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

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The Crop Journal 2024年第2期12卷 594-604页

作者： Cuicun Wang Ke Zhang Qing Liu Xiufeng Zhang Zhikuan Shi Xue Wang Caili Guo Qiang cao Yongchao Tian Yan Zhu Xiaojun Liu weixing cao National Engineering and Technology Center for Information Agriculture Nanjing Agricultural UniversityNanjing 210095JiangsuChina MOE Engineering Research Center of Smart Agricultural Nanjing Agricultural UniversityNanjing 210095JiangsuChina MARA Key Laboratory for Crop System Analysis and Decision Making Nanjing Agricultural UniversityNanjing 210095JiangsuChina Jiangsu Key Laboratory for Information Agriculture Nanjing Agricultural UniversityNanjing 210095JiangsuChina Institute of Smart Agriculture Nanjing Agricultural UniversityNanjing 210095JiangsuChina

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.

关键词： Chlorophyll density Densification method Nitrogen Photosynthetically active radiation interception Wheat

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

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The Crop Journal 2024年第3期12卷 927-937页

作者： Haiyu Tao Ruiheng Zhou Yining Tang Wanyu Li Xia Yao Tao Cheng Yan Zhu weixing cao Yongchao Tian National Engineering and Technology Center for Information Agriculture Key Laboratory of Crop System Analysis and Decision MakingMinistry of Agriculture and Rural AffairsJiangsu Key Laboratory for Information AgricultureNanjing Agricultural UniversityNanjing 210095JiangsuChina

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.

关键词： Wheat spike photosynthesis Yield-related phenotypic trait Spectral indices Machine learning Estimation

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

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Plant Phenomics 2023年第3期5卷 612-625页

作者： Bo Yao Xiaolong Wang Yancheng Wang Tianyang Ye Enli Wang Qiang cao Xia Yao Yan Zhu weixing cao Xiaojun Liu Liang Tang National Engineering and Technology Center for Information Agriculture Engineering Research Center for Smart AgricultureMinistry of EducationKey Laboratory for Crop System Analysis and Decision MakingMinistry of Agriculture and Rural AffairsJiangsu Key Laboratory for Information AgricultureJiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjing 210095JiangsuPR China CSIRO Agriculture and Food Cluniess Ross StreetBlack MountainACTAustralia

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.

关键词： Wheat diagnosis starting

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

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Plant Phenomics 2023年第3期5卷 642-656页

作者： Yapeng Wu Wenhui Wang Yangyang Gu Hengbiao Zheng Xia Yao Yan Zhu weixing cao Tao Cheng National Engineering and Technology Center for Information Agriculture(NETCIA) MARA Key Laboratory for Crop System Analysis and Decision MakingMOE Engineering Research Center of Smart AgricultureJiangsu Key Laboratory for Information AgricultureNanjing Agricultural UniversityOne WeigangNanjingJiangsu 210095PR China Langfang Normal University 100 Aimin West RoadLangfangHebei 065000PR China

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.

关键词： heading Wheat indices

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

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Pedosphere 2022年第3期32卷 463-474页

作者： Ke ZHANG Jifeng MA Yu WANG weixing cao Yan ZHU Qiang cao Xiaojun LIU Yongchao TIAN National Engineering and Technology Center for Information Agriculture Nanjing Agricultural UniversityNanjing 210095(China) Engineering Research Center of Smart Agriculture Ministry of EducationNanjing Agricultural UniversityNanjing 210095(China) Key Laboratory for Crop System Analysis and Decision Making Ministry of Agriculture and Rural AffairsNanjing Agricultural UniversityNanjing 210095(China) Jiangsu Key Laboratory for Information Agriculture Nanjing Agricultural UniversityNanjing 210095(China) Jiangsu Collaborative Innovation Center for Modern Crop Production Nanjing Agricultural UniversityNanjing 210095(China)

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.

关键词： agronomic parameter grain yield leaf area index nitrogen nutrition index nitrogen status nitrogen use efficiency

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

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Plant Phenomics 2023年第4期5卷 779-789页

作者： Jianqing Zhao Yucheng Cai Suwan Wang Jiawei Yan Xiaolei Qiu Xia Yao Yongchao Tian Yan Zhu weixing cao Xiaohu Zhang National Engineering and Technology Center for Information Agriculture Nanjing Agricultural UniversityNanjing 210095China Key Laboratory for Crop System Analysis and Decision Making Ministry of Agriculture and Rural AffairsNanjing 210095China Jiangsu Key Laboratory for Information Agriculture Nanjing 210095China Jiangsu Collaborative Innovation Center for Modern Crop Production Nanjing 210095China

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 *** 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.

关键词： Wheat maturity wheat

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

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International Journal of Agricultural and Biological Engineering 2018年第4期11卷 207-216页

作者： Zhenran Gao Jun Ni Yan Zhu Qi Jiang weixing cao National Engineering and Technology Center for Information Agriculture Jiangsu Key Laboratory for Information AgricultureJiangsu Collaborative Innovation Center for Modern Crop ProductionJiangsu Collaborative Innovation Center for the Technology and Application of Internet of ThingsNanjing Agricultural UniversityNanjing 210095China

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.

关键词： paddy field profile soil moisture perception clustering algorithm path analysis

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

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The Crop Journal 2021年第4期9卷 889-900页

作者： Raheel Osman Yan Zhu weixing cao Zhifeng Ding Meng Wang Leilei Liu Liang Tang Bing Liu National Engineering and Technology Center for Information Agriculture/Engineering Research Center of Smart Agriculture Ministry of Education/Key Laboratory for Crop System Analysis and Decision MakingMinistry of Agriculture and Rural Affairs/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjing 210095JiangsuChina

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.

关键词： Heat stress Total aboveground N Grain N accumulation Grain protein concentration Model improvement WheatGrow model

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

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The Crop Journal 2020年第2期8卷 194-212页

作者： Fengxian Zhen Junjie Zhou Aqib Mahmood Wei Wang Xini Chang Bing Liu Leilei Liu weixing cao Yan Zhu Liang Tang National Engineering and Technology Center for Information Agriculture Key Laboratory for Crop System Analysis and Decision MakingMinistry of Agriculture and Rural Affairs of the People’s Republic of ChinaJiangsu Key Laboratory for Information AgricultureJiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjing 210095JiangsuChina

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.

关键词： Rice(Oryza sativa L.) Heat stress Yield components Nonstructural carbohydrates Translocation

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秸秆还田与氮肥施用对稻田温室气体排放的影响

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生态学报 2024年第12期44卷 5328-5339页

作者：吴健成刘卿汪翠存陆汝华刘艳曹强田永超朱艳曹卫星刘小军南京农业大学国家信息农业工程技术中心南京210095 智慧农业教育部工程研究中心南京210095 农业农村部农作物系统分析与决策重点实验室南京210095 江苏省信息农业重点实验室南京210095 现代作物生产省部共建协同创新中心南京210095

秸秆还田与氮肥施用是农田生态系统中碳氮元素的两大主要补给途径,其在调控稻田甲烷(CH_(4))和氧化亚氮(N_(2)O)排放以及水稻产量方面具有重要作用。以往的研究主要关注秸秆还田或氮肥施用单因素对稻田温室气体排放的影响,而双因素互作... 详细信息

秸秆还田与氮肥施用是农田生态系统中碳氮元素的两大主要补给途径,其在调控稻田甲烷(CH_(4))和氧化亚氮(N_(2)O)排放以及水稻产量方面具有重要作用。以往的研究主要关注秸秆还田或氮肥施用单因素对稻田温室气体排放的影响,而双因素互作对甲烷和氧化亚氮排放的影响尚未明确。同时,在秸秆还田条件下如何进行合理的氮肥施用鲜有深入研究。本研究基于3个氮肥处理(0、180、360 kg N/hm^(2))和3个秸秆还田处理(0、2.25、3.75 t/hm^(2))进行多年水稻田间定位试验,研究结果表明:CH_(4)季节累积排放随秸秆还田量增加而增加,与施氮量无显著正相关关系;N_(2)O季节累积排放随施氮量增加而增加,与秸秆还田量无显著正相关关系;秸秆还田对于产量的影响具有不确定性,两年均在秸秆不还田+不施氮处理(S0N0)出现最低产量,2021与2022年最低产量分别为5740.64和4903.75 kg/hm^(2)。2021与2022年最高产量分别在秸秆不还田+高氮(S0N_(2))和高量秸秆还田+高氮(S2N_(2))出现,分别为10938.48和10384.83 kg/hm^(2)。同时,本研究发现在低量秸秆还田条件下,在碳足迹(CF, Carbon Footprint)方面,施氮量为251 kg N/hm^(2)时碳足迹达到最低点,为1.01 kg C/kg;而在生态经济净收益(NEEB, Net Ecosystem Economic Benefits)方面,施氮量为294 kg N/hm^(2)时生态经济净收益达到最高点,为11778.15元/hm^(2)。为协同生态经济净收益与碳排放,在低量秸秆还田(S1)下,配合251—294 kg N/hm^(2)的施氮量为最优施肥方案。研究结果为指导稻田温室气体减排、实现稻田碳中和以及农田管理提供了理论支撑,为实现水稻的高产稳产与低碳生产科学依据。

关键词：水稻温室气体秸秆还田氮肥推荐施肥

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