-MCP treatment, which indicates that effective control of ACC production and ethylene synthesis is possible primarily through the management of shelf life and the use of an ethylene inhibitor after apple harvest. The separate effects of treatment, harvest date, length of storage, and storage conditions on ACC content for both years of the study were analyzed by analysis of variance and are presented in the Supplementary Materials [Tables S9–S16].

4. Discussion

The analyses clearly indicate that maintaining high post-harvest quality in ‘Gala Schniga® SchniCo Red(s)’ apples requires an integrated approach that combines an optimal harvest date, an appropriate storage technology, and the use of 1-MCP. Determining the optimal harvest date largely relies on the ethylene concentration in the seed cavities, which should be 0.1–0.5 µL·L−1. The most effective single factor influencing firmness, acidity, and ACC content was the use of 1-MCP after harvest (SmartFresh™), whose contribution to explaining the variability was 70.4% for firmness, 38.0% for titration acidity, and 21.3% for ACC content. These results confirm the high efficacy of 1-MCP in blocking ethylene receptors and slowing maturation [49,50,51], which is particularly important in the case of cultivars prone to early firmness loss.
Thanks to the use of 1-MCP, it was possible to maintain the firmness of apples above the threshold of 55 N, which is considered the minimum value accepted by consumers [3,7], even after 9 months of storage and a shelf life period. The effectiveness of the treatment was further enhanced by favorable weather conditions before harvest in 2022—a higher number of cold days (below 15 °C) and precipitation (above 6 mm)—which significantly affected firmness (η2 = 9.1% and 36.2%). These observations are consistent with previous studies showing that precipitation and temperatures in the final stage of fruit development can significantly shape the quality characteristics of apples, including firmness [52]. For example, precipitation between 61 and 90 days after full flowering explained up to 39% of the variation in the firmness of ‘McIntosh’ apples [42].
Importantly, both cold conditions and heat stress can modify the intensity of ethylene biosynthesis. As shown by Sharma et al. [53] and Honda et al. [54], high temperatures before harvest can interfere with the production and performance of ethylene, leading to irregular ripening and deterioration of fruit firmness and flavor. Paull and Chen [55], on the other hand, noted that short-term exposure to heat shock can have both beneficial and adverse effects, depending on the time and intensity of treatment. In our own studies (unpublished data), a small number of hot days (>25 °C) in the second half of the growing season was found to promote the proper course of the ripening process, which facilitated the effective action of 1-MCP and ensured even ripeness of the fruit.
In this study, it was shown that the harvest date (η2 = 47.8%) and the shelf life period (η2 = 23.1%) played a key role in shaping the firmness of apples. Fruit harvested later and kept at room temperature showed a greater decrease in firmness, which is consistent with the observations of Thongkum et al. [56] regarding the effect of harvest maturity on the expression of ethylene genes. Additionally, a positive correlation was found between delayed harvest and increased ACC content, confirming the activation of the ethylene biosynthesis pathway as maturation progressed. The use of 1-MCP both before harvest (Harvista™) and after harvest (SmartFresh™), combined with the optimal harvest date, significantly supported the maintenance of apple firmness, which makes this strategy effective in the production of fruit intended for export to distant markets [26,57].
The storage period (η2 = 26.8%) and the harvest date (η2 = 26.6%) played an important role for TA, with prolonged storage and delayed harvest leading to a decrease in TA, which may adversely affect the taste of ‘Gala Schniga® SchniCo Red(s)’ apples, which are characterized by relatively low acidity [3,7]. Storage technology had a relatively small, although statistically proven, impact (η2 = 3.1%).
SSC was less dependent on the analyzed factors than firmness or acidity. The use of 1-MCP before harvest (η2 = 22.7%) and after harvest (η2 = 16.2%) had the strongest impact, with the pre-harvest treatment reducing SSC and the post-harvest treatment showing the opposite effect. These effects have not been clearly stated in the literature [31,58,59,60,61,62]. Storage period (η2 = 8.5%), harvest date (η2 = 9.3%), and shelf life period (η2 = 3.2%) had a moderate impact, suggesting that SSC is more dependent on agrotechnical and environmental factors.
The SSC/TA ratio, reflecting the taste balance of apples, was most strongly shaped by the storage period (η2 = 26.4%), the harvest date (η2 = 22.5%) and the shelf life period (η2 = 16.7%). On the other hand, the 1-MCP treatment after harvest lowered the ratio (η2 = 35.4%), mainly by maintaining higher acidity with relatively stable SSC. These effects are important in the context of export, where the taste balance of apples is a key factor affecting consumer acceptance [3,7,8].
Regarding ACC content, which is an indicator of the metabolic activity of apples, the application of 1-MCP after harvest turned out to be a key factor (η2 = 21.3%). The storage period (η2 = 14.4%) and shelf life period (η2 = 6.2%) also showed a significant impact. The number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest had a marginal effect (η2 = 1.9%), and the use of 1-MCP before harvest did not significantly affect the ACC content (η2 = 0.0%; p = 0.726), which confirms previous observations on the possibility of regeneration of ethylene receptors [37,40].
Storage technology (ULO vs. DCA) showed a significant but only limited effect on ACC content (η2 = 4.9%). With respect to firmness and SSC, its effect was negligible (η2 = 0.0–0.5%). These results suggest that in the studied seasons, atmospheric composition did not play a significant role in shaping the quality the ‘Gala Schniga® SchniCo Red(s)’ apples, which is in agreement with previous observations on seasonal variation in the effectiveness of DCA technology [22,36,37,38].
To summarize, the results of this study clearly indicate that the most effective strategy to maintain the high quality of stored ‘Gala Schniga® SchniCo Red(s)’ apples is the post-harvest application of 1-MCP, especially in combination with an optimal harvest date (typically 121–122 DAFB) and a controlled storage period and exposure under shelf life conditions. The stepwise regression analysis in combination with the effect size assessment (η2) showed a dominant role of post-harvest factors in shaping firmness, titration acidity, and ACC content, while factors such as storage technology or weather conditions in the final vegetation period had a marginal, although statistically proven, effect. The results underline the importance of an integrated approach combining chemical treatments with appropriate harvest planning and storage logistics, which can be the basis for the development of practical recommendations for producers and exporters and for the development of decision-making models to support the planning of storage processes, especially for cultivars intended for markets with high quality requirements.

5. Conclusions

The use of 1-MCP before apple harvest showed a relatively small effect in promoting the maintenance of higher flesh firmness and lowering SSC and the SSC/TA ratio. On the other hand, the post-harvest application of 1-MCP proved to be the most effective factor in maintaining the firmness of apples, reducing the production of ACC, and maintaining higher TA. This treatment contributed to a reduction in the SSC/TA ratio and supported a favorable balance between sweetness and acidity in the fruit, which is particularly important in demanding export markets.
Delayed harvest dates and extended shelf life–both in refrigerated conditions and during shelf life–increased SSC and SSC/TA value, while causing a significant decrease in firmness and acidity, which can negatively affect the sensory quality and shelf life of the fruit. The most beneficial results in maintaining the quality of ‘Gala Schniga® SchniCo Red(s)’ apples were obtained by combining an optimal harvest date with post-harvest application of 1-MCP and a shorter shelf life period.
The used controlled atmosphere technology (ULO vs. DCA) showed little effect on most of the quality parameters of apples, which indicates the possibility of flexible selection of storage conditions, especially with the simultaneous use of an ethylene inhibitor. The obtained results can be the basis for the development of practical recommendations for producers and exporters of apples intended for fresh consumption and can support decisions on the optimal harvest date, storage strategy, and the timing of ethylene receptor blocking, depending on the specifics and requirements of the target markets.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agriculture15222363/s1, Table S1: Flesh firmness (N) of Gala Schniga® SchniCo Red(s) apples directly after storage, depending on the use of 1-MCP treatment, harvest date, and length of storage under ULO conditions; Table S2: Flesh firmness (N) of Gala Schniga® SchniCo Red(s) apples directly after shelf life, depending on the use of 1-MCP treatment, harvest date, and length of storage under ULO conditions; Table S3: Flesh firmness (N) of Gala Schniga® SchniCo Red(s) apples directly after storage, depending on the use of 1-MCP treatment, harvest date, and length of storage under DCA conditions; Table S4: Flesh firmness (N) of Gala Schniga® SchniCo Red(s) apples directly after shelf life, depending on the use of 1-MCP treatment, harvest date, and length of storage under DCA conditions; Table S5: Flesh firmness (N) of Gala Schniga® SchniCo Red(s) apples directly after storage, depending on the use of 1-MCP treatment, harvest date, and length of storage under ULO conditions; Table S6: Flesh firmness (N) of Gala Schniga® SchniCo Red(s) apples directly after shelf life, depending on the use of 1-MCP treatment, harvest date, and length of storage under ULO conditions; Table S7: Flesh firmness (N) of Gala Schniga® SchniCo Red(s) apples directly after storage, depending on the use of 1-MCP treatment, harvest date, and length of storage under DCA conditions; Table S8: Flesh firmness (N) of Gala Schniga® SchniCo Red(s) apples directly after shelf life, depending on the use of 1-MCP treatment, harvest date, and length of storage under DCA conditions; Table S9: ACC content (nmol·kg−1) in Gala Schniga® SchniCo Red(s) apples directly after storage, depending on the use of 1-MCP treatment, harvest date, and length of storage under ULO conditions; Table S10: ACC content (nmol·kg−1) in Gala Schniga® SchniCo Red(s) apples directly after shelf life, depending on the use of 1-MCP treatment, harvest date, and length of storage under ULO conditions; Table S11: ACC content (nmol·kg−1) in Gala Schniga® SchniCo Red(s) apples directly after storage, depending on the use of 1-MCP treatment, harvest date, and length of storage under DCA conditions; Table S12: ACC content (nmol·kg−1) in Gala Schniga® SchniCo Red(s) apples directly after shelf life, depending on the use of 1-MCP treatment, harvest date, and length of storage under DCA conditions; Table S13: ACC content (nmol·kg−1) in Gala Schniga® SchniCo Red(s) apples directly after storage, depending on the use of 1-MCP treatment, harvest date, and length of storage under ULO conditions; Table S14: ACC content (nmol·kg−1) in Gala Schniga® SchniCo Red(s) apples directly after shelf life, depending on the use of 1-MCP treatment, harvest date, and length of storage under ULO conditions; Table S15: ACC content (nmol·kg−1) in Gala Schniga® SchniCo Red(s) apples directly after storage, depending on the use of 1-MCP treatment, harvest date, and length of storage under DCA conditions; Table S16: ACC content (nmol·kg−1) in Gala Schniga® SchniCo Red(s) apples directly after shelf life, depending on the use of 1-MCP treatment, harvest date, and length of storage under DCA conditions.

Author Contributions

Conceptualization, M.M. and K.T.; methodology, M.M.; software, M.M.; validation, M.M.; formal analysis, M.M. and K.T.; investigation, M.M. and K.T.; resources, M.M. and K.T.; data curation, M.M. and K.T.; writing—original draft preparation, M.M. and K.T.; writing—review and editing, M.M. and K.T.; visualization, M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data is contained within the article or Supplementary Material. The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors sincerely thank Paweł Jankowski (Department of Information Systems, Warsaw University of Life Sciences—SGGW) for his valuable statistical consultation.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agriculture 15 02363 g001
Figure 1. Average monthly temperatures and total rainfall in 2022 from April to September for the Wilanów station, Warsaw, Poland (N 52°9′36.1″, E 21°5′58.2″). Areas indicating an excess of precipitation—cumulative monthly precipitation exceeds 4.5 mm per 1 °C of mean monthly temperature (i.e., >4.5 mm °C−1). Areas indicating insufficient precipitation—this value is <4.5 mm °C−1.
Figure 1. Average monthly temperatures and total rainfall in 2022 from April to September for the Wilanów station, Warsaw, Poland (N 52°9′36.1″, E 21°5′58.2″). Areas indicating an excess of precipitation—cumulative monthly precipitation exceeds 4.5 mm per 1 °C of mean monthly temperature (i.e., >4.5 mm °C−1). Areas indicating insufficient precipitation—this value is <4.5 mm °C−1.
Agriculture 15 02363 g001
Agriculture 15 02363 g002
Figure 2. Average monthly temperatures and total rainfall in 2023 from April to September for the Wilanów station, Warsaw, Poland (N 52°9′36.1″, E 21°5′58.2″). Areas indicating an excess of precipitation—cumulative monthly precipitation exceeds 4.5 mm per 1 °C of mean monthly temperature (i.e., >4.5 mm °C−1). Areas indicating insufficient precipitation—this value is <4.5 mm °C−1.
Figure 2. Average monthly temperatures and total rainfall in 2023 from April to September for the Wilanów station, Warsaw, Poland (N 52°9′36.1″, E 21°5′58.2″). Areas indicating an excess of precipitation—cumulative monthly precipitation exceeds 4.5 mm per 1 °C of mean monthly temperature (i.e., >4.5 mm °C−1). Areas indicating insufficient precipitation—this value is <4.5 mm °C−1.
Agriculture 15 02363 g002
Table 1. Independent variables included in statistical models and their variants and assigned numerical codes used in regression analysis and partial effect size calculations (η2). Independent variables included in the statistical models, their categorical variants, and corresponding numerical codes used in regression analysis and partial eta squared (η2) effect size calculations.
Table 1. Independent variables included in statistical models and their variants and assigned numerical codes used in regression analysis and partial effect size calculations (η2). Independent variables included in the statistical models, their categorical variants, and corresponding numerical codes used in regression analysis and partial eta squared (η2) effect size calculations.
Independent VariablesCategoryCode
Number of days with an average air temperature below 15 °C in the last 30 (or 44) days before harvest (X1) *2022/2023optimal harvest date13
delayed harvest25
2023/2024optimal harvest date1
delayed harvest3
Number of days when cumulated precipitation was above 6 mm (X2) *2022/2023optimal harvest date22
delayed harvest22
2023/2024optimal harvest date15
delayed harvest17
1-MCP treatment before harvest (X3)without treatment0
with treatment1
Date of harvest (X4)optimal harvest date0
delayed harvest1
1-MCP treatment after harvest (X5)without treatment0
with treatment1
Storage technology (X6)ULO (Ultra Low Oxygen)0
DCA (Dynamic Controlled Atmosphere)1
Storage period (X7)5 months5
7 months7
9 months9
Shelf life (X8)0 days (directly after storage)0
7 days1
* Code explanation: Values indicate the number of days meeting the condition in the last 30 (or 44) days preharvest (e.g., OHD: 13 in 2022/23 and 1 in 2023/24; DH: 25 and 3); for “days with cumulated precipitation > 6 mm,” OHD and DH counts were identical (e.g., both 22 in 2022/23), meaning no >6 mm days occurred between OHD and DH.
Table 2. Descriptive statistics of the results obtained from analyses of Gala apples in the 2022/2023 and 2023/2024 storage seasons.
Table 2. Descriptive statistics of the results obtained from analyses of Gala apples in the 2022/2023 and 2023/2024 storage seasons.
IndicatornMin.Max.AverageSDR2Adjusted R2
Firmness (N)76833.083.859.512.40.920.85
Titratable acidity (% malic acid)0.140.360.260.040.770.60
Soluble solids content (°Bx)11.516.213.30.600.610.36
SSC/TA39.094.051.58.000.730.53
ACC content (nmol·kg−1)0.001.120.180.160.610.37
SD—standard deviation. Model fit was summarized with R2 and adjusted R2 (which corrects for the number of predictors and sample size).
Table 3. Results of the stepwise multiple regression analysis for the firmness of ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, the number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, the date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. (n = 768.) This research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Table 3. Results of the stepwise multiple regression analysis for the firmness of ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, the number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, the date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. (n = 768.) This research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Unstandardized CoefficientsStandardized Coefficients βp
βSE *
Constant 37.91<0.001
Number of days with an average air temperature below 15 °C in the last 30 (or 44) days before harvest0.3700.0450.284<0.001
Number of days when cumulated precipitation was above 6 mm1.1520.1300.286<0.001
1-MCP treatment before harvest4.1610.3460.168<0.001
Date of harvest−9.1360.346−0.368<0.001
1-MCP treatment after harvest14.7310.3460.594<0.001
Storage period−0.6470.106−0.085<0.001
Shelf life−5.2370.346−0.211<0.001
* SE—standard error.
Table 4. Results of the eta squared (η2) analysis for firmness of ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, the number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, the date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Table 4. Results of the eta squared (η2) analysis for firmness of ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, the number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, the date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Independent Variablesη2 (%) *p
Number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest9.1<0.001
Number of days when cumulated precipitation was above 6 mm36.2<0.001
1-MCP treatment before harvest15.9<0.001
Date of harvest47.8<0.001
1-MCP treatment after harvest70.4<0.001
Storage period4.7<0.001
Shelf life23.1<0.001
* Method note (effect sizes). Partial η2 values quantify variance attributed to each factor controlling for others, ηp2 = SSeffect/(SSeffect + SSerror). Consequently, partial η2 do not sum to 100%. The constant is not an effect.
Table 5. Results of the stepwise multiple regression analysis for titratable acidity (TA) of ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C, within the last 30 (or 44) days before harvest, the number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, the date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. (n = 768). The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Table 5. Results of the stepwise multiple regression analysis for titratable acidity (TA) of ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C, within the last 30 (or 44) days before harvest, the number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, the date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. (n = 768). The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Unstandardized CoefficientsStandardized Coefficients βp
βSE *
Constant 0.322<0.001
1-MCP treatment before harvest0.0070.0020.089<0.001
Date of harvest−0.0300.002−0.381<0.001
1-MCP treatment after harvest0.0380.0020.496<0.001
Storage technology0.0090.0020.113<0.001
Storage period−0.0090.001−0.383<0.001
Shelf life−0.0160.002−0.203<0.001
* SE—standard error.
Table 6. Results of the eta squared (η2) analysis for titratable acidity (TA) of ‘Gala’ apples based on a model including 1-MCP treatment before harvest, date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Table 6. Results of the eta squared (η2) analysis for titratable acidity (TA) of ‘Gala’ apples based on a model including 1-MCP treatment before harvest, date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Independent Variablesη2 (%) *p
1-MCP treatment before harvest2.0<0.001
Date of harvest26.6<0.001
1-MCP treatment after harvest38.0<0.001
Storage technology3.1<0.001
Storage period26.8<0.001
Shelf life9.3<0.001
* Method note (effect sizes). Partial η2 values quantify variance attributed to each factor controlling for others, ηp2 = SSeffect/(SSeffect + SSerror). Consequently, partial η2 do not sum to 100%. The constant is not an effect.
Table 7. Results of the stepwise multiple regression analysis for soluble solids content (SSC) of ‘Gala’ apples based on a model number of days in the last 30 (or 44) days before harvest with an average air temperature below 15 °C, number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, date of harvest, 1-MCP treatment after harvest, storage technology, month of storage, and days of shelf life. (n = 768). The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Table 7. Results of the stepwise multiple regression analysis for soluble solids content (SSC) of ‘Gala’ apples based on a model number of days in the last 30 (or 44) days before harvest with an average air temperature below 15 °C, number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, date of harvest, 1-MCP treatment after harvest, storage technology, month of storage, and days of shelf life. (n = 768). The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Unstandardized CoefficientsStandardized Coefficients βp
βSE *
Constant 13.9<0.001
1-MCP treatment before harvest−0.4810.036−0.387<0.001
Date of harvest−0.2850.036−0.2290.011
1-MCP treatment after harvest0.3900.0360.314<0.001
Storage period−0.0810.011−0.212<0.001
Shelf life0.1620.0360.130<0.001
* SE—standard error.
Table 8. Results of the eta squared (η2) analysis for soluble solids content (SSC) of ‘Gala’ apples based on a model including 1-MCP treatment before harvest, date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Table 8. Results of the eta squared (η2) analysis for soluble solids content (SSC) of ‘Gala’ apples based on a model including 1-MCP treatment before harvest, date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Independent Variablesη2 (%) *p
1-MCP treatment before harvest22.7<0.001
Date of harvest9.30.011
1-MCP treatment after harvest16.2<0.001
Storage technology0.50.061
Storage period8.5<0.001
Shelf life3.2<0.001
* Method note (effect sizes). Partial η2 values quantify variance attributed to each factor controlling for others, ηp2 = SSeffect/(SSeffect + SSerror). Consequently, partial η2 do not sum to 100%. The constant is not an effect.
Table 9. Results of the stepwise multiple regression analysis for SSC/TA ratio of ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, the number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, date of harvest, 1-MCP treatment after harvest, storage technology, storage period and shelf life. (n = 768). This research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Table 9. Results of the stepwise multiple regression analysis for SSC/TA ratio of ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, the number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, date of harvest, 1-MCP treatment after harvest, storage technology, storage period and shelf life. (n = 768). This research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Unstandardized CoefficientsStandardized Coefficients βp
βSE *
Constant 41.576<0.001
Number of days in the last 30 (or 44) days before harvest with an average air temperature below 15 °C−0.0470.022−0.0570.034
1-MCP treatment before harvest−3.7010.395−0.233<0.001
Date of harvest5.2810.4240.332<0.001
1-MCP treatment after harvest−6.7940.395−0.427<0.001
Storage technology−1.5390.395−0.097<0.001
Storage period1.6810.1210.345<0.001
Shelf life4.1020.3950.258<0.001
* SE—standard error.
Table 10. Results of the eta squared (η2) analysis for SSC/TA ratio of ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, 1-MCP treatment before, date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Table 10. Results of the eta squared (η2) analysis for SSC/TA ratio of ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, 1-MCP treatment before, date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Independent Variablesη2 (%) *p
Number of days in the last 30 (or 44) days before harvest with an average air temperature below 15 °C4.4<0.001
1-MCP treatment before harvest14.0<0.001
Date of harvest22.5<0.001
1-MCP treatment after harvest35.4<0.001
Storage technology2.7<0.001
Storage period26.4<0.001
Shelf life16.7<0.001
* Method note (effect sizes). Partial η2 values quantify variance attributed to each factor controlling for others, ηp2 = SSeffect/(SSeffect + SSerror). Consequently, partial η2 do not sum to 100%. The constant is not an effect.
Table 11. Results of the stepwise multiple regression analysis for 1-aminocyclopropane-1-carboxylic acid (ACC) content in ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, the number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. (n = 768). This research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Table 11. Results of the stepwise multiple regression analysis for 1-aminocyclopropane-1-carboxylic acid (ACC) content in ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, the number of days when cumulated precipitation was above 6 mm, 1-MCP treatment before harvest, date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. (n = 768). This research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Unstandardized CoefficientsStandardized Coefficients βp
βSE *
Constant −0.0520.023
Number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest−0.0010.001−0.0780.012
Date of harvest0.0470.0100.144<0.001
1-MCP treatment after harvest−0.1350.009−0.413<0.001
Storage technology0.0590.0090.181<0.001
Storage period0.0330.0030.326<0.001
Shelf life0.0670.0090.204<0.001
* SE—standard error.
Table 12. Results of the eta squared (η2) analysis for 1-aminocyclopropane-1-carboxylic acid (ACC) content in ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, 1-MCP treatment before harvest, date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Table 12. Results of the eta squared (η2) analysis for 1-aminocyclopropane-1-carboxylic acid (ACC) content in ‘Gala’ apples based on a model including the number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest, 1-MCP treatment before harvest, date of harvest, 1-MCP treatment after harvest, storage technology, storage period, and shelf life. The research was conducted during the 2022/2023 and 2023/2024 storage seasons.
Independent Variablesη2 (%) *p
Number of days with an average air temperature below 15 °C within the last 30 (or 44) days before harvest 1.9<0.001
1-MCP treatment before harvest0.00.726
Date of harvest2.1<0.001
1-MCP treatment after harvest21.3<0.001
Storage technology4.9<0.001
Storage period14.4<0.001
Shelf life6.2<0.001
* Method note (effect sizes). Partial η2 values quantify variance attributed to each factor controlling for others, ηp2 = SSeffect/(SSeffect + SSerror). Consequently, partial η2 do not sum to 100%. The constant is not an effect.
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Małachowska, M.; Tomala, K. Determinants of Postharvest Quality in ‘Gala Schniga® SchniCo Red(s)’ Apples: The Role of Harvest Date, Storage Duration, and 1-MCP Application. Agriculture 2025, 15, 2363. https://doi.org/10.3390/agriculture15222363

AMA Style

Małachowska M, Tomala K. Determinants of Postharvest Quality in ‘Gala Schniga® SchniCo Red(s)’ Apples: The Role of Harvest Date, Storage Duration, and 1-MCP Application. Agriculture. 2025; 15(22):2363. https://doi.org/10.3390/agriculture15222363

Chicago/Turabian Style

Małachowska, Maria, and Kazimierz Tomala. 2025. "Determinants of Postharvest Quality in ‘Gala Schniga® SchniCo Red(s)’ Apples: The Role of Harvest Date, Storage Duration, and 1-MCP Application" Agriculture 15, no. 22: 2363. https://doi.org/10.3390/agriculture15222363

APA Style

Małachowska, M., & Tomala, K. (2025). Determinants of Postharvest Quality in ‘Gala Schniga® SchniCo Red(s)’ Apples: The Role of Harvest Date, Storage Duration, and 1-MCP Application. Agriculture, 15(22), 2363. https://doi.org/10.3390/agriculture15222363

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