Antibiotics and Pesticides Enhancing the Transfer of Resistomes among Soil-Bayberry-Fruit Fly Food Chain in the Orchard Ecosystem.

While substantial amounts of antibiotics and pesticides are applied to maintain orchard yields, their influence on the dissemination and risk of antibiotic resisitome in the orchard food chain remains poorly understood. In this study, we characterized the bacterial and fungal communities and differentiated both antibiotic resistance genes (ARGs) and virulence factor genes (VFGs) in the soil, Chinese bayberry (matured and fallen), and fruit fly gut, collected from five geographic locations. Our results showed that fruit fly guts and soils exhibit a higher abundance of ARGs and VFGs compared with bayberry fruits. We identified 112 shared ARGs and 75 shared VFGs, with aminoglycoside and adherence factor genes being among the most abundant. The co-occurrence network revealed some shared microbes, such as Bacillus and Candida, as potential hosts of ARGs, highlighting the vector risks for both above- and below-ground parts of the orchard food chain. Notably, the elevated levels of antibiotics and pesticide residues in orchard soils increase ARGs, mobile genetic elements (MGEs), and VFGs in the soil-bayberry-fruit fly food chain. Our study highlighted that agricultural management, including the overuse of antibiotics and pesticides, could be the key factor in accumulating resistomes in the orchard food chain.

Exploring microbial diversity and function in companion planting systems of white clover and orchard grass.

Companion planting of white clover (Trifolium repens L.) with orchard grass (Dactylis glomerata L.), a famous hay grass, improves the forage quality of orchard grass. Microbiome profiling techniques can reveal the specific role of white clover companion planting with orchard grass. This study aimed to explore the microbiome distribution and gene functions of rhizosphere and non-rhizosphere soil via companion planting systems of white clover and orchard grass. From metagenomics sequencing analysis, we confirmed the significant role of white clover on soil environment modeling during companion planting with orchard grass. Twenty-eight biomarkers of rhizosphere soil organisms were identified during companion planting, including Proteobacteria, Betaproteobacteria, Flavobacteriia, and Caulobacterales. The number of gene functions of nitrogen and carbon fixation in companion planting was higher than that in single plants, indicating new functional flora for companion planting. We characterized specific rhizosphere effects, typical biomarker flora, and potential regulatory mechanisms for white clover-related companion planting by metagenomics analyses.

Effects of ground flowering plant traits on the diversity of wild bees in apple orchards during flowering.

Pollination is one of the important ecosystem services related to sustainable development of human society. However, the population diversity and abundance of wild bees, important pollinators, have been significantly reduced by climate change, agricultural intensification, and landscape transformation. Re-establishment of pollinator habitat by planting nectar-producing plants is an important way to maintain pollination service. In this study, we investigated the status of wild bees and the traits of flowering plants in 22 apple orchards during flowering stage in Changping District, Beijing in 2019. We analyzed the response of wild bee diversity to the flowering plant richness, flower color richness, inflorescence type richness, flowering plant coverage, herbaceous layer coverage and different flower color coverage in apple orchards, aiming to provide guidance for the selection of nectar-producing plants to establish the habitat of wild bees. A total of 3517 wild bees were captured during the apple flowering season, representing 49 species, 13 genera, and 5 families. We identified 21 flowering plants species that shared a similar flo-wering period with apple, exhibiting a range of 5 colors and 9 inflorescence types. The Shannon diversity index, evenness index, and social bee richness of wild bee community were positively correlated with flowering plant richness. The total wild bee community richness, social bee richness, underground nesting bee richness were positively correlated with the richness of flowering plant color, but Halictidae bee abundance was negatively correlated with the richness of flowering plant color. The Shannon diversity index and evenness index of wild bee community were positively correlated with the richness of inflorescence types. Megachilidae bee richness was negative correlated with the white flower coverage. Megachilidae bee richness, social bee abundance, and ground nesting bee richness were positively correlated with the purple flower coverage. There was no significant correlation between wild bees and flowering plant richness, flower color richness, inflorescence type richness, flowering plant coverage, herbaceous layer coverage and different flower color coverage in other communities of different families, lifestyles and nesting types. Maintaining diverse ground flowering plants with various traits in orchards is important to improve the diversity of wild bees. In particular, increasing the coverage of purple flower during apple flowering period is helpful to promote the diversity of Megachilidae bee, social bees, and ground nesting bees.

Temporal changes in net ecosystem CO2 exchange and influential factors in an apple orchard in Northeast China.

The apple orchards in Liaoning, one of the four major apple-producing areas in Bohai Bay, Northeast China, play a crucial role in regulating the carbon sink effect. However, there is limited information on the variation in carbon flux and its influential factors in apple orchards in this region. To address this, CO2 flux data were monitored throughout the entire apple growth seasons from April to November in 2017 and 2018 in the apple (Malus pumila Mill. cv Hanfu) orchard in Shenyang, China. The energy closure of the apple orchard was calculated, and variations in net ecosystem exchange (NEE) at different time scales and its response to environmental factors were analyzed. Our results showed that the energy balance ratio of the apple was 0.74 in 2017 and 1.38 in 2018. NEE was generally positive in April and November and negative from May to October, indicating a strong carbon sink throughout the growth season. The daily average NEE ranged from - 0.103 to 0.094 mg m-2 s-1 in 2017 and from - 0.134 to 0.059 mg m-2 s-1 in 2018, with the lowest values observed in June and July. NEE was negatively correlated with net radiation, atmospheric temperature, saturated vapor pressure deficit, and soil temperature. These findings provide valuable insights for predicting carbon flux in orchard ecosystems within the context of global climate change.

A Hierarchical Model to Predict Time of Flowering of Kiwifruit Using Weather Data and Budbreak Dynamics.

Accurate prediction of flowering times is essential for efficient orchard management for kiwifruit, facilitating timely pest and disease control and pollination interventions. In this study, we developed a predictive model for flowering time using weather data and observations of budbreak dynamics for the 'Hayward' and 'Zesy002' kiwifruit. We used historic data of untreated plants collected from 32 previous studies conducted between 2007 and 2022 and analyzed budbreak and flowering timing alongside cumulative heat sum (growing degree days, GDDs), chilling unit (CU) accumulation, and other environmental variables using weather data from the weather stations nearest to the study orchards. We trained/parameterized the model with data from 2007 to 2019, and then evaluated the model's efficacy using testing data from 2020 to 2022. Regression models identified a hierarchical structure with the accumulation of GDDs at the start of budbreak, one of the key predictors of flowering time. The findings suggest that integrating climatic data with phenological events such as budbreak can enhance the predictability of flowering in kiwifruit vines, offering a valuable tool for kiwifruit orchard management.

An Evaluation of Multi-Channel Sensors and Density Estimation Learning for Detecting Fire Blight Disease in Pear Orchards.

Fire blight is an infectious disease found in apple and pear orchards. While managing the disease is critical to maintaining orchard health, identifying symptoms early is a challenging task which requires trained expert personnel. This paper presents an inspection technique that targets individual symptoms via deep learning and density estimation. We evaluate the effects of including multi-spectral sensors in the model's pipeline. Results show that adding near infrared (NIR) channels can help improve prediction performance and that density estimation can detect possible symptoms when severity is in the mid-high range.

Fruit surface temperature data at different ripeness stages and ambient temperature provided as temperature-annotated 3D point clouds of apple trees.

By means of a unique, low vibration circular conveyor system, plant sensors capturing light detection and ranging (LiDAR) unit and thermal camera were moved on the same route, around blocks of apple trees, with seven Malus x domestica Borkh. 'Gala' apple trees in each block. Measurements took place four times during the season. Additionally at harvest, diurnal courses were recorded with 18 readings during three days. The data are provided as [i] raw data (3D point clouds of 3 blocks of trees scanned from right and left sides and thermal images), [ii] processed 3D point clouds of canopies annotated with temperature data from the thermal camera, and [iii] manually segmented 3D point clouds of fruit, representing the spatially-resolved fruit surface temperature (FST). Manual FST readings are provided on each measuring date and during diurnal courses. The fruit data are capturing 1236 FST, providing temperature distribution as 3D point cloud and one manually recorded reference FST per fruit. Additionally, fruit size and colour were measured for each fruit, despite for the first date, when fruit were too small for colour readings. Weather data are provided from a station located in the orchard. Usage of data could be (a) in developing methodology for 3D point cloud processing based on raw data, accomplished with reference FST data. Furthermore, (b) the pre-processed point clouds of fruit surface temperature can be reused in ecophysiological studies related to global warming, optimizing fruit production systems, and other. Because the sensors and trees were measured from the same angle and distance, time series analysis of the canopies would be possible.

Effects of fertilization on soil ecological stoichiometry and fruit quality in Karst pitaya orchard.

Pitaya (Hylocereus undulatus) is a significant cash crop in the karst region of Southwest China. Ecological stoichiometry is an essential method to research biogeochemical cycles and limiting elements. The purpose of this study was to explore the stoichiometric characteristics of C, N, and P in Karst pitaya orchards and fruit quality and to elucidate the mechanism and process of nutrient cycling. The results showed that: (1) Fruit quality was highest under the combination of chemical and organic fertilizers. Compared to the control, the contents of per-fruit weight, vitamin C, and soluble sugar increased significantly by 55.5%, 60.7%, and 23.0%, respectively, while the content of titratable acidity decreased significantly by 22.0%. (2) The content of soil nutrients under fertilization stress showed a downward trend in general, as did microbial biomass and extracellular enzyme activities. (3) Different fertilization treatments significantly affected the soil-microbial stoichiometry C:N ratio, C:P ratio, with research areas being significantly limited by C and P. (4) Spearman and PLS-SEM (partial least squares-structural equation model) analysis results showed that under the influence of fertilization, there was a significant positive effect between microorganisms and soil nutrients, but a significant negative effect between soil nutrients and quality. The results of this study offer an innovative perspective on pitaya quality research in Karst areas.

Osmia lignaria (Hymenoptera: Megachilidae) increase pollination of Washington sweet cherry and pear crops.

Apis mellifera Linnaeus (Hymenoptera: Apis), honey bees, are the most widely used managed crop pollinators. However, their high rental cost and uncertain availability for North American orchard crops have motivated growers to explore alternative pollination options. We examined whether adding solitary, spring-flying Osmia lignaria Say (Hymenoptera: Megachilidae), blue orchard bees, as co-pollinators with A. mellifera in Washington sweet cherry and pear orchards enhances fruit set and yield compared to the use of A. mellifera alone. We added managed O. lignaria to orchard sites where A. mellifera hives were already present. Fruit set, fruit yield, and O. lignaria reproduction at O. lignaria-supplemented sites were compared to nearby, paired sites pollinated only by A. mellifera (3 paired cherry and 3 paired pear sites). For both crops, the addition of O. lignaria significantly increased fruit set but did not yield at harvest. Microscopic inspection of pollen grains from O. lignaria nest cell provisions confirmed that O. lignaria primarily visited orchard flowers. Mean retention of O. lignaria in cherry orchards was slightly higher (65%) than O. lignaria retention reported in other orchard crops (30%-60%). However, retention in pear orchards was much lower (≤20%). These results show that supplementing hives with O. lignaria in Washington spring orchard crops can increase overall pollination, but that trees fail to bear developing fruit to maturity. The strategy of using co-pollinators, O. lignaria and A. mellifera, in US orchards may act as "pollination insurance" when A. mellifera hives are in low supply or when the weather is not amenable for A. mellifera flight during the bloom period.

A Detection Algorithm for Citrus Huanglongbing Disease Based on an Improved YOLOv8n.

Given the severe impact of Citrus Huanglongbing on orchard production, accurate detection of the disease is crucial in orchard management. In the natural environments, due to factors such as varying light intensities, mutual occlusion of citrus leaves, the extremely small size of Huanglongbing leaves, and the high similarity between Huanglongbing and other citrus diseases, there remains an issue of low detection accuracy when using existing mainstream object detection models for the detection of citrus Huanglongbing. To address this issue, we propose YOLO-EAF (You Only Look Once-Efficient Asymptotic Fusion), an improved model based on YOLOv8n. Firstly, the Efficient Multi-Scale Attention Module with cross-spatial learning (EMA) is integrated into the backbone feature extraction network to enhance the feature extraction and integration capabilities of the model. Secondly, the adaptive spatial feature fusion (ASFF) module is used to enhance the feature fusion ability of different levels of the model so as to improve the generalization ability of the model. Finally, the focal and efficient intersection over union (Focal-EIOU) is utilized as the loss function, which accelerates the convergence process of the model and improves the regression precision and robustness of the model. In order to verify the performance of the YOLO-EAF method, we tested it on the self-built citrus Huanglongbing image dataset. The experimental results showed that YOLO-EAF achieved an 8.4% higher precision than YOLOv8n on the self-built dataset, reaching 82.7%. The F1-score increased by 3.33% to 77.83%, and the mAP (0.5) increased by 3.3% to 84.7%. Through experimental comparisons, the YOLO-EAF model proposed in this paper offers a new technical route for the monitoring and management of Huanglongbing in smart orange orchards.

[Effect of Partial Substitution of Chemical Fertilizers with Organic Fertilizers on N2O and NO Emissions from a Peach Orchard].

Organic fertilizer substitution has been promoted as a weight loss, efficient, and diversified fertilizer substitution technology in agricultural production. However, there is a lack of comprehensive assessment of the impact of organic fertilizers on N2O and NO emissions from orchards. In this study, N2O and NO emissions from peach orchards were observed annually using static dark box-gas chromatography to compare the effects of chemical fertilizer application alone and partial replacement of chemical fertilizer treatment on NO emissions from peach orchards. The results showed that the partial replacement of chemical fertilizers with organic fertilizers reduced the total N2O and NO emissions from peach orchards by 15.0 % and 9.4 %, respectively. The N2O and NO emission factors were reduced by 21.3 % and 21.1 %. The mineral N content of the soil in the organic fertilizer treatment was lower than that in the chemical fertilizer treatment alone. The organic fertilizer treatment increased the contribution of AOA to nitrification and decreased the contribution of AOB, thus reducing N2O and NO from nitrification. In addition, the results of the dual isotope mixing model[δ18O(N2O/H2O) vs. δ15NSP] indicated that the bacterial denitrification/nitrifying bacterial denitrification (bD/nD) process served as the primary pathway for N2O emissions in peach orchards. Partial substitution with organic fertilizers enhanced soil denitrification, resulting in larger reductions in the amounts of N2O and NO. Therefore, partial substitution of organic fertilizer is a viable measure to mitigate nitrogen oxide emissions from orchards and to achieve green and low-carbon development in agriculture.

[A Pilot Test of Olive Weevil Repellents in an Olive Orchard].

Recently, feeding damage by the olive weevil Pimelocerus (Dyscerus) perforatus Roelofs, which utilizes olive trees (Olea europaea Linne) as a host plant, has become the biggest obstacle to olive cultivation in Japan. We previously identified several volatile plant-derived natural products that exhibit repellent activity against olive weevils. In this study, we conducted a pilot test of repellents in an olive orchard along with the use of insecticide. During three consecutive years from 2021 to 2023, the first year was the observation period, and the second and third years were set aside for a trial period for o-vanillin and geraniol as repellents, respectively. Using o-vanillin, the number of adult olive weevil outbreaks decreased to almost half a year in the experimental area, the use of geraniol then resulted in a drastic reduction of the number of individual olive weevils in the experimental area. In contrast, adults and larvae outbreaks increased in the control area without a repellent, despite the use of insecticide. These results indicate that the volatile repellents drove the olive weevils away and kept them at bay in the field. Based on the observations, we will be able to provide a new approach for the control of olive cultivation, including fruit and leaves used for commercial purposes, following integrated pest management (IPM) practices, such as reducing environmental poisoning from intense insecticides, and returning olive weevils to their original habitat outside of olive orchards.

Canopy Architectural Characteristics of Ten New Olive (Olea europaea L.) Genotypes and Their Potential for Cultivation in Super-High-Density Orchards.

In recent years, there has been growing interest in olive genotypes (Olea europaea L.) suitable for super-high-density (SHD > 1200 trees/hectare) orchards. To date, only a few cultivars are considered fitting for such cultivation system. In this study, the first results on the architectural characteristics of the canopy of ten new olive genotypes are presented. Their suitability for SHD orchards was evaluated and compared with the cultivar 'Arbequina', which is considered suitable for SHD olive orchards and, for this reason, was used as the control. Several canopy measurements were taken, and some architectural parameters, such as branching frequency, branching density, and branch diameter/stem diameter ratio were calculated. The branching frequency value was greater than 0.20 in 'Arbequina' and in only four of the genotypes. The branching density in five genotypes was similar to 'Arbequina'. 'Arbequina' had the lowest value for the branch diameter/stem diameter ratio, and only three genotypes had similar values. These initial results showed that only one genotype has all canopy architectural characteristics comparable to those of the cv. 'Arbequina'. Further studies are needed to evaluate the production traits of these new genotypes and complete their characterization.

Improving path planning for mobile robots in complex orchard environments: the continuous bidirectional Quick-RRT* algorithm.

Efficient obstacle-avoidance path planning is critical for orchards with numerous irregular obstacles. This paper presents a continuous bidirectional Quick-RRT* (CBQ-RRT*) algorithm based on the bidirectional RRT (Bi-RRT) and Quick-RRT* algorithms and proposes an expansion cost function that evaluates path smoothness and length to overcome the limitations of the Quick-RRT* algorithm for non-holonomic mobile robot applications. To improve the zigzag between dual trees caused by the dual-tree expansion of the Bi-RRT algorithm, CBQ-RRT* proposes the CreateConnectNode optimization method, which effectively solves the path smoothness problem at the junction of dual trees. Simulations conducted on the ROS platform showed that the CBQ-RRT* outperformed the unidirectional Quick-RRT* in terms of efficiency for various orchard layouts and terrain conditions. Compared to Bi-RRT*, CBQ-RRT* reduced the average path length and maximum heading angle by 8.5% and 21.7%, respectively. In addition, field tests confirmed the superior performance of the CBQ-RRT*, as evidenced by an average maximum path lateral error of 0.334 m, a significant improvement over Bi-RRT* and Quick-RRT*. These improvements demonstrate the effectiveness of the CBQ-RRT* in complex orchard environments.

Assessment of pesticide exposure to applicators during spraying in orchards with a stretcher-mounted sprayer.

Various health risk assessment models have been developed to evaluate occupational pesticide exposure in China. However, there has been limited investigation into the relationship between health risks and pesticide spraying in orchards. In this study, we analyzed pesticide exposure of applicators while spraying with a stretcher-mounted sprayer in orchards located in four different climatic regions. All garments' unit exposure (UE) demonstrated a right-skewed distribution, with gloves and shins accounting for the highest proportion of dermal pesticide exposure. We observed little difference in dermal and inhalation UE levels between apple and citrus orchards, except for pesticide exposure levels on wipes and faces. While 57% of the inhalation UE distribution variance was attributed to clustering and location effects, no significant differences were observed in dermal exposure levels. We evaluated the impact of different levels of protective clothing on pesticide exposure levels, according to applicators' working habits in China. Our findings revealed that improved levels of protection significantly reduced dermal exposure to pesticides, particularly when wearing gloves during spraying with a stretcher-mounted sprayer. Based on our empirical data, we utilized a simple random sampling model and an intercept-only lognormal mixed model to estimate dermal and inhalation exposure levels. The estimated dermal UE was accurate to within 3-fold with 95% confidence, and half of the estimated inhalation UE was acceptable according to the fold relative accuracy (fRA). Our established and verified statistics for dermal and inhalation UE can be utilized to evaluate the potential pesticide exposure to applicators during spraying in orchards with a stretcher-mounted sprayer.

Isolation and Characterization of a Lytic Bacteriophage RH-42-1 of Erwinia amylovora from Orchard Soil in China.

Fire blight, caused by the bacterium Erwinia amylovora, is a major threat to pear production worldwide. Bacteriophages, viruses that infect bacteria, are a promising alternative to antibiotics for controlling fire blight. In this study, we isolated a novel bacteriophage, RH-42-1, from Xinjiang, China. We characterized its biological properties, including host range, plaque morphology, infection dynamics, stability, and sensitivity to various chemicals. RH-42-1 infected several E. amylovora strains but not all. It produced clear, uniform plaques and exhibited optimal infectivity at a multiplicity of infection (MOI) of 1, reaching a high titer of 9.6 × 109 plaque-forming units (PFU)/mL. The bacteriophage had a short latent period (10 min), a burst size of 207 PFU/cell, and followed a sigmoidal one-step growth curve. It was stable at temperatures up to 60 °C but declined rapidly at higher temperatures. RH-42-1 remained viable within a pH range of 5 to 9 and was sensitive to extreme pH values. The bacteriophage demonstrates sustained activity upon exposure to ultraviolet radiation for 60 min, albeit with a marginal reduction. In our assays, it exhibited a certain level of resistance to 5% chloroform (CHCl3), 5% isopropanol (C3H8O), and 3% hydrogen peroxide (H2O2), which had little effect on its activity, whereas it showed sensitivity to 75% ethanol (C2H5OH). Electron microscopy revealed that RH-42-1 has a tadpole-shaped morphology. Its genome size is 14,942 bp with a GC content of 48.19%. Based on these characteristics, RH-42-1 was identified as a member of the Tectiviridae family, Alphatectivirus genus. This is the first report of a bacteriophage in this genus with activity against E. amylovora.

Targeting optimal power consumption: Optimizing operational parameters for orchard furrowing and fertilizing machine.

In response to the problem of excessive power consumption during the furrowing operation of orchard furrowing fertilizer machines, an optimization experiment of furrowing operation parameters for orchard furrowing fertilizer machine was conducted based on discrete element simulations. This research focused on the impact of furrowing device operation parameters on furrowing power consumption under full machine operating conditions. Firstly, a kinematics analysis of the soil granules during cutting was done. The mathematical model of soil granules through three movement processes of rising, detachment, and falling was established to determine the main factors affecting the power consumption of furrowing. Secondly, in assessing the furrowing power consumption, the stability coefficient of the furrowing depth, and the percentage of soil cover, alongside the key parameters of furrowing depth, forward propulsion velocity, and furrowing blade rotation speed, a comprehensive quadratic orthogonal rotation regression experiment was meticulously conducted. It was established that test metrics and test parameters regress. Finally, the test parameters were comprehensively optimized after analyzing each factor's impact on the test metrics. The orchard furrowing fertilizer machine's optimal operating parameters were determined, and the verification test was performed. According to the field test findings, the forward propulsion velocity was 785 m/h, and the furrowing blade rotation speed was 190 r/min when the furrowing depth was 275 mm. At this point, the furrowing power consumption was 2.39 kW, the soil cover percentage was 69.06%, and the furrowing depth stability coefficient was 95.08%. These results were in line with the requirements of orchard furrowing operation. The findings of the study can be utilized as a guide for structural changes to orchard furrowing equipment and the management of furrowing operation parameters.

Green manure incorporation enhanced soil labile phosphorus and fruit tree growth.

Introduction: The incorporation of green manures substantially enhances the conversion of external phosphorus (P) fertilizers and soil-reserved P into forms readily available to plants. The study aims to evaluate the influence of green manure additions on soil phosphorus dynamics and citrus growth, considering different green manure species and initial soil phosphorus levels. Additionally, the research seeks to elucidate the microbiological mechanisms underlying the observed effects. Methods: A citrus pot experiment was conducted under both P-surplus (1.50 g·P·kg-1) and P-deficient (0.17 g·P·kg-1) soils with incorporating legume (Leg), non-legume (Non-Leg) or no green manure residues (CK), and 18O-P labeled KH2PO4 (0.5 g, containing 80‰ δ18Op) was additionally introduced to trace the turnover characteristics of chemical P fertilizer mediated by soil microorganisms. Results and discussion: In P-surplus soil, compared with the CK treatment, the Leg treatment significantly increased soil H2O-Pi (13.6%), NaHCO3-Po (8.9%), NaOH-Pi (9.5%) and NaOH-Po (30.0%) content. It also promoted rapid turnover of P sources into H2O-Pi and NaHCO3-Pi pools by enhancing the phoC (576.6%) gene abundance. In contrast, the Non-Leg treatment significantly augmented soil H2O-Pi (9.2%) and NaHCO3-Po (8.5%) content, facilitating the turnover of P sources into NaHCO3-Pi pools. Under P-deficient soil conditions, compared with the CK treatment, the Leg treatment notably raised soil H2O-Pi (150.0%), NaHCO3-Pi (66.3%), NaHCO3-Po (34.8%) and NaOH-Pi (59.0%) content, contributing to the transfer of P sources into NaHCO3-Pi and NaOH-Pi pools. This effect was achieved through elevated ALP (33.8%) and ACP (12.9%) activities and increased pqqC (48.1%), phoC (42.9%), phoD (21.7%), and bpp (27.4%) gene abundances. The Non-Leg treatment, on the other hand, led to significant increases in soil NaHCO3-Pi (299.0%) and NaHCO3-Po (132.6%) content, thereby facilitating the turnover of P sources into NaHCO3-Pi and NaOH-Pi pools, except for the phoC gene abundance. Both Leg and Non-Leg treatments significantly improved citrus growth (7.3-20.0%) and P uptake (15.4-42.1%) in P-deficient soil but yielded no substantial effects in P-surplus soil. In summary, introducing green manure crops, particularly legume green manure, emerges as a valuable approach to enhance soil P availability and foster fruit tree growth in orchard production.

Evaluating the use of unmanned aerial vehicles for spray applications in mountain Nanguo pear orchards.

BACKGROUND: Nanguo pear is a distinctive pear variety in northeast China, grown mainly in mountainous areas. Due to terrain limitations, ground-based pesticide application equipment is difficult to use. This limitation could be overcome by using unmanned aerial vehicles (UAVs) for pesticide application in Nanguo pear orchards. This study evaluated the spraying performance of two UAVs in the Nanguo pear orchards and compared them with a manually used backpack electric sprayer (BES). The study also analyzed the effect of canopy size on droplet deposition and ground loss, and evaluated two sampling methods, leaf sampling and telescopic rod sampling. RESULTS: Compared to BESs, droplet deposition is lower for UAVs, but the actual pesticide active ingredient deposition is not necessarily lower given the solution concentration. The droplet deposition varies among different UAVs due to structural differences. Under the same UAV operating parameters, droplet deposition on trees with smaller canopy sizes is typically greater than that on trees with larger canopy sizes, and the ground loss was also more severe. Although telescopic rod sampling is a quick and convenient method, it can only reflect the trend of droplet deposition, and the data error is greater compared with leaf sampling. CONCLUSION: UAVs can achieve better droplet deposition in mountainous Nanguo pear orchards and does almost no harm to the operators compared with the BES. However, canopy size needs to be considered to adjust the application volume rate. Telescopic rods can be used for qualitative analyses, but are not recommended for quantitative analyses. © 2024 Society of Chemical Industry.

Development of Location-Data-Based Orchard Passage Map Generation Method.

Currently, pest control work using speed sprayers results in increasing numbers of safety accidents such as worker pesticide poisoning and rollover of vehicles during work. To address this, there is growing interest in autonomous driving technology for speed sprayers. To commercialize and rapidly expand the use of self-driving speed sprayers, an economically efficient self-driving speed sprayer using a minimum number of sensors is essential. This study developed an orchard passage map using location data acquired from positioning sensors to generate autonomous driving paths, without installing additional sensors. The method for creating the orchard passage map presented in this study was to create paths using location data obtained by manually driving the speed sprayer and merging them. In addition, to apply the orchard passage map when operating autonomously, a method is introduced for generating an autonomous driving path for the work start point movement path, work path, and return point movement path.