Search

Soil health is a critical factor in sustainable agriculture, particularly in fruit production, where fertilization strategies play a vital role in maintaining the soil quality and enhancing fruit production and quality. This study investigates the effects of different fertilization strategies on soil bacterial communities and honey pomelo (Citrus maxima) properties in Ji'an City, Jiangxi Province, China. Three fertilization treatments were compared: conventional fertilization (CF: botanical organic plus chemical compound fertilizers), organic material fermented fertilization (OF: organic material including duck manure fermented fertilizer plus chemical compound fertilizer), and a special honey pomelo fertilizer (SF: organic material fermented fertilizer only during the whole honey pomelo growing season). Soil samples were collected at two depths (0–20 cm and 20–40 cm) from nine plots (three treatments × three replicates) and analyzed for their soil properties, bacterial community diversity and composition, and fruit characteristics. The results indicate that the OF and SF significantly improved the soil pH, soil organic matter (SOM), and nutrient availability compared to the CF. Additionally, the OF and SF treatments led to a 13.6% and 16.6% increase in fruit weight, respectively, and higher bacterial diversity, although no significant differences were observed in fruit quality parameters such as vitamin C, soluble sugar, and titratable acid. Acidobacteriota, Proteobacteria, Actinobacteria, and Chloroflexi were the dominant bacterial phyla. The soil bacterial composition structures were significantly different among the different fertilization strategies, and were well explained by soil properties such as the pH, SOM, total phosphorus, and available nutrients. Our study suggests that applying fermented organic fertilizers which use duck manure as part of the raw materials, either alone or in combination with chemical compound fertilizers, increases honey pomelo fruit production and improves soil health, contributing to the sustainable development of orchards. [ABSTRACT FROM AUTHOR]

The significant contributions of anthropogenic activity to global warming are evidenced by the increasing average atmospheric temperatures and CO2 concentration. To date, data on the impact of global warming on crops, particularly field-scale fruit growth and the soil environment, remain lacking. Therefore, we conducted a 4-year monitoring experiment on semi-closed apple tree populations cultivated under the present or near-future climate to assess the effects of atmospheric temperature and CO2 concentrations on soil moisture, temperature, and inorganic ion concentration. Apple saplings were grown in three independent environment-controlled greenhouses, namely greenhouses A (GH_A; control), B (GH_B; high-temperature), and C (GH_C; high-temperature and high CO2), representing climate modification experiments. The pH increased by 0.02, 0.20, and 0.12/year for GH_A, GH_B, and GH_C, respectively. In particular, GH_B (standard irrigation plot; STD) showed a significant pH increase of 0.25/year. The EC increased by 0.014 mS/cm/year for GH_A, decreased by -0.010 mS/cm/year for GH_B, and increased by 0.006 mS/cm/year for GH_C, showing no clear overall increasing or decreasing trend. The NO3 concentration increased by 1.2 mg/L/year for GH_A, decreased by 10.2 mg/L/year for GH_B and decreased by 0.7 mg/L/year for GH_C. The apparent activation energy (Ea) ranged from 85.3 to 214.4 (kJ/mol NH4+) and, regardless of watering treatment, GH_A > GH_B > GH_C. The values in STD plots were 1.4 to 1.7 times those in abundant irrigation plots. Using GH_A (STD) as a standard, the soil temperature dependence of the nitrification rate did not change overall at high or low air temperatures with increasing watering (Ea = 150.5 kJ/mol) or heating (Ea = 154.1 kJ/mol). The soil temperature dependence of the nitrification rate did not vary under heating and increased watering (Ea = 89.5 kJ/mol) or under heating, increased watering, and increased CO2 (Ea = 85.3 kJ/mol). This study provides novel insights into determining the environmental impacts of apple tree community soils under near-future climatic conditions. [ABSTRACT FROM AUTHOR]

Fruit industry is one of the important economic pillars of China’s agriculture and rural areas. The current orchard production management level,especially the mechanization and information level,is relatively backward. Orchard mobile robot based on multi-source information fusion can realize stable and high-precision autonomous navigation in complex environment,provide intelligent and efficient autonomous navigation means for orchard mobile platform,and strongly support the construction of smart orchard. By analyzing the research progress of orchard mobile robot autonomous navigation system based on multi-source information fusion,this paper proposes to combine the actual complex and diverse working conditions of orchard,focus on key technologies such as positioning and mapping,path planning and decision control strategy,and based on the existing mobile platform,study the multi-source sensor information fusion strategy to achieve autonomous navigation in complex environment. The performance of the autonomous navigation system is verified by field tests. [ABSTRACT FROM AUTHOR]

A typical orchard's mechanical operation consists of three or four stages: lining and digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging of the planting hole is the most time-consuming operation. In fruit orchards, the use of robots is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can be highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GISs), to develop an autonomous drilling machine and use the developed robot to open planting holes. There is no comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four=wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with a reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS 2020 software was used for designing and drawing the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards. [ABSTRACT FROM AUTHOR]

Recently, the agriconstruction machinery sector has been involved in a great technological revolution. The reasons that may explain this are strictly connected to the mitigation of climate change. At the same time, there is a necessity to ensure an adequate production level in order to meet the increasing food demand due to the current population growth trend. In this context, the development of autonomously driven agricultural vehicles is one of the areas on which tractor manufacturers and academics are focusing. The fundamental prerequisite for an autonomous driving vehicle is the development of an appropriate motion strategy. Hence, the vehicle will be able to follow predetermined routes, accomplishing its missions. The aim of this study was the development of path-planning and path-following algorithms for an agricultural four-whee differential-drive vehicle operating in vineyard/orchard environments. The algorithms were completely developed within the MATLAB software environment. After a brief description of the geometrical characteristics of the vehicle, a parametric process to build a virtual orchard environment is proposed. Then, the functional principles of the autonomous driving algorithms are shown. Finally, the algorithms are tested, varying their main tuning parameters, and an indicator to quantify the algorithms' efficiency, named relative accuracy, is defined. The results obtained show the strong dependence between the relative accuracy and lookahead distance value assigned to the rover. Furthermore, an analysis of rover positioning errors was performed. The results in this case show a lower influence of the location error when the accuracy of the positioning device is within 2 cm. [ABSTRACT FROM AUTHOR]

A typical orchard’s mechanical operation consists of three or four stages: lining and digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging of the planting hole is the most time-consuming operation. In fruit orchards, the use of robots is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can be highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GISs), to develop an autonomous drilling machine and use the developed robot to open planting holes. There is no comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four=wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with a reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS 2020 software was used for designing and drawing the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards.

Recently, the agriconstruction machinery sector has been involved in a great technological revolution. The reasons that may explain this are strictly connected to the mitigation of climate change. At the same time, there is a necessity to ensure an adequate production level in order to meet the increasing food demand due to the current population growth trend. In this context, the development of autonomously driven agricultural vehicles is one of the areas on which tractor manufacturers and academics are focusing. The fundamental prerequisite for an autonomous driving vehicle is the development of an appropriate motion strategy. Hence, the vehicle will be able to follow predetermined routes, accomplishing its missions. The aim of this study was the development of path-planning and path-following algorithms for an agricultural four-whee differential-drive vehicle operating in vineyard/orchard environments. The algorithms were completely developed within the MATLAB software environment. After a brief description of the geometrical characteristics of the vehicle, a parametric process to build a virtual orchard environment is proposed. Then, the functional principles of the autonomous driving algorithms are shown. Finally, the algorithms are tested, varying their main tuning parameters, and an indicator to quantify the algorithms’ efficiency, named relative accuracy, is defined. The results obtained show the strong dependence between the relative accuracy and lookahead distance value assigned to the rover. Furthermore, an analysis of rover positioning errors was performed. The results in this case show a lower influence of the location error when the accuracy of the positioning device is within 2 cm.

Fertilizer application has been one of the most important facilities to advance agricultural mechanization. Particularly, manual fertilization cannot fully meet the large-scale production in recent years. In this article, a chain reversal trenching and application device was presented for the organic fertilizer in orchards, in order to enhance the trenching depth and uniform fertilization. A reverse-chain trenching mechanism was employed with a fixed trenching blade that moved in the opposite direction of the chain. The soil cutting and simultaneous backfilling were allowed within the trench, as the device progressed. A trenching depth of up to 600mm was achieved in the chain trenching mechanisms. The separate backfilling was reduced to leave no visible trenches on the surface after completion. This design was also a streamlined operation. The mathematical analysis of the trenching blade was conducted to establish a mathematical model using Cartesian coordinate transformations. The relationship equation was derived for the cutting angle. Optimal angle parameters were also provided. According to the trenching depth analysis, the hydraulic adjustment cylinders were selected for the trenching device. A scrapertype fertilizer application mechanism was adopted to develop the discrete element simulation models for both upright and sideways fertilizer application modes. The fertilizer distribution patterns were analyzed under the two modes. While both modes were similarly distributed fertilizer, and the forward-facing mode was more suitable for the operations with the larger fertilizer widths, while the sideward-facing mode was with the smaller fertilizer widths. In the two fertilizer application modes, the sideways application mode had better uniformity, especially at the lower forward speeds. Therefore, the application mode of sideward-facing fertilizer was selected to ensure that all organic fertilizer accurately fell into the trench. A single-factor experiment was carried out to explore the effects of scraper height, angle, and spacing on the normal contact force and coefficient of variation. All three parameters shared a significant impact on the coefficient of variation. In a coefficient of variation of less than 15%, the optimal height, angle, and spacing of the scraper were selected to minimize the normal contact force, resulting in values of 20 mm, 90°, and 180 mm, respectively. According to these optimal parameters of the scraper, the simulation was conducted to yield a coefficient of variation of 6.02% and a normal contact force of 9.03 N, both of which were lower than those before optimization. Subsequently, a physical prototype was fabricated and field-tested. An average coefficient of variation of 8.34% was obtained for fertilizer distribution, indicating better uniformity. Furthermore, the maximum and minimum fertilizer application tests showed that the quantities ranged from 0.8 to 6 kg/m, thus meeting the fertilization requirements of different plots. The trenching performance tests show that the average trenching depths of two operations were 616.0 and 624.4 mm, respectively, with stability coefficients of 93.57% and 92.58%, respectively, both exceeding 90%. The average trenching widths were 305.4 and 295.4 mm, respectively, with consistency coefficients of 96.16% and 95.04%, respectively, both exceeding 95%. Therefore, the device exhibited excellent performance in the fertilizer distribution and trenching operations, fully meeting the agronomic requirements. The findings can provide a new tool for the deep application of organic fertilizers in orchards, indicating the promising potential prospects. Innovative design and efficiency can also offer valuable insights into the agricultural fields. [ABSTRACT FROM AUTHOR]

Mechanized harvesting of fruit has been the ever-increasing trend in smart agriculture. However, the picking quality has been confined to the commonly-used unstructured planting for fruit trees. The robots can easily encounter the collisions with the dense branches and leaves during harvesting. The random spatial distribution of orchard obstacles can be highly influenced by the variation in the lighting conditions. It is very challenging on the real-time reconstruction of obstacles. In this study, a real-time reconstruction of orchard obstacles was proposed to combine the semantic segmentation and 3D point cloud analysis. Firstly, taking guava orchard as an example, the image acquisition was carried out using Intel RealSense D435i depth camera at a distance of about 15-50 cm from the guava fruit trees with a resolution of 640×480 in the orchard of Haiou Island Jiashuo Farm, Guangzhou City, Guangdong Province, China. Data enhancement was performed to improve the generalization and robustness of the model, according to the mirroring, luminance enhancement, luminance attenuation, adding Gaussian noise and pretzel noise. A total of 1,250 samples were obtained to randomly divided into the training, the validation and the test set in a ratio of 6:3:1, where there were 750 samples in the training set and 375 samples in the validation set; The test set was 125 samples. Then, a real-time semantic segmentation model was built using DeepLabV3+. The CoT module was introduced on the low-level feature map and high-level feature map of the encoder, with the MobileNetV3-large as the backbone network. The segmentation accuracy was improved on the thin obstacles like twig groups. The detail information of feature maps was strengthened to suppress the useless information. Furthermore, an edge-assisted loss function was proposed using the Sobel operator. Laplace edge loss function was used to further increase the accuracy of semantic segmentation, in order to improve the accuracy of picture edge segmentation. Next, the intrinsic parameters of the depth camera were combined to transform the semantic map of obstacles into a 3D point cloud. Statistical analysis was applied to remove the outliers. The voxelization was used to approximate the reconstruction of tree trunks and branches. Moreover, Euclidean clustering was employed to identify the individual 3D fruits. Among them, the minimum bounding box of the fruit was served as the reconstruction. Finally, the pose change matrix between the camera and the robot was obtained using the hand-eye calibration with a chessboard pattern. The minimum bounding box of the fruit and the voxelized cubes of branches and tree trunks were transformed into the working space of robot picking. The experimental results show that the MIoU (mean intersection over union) of obstacles in MobileNetV3-large-DeepLabV3+ was improved from 69.7% to 74.3% after the introduction of the CoT block, indicating an increase of 4.6 percentage points; The Sobel algorithm also considered both horizontal and vertical gradient information, indicating some anti-noise performance. The MIoU of the obstacle was further improved from 74.3% to 77.2%, indicating an improvement of 2.9 percentage points. The overall MIoU of the improved model was 7.0 percentage points, 0.6 percentage points, and 1.0 percentage points higher than those of Xception-DeepLabV3+, BranchNet, and LRASPP, respectively. There was 1.7 percentage points lower than BiSeNetV3; The ACC was 3.1 percentage points, 0.5 percentage points, and 0.6 percentage points higher than those of Xception-DeepLabV3+, BranchNet, and LRASPP, respectively, while there was 2.2 percentage points lower than BiSeNetV3; The FPS of the improved model was as high as 78.8 frames per second, which was faster than the comparison; The number of parameters was 4.4 M, which is 8% and 31% of Xception-DeepLabV3+ and BiSeNetV3, respectively, while BranchNet and LRASPP are 68% and 73% of the parameters in this paper, respectively. Besides, the IoU (intersection over union) values of the voxelized cubes for the fruit and branches were 65.7% and 56.6%, respectively, and the voxelization time was 0.34 s and 0.84 s, respectively. The better robustness and real-time performance can fully meet the obstacle avoidance requirements of guava picking robots. The real-time semantic segmentation of irregular obstacles was optimized to utilize more sophisticated attention modules or backbone networks. The finding can provide the better ways to enhance the effects of obstacle reconstruction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 flowering 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. [ABSTRACT FROM AUTHOR]

Copyright of Xinjiang Agricultural Sciences is the property of Xinjiang Agricultural Sciences Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geology & Exploration is the property of Geology & Exploration Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate positioning at the inter-row canopy can provide data support for precision variable-rate spraying. Therefore, there is an urgent need to design a reliable positioning method for the inter-row canopy of closed orchards (planted forests). In the study, the Extended Kalman Filter (EKF) fusion positioning method (method C) was first constructed by calibrating the IMU and encoder with errors. Meanwhile, 3D Light Detection and Ranging (LiDAR) observations were introduced to be fused into Method C. An EKF fusion positioning method (method D) based on 3D LiDAR corrected detection was designed. The method starts or closes method C by the presence or absence of the canopy. The vertically installed 3D LiDAR detected the canopy body center, providing the vehicle with inter-row vertical distance and heading. They were obtained through the distance between the center of the body and fixed row spacing. This can provide an accurate initial position for method C and correct the positioning trajectory. Finally, the positioning and canopy length measurement experiments were designed using a GPS positioning system. The results show that the method proposed in this study can significantly improve the accuracy of length measurement and positioning at the inter-row canopy, which does not significantly change with the distance traveled. In the orchard experiment, the average positioning deviations of the lateral and vertical distances at the inter-row canopy are 0.1 m and 0.2 m, respectively, with an average heading deviation of 6.75°, and the average relative error of canopy length measurement was 4.35%. The method can provide a simple and reliable inter-row positioning method for current remote-controlled and manned agricultural machinery when working in standardized 3D crops. This can modify the above-mentioned machinery to improve its automation level. [ABSTRACT FROM AUTHOR]

Purpose: Dissolved organic matter (DOM) plays a key role in agroecosystems' carbon cycling. However, the DOM evolutionary signatures and the timing of equilibrium being achieved in dryland inter-planting systems remain unclear. This study aimed to determine the characteristics of DOM dynamics using a three-dimensional fluorescence excitation-emission matrix combined with parallel factor analysis (EEMs-PARAFAC). Methods: DOM contents, different related components accumulation were assessed using clear tillage (CT) as the control, white clover (Trifolium repens L.) of three different ages (4, 8, 12 years), representing a chronosequence, in apple orchards on the Loess Plateau. Results: At a depth of 0–10 cm, DOM concentration decreased with depth and significantly increased with longer inter-planting, and the five fluorescence components contents of soil DOM showed a similar trend. Moreover, the relative abundances of humic-like (C2, C3) was higher than CT, but the protein-like was lower. The ultraviolet–visible spectral and optical indices indicated that DOM mainly originated from mixed sources, and gradually tended towards greater DOM molecular complexity with increasing time since sowing. Microbial biomass nitrogen and soil organic carbon (SOC) were the most important two factors affecting DOM over time, contributing to 87% and 77% of the total variation. Conclusions: This study demonstrated that inter-planting time and soil depth played important roles in determining DOM content and component accumulation. Overall, inter-planting with white clover in orchards can effectively improve the accumulation of macro-molecular humic-like DOM, which probably promotes SOC storage. Long-term experiments are required exploring DOM dynamics and the timing of equilibrium being reached. [ABSTRACT FROM AUTHOR]

Exposure to pesticide spray drift during the application of plant protection products (PPP) in orchards poses potential health risks to bystanders and residents. To address this concern, this study aimed to gather novel field data on exposure to spray drift in orchards. The research was conducted by the Julius Kuehn-Institute (JKI), the Federal Office of Consumer Protection and Food Safety (BVL), and the German Federal Institute for Risk Assessment (BfR). From 2018 to 2021, the method for conducting the field trials and analysis was implemented. The data from multiple series of performed field trials conducted between 2021 and 2022 allowed for collecting data on potential dermal exposure (PDE), inhalation exposure, and ground sediment. The trials considered various distances from the treatment area and drift-reducing techniques. Adult and child mannequins were used to simulate bystanders' exposure, and petri dishes were used to measure ground sediment. The results showed that drift reduction settings (75% drift reduction) considerably reduced exposure levels on the mannequins. Furthermore, a strong correlation between PDE values for adults and children located next to each other at the same distance was observed. This finding suggests a possibility to convert exposure results from adult to child mannequins. The study produced realistic exposure data for bystanders and residents next to a treated area. It contributes to a better understanding of exposure risks from PPP application and includes data for different distances and spray drift reducing technique. If taken into account for further development of the current risk assessment models, new risk management options for the safe application of PPP, including buffer strips or application of drift reducing systems, might be available in near future. [ABSTRACT FROM AUTHOR]

Purpose: to determine the parameters and justification of the design of the irrigation module of the drip irrigation system for orchards. Materials and methods. Typical crop planting patterns in an orchard, microclimatic, soil and technological conditions of drip irrigation are considered as initial factors. The results of the study were obtained in the process of methods and recommendations generalizing for predicting the technological parameters of a drip irrigation network. Results and discussion. As a result of the research, a methodology to justify the design of the irrigation module, which provides for establishing: the area of the crop nutrition zone; coefficient determining the area of the nutrition zone to be moistened; diameter and area of the horizontal projection of the soil moisture contour; the number of moisture contours and emitters ensuring their formation; diagrams for placing emitters in the plant nutrition zone was developed. As a criterion for choosing the design of an irrigation module, the ratio of the area of moistened soil space required for cultivating plants and the area of drip soil moisture zones formed by the irrigation module is taken. The methodology was tested on an irrigation module in single- and double-line versions. Conclusions. A single-line irrigation module meets the needs of perennial fruit crops in areas with a natural moisture coefficient of 1.0, and in rare combinations of conditions with a coefficient of 0.8. The double-line irrigation module consistently provides the required soil moisture area in areas with a natural moisture coefficient ≥ 0.6 and cannot form the required moisture area for the nutrition zone of fruit crops in areas with a moisture coefficient of 0.4. For drip irrigation of perennial fruit crops in areas with a moisture coefficient ≤ 0.6, it is proposed to use an irrigation module with three drip lines.

Yield prediction has long been a valuable tool for farmers seeking to enhance crop production. Among the many ways to predict yield, the integration of machine learning (ML) techniques is becoming more common for refining prediction methodologies. This study highlights the current landscape of remote sensing and ML techniques employed in predicting tree crop yield while also identifying critical gaps and areas for further exploration. Studies with limited datasets for training often use simpler models such as linear regression, while studies with larger datasets use more complex models, including techniques such as deep learning, ensemble methods, and hyperparameter tuning; in these cases, the performance evaluation tends to be more sophisticated. Yield prediction using ML has demonstrated accuracy levels ranging from 50 % to 99 %. Studies using smaller datasets consistently demonstrate higher accuracy rates. While ML techniques can enhance yield prediction, their effectiveness depends on strategic data collection and a multi-factor and multi-method approach. Integration of various data sources, including weather, soil, and plant data, could enhance model resilience and applicability. Enhancing research in this field could be achieved through overcoming challenges in accurate data collection and fostering the development of open datasets. This comprehensive analysis lays the groundwork for future research endeavors aimed at refining and advancing the application of remote sensing and ML techniques in accurately predicting tree crop yield.

To reduce pesticide pollution and promote sustainable agricultural development in China, we designed a pilot-scale biofilter system to treat residual imidacloprid wastewater in an orchard. The biofilter system demonstrated a high rate of removal of imidacloprid from the biodegradation wastewater, with removal rates from the outlet exceeding 99% at different concentrations of pesticides. Among environmental factors, imidacloprid concentration at the inlet and biomixture significantly affected the activity of imidacloprid-degrading bacteria. The dominant microbial communities during the stable operation of the biofilter system included Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes at the phylum level and Bacillus, Methylobacter, and unclassified_f__Microbacteriaceae at the genus level. In future initiatives to improve biofilter performance and applicability, increasing attention should be paid to the dominant microbial communities, the number of biofilter units, and important environmental factors. Orchard workers in China should improve the existing treatment of residual pesticide wastewater to mitigate agricultural non-point source pollution. [ABSTRACT FROM AUTHOR]

Fruit tree frost, an agricultural meteorological disaster primarily occurring in spring, severely impacts the growth and development of trees and fruit set. Therefore, exploring the temperature change rules of fruit trees during frost events is of considerable importance. This study used a T40 unmanned aerial vehicle (UAV) as the test vehicle based on the air disturbance frost prevention principle. The airflow field change pattern of the UAV was explored using the computational fluid dynamics simulation method, and the reliability of the multi-rotor UAV in frost prevention was verified using field tests. Setting the UAV height at 6.0 m above the ground and the rotor speed at 1000 r min−1, it was found that under the conditions of the inverse temperature phenomenon, the temperatures of different altitude layers will eventually converge to a stable value when the UAV down-rotating airflow is disturbed, which could be completed rapidly within 20 s. The results of the field test also showed that when the fruit trees reached the critical temperature of frost at the flowering stage, UAV intervention could have a substantial effect on the warming of the near-surface layers, whose warming amplitude was in the range of 2.5 °C–3 °C. This study provides a reliable method for mitigating frost, which serves as an important theoretical and guiding basis for improving agricultural frost disasters in China. • A multi-rotor UAV is used to test frost protection performance on fruit trees. • The temperature field characterizations are obtained. • The anti-frost effectiveness of UAV intervention is confirmed. [ABSTRACT FROM AUTHOR]

Fallen fruits provide important feeding and breeding substrates for insects such as Drosophilidae and can be a potential trophic reservoir when usual host fruits become scarce.Recently, two invasive fruit fly species, Drosophila suzukii and Chymomyza amoena, have become established in Europe and are expected to alter existing Drosophilidae communities.In this study, carried out between September 2021 and April 2022 in northern France, we aimed to disentangle the relative roles of microclimatic, landscape and local factors driving the diversity of the Drosophilidae community in decaying fruit across seasons.Minimum site temperature during the week preceding sampling and the proportion of rotten fruit tissue had the strongest positive influence on Drosophilidae abundance and species richness. Drosophilidae abundance also increased with urbanisation (portion of building cover) around the sampled trees.Decaying apples were important breeding sites for C. amoena across seasons, but provided a suboptimal substrate for D. suzukii, which was only present in late summer.This study sheds light on the important role of unharvested fallen crop fruit in maintaining the diversity of an insect family that is generally overlooked in field studies. It also emphasises the importance of considering multiple scales and factors when studying the interactions between invasive species, native species and their shared trophic resources. Finally, our data highlight the importance of the Drosophilidae community in recycling agricultural waste. [ABSTRACT FROM AUTHOR]

The use of drones for collecting information and detecting bugs in orchards covered by nets is a challenging problem. The nets help in reducing pest damage, but they also constrain the drone's flight path, making it longer and more complex. To address this issue, we model the orchard as an aisle-graph, a regular data structure that represents consecutive aisles where trees are arranged in straight lines. The drone flies close to the trees and takes pictures at specific positions for monitoring the presence of bugs, but its energy is limited, so it can only visit a subset of positions. To tackle this challenge, we introduce the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the orienteering problem, where likely infested locations are prioritized by assigning them a larger profit. Additionally, the drone's movements have a cost in terms of energy, and the objective is to plan a drone's route in the most profitable locations under a given drone's battery. We show that SOAP can be optimally solved in polynomial time, but for larger orchards/instances, we propose faster approximation and heuristic algorithms. Finally, we evaluate the algorithms on synthetic and real datasets to demonstrate their effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

This study aims to enhance the accuracy, real-time performance, and practicality of real-time target detection in spray robots in forest and fruit orchards. A novel approach was introduced using a lightweight PointNet deep learning network. Efficient detection of target was realized during spray under complex natural environments. Several key steps were consisted as follows. Firstly, the target was extracted from the original point cloud. Preprocessing techniques included the region extraction downsampling, ground segmentation, and an improved density-based spatial clustering of application with noise. The target points were isolated to facilitate the subsequent recognition. After extraction, the target point cloud was transformed into the format suitable for the input into the point cloud recognition network. The moving least squares upsampling was carried out to collect the appropriately prepared data. The recognition network of point cloud was constructed to specifically designed for the forest and fruit tree environments. The residual module was introduced to enhance the cyclic pruning algorithm into the PointNet network. The representation power of the network was improved to capture more complex patterns and features of target objects. The cyclic pruning algorithm was used to refine the network architecture, and then remove unnecessary connections, in order to reduce the computational overhead with high performance. The experimental results demonstrate that an impressive accuracy of 89.7% was achieved in the widely-used ModelNet40 dataset. The accuracy of target recognition reached 92.49% in actual nursery environments. Moreover, the misidentification and rejection rates were reduced to 13.4% and 6.47%, respectively. The accuracy of the improved PointNet was outperformed by 4.38 percentage points, compared with the original. While the misidentification and rejection rates were reduced significantly. Comparative analysis revealed that the recognition accuracy of the improved model was only 1.14 percentage points lower than that of PointNet++. Additionally, the misidentification rate and false rejection rate were slightly higher by 0.9 and 1.12 percentage points, respectively. Nevertheless, the number of parameters and computational operations were reduced remarkably in the lightweight PointNet model, only 11.5% of PointNet and 27.02% of PointNet++. The operations were reduced by 13.3 and 76.79 percentage points, respectively. The efficiency and response performance were promoted to reduce the computational complexity suitable for real-time applications. In conclusion, the improved lightweight PointNet deep learning network can offer the high real-time performance, accuracy, and robustness, which was well-suited for the target recognition in spraying operations within forest and fruit orchard environments. These findings can provided the strong reference to advance the field of target detection and recognition in spray operations. [ABSTRACT FROM AUTHOR]

Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method aims to enhance the research and application of autonomous operational equipment in orchards. Firstly, we design and integrate robot vehicles; secondly, we unify the positioning information of GNSS/INS and laser odometer through coordinate system transformation; next, we propose a dynamic switching strategy, whereby the system switches to LiDAR positioning when the GNSS signal is unavailable; and finally, we combine the kinematic model of the robot vehicles with PID and propose a path-tracking control system. The results of the orchard navigation experiment indicate that the maximum lateral deviation of the robotic vehicle during the path-tracking process was 0.35 m, with an average lateral error of 0.1 m. The positioning experiment under satellite signal obstruction shows that compared to the GNSS/INS integrated with adaptive Kalman filtering, the navigation system proposed in this article reduced the average positioning error by 1.6 m. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Spatially enabled yield forecasting is a key component of farm Management Information Systems (MISs) for broadacre grain production, enabling management decisions such as variable rate fertilization. However, such a capability has been lacking for soft (fleshy)-tree-fruit harvest load, with relevant tools for automated assessment having been developed only recently. Such tools include improved estimates of the heat units required for fruit maturation and in-field machine vision for flower and fruit count and fruit sizing. Feedback on the need for and issues in forecasting were documented. A mango 'harvest forecast engine' was designed for the forecasting of harvest timing and fruit load, to aid harvest management. Inputs include 15 min interval temperature data per orchard block, weekly manual or machine-vision-derived estimates of flowering, and preharvest manual or machine-vision-derived estimates of fruit load on an orchard block level across the farm. Outputs include predicted optimal harvest time and fruit load, on a per block and per week basis, to inform harvest scheduling. Use cases are provided, including forecast of the order of harvest of blocks within the orchard, management of harvest windows to match harvesting resources such as staff availability, and within block spatial allocation of resources, such as adequate placement of harvest field bin and frost fans. Design requirements for an effective harvest MIS software artefact incorporating the forecast engine are documented, including an integrated database supporting spatial query, data analysis, processing and mapping, an integrated geospatial database for managing of large spatial–temporal datasets, and use of dynamic web map services to enable rapid visualization of large datasets. [ABSTRACT FROM AUTHOR]

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.

This article provides a systematic review of innovations in smart fruit-growing. The research aims to highlight the technological gap and define the optimal studies in the near future moving toward smart fruit-growing based on a systematic review of literature for the period 2021–2022. The research object is the technological gap until the smart fruit-growing. The research question of the systematic review was related to understanding the current application of vehicles, IoT, satellites, artificial intelligence, and digital twins, as well as active studies in these directions. The authors used the PRISMA 2020 approach to select and synthesise the relevant literature. The Scopus database was applied as an information source for the systematic review, completed from 10 May to 14 August 2022. Forty-three scientific articles were included in the study. As a result, the technology gap analysis was completed to highlight the current studies and the research trends in the near future moving toward smart fruit-growing. The proposed material will be useful background information for leaders and researchers working in smart agriculture and horticulture to make their strategic decisions considering future challenges and to optimise orchard management or study directions. Considering the current challenges, authors advise paying attention to decision-making, expert, and recommendation systems through the digital twin paradigm. This study will help the scientific community plan future studies optimising research to accelerate the transfer to new smart fruit-growing technologies as it is not sufficient to develop an innovation, but it must be done at the appropriate time.

Soil health is a critical factor in sustainable agriculture, particularly in fruit production, where fertilization strategies play a vital role in maintaining the soil quality and enhancing fruit production and quality. This study investigates the effects of different fertilization strategies on soil bacterial communities and honey pomelo (Citrus maxima) properties in Ji’an City, Jiangxi Province, China. Three fertilization treatments were compared: conventional fertilization (CF: botanical organic plus chemical compound fertilizers), organic material fermented fertilization (OF: organic material including duck manure fermented fertilizer plus chemical compound fertilizer), and a special honey pomelo fertilizer (SF: organic material fermented fertilizer only during the whole honey pomelo growing season). Soil samples were collected at two depths (0–20 cm and 20–40 cm) from nine plots (three treatments × three replicates) and analyzed for their soil properties, bacterial community diversity and composition, and fruit characteristics. The results indicate that the OF and SF significantly improved the soil pH, soil organic matter (SOM), and nutrient availability compared to the CF. Additionally, the OF and SF treatments led to a 13.6% and 16.6% increase in fruit weight, respectively, and higher bacterial diversity, although no significant differences were observed in fruit quality parameters such as vitamin C, soluble sugar, and titratable acid. Acidobacteriota, Proteobacteria, Actinobacteria, and Chloroflexi were the dominant bacterial phyla. The soil bacterial composition structures were significantly different among the different fertilization strategies, and were well explained by soil properties such as the pH, SOM, total phosphorus, and available nutrients. Our study suggests that applying fermented organic fertilizers which use duck manure as part of the raw materials, either alone or in combination with chemical compound fertilizers, increases honey pomelo fruit production and improves soil health, contributing to the sustainable development of orchards.

Background: Crop residues used as organic matter amendments have been shown to release potassium (K) into the soil, promoting K cycling in agronomic systems. Orchard field trials are needed to evaluate K dynamics under almond hull and shell amendments, which contain high K concentrations. Methods: Three field trials in commercial almond orchards were conducted to assess the effects of surface-applied almond hull and shell amendments on K cycling within plant and soil systems. Amendment K concentrations over time, soil exchangeable K, and tree K status were measured as well as decomposition rate and crop yield. Results: Hulls and shells released K rapidly under irrigation and rainfall, significantly increasing soil exchangeable K in the upper 0–10 cm soil within 2–7 weeks. Amendments increased tree leaf K status within the first 1–3 years to varying degrees depending on site. Initial amendment K concentrations decreased by at least half by dry weight within the first 25.4 cm (10 inches) of water (irrigation and precipitation) within the irrigated zone. Conclusions: Almond hulls and shells can increase soil and plant K status when used as amendments on the soil surface. This practice can address byproduct utilization issues, recycle potassium (K), and reduce orchard K fertilizer demand by replacing the majority of tree K demand. Growers can tailor application rates to meet orchard-specific K management goals. Off-ground harvest preserved the hull/shell organic layer over time and maximized K cycling. Hull/shell amendments applied on the soil surface cover more soil area within the irrigated wetted zone compared to banded K fertilizer. This practice can reduce reliance on K fertilizers and reduce associated costs while providing a convenient outlet for hulls and shells. [ABSTRACT FROM AUTHOR]

As one of the most important topics in contemporary computer vision research, object detection has received wide attention from the precision agriculture community for diverse applications. While state-of-the-art object detection frameworks are usually evaluated against large-scale public datasets containing mostly non-agricultural objects, a specialized dataset that reflects unique properties of plants would aid researchers in investigating the utility of newly developed object detectors within agricultural contexts. This article presents AriAplBud: a close-up apple flower bud image dataset created using an unmanned aerial vehicle (UAV)-based red–green–blue (RGB) camera. AriAplBud contains 3600 images of apple flower buds at six growth stages, with 110,467 manual bounding box annotations as positive samples and 2520 additional empty orchard images containing no apple flower bud as negative samples. AriAplBud can be directly deployed for developing object detection models that accept Darknet annotation format without additional preprocessing steps, serving as a potential benchmark for future agricultural object detection research. A demonstration of developing YOLOv8-based apple flower bud detectors is also presented in this article. Dataset:  https://osf.io/wexu7/. Dataset License: CC BY 4.0 DEED [ABSTRACT FROM AUTHOR]

Based on multi temporal and medium resolution remote sensing data, combined with field sampling data and object-oriented remote sensing image analysis methods, information extraction of orchards in the ancient canal of Yellow River was carried out. Combining basic geographic data such as terrain factors and coastline distribution, the spatial distribution pattern of orchards was explored, and the clustering characteristics of orchards at different spatial scales were analyzed. The results showed that the area of orchards along the ancient canal of Yellow River was about 1 268.30 km². Dangshan County, Feng County and Xiao County were the major planting areas. Orchards were mainly distributed in flat terrain areas with an elevation of >20-60 m and a slope of ≤4°, accounting for 86.84% and 88.43% of the total orchard area, respectively; orchards were mainly distributed in areas more than 280 km away from the coastline, accounting for 91.20% of the total area of orchards. The planting density of orchards at the county (district) scale, 10 km x 10 km, 5 km x 5 km and 1 km x 1 km grid scale showed a significant spatial positive correlation, belonging to a spatial aggregation distribution pattern. As the spatial scale decreased, the aggregation degree increased. It could be seen that orchards in the ancient canal Yellow River were gradually showing the characteristics of large-scale cultivation and contiguity. [ABSTRACT FROM AUTHOR]

The object of the investigation has been to analyze the perception of the public about the orchard and its associated hydraulic heritage, within the set of cultural assets of the municipality of Mula, located in the interior of the Region of Murcia. The study was elaborated from the methodology of geographic analysis and cultural geography. For data collection, a mixed and semi-structural questionnaire was developed in which 102 people of different ages, studies and occupations participated. The results reveal a lower valuation of the hydraulic heritage and the landscape of the orchard compared to other cultural assets, both material (castle or religious buildings) and immaterial (festivities and local traditions). The importance of academic training in the valuation of cultural heritage and, above all, of the infrastructures related to the use of water is also observed. A better consideration of farmers who have direct contact with the territory is observed. In contrast, young participants place less value on hydraulic heritage. [ABSTRACT FROM AUTHOR]

Phylogenetic networks are an extension of phylogenetic trees that allow for the representation of reticulate evolution events. One of the classes of networks that has gained the attention of the scientific community over the last years is the class of orchard networks, that generalizes tree-child networks, one of the most studied classes of networks. In this paper we focus on the combinatorial and algorithmic problem of the generation of binary orchard networks, and also of binary tree-child networks. To this end, we use that these networks are defined as those that can be recovered by reversing a certain reduction process. Then, we show how to choose a “minimum” reduction process among all that can be applied to a network, and hence we get a unique representation of the network that, in fact, can be given in terms of sequences of pairs of integers, whose length is related to the number of leaves and reticulations of the network. Therefore, the generation of networks is reduced to the generation of such sequences of pairs. Our main result is a recursive method for the efficient generation of all minimum sequences, and hence of all orchard (or tree-child) networks with a given number of leaves and reticulations. An implementation in C of the algorithms described in this paper, along with some computational experiments, can be downloaded from the public repository . Using this implementation, we have computed the number of binary orchard networks with at most 6 leaves and 8 reticulations. [ABSTRACT FROM AUTHOR]

This article examines the morphological characteristics of apple varieties in northeastern Algeria, specifically focusing on the leaves, fruits, and terminal shoots of three apple varieties: Golden Delicious, Royal Gala, and Starkrimson. The study reveals limited morphological diversity among the investigated apple cultivars, with some descriptors showing insignificant differences between cultivar types. The findings emphasize the distinct physiological and morphological characteristics of each variety and highlight the need for further exploration and conservation efforts. Another document provides information on the application rate of sheep manure and urea for soil salinity levels, as well as the physical and chemical analysis of soil in an orchard. It also discusses the growth habit and length of annual shoots for different apple tree varieties, as well as the flowering characteristics and fruit characteristics such as weight, length, width, shape, color, and ripening period. The document is valuable for understanding the cultivation and quality of apple trees in the studied orchard. Lastly, a document discusses the characterization of new apple cultivars and their implications for sustainable development and consumption. The study found significant variation in vegetative growth, fruit weight, and color among the three cultivated apple tree varieties examined. The authors suggest further research and evaluation to fully understand the genetic diversity and potential uses of these cultivars, emphasizing the importance of rigorous protocols for measurement and observation. The document includes a list of references for further reading on apple diversity and characterization. [Extracted from the article]

The life-cycle cost analysis is a method used to assess long-term economic efficiency among equivalent competing processes or products. The purpose of this paper is to investigate the nature and level of costs for an organic orchard located in Southern Romania, using a complex approach covering the entire chain of production, through its life span. The research results, based on a dynamic analysis and an integrated evaluation of the orchard's performance, were ranked on investment and operational costs and broken down into three categories (establishment, production, post-harvest, transport costs). The highest costs, representing 151,726 EUR/ha/20 years, about 52.72% of the total operational costs and 50.4% of the total farm costs/ha/20 years, were recorded in the exploitation stage. The scenarios for the sensitivity analysis considered different levels of average yields (40 and 60 tons/ha, respectively) with different rates of sold productions (85%, optimistic scenario; 70%, pessimistic scenario). The hot points identified at the production stage were the use of agricultural machinery, several pesticides, the costs of seedlings, anti-hail nets, plastic boxes, and labor costs, while at the post-harvest stage, there were those related to labor and energy consumption. The transport stage had important costs with respect to tractor operations and the track. [ABSTRACT FROM AUTHOR]

The mobility of nitrogen (N) in the environment is conditioned by its cycling between atmospheric, terrestrial, and marine ecosystems. It is a key element for global biogeochemistry, and although isotope analysis has been an integral part of many studies over the past eighty years, the complexity of the nitrogen cycle hinders a correct and detailed understanding of the mechanisms behind its processes. It could be argued that the interpretation of the isotopic signatures of nitrogen in soils is still in its infancy. In Croatia, such research has recently begun and is driven by a need for the comprehensive study of nitrogen isotopes in terrestrial ecosystems. The aim of this study was to compare the abundance of the 15N isotope in soils from continental and coastal parts of Croatia with different types of land use (arable land/crop production, meadows, forests, orchards, ski slopes, urban soil/city roads) and to authenticate the nitrogen origin in soils in relation to different soil management practices. This research was based on 27 soil samples collected at 11 locations in Croatia. The samples differed according to soil type, land use, applied mineral and organic nitrogen fertilization, and climatic condition at each specific location. The determination of δ15NT (T—total nitrogen) values in bulk samples was performed in duplicate with the IRMS (Isotope Ratio Mass Spectrometry) method using an IsoPrime100-Vario PYRO Cube (OH/CHN Pyrolyser/Elemental Analyzer). The results reveal that the mean δ15N abundance in soils according to different land use declines in the following order: crop production (+5.66 ± 1.06‰) > apple orchard (+5.60 ± 0.10‰) > city road (+4.33 ± 0.38‰) > meadow (+3.71 ± 0.85‰) > ski slope (+2.20 ± 0.10‰) > forest (+2.15 ± 1.86‰). The individual values were in the range from 0.00 ± 0.10‰ in the forest soil in continental Croatia to +7.19 ± 0.07‰ in the vegetable garden (crop production) soil in coastal Croatia. Among the investigated soil properties and weather conditions, PCA analysis identified close correlations between P2O5 content and δ115N abundance in arable soils, as well as between soil reaction (pH) and mean annual temperatures, while high C/N ratio values explained the isotopic distribution in non-arable soils (city roads and forests). Despite the long-term application of mineral nitrogen fertilizers, the results represent nitrogen of organic origin in the arable soils (crop production), which partly confirms the sustainable management of those agroecosystems. [ABSTRACT FROM AUTHOR]

Reducing nonpoint-source pollution and improving soil quality by applying best management practices (BMPs) on sloping farmland are essential for ensuring water environmental safety and sustainable agricultural development. However, little is known about the accumulative effect of the long-term application of BMPs on reducing pollution and improving soil quality in major land use types of sloping farmland in watersheds. We conducted a nine-year field experiment for citrus orchard and cropland of sloping farmland in the Three Gorges Reservoir area of China. The treatment settings for the citrus experimental plots were as follows: (1) citrus intercropped with white clover (WC), (2) citrus orchard soil mulched with straw (SM), (3) citrus intercropped with Hemerocallis flava contour hedgerows (HF) and (4) conventional citrus planting pattern (C-CK). The treatment settings for the crop experimental plots were as follows: (1) wheat-peanut rotation intercropped with Toona sinensis contour hedgerows (TS), (2) wheat-peanut rotation intercropped with alfalfa contour hedgerows (AF), (3) ryegrass-sesame rotation (RS) and (4) conventional wheat-peanut rotation (W-CK). We found that compared with C-CK and W-CK, (1) runoff, sediment, total nitrogen and total phosphorus losses were reduced by 18.0 %, 66.1 %, 35.6 % and 40.2 %, respectively, in WC; by 22.4 %, 61.2 %, 43.9 % and 52.4 %, respectively, in SM; by 25.4%, 69.5 %, 52.0 % and 58.1 %, respectively, in HF; by 45.1 %, 73.7 %, 64.6 % and 66.0 %, respectively, in TS; by 41.8 %, 67.4 %, 59.3 % and 60.8 %, respectively, in AF; and by 24.1 %, 42.9 %, 39.3 % and 40.3 %, respectively, in RS; (2) the soil quality index values of the BMP treatments increased; and (3) the application of these BMPs did not significantly affect yield or product value. We suggest the application of these BMPs on sloping farmland to (1) efficiently reduce total runoff, total sediment, and soil total nitrogen and total phosphorus losses; (2) improve soil quality; and (3) have no significant effect on output, of which the BMP HF applied to citrus orchard and the BMP TS applied to cropland are the most effective.

Accurate positioning at the inter-row canopy can provide data support for precision variable-rate spraying. Therefore, there is an urgent need to design a reliable positioning method for the inter-row canopy of closed orchards (planted forests). In the study, the Extended Kalman Filter (EKF) fusion positioning method (method C) was first constructed by calibrating the IMU and encoder with errors. Meanwhile, 3D Light Detection and Ranging (LiDAR) observations were introduced to be fused into Method C. An EKF fusion positioning method (method D) based on 3D LiDAR corrected detection was designed. The method starts or closes method C by the presence or absence of the canopy. The vertically installed 3D LiDAR detected the canopy body center, providing the vehicle with inter-row vertical distance and heading. They were obtained through the distance between the center of the body and fixed row spacing. This can provide an accurate initial position for method C and correct the positioning trajectory. Finally, the positioning and canopy length measurement experiments were designed using a GPS positioning system. The results show that the method proposed in this study can significantly improve the accuracy of length measurement and positioning at the inter-row canopy, which does not significantly change with the distance traveled. In the orchard experiment, the average positioning deviations of the lateral and vertical distances at the inter-row canopy are 0.1 m and 0.2 m, respectively, with an average heading deviation of 6.75°, and the average relative error of canopy length measurement was 4.35%. The method can provide a simple and reliable inter-row positioning method for current remote-controlled and manned agricultural machinery when working in standardized 3D crops. This can modify the above-mentioned machinery to improve its automation level.