Effects of drip irrigation frequency on the yield and nutrient utilization efficiency of tomato under long-season cultivation in solar greenhouse.

To improve the utilization efficiency of nutrients and water and determine the best drip irrigation frequency for long-season tomato cultivation in solar greenhouses, we cultivated tomato grafted seedlings in soil using an integrated water and fertilizer technology: drip irrigation under mulch. Seedlings drip-irrigated with balanced fertilizer (containing 20% N, 20% P2O5, and 20% K2O) and high-K fertilizer (containing 17% N, 8% P2O5, and 30% K2O) once every 12 days were set as control (CK) and that with water once every 12 days as CK1, while other seedling groups, drip-irrigated with a nutrient solution of Yamazaki (1978) formula for tomato, were set as treatments (T1-T4). There were four drip-irrigation frequencies, i.e., once every 2 days (T1), 4 days (T2), 6 days (T3), or 12 days (T4), who received the same total amounts of fertilizer and water over the 12 experimental days. The results showed that, with the decreases of drip irrigation frequency, tomato yield, the accumulation of N, P and K in plant dry matter, the fertilizer partial productivity, and the nutrient utilization rate first increased and then decreased, peaking at the T2 treatment. Compared with CK, under the T2 treatment, plant dry matter accumulation and the accumulation of N, P and K increased by 4.9%, 8.0%, 8.0%, 16.8%, the partial productivity of fertilizer and the utilization efficiency of water increased by 142.8% and 12.2%, the use efficiency of N, P and K was better than CK by 241.4%, 466.6% and 235.9%, respectively, and the tomato yield increased by 12.2%. Under the experimental conditions, drip irrigation with the Yamazaki nutrient solution at a frequency of 4 days could increase the tomato yield, as well as the use efficiency of nutrients and water. Under long-season cultivation, these trends would result in considerable saving of water and fertilizer. Overall, our findings provided a basis for improving the scientific management of water and fertilizers under long-season tomato cultivation in protected facilities.

Comparison of the effectiveness of high-flow nasal oxygen vs. standard facemask oxygenation for pre- and apneic oxygenation during anesthesia induction: a systematic review and meta-analysis.

BACKGROUND: In recent years, high flow nasal oxygen (HFNO) has been widely used in clinic, especially in perioperative period. Many studies have discussed the role of HFNO in pre- and apneic oxygenation, but their results are controversial. Our study aimed to examine the effectiveness of HFNO in pre- and apneic oxygenation by a meta-analysis of RCTs. METHODS: EMBASE, PUBMED, and COCHRANE LIBRARY databases were searched from inception to July 2021 for relevant randomized controlled trails (RCTs) on the effectiveness of HFNO versus standard facemask ventilation (FMV) in pre- and apenic oxygenation. Studies involving one of the following six indicators: (1) Arterial oxygen partial pressure (PaO2), (2) End expiratory oxygen concentration (EtO2), (3) Safe apnoea time, (4) Minimum pulse oxygen saturation (SpO2min), (5) Oxygenation (O2) desaturation, (6) End expiratory carbon dioxide (EtCO2) or Arterial carbon dioxide partial pressure(PaCO2) were included. Due to the source of clinical heterogeneity in the observed indicators in this study, we adopt random-effects model for analysis, and express it as the mean difference (MD) or risk ratio (RR) with a confidence interval of 95% (95%CI). We conducted a risk assessment of bias for eligible studies and assessed the overall quality of evidence for each outcome. RESULTS: Fourteen RCTs and 1012 participants were finally included. We found the PaO2 was higher in HFNO group than FMV group with a MD (95% CI) of 57.38 mmHg (25.65 to 89.10; p = 0.0004) after preoxygenation and the safe apnoea time was significantly longer with a MD (95% CI) of 86.93 s (44.35 to 129.51; p < 0.0001) during anesthesia induction. There were no significant statistical difference in the minimum SpO2, CO2 accumulation, EtO2 and O2 desaturation rate during anesthesia induction between the two groups. CONCLUSIONS: This systematic review and meta-analysis suggests that HFNO should be considered as an oxygenation tool for patients during anesthesia induction. Compared with FMV, continuous use of HFNO during anesthesia induction can significantly improve oxygenation and prolong safe apnoea time in surgical patients.

Evaluation of Self-Propelled High-Energy Ultrasonic Atomizer on Azoxystrobin and Tebuconazole Application in Sunlit Greenhouse Tomatoes.

In this study, a self-propelled high-energy ultrasonic atomizer was evaluated in terms of deposition on the canopy, the loss to the ground, and fungicide residues in cherry tomato and tomato. Artificial collectors fixed to the upper side and underside of the leaves at different depths and heights were used to collect the depositions. A reliable analytical method for determination of azoxystrobin and tebuconazole in artificial collectors and residue samples was developed by using liquid chromatography triple-quadrupole mass spectrometry. The results showed that the atomizer distributed the droplets evenly throughout the greenhouse with good uniformity (CVs below 39%). The ratio of depositions on the internal and external sides was 66⁻83%, and the ratio of depositions on the underside and upper side was 39⁻50%. There were no significant differences in depositions between two different height crops. The residues of azoxystrobin and tebuconazole in tomato and cherry tomato fruits were far below the maximum residue limits at harvest time. In general, self-propelled high-energy ultrasonic atomizer used in a greenhouse could increase the depositions, especially on the underside and internal side of the canopies, and lead to a reduction of operator exposure risk.

Utilization of thermal infrared image for inversion of winter wheat yield and biomass.

The present paper utilizes thermal infrared image for inversion of winter wheat yield and biomass with different technology of irrigation (drip irrigation, sprinkler irrigation, flood irrigation). It is the first time that thermal infrared image is used for predicting the winter wheat yield and biomass. The temperature of crop and background was measured by thermal infrared image. It is necessary to get the crop background separation index (CBSI(L), CBSI(H)), which can be used for distinguishing the crop value from the image. CBSI(L) and CBSI(H) (the temperature when the leaves are wet adequately; the temperature when the stomata of leaf is closed completely) are the threshold values. The temperature of crop ranged from CBSI(L) to CBSI(H). Then the ICWSI was calculated based on relevant theoretical method. The value of stomata leaf has strong negative correlation with ICWSI proving the reliable value of ICWSI. In order to construct the high accuracy simulation model, the samples were divided into two parts. One was used for constructing the simulation model, the other for checking the accuracy of the model. Such result of the model was concluded as: (1) As for the simulation model of soil moisture, the correlation coefficient (R2) is larger than 0.887 6, the average of relative error (Er) ranges from 13.33% to 16.88%; (2) As for the simulation model of winter wheat yield, drip irrigation (0.887 6, 16.89%, -0.12), sprinkler irrigation (0.970 0, 14.85%, - 0.12), flood irrigation (0.969 0, 18.87%, -0.18), with the values of R2, Er and CRM listed in the parentheses followed by the individual term. (3) As for winter wheat biomass, drip irrigation (0.980 0, 13.70%, -0.13), sprinkler irrigation (0.95, 13.15%, -0.14), flood irrigation (0.970 0, 14.48%, -0.13), and the values in the parentheses are demonstrated the same as above. Both the CRM and Er are shown to be very low values, which points to the accuracy and reliability of the model investigated. The accuracy of model is high and reliable. The results indicated that thermal infrared image can be used potentially for inversion of winter wheat yield and biomass.

[SVM-based spectral recognition of corn and weeds at seedling stage in fields].

A handheld FieldSpec 3 Spectroradiometer manufactured by ASD Incorporated Company in USA was used to measure the spectroscopic data of canopies of seedling corns, Dchinochloa crasgalli, and Echinochloa crusgalli weeds within the 350-2 500 nm wavelength range in the field. Each canopy was measured five times continuously. The five original spectroscopic data were averaged over the whole wavelength range in order to eliminate random noise. Then the averaged original data were converted into reflectance data, and the unsmooth parts of reflectance spectral curves with large noise were removed. The effective wavelength range for spectral data process was selected as 350-1 300 and 1 400-1 800 nm. Support vector machine (SVM) was chosen as a method of pattern recognition in this paper. SVM has the advantages of solving the problem of small sample size, being able to reach a global optimization, minimization of structure risk, and having higher generalization capability. Two classes of classifier SVM models were built up respectively using "linear", "polynomial", "RBF"(radial basis function), and "mlp (multilayer perception)" kernels. Comparison of different kernel functions for SVM shows that higher precision can be obtained by using "polynomial" kernel function with 3 orders. The accuracy can be above 80%, but the SV ratio is relatively low. On the basis of two-class classification model, taking use of voting procedure, a model based on one-against-one-algorithm multi-class classification SVM was set up. The accuracy reaches 80%. Although the recognition accuracy of the model based on SVM algorithm is not above 90%, the authors still think that the research on weeds recognition using spectrum technology combining SVM method discussed in this paper is tremendously significant. Because the data used in this study were measured over plant canopies outdoor in the field, the measurement is affected by illumination intensity, soil background, atmosphere temperature and instrument accuracy. This method proposes a kind of research ideology and application foundation for weeds recognition in the field.

[Target infrared detection in target spray].

Crops in agriculture and forestry are normally planted discretely. The chemical sprayed between crops would cause great waste and serious environment pollution. Therefore realization of the precision spray has great significance. This research discussed the method to realize automatic target detection using infrared detect technology. The infrared can avoid the interference of the visible light effectively and the response speed is very fast. Therefore it can be used to implement non-tough detection. Photoelectric detection systems based on infrared detect technology are normally stable, reliable, low cost, simple structure, and easy to be practically utilized. Therefore it is widely used in the on-line real time detection field. Its key point is to determinate the characteristic wavelength or wave band. The infrared lights emitted from the infrared light emitting diode were irradiated to the detected objects. The reflected infrared lights could be received by the photoelectric device. Then control signal was triggered and automatic target spray was realized. Code-division infrared detection circuit was used in the system. Modulated pulse infrared signals using different coding were used in different photodetector units in the built system so as to eliminate the light path interference between different detector units and other light signal interferences. Therefore the interference capacity of the system is high. The test results showed that the automatic target spray equipment set up in the study could detect crop targets automatically. The light wavelength used in the test is 850 nm. The detection range was tunable within 0.1-0.5 m. The least targets detectable distance was less than 0.3 m.