Greenhouse Tomato Research Articles

Prediction Model of Nitrogen, Phosphorus, and Potassium Fertilizer Application Rate for Greenhouse Tomatoes under Different Soil Fertility Conditions

To reach the target yield of crops, nutrient management is essential. Selecting the appropriate prediction model and adjusting the nutrient supply based on the actual situation can effectively improve the nutrient utilization efficiency, crop yield, and product quality. Therefore, a prediction model of the NPK fertilizer application rate for greenhouse tomatoes under the target yield was studied in this study. Under low, medium, and high soil fertility conditions, a neural network prediction model based on the sparrow search algorithm (SSA-NN), a neural network prediction model based on the improved sparrow search algorithm (ISSA-NN), and a neural network prediction model based on the hybrid algorithm (HA-NN) were used to predict the NPK fertilizer application rate for greenhouse tomatoes. The experimental results indicated that the evaluation indexes (i.e., the mean square error (MSE), explained variance score (EVS), and coefficient of determination (R2)) of the HA-NN prediction model proposed in this study were superior than the SSA-NN and ISSA-NN prediction models under three different soil fertility conditions. Under high soil fertility, compared with the SSA-NN prediction model, the MSE of the ISSA-NN and HA-NN prediction models decreased to 0.007 and 0.005, respectively; the EVS increased to 0.871 and 0.908, respectively; and the R2 increased to 0.862 and 0.899, respectively. This study showed that the HA–NN prediction model was superior in predicting the NPK fertilizer application rate for greenhouse tomatoes under three different soil fertility conditions. Due to the significance of NPK fertilizer application rate prediction for greenhouse tomatoes, this technique is expected to bring benefits to agricultural production management and decision support.

Foliar applications of a Malvaceae-derived protein hydrolysate and its fractions differentially modulate yield and functional traits of tomato under optimal and suboptimal nitrogen application.

Protein hydrolysates (PHs) can enhance plant nitrogen nutrition and improve the quality of vegetables, depending on their bioactive compounds. A tomato greenhouse experiment was conducted under both optimal (14 mM) and suboptimal (2 mM) nitrogen (N-NO3) conditions. Tomatoes were treated with a new Malvaceae-derived PH (MDPH) and its molecular fractions (MDPH1, >10 kDa; MDPH2, 1-10 kDa and MDPH3, <1 kDa). Under optimal N conditions, the plants increased biomass and fruit yield, and showed a higher photosynthetic pigment content in leaves in comparison with suboptimal N, whereas under N-limiting conditions, an increase in dry matter, soluble solid content (SSC) and lycopene, a reduction in firmness, and changes in organic acid and phenolic compounds were observed. With 14 mM N-NO3, MDPH3 stimulated an increase in dry weight and increased yield components and lycopene in the fruit. The MDPH2 fraction also resulted in increased lycopene accumulation in fruit under 14 mM N-NO3. At a low N level, the PH fractions showed distinct effects compared with the whole MDPH and the control, with an increase in biomass for MDPH1 and MDPH2 and a higher pigment content for MDPH3. Regardless of N availability, all the fractions affected fruit quality by increasing SSC, whereas MDPH2 and MDPH3 modified organic acid content and showed a higher concentration of flavonols, lignans, and stilbenes. The molecular weight of the peptides modifies the effect of PHs on plant performance, with different behavior depending on the level of N fertilization, confirming the effectiveness of fractioning processes. © 2024 Society of Chemical Industry.

Autonomous navigation system for greenhouse tomato picking robots based on laser SLAM

Aiming at the specific operation path cruising problem of greenhouse tomato picking robots, this paper designs a laser SLAM (Simultaneous Localization And Mapping)-based autonomous navigation system for greenhouse tomato picking robots.The autonomous navigation system utilizes LiDAR to detect the greenhouse environment, creates an environment map with the Gmapping algorithm employing particle filtering, locates the robot with the AMCL (Adaptive Monte Carlo Localization) algorithm, devises the robot's path for picking tasks with the A* algorithm, and dynamically navigates around obstacles during operations with the DWA (Dynamic Window Approach) algorithm. The greenhouse experiment revealed that the robot's average deviance for both directions (X and Y) is 2.87 and 0.80 centimeters, respectively, when traveling a 320 centimeter path. On the other hand, its average variation across the directions of X and Y is 1.48 versus 1.08 centimeters when it passes through a 40 centimeter zone. The robot exhibits averaged variations of 1.73 and 2.24 millimeters in the X and Y axes, each, when navigating a 40-centimeter-radius turn. Furthermore, the robot exhibits average variations of 1.12 centimeters in the X and 0.78 centimeters in the Y directions when it follows a certain "S"-shaped course. Based on the fulfillment of prescribed path cruising, the system has a high level of navigation and positioning accuracy, better meeting the requirements for greenhouse tomato harvesting robot direction and placement.

Vapour pressure deficit affects crop water productivity, yield, and quality in tomatoes

A high atmospheric vapour pressure deficit (VPD) hinders calcium absorption in tomatoes (Solanum lycopersicum L.), which severely reduces tomato yield, crop water productivity (WPcrop), and quality. Although reducing the VPD is effective for increasing tomato yield and water productivity, regulating the VPD is costly. Therefore, this study aimed to explore the effects of VPD regulation (high VPD=2.22 kPa, low VPD=0.95 kPa) during different growth stages on tomato WPcrop, yield, and quality to identify a VPD management method that saves costs and enhances the above parameters. The results showed that the yield was significantly positively correlated with fruit calcium absorption. Decreasing the VPD during the fruit expansion stage and increasing it during the flowering stage significantly increased calcium accumulation in the fruits, thereby improving tomato WPcrop, yield, and quality. Additionally, a high VPD during the flowering stage increased stomatal conductance and mesophyll conductance, thereby increasing intercellular and chloroplast CO2 concentrations and enhancing plant photosynthetic capacity, and reduced the stem water potential, leaf water potential, and leaf relative water content, thereby increasing the water potential difference, leaf hydraulic conductance, and calcium absorption. By integrating game theory and the technique for order of preference by similarity to ideal solution method, a comprehensive analysis of tomato appearance, nutrients, and flavour quality revealed that growing plants under a high VPD during the seedling, flowering, and fruit ripening stages and a low VPD during the fruit expansion stage not only enhanced the comprehensive tomato quality value but also maximally reduced the humidification cost. Conclusively, controlling the environmental VPD at 2.22 kPa during the seedling, flowering, and fruit ripening stages and at 0.95 kPa during the fruit expansion stage can effectively increase tomato WPcrop, yield, and quality. This study provides a theoretical basis for the environmental regulation of high-quality, efficient, and water-conserving cultivation of greenhouse tomatoes.

A new pest suction machine to control Bemisia tabaci (Hemiptera: Aleyrodidae) in tomato greenhouses.

The sweetpotato whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is challenging to control using chemical pesticides owing to its resistance to many insecticides. Thus, there has been an increasing demand for alternative control measures. Thus, this study evaluated the efficacy of a newly designed pest suction machine to manage whiteflies on tomato plants (Solanum lycopersicum L.) (Solanales: Solanaceae) in greenhouses over 2 seasons. The suction machine comprised a battery-powered cart with a mounted suction unit, an ultrasonic device, and green lights. Ultrasonic irradiation provided non-contact vibration, facilitating the movement of adult whiteflies away from the plants, and green lights attracted them to the suction device. This combination effectively captured whitefly adults, even with a weak suction force, saving electricity consumption. The efficacy of suction machine was further evaluated by measuring the number of whitefly adults caught by the machine and the number of adults and nymphs remaining on the tomato leaves. The whitefly population was considerably lower in the treated blocks than in the non-treated blocks in the autumn trial. The machine reduced the density of whitefly adults without using chemical pesticides. Although a lot of optimizations would be required, suction control is an additional and alternative strategy that may be incorporated in the integrated pest management of whiteflies on greenhouse tomato plants.

Precision Regulation and Forecasting of Greenhouse Tomato Growth Conditions Using an Improved GA-BP Model

Tomatoes are annual herbaceous plants of the family Solanaceae. They have very stringent requirements for their growing environment and climate conditions. To precisely control the greenhouse environment for tomato growth, this project designed and implemented a monitoring system utilizing programmable logic controllers and a data acquisition system for monitoring. Sensors are installed at key locations in the greenhouse, such as near the ground, in the plant canopy, and under the roof, to monitor environmental parameters such as temperature, humidity, light intensity, and carbon dioxide concentration in real time. A three-layer feedforward GA-BP neural network model was established using soil temperature, soil humidity, air humidity, and illumination as predictive parameters. This technology predicts optimal environmental parameters and performs real-time monitoring and intelligent control, overcoming the traditional BP neural network’s drawbacks of slow convergence, susceptibility to disturbance, and poor generalization ability, with the current model’s average prediction error being less than 5%. It provides a theoretical basis and decision support for precision control and related environmental forecasting in agricultural projects. Based on the growth characteristics, physiological and morphological features of tomatoes, and the comprehensive impact of planting conditions, the technology enhances tomato yield and quality while reducing production costs and labor inputs. It mainly includes the establishment of tomato growth models, measurement and control of environmental parameters, and the design and optimization of intelligent decision control systems. Research outcomes in this field are significant for improving agricultural production efficiency, ensuring food security, and promoting sustainable agricultural development.

Evaluation of the green lacewing, Mallada signatus as a biological control agent for the invasive tomato potato psyllid, Bactericera cockerelli

Evaluation of the green lacewing, <i>Mallada signatus</i> as a biological control agent for the invasive tomato potato psyllid, <i>Bactericera cockerelli</i>

Aerated irrigation improves soil gross nitrogen transformations in greenhouse tomato: Insights from a 15N-tracing study

Aerated irrigation (AI) significantly influences soil nitrogen (N) dynamics and biochemical characteristics, which are crucial for enhancing N absorption and utilization efficiency. However, AI’s potential effects and mechanisms on gross soil N transformations remain unclear. In this study, a 15N tracing incubation experiment was conducted with four treatments, each receiving an equivalent N input (150 kg N ha–1): (1) AINPK, chemical fertilizer under AI; (2) AIOM, organic fertilizer (OM) under AI; (3) AINPK+OM, chemical fertilizer (75 % of applied N) with organic fertilizer (NPK+OM) under AI; (4) NPK, chemical fertilizer (NPK) under traditional drip irrigation. The results revealed that AINPK significantly increased crop yield by 12.42 %, gross N mineralization by 2.58-fold, and gross nitrification by 1.27-fold compared to NPK. The AIOM and AINPK+OM significantly increased yield by 18.74–12.34 %, gross N mineralization by 1.14–1.27-fold, and autotrophic nitrification by 1.05–1.11-fold compared to AINPK. The AIOM exhibits the highest rate of NH4+ immobilization, being 1.16–1.26-fold greater than the other three treatments. Moreover, AINPK significantly reduced the turnover time of organic N compared to NPK, and the turnover time of organic N of AIOM and AINPK+OM was shorter than AINPK. Overall, AI increased gross N mineralization and gross nitrification rates, enhancing soil N availability. AINPK and AIOM elevated the NH4+ immobilization rates compared to NPK, while the AIOM and AINPK+OM also enhanced NO3– immobilization rates, stimulating inorganic N retention capacity. Therefore, based on the N transformation dynamics observed in this study, AIOM and AINPK+OM, which accelerate organic N turnover, enhance N availability and retention capacity, and increase crop yields, are recommended for crop production in greenhouse tomato.

Investigating Precise Decision-Making in Greenhouse Environments Based on Intelligent Optimization Algorithms

The precise control of a greenhouse environment is vital in production. Currently, environmental control in traditional greenhouse production relies on experience, making it challenging to accurately control it, leading to environmental stress, resource waste, and pollution. Hence, this paper proposes a decision-making greenhouse environment control strategy that employs an existing monitoring system and intelligent algorithms to enhance greenhouse productivity and reduce costs. Specifically, a model library is created based on machine learning algorithms, and an intelligent optimization algorithm is designed based on the Non-Dominated Sorting Genetic Algorithm III (NSGA-3) and an expert experience knowledge base. Then, optimal environmental decision-making solutions under different greenhouse environments are obtained by adjusting the greenhouse environmental parameters. Our method’s effectiveness is verified through a simulated fertilization plan that was simulated for a real greenhouse tomato environment. The proposed optimization solution can reduce labor and time costs, enable accurate decision-making in the greenhouse environment, and enhance agricultural production efficiency.

Potential Role of the Yeast Papiliotrema terrestris Strain PT22AV in the Management of the Root-Knot Nematode Meloidogyne incognita

The nematicidal potential of the yeast Papiliotrema terrestris strain PT22AV (YSY) was investigated against the root nematode (RKN) Meloidogyne incognita in in vitro bioassays on infective juveniles (J2) and experiments on tomatoes in pot and greenhouse conditions. The J2 nematodes were exposed to YSY solutions for 19 days, using abamectin (ABA), fosthiazate (FOS) and distilled water as controls. In the experiments on potted and greenhouse tomatoes, 0.5 and 1 kg ha−1 doses of YSY were tested in comparison to ABA, biocontrol agents Purpureocillium lilacinus strain 251 (PUL) and Bacillus firmus strain 1-1582 (BAF), a plant biostimulant/fertilizer (ERG) and the nematicide Fluopyram (FLU). J2’s viability was affected by YSL after 7 days, decreasing to zero on the 15th exposure day, while ABA and FOS resulted in 83 and 100% J2 mortality within 24 h. Only the 1.0 kg ha−1 dose of YSY was able to significantly reduce the final nematode population in soil and gall formation on tomato roots, without significant differences from PUL and BAF. All treatments in comparison also resulted in a significant increase in tomato growth and crop yield, except for 0.5 kg ha−1 of YSY. Data indicated that YSY could represent an additional tool for organic and integrated RKN management.

First Report of Enterobacter mori Causing Bacterial Wilt on Tomato in China.

Tomato (Solanum lycopersicum L.) is one of the most important vegetable crops in China. In October 2023, a new bacterial disease was discovered on tomato plants in a 0.3-acre farm's greenhouse (35.514806N, 118.996106E) in Longshan Town, Shandong Province, China. Over 50% of the tomato plants showed symptoms of stem rot, leaf wilt, or plant death. Three diseased tomato plants were collected for pathogen isolation and purification. Two leaf samples, each about 1 cm2, were cut from the junction area of healthy and diseased parts and disinfected with 75% alcohol for 60 s, followed by 0.1% HgCl2 for 90 s, and then washed three times with sterilized H2O. The samples were subsequently ground with 1.0 mL sterilized H2O. The ground samples were diluted to 10-4, 10-5, and 10-6 and then were plated on a potato dextrose agar (PDA) plate, respectively. White mucous bacterial colonies appeared at 28℃ for 24~48 h, no fungal colony was observed. Six bacterial colonies were randomly selected for gram staining and found to be gram-negative. To further determine their species classification, fragments of the 16SrDNA, hsp60, gyrB, and rpoB genes were separately amplified using previously reported PCR conditions and with primer pairs, including 27F/1492R (Wu et al., 2023), HSP60-F /HSP60-R (Gül et al., 2023), gyrB UP-1 / gyrB UP-2r (Yamamoto et al., 1995) and rpoB CM81-F / rpoB CM32b-R (Brady et al., 2008). Sequence analysis showed that the obtained sequences of the 16SrDNA, hsp60, gyrB, and rpoB genes among these six colonies were identical and 100%, 100%, 99.31%, and 99.36% similar to those of Enterobacter mori accessions OP601841 (with a coverage of 100%), MT199160 (83%), OP676246 (100%), and MN594495 (100%), at nucleotide level, respectively. Sequences of the above four genes of 23LSFQ were submitted to GenBank under the accession numbers PP461247, PP474090, PP136037, and PP136038, respectively. We selected one of these six colonies, 23LSFQ, for further analysis. The phylogenetic tree based on the concatenated sequences of the above four genes using the maximum likelihood method with MEGAX software showed that 23LSFQ is grouped with E. mori LMG25706 (NCBI: txid980518). To determine the pathogenicity of 23LSFQ , we sprayed 23LSFQ (OD600=0.8) onto five 30-day-old healthy plants of the tomato cultivars Alisa Craig, Jinpeng NO.1, and Chaobei, respectively. These seedlings were incubated in a chamber at 28°C with a 16 h light/ 8h dark photoperiod and 60% relative humidity. The leaves of the inoculated plants became curled and wilted at two days post inoculation (dpi) and appeared necrotic at 10 dpi. The symptoms were similar to those observed in field-infected tomato plants. No symptoms were observed on the plants inoculated with water. We further sequenced the re-isolated bacteria from the symptomatic inoculated seedlings. Results showed that they belong to E. mori. The experiment was repeated three times. E. mori has been found to cause diseases on peaches (Ahmad et al., 2021), watermelons (Wu et al., 2023), Canna indica, (Zhang et al., 2023), and strawberries (Ji et al., 2023). E. cloacae has been found to cause diseases on tomatoes in Heilongjiang province (Jin et al., 2023). This is the first report of E. mori causing leaf yellowing and wilting on tomatoes in China. These results are significant for the safe production and disease control of greenhouse tomatoes.

Applying Different Organic Amendments Optimizes Soil Nitrogen Management and Increases Greenhouse Tomato Yield

Applying Different Organic Amendments Optimizes Soil Nitrogen Management and Increases Greenhouse Tomato Yield

Tomato pose estimation using the association of tomato body and sepal

In facility horticulture smart farms, harvesting robotic systems to automate harvesting tasks are challenging due to the complex environment, irregular growth and fruits pose. To harvest a fruits, the vision information required by a harvesting robot is accurate position and pose. Especially, fruit pose information is essential for planning paths to avoid damaging stems, leaves, branches, and obstacles, preventing damage to the fruit and harvesting robots, and planning efficient harvest sequences. This paper presents a method that uses information from tomato sepals to estimate the orientation of tomatoes, which are a commonly grown crop in horticulture. First, we train a YOLOv8 model to detect and segment bodies and sepals of tomatoes. For robust training of the model, the training data is constructed using effective data augmentation, which synthesizes segmented foreground objects and inserting them into the background. Then, for finding association between the body and sepal of a tomato, we apply IoU score based matching and the Hungarian algorithm. Consequently, we obtain point clouds for both parts using RGB-D data. Finally, we compute the center point of each object by using spherical fitting and the statistics of the point cloud, respectively. Then, we estimate the pose of the tomato as a vector between two center points of the body and sepal. To accurately and practically evaluate the proposed method, we generated ground truth data using calibration patterns in tomato greenhouse. As the experimental results show, The segmentation results show that AP50,sepal is 94.7, AP50,tomato is 96.3, and mAP is 61.5. The result of the pose estimation for the pose validation dataset is a mean error angle of 6.79 ± 3.18 and angle errors of less than 10 degrees account for 87.2%. The total algorithm proposed in this paper requires about 0.038s for each tomato greenhouse image. As a result, our proposed pose estimation can be practically utilized in robot systems for tomato harvesting.

Assessing Shelf-life Ripening of Newly Imported Greenhouse Tomato Cultivars in Saudi Arabia: A qPCR Approach

Assessing Shelf-life Ripening of Newly Imported Greenhouse Tomato Cultivars in Saudi Arabia: A qPCR Approach

Rearing of Native Bumblebee Species Bombus haemorrhoidalis for Greenhouse Pollination in Pakistan

Greenhouse tomato production is a growing business worldwide, and it is strongly promoted by bumblebee pollination. Although there are over 250 bumblebee species worldwide, very few species have been reared successfully for greenhouse tomato pollination. Those successfully managed species, especially Bombus terrestris, are shipped around the world for commercial use. However, managed bumblebees are known to escape greenhouse facilities, have established local populations, spread disease to local bumblebees, and are blamed for the declines of some indigenous bee species. An alternative to shipping exotic bumblebees around the world is to develop local species for greenhouse pollination. Such an approach has the dual benefits of creating a new industry of insect rearing while reducing threats to local bee communities. In this study, we successfully reared Bombus haemorrhoidalis, which is the most common bumblebee species in Northern Pakistan, in a laboratory and compared its effectiveness as a tomato pollinator with that of commercial B. terrestris in a greenhouse. We found that the effectiveness of B. haemorrhoidalis in tomato pollination in a greenhouse is very similar to that of B. terrestris when it comes to the fruit size, number of seeds, and fruit weight. Our study provides an example of how to rear a native bumblebee species to pollinate local crops, which is a method that could potentially substitute the importation of non-ingenious bumblebees.

A Study of Irrigation Management in Smart Farming and IoT for Greenhouse Tomato Production

In this study of the relevant literature, we look at the development of smart farming and precision agriculture in greenhouse tomato cultivation, paying particular attention to the role that the Internet of Things (IoT) and automated irrigation systems have played thus far. Five terms were used to compile this literature review: automated watering systems, greenhouses, the Internet of Things, tomatoes, and smart farming. This article summarizes recent advancements in smart farming tech, their impact on greenhouse tomato farming, and their potential to boost future crop yield and quality. The evaluation starts by introducing 'smart farming' and its significance in agriculture, followed by a review of the greenhouse sector and an overview of the Internet of Things (IoT) within it, with subsequent examination of the pros and cons of employing smart farming and IoT in greenhouse tomato cultivation through specific industry technology examples. The following overview section discusses the benefits of cultivating tomatoes in a smart greenhouse, techniques for achieving optimal results in greenhouse tomato farming, and includes case studies to demonstrate the advantages of automated irrigation for smart greenhouse tomato cultivation, while this literature review analyzes the current state of innovative farming technology and its effect on greenhouse tomato cultivation, covering smart farming, greenhouse cultivation, the Internet of Things, tomato cultivation, and automated irrigation, summarizing significant results and recommendations for future research, including promising increases in crop yield and quality.

Molecular Mechanisms and Biological Characteristics of Botrytis cinerea Field Isolate Resistance to Pyrisoxazole in Liaoning Province.

Botrytis cinerea is a broad-host-range necrotrophic phytopathogen responsible for serious diseases in leading crops worldwide. The novel sterol 14α-demethylase inhibitor (DMI) pyrisoxazole was recently registered for the control of tomato gray mold caused by B. cinerea in China. One hundred fifty-seven isolates of B. cinerea were collected from tomato greenhouses in 14 cities of Liaoning Province from 2016 to 2021 and examined for sensitivity to pyrisoxazole, with a mean EC50 value of 0.151 μg/ml. Three highly resistant isolates, XD-5, DG-4, and GQ-3, were screened, and the EC50 values were 0.734, 0.606, and 0.639 μg/ml with corresponding resistance factors of 12.88, 10.63, and 11.21, respectively. Compared with field-sensitive strains, the highly resistant isolate XD-5 exhibited fitness defects in traits, including mycelial growth, conidial production, and pathogenicity, but DG-4 and GQ-3 did not experience fitness costs. Positive cross-resistance was observed only between pyrisoxazole and the DMIs tebuconazole and prochloraz but not between pyrisoxazole and the non-DMIs iprodione, procymidone, pyrimethanil, fludioxonil, fluazinam, and fluopyram. Sequence alignment of the CYP51 gene indicated that three point mutations were observed in the highly resistant mutant, namely, V24I in XD-5, G461S in GQ-3, and R464K in DG-4. When exposed to pyrisoxazole, the induced expression levels of the ABC transporter AtrD and MFS transporter Mfs1 increased in the resistant isolates compared with those in the sensitive isolates, whereas the expression level of the CYP51 gene did not change significantly. Molecular docking suggested that the G461S and R464K mutations both led to a decrease in the binding energy between CYP51 and pyrisoxazole, whereas no change was found with the V24I mutation. Thus, two point mutations in the CYP51 protein combined with induced expression of the Mfs1 and AtrD genes appeared to mediate the pyrisoxazole resistance of the highly resistant mutants DG-4 and GQ-3, while the overexpression of the Mfs1 and AtrD genes was responsible for the highly resistant mutant XD-5.

Simultaneous use of Beauveria bassiana and Bacillus subtilis-based biopesticides contributed to dual control of Trialeurodes vaporariorum (Hemiptera: Aleyrodidae) and tomato powdery mildew without antagonistic interactions

BackgroundImplementing pest and disease control techniques that have low environmental impact is important for sustainable agriculture. Microbial biopesticides are an effective approach due to their low environmental impact and low risk of resistance development. Because it is not usually possible to control multiple pests and diseases with a single microbial biopesticide, it is essential to investigate the potential for combining microbial biopesticides with varying control spectrums effectively. Many biopesticides have antimicrobial activity and may therefore interact negatively in combination.ResultsThis study demonstrated that a mixture of Beauveria bassiana and Bacillus subtilis formulations proved potential for simultaneous control of greenhouse whitefly (Trialeurodes vaporariorum Westwood) and tomato powdery mildew (Oidium neolycopersici). Three greenhouse experiments were conducted to assess the efficacy of mixed and single-use treatments. A laboratory experiment comparing the insecticidal effect of each treatment was also conducted. In all greenhouse experiments, the combined treatment controlled the greenhouse whitefly (78.9–88.3%) and tomato powdery mildew (47.2–81.0%) compared to untreated controls, which was as well as each treatment alone. In some greenhouse and laboratory experiments, the mixed treatment showed an approximately 1.32 to 1.78 times higher insecticidal effect compared to single-use treatments. Regarding the control efficacy against the pest and disease, negative effects of microbial agents on each other were not observed.ConclusionsThese results demonstrated the effectiveness of concurrent use of two microbial pesticides examined on dual control of pest and disease and showed potential for improved control of certain pests. The knowledge of this work could suggest the possibility of more environmentally friendly pest control systems with the use of microbial pesticides.

Bumblebee Foraging Dynamics and Pollination Outcomes for Cherry Tomato and Pear Varieties in Northern China.

Bumblebees (Bombus terrestris) have strong environmental adaptability and high pollen transfer efficiency, making them well-suited pollinators of economic crops. However, bumblebee pollination is still not widely applied in northern China due to the lack of data on foraging behavior and pollination effects. We conducted a three-year experiment involving cherry tomatoes (Solanum lycopersicum L.) and pears (Pyrus spp.) treated with bumblebee pollination to evaluate the foraging behavior and pollination effects on these two crops. Results showed that B. terrestris had enhanced foraging activities as daytime temperatures rose from 18 °C to 26 °C, as indicated by the increased number of bees leaving the hive and returning bees carrying pollen in greenhouses in winter. There were two peaks in the foraging activity of bumblebees in pear orchards in early spring, which was closely related to the temperature change in the daytime. Undoubtedly, cherry tomatoes treated with B. terrestris had higher fruit setting rate, weight, seed number, and fruit yields compared to those with hormone 2,4-dichlorophenoxyacetic acid treatments, as well as a lower rate of deformed fruits. B. terrestris pollination can significantly increase the fruit setting rate and fruit yield of pears, compared with open pollination, and can fully achieve the effect of hand pollination. B. terrestris pollination can improve cultivation efficiency, increase yield, and produce more economic benefits. Moreover, it can also contribute to reducing hormone residues and ensure the safety of agricultural products. We recommend its application to cherry tomatoes in greenhouses in winter and potential application to pears in orchards in early spring in northern China. However, the risk to local bumblebee species of introducing commercially available bumblebees into orchards should be considered and evaluated in future research. This study provides both empirical support and a theoretical basis for the selection of bumblebees as pollinators in the production of economically important crops and the improvement of crop cultivation management in northern China.

Effects of Nutrient Solution Application Rates on Yield, Quality, and Water-Fertilizer Use Efficiency on Greenhouse Tomatoes Using Grown-in Coir.

The yield, quality, and water-fertilizer use efficiency of crops are important parameters for assessing rational water and fertilizer management. For an optimal water and fertilizer system with respect to the nutrient solution irrigation of greenhouse tomatoes using cultivation substrates, a two-year greenhouse cultivation experiment was conducted from 2022 to 2023. Three drip fertigation treatments (T1, T2, and T3) were implemented in the experiment, where nutrient solutions were supplied when the substrate's water content reached 60%, 70%, and 80%. The frequency of nutrient solution applications is based on weighing coconut coir strips in the morning and evening at 7:00 to determine the daily water consumption of plants. Nutrient solutions were supplied when the substrate's water content reached the lower limit, and the upper limit for nutrient supply was set at 100% of the substrate water content. The nutrient solution application was carried out multiple times throughout the day, avoiding the midday heat. The nutrient solution formula used was the soilless tomato cultivation formula from South China Agricultural University. The results show that plant height and the leaf area index rapidly increased in the early and middle stages, and later growth tended to stabilize; the daily transpiration of tomatoes increased with an increase in nutrient solution supply, and it was the greatest in the T3 treatment. Between the amount of nutrient solution application and the number of years, the yield increased with the increase of the amount of nutrient solution, showing T3 > T2 > T1. Although the average yield of the T2 treatment was slightly lower than that of the T3 treatment by 3.65%, the average irrigation water use efficiency, water use efficiency, and partial fertilizer productivity of the T2 treatment were significantly higher than those of the T3 treatment by 29.10%, 19.99%, and 28.89%, respectively (p < 0.05). Additionally, soluble solid, vitamin C, and soluble sugar contents and the sugar-acid ratio of tomatoes in the T2 treatment were greater than those in the other two treatments (p < 0.05). Using the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method, it was concluded that the nutrient solution application rate of 70% can significantly increase water and fertilizer use efficiency and markedly improve the nutritional and flavor quality of the fruit without a significant reduction in yield. This finding provides significant guidance for the high-yield, high-quality, and efficient production of coconut coir-based cultivated tomatoes in greenhouses.