Apple Yield Research Articles

MdVQ17 negatively regulates apple resistance to Glomerella leaf spot by promoting MdWRKY17-mediated salicylic acid degradation and pectin lyase activity.

Glomerella leaf spot (GLS) is a fungal disease caused by Colletotrichum fructicola, which severely restricts the yield and quality of apples. Valine-glutamine (VQ) proteins are transcriptional regulators involved in the regulation of plant growth and stress responses. However, little is known about the role of VQ proteins in the biotic stress response in apple. Here, a VQ gene, MdVQ17, that was highly induced by C. fructicola infection was identified. Overexpression of MdVQ17 in apple increased susceptibility to C. fructicola and significantly reduced the salicylic acid content and β-1,3-glucanase and chitinase activities. Based on yeast two-hybrid screening, MdWRKY17, which promotes susceptibility to C. fructicola, was identified as an MdVQ17-interacting protein. Co-expression of MdVQ17 can promote the binding and transcriptional activation activity of MdWRKY17 on the promoter of Downy Mildew Resistant 6 (MdDMR6), thereby promoting MdWRKY17-mediated salicylic acid degradation. Based on DNA affinity purification sequencing, the pectin lyase-encoding gene MdPL-like was identified as a direct target of MdWRKY17. MdWRKY17 can directly bind to the promoter of MdPL-like and activate its transcription, and the binding and activation of MdWRKY17 on the MdPL-like promoter were significantly enhanced by MdVQ17 co-expression. Functional identification showed that MdPL-like promoted pectin lyase activity and susceptibility to C. fructicola. In sum, these results demonstrate that the MdVQ17-MdWRKY17 module mediates the response to C. fructicola infection by regulating salicylic acid accumulation and pectin lyase activity. Our findings provide novel insights into the mechanisms by which the VQ-WRKY complex modulates plant pathogen defense responses.

Effect of Biochar on Apple Yield and Quality in Aged Apple Orchards on the Loess Plateau (China)

Apples are not only a foodstuff, but also a raw material for many industrial production fields, and the market demand is constantly increasing. The Loess Plateau is one of the world’s largest apple-producing areas, with about 85% are aged orchards (more than 20 years old), facing problems such as poor soil water retention, degradation of tree strength, and declining yield and quality, etc., so do aged orchards in other regions of the world, and study on improving quality and increasing yield of aged orchards is of great significance to the sustainable development of the apple industry. Here, 6 treatments (2, 4, 6, 8, 10 and 12 kg/plant) were designed in the experiment to study the effects of biochar on yield and quality in aged apple orchards. The study showed that: biochar could improve soil water retention, but caused the soil alkalization; biochar could improve apple quality and increase yield, T3 and T4 were of better quality, and T3 had the highest yield. Comprehensive analysis, T3 is the optimal scheme. The results not only provide a reference for aged orchards worldwide, but also have great significance for the sustainable development of the apple industry.

Brassinosteroids biosynthetic gene MdBR6OX2 regulates salt stress tolerance in both apple and Arabidopsis

Salt stress is a critical limiting factor for fruit yield and quality of apples. Brassinosteroids (BRs) play an important role in response to abiotic stresses. In the present study, application of 2,4- Epicastasterone on seedlings of Malus ‘M9T337’ and Malus domestica ‘Gala3’ alleviated the physiological effects, such as growth inhibition and leaf yellowing, induced by salt stress. Further analysis revealed that treatment with NaCl induced expression of genes involved in BR biosynthesis in ‘M9T337’ and ‘Gala3’. Among which, the expression of BR biosynthetic gene MdBR6OX2 showed a three-fold upregulation upon salt treatment, suggesting its potential role in response to salt stress in apple. MdBR6OX2, belonging to the CYP450 family, contains a signal peptide region and a P450 domain. Expression patterns analysis showed that the expression of MdBR6OX2 can be significantly induced by different abiotic stresses. Overexpressing MdBR6OX2 enhanced the tolerance of apple callis to salt stress, and the contents of endogenous BR-related compounds, such as Typhastero (TY), Castasterone (CS) and Brassinolide (BL) were significantly increased in transgenic calli compared with that of wild-type. Extopic expression of MdBR6OX2 enhanced tolerance to salt stress in Arabidopsis. Genes associated with salt stress were significantly up-regulated, and the contents of BR-related compounds were significantly elevated under salt stress. Our data revealed that BR-biosynthetic gene MdBR6OX2 positively regulates salt stress tolerance in both apple calli and Arabidopsis.

Evolution of the SPX gene family and its role in the response mechanism to low phosphorus stress in self-rooted apple stock

BackgroundPhosphorus plays a key role in plant adaptation to adversity and plays a positive role in the yield and quality formation of apples. Genes of the SPX domain-containing family are widely involved in the regulation of phosphorus signalling networks. However, the mechanisms controlling phosphorus deficiency are not completely understood in self-rooted apple stock.ResultsIn this study, 26 members of the apple SPX gene family were identified by genome-wide analysis, and further divided into four subfamilies (SPX, SPX-MFS, SPX-EXS, and SPX-RING) based on their structural features. The chromosome distribution and gene duplications of MdSPXs were also examined. The promoter regions of MdSPXs were enriched for multiple biotic/abiotic stresses, hormone responses and typical P1BS-related elements. Analysis of the expression levels of 26 MdSPXs showed that some members were remarkably induced when subjected to low phosphate (Pi) stress, and in particular MdSPX2, MdSPX3, and MdPHO1.5 exhibited an intense response to low Pi stress. MdSPX2 and MdSPX3 showed significantly divergent expression levels in low Pi sensitive and insensitive apple species. Protein interaction networks were predicted for 26 MdSPX proteins. The interaction of MdPHR1 with MdSPX2, MdSPX3, MdSPX4, and MdSPX6 was demonstrated by yeast two-hybrid assay, suggesting that these proteins might be involved in the Pi-signaling pathway by interacting with MdPHR1.ConclusionThis research improved the understanding of the apple SPX gene family and contribute to future biological studies of MdSPX genes in self-rooted apple stock.

Effectiveness of sod and humus soil maintenance and biologized protection in intensive apple orchards (Malus domestica Borkh.) in the south of Russia

Application of intensive technologies in horticulture with a high mechanical and chemical load on soils and fruit plants leads to deterioration of soil properties, environmental pollution, and development of resistance among pathogens and phytophages. In this study, we aim to developing a biologized technology for apple tree cultivation using a sod and humus system of inter-row maintenance and biopreparations based on active strains of microorganisms, entomoacariphages, and pheromones. The research was conducted in 2019-2023 in intensive apple orchards of the central plain-steppe region of Crimea by the methods conventionally used in soil science, agrochemistry, horticulture, and plant protection. The use of a mixture of cereal-legume grasses in combination with microbiological fertilizers based on nitrogen-fixing microorganisms was found to increase apple yields by 11–12 t/ha, or 64 % compared to the control, by increasing the number of fruits and the average weight of one fruit. A positive balance of nutrition elements in soil with an intensity of 135-270 % was established. The efficiency of controlling the number of phytophages and pathogen harmfulness through the integrated use of biological and biotechnical methods was shown. Thus, the “attract-and-kill” method for Cydia pomonella L. pheromones demonstrated a 98 % efficiency along with a reduction in pesticide load to 7.7 kg/ha season by the preparation. Release of phitoseiid mites for control of tetranychus mites by the method of seasonal colonization provides for a reduction and retention of phytophagous population at the economic threshold of harmfulness during the entire vegetation period. The use of fungicides based on pathogenic bacteria of the Bacillus genus and antagonist fungi of the Trichoderma genus against scab is eff ective only in the years unfavorable for the development of the disease. The results obtained form the basis for a biologized resource-saving technology for exploitation of intensive apple orchards. This technology contributes to increased soil fertility, reduced chemical and pesticide load, and increased productivity of horticultural agrocenosis under the conditions of the Crimea and southern Russia.

Optimizing irrigation and nitrogen fertilizer management to improve apple yield, quality, water productivity and nitrogen use efficiency: A global meta-analysis

Inappropriate strategies in water use and nitrogen (N) fertilizer application in apple production can decrease apple yield, water productivity (WP), nitrogen use efficiency (NUE), and quality, leading to severe environmental pollution. However, there is a lack of systematic analysis on the effects of water use and N fertilizer inputs on apple yield, quality, WP, and NUE at the global scale. Therefore, we conducted a comprehensive meta-analysis with 1834 observations from 72 published studies across 18 countries to assess the effects of water use and N inputs on apple yield, quality, WP, and NUE. We aim to quantify the potential for water-saving and N-saving for global apple production. Our results showed that the global average apple yield, organic acids (OA), total soluble solids (TSS), vitamin C (VC), firmness (FN), WP, and NUE were 34.7 t ha−1, 0.51 %, 13.9 %, 4.7 mg 100 g−1, 7.4 kg cm−2, 6.8 kg m−3, and 214.5 kg kg−1, respectively. Reducing above-optimal water input to optimal water input may increase apple yield by 19.4 %, WP by 36.1 %, OA by 12.5 %, TSS by 8.4 %, VC by 17.5 % and FN by 9.8 %, respectively. Reducing above-optimal N inputs to optimal N inputs may increase apple yield by 8.9 %, NUE by 48.1 %, OA by 10.7 %, TSS by 6.1 %, VC by 13.3 % and FN by 5.2 %, respectively. The overall global mean water-saving and N-saving potentials for apple production were 20.4 % (136 mm less water irrigated) and 37 % (203 kg ha−1 less N fertilizer used), respectively. Our findings provide guidance for water use and N fertilizer management of apple production and can thus help to improve/ maintain yield and quality for greatly saving water and N fertilizer.

Foundation and evaluation of a diagnosis and recommendation integrated system for star apple (Chrysophyllum cainito L.)

Diagnosis and recommendation integrated system (DRIS) is a greatly accurate and reliable mean of evaluating the exact nutrition status of crops via foliar analysis. DRIS has been applied to many types of crops, but DRIS has not been established for and applied to star apple, which is an important fruit in many countries. Therefore, this study aims to (i) detect soil obstacles in star apple cultivation, (ii) create a DRIS for star apple, and (iii) apply the created DRIS norms to evaluate the macro-nutrition status of star apple. In total, 90 leaf samples were obtained from 90 different star apple farms, where 90 soil samples were also collected. The soil analysis results revealed that concentrations of available nitrogen and soluble phosphorus were low and medium, indicating a high risk of reduction in star apple yield. The average yield of star apple was 200.0 kg plant−1. The high-yielding group had higher concentrations of N, K, and Mg in the leaf than the low-yielding group; however, the concentrations of Fe and Mn in the leaf were opposite, and the concentrations of P, Ca, Cu, and Zn were equivalent in the two groups. The DRIS norms have been established for star apple with 28 nutrient ratio pairs and consisted of means, coefficients of variation, variances (S2), and variance ratios (S2 l/S2 h), whose values were 0.136–182.6, 21.0–69.5%, 0.0024–10400.9, and 1.89–34.3, respectively. The results of applying the DRIS norms revealed that the nutrient status of star apple was deficient of N, K, and Mg.

Selection of Spectral Parameters and Optimization of Estimation Models for Soil Total Nitrogen Content during Fertilization Period in Apple Orchards

The rapid and accurate diagnosis of nitrogen content in apple orchard soil is of great significance for the rational application of nitrogen fertilizer in orchards to improve apple yield and quality. An apple orchard in Shuangquan Town, Changqing District, Jinan City, Shandong Province, was taken as the experimental area. The optimal method for extracting spectral characteristic bands and screening spectral characteristic indices (SCIs) of soil total nitrogen (TN) for independent and comprehensive fertilization periods was explored. Independent and comprehensive soil TN content estimation models were constructed and optimized for each and the entire fertilization period, respectively. The results show that compared with the correlation coefficient method, stepwise multiple linear regression (SMLR) performs better in extracting hyperspectral characteristic bands of soil TN content. It helps to achieve a higher modeling accuracy, smaller root mean square error (RMSE), and is more conducive to avoiding the influence of multicollinearity of model variables. The sensitive areas of soil TN content in the SCI do not undergo significant changes due to different fertilization periods. Among them, the ratio spectral indices (RSIs) are in the range of 800–900 nm, 1900–1950 nm, and 2200–2300 nm, while the sensitive areas of the difference spectral index (DI) and Normalized difference spectral index (NDSI) are in the range of 1900–1950 nm and 2200–2300 nm. The combination of SCI and characteristic bands significantly improves the prediction accuracy of soil TN estimation models. The independent and comprehensive estimation models for each fertilization period based on the BP (back propagation) neural network optimized by the Mind Evolution Algorithm (MEA-BPNN) can achieve a more stable and accurate estimation of soil TN. Finally, using soil spectral characteristic bands selected through continuum removal (CR) transformation and SMLR, combined with SCI, the model based on the MEA-BPNN (CR-SCI-MEA-BPNN) has the best prediction performance. The modeling determination coefficients R2 for each fertilization period reached 0.94, 0.95, 0.92, and 0.94, respectively, with RMSE of 0.0032, 0.0024, 0.0035, and 0.0027. The R2 and RMSE of the modeling and validation set of the entire fertilization period comprehensive model are 0.899, 0.0038, and 0.89, 0.0041, respectively. The results of this article provide technical support for promoting the timely monitoring of soil TN content and guiding rational fertilization in apple orchards.

基于改进 YOLOv7 的苹果叶病害目标检测

Apple leaf diseases significantly threaten the yield and quality of apples. In order to detect apple leaf diseases in a timely and accurate manner, this study proposed a detection method for apple leaf diseases based on an improved YOLOv7 model. The method integrated a Similarity-based Attention Mechanism(SimAM) into the traditional YOLOv7 model. Additionally, the regression loss function is modified from Complete Intersection over Union (CIoU) to Structured Intersection over Union (SIoU). Experimental results demonstrates that the improved model exhibits an overall recognition precision of 92%, a recall rate of 99%, and a mean average precision (mAP) of 96.1%. These metrics show a respective improvement of 14.4%, 38.85%, and 18.69% compared to the preimproved YOLOv7. When compared with seven other target detection models in comparative experiments, the improved YOLOv7 model achieves higher accuracy, lower rates of missed and false detections in disease target detection. The model excels in detecting disease categories in complex environments and identifying small targets at early disease stages. It can provide technical support for effective detection of apple leaf diseases.

Optimizing deficit drip irrigation to improve yield,quality, and water productivity of apple in Loess Plateau of China

ContextDeficit drip irrigation is an advanced agricultural technology widely promoted and applied around the world to maintain/increase yield and quality while simultaneously minimizing water consumption. However, there is still a lack of comprehensive research on the effects of deficit drip irrigation at different stages on apple yield, water productivity, and quality. ObjectiveThe aim was to investigate the effects of deficit drip irrigation treatments (DDITs) on apple yield, quality, and water productivity (WP), and optimize the deficit drip irrigation strategy for apples in the Loess Plateau of China. Methodsa two-year field experiment was conducted with 17 irrigation treatments, including a control treatment (CK, full irrigation (FI)) and four DDITs (D15%, D30%, D45%, D60%) during the bud burst to leafing stage (I), flowering to fruit set stage (II), fruit expansion stage (III), and fruit maturation stage (IV). The optimal irrigation management was determined using the fuzzy Borda combined model, which was based on four different single evaluation models of the TOPSIS method, principal component analysis (PCA), grey correlation analysis (GCA), and membership function analysis (MFA). ResultsThe DDITs at stage IV decreased midday leaf water potential (Ψs) by 2.57–20.00% compared with CK, and the difference of Ψs between IV-D30%, IV-D45%, IV-D60%, and CK were significant in two years (P<0.05). DDITs at stages I, II, and IV improved fruit weight (FW), fruit shape index (FSI), and fruit firmness (FN), but did not significantly affect fruit water content (FWC). The DDITs at stages III and IV significantly enhanced total soluble solids (TSS), soluble sugars (SS), and vitamin C (Vc) in 2021 and 2022, but a notable decrease in titratable acidity (TA) was observed. ConclusionsDDITs at stage IV exhibited the highest comprehensive benefit of apple, followed by DDITs at stages I and II, and DDITs at stage III had the lowest values. The DDITs of D15%, D30%, CK, and D15% at stages I, II, III, and IV were identified as the optimal irrigation strategy to improve apple yield, quality, and WP. ImplicationsOur findings can guide the development of appropriate irrigation strategies for apple cultivation in Loess Plateau of China, promoting efficient and sustainable apple production.

Optimization of irrigation and fertilization of apples under magnetoelectric water irrigation in extremely arid areas.

Apple (Malus pumila Mill.) is one of the important economic crops in the arid areas of Xinjiang, China. For a long time, there has been a problem of high consumption but low yield in water and fertilizer management, prevent improvements in apple quality and yield. In this study, 5-year-old 'Royal Gala' apple trees in extremely arid areas of Xinjiang were used as experimental materials to carry out field experiments. considering 5 irrigation levels (W1, 30 mm; W2, 425 mm; W3, 550 mm; W4, 675 mm; W5, 800 mm) and 5 fertilization levels (F1, 280 kg·ha-1; F2, 360 kg·ha-1; F3, 440 kg·ha-1; F4, 520 kg·ha-1; F5, 600 kg·ha-1) under magnetoelectric water irrigation conditions. The results demonstrated that magnetoelectric water combined with the application of 675 mm irrigation amount and 520 kg·ha-1 fertilization amount was the most effective combination. These results occurred by increasing net photosynthetic rate of apple leaves, improved the quality of apples, increased apple yield, and promoted the improvement of water and fertilizer use efficiency. Additionally, the quadratic regression model was used to fit the response process of yield, IWUE and PFP to irrigation amount and fertilization amount, and the accuracy was greater than 0.8, indicating good fitting effects. The synergistic effect of water and fertilizer has a positive effect on optimizing apple water and fertilizer management. Principal component analysis showed that the magnetoelectric treatment combined water and fertilizer mainly affected apple yield, water and fertilizer use efficiency and vitamin C content related to quality. This study provides valuable guidance for improving water and fertilizer productivity, crop yield and quality in extreme arid areas of Xinjiang by using Magnetoelectric water irrigation.

Optimizing irrigation and nitrogen management improves soil soluble nitrogen pools and reduces nitrate residues in a drip-fertigated apple orchard on the Loess Plateau

Appropriate water and nitrogen fertilizer management is crucial for achieving sustainable development in the apple industry. However, the soil nitrogen supply capacity and residual nitrate characteristics under drip fertigation remain poorly understood. Therefore, this study aims to (1) investigate the coupling effects of irrigation and nitrogen levels on the concentrations and distribution characteristics of soil soluble nitrogen fractions and residual soil nitrate; (2) explore the relationships between Christiansen uniformity coefficient (CU) of soil nitrate and apple yield, water productivity (WPc) and nitrogen use efficiency (nitrogen agronomic efficiency, AEn). The field experiment was conducted in a drip-fertigated apple orchard starting in 2017, which included two irrigation levels, 85% (W1) and 100% (W2) of field capacity (FC), and four nitrogen rates, 0 (N1), 120 (N2), 240 (N3) and 360 (N4) kg ha–1. The results showed that nitrogen inputs significantly increased soil soluble inorganic nitrogen and soluble organic nitrogen (SON) contents. However, the SON content significantly decreased when 360 kg N ha–1 was applied, particularly at W1. The maximum nitrate accumulation in the 0–3 m soil layer at N4 reached 2540.97 kg ha–1 in 2021, but the maximum value decreased to 1251.62 kg ha–1 in 2022 due to extreme rainfall. The average CU of residual nitrate across irrigation levels in the 0–3 m soil layer was highest at N3, with values of 0.68 and 0.57 in 2021 and 2022, respectively. High irrigation level increased the diffusion distances of nitrate both vertically and horizontally, resulting in a significant effect on CU. Apple yield and WPc first increased and then decreased with increasing nitrogen rates, while AEn consistently decreased, but they were all greater with a higher CU. The recommended combination of irrigation and nitrogen application was 85% FC and 240 kg N ha–1, which can achieve the dual goals of enhancing soil nitrogen supply capacity and minimizing environmental risks while improving orchard productivity.

Effect of plant bio stimulant application on plant health, growth, fruit yield and quality of apple (Malus x domestica) under temperate conditions of Jammu and Kashmir, India

Effect of plant bio stimulant application on plant health, growth, fruit yield and quality of apple (Malus x domestica) under temperate conditions of Jammu and Kashmir, India

Effects of Organic Substitution on the Yield and Quality of Apples and Residual Nitrate-N Leaching in Soil

The substitution of chemical nitrogen (N) fertilizer with organic fertilizer (organic substitution, OS) is increasingly applied in crop production, due to its environmentally friendly characteristics, low price, and high crop and soil improvement efficacies. Here, we studied the effects of chemical N fertilizer with organic fertilizer treatment at different proportions (no organic substitution (NOS), 20% (OS-20), 40% (OS-40), 60% (OS-60), 100% (OS-100), and 200% (OS-200, double the organic fertilizer application amount of OS-100) on the yield and quality of apples in the Shanxi Province of China. The results revealed that, compared to the NOS, the total apple yields of OS treatments, especially the OS-60 and OS-100 treatments, decreased. However, all OS treatments, except OS-200, increased the yield of large-sized fruits (transverse diameter ≥ 85 mm) and the mean mass of apple fruits, and significantly decreased yield of small-sized fruits (transverse diameter &lt; 75 mm). All OS treatments, especially OS-40, promoted the total sugar and vitamin C (Vc) contents and fruit hardness of apples, and OS-40, OS-60, and OS-200 resulted in significantly decreased titratable acid contents in apples. The influence of organic substitutions on soil quality was further investigated in a two-year field experiment. The results showed that the influence of organic substitution on soil chemical properties differed between the two years. Notably, 40% OS increased the soil organic carbon (SOC) content and the C/N ratio in the upper 20 cm of the soil in both years. Additionally, OS treatments reduced the residual nitrate (NO3−)-N (RN) content in deep soil layers, suggesting that OS has the potential to alleviate N leaching. Moreover, redundancy analysis (RDA) of the soil, fruit yield, and fruit quality parameters revealed that the SOC content in the 0–20 cm soil layer and the RN content in the 0–100 cm soil layer had the greatest impact on the fruit quality and yield variables, respectively. This study showed that the proper substitution (40%) of chemical N fertilizer with organic fertilizer could improve the yield of large-sized fruits, the mean mass and fruit quality of apples, and soil chemical properties. Our study will provide a basis for rational organic substitution in apple orchards.

BLSENet: A Novel Lightweight Bilinear Convolutional Neural Network Based on Attention Mechanism and Feature Fusion Strategy for Apple Leaf Disease Classification

Accurate identification of apple leaf diseases is of great significance for improving apple yield. The lesion area of the apple leaf disease image is small and vulnerable to background interference, which easily leads to low recognition accuracy. To solve this problem, a lightweight bilinear convolutional neural network (CNN) model named BLSENet based on attention mechanism is designed. The model consists of two subnetworks, and each subnetwork is embedded with a Squeeze-and-Excitation (SE) module. By using the feature extraction ability of the two subnetworks and combining the bilinear feature CONCAT operation, the multiscale features of the image are obtained. Compared with the unimproved model LeNet-5 (84.63%), BLSENet has higher accuracy in the test set, which indicates that SE module and bilinear feature fusion have a positive effect on the performance of the model, and BLSENet has the ability to identify apple leaf diseases. The model has achieved the expected goal and can provide technical support for accurate identification and real-time monitoring of apple disease images.

The function of an apple ATP-dependent Phosphofructokinase gene MdPFK5 in regulating salt stress.

Salt stress severely affects the growth and yield of apples (Malus domestica Borkh). Although salt-tolerant genes have been extensively studied, documentation on the role of the ATP-dependent phosphofructokinase gene MdPFK5 in salt stress is limited. This study conducted an evolutionary tree and three-dimensional structure analysis of the PFK gene family in Arabidopsis thaliana and MdPFK (MD01G1037400), revealing a close phylogenetic relationship between MdPFK (MD01G1037400) and AtPFK5. Given the similarity in their protein tertiary structures, MdPFK was designated as MdPFK5, suggesting functional similarities with AtPFK5. Further investigation revealed elevated expression levels of MdPFK5 in apple leaves and flowers, particularly showing significant upregulation 120 days after blooming and differential expression beginning at 3 hours of salt stress. Overexpression of MdPFPK5 conferred salt tolerance in both apple calli and transgenic lines of Arabidopsis thaliana. Moreover, NaCl treatment promoted soluble sugar accumulation in apple calli and transgenic lines of Arabidopsis thaliana overexpressing MdPFK5. This study provides new insights into the salt tolerance function of MdPFK5.

Predicting Apple Yield Based on Occurrence of Phenological Stage in Conjunction With Soil and Weather Parameters

Predicting Apple Yield Based on Occurrence of Phenological Stage in Conjunction With Soil and Weather Parameters

Apple production: area, gross yield, productivity in the world and main countries

At present, the location of fruit and berry production in specific regions and countries of the world is determined by natural and climatic conditions and the biological characteristics of the crops concerned. Apples are one of the most important fruits in the world, both in terms of production volume and international trade parameters. For some countries, including Russia, they are the basis for the formation of the corresponding product balance for fruit and berry products. In this regard, the authors set the goal of analyzing the trends in the development of apple orchards for 2011‑2021 in the world as a whole and in the countries ranked in the top 20 by a number of indicators: harvested area of apple orchards, gross yield and productivity. Based on FAO statistics, it was found that over this period, the global harvested area of apple orchards decreased from 4.961 million ha to 4.822 million ha. However, it increased from 0.289 million ha to 0.313 million ha in India, which ranks second behind China, and from 0.188 million ha to 0.225 million ha in Russia (third in this ranking). These three countries will rank first, fifth and seventh in terms of gross apple yield in 2021. At the same time, the production of this seed fruit in Russia increased from 1.198 to 2.216 million tons between 2011 and 2021, i.e. by a factor of 1.85. In terms of productivity, none of these three countries was among the top 20 for this indicator. In order to strengthen Russia’s food security in fruit and berry products, it is necessary to further promote the development of domestic apple horticulture on the basis of intensive technologies.

Supplemental nesting habitat increases bee abundance in apple orchards

Abstract Identifying the resources that limit bee populations is essential for both bee conservation and pollination management in agroecosystems. Land‐use change typically leads to decreased habitat availability for wild pollinators, including loss of nesting habitat, which is an essential but often‐overlooked resource for wild bees. Cavity‐nesting bees, such as many Osmia spp. (Hymenoptera: Megachilidae), occupy holes in wood or reeds to build their nests; due to their nesting habits, they are frequently scarce in agricultural settings. Nevertheless, under the right circumstances, these bees can be ideal pollinators of apple and other orchard crops. Artificial nesting structures (‘bee hotels’ or ‘trap‐nests’) are often used to study cavity‐nesting bees and have been proposed as tools for bee conservation. To evaluate the effects of additional nesting habitat on the local abundance of pollinators, we selected 24 sites in apple orchards in eastern Canada in 2021 and 2022. Each site comprised two study plots: one in which we installed artificial nesting structures for cavity‐nesting bees, and one without added nesting habitat (control). Pollinator surveys were conducted in both plot types to measure pollinator visits to apple blossoms and, after apple bloom, to flowers in the undergrowth and/or in shrubs. Numbers and size of developing fruit were also recorded. Both Osmia bees and overall pollinator numbers were significantly higher in the treatment with trap‐nests (respectively, 33% and 22% higher during the 2021 growing season, and 113% and 30% higher during apple bloom across the 2 years of the study); however, there was no consistent difference in apple size or fruit set between the two treatments. Thus, trap‐nests locally increased the numbers of cavity‐nesting bees as well as total pollinator numbers, but they had little effect on apple yield, likely because apple production was not pollinator limited in the years of this study. Synthesis and applications. These findings suggest that bee populations in apple orchards can be limited by nesting resources such that addition of constructed nesting habitat could be an effective means of increasing pollinator numbers in orchards, and potentially, supporting bee conservation.

Neural Network Model for Predicting Apple Yield Based on Arrival of Phenological Stage in Conjunction with Leaf disease, Soil and Weather Parameters

Neural Network Model for Predicting Apple Yield Based on Arrival of Phenological Stage in Conjunction with Leaf disease, Soil and Weather Parameters