Organic Agricultural Practice: Crop Load Management Enhancing Quality and Storability of High-Russet Pears

Abstract

The variability of crop load in Conference pears significantly influences fruit quality and postharvest losses. This study aimed to investigate and implement a novel technique for managing crop load in Conference pear orchards, with a focus on contributing to sustainable orchard management practices. High-russet pear cv. Conference fruit was harvested from trees categorized into two groups based on yield efficiency. A two-way ANOVA was used to test the effects of crop load level and year on yield properties and fruit quality parameters. Results demonstrated that low crop loads exerted a positive influence on fruit quality, resulting in increased fresh-market yield, enhanced fruit-mass parameters, and improved firmness and acidity. Additionally, fruit from low crop load levels exhibited a heightened level of characteristic skin russeting, a desirable attribute that enhances product appeal, compared to fruit from high crop load levels. Thus, crop load management in Conference pear orchards emerged as an effective preharvest organic strategy for mitigating postharvest quality losses during storage, thereby promoting overall orchard sustainability.

1. Introduction

The Conference pear (Pyrus communis L.) holds a prominent position in Spain and throughout the European Union [1]. This cultivar is distinguished by its Guarantee Mark (Pera Conferencia del Bierzo), which certifies compliance with quality and origin standards under Law 17/2001, 7 December 2001. Typically, russet on pear fruit skin diminishes its commercial value [2]. However, intriguingly, the Conference pear’s Guarantee Mark highlights a notable feature: a pronounced degree of russeting on its skin, enhancing its value compared to pears from other regions in Spain. A substantial portion of the Conference fruit surface is covered by russeting, that is, cork cells, which exfoliated and revealed deeper layers [3]. Russeting occurs during the fruit’s rapid growth phase when micro-cracks develop in the epidermis, leading to the formation of a waterproofing periderm to regulate water loss through the skin [4]. While russeting is genetically determined, environmental factors and cultivation practices significantly influence its formation [5]. Contrary to earlier beliefs that russeting detracted from fruit quality and market value, it is now considered a desirable trait, enhancing fruit quality and providing protection against environmental stress [3,5,6,7].

The Conference pear, characterized by its abundant russeting, is a prolific and high-yielding cultivar [8]. However, within an orchard, crop load in Conference pears may vary annually [9] due to its classification as a biennial bearing cultivar, yielding heavily in ‘on’-years and minimally in ‘off’-years [10]. Additionally, adverse weather conditions during pear blossom time can lead to low yields (less than 1.5 kg/tree) [11,12]. Apart from biennial bearing and climatic influences, seasonal crop load is determined by carbohydrate availability, rootstock, and flower- and fruit-thinning practices [13].

Variability in crop load profoundly impact fruit quality and tree physiology [13,14], with significant economic implications due to their effects on orchard productivity and fruit quality [15]. The value of harvested Conference pears, like other cultivars, heavily depends on fruit size distribution [16]. Harvesting pears with diameters of 65 mm or larger is economically advantageous as they fetch higher returns compared to smaller fruit sizes [9]. Market prices for Conference fruit with diameters exceeding 60 mm are twice those of smaller-sized fruit (<55 mm) [17]. Thus, limiting crop load to a specific number of fruits per tree enhances fruit quality and economic returns [18]. To achieve optimal productivity and adjust crop load to enhance fruit quality and economic returns [18,19], thinning treatments are employed. These treatments aim to reduce the crop load of the tree, increase mean fruit weight and size, and improve fruit quality [10,20,21].

Historically, orchards focused on maximizing yield through excessive fertilizer and pesticide use, often neglecting fruit quality [18]. However, recent years have witnessed a shift towards organic agricultural practices, optimizing input efficiency and minimizing environmental impacts [22,23]. According to European Union regulations CEE/2092/91 and CEE/1840/99, organic agricultural practices are defined as an organic production management system that promotes and enhances biodiversity, biological cycles, and soil biological activity [24], being recognized as a sustainable agricultural practice [25]. Organic agricultural practices include the addition of nutrient amendments that enhance soil fertility and health and the exclusion of mineral inorganic fertilizers [24]. Thus, research suggests that incorporating organic fertilization practices, which involve replacing chemical fertilizers with organic alternatives, is crucial for enhancing crop growth, soil fertility, and sustainability compared to relying solely on chemical fertilizers [26]. This approach also protects the local environment, making it compatible with environmentally sustainable development [27]. In addition to biofertilizers, other organic agricultural practices employed include: the use of biopesticides, biocontrol agents, preparation of farmyard manure, and trash composting [28]. According to [29], organic control management ranges from the use of host resistance, rootstock, and scion cultivar selection to the planting location of trees, the application of biological control agents, soil amendments, cultural management, postharvest treatments, disease modelling, and forecasting [29]. Regarding cultural management, there is a growing need to replace chemical thinners, particularly for pear blossom thinning, as no approved chemicals are available in Europe [30]. Organic crop load management techniques, such as mechanical or hand thinning, offer environmentally friendly alternatives, aligning with the principles of organic horticulture. They provide a low-environmental-impact method for crop load management and are a good fit for organic orchards [31]. Such practices gain significance for premium fruits, such as the Conference pear, awarded with a Guarantee Mark, as customers are willing to pay premium prices for organically produced high-quality fruit [32].

The impact of crop load and management techniques on pear yield and fruit weight is well documented [33]. However, there is limited information available for managing crop load in European pear cultivars, and even fewer studies focus on the impact of crop load on pear fruit quality [31]. Moreover, the comprehensive effects of crop load management, implemented as an organic agricultural practice, on overall fruit quality, both at harvest and after cold storage, particularly in high-russet Conference pears expressed as kg/TCSA (Trunk Cross-Sectional Area), remain understudied. Furthermore, little is known about the influence of crop load on the distinctive russeting characteristic of Conference pears. Therefore, this study aims to investigate the effects of organic agricultural practices, specifically crop load management, on fruit quality properties, including skin russet, in high-russet Conference pears.

2. Materials and Methods

2.1. Plant Material and Tree Selection

The experiment was conducted over the 2017 (year 1) and 2018 (year 2) growing seasons as a means of replication over time. Pear cv. Conference from 19-year-old trees on BA-29 rootstock, planted on 3.5 × 1.5 m spacing and trained as central leaders, were harvested in accordance with commercial harvesting standards based on days after full bloom and colour. The trees were sourced from a commercial orchard located in Cabañas Raras, El Bierzo Valley (León, Castilla y León, Spain) (Lat. 42°37′ N, Long. −6°38′ W, elevation 573 m). The orchard has 15 rows planted in an east–west orientation and includes three pear cultivars: Conference as the main cultivar and Buena Luisa and Passe Crassanne as pollinators. Orchard and nutritional management practices remained consistent between years and were those commonly used by the growers. The orchard was irrigated by gravity during the dry season. Dormant pruning was carried out each preceding winter season following recommendations for this cultivar [34]. Soil characteristics were consistent across both years, characterized by loam texture, neutral pH, and medium organic matter content. However, average annual rainfall increased from 681 mm in year 1 to 787 mm in year 2. Initially, 60 trees were randomly selected within the orchard in both years.

Tree selection for each crop load level changed slightly depending on the year, since climatic conditions or biennial bearing greatly affected the crop load of the trees during the second year of the study. Additionally, different trees were used for sample collection each year because the crop load of each tree varied across years.

In year 1, when climatic conditions or biennial bearing did not greatly affect the crop load of the trees, 300 suitable trees were selected out of the 826 trees in the orchard, focusing particularly on those with a sufficient crop load level to study the effect of thinning. From this selection, 60 trees were randomly chosen to conduct the study. Once the 60 trees were selected, it was necessary to conduct hand thinning on 30 of these trees to obtain a wide range of crop loads suitable for studying their impact on fruit quality.

In year 2, all the fruit trees in the orchard were examined to determine their estimated production levels. Subsequently, a random selection of 30 trees with a low crop load and a random selection of 30 trees with a high crop load were made, from which fruit samples were later collected. In this year, thinning was unnecessary due to reduced crop loads resulting from rainfall during flowering.

2.2. Crop Load Management and Experimental Design

During the first year, trees received two fruit thinning levels: no thinning and a light hand thinning carried out 75 days after full bloom (leaving an average of 75% of the fruit compared to trees with no thinning). Thinning aimed to achieve 2–3 fruits per cluster, with the number of fruits retained within the cluster depending on the fruit size (when the fruits within a cluster were large, three fruits per cluster were retained, whereas when the fruits were small, only two fruits per cluster were left) (Figure 1). Besides the advantages that the hand thinning method has over chemical methods, such as being more environmentally friendly and applicable in organic farming, this thinning method was chosen because it facilitates the retention of the largest and healthiest fruits within the cluster while removing the smaller and damaged ones. Thinned fruits averaging a diameter of 22.8 mm and a weight of 10.1 g.

Tree vigour was evaluated annually in October, marking the end of the growing season, and expressed as TCSA measured 20 cm above the graft [35].

Organic management in this study comprised two key strategies: firstly, the adoption of environmentally friendly hand thinning when necessary, and secondly, the sorting of trees based on yield efficiency. These organic practices were implemented to effectively address the variability of crop load levels.

The experiments were conducted using a completely randomized design with twenty replicates, focusing solely on the factor of crop load level.

2.3. Skin Russeting Quantification

The quantification of skin russeting was conducted through digital image processing and classification. The sample consisted of three pears randomly chosen per tree in the first year, each fruit representing one of three size classes: large, medium, and small. In the second year, the sample consisted of two pears per tree, representing large and medium/small sizes. Overall, the russetted area (%) was assessed in 180 pears during the first year and 120 pears during the second year. Three close-up photographs, using a Canon Powershot A3300 IS 16 MgP digital camera with a 28 mm focal lens and optical image stabilizer, were taken per pear to cover the entire surface area. A total of 900 images were taken and processed, with 540 in the first year and 360 in the second year.

An object-based image classification approach was applied to each image, defining two classes: green (non-russetted area) and brown (russetted area). The multiresolution segmentation algorithm in eCOGNITION^® 8.9 (Trimble Germany GmbH, Munich, Germany, 2021). [36] was used to merge pixels and obtain homogeneous non-overlapping polygons, which were then classified using the nearest neighbour algorithm. The accuracy assessment of the classification was assessed using an independent sample of validation points [37]. A total of 19 out of the 900 images were randomly selected. Within each of these 19 images, a systematic sampling design (1 cm × 1 cm) was applied to collect the validation points (one point corresponded to one object resulting from the segmentation). Overall, 1375 points belonging to the green class and 2108 to the brown class were used for validation, resulting in an overall accuracy of 98%, indicating that the method is sufficiently reliable to calculate russeting for all samples.

Once the classification method was validated, each classified image was exported as a raster file. The number of pixels assigned to the class ‘russetted area’ was calculated, along with the total number of pixels representing the pear (including both ‘non-russetted’ and ‘russetted’ areas). The russetted area of the pear (%) was determined by dividing the number of ‘russetted skin’ pixels by the total number of ‘pear’ pixels in the three images per pear. The mean russetted area (%) per tree was then calculated as the mean value of russeting obtained for the three pears in the first year and two pears in the second year. To account for pear size in the estimation of russeting per tree, a weighed mean of russeting was calculated (Equation (1)).

$$\mathrm{R}\mathrm{u}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{e}\mathrm{d} \mathrm{m}\mathrm{e}\mathrm{a}\mathrm{n}=\frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}}{\mathrm{x}}_{\mathrm{i}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathsf{\omega }}_{\mathrm{i}}}$$

(1)

where:

russeting weighted mean: russeting weighted mean per tree (%)

n: number of pears in the sample (3 in year 1, and 2 in year 2)

x: russetted area of the pear (%)

w: weight of the pear (g)

2.4. Yield and Fruit Quality Evaluations

Yield efficiency, total number of fruits per tree, and yield per tree (kg) were analysed. The collected fruits were subdivided into five classes according to their sizes based on a size card in steps of 5 mm. Yield parameters were then calculated, including the number of fruits per size class and the weight of fruits per size class (<55, 55/60, 60/65, 65/70, and >70 mm). Fresh-market yield for each tree was determined based on fruit with a maximum cheek diameter equal to or higher than 60 mm. The financial return from fruits with a diameter higher than 60 mm is twice that of smaller-sized fruits (less than 55 mm) [38]. Mean fruit mass was calculated by dividing the total yield per tree (kg) by the total number of fruits per tree.

In both seasons, samples consisting of 20 commercial fruits, representative in size from the total fruit of the tree, were selected for quality evaluation. Then, once in the lab, samples were divided into two sub-samples of 10 fruits each, one for quality evaluation at harvest and the other for postharvest quality evaluation. The latter were stored in plastic containers for 70 days in standard cold storage at 1.5 °C, 95% RH. To simulate commercial storage conditions, the fruit was placed on plastic trays enclosed with paper.

Weight loss (%) was determined by measuring the total fresh weight of the 10-fruit samples both at harvest and after storage. Fruit quality evaluation included skin ground colour, firmness, starch index (SI) (only at harvest), total soluble solids (TSS), and titratable acidity (TA) assessments. For each fruit, skin ground colour was determined at three points in the area free from russet with a colorimeter (Minolta, CR-200, Ahrensburg, Germany), expressed in L*, a*, b* system and converted to hue angle (h°), where h° = tan − 1 b*/a*. If the background colour was obscured by russetted areas covering the entire fruit, colour analysis for that particular fruit was omitted to prevent measurement errors. Three determinations of the colour parameters were made along the equatorial axis of each fruit. Flesh firmness was measured using a penetrometer (Effegi TR Turoni & C., Forlì, Italy) mounted on a hand-operated press fitted with a 7.9 mm diameter plunger. Measurements were taken at two equidistant points on the equatorial axis of the fruit. The iodine–starch index was evaluated by dipping an equatorial slide of the fruit into iodine–iodize solution (20 g IK + 10 g I2 + 1 L H₂O) for 1 min and comparing the colour pattern with a reference chart, Ctifl-Eurofru Code, using a scale of 1 (fully stained, all starch) to 10 (no staining, no starch).

The 10-fruit sub-samples were divided into two 5-fruit groups. Juice extraction was performed by homogenizing the fruit flesh in a blender to determine TSS and TA. The TSS (%) of the juice was measured with a digital refractometer (Atago, DR-A1, Tokyo, Japan), while TA was determined by titrating 10 mL of juice with NaOH 0.1 N up to pH 8.1. Acidity of pear fruit was reported as a percentage of malic acid concentration.

2.5. Incidence of Physiological Disorders

For stored samples, the incidence of fruit affected by disorders such as shrivelling or rot incidence was recorded. Core breakdown was visually scored on fruit cut through the equator.

2.6. Statistical Analysis

A two-way analysis of variance (ANOVA) was used to test the effects of crop load level and year on yield properties and fruit quality parameters, followed by a Fisher’s LSD post-hoc test to compare means. The significance level for the analyses was set at 5% (p ≤ 0.05). All statistical analyses were carried out using SPSS software v. 29.0 (SPSS Inc., Chicago, IL, USA).

3. Results

3.1. Yield Efficiency and Yield Properties

Each year, all fruits from every experimental tree were harvested, counted, and weighed. Yield efficiency was then calculated as the ratio of the total cumulative yield of each tree (g) per TCSA (cm²). Subsequently, each year, the 60 trees, out of the 826 trees on the orchard, were ranked according to yield efficiency, forming two categories of crop load: a high crop load level, consisting of the 20 trees with the highest yield efficiency (high TCSA), and a low crop level, consisting of the 20 trees with the lowest yield efficiency (low TCSA) (

Table 1. Yield efficiency (g cm⁻²).

Crop Load Level	Year 1			Year 2
	Minimum	Mean	Maximum	Minimum	Mean	Maximum
Low	168.06	288.77	365.93	34.58	81.10	119.85
High	510.03	651.46	919.60	219.59	332.95	760.51
Mean		470.12			207.02

). The 20 trees in each crop load level were selected as replicates, while the 20 remaining trees in the middle were excluded from the analysis. Each sample tree in the orchard was labelled with a unique code. The number of fruits per tree in the orchard was 329.9 in year 1 and 53.1 in year 2.

The mean yield efficiency varied between the two years, decreasing from 470.12 g cm⁻² of TCSA in year 1 to 207.02 g cm⁻² of TCSA in year 2 (Table 1). Consequently, the crop load of pear cv. Conference, measured in kg per tree, varied considerably from high (27.92 kg per tree in year 1) to low (13.57 kg per tree in year 2). Yield efficiency (g cm⁻²) ranged from 168.06 (representing the tree with the lowest crop load in year 1) to 919.60 (indicating the tree with the highest crop load in the same year). In year 2, the range was from 34.58 (lowest) to 760.51 (highest). When considering crop load levels, during the first season, yield efficiency ranged from 168.06 to 365.93 g cm⁻² in low crop load trees and from 510.03 to 919.60 g cm⁻² in high crop load trees. In the second season, yield efficiency varied from 34.58 to 119.85 g cm⁻² in low crop load trees and from 219.59 to 760.51 g cm⁻² in high crop load trees. The mean yield efficiency changed from 288.77 g cm⁻² of TCSA (low crop load) to 651.46 g cm⁻² of TCSA (high crop load) in year 1 and from 81.10 g cm⁻² of TCSA (low crop load) to 332.95 g cm⁻² of TCSA (high crop load) in year 2.

Trees with low crop load produced larger fruits, characterized by higher percentages in the upper size category (>70) and lower percentages in the lower size categories (<55, 55–60). These trees also yielded a higher percentage of fresh-market produce and had greater fruit mass (

Table 2. Means for yield properties.

Factor		<55 mm (%)	55–60 mm (%)	60–65 mm (%)	65–70 mm (%)	>70 mm (%)	Fresh-Market Yield (%)	Fruit Mass (g)
Crop load	Low	19.26 b 1	10.39 b	11.60 a	13.04 a	45.72 a	70.35 a	237.16 a
	High	24.55 a	12.44 a	11.92 a	12.08 a	39.01 b	63.01 b	219.22 b
Year	1	41.51 a	20.07 a	17.67 a	12.96 a	7.80 b	38.42 b	170.55 b
	2	2.30 b	2.76 b	5.85 b	12.16 a	76.93 a	94.94 a	285.83 a
Main effects 2
Crop load		*	*	ns	ns	**	**	*
Year		***	***	***	ns	***	***	***
Interaction
Crop load × Year		ns	ns	*	*	ns	ns	ns

¹ For each yield property, means with different letters within the same factor are significantly different (p value ≤ 0.05, according to the two-way ANOVA). ² p value: ns—not significantly different; * p < 0.05; ** p < 0.01; *** p < 0.001.

). Fresh-market yield varied from 63.01% to 70.35%, and fruit mass varied from 219.22 g to 237.16 g between crop loads, whereas fresh-market yield varied from 38.42% to 94.94%, and fruit mass varied from 170.55 g to 285.83 g between years. The two-way ANOVA test revealed statistically significant interactions between crop load and year for the 60–65 mm and 65–70 mm size classes.

3.2. Quality Properties

Skin colour and TSS were not influenced by crop load either at harvest or after storage. However, significant differences in firmness, starch index (analysed only at harvest), and TA were found between crop loads (

Table 3. Means for fruit quality properties.

Storage Stage	Factor		h°	Firmness (N)	SI (1–10)	TSS (%)	TA (%)
Harvest	Crop load	Low	115.00 a 1	52.18 a	5.02 b	12.64 a	0.217 a
		High	114.84 a	48.81 b	5.78 a	12.62 a	0.201 b
	Year	1	114.26 b	51.05 a	5.93 a	12.06 b	0.203 b
		2	115.58 a	49.94 a	4.87 b	13.20 a	0.215 a
Main effects 2
Crop load			ns	***	**	ns	**
Year			***	ns	***	***	*
Interaction
Crop load × Year			ns	ns	*	ns	ns
Postharvest	Crop load	Low	106.83 a	21.89 a	-	13.78 a	0.180 a
		High	106.12 a	18.74 b	-	13.88 a	0.159 b
	Year	1	106.74 a	24.35 a	-	13.20 b	0.171 a
		2	106.22 a	16.29 b	-	14.46 a	0.169 a
Main effects
Crop load			ns	*	-	ns	***
Year			ns	***	-	***	ns
Interaction
Crop load × Year			ns	ns	-	ns	ns

¹ For each quality parameter, means with different letters within the same factor are significantly different (p value ≤ 0.05, according to the two-way ANOVA). ² p value: ns—not significantly different; * p < 0.05; ** p < 0.01; *** p < 0.001. h°, hue angle; SI, starch index; TSS, total soluble solids; TA, titratable acidity.

).

Fruit from low crop load trees exhibited greater hardness compared to those from high crop load trees. This trend was consistent both at harvest and after storage. Differences in fruit firmness between years were only observed at the end of storage. Fruit coming from low crop load trees had a lower SI value compared to fruit from high crop load trees, while fruit harvested in year 2 had lower SI values than fruit harvested in year 1. TSS was the only parameter affected by year during both storage stages, with TSS being higher in year 2 compared to year 1. Higher TA values were observed in fruit coming from low crop loads compared to fruit from high crop loads. Differences in TA between years were observed only at harvest. Significant interactions between crop load and year were observed only for SI at harvest.

The russetted area was influenced by crop load, with russeting being more pronounced in fruit from low crop load trees (

Table 4. Means for fruit russeting parameters.

Factor		Weighted Mean	Mean
Crop load	Low	47.51 a 1	47.22 a
	High	39.03 b	38.22 b
Year	1	39.58 b	38.20 b
	2	46.96 a	47.24 a
Main effects 2
Crop load		**	**
Year		*	**
Interaction
Crop load × Year		ns	ns

¹ For each russeting parameter, means with different letters within the same factor are significantly different (p value ≤ 0.05, according to the two-way ANOVA). ² p value: ns—not significantly different; * p < 0.05; ** p < 0.01.

). The mean russetted area was higher in low crop load trees compared to high crop load trees (47.22% and 38.22%, respectively). Similarly, the weighed mean russetted area was higher in low crop load trees compared to high crop load trees (47.51% and 39.03%, respectively). Skin russeting was also affected by year, with both russeting parameters being more pronounced in year 2 than in year 1. Interactions between crop load and year for russeting parameters were not significant.

No significant differences were found for disorders or weight loss between crop load levels.

4. Discussion

The highest yield efficiency observed for the high crop load aligns closely with the yield observed by a previous study on pear cv. Conference, which recorded a yield of 690 g cm⁻² [39]. When considering the mean yield efficiency in terms of kg/ha, both low and high crop load levels in year 1 were consistent with high yield levels, as defined by [1] for Conference pear trees (above 38t per ha). However, in year 2, only the mean yield efficiency from the high crop load level could meet this criterion. It is important to note that crop load, as measured by yield efficiency, is considered low in Conference pear due to its high TCSA and low productivity [39].

The notable difference in yield efficiencies between the two years could be attributed to several factors. One major factor is the impact of rainfall during flowering in the second season, which likely led to a reduction in crop load. Additionally, the phenomenon of biennial bearing may have influenced yield efficiency. The authors of [40] highlighted the importance of crop load management in order to avoid biennial bearing and regulate fruit size and quality in apple cultivars.

Despite the pear trees in this orchard being winter-pruned not only during the two years of the experiment but also in previous years, and the well-established understanding of how management practices such as pruning are utilized to restore the balance between vegetative and reproductive shoots in alternate bearing varieties [41], thereby limiting the detrimental effect of alternate bearing [42], there are certain cultivars for which alternate bearing is less affected by pruning and training [43], such as the Conference pear variety.

Environmental factors can moderate fruit levels through suboptimal conditions for flower induction or by reducing the number of fruits, while growers can intervene by providing optimal conditions for the tree through practices such as pruning [41]. However, growers are often hesitant to remove flowers and fruit from trees as unexpected climatic events, such as those experienced during the second year, can further reduce crop load after grower intervention.

The differences in production between the two years likely contributed to the larger discrepancies in fresh-market yield between years (56.52%). Additionally, better fruit quality, particularly in terms of fruit size, was achieved with low crop load. The tendency of fruit trees to produce heavy crop loads often results in the production of numerous small fruits that are unsuitable for fresh market sale [44]. Therefore, when comparing both years, fruit quality in terms of fruit size was superior during the year with a lower yield efficiency, which was the second season. Variations in fruit weight between years has been observed in other studies of pear cv. Conference [45]. The interaction between crop load and year on intermediate size classes, specifically the 60–65 mm and 65–70 mm classes, is likely influenced by the significant difference in fruit mass between the two years. In year 2, when the average fruit mass is significantly higher compared to year 1, the majority of the fruit falls into the >70 mm size class, especially in low crop load level. This leaves a smaller percentage of fruit for the other size classes, including 60–65 mm and 65–70 mm. Conversely, the smaller fruit size in year 1 resulted in higher percentages of fruit in the intermediate size classes under low crop load.

The natural tendency of fruit trees to produce heavy crop loads causes the sugars produced in the leaves (sources) to be distributed over too many fruits (sinks) [44]. In contrast, thinning, as a preharvest practice that decreases crop load, can enhance fruit quality in Conference pears, particularly fruit size, by improving the source–sink relationship and increasing the allocation of photoassimilates to the remaining fruit [33]. Moreover, [23] studies have found that the eating quality of fruit such as Deveci pears was better in larger fruit, as lower fruit size led to reduced juiciness and crispness. Lower crop loads have also been associated with enhanced fruit quality in terms of sugar content, as there is less competition for soluble carbohydrates between plant organs [23], as well as improved firmness [22,33,40,46] and even acidity in apples [15]. The authors of [47] found that low crop load reduced the photosynthetic performance of Honeycrisp apple trees, possibly due to the increase in leaf fructose, glucose, and sorbitol concentrations. This suggested that the pathways underlying the distribution and concentration of these sugars could be sensitive to changes in crop load treatments, highlighting the key role of crop load in the allocation of photoassimilates. In our experiment, these changes between crop load levels were clear for firmness and TA, while TSS remained invariable regardless of the crop load.

After 70 days of storage, firmness in all cases exceeded the optimum level recommended for acceptable consumption in pear cv. Conference, which is typically between 10.8 and 12.8 N according to [48], or within the range of 10–30 N as suggested by [49]. However, the potential storability of fruit from trees with low crop loads would be extended compared to fruit from trees with high crop loads, especially in years like year 2, where the firmness value in postharvest was close to the minimum recommended level for consumption. Hence, the storability of fruit from trees with low crop load would be greater compared to fruit from trees with high crop loads, particularly in terms of mid-term storage. Firmness during storage was also dependent on the year. This could be attributed to the fact that, as explained in the Materials and Methods Section, the samples of fruit selected for quality evaluation were representative in size of the total fruit from each tree. Since the majority of the sample by weight (and even more in terms of the number of pieces) consisted of small fruits in the first year, this could have had a large impact on firmness during postharvest, which was contrary to the effect of the factor under study (crop load). Johnston et al. [50] noted that larger-sized apple fruits harvested at advanced maturity usually soften more rapidly than smaller fruits.

TSS exhibited notable variation across years, making it the quality property with the greatest change from year to year, whereas the crop load factor did not affect TSS. Specifically, TSS varied from 12.06% (at harvest) in year 1, the year with higher yield efficiency, to 13.20% (at harvest) in year 2, the year with lower yield efficiency. These findings align with previous research [10], which highlights that the individual sugar content in pear cv. Conference is highly dependent on the year rather than on treatments such as chemical thinning. Similarly to the effect observed with firmness, the variation in fruit size between years could have had influenced TSS, as lower TSS was found in year 1 (the year with a higher proportion of small fruits) compared to year 2. Furthermore, disparities in TSS between years could also be attributed to the average solar radiation over a 4-week period [51].

Conference pear is considered a low-acidity cultivar, with a TA level around 0.2–0.3% [35]. In our experiment, the TA levels in fruit from trees with low crop loads were consistently above the threshold representing ripe Conference pears (0.16%) [52]. However, fruit from trees with high crop loads showed TA values after storage that were significantly lower than those for fruit from trees with low crop loads, and, in fact, they were lower than that threshold. Similarly, the SI values were consistent with the firmness and TA values. Fruit from low crop levels were less ripe as their starch content was higher (on a scale of 1 (all starch) to 10 (no starch)), suggesting they could withstand longer storage periods. However, while crop load affected SI in year 1, with lower values observed in low crop load compared to high crop load, significant differences between crop load levels could not be found in year 2. Thus, the effect of crop load on SI was contingent upon the year, indicating that it cannot be definitively stated that crop load affects SI. Ultimately, except for SI, it could be inferred that regardless of the season, fruit coming from low crop loads would have better behaviour during postharvest as its storability would be increased compared to fruit coming from high crop loads, which will be riper and therefore would have lower storability.

As previously mentioned, russeting in pear cv. Conference is considered a significant quality attribute of the fruit. Research has shown that differential growth rates between regions in pear cv. Conference contributed to variations in russeting [53]. The russetted area for Conference pears grown in El Bierzo was found to be higher than in other areas where Conference pear are cultivated [54]. Thus, [1] estimated that the russetted area in Conference pears grown in Dąbrowice (Poland) was minimal, mainly around the calyx, averaging 1.9 and 2.2 on a scale from 1 (no russeting) to 5 (russeting > 75% of fruit skin surface), indicating that the percentage of russeting was around 20%, significantly lower than the percentage observed in our work.

According to [53], russeting in Conference pear increases when the relative growth rates of the surface area exceeded 0.03 per day. This finding could explain the differences observed in russetted area between crop loads. Larger fruit, such as those coming from low crop loads, compared to those from high crop loads, would have higher growth rates, resulting in a higher percentage of russetted area. Therefore, in addition to location, the higher percentage of russetted area found in fruit from low crop loads will further enhance the quality of this cultivar, making it more desirable. This aspect contributed to achieving organic management practices, as it eliminated the need for farmers to use chemical products such as thinners to increase the percentage of russetted area in the pear fruit to enhance its appearance [18].

It could be asserted that thinning to manage the crop load of Conference pear trees will not be necessary every year. Therefore, only years with high crop loads will necessitate hand thinning as an organic preharvest technique to optimize fruit quality and enhance environmental sustainability. Conversely, in years with low crop loads, it would be more productive to concentrate on identifying or classifying fruit according to yield efficiency to improve the quality and storability of the different batches picked. This approach could be implemented by farmers to contribute to organic management.

5. Conclusions

Despite the significant influence of annual variations on fruit quality, particularly evident in cultivars susceptible to biennial bearing, our study underscores a consistent finding: maintaining a low crop load in pear cv. Conference reliably enhances fruit quality. Specifically, it improves fruit size, firmness, and acidity, and increases mid-term storability. Additionally, our findings highlight the superiority of fruit from low crop load levels in terms of russetted area, a key factor in consumer preference for this variety, where russeting importantly contributes to its commercial value; hence, crop load management should be the key area of focus for increasing russetted area in high-russetted cultivars such as Conference pears. Considering horticultural management, the implementation of organic agricultural practices such as crop load management not only optimizes orchard productivity but also fosters organic practices by minimizing postharvest quality losses in Conference pears. Procedures for classifying fruit trees according to yield efficiency should be implemented to improve the quality and storability of the different harvested batches.