Recently,information acquired at the canopy top,such as spectral and textural data,has been widely used to estimate plant nitrogen(N)accumulation(PNA).The response of crops to N uptake involves not only changes in hor...Recently,information acquired at the canopy top,such as spectral and textural data,has been widely used to estimate plant nitrogen(N)accumulation(PNA).The response of crops to N uptake involves not only changes in horizontal canopy top information but also an increase in vertical plant height(PH).It remains unclear whether the fusion of spectral indices with PH can improve the estimation performance of PNA models based on spectral remote sensing across different growth stages.展开更多
The distributions of light and nitrogen within a plant's canopy reflect the growth adaptation of crops to the environment and are conducive to improving the carbon assimilation ability.So can the yield in crop pro...The distributions of light and nitrogen within a plant's canopy reflect the growth adaptation of crops to the environment and are conducive to improving the carbon assimilation ability.So can the yield in crop production be maximized by improving the light and nitrogen distributions without adding any additional inputs?In this study,the effects of different nitrogen application rates and planting densities on the canopy light and nitrogen distributions of two highyielding maize cultivars(XY335 and DH618)and the regulatory effects of canopy physiological characteristics on radiation use efficiency(RUE)and yield were studied based on high-yield field experiments in Qitai,Xinjiang Uygur Autonomous Region,China,during 2019 and 2020.The results showed that the distribution of photosynthetically active photon flux density(PPFD)in the maize canopy decreased from top to bottom,while the vertical distribution of specific leaf nitrogen(SLN)initially increased and then decreased from top to bottom in the canopy.When SLN began to decrease,the PPDF values of XY335 and DH618 were 0.5 and 0.3,respectively,corresponding to 40.6 and49.3%of the total leaf area index(LAI).Nitrogen extinction coefficient(K_(N))/light extinction coefficient(K_(L))ratio in the middle and lower canopy of XY335(0.32)was 0.08 higher than that of DH618(0.24).The yield and RUE of XY335(17.2 t ha^(-1)and 1.8g MJ^(-1))were 7.0%(1.1 t ha^(-1))and 13.7%(0.2 g MJ^(-1))higher than those of DH618(16.1 t ha^(-1)and 1.6 g MJ^(-1)).Therefore,better light conditions(where the proportion of LAI in the upper and middle canopy was small)improved the light distribution when SLN started to decline,thus helping to mobilize the nitrogen distribution and maintain a high K_(N)and K_(N)/K_(L)ratio.In addition,K_(N)/K_(L)was a key parameter for yield improvement when the maize nutrient requirements were met at 360 kg N ha^(-1).At this level,an appropriately optimized high planting density could promote nitrogen utilization and produce higher yields and greater efficiency.The results of this study will be important for achieving high maize yields and the high efficiency cultivation and breeding of maize in the future.展开更多
Dense cropping increases crop yield but intensifies resource competition,which reduces single plant yield and limits potential yield growth.Optimizing canopy spacing could enhance resource utilization,support crop mor...Dense cropping increases crop yield but intensifies resource competition,which reduces single plant yield and limits potential yield growth.Optimizing canopy spacing could enhance resource utilization,support crop morphological development and increase yield.Here,a three-year study was performed to verify the feasibility of adjusting row spacing to further enhance yield in densely planted soybeans.Of three row-spacing configurations(40-40,20-40,and 20-60 cm)and two planting densities(normal 180,000 plants ha 1 and high 270,000 plants ha 1).The differences in canopy structure,plant morphological development,photosynthetic capacity and their impact on yield were analyzed.Row spacing configurations have a significant effect on canopy transmittance(CT).The 20-60 cm row spacing configuration increased CT and creates a favorable canopy light environment,in which plant height is reduced,while branching is promoted.This approach reduces plant competition,optimizes the developments of leaf area per plant,specific leaf area,leaf area development rate,leaf area duration and photosynthetic physiological indices(F_(v)/F_(m),ETR,P_(n)).The significant increase of 11.9%-34.2%in canopy apparent photosynthesis(CAP)is attributed to the significant optimization of plant growth and photosynthetic physiology through CT,an important contributing factor to yield increases.The yield in the 20-60 cm treatment is 4.0%higher than in equidistant planting under normal planting density,but 5.9%under high density,primarily driven by CAP and pod number.These findings suggest that suitable row spacing configurations optimize the light environment for plants,promote source-sink transformation in soybeans,and further improve yield.In practice,a 20-60 cm row spacing configuration could be employed for high-density soybean planting to achieve a more substantial yield gain.展开更多
Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planti...Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.展开更多
Intercropping has been widely used in arid and semi-arid regions because of its high yield,stable productivity,and efficient utilization of resources.However,in recent years,the high yield of traditional intercropping...Intercropping has been widely used in arid and semi-arid regions because of its high yield,stable productivity,and efficient utilization of resources.However,in recent years,the high yield of traditional intercropping is mainly attributed to the large amount of purchased resources such as water and fertilizer,plastic film,and mechanical power.These lead to a decline in cultivated land quality and exacerbate intercrops'premature root and canopy senescence.So,the application of traditional intercropping faces major challenges in crop production.This paper analyzes the manifestations,occurrence mechanisms,and agronomic regulatory pathways of crop senescence.The physiological and ecological characteristics of intercropping to delay root and canopy senescence of crops are reviewed in this paper.The main agronomic regulatory pathways of intercropping to delay root and canopy senescence of crops are based on above-and blow-ground interactions,including collocation of crop varieties,spatial arrangement,water and fertilizer management,and tillage and mulch practices.Future research fields of intercropping to delay root and canopy senescence should focus on the aspects of selecting and breeding special varieties,application of molecular biology techniques,and developing or applying models to predict and evaluate the root and canopy senescence process of intercrops.Comprehensive analysis and evaluation of different research results could provide a basis for enhancing intercropping delay root and canopy senescence through adopting innovative technologies for regulating the physio-ecological characteristics of intercrops.This would support developing and adopting high-yield,efficient,and sustainable intercropping systems in arid and semi-arid areas with high population density,limited land,and abundant light and heat resources.展开更多
[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the cha...[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.展开更多
Temperate woodland vegetation is initially determined by spatiotemporal and historical factors,mediated by complex biotic interactions.However,catastrophic events such as disease outbreaks(e.g.,sweet chestnut blight,a...Temperate woodland vegetation is initially determined by spatiotemporal and historical factors,mediated by complex biotic interactions.However,catastrophic events such as disease outbreaks(e.g.,sweet chestnut blight,ash dieback),infestations of insect pests,and human-accelerated climate change can create canopy gaps due to systematic decline in,or loss of,tree species that was once an important part of the canopy.Resultant cascade effects have the potential to alter the composition of woodland ecosystems quickly and radically,but inherent lag times make primary research into these effects challenging.Here,we explore change in woodland vegetation at 10 sites in response to canopy opening using the Elm Decline,a rapid loss of Ulmus in woodlands across northwestern Europe~5800 years ago that coexisted alongside other stressors such as increasing human activity,as a palaeoecological analogue.For arboreal taxa,community evenness significantly decreased,within-site turnover significantly increased,and richness remained unchanged.Changes in arboreal taxa were highly site-specific but there was a substantial decline in woody climbing taxa,especially Hedera(ivy),across the majority of sites.For shrub taxa,richness significantly increased but evenness and turnover remained consistent.Interestingly,however,there was a significant increase in abundance of shrubs at 70%of sites,including Calluna(heather),Ilex(holly)and Corylus(hazel),suggesting structural change.Surprisingly,there was no change in richness,evenness or turnover for herb taxa,possibly because change was highly variable spatially.However,there was a marked uptick in the disturbance indicator Plantago(plantain).Overall,these findings suggest that woodlands with sustained reduction in,or loss of,a tree species that once formed an important part of the canopy has profound,but often spatially idiosyncratic,impacts on vegetation diversity(richness),composition(evenness),stability(turnover),and on abundance of specific taxa,especially within the shrub layer.Use of this palaeoecological analogue,which was itself complicated by cooccurring changes in human activity,provides a valuable empirical insight into possible cascade effects of similar change in canopy opening in contemporary settings,including Ash Dieback.展开更多
Unmanned aerial vehicle light detection and ranging(UAV–LiDAR)is a new method for collecting understory terrain data.The high estimation accuracy of understory terrain is crucial for accurate tree height measurement ...Unmanned aerial vehicle light detection and ranging(UAV–LiDAR)is a new method for collecting understory terrain data.The high estimation accuracy of understory terrain is crucial for accurate tree height measurement and forest resource surveys.The UAV–LiDAR flight altitude and forest canopy cover significantly impact the accuracy of understory terrain estimation.However,since no research examined their combined effects,we aimed to investigate this relationship.This will help optimize UAV–LiDAR flight parameters for understory terrain estimation and forest surveys across various canopy cover.This study analyzed the impacts of three flight altitudes and three canopy cover on the estimation accuracy of understory terrain.The results showed that when canopy cover exceeded a specific value,UAV–LiDAR flight altitudes significantly affected understory terrain estimation.Given a forest canopy cover,the reduction in ground point coverage increased significantly as the flight altitude increased;given a flight altitude,the higher the canopy cover,the more significant the reduction in ground point coverage.In forests with a canopy cover≥0.9,there were substantial differences in the accuracies of understory digital elevation models(DEMs)generated using UAV–LiDAR at different flight altitudes.For forests with a canopy cover<0.9,the mean absolute error(MAE)of understory DEMs from UAV–LiDAR at different flight altitudes was≤0.17 m and the root mean square error(RMSE)was≤0.24 m.However,for forests with a canopy cover≥0.9,the UAV–LiDAR flight altitude significantly affected the accuracy of understory DEMs.At the same flight altitude,the MAE and RMSE of the estimated elevation for forests with a canopy cover≥0.9 were approximately twice those of the estimated elevation for forests with a canopy cover<0.9.In forests with low canopy cover,it is possible to improve data collection efficiency by selecting a higher flight altitude.However,UAV–LiDAR flight altitudes significantly affected understory terrain estimation in forests with high canopy cover,it is essential to adopt terrain-following flight modes,reduce flight altitudes,and maintain a consistent flight altitude during longterm monitoring in high canopy cover forests.展开更多
Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on t...Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season.A recent development in maize phenology detection research is to use canopy height(CH)data instead of spectral indices,but its robustness in multiple treatments and stages has not been confirmed.Meanwhile,because data of a complete growth season are needed,the need for timely in-season TD identification remains unmet.This study proposed an approach to timely identify and forecast the maize TD.We obtained RGB and light detection and ranging(Li DAR)data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments.After CH estimation,the feature points(inflection point)from the Logistic curve of the CH time series were extracted as TD.We examined the impact of various independent variables(day of year vs.accumulated growing degree days(AGDD)),sensors(RGB and Li DAR),time series denoise methods,different feature points,and temporal resolution on TD identification.Lastly,we used early CH time series data to predict height growth and further forecast TD.The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from Li DAR to estimate maize CH was the most stable across treatments and stages(R~2:0.928 to0.943).For TD identification,the best performance was achieved by using Li DAR data with AGDD as the independent variable,combined with the knee point method,resulting in RMSE of 2.95 d.The high accuracy was maintained at temporal resolutions as coarse as 14 d.TD forecast got more accurate as the CH time series extended.The optimal timing for forecasting TD was when the CH exceeded half of its maximum.Using only Li DAR CH data below 1.6 m and empirical growth rate estimates,the forecasted TD showed an RMSE of 3.90 d.In conclusion,this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.展开更多
High-density planting increases maize yield but also canopy crowding and stalk lodging.Aiming this contradiction,a wavy canopy was created using interlaced chemical application(IC)of a plant growth retardant at the V1...High-density planting increases maize yield but also canopy crowding and stalk lodging.Aiming this contradiction,a wavy canopy was created using interlaced chemical application(IC)of a plant growth retardant at the V14 stage with three densities(60,000,75,000,and 90,000 plants ha-1,indicated by D1,D2,and D3,respectively)for two seasons.The results showed that the IC-treated wavy canopy featuring both natural height(IC-H)and dwarfed(IC-L)plants,improved light transmission by 8.54%,8.49%,and 16.49%on average than the corresponding controls(CK)at D1,D2,and D3,respectively.The alleviation of canopy crowding stimulated leaf photosynthesis,sugar availability,basal-internode strength,and decreased plant lodging ratios in both IC-H and IC-L,particularly under higher densities.Meanwhile,the IC populations produced significantly higher yield than CK,with an average increase of 3.38%,16.70%,and 15.28%at D1,D2,and D3,respectively.Collectively,this study proposed a new wavy canopy strategy using plant growth retardant to simultaneously increase yield performance and lodging resistance,thus offering a sustainable solution for further development of high-density maize production.展开更多
雷达信号分选是电子战系统中的关键技术,是战场态势感知的重要环节,新体制雷达技术的快速发展给复杂电磁环境下信号分选带来了严峻挑战。针对传统K-means聚类算法在对雷达全脉冲数据进行信号分选时存在对聚类数K和初始点选择较为敏感的...雷达信号分选是电子战系统中的关键技术,是战场态势感知的重要环节,新体制雷达技术的快速发展给复杂电磁环境下信号分选带来了严峻挑战。针对传统K-means聚类算法在对雷达全脉冲数据进行信号分选时存在对聚类数K和初始点选择较为敏感的问题,提出了一种基于优化K-means的雷达信号分选算法。通过将水波中心扩散(water wave center diffusion,WWCD)优化算法和Canopy算法相结合,实现了Canopy算法距离阈值的优选,并为后续K-means聚类优化了K值的选择,有效降低了K-means算法对初始聚类数选择的敏感性。实验中,主要通过3个UCI公开数据集和3类频率跳变雷达脉冲数据进行聚类分选效果验证,并与常见的DBSCAN、OPTICS、Canopy-K-means等聚类算法进行了聚类效果对比。结果表明,所提方法有较高的聚类分选准确率,且对初始参数的设置不敏感。展开更多
Tree canopy landscapes are an important component of urban forests and have the potential to influence human emotions.However,their influence on emotional responses remains unclear.The aims of this study were:(1)to de...Tree canopy landscapes are an important component of urban forests and have the potential to influence human emotions.However,their influence on emotional responses remains unclear.The aims of this study were:(1)to determine whether the canopy landscape affects human emotions;(2)to clarify the influence of canopy landscape on individual emotional indicators;and(3)to identify the ratio of canopy landscape elements with the most beneficial effects on human emotions.Different canopy landscape images were generated,and the self-reported emotions and neural activity of the subjects assessed before and after they viewed the images.The results of the statistical analysis were intuitively displayed by a ternary phase diagram.We found that the canopy landscape affected human emotions and different proportions of canopy landscape elements led to significant differences in excitement,depression and confusion.Higher proportions of blue elements and lower proportions of green and other elements characterized the canopy landscape with the most beneficial effect on human emotions.These findings will promote further research on canopy landscapes,inform the planning and design of urban forests,and contribute to the field of landscape architecture.展开更多
Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen...Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen and carbon cycles in the atmosphere. This study aimed to assess maize CNC and CCC using canopy hyperspectral information and uninformative variable elimination(UVE). Vegetation indices(VIs) and wavelet functions were adopted for estimating CNC and CCC under varying water and nitrogen regimes. Linear, nonlinear, and partial least squares(PLS) regression models were fitted to VIs and wavelet functions to estimate CNC and CCC, and were evaluated for their prediction accuracy.UVE was used to eliminate uninformative variables, improve the prediction accuracy of the models, and simplify the PLS regression models(UVE-PLS). For estimating CNC and CCC, the normalized difference vegetation index(NDVI, based on red edge and NIR wavebands) yielded the highest correlation coefficients(r > 0.88). PLS regression models showed the lowest root mean square error(RMSE) among all models. However, PLS regression models required nine VIs and four wavelet functions, increasing their complexity. UVE was used to retain valid spectral parameters and optimize the PLS regression models.UVE-PLS regression models improved validation accuracy and resulted in more accurate CNC and CCC than the PLS regression models. Thus, canopy spectral reflectance integrated with UVE-PLS can accurately reflect maize leaf nitrogen and carbon status.展开更多
This paper compares the predictions by two radiative transfer models-the two-stream approximation model and the generalized layered model （developed by the authors） in land surface processes -for different canopies ...This paper compares the predictions by two radiative transfer models-the two-stream approximation model and the generalized layered model （developed by the authors） in land surface processes -for different canopies under direct or diffuse radiation conditions. The comparison indicates that there are significant differences between the two models, especially in the near infrared （NIR） band. Results of canopy reflectance from the two-stream model are larger than those from the generalized model. However, results of canopy absorptance from the two-stream model are larger in some cases and smaller in others compared to those from the generalized model, depending on the cases involved. In the visible （VIS） band, canopy reflectance is smaller and canopy absorptance larger from the two-stream model compared to the generalized model when the Leaf Area Index （LAI） is low and soil reflectance is high. In cases of canopies with vertical leaf angles, the differences of reflectance and absorptance in the VIS and NIR bands between the two models are especially large. Two commonly occurring cases, with which the two-stream model cannot deal accurately, are also investigated. One is for a canopy with different adaxial and abaxial leaf optical properties; and the other is for incident sky diffuse radiation with a non-uniform distribution. Comparison of the generalized model within the same canopy for both uniform and non-uniform incident diffuse radiation inputs shows smaller differences in general. However, there is a measurable difference between these radiation inputs for a canopy with high leaf angle. This indicates that the application of the two-stream model to a canopy with different adaxial and abaxial leaf optical properties will introduce non-negligible errors.展开更多
The increase of atmospheric CO 2 concentration is indisputable. In such condition, photosynthetic response of leaf is relatively well studied, while the comparison of that between single leaf and whole canopy is less...The increase of atmospheric CO 2 concentration is indisputable. In such condition, photosynthetic response of leaf is relatively well studied, while the comparison of that between single leaf and whole canopy is less emphasized. The stimulation of elevated CO 2 on canopy photosynthesis may be different from that on single leaf level. In this study, leaf and canopy photosynthesis of rice (Oryza sativa L.) were studied throughout the growing season. High CO 2 and temperature had a synergetic stimulation on single leaf photosynthetic rate until grain filling. Photosynthesis of leaf was stimulated by high CO 2, although the stimulation was decreased by higher temperature at grain filling stage. On the other hand, the simulation of elevated CO 2 on canopy photosynthesis leveled off with time. Stimulation at canopy level disappeared by grain filling stage in both temperature treatments. Green leaf area index was not significantly affected by CO 2 at maturity, but greater in plants grown at higher temperature. Leaf nitrogen content decreased with the increase of CO 2 concentration although it was not statistically significant at maturity. Canopy respiration rate increased at flowering stage indicating higher carbon loss. Shading effect caused by leaf development reached maximum at flowering stage. The CO 2 stimulation on photosynthesis was greater in single leaf than in canopy. Since enhanced CO 2 significantly increased biomass of rice stems and panicles, increase in canopy respiration caused diminishment of CO 2 stimulation in canopy net photosynthesis. Leaf nitrogen in the canopy level decreased with CO 2 concentration and may eventually hasten CO 2 stimulation on canopy photosynthesis. Early senescence of canopy leaves in high CO 2 is also a possible cause.展开更多
基金supported by the National Key Research and Development Plan Project Sub-Topic of China(Grant Nos.2022YFD1901500 and 2022YFD1901505-07)the National Natural Science Foundation of China(Grant No.32260531)+1 种基金the Key Laboratory of Molecular Breeding for Grain and Oil Crops in Guizhou Province,China(Grant No.Qiankehezhongyindi[2023]8)the Key Laboratory of Functional Agriculture of Guizhou Provincial Higher Education Institutions,China(Grant No.Qianjiaoji[2023]007).
文摘Recently,information acquired at the canopy top,such as spectral and textural data,has been widely used to estimate plant nitrogen(N)accumulation(PNA).The response of crops to N uptake involves not only changes in horizontal canopy top information but also an increase in vertical plant height(PH).It remains unclear whether the fusion of spectral indices with PH can improve the estimation performance of PNA models based on spectral remote sensing across different growth stages.
基金supported by the National Natural Science Foundation of China(32172118)the National Key Research and Development Program of China(2016YFD0300110 and 2016YFD0300101)+1 种基金the Basic Scientific Research Fund of Chinese Academy of Agricultural Sciences,China(S2022ZD05)the Agricultural Science and Technology Innovation Program,China(CAAS-ZDRW202004)。
文摘The distributions of light and nitrogen within a plant's canopy reflect the growth adaptation of crops to the environment and are conducive to improving the carbon assimilation ability.So can the yield in crop production be maximized by improving the light and nitrogen distributions without adding any additional inputs?In this study,the effects of different nitrogen application rates and planting densities on the canopy light and nitrogen distributions of two highyielding maize cultivars(XY335 and DH618)and the regulatory effects of canopy physiological characteristics on radiation use efficiency(RUE)and yield were studied based on high-yield field experiments in Qitai,Xinjiang Uygur Autonomous Region,China,during 2019 and 2020.The results showed that the distribution of photosynthetically active photon flux density(PPFD)in the maize canopy decreased from top to bottom,while the vertical distribution of specific leaf nitrogen(SLN)initially increased and then decreased from top to bottom in the canopy.When SLN began to decrease,the PPDF values of XY335 and DH618 were 0.5 and 0.3,respectively,corresponding to 40.6 and49.3%of the total leaf area index(LAI).Nitrogen extinction coefficient(K_(N))/light extinction coefficient(K_(L))ratio in the middle and lower canopy of XY335(0.32)was 0.08 higher than that of DH618(0.24).The yield and RUE of XY335(17.2 t ha^(-1)and 1.8g MJ^(-1))were 7.0%(1.1 t ha^(-1))and 13.7%(0.2 g MJ^(-1))higher than those of DH618(16.1 t ha^(-1)and 1.6 g MJ^(-1)).Therefore,better light conditions(where the proportion of LAI in the upper and middle canopy was small)improved the light distribution when SLN started to decline,thus helping to mobilize the nitrogen distribution and maintain a high K_(N)and K_(N)/K_(L)ratio.In addition,K_(N)/K_(L)was a key parameter for yield improvement when the maize nutrient requirements were met at 360 kg N ha^(-1).At this level,an appropriately optimized high planting density could promote nitrogen utilization and produce higher yields and greater efficiency.The results of this study will be important for achieving high maize yields and the high efficiency cultivation and breeding of maize in the future.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD0403305)National Natural Science Foundation of China(32101845)+1 种基金the National Key Research and Development Program of China(2023YFE0105000)the China Agriculture Research System(CARS-04).
文摘Dense cropping increases crop yield but intensifies resource competition,which reduces single plant yield and limits potential yield growth.Optimizing canopy spacing could enhance resource utilization,support crop morphological development and increase yield.Here,a three-year study was performed to verify the feasibility of adjusting row spacing to further enhance yield in densely planted soybeans.Of three row-spacing configurations(40-40,20-40,and 20-60 cm)and two planting densities(normal 180,000 plants ha 1 and high 270,000 plants ha 1).The differences in canopy structure,plant morphological development,photosynthetic capacity and their impact on yield were analyzed.Row spacing configurations have a significant effect on canopy transmittance(CT).The 20-60 cm row spacing configuration increased CT and creates a favorable canopy light environment,in which plant height is reduced,while branching is promoted.This approach reduces plant competition,optimizes the developments of leaf area per plant,specific leaf area,leaf area development rate,leaf area duration and photosynthetic physiological indices(F_(v)/F_(m),ETR,P_(n)).The significant increase of 11.9%-34.2%in canopy apparent photosynthesis(CAP)is attributed to the significant optimization of plant growth and photosynthetic physiology through CT,an important contributing factor to yield increases.The yield in the 20-60 cm treatment is 4.0%higher than in equidistant planting under normal planting density,but 5.9%under high density,primarily driven by CAP and pod number.These findings suggest that suitable row spacing configurations optimize the light environment for plants,promote source-sink transformation in soybeans,and further improve yield.In practice,a 20-60 cm row spacing configuration could be employed for high-density soybean planting to achieve a more substantial yield gain.
文摘Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.
基金supported by the National Natural Science Foundation of China(32101857 and U21A20218)the China Agricultural University Corresponding Support Research Joint Fund(GSAU-DKZY-2024-001)+1 种基金the Science and Technology Program in Gansu Province,China(24ZDNA008and23JRRA1407)the Fuxi Young Talents Fund of Gansu Agricultural University,China(Gaufx-03Y10).
文摘Intercropping has been widely used in arid and semi-arid regions because of its high yield,stable productivity,and efficient utilization of resources.However,in recent years,the high yield of traditional intercropping is mainly attributed to the large amount of purchased resources such as water and fertilizer,plastic film,and mechanical power.These lead to a decline in cultivated land quality and exacerbate intercrops'premature root and canopy senescence.So,the application of traditional intercropping faces major challenges in crop production.This paper analyzes the manifestations,occurrence mechanisms,and agronomic regulatory pathways of crop senescence.The physiological and ecological characteristics of intercropping to delay root and canopy senescence of crops are reviewed in this paper.The main agronomic regulatory pathways of intercropping to delay root and canopy senescence of crops are based on above-and blow-ground interactions,including collocation of crop varieties,spatial arrangement,water and fertilizer management,and tillage and mulch practices.Future research fields of intercropping to delay root and canopy senescence should focus on the aspects of selecting and breeding special varieties,application of molecular biology techniques,and developing or applying models to predict and evaluate the root and canopy senescence process of intercrops.Comprehensive analysis and evaluation of different research results could provide a basis for enhancing intercropping delay root and canopy senescence through adopting innovative technologies for regulating the physio-ecological characteristics of intercrops.This would support developing and adopting high-yield,efficient,and sustainable intercropping systems in arid and semi-arid areas with high population density,limited land,and abundant light and heat resources.
文摘[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.
文摘Temperate woodland vegetation is initially determined by spatiotemporal and historical factors,mediated by complex biotic interactions.However,catastrophic events such as disease outbreaks(e.g.,sweet chestnut blight,ash dieback),infestations of insect pests,and human-accelerated climate change can create canopy gaps due to systematic decline in,or loss of,tree species that was once an important part of the canopy.Resultant cascade effects have the potential to alter the composition of woodland ecosystems quickly and radically,but inherent lag times make primary research into these effects challenging.Here,we explore change in woodland vegetation at 10 sites in response to canopy opening using the Elm Decline,a rapid loss of Ulmus in woodlands across northwestern Europe~5800 years ago that coexisted alongside other stressors such as increasing human activity,as a palaeoecological analogue.For arboreal taxa,community evenness significantly decreased,within-site turnover significantly increased,and richness remained unchanged.Changes in arboreal taxa were highly site-specific but there was a substantial decline in woody climbing taxa,especially Hedera(ivy),across the majority of sites.For shrub taxa,richness significantly increased but evenness and turnover remained consistent.Interestingly,however,there was a significant increase in abundance of shrubs at 70%of sites,including Calluna(heather),Ilex(holly)and Corylus(hazel),suggesting structural change.Surprisingly,there was no change in richness,evenness or turnover for herb taxa,possibly because change was highly variable spatially.However,there was a marked uptick in the disturbance indicator Plantago(plantain).Overall,these findings suggest that woodlands with sustained reduction in,or loss of,a tree species that once formed an important part of the canopy has profound,but often spatially idiosyncratic,impacts on vegetation diversity(richness),composition(evenness),stability(turnover),and on abundance of specific taxa,especially within the shrub layer.Use of this palaeoecological analogue,which was itself complicated by cooccurring changes in human activity,provides a valuable empirical insight into possible cascade effects of similar change in canopy opening in contemporary settings,including Ash Dieback.
基金supported by the National Natural Science Foundation of China(No.32271876)the Research on Key Technologies of Intelligent Monitoring and Carbon Sink Metering of Forest Resources in Fujian Province(No.2022FKJ03)the Science and Technology Innovation Project of Fujian Agriculture and Forestry University(No.KFB23172A,KFB23173A).
文摘Unmanned aerial vehicle light detection and ranging(UAV–LiDAR)is a new method for collecting understory terrain data.The high estimation accuracy of understory terrain is crucial for accurate tree height measurement and forest resource surveys.The UAV–LiDAR flight altitude and forest canopy cover significantly impact the accuracy of understory terrain estimation.However,since no research examined their combined effects,we aimed to investigate this relationship.This will help optimize UAV–LiDAR flight parameters for understory terrain estimation and forest surveys across various canopy cover.This study analyzed the impacts of three flight altitudes and three canopy cover on the estimation accuracy of understory terrain.The results showed that when canopy cover exceeded a specific value,UAV–LiDAR flight altitudes significantly affected understory terrain estimation.Given a forest canopy cover,the reduction in ground point coverage increased significantly as the flight altitude increased;given a flight altitude,the higher the canopy cover,the more significant the reduction in ground point coverage.In forests with a canopy cover≥0.9,there were substantial differences in the accuracies of understory digital elevation models(DEMs)generated using UAV–LiDAR at different flight altitudes.For forests with a canopy cover<0.9,the mean absolute error(MAE)of understory DEMs from UAV–LiDAR at different flight altitudes was≤0.17 m and the root mean square error(RMSE)was≤0.24 m.However,for forests with a canopy cover≥0.9,the UAV–LiDAR flight altitude significantly affected the accuracy of understory DEMs.At the same flight altitude,the MAE and RMSE of the estimated elevation for forests with a canopy cover≥0.9 were approximately twice those of the estimated elevation for forests with a canopy cover<0.9.In forests with low canopy cover,it is possible to improve data collection efficiency by selecting a higher flight altitude.However,UAV–LiDAR flight altitudes significantly affected understory terrain estimation in forests with high canopy cover,it is essential to adopt terrain-following flight modes,reduce flight altitudes,and maintain a consistent flight altitude during longterm monitoring in high canopy cover forests.
基金supported by National Science and Technology Major Project(2022ZD0115701)Nanfan Special Project,CAAS(YBXM2305,YBXM2401,YBXM2402,PTXM2402)+1 种基金National Natural Science Foundation of China(42071426,42301427)the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences。
文摘Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season.A recent development in maize phenology detection research is to use canopy height(CH)data instead of spectral indices,but its robustness in multiple treatments and stages has not been confirmed.Meanwhile,because data of a complete growth season are needed,the need for timely in-season TD identification remains unmet.This study proposed an approach to timely identify and forecast the maize TD.We obtained RGB and light detection and ranging(Li DAR)data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments.After CH estimation,the feature points(inflection point)from the Logistic curve of the CH time series were extracted as TD.We examined the impact of various independent variables(day of year vs.accumulated growing degree days(AGDD)),sensors(RGB and Li DAR),time series denoise methods,different feature points,and temporal resolution on TD identification.Lastly,we used early CH time series data to predict height growth and further forecast TD.The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from Li DAR to estimate maize CH was the most stable across treatments and stages(R~2:0.928 to0.943).For TD identification,the best performance was achieved by using Li DAR data with AGDD as the independent variable,combined with the knee point method,resulting in RMSE of 2.95 d.The high accuracy was maintained at temporal resolutions as coarse as 14 d.TD forecast got more accurate as the CH time series extended.The optimal timing for forecasting TD was when the CH exceeded half of its maximum.Using only Li DAR CH data below 1.6 m and empirical growth rate estimates,the forecasted TD showed an RMSE of 3.90 d.In conclusion,this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.
基金supported by the National Key Research and Development Program of China(2023YFD2303302,2022YFD2300803)the National Natural Science Foundation of China(32160445)the China Agriculture Research System of MOF and MARA(CARS-02-16).
文摘High-density planting increases maize yield but also canopy crowding and stalk lodging.Aiming this contradiction,a wavy canopy was created using interlaced chemical application(IC)of a plant growth retardant at the V14 stage with three densities(60,000,75,000,and 90,000 plants ha-1,indicated by D1,D2,and D3,respectively)for two seasons.The results showed that the IC-treated wavy canopy featuring both natural height(IC-H)and dwarfed(IC-L)plants,improved light transmission by 8.54%,8.49%,and 16.49%on average than the corresponding controls(CK)at D1,D2,and D3,respectively.The alleviation of canopy crowding stimulated leaf photosynthesis,sugar availability,basal-internode strength,and decreased plant lodging ratios in both IC-H and IC-L,particularly under higher densities.Meanwhile,the IC populations produced significantly higher yield than CK,with an average increase of 3.38%,16.70%,and 15.28%at D1,D2,and D3,respectively.Collectively,this study proposed a new wavy canopy strategy using plant growth retardant to simultaneously increase yield performance and lodging resistance,thus offering a sustainable solution for further development of high-density maize production.
文摘雷达信号分选是电子战系统中的关键技术,是战场态势感知的重要环节,新体制雷达技术的快速发展给复杂电磁环境下信号分选带来了严峻挑战。针对传统K-means聚类算法在对雷达全脉冲数据进行信号分选时存在对聚类数K和初始点选择较为敏感的问题,提出了一种基于优化K-means的雷达信号分选算法。通过将水波中心扩散(water wave center diffusion,WWCD)优化算法和Canopy算法相结合,实现了Canopy算法距离阈值的优选,并为后续K-means聚类优化了K值的选择,有效降低了K-means算法对初始聚类数选择的敏感性。实验中,主要通过3个UCI公开数据集和3类频率跳变雷达脉冲数据进行聚类分选效果验证,并与常见的DBSCAN、OPTICS、Canopy-K-means等聚类算法进行了聚类效果对比。结果表明,所提方法有较高的聚类分选准确率,且对初始参数的设置不敏感。
基金co-funded by the Talent Initiation Program of the Scientific Research Development Fund of Zhejiang A&F University(2021LFR041and 2022LFR040)the National Natural Science Foundation of China(52278084).
文摘Tree canopy landscapes are an important component of urban forests and have the potential to influence human emotions.However,their influence on emotional responses remains unclear.The aims of this study were:(1)to determine whether the canopy landscape affects human emotions;(2)to clarify the influence of canopy landscape on individual emotional indicators;and(3)to identify the ratio of canopy landscape elements with the most beneficial effects on human emotions.Different canopy landscape images were generated,and the self-reported emotions and neural activity of the subjects assessed before and after they viewed the images.The results of the statistical analysis were intuitively displayed by a ternary phase diagram.We found that the canopy landscape affected human emotions and different proportions of canopy landscape elements led to significant differences in excitement,depression and confusion.Higher proportions of blue elements and lower proportions of green and other elements characterized the canopy landscape with the most beneficial effect on human emotions.These findings will promote further research on canopy landscapes,inform the planning and design of urban forests,and contribute to the field of landscape architecture.
基金supported by the National Key Research and Development Program of China (2016YFD0300602)China Agricultural Research System (CARS-04-PS19)Chengdu Science and Technology Project (2020-YF09-00033-SN)。
文摘Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen and carbon cycles in the atmosphere. This study aimed to assess maize CNC and CCC using canopy hyperspectral information and uninformative variable elimination(UVE). Vegetation indices(VIs) and wavelet functions were adopted for estimating CNC and CCC under varying water and nitrogen regimes. Linear, nonlinear, and partial least squares(PLS) regression models were fitted to VIs and wavelet functions to estimate CNC and CCC, and were evaluated for their prediction accuracy.UVE was used to eliminate uninformative variables, improve the prediction accuracy of the models, and simplify the PLS regression models(UVE-PLS). For estimating CNC and CCC, the normalized difference vegetation index(NDVI, based on red edge and NIR wavebands) yielded the highest correlation coefficients(r > 0.88). PLS regression models showed the lowest root mean square error(RMSE) among all models. However, PLS regression models required nine VIs and four wavelet functions, increasing their complexity. UVE was used to retain valid spectral parameters and optimize the PLS regression models.UVE-PLS regression models improved validation accuracy and resulted in more accurate CNC and CCC than the PLS regression models. Thus, canopy spectral reflectance integrated with UVE-PLS can accurately reflect maize leaf nitrogen and carbon status.
基金supported by the National Natural Science Foundation of China under Grant Nos.40233034,40605024,40575043,and 40305011.
文摘This paper compares the predictions by two radiative transfer models-the two-stream approximation model and the generalized layered model （developed by the authors） in land surface processes -for different canopies under direct or diffuse radiation conditions. The comparison indicates that there are significant differences between the two models, especially in the near infrared （NIR） band. Results of canopy reflectance from the two-stream model are larger than those from the generalized model. However, results of canopy absorptance from the two-stream model are larger in some cases and smaller in others compared to those from the generalized model, depending on the cases involved. In the visible （VIS） band, canopy reflectance is smaller and canopy absorptance larger from the two-stream model compared to the generalized model when the Leaf Area Index （LAI） is low and soil reflectance is high. In cases of canopies with vertical leaf angles, the differences of reflectance and absorptance in the VIS and NIR bands between the two models are especially large. Two commonly occurring cases, with which the two-stream model cannot deal accurately, are also investigated. One is for a canopy with different adaxial and abaxial leaf optical properties; and the other is for incident sky diffuse radiation with a non-uniform distribution. Comparison of the generalized model within the same canopy for both uniform and non-uniform incident diffuse radiation inputs shows smaller differences in general. However, there is a measurable difference between these radiation inputs for a canopy with high leaf angle. This indicates that the application of the two-stream model to a canopy with different adaxial and abaxial leaf optical properties will introduce non-negligible errors.
文摘The increase of atmospheric CO 2 concentration is indisputable. In such condition, photosynthetic response of leaf is relatively well studied, while the comparison of that between single leaf and whole canopy is less emphasized. The stimulation of elevated CO 2 on canopy photosynthesis may be different from that on single leaf level. In this study, leaf and canopy photosynthesis of rice (Oryza sativa L.) were studied throughout the growing season. High CO 2 and temperature had a synergetic stimulation on single leaf photosynthetic rate until grain filling. Photosynthesis of leaf was stimulated by high CO 2, although the stimulation was decreased by higher temperature at grain filling stage. On the other hand, the simulation of elevated CO 2 on canopy photosynthesis leveled off with time. Stimulation at canopy level disappeared by grain filling stage in both temperature treatments. Green leaf area index was not significantly affected by CO 2 at maturity, but greater in plants grown at higher temperature. Leaf nitrogen content decreased with the increase of CO 2 concentration although it was not statistically significant at maturity. Canopy respiration rate increased at flowering stage indicating higher carbon loss. Shading effect caused by leaf development reached maximum at flowering stage. The CO 2 stimulation on photosynthesis was greater in single leaf than in canopy. Since enhanced CO 2 significantly increased biomass of rice stems and panicles, increase in canopy respiration caused diminishment of CO 2 stimulation in canopy net photosynthesis. Leaf nitrogen in the canopy level decreased with CO 2 concentration and may eventually hasten CO 2 stimulation on canopy photosynthesis. Early senescence of canopy leaves in high CO 2 is also a possible cause.
