Effects of dense planting patterns on photosynthetic traits of different vertical layers and yield of wheat under different nitrogen rates

A two-year field experiment was conducted to measure the effects of densification methods on photosynthesis and yield of densely planted wheat.Inter-plant and inter-row distances were used to define ratefixed pattern(RR)and row-fixed pattern(RS)density treatments.Meanwhile,four nitrogen(N)rates(0,144,192,and 240 kg N ha-1,termed N0,N144,N192,and N240)were applied with three densities(225,292.5,and 360×10^(4)plants ha^(-1),termed D225,D292.5,and D360).The wheat canopy was clipped into three equal vertical layers(top,middle,and bottom layers),and their chlorophyll density(Ch D)and photosynthetically active radiation interception(FIPAR)were measured.Results showed that the response of Ch D and FIPAR to N rate,density,and pattern varied with different layers.N rate,density,and pattern had significant interaction effects on Ch D.The maximum values of whole-canopy Ch D in the two seasons appeared in N240 combined with D292.5 and D360 under RR,respectively.Across two growing seasons,FIPAR values of RR were higher than those of RS by 29.37%for the top layer and 5.68%for the middle layer,while lower than those of RS by 20.62%for the bottom layer on average.With a low N supply(N0),grain yield was not significantly affected by density for both patterns.At N240,increasing density significantly increased yield under RR,but D360 of RS significantly decreased yield by 3.72%and 9.00%versus D225 in two seasons,respectively.With an appropriate and sufficient N application,RR increased the yield of densely planted wheat more than RS.Additionally,the maximum yield in two seasons appeared in the combination of D360 with N144 or N192 rather than of D225 with N240 under both patterns,suggesting that dense planting combined with an appropriate N-reduction application is feasible to increase photosynthesis capacity and yield.

Estimating wheat spike-leaf composite indicator(SLI)dynamics by coupling spectral indices and machine learning

The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis.Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index.ETVI3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike emergence.Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,respectively.These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY.This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

Quantifying the effects of short-term heat stress at booting stage on nonstructural carbohydrates remobilization in rice

Extreme heat stress events are becoming more frequent under anticipated climate change,which can have devastating impacts on rice growth and yield.To quantify the effects of short-term heat stress at booting stage on nonstructural carbohydrates(NSC)remobilization in rice,two varieties(Nanjing 41 and Wuyunjing 24)were subjected to 32/22/27°C(maximum/minimum/mean),36/26/31°C,40/30/35°C,and 44/34/39°C for 2,4 and 6 days in phytotrons at booting stage during 2014 and 2015.Yield and yield components,dry matter partitioning index(DMPI),NSC accumulation and translocation were measured and calculated.The results showed that the increase of high-temperature level and duration significantly reduced grain yield by suppressing spikelet number per panicle,seed-setting rate,and grain weight.Heat stress at booting decreased DMPI in panicles,increased DMPI in stems,but had no significant effect on photosynthetic rate.Stem NSC concentration increased whereas panicles NSC concentration,stem NSC translocation efficiency,and contribution of stem NSC to grain yield decreased.Severe heat stress even transformed the stem into a carbohydrate sink during grain filling.The heat-tolerant Wuyunjing 24 showed a higher NSC transport capacity under heat stress than the heat-sensitive Nanjing 41.Heat degree-days(HDD),which combines the effects of the intensity and duration of heat stress,used for quantifying the impacts of heat stress indicates the threshold HDD for the termination of NSC translocation is 9.82°C day.Grain yield was negatively correlated with stem NSC concentration and accumulation at maturity,and yield reduction was tightly related to NSC translocation reduction.The results suggest that heat stress at booting inhibits NSC translocation due to sink size reduction.Therefore,genotypes with higher NSC transport capacity under heat stress could be beneficial for rice yield formation.

Modeling the effects of extreme high-temperature stress at anthesis and grain filling on grain protein in winter wheat

Extreme high-temperature stress(HTS) associated with climate change poses potential threats to wheat grain yield and quality. Wheat grain protein concentration(GPC) is a determinant of wheat quality for human nutrition and is often neglected in attempts to assess climate change impacts on wheat production. Crop models are useful tools for quantification of temperature impacts on grain yield and quality.Current crop models either cannot simulate or can simulate only partially the effects of HTS on crop N dynamics and grain N accumulation. There is a paucity of observational data on crop N and grain quality collected under systematic HTS scenarios to develop algorithms for model improvement as well as evaluate crop models. Two-year phytotron experiments were conducted with two wheat cultivars under HTS at anthesis, grain filling, and both stages. HTS significantly reduced total aboveground N and increased the rate of grain N accumulation, while total aboveground N and the rate of grain N accumulation were more sensitive to HTS at anthesis than at grain filling. The observed relationships between total aboveground N, rate of grain N accumulation, and HTS were quantified and incorporated into the WheatGrow model. The new HTS routines improved simulation of the dynamics of total aboveground N, grain N accumulation, and GPC by the model. The improved model provided better estimates of total aboveground N, grain N accumulation, and GPC under HTS(the normalized root mean square error was reduced by 40%, 85%, and 80%, respectively) than the original WheatGrow model. The improvements in the model enhance its applicability to the assessment of climate change effects on wheat grain quality by reducing the uncertainties of simulating N dynamics and grain quality under HTS.

温度和日照长度对小麦和大麦现叶和叶片衰亡的影响

为了模拟作物地上部分生长,了解环境条件对叶片形成的影响十分必要。本研究的目的是测定控制温度和日照长度对四个冬小麦和四个春大麦品种叶片出现速率和衰亡的影响。利用不同温度处理(7.5-25℃)在生长室内进行了9个试验;在15℃恒温下采用日长8-24h,进行了10个试验;还做了三种温度(10℃,15℃,20℃)和四种日长(6h,10h,14h和18h)的全组合复因子试验。从出苗到第四叶片定型,逐日记载每个茎秆的叶片数。在一定温度或日长下,所有基因型品种的新叶出现与时间呈直线关系,其决定系数R2≥0.94-0.95。然而表示现叶速率的直线回归斜率在不同基因型品种间、各基因型品种间不同温度处理、不同日长间以及不同温度与日短的组合间均有差异。所有品种的叶片出现速率随温度增加呈抛物线型增加,直到最适温度以后开始下降。这种关系可以用二次多项式表示,且所有品种的R2≥0.96。随温度增加,叶片衰亡(度-日/叶)呈指数型增加,所有品种在叶衰亡与温度之间指数方程的R2≥0.97。随日长增加,叶片出现速率增加,叶片衰亡值减小,两者的变化均呈曲线型,所有品种拟合曲线的R2≥0.98。大麦叶片衰亡对日长的反应比小麦敏感。在温度和日长组合的两因子试验中,所有品种的叶衰亡值与热/光比[即每日的积温(度-日)与日长的比值]呈直线相关,R2≥0.94。这些结果表明,在模拟小麦和大麦的叶片衰亡时必须考虑温度和日长的影响以及两者间的互作。

SPSI:A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery

Rapid and accurate estimation of panicle number per unit ground area(PNPA)in winter wheat before heading is crucial to evaluate yield potential and regulate crop growth for increasing the final yield.The accuracies of existing methods were low for estimating PNPA with remotely sensed data acquired before heading since the spectral saturation and background effects were ignored.This study proposed a spectral-textural PNPA sensitive index(SPSI)from unmanned aerial vehicle(UAV)multispectral imagery for reducing the spectral saturation and improving PNPA estimation in winter wheat before heading.The effect of background materials on PNPA estimated by textural indices(TIs)was examined,and the composite index SPSI was constructed by integrating the optimal spectral index(SI)and TI.Subsequently,the performance of SPSI was evaluated in comparison with other indices(SI and TIs).The results demonstrated that green-pixel TIs yielded better performances than all-pixel TIs apart from TI_([HOM]),TI_([ENT]),and TI_([SEM])among all indices from 8 types of textural features.SPSI,which was calculated by the formula DATT_([850,730,675])+NDTICOR_([850,730]),exhibited the highest overall accuracies for any date in any dataset in comparison with DATT_([850,730,675]),TINDRE_([MEA]),and NDTICOR_([850,730]).For the unified models assembling 2 experimental datasets,the RV^(2) values of SPSI increased by 0.11 to 0.23,and both RMSE and RRMSE decreased by 16.43%to 38.79%as compared to the suboptimal index on each date.These findings indicated that the SPSI is valuable in reducing the spectral saturation and has great potential to better estimate PNPA using high-resolution satellite imagery.

Interaction of Genotype,Environment,and Management on Organ-Specific Critical Nitrogen Dilution Curve in Wheat

The organ-specific critical nitrogen(N_(c))dilution curves are widely thought to represent a new approach for crop nitrogen(N)nutrition diagnosis,N management,and crop modeling.The N_(c) dilution curve can be described by a power function(N_(c)=A_(1)·W^(−A2)),while parameters A_(1) and A_(2) control the starting point and slope.This study aimed to investigate the uncertainty and drivers of organ-specific curves under different conditions.By using hierarchical Bayesian theory,parameters A_(1) and A_(2) of the organ-specific N_(c) dilution curves for wheat were derived and evaluated under 14 different genotype×environment×management(G×E×M)N fertilizer experiments.Our results show that parameters A_(1) and A_(2) are highly correlated.Although the variation of parameter A_(1) was less than that of A_(2),the values of both parameters can change significantly in response to G×E×M.Nitrogen nutrition index(NNI)calculated using organ-specific N_(c) is in general consistent with NNI estimated with overall shoot N_(c),indicating that a simple organ-specific N_(c) dilution curve may be used for wheat N diagnosis to assist N management.However,the significant differences in organ-specific N_(c) dilution curves across G×E×M conditions imply potential errors in N_(c) and crop N demand estimated using a general N_(c) dilution curve in crop models,highlighting a clear need for improvement in N_(c) calculations in such models.Our results provide new insights into how to improve modeling of crop nitrogen–biomass relations and N management practices under G×E×M.

Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming.Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes.However,wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background.Existing models for wheat spike detection are typically designed for a specific growth stage.Their adaptability to other growth stages or field scenes is limited.Such models cannot detect wheat spikes accurately caused by the difference in color,size,and morphological features between growth stages.This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage.WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy.Moreover,a Detection Network is designed to improve wheat spike detection capability.A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery.A new micro-scale detection layer is added to the network to extract the features of small spikes.Localization loss is improved by Complete Intersection over Union to reduce the impact of the background.The results show that WheatNet can achieve greater accuracy than classical detection methods.The detection accuracy with average precision of spike detection at the filling stage is 90.1%,while it is 88.6%at the maturity stage.It suggests that WheatNet is a promising tool for detection of wheat spikes.

Water-efficient sensing method for soil profiling in the paddy field

The comprehensive and reliable perception of moisture in a soil profile is essential to irrigation.To establish an efficient method for sensing soil moisture,field trials were conducted to analyze the spatial and temporal variations of moisture in a paddy soil profile by using coefficients of variation.The results showed that soil layers at shallow depths undergo more extensive changes in the coefficients of variation.Moisture perception is most sensitive within a depth range of 0-60 cm in the vertical soil profile of a paddy field.By using the clustering algorithm of Euclidean distance,the paddy soil profile was divided into three categories based on soil depth.The first category includes depths ranging from 10-20 cm,the second is from 30-40 cm,and the third is from 50-100 cm.Path analysis indicated that the most sensitive depths for moisture sensing in a paddy soil profile were 20 cm,30 cm,and 50 cm,whereas the most sensitive depths for moisture sensing by time stability analysis were 20 cm,40 cm,and 60 cm.Based on the multiple regression of sensitive depths,the results of quantitative inversion indicated that the time stability analysis results were 0.962 when the(y,y)Coryy of path analysis was 0.980,and the time stability analysis was 0.61 when the root mean square error(RMSE)of path analysis was 0.40.The relative error range between the measured and predicted values of path analysis was less than that of time stability analysis.These findings suggest that it is feasible to effectively sense the moisture of the entire vertical soil profile based on the sensitive depth.The present study has also determined that path analysis is superior to time stability analysis.

Key variable for simulating critical nitrogen dilution curve of wheat:Leaf area ratio-driven approach

Nitrogen(N)dilution curves,a pivotal tool for N nutrition diagnosis,have been developed using different winter wheat(Triticum aestivum L.)tissues.However,few studies have attempted to establish critical nitrogen(N_(c))dilution curves based on the leaf area ratio(LAR)to improve the monitoring accuracy of N status.In this study,three field experiments using eight N treatments and four wheat varieties were conducted in Jiangsu Province of China from 2013 to 2016.The empirical relationship of LAR with shoot biomass(expressed as dry matter)was developed under different N conditions.The results showed that LAR was a reliable index,which reduced the effects of wheat varieties and years compared with the traditional indicators.The N nutrition index(NNI)based on the LAR approach(NNI-LAR)produced equivalent results to that based on shoot biomass.Moreover,the NNI-LAR better predicted accumulated N deficit and best estimated the relative yield compared with the other two indicator-based NNI models.Therefore,the LAR-based approach improved the prediction accuracy of N_(c),accumulated N deficit,and relative yield,and it would be an optimal choice to conveniently diagnose the N status of winter wheat under field conditions.