基于马氏距离和Canopy改进K-means的交通聚类算法

在对交通数据的研究中经常会使用到聚类算法,且不同的聚类算法有不同的特性。K-means作为其中的一种聚类算法,具有较高的准确性和实用性,但其准确性易受主观选取K值和确定初始聚类中心的影响。为了优化聚类中心和K值的选取问题,提出MC-Kmeans算法。在所提方法中,首先通过Canopy算法选取K值,然后依据马氏距离的计算准则来确定初始聚类中心,最后将K值和聚类中心的值作为K-means的参数进行聚类。将MC-Kmeans算法应用到某时间段的纽约出租车交通数据中进行实际的验证。结果表明,与K-means算法比较,所提方法准确度更高,与实际交通情况更加相匹配,更能反映区域内的交通热点情况。

基于信息熵和Canopy-K-Means算法的货车驾驶风格识别

为识别货车的激进驾驶行为,保障货车行车安全,提出1种基于信息熵和Canopy-K-Means算法的货车驾驶风格识别方法。首先,从货车自然驾驶数据中提取出604个驾驶片段,根据信息熵理论计算各个驾驶片段的速度熵值、横向加速度熵值和纵向加速度熵值,构成货车驾驶风格表征指标集;其次,针对K-Means算法的聚类数量主观选取、初始聚类中心随机选取的问题,使用Canopy算法改进K-Means算法(Canopy-K-Means算法);最后,分别使用K-Means算法和Canopy-K-Means算法对货车驾驶风格进行识别。研究结果显示,Canopy-K-Means算法的轮廓系数和Calinski-Harabasz指数均大于K-Means算法,表现出更优的聚类性能。根据Canopy-K-Means算法,可将货车驾驶风格分为沉稳型、常规型和激进型3类,其中激进型货车驾驶风格的指标熵值和极差均较大,存在较高的安全隐患,需要引起相关部门的高度重视。

核电站控制棒驱动机构Canopy焊缝焊接温度场和应力-应变场模拟

目的研究机加工和拉拔2种成形方式下得到的填充环对Canopy焊缝的影响,获取焊接焊缝成形、焊接残余应力和变形的相关数据,以指导Canopy焊缝焊接工艺。方法采用数值模拟的方法,建立Canopy焊缝焊接数值分析模型,模拟焊接温度场、焊接残余应力和焊接残余变形。结果拉拔成形环焊接熔池高度为9 mm,机加工成形环焊接熔池高度为8.3 mm;机加工成形环焊接最大残余应力为255.6 MPa,而拉拔成形环焊接最大残余应力为277.8 MPa,均出现在管座紧贴焊缝的位置;机加工成形环焊接残余变形为0.19 mm,拉拔成形环焊接残余变形为0.186 mm,最大残余变形均出现在焊接起始位置附近,在焊缝与管座交接的位置。结论熔池形貌直接影响了热影响区域的大小,拉拔Y型环焊接熔池高度更大,焊接的热影响区域更大;拉拔Y型环焊接残余应力略大于机加工Y型环焊接残余应力;机加工成形环和拉拔成形环焊接残余变形相近。

Rice canopy temperature is affected by nitrogen fertilizer

Canopy temperature strongly influences crop yield formation and is closely related to plant physiological traits.However, the effects of nitrogen treatment on canopy temperature and rice growth have yet to be comprehensively examined. We conducted a two-year field experiment with three rice varieties(HD-5, NJ-9108, and YJ-805) and three nitrogen treatments(zero-N control(CK), 200 kg ha~(–1)(MN), and 300 kg ha~(–1)(HN)). We measured canopy temperature using a drone equipped with a high-precision camera at the six stages of the growth period. Generally,canopy temperature was significantly higher for CK than for MN and HN during the tillering, jointing, booting, and heading stages. The temperature was not significantly different among the nitrogen treatments between the milky and waxy stages. The canopy temperature of different rice varieties was found to follow the order: HD-5>NJ-9108>YJ-805, but the difference was not significant. The canopy temperature of rice was mainly related to plant traits, such as shoot fresh weight(correlation coefficient r=–0.895), plant water content(–0.912), net photosynthesis(–0.84), stomatal conductance(–0.91), transpiration rate(–0.90), and leaf stomatal area(–0.83). A structural equation model(SEM) showed that nitrogen fertilizer was an important factor affecting the rice canopy temperature.Our study revealed:(1) A suite of plant traits was associated with the nitrogen effects on canopy temperature,(2) the heading stage was the best time to observe rice canopy temperature, and(3) at that stage, canopy temperature was negatively correlated with rice yield, panicle number, and grain number per panicle. This study suggests that canopy temperature can be a convenient and accurate indicator of rice growth and yield prediction.

Drought events influence nutrient canopy exchanges and green leaf partitioning during senescence in a deciduous forest

The increase in the frequency and intensity of drought events expected in the coming decades in Western Europe may disturb forest biogeochemical cycles and create nutrient deficiencies in trees.One possible origin of nutrient deficiency is the disturbance of the partitioning of the green leaf pool during the leaf senescence period between resorption,foliar leaching and senesced leaves.However,the effects of drought events on this partitioning and the consequences for the maintenance of tree nutrition are poorly documented.An experiment in a beech forest in Meuse(France)was conducted to assess the effect of drought events on nutrient canopy exchanges and on the partitioning of the green leaf pool during the leaf senescence period.The aim was to identify potential nutritional consequences of droughts for trees.Monitoring nutrient dynamics,including resorption,chemistry of green and senesced leaves,foliar absorption and leaching in mature beech stands from 2012 to 2019 allowed us to compare the nutrient exchanges for three nondry and three dry years(i.e.,with an intense drought event during the growing season).During dry years,we observed a decrease by almost a third of the potassium(K)partitioning to resorption(i.e.resorption efficiency),thus reducing the K reserve in trees for the next growing season.This result suggests that with the increased drought frequency and intensity expected for the coming decades,there will be a risk of potassium deficiency in trees,as already observed in a rainfall exclusion experiment on the same study site.Reduced foliar leaching and higher parititioning to the senesced leaves for K and phosphorus(P)were also observed.In addition,a slight increase in nitrogen(N)resorption efficiency occurred during dry years which is more likely to improve tree nutrition.The calcium(Ca)negative resorption decreased,with no apparent consequence in our study site.Our results show that nutrient exchanges in the canopy and the partitioning of the green leaf pool can be modified by drought events,and may have consequences on tree nutrition.

Effects of dust controls on respirable coal mine dust composition and particle sizes:case studies on auxiliary scrubbers and canopy air curtain

Control of dust in underground coal mines is critical for mitigating both safety and health hazards.For decades,the National Institute of Occupational Safety and Health(NIOSH)has led research to evaluate the effectiveness of various dust control technologies in coal mines.Recent studies have included the evaluation of auxiliary scrubbers to reduce respirable dust downstream of active mining and the use of canopy air curtains(CACs)to reduce respirable dust in key operator positions.While detailed dust characterization was not a focus of such studies,this is a growing area of interest.Using preserved filter samples from three previous NIOSH studies,the current work aims to explore the effect of two different scrubbers(one wet and one dry)and a roof bolter CAC on respirable dust composition and particle size distribution.For this,the preserved filter samples were analyzed by thermogravimetric analysis and/or scanning electron microscopy with energy dispersive X-ray.Results indicate that dust composition was not appreciably affected by either scrubber or the CAC.However,the wet scrubber and CAC appeared to decrease the overall particle size distribution.Such an effect of the dry scrubber was not consistently observed,but this is probably related to the particular sampling location downstream of the scrubber which allowed for significant mixing of the scrubber exhaust and other return air.Aside from the insights gained with respect to the three specific dust control case studies revisited here,this work demonstrates the value of preserved dust samples for follow-up investigation more broadly.

The influences of canopy temperature measuring on the derived crop water stress index

Crop water stress index(CWSI)is widely used for efficient irrigation management.Precise canopy temperature(T_(c))measurement is necessary to derive a reliable CWSI.The objective of this research was to investigate the influences of atmospheric conditions,settled height,view angle of infrared thermography,and investigating time of temperature measuring on the performance of the CWSI.Three irrigation treatments were used to create different soil water conditions during the 2020-2021 and 2021-2022 winter wheat-growing seasons.The CWSI was calculated using the CWSI-E(an empirical approach)and CWSI-T(a theoretical approach)based on the T_(c).Weather conditions were recorded continuously throughout the experimental period.The results showed that atmospheric conditions influenced the estimation of the CWSI;when the vapor pressure deficit(VPD)was>2000 Pa,the estimated CWSI was related to soil water conditions.The height of the installed infrared thermograph influenced the T_(c)values,and the differences among the T_(c)values measured at height of 3,5,and 10 m was smaller in the afternoon than in the morning.However,the lens of the thermometer facing south recorded a higher T_(c)than those facing east or north,especially at a low height,indicating that the direction of the thermometer had a significant influence on T_(c).There was a large variation in CWSI derived at different times of the day,and the midday measurements(12:00-15:00)were the most reliable for estimating CWSI.Negative linear relationships were found between the transpiration rate and CWSI-E(R^(2)of 0.3646-0.5725)and CWSI-T(R^(2)of 0.5407-0.7213).The relations between fraction of available soil water(FASW)with CWSI-T was higher than that with CWSI-E,indicating CWSI-T was more accurate for predicting crop water status.In addition,The R^(2)between CWSI-T and FASW at 14:00 was higher than that at other times,indicating that 14:00 was the optimal time for using the CWSI for crop water status monitoring.Relative higher yield of winter wheat was obtained with average seasonal values of CWSI-E and CWSI-T around 0.23 and 0.25-0.26,respectively.The CWSI-E values were more easily influenced by meteorological factors and the timing of the measurements,and using the theoretical approach to derive the CWSI was recommended for precise irrigation water management.

Fuzzy C-Means Algorithm Based on Density Canopy and Manifold Learning

Fuzzy C-Means(FCM)is an effective and widely used clustering algorithm,but there are still some problems.considering the number of clusters must be determined manually,the local optimal solutions is easily influenced by the random selection of initial cluster centers,and the performance of Euclid distance in complex high-dimensional data is poor.To solve the above problems,the improved FCM clustering algorithm based on density Canopy and Manifold learning(DM-FCM)is proposed.First,a density Canopy algorithm based on improved local density is proposed to automatically deter-mine the number of clusters and initial cluster centers,which improves the self-adaptability and stability of the algorithm.Then,considering that high-dimensional data often present a nonlinear structure,the manifold learning method is applied to construct a manifold spatial structure,which preserves the global geometric properties of complex high-dimensional data and improves the clustering effect of the algorithm on complex high-dimensional datasets.Fowlkes-Mallows Index(FMI),the weighted average of homogeneity and completeness(V-measure),Adjusted Mutual Information(AMI),and Adjusted Rand Index(ARI)are used as performance measures of clustering algorithms.The experimental results show that the manifold learning method is the superior distance measure,and the algorithm improves the clustering accuracy and performs superiorly in the clustering of low-dimensional and complex high-dimensional data.

不同Y形环应用于核电站控制棒驱动机构Canopy焊缝焊接工艺及焊缝性能的研究

试验采用两种机械加工Y形环和一种拉拔Y形环应用于核电站控制棒驱动机构Canopy焊缝,研究不同Y形环对Canopy焊缝焊接工艺及焊缝性能的影响。结果表明:机械加工Y形环为整圈环,相比拉拔Y形环,更有利于定位焊;机械加工Y形环(控公差)与拉拔Y形环装配间隙范围相当,比机械加工Y形环装配间隙范围大,更有利于工程应用;3种Y形环熔成的Canopy焊缝组织主要由铁素体和柱状奥氏体组成,无明显差异;3种Y形环熔成的Canopy焊缝经23.1~23.3MPa水压试验,焊缝表面均无渗漏,无可见变形。该研究为机械加工Y形环应用于核电站控制棒驱动机构Canopy焊缝提供了宝贵的经验。

核电站控制棒驱动机构Canopy焊缝组织和性能研究

试验采用专用自动TIG焊机焊接核电站控制棒驱动机构Canopy焊缝,选用机械加工Y形环作为填充材料,研究Canopy焊缝组织和性能。结果表明:通过自动TIG焊,机械加工Y形环熔成的Canopy焊缝,成形良好,宽度适中,焊缝与母材冶金熔合良好;焊缝组织主要由板条状或骨架状铁素体与快速凝固的柱状奥氏体组成;经23.1~23.3MPa水压试验,Canopy焊缝表面无渗漏,无可见变形;维氏硬度结果表明:焊缝硬度在210HV左右,焊缝硬度分布均匀。该研究为核电站控制棒驱动机构Canopy焊缝用机械加工Y形环提供了宝贵的经验。

屏蔽主泵Canopy密封环自动焊接技术研究

为了降低作业人员在AP1000机组屏蔽主泵在役检修拆装过程中的辐照剂量,研究了与实际作业工序相同的Canopy密封环自动焊接工艺,开发了可远程控制、对称施焊的自动焊接装置,并在模拟体上进行了焊接试验。试验结果表明,在优化的焊接参数下,Canopy密封环打底焊成形良好,多层多道焊缝无层间未熔合等缺陷,起弧、收弧过渡良好。该自动焊接装置结构紧凑,可实现模拟件的稳定焊接,焊接质量良好,能够满足屏蔽主泵在役检修更换时的自动焊接需求。

基于加权密度Canopy的K-means文本聚类

针对现有文本聚类性能不高的问题,提出了一种改进质心初始化的K-means文本聚类算法.该算法首先利用Canopy算法进行文本预聚类,并且对Canopy算法的阈值选取策略进行改进,定义加权密度进行Canopy中心的选取,得到更准确的聚类数以及初始聚类中心;然后将所得结果作为K-means算法的初始化参数进行后续迭代聚类,有效解决了传统算法因随机选取初始聚类中心而陷入局部最优解的问题,减少了算法的迭代次数,提高了聚类准确性.实验结果表明:与其他同类型算法相比,该算法在文本聚类分析中更具优势.

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

Effect of the L-D_(1)alleles on leaf morphology,canopy structure and photosynthetic productivity in upland cotton(Gossypium hirsutum L.)

One of the most important objectives for breeders is to develop high-yield cultivars.The increase in crop yield has met with bottlenecks after the first green revolution,and more recent efforts have been focusing on achieving high photosynthetic efficiency traits in order to enhance the yield.Leaf shape is a significant agronomic trait of upland cotton that affects plant and canopy architecture,yield,and other production attributes.The major leaf shape types,including normal,sub-okra,okra,and super-okra,with varying levels of lobe severity,are controlled by a multiple allelic series of the D-genome locus L-D_(1).To analyze the effects of L-D_(1)alleles on leaf morphology,photosynthetic related traits and yield of cotton,two sets of near isogenic lines(NILs)with different alleles were constructed in Lumianyan 22(LMY22)and Lumianyan 28(LMY28)backgrounds.The analysis of morphological parameters and the results of virus-induced gene silencing(VIGS)showed that the regulation of leaf shape by L-D_(1)alleles was similar to a gene-dosage effect.Compared with the normal leaf,deeper lobes of the sub-okra leaf improved plant canopy structure by decreasing the leaf area index(LAI)and increasing the light transmittance rate(LTR),and the mid-range LAI of sub-okra leaf also guaranteed the accumulation of cotton biomass.Although the chlorophyll content(SPAD)of sub-okra leaf was lower than those of the other two leaf shapes,the net photosynthetic rate(Pn)of sub-okra leaf was higher than those of okra leaf and normal leaf at most stages.Thus,the improvements in canopy structure,as well as photosynthetic and physiological characteristics,contributed to optimizing the light environment,thereby increasing the total biomass and yield in the lines with a sub-okra leaf shape.Our results suggest that the sub-okra leaf may have practical application in cultivating varieties,and could enhance sustainable and profitable cotton production.

Visual rating and the use of image analysis for assessing canopy density in a pecan provenance collection during leaf fall

A collection representing the native range of pecan was planted at the US DA-ARS Southeastern Fruit and Tree Nut Research Station,Byron,GA.The collection(867 trees)is a valuable genetic resource for characterizing important horticultural traits.Canopy density during leaf fall is important as the seasonal canopy dynamics provides insights to environmental cues and breeding potential of germplasm.The ability of visual raters to estimate canopy density on a subset of the provenance collection(76 trees)as an indicator of leaf shed during autumn along with image analysis values was explored.Mean canopy density using the image analysis software was less compared to visual estimates(11.9%vs 18.4%,respectively).At higher canopy densities,the raters overestimated foliage density,but overall agreement between raters and measured values was good(ρc=0.849 to 0.915),and inter-rater reliability was high(R^(2)=0.910 to 0.953).The provenance from Missouri(MOL),the northernmost provenance,had the lowest canopy density in November,and results show that the higher the latitude of the provenance,the lower the canopy density.Based on regression,the source provenance latitude explained 0.609 of the variation using image analysis,and0.551 to 0.640 when based on the rater estimates of canopy density.Visual assessment of pecan canopy density due to late season leaf fall for comparing pecan genotypes provides accurate and reliable estimates and could be used in future studies of the whole provenance collection.

Remote sensing of subtropical tree diversity:The underappreciated roles of the practical definition of forest canopy and phenological variation

Tree species diversity is vital for maintaining ecosystem functions,yet our ability to map the distribution of tree diversity is limited due to difficulties in traditional field-based approaches.Recent developments in spaceborne remote sensing provide unprecedented opportunities to map and monitor tree diversity more efficiently.Here we built partial least squares regression models using the multispectral surface reflectance acquired by Sentinel-2 satellites and the inventory data from 74 subtropical forest plots to predict canopy tree diversity in a national natural reserve in eastern China.In particular,we evaluated the underappreciated roles of the practical definition of forest canopy and phenological variation in predicting tree diversity by testing three different definitions of canopy trees and comparing models built using satellite imagery of different seasons.Our best models explained 42%–63%variations in observed diversities in cross-validation tests,with higher explanation power for diversity indices that are more sensitive to abundant species.The models built using imageries from early spring and late autumn showed consistently better fits than those built using data from other seasons,highlighting the significant role of transitional phenology in remotely sensing plant diversity.Our results suggested that the cumulative diameter(60%–80%)of the biggest trees is a better way to define the canopy layer than using the subjective fixeddiameter-threshold(5–12 cm)or the cumulative basal area(90%–95%)of the biggest trees.Remarkably,these approaches resulted in contrasting diversity maps that call attention to canopy structure in remote sensing of tree diversity.This study demonstrates the potential of mapping and monitoring tree diversity using the Sentinal-2 data in species-rich forests.

Assessing the severity of cotton Verticillium wilt disease from in situ canopy images and spectra using convolutional neural networks

Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evaluation of VW severity(VWS)forms the basis of field cotton VW control,which has great significance to cotton production.Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses,which require abundant time and professional expertise.Remote and proximal sensing using imagery and spectrometry have great potential for this purpose.In this study,we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values,in situ images,and spectra of 361 cotton canopies.To estimate cotton VWS values at the canopy scale,we developed two deep learning approaches that use in situ images and spectra,respectively.For the imagery-based method,given the high complexity of the in situ environment,we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes(CFS)dataset with over 1000 images for each scene-unit type.We performed pretrained convolutional neural networks(CNNs)training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy.The results showed that the Dark Net-19 model achieved satisfactory performance in CFS classification and VWS values estimation(R^(2)=0.91,root-mean-square error(RMSE)=6.35%).For the spectroscopy-based method,we first designed a one-dimensional regression network(1D CNN)with four convolutional layers.After dimensionality reduction by sensitive-band selection and principal component analysis,we fitted the 1D CNN with varying numbers of principal components(PCs).The 1D CNN model with the top 20 PCs performed best(R^(2)=0.93,RMSE=5.77%).These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.

平邑甜茶高位嫁接丽嘎拉苹果树冠层内枝类结构和果实品质、产量的分布

【目的】掌握以平邑甜茶为砧木,采用高位嫁接方式的丽嘎拉苹果树冠层内枝类结构、果实产量和品质的分布特点,为辽宁寒地苹果园建设中应用该种栽培模式,提升果品质量、优化栽培措施提供参考。【方法】以丽嘎拉/GM256/平邑甜茶、丽嘎拉/平邑甜茶(高接)和丽嘎拉/平邑甜茶(低接)苹果树为试材,按照冠层分格的方法,将试验树从地面到顶部分成4个冠层,比较不同冠层高度内枝类结构、果实产量和品质的分布差异。【结果】丽嘎拉/平邑甜茶(高接)苹果树高、冠径、覆盖率生长速度高于丽嘎拉/GM256/平邑甜茶,丽嘎拉/平邑甜茶(高接)树体外观表现为“小脚”现象,而丽嘎拉/GM256/平邑甜茶表现为“大脚”现象。丽嘎拉/平邑甜茶(高接)苹果树枝量、短枝比例高于丽嘎拉/平邑甜茶(低接)和丽嘎拉/GM256/平邑甜茶。丽嘎拉/平邑甜茶(高接)、丽嘎拉/平邑甜茶(低接)和丽嘎拉/GM256/平邑甜茶枝量分布集中区域为冠层高度>2.00~3.00 m、>3.00 m和>1.00~2.00 m,占全树枝量比例的43.51%、45.37%和47.96%。相同冠层高度内,丽嘎拉/平邑甜茶(高接)果实单果质量、果形指数、硬度、可滴定酸含量、色差L和a值高于丽嘎拉/GM256/平邑甜茶。随着冠层高度的增加,丽嘎拉/平邑甜茶(高接)、丽嘎拉/平邑甜茶(低接)和丽嘎拉/GM256/平邑甜茶果实硬度、可滴定酸含量呈现下降趋势,而固酸比和色差值(L、a、b)呈现上升趋势。丽嘎拉/平邑甜茶(高接)和丽嘎拉/平邑甜茶(低接)果实产量主要分布在冠层高度>2.00 m的空间区域,占全树产量的69.79%和75.55%,而丽嘎拉/GM256/平邑甜茶为>1.00~3.00 m,占比全树产量的74.59%。【结论】以平邑甜茶为砧木,采用高位嫁接的丽嘎拉苹果树枝条数量多、短枝比例高、外观色泽好,树体枝量和果实产量分布集中区域为冠层高度>2.00~3.00 m,该冠层生产的苹果单果质量大、果形指数适中、可溶性固形物含量高。

拱架送/回风方式对日光温室冬季作物冠层区热环境的影响

传统日光温室被动的热环境调控模式难以满足温室作物冠层区空气温度和速度调控需求,且能源利用效率低下。为了改进日光温室热环境精准调控方法和提高温室能源利用效率,该研究结合日光温室围护结构特点,提出了一种日光温室拱架的送/回风方法,并基于温室作物多孔介质模型,建立了拱架送/回风系统温室的数值传热模型。采用空气温度与速度不均匀系数、气流速度适宜区面积比、能量利用系数以及累计有效积温等评价参数,研究了下送上回、中间回风和上送下回等3种温室拱架送/回风方式对日光温室冬季作物冠层区热环境的影响。结果表明,与中间回风和上送下回通风方式相比,下送上回通风方式对不同作物冠层高度处的空气温度和速度调控的结果最优,且不同作物冠层高度处气流速度适宜区面积比和累计有效积温都最大。当采用下送上回通风方式时,与送风干管风速为9、11和12 m/s相比,送风干管风速为10 m/s的能量利用系数最大,在作物冠层高度0.4、0.6、0.8和1.0 m处的能量利用系数分别为0.976、0.982、0.985和0.987,并且不同作物冠层高度处的空气温度不均匀系数和速度不均匀系数也都最小。因此,下送上回通风方式的推荐送风干管风速为10 m/s。该研究可为日光温室热环境的精准调控提供参考。

不同小麦品种光能利用特征及产量构成差异

光能利用不仅是小麦品种资源的重要反应参数,而且是其干物质生产及籽粒产量形成的生理基础。以近10年河南地区栽培的10个主要冬播小麦品种为实验材料,探讨了各品种小麦的光能利用特征及产量效应。结果表明:天麦535和伟隆169的净光合速率P_(n)与蒸腾速率T_(r)较高,显著高于圣源608、周麦28、百麦5811、百农889、郑麦22的P_(n)与T_(r);光能利用各指标对产量的贡献依次为P_(n)>T_(r)>胞间CO_(2)浓度C_(i)>叶面积指数LAI>气孔导度G s>冠层开度DIFN>叶倾角MTA,表明P_(n)对小麦籽粒产量的直接效应最大,其次为T_(r);产量排序为天麦535>滑育麦1号>伟隆169>百麦5811>百农889>郑麦132>郑麦22>百农201>周麦28>圣源608,其中天麦535的产量、P_(n)和T_(r)最高。笔者比较了不同冬小麦品种在抽穗期的光能利用相关指标及籽粒产量,旨在探究该地区不同小麦品种的光合机制,为高光效小麦品种天麦535在该地区的推广提供理论参考。