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winter wheat yield improvement by genetic gain across different provinces in China

Journal of Integrative Agriculture 2024年第2期23卷 468-483页

作者： Wei Chen Jingjuan Zhang Xiping Deng Key Laboratory of Disaster Monitoring and Mechanism Simulating of Shaanxi Province/School of Geography and Environment Baoji University of Arts and SciencesBaoji 721013China Centre for Crop and Food Innovation Murdoch University90 South StreetMurdochWA 6150Australia State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Northwest A&F UniversityYangling 712100China

The replacement of winter wheat varieties has contributed significantly to yield improvement worldwide,with remarkable progress in China.Drawing on two sets of data,production yield from the National Bureau of Statistics of China and experimental yield from literature,this study aims to(1)illustrate the increasing patterns of production yield among different provinces from 1978 to 2018 in China,(2)explore the genetic gain in yield and yield relevant traits through the variety replacement based on experimental yield from 1937 to 2016 in China,and(3)compare the yield gap between experimental yield and production yield.The results show that both the production and experimental yields significantly increased along with the variety replacement.The national annual yield increase ratio for the production yield was 1.67%from 1978 to 2018,varying from 0.96%in Sichuan Province to 2.78%in Hebei Province;such ratio for the experimental yield was 1.13%from 1937 to 2016.The yield gap between experimental and production yields decreased from the 1970s to the 2010s.This study reveals significant increases in some yield components consequent to variety replacement,including thousand-grain weight,kernel number per spike,and grain number per square meter;however,no change is shown in spike number per square meter.The biomass and harvest index consistently and significantly increased,whereas the plant height decreased significantly.

关键词： genetic gain winter wheat yield yield components

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winter wheat leaf area index inversion by the genetic algorithms neural network model based on SAR data

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International Journal of Digital Earth 2022年第1期15卷 362-380页

作者： Xiaoping Lu Xiaoxuan Wang Xiangjun Zhang Jun Wang Zenan Yang Key Laboratory of Spatio-Temporal Information and Ecological Restoration of Mines of Natural Resources of the People's Republic of China Henan Polytechnic UniversityJiaozuoPeople’s Republic of China Institute of Remote Sensing and Surveying and Mapping ZhengzhouPeople’s Republic of China

The leaf area index(LAI)is an important agroecological physiological parameter affecting vegetation growth.To apply the genetic algorithms neural network model(GANNM)to the remote sensing inversion of winter wheat LAI throughout the growth cycle and based on GaoFen-3 Synthetic aperture radar(GF-3 SAR)images and GaoFen-1 Wide Field of View(GF-1 WFV)images,the Xiangfu District in the east of Kaifeng City,Henan Province,was selected as the testing region.winter wheat LAI data from five growth stages were combined,and optical and microwave polarization decomposition vegetation index models were used.The backscattering coefficient was extracted by modified water cloud model(MWCM),and the LAI was obtained by MWCM inversion as input factors to construct GANNM to invert LAI.The root mean square error(RMSE)and determination coefficient(R2)were used as evaluation indicators of the model.The fitting accuracy of winter wheat LAI in five growth stages by GANNM inversion was better than that of the BP neural network model;the R2 was higher than 0.8,and RMSE was lower than 0.3,indicating that the model could accurately invert the growth status of winter wheat in five growth stages.

关键词： Leaf area index(LAI) GF-3 BP neural network model(BPNNM) genetic algorithms neural network model winter wheat

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Assessment of drought and its impact on winter wheat yield in the Chinese Loess Plateau

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Journal of Arid Land 2022年第7期14卷 771-786页

作者： WANG Fengjiao FU Bojie LIANG Wei JIN Zhao ZHANG Liwei YAN Jianwu FU Shuyi GOU Fen School of Geography and Tourism Shaanxi Normal UniversityXi'an 710119China State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China National Demonstration Center for Experimental Geography Education Shaanxi Normal UniversityXi'an 710119China State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Northwest A&F UniversityYangling 712100China School of Geography and Planning Sun Yat-Sen UniversityGuangzhou 510275China

Drought has pronounced and immediate impacts on agricultural production,especially in semi-arid and arid rainfed agricultural regions.Quantification of drought and its impact on crop yield is essential to agricultural water resource management and food security.We investigated drought and its impact on winter wheat(Triticum aestivum L.)yield in the Chinese Loess Plateau from 2001 to 2015.Specifically,we performed a varimax rotated principal component analysis on drought severity index(DSI)separately for four winter wheat growth periods:pre-sowing growth period(PG),early growth period(EG),middle growth period(MG),and late growth period(LG),resulting in three major subregional DSI dynamics for each growth period.The county-level projections of these major dynamics were then used to evaluate the growth period-specific impacts of DSI on winter wheat yields by using multiple linear regression analysis.Our results showed that the growth period-specific subregions had different major DSI dynamics.During PG,the northwestern area exhibited a rapid wetting trend,while small areas in the south showed a slight drying trend.The remaining subregions fluctuated between dryness and wetness.During EG,the northeastern and western areas exhibited a mild wetting trend.The remaining subregions did not display clear wetting or drying trends.During MG,the eastern and southwestern areas showed slight drying and wetting trends,respectively.The subregions scattered in the north and south had a significant wetting trend.During LG,large areas in the east and west exhibited wetting trends,whereas small parts in south-central area had a slight drying trend.Most counties in the north showed significant and slight wetting trends during PG,EG,and LG,whereas a few southwestern counties exhibited significant drying trends during PG and MG.Our analysis identified close and positive relationships between yields and DSI during LG,and revealed that almost all of the counties were vulnerable to drought.Similar but less strong relationships existed for

关键词： drought severity index winter wheat crop yield principal component analysis Loess Plateau

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Modeling leaf color dynamics of winter wheat in relation to growth stages and nitrogen rates

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Journal of Integrative Agriculture 2022年第1期21卷 60-69页

作者： ZHANG Yong-hui YANG Yu-bin CHEN Chun-lei ZHANG Kui-ting JIANG Hai-yan CAO Wei-xing ZHU Yan School of Computer Engineering Weifang UniversityWeifang 261061P.R.China Texas A&M University System AgriLife Research&Extension CenterBeaumontTX 77713United States College of Information Science and Technology Nanjing Agricultural UniversityNanjing 210095P.R.China National Engineering and Technology Center of Information Agriculture Nanjing Agricultural UniversityNanjing 210095P.R.China

The objective of this work was to develop a model for simulating the leaf color dynamics of winter wheat in relation to crop growth stages and leaf positions under different nitrogen(N) rates. RGB(red, green and blue) data of each main stem leaf were collected throughout two crop growing seasons for two winter wheat cultivars under different N rates. A color model for simulating the leaf color dynamics of winter wheat was developed using the collected RGB values. The results indicated that leaf color changes went through three distinct stages, including early development stage(ES), early maturity stage(MS) and early senescence stage(SS), with respective color characteristics of light green, dark green and yellow for the three stages. In the ES stage, the R and G colors gradually decreased from their initial values to steady values, but the B value generally remained unchanged. RGB values remained steady in the MS, but all three gradually increased to steady values in the SS. Different linear functions were used to simulate the dynamics of RGB values in time and space.A cultivar parameter of leaf color matrix(MRGB) and a nitrogen impact factor(FN) were added to the color model to quantify their respective effects. The model was validated with an independent experimental dataset. RMSEs(root mean square errors) between the observed and simulated RGB values ranged between 7.0 and 10.0, and relative RMSEs(RRMSEs)ranged between 7 and 9%. In addition, the model was used to render wheat leaves in three-dimensional space(3 D). The 3 D visualizations of leaves were in good agreement with the observed leaf color dynamics in winter wheat. The developed color model could provide a solid foundation for simulating dynamic crop growth and development in space and time.

关键词： winter wheat crop model virtual crop leaf color crop phenotype RGB Model

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Should phenological information be applied to predict agronomic traits across growth stages of winter wheat?

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The Crop Journal 2022年第5期10卷 1346-1352页

作者： Yu Zhao Yang Meng Shaoyu Han Haikuan Feng Guijun Yang Zhenhai Li Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs Information Technology Research CenterBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry Nanjing Agricultural UniversityNanjing 210095JiangsuChina College of Geomatics Shandong University of Science and TechnologyQingdao 266590ShandongChina

Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index(VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model(HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model’s performance by sensitivity analysis. Results demonstrated that(1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD.(2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81–0.86 and 0.66–0.73, whereas LAI, LGB, LNC, and SPAD prediction models produced sensitivity indexes of 0.01–0.02, 0.01–0.02, 0.01–0.02, and 0.02–0.08, respectively.(3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB,LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons.

关键词： Agronomic traits Phenological effect Vegetation index Hierarchical linear model winter wheat

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Genomic prediction of Fusarium head blight resistance in early stages using advanced breeding lines in hard winter wheat

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The Crop Journal 2022年第6期10卷 1695-1704页

作者： Jinfeng Zhang Harsimardeep S.Gill Navreet K.Brar Jyotirmoy Halder Shaukat Ali Xiaotian Liu Amy Bernardo Paul St.Amand Guihua Bai Upinder S.Gill Brent Turnipseed Sunish K.Sehgal Department of Agronomy Horticulture&Plant ScienceSouth Dakota State UniversityBrookingsSD 57007USA School of Computing Queen’s UniversityKingstonON K7L 3N6Canada USDA-ARS Hard Winter Wheat Genetics Research UnitManhattanKS 66506USA Department of Plant Pathology North Dakota State UniversityFargoND 58108USA

Fusarium head blight(FHB),also known as scab,is a devastating fungal disease of wheat that causes significant losses in grain yield and quality.Quantitative inheritance and cumbersome phenotyping make FHB resistance a challenging trait for direct selection in wheat breeding.Genomic selection to predict FHB resistance traits has shown promise in several studies.Here,we used univariate and multivariate genomic prediction models to evaluate the prediction accuracy(PA)for different FHB traits using 476elite and advanced breeding lines developed by South Dakota State University hard winter wheat breeding program.These breeding lines were assessed for FHB disease index(DIS),and percentage of Fusarium damaged kernels(FDK)in three FHB nurseries in 2018,2019,and 2020(TP18,TP19,and TP20)and were evaluated as training populations(TP)for genomic prediction(GP)of FHB traits.We observed a moderate PA using univariate models for DIS(0.39 and 0.35)and FDK(0.35 and 0.37)using TP19 and TP20,respectively,while slightly higher PA was observed(0.41 for DIS and 0.38 for FDK)when TP19 and TP20(TP19+20)were combined to leverage the advantage of a large training population.Although GP with multivariate approach including plant height and days to heading as covariates did not significantly improve PA for DIS and FDK over univariate models,PA for DON increased by 20%using DIS,FDK,DTH as covariates using multi-trait model in 2020.Finally,we used TP19,TP20,and TP19+20 in forward prediction to calculate genomic-estimated breeding values(GEBVs)for DIS and FDK in preliminary breeding lines at an early stage of the breeding program.We observed moderate PA of up to 0.59 for DIS and 0.54for FDK,demonstrating the promise in genomic prediction for FHB resistance in earlier stages using advanced lines.Our results suggest GP for expensive FHB traits like DON and FDK can facilitate the rejection of highly susceptible materials at an early stage in a breeding program.

关键词： FHB GBS Genomic selection Multi-trait models winter wheat wheat scab

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Establishment of soil moisture model based on hyperspectral data and growth parameters of winter wheat

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International Journal of Agricultural and Biological Engineering 2023年第3期16卷 160-168页

作者： Xizhi Lyu Weimin Xing Yuguo Han Zhigong Peng Baozhong Zhang Muhammad Roman Key Laboratory of State Forestry Administration on Soil and Water Conservation College of Soil and Water ConservationBeijing Forestry UniversityBeijing 100083China Yellow River Institute of Hydraulic Research Key Laboratory of the Loess Plateau Soil Erosion and Water Loss Process and Control of Ministry of Water ResourcesZhengzhou 450003China College of Hydraulic Science and Engineering Yangzhou UniversityYangzhou 225009China Forest Ecosystem Studies National Observation and Research StationJixianShanxi 042200China Key Laboratory of Non-point Source Pollution Control Ministry of Agriculture and Rural AffairsBeijing 100081China State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijing 100038China National Center of Efficient Irrigation Engineering and Technology Research-Beijing Beijing 100048China

Large area of soil moisture status diagnosis based on plant canopy spectral data remains one of the hot spots of agricultural irrigation.However,the existing soil water prediction model constructed by the spectral parameters without considering the plant growth process will inevitably increase the prediction errors.This study carried out research on the correlations among spectral parameters of the canopy of winter wheat,crop growth process,and soil water content,and finally constructed the soil water content prediction model with the growth days parameter.The results showed that the plant water content of winter wheat tended to decrease during the whole growth period.The plant water content had the best correlations with the soil water content of the 0-50 cm soil layer.At different growth stages,even if the soil water content was the same,the plant water content and characteristic spectral reflectance were also different.Therefore,the crop growing days parameter was added to the model established by the relationships between characteristic spectral parameters and soil water content to increase the prediction accuracy.It is found that the determination coefficient(R^(2))of the models built during the whole growth period was greatly increased,ranging from 0.54 to 0.60.Then,the model built by OSAVI(Optimized Soil Adjusted Vegetation Index)and Rg/Rr,two of the highest precision characteristic spectral parameters,were selected for model validation.The correlation between OSAVI and soil water content,Rg/Rr,and soil water content were still significant(pwinter wheat in northern China.

关键词： winter wheat canopy spectra growth process soil water content irrigation threshold soil moisture model prediction

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Fast extraction of winter wheat planting area in Huang-Huai-Hai Plain using high-resolution satellite imagery on a cloud computing platform

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International Journal of Agricultural and Biological Engineering 2022年第1期15卷 241-250页

作者： Dongyan Zhang Mengru Zhang Fenfang Lin Zhenggao Pan Fei Jiang Liang He Hang Yang Ning Jin National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions(Henan University) Ministry of EducationKaifeng 475004HenanChina School of Informatics and Engineering Suzhou UniversitySuzhou 234000AnhuiChina National Meteorological Center Beijing 100081China Aerospace Information Research Institute Chinese Academy of ScienceBeijing 100101China

To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing classification.Google Earth Engine(GEE),a cloud computing analysis platform based on global geospatial analysis,provides a new opportunity for rapid analysis of remote sensing data.In this study,high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in China.The random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020,and Sentinel-2 imagery was used to verify the results.The spectral indices,texture,and terrain features of the image were derived,and their contribution to the classification accuracy of winter wheat was evaluated by scoring.Then the top nine features were selected to form an optimal feature subset.Comparing the set of thirty-four features and the optimized feature subset as the input variables of the random forest classifier,the results show that the accuracy difference between the two feature classification schemes is small,but the classification effect of all feature sets is slightly better than the optimal feature subset.The overall classification accuracy of sample plots verification was 86%-95%,the Kappa coefficient was between 0.7 and 0.85,and the percentage error of the total area was 5.42%.The research demonstrates a reliable method for mapping a wide range of winter wheat planting area,and provides a good prospect for exploring the precise mapping of other crops,which is of great significance to crop monitoring and agricultural development.

关键词： Google Earth Engine regional scale winter wheat rapid mapping

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Retrieval of winter wheat Canopy Carotenoid Content with Ground-and Airborne-Based Hyperspectral Data

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Phyton-International Journal of Experimental Botany 2023年第9期92卷 2633-2648页

作者： Ting Cui Xianfeng Zhou Yufeng Huang Yanting Guo Yunrui Lin Leyi Song Jingcheng Zhang College of Artificial Intelligence Hangzhou Dianzi UniversityHangzhou310018China

Accurate assessment of canopy carotenoid content(CC_(x+c)C)in crops is central to monitor physiological conditions in plants and vegetation stress,and consequently supporting agronomic decisions.However,due to the overlap of absorption peaks of carotenoid(C_(x+c))and chlorophyll(C_(a+b)),accurate estimation of carotenoid using reflectance where carotenoid absorb is challenging.The objective of present study was to assess CC_(x+c)C in winter wheat(Triticum aestivum L.)with ground-and aircraft-based hyperspectral measurements in the visible and near-infrared spectrum.In-situ hyperspectral reflectance were measured and airborne hyperspectral data were acquired during major growth stages of winter wheat in five consecutive field experiments.At the canopy level,a remarkable linear relationship(R^(2)=0.95,pwinter wheat with red edge chlorophyll index provided that C_(x+c) correlated well with C_(a+b) at the canopy scale.it is therefore a promising method for CC_(x+c)C retrieval at regional scale from aerial hyperspectral imagery.

关键词： Hyperspectra carotenoid spectral index partial least squares regression winter wheat

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winter wheat biomass estimation based on canopy spectra

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International Journal of Agricultural and Biological Engineering 2015年第6期8卷 30-36页

作者： Zheng Ling Zhu Dazhou Liang Dong Zhang Baohua Wang Cheng Zhao Chunjiang Beijing Research Center for Information Technology in Agriculture Beijing 100097China School of Electronic Information Engineering Anhui UniversityHefei 230039China

The winter wheat aboveground biomass is an important agronomic parameter to estimate the growth status,and evaluate the yield and quality.Spectrum technique provides a nondestructive and fast method for estimating the winter wheat biomass.In order to find the optimum model by analyzing the wheat canopy spectral characteristic during the whole growth period,field trails were conducted at the National Demonstration Base of Precision Agriculture in Beijing Xiaotangshan town.A portable spectrometer(200-1100 nm)was used to collect the wheat canopy spectra of different varieties at the different growth stages(green stage,jointing stage,booting stage,heading stage and filling stage),clipping the winter wheat at ground level at the same time.Regression and correlation analysis were used to establish the winter wheat biomass estimation models in this study.The results showed that the biggest different bands of the winter wheat canopy spectral reflection curves mainly lied along the blue and near-infrared bands.The spectral reflectance at 678 nm in the visible light range had the best correlation with the biomass(correlation=0.724).The monadic regression analysis,the multiple regression analysis and the partial least squares regression analysis were applied to establish the biomass estimation models,among which the partial least squares regression(PLS)model had higher modeling precision.The R2 of the calibration and validation were 0.916 and 0.911,respectively.The root-mean-square error(RMSE)of the calibration and validation were 0.090 kg and 0.094 kg(Sample area 50 cm×60 cm).The results indicated that the PLS model(400-1000 nm)could fully estimate the aboveground biomass in the whole growth period of wheat with a better measurement accuracy.

关键词： winter wheat biomass canopy spectra crop growth period partial least square regression

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