小麦条锈病气象等级预测方法研究(英文)

[Objective] The aim of this study is to establish the model for forecasting wheat stripe rust occurrence condition using meteorological factors. [Method] Based on the data of wheat stripe rust occurrence degrees in its past prevalent years and the meteorological data at corresponding periods, the methods of grey correlation analysis and fuzzy mathematics were employed to establish the forecast model for four pathogenesis indices according to the time sequence before winter, Early March, Early April and Middle May. Thus, the criterion for forecasting the occurrence degree of wheat stripe rust was obtained based on the distribution method of arithmetic progression. [Result] The model corresponding to meteorological conditions for forecasting wheat stripe rust was successfully established. According to the verification, the forecasting results before winter and in Early Mar. were more severer than the real occurrence condition, while the forecasting results in Early Apr. and Middle May were basically consistent with real values. [Conclusion] The results of the present study may avail the control of wheat stripe rust in Henan Province.     

Monitoring Winter Wheat Freeze Injury Using Multi-Temporal MODIS Data

Freeze injury is an usual disaster for winter wheat in Shanxi Province, China, and monitoring freeze injury is of important economic significance. The aim of this article is to monitor and analyze the winter wheat freeze injury using remote sensing data, to monitor the occurrence and spatial distribution of winter wheat freeze in time, as well as the severity of the damage. The winter wheat freeze injury was monitored using multi-temporal moderate-resolution imaging spectroradiometer （MODIS） data, combined with ground meteorological data and field survey data, the change of normalized difference vegetation index （NDVI） before and after freeze injury was analyzed, as well as the effect of winter wheat growth recovery rate on yield. The results showed that the NDVI of winter wheat decreased dramatically after the suffering from freeze injury, which was the prominent feature for the winter wheat freeze injury monitoring. The degrees of winter wheat freeze injury were different in the three regions, of which, Yuncheng was the worst severity and the largest freeze injury area, the severity of freeze injury correlates with the breeding stage of the winter wheat. The yield of winter wheat showed positive correlation with its growth recovery rate （r=0.659^＊＊ which can be utilized to monitor the severity of winter wheat freeze injury as well as its impact on yield. It can effectively monitor the occurrence and severity of winter wheat freeze injury using horizontal and vertical profile distribution and growth wheat freeze injury in Shanxi Province. recovery rate, and provide a basis for monitoring the winter     

Study on the Change of GAs,ABA Level and ABA／GAs Ratio in the Embryos of Spring Wheat and Winter Wheat During Vernalization

Spring wheat(cv.7742)and winter wheat (cv.Nongda 015) were vernalized by seed chilling,and the changes of GAs,ABA content and GAs/ABA ration in embryo wer investigated.The differences between cultivars,treatments and variety-treatment inleractions were significant for both GAs and ABA.The level of GAs and ABA as well as GAs/ABA ratio were hardly changed by seed chilling in spriug wheat Howerve,in winter wheat,the GAs content decreased much less compared with ABA that reduced sharply after chilling.It showed that the GAs/ABA ratio in winter wheat was increased remarkably(4.55 times)than in spring wheat.It was suggested that the absolute GAs Ievel was independent of the vernalization effect because there was a higher GAs Level before and after seed chilling in winter wheat when compared with spring wheat,One of the vemalization effects was the decline of ABA level that led to the increase of GAs/ABA ratio.     

Exploring the Feasibility of Winter Wheat Freeze Injury by Integrating Grey System Model with RS and GIS

Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system model (GSM) to monitor the degree and the distribution of the winter wheat freeze injury. The model combines remote sensing (RS) and geographic information system (GIS) technology. It gave examples of wheat freeze injury monitoring applications in Gaocheng and Jinzhou of Hebei Province, China. We carried out a quantitative evaluation method study on the severity of winter wheat freeze injury. First, a grey relational analysis (GRA) was conducted. At the same time, the weights of the stressful factors were determined. Then a wheat freezing injury stress multiple factor spatial matrix was constructed using spatial interpolation technology. Finally, a winter wheat freeze damage evaluation model was established through grey clustering algorithm (GCA), and classifying the study area into three sub-areas, affected by severe, medium or light disasters. The evaluation model were verified by the Kappa model, the overall accuracy reached 78.82% and the Kappa coefficient was 0.6754. Therefore, through integration of GSM with RS images as well as GIS analysis, quantitative evaluation and study of winter wheat freeze disasters can be conducted objectively and accurately, making the evaluation model more scientific.     

Sampling Survey and Identification of Races of Soybean Cyst Nematode （Heterodera glycines Ichinohe） in Huang-Huai Valleys

Soybean cyst nematode （SCN Heterodera glycines Ichinohe） is one of the most important nationwide soybean diseases in China. A total of 38 soil specimens or locations in the area was sampled and tested for SCN races during 2001-2003 for the inspection of race distribution in Huang-Huai Valleys. A map of race distribution was constructed according to the data from both the present study and the published reports cited. Three areas, namely, the area of southeast to Jinan in Shangdong Province; the area of northern Henan Province and its border region to south of Hebei Province; and the area of Luohe, Zhoukou of Henan Province and Fuyang of Anhui Province mainly infested with Race 1 were identified. Race 4 was predominant in Shanxi Province, Beijing and the adjacent area of Henan, Shandong, and Anhui provinces, and the delta of Huanghe River in Shandong Province. Race 2 was mainly found in Liaocheng, Dezhou of Shangdong Province and Shijiazhuang of Hebei Province, and Jiaozuo and Huojia of Henan Province. Race 7 was distributed in the west part of Jiaodong Peninsula of Shandong Province and Kaifeng, Huaxian, Wenxian of Henan Province. Race 5 was found and scattered in Hebei and Henan Province. Race 9 was found in Shangqiu of Henan Province, which was reported for the first time in China. It can be seen that Race 1 and Race 4 were the two predominant races in Huang-Huai Valleys, and that research should focus on developing resistant cultivars of these races. There might exist other races in an area with some predominant races. The race substitution in the past decade was not obviously found, therefore, the results should be meaningful to future breeding for resistance to SCN in Huang-Huai Valleys.     

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m2 m–2, respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha–1 in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.     

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

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 ...     

Spatiotemporal Characteristics of Reference Evapotranspiration and Its Sensitivity Coefficients to Climate Factors in Huang-Huai-Hai Plain,China

Climate change will have important implications in water shore regions,such as Huang-Huai-Hai（3H） plain,where expected warmer and drier conditions might augment crop water demand.Sensitivity analysis is important in understanding the relative importance of climatic variables to the variation in reference evapotranspiration（ET 0）.In this study,the 51-yr ET 0 during winter wheat and summer maize growing season were calculated from a data set of daily climate variables in 40 meteorological stations.Sensitivity maps for key climate variables were estimated according to Kriging method and the spatial pattern of sensitivity coefficients for these key variables was plotted.In addition,the slopes of the linear regression lines for sensitivity coefficients were obtained.Results showed that ET 0 during winter wheat growing season accounted for the largest proportion of annual ET 0,due to its long phenological days,while ET 0 was detected to decrease significantly with the magnitude of 0.5 mm yr-1in summer maize growing season.Solar radiation is considered to be the most sensitive and primarily controlling variable for negative trend in ET 0 for summer maize season,and higher sensitive coefficient value of ET 0 to solar radiation and temperature were detected in east part and southwest part of 3H plain respectively.Relative humidity was demonstrated as the most sensitive factor for ET 0 in winter wheat growing season and declining relativity humidity also primarily controlled a negative trend in ET 0,furthermore the sensitivity coefficient to relative humidity increased from west to southeast.The eight sensitivity centrals were all found located in Shandong Province.These ET 0 along with its sensitivity maps under winter wheat-summer maize rotation system can be applied to predict the agricultural water demand and will assist water resources planning and management for this region.     

Calibration and validation of SiBcrop Model for simulating LAI and surface heat fluxes of winter wheat in the North China Plain

The accurate representation of surface characteristic is an important process to simulate surface energy and water flux in land-atmosphere boundary layer. Coupling crop growth model in land surface model is an important method to accurately express the surface characteristics and biophysical processes in farmland. However, the previous work mainly focused on crops in single cropping system, less work was done in multiple cropping systems. This article described how to modify the sub-model in the SiBcrop to realize the accuracy simulation of leaf area index(LAI), latent heat flux(LHF) and sensible heat flux(SHF) of winter wheat growing in double cropping system in the North China Plain(NCP). The seeding date of winter wheat was firstly reset according to the actual growing environment in the NCP. The phenophases, LAI and heat fluxes in 2004–2006 at Yucheng Station, Shandong Province, China were used to calibrate the model. The validations of LHF and SHF were based on the measurements at Yucheng Station in 2007–2010 and at Guantao Station, Hebei Province, China in 2009–2010. The results showed the significant accuracy of the calibrated model in simulating these variables, with which the R~2, root mean square error(RMSE) and index of agreement(IOA) between simulated and observed variables were obviously improved than the original code. The sensitivities of the above variables to seeding date were also displayed to further explain the simulation error of the SiBcrop Model. Overall, the research results indicated the modified SiBcrop Model can be applied to simulate the growth and flux process of winter wheat growing in double cropping system in the NCP.     

Population genetic structure of Chinese Puccinia triticina races based on multi-locus sequences

Puccinia triticina, the causal agent of wheat leaf rust, is one of the most devastating rust fungi attacking wheat worldwide. Seventy-six isolates of the wheat leaf rust pathogen from Yunnan, Sichuan, Gansu and Henan provinces, China, were tested on wheat leaf rust differentials and the population structure was analyzed using four presumably neutral partial sequence markers such as elongation factor-1α(EF-1α), glyceraldehyde-3-phosphate dehydrogenase(GAPDH), β-tubulin(TUB) and the second largest RNA polymerase subunit(RPB2). The phenotypic diversity of Yunnan and Sichuan populations was higher than that of Gansu and Henan populations. The four populations were separated into two clusters based on the pathogenic data. A total of 12 single nucleotide polymorphisms(SNPs) and 32 haplotypes were identified among the four sequences. The 32 haplotypes were divided into two clusters in a neighbor-joining tree. Bayesian analyses also identified two clusters. Pairwise FST between populations in different regions were significantly different(P0.05). Analysis of molecular variance(AMOVA) indicated that 68% of the total genetic variation was within populations.     

不同时序EOS/MODIS-NDVI监测河南省冬小麦面积

[目的]利用不同时序EOS/MODIS-NDVI数据计算河南省冬小麦面积。[方法]对MODIS图像数据进行转换、地理较正和波段组合等处理后,从而获得不同时间序列的归一化植被指数(Normal Difference Vegetation Index,NDVI),然后在地面光谱测量基础上进行监督分类,得出河南省2005年小麦种植面积。[结果]该法所测河南冬小麦像元总数92 208个,种植面积为576万hm2,与统计部门统计数据相比,误差为9.66%。[结论]该研究为冬小麦种植面积监测提供了新途径。     

县域尺度上基于GF-1PMS影像的冬小麦种植面积遥感监测

为探究县域尺度上基于高分一号卫星(GF-1)PMS影像进行冬小麦遥感监测的可行性及精准性,以河南省滑县为研究区,遴选2015年2月上旬GF-1 PMS影像6景,对影像进行辐射定标、FLAASH大气校正、NNDiffuse融合、几何精校正、地图投影转换等预处理后,在外业调查和样本分析的基础上构建一种新的冬小麦决策树分类模型,模型第1层决策方案中NDVI0.311的像元为冬小麦,得到冬小麦的粗分类结果;在此基础上进行第2层决策分类,以进一步提高冬小麦的分类精度,分类方案为第1波段地表反射率0.146、第2波段地表反射率0.148、第3波段地表反射率0.135、第4波段地表反射率0.250的像元为冬小麦。对分类结果进行形态学滤波处理,以消除或减少分类结果中孤立的像元。分别基于决策树分类模型与ENVI软件自带的IsoData非监督分类模型,对比分析GF-1PMS影像和同时期Landsat-8OLI影像在冬小麦面积提取上的精度。结果表明:基于新构建的决策树分类模型,2015年滑县冬小麦种植面积为115 715.81hm2,混淆矩阵检验总体精度为99.62%,Kappa系数为0.99;PMS影像提取冬小麦的混淆矩阵总体精度比OLI影像高出9个百分点。说明县域尺度上基于单时相GF-1PMS影像在冬小麦收获前提取冬小麦种植面积是可行的,提取精度较高。     

小麦条锈病气象等级预测方法研究

[目的]建立通过气象因子预测小麦条锈病发生面积的模型。[方法]在统计河南省条锈病发生轻重年份的基础上,结合相应年份对应时段的气象资料,采用灰色关联分析、模糊数学等方法,按时间顺序建立了冬前、3月上旬4、月上旬和5月中旬4个条锈病促病指数预测模型。根据促病指数计算原理,采用等差分布方法得出条锈病发生程度预测标准,并进行了历史资料检验及2006年发生程度预测。[结果]成功建立了对应于气象条件的小麦锈病发生模型。检验结果表明,冬前及3月上旬预测结果相比实际值偏重,4月上旬及5月中旬预测结果与实际值较为吻合。[结论]该研究结果将对河南省小麦条锈病的预防起到积极作用。     

小麦冬前异常旺长对秋冬气候变暖的响应分析

[目的]分析冬小麦冬前生长高度、叶面积指数和大蘖数对气候变暖的响应,为河南省冬小麦的安全越冬、安全生产提供重要依据。[方法]使用2006～2007年郑州农业气象试验田的地面平行观测资料,并利用Excel2000软件绘制图表,进行统计分析。[结果]11月平均气温≥6．5℃时,多年冬前小麦生长高度随气温的上升而明显增高;叶面积指数与≥5℃积温呈0．01水平显著正相关关系,积温每增加15℃,叶面积指数将提高0．1。[结论]为有效地应对气候变暖对冬小麦的负面影响,小麦播期应推迟4d左右。     

绿色植物生长调节剂GGR6号和GGR8号对冬小麦生长发育及产量的影响

[目的]筛选适宜冬小麦的生长调节剂,探讨其适宜的浓度和施用方法。[方法]以邯生329为试验材料,分别采用不同浓度的新型绿色植物生长调节剂GGR6号和GGR8号对河北地区冬小麦进行试验,分析其对冬小麦生长发育及产量的影响。[结果]2种生长调节剂均能促进根茎的发育、干物质的积累、产量性状的改善及产量的增加,尤其以GGR8号20 mg/kg拌种+拔节期20 mg/kg喷施对冬小麦促进效果最好。[结论]该研究可为冬小麦生产中应用植物生长调节剂提供参考。     

播期对不同类型小麦生育进程及产量构成的影响

[目的]研究播期对不同感温性小麦生育进程及产量构成的影响。[方法]分别以2个弱春性(郑麦9023、04中36)和半冬性(西农979、矮抗58)小麦品种为试验对象,通过5个不同播种期(10月1日、10月7日、10月14日、10月21日、10月28日)的设置,研究播期对小麦生育进程及产量构成因素的影响。[结果]随播期推迟,小麦株高呈下降趋势;晚播推迟小麦的出苗期和三叶期,总生育天数缩短;不同播期中,弱春性品种的生育时期均较半冬性品种提前。弱春性品种早播或晚播均影响群体产量形成,籽粒产量下降,适期播种(10月14日)最好;半冬性品种应适时早播,播量一致的前提下以10月1日播种籽粒产量较高。[结论]该研究可为豫北地区不同感温性小麦品种选择适宜播期提供理论依据。     

河南省冬小麦干旱指数特征分析及风险评估

为了研究干旱对河南省冬小麦产量形成的影响,准确评估干旱造成的危害程度.笔者利用河南省气候资料、冬小麦产量资料,商丘灌溉资料分析冬小麦完成各发育期的气象条件,利用Z指数等方法,讨论冬小麦干旱的时空分布特征,并对河南省冬小麦干旱风险区划.结果表明:(1)河南省冬小麦丰年和歉年降水条件东西部差异较大,当东部(西部)全生育期降...     

用高稳系数法评价小麦新品种（系）的高产稳产性

根据1997～1998年河南省小麦超高产冬水组以及黄淮南片小麦春水组的区试结果。利用高穗系数法对参试小麦新品种(系)的高产稳产性进行了分析．并将高稳系数与常规的几种稳定性统计数进行了比较。结果表明,高稳系数是一个简单、准确地反映品种高产稳产性的综合指标．高稳系数法是评价小麦新品种(系)高产稳产性的良好方法。     

GEE支持下的河南省冬小麦面积提取及长势监测

[目的]使用遥感技术对2017—2020年河南省冬小麦的空间分布信息进行高精度的提取,然后对2020年冬小麦的长势进行高频度的监测并结合气象条件进行分析.[方法]本文基于谷歌地球引擎(Google Earth Engine,GEE)云平台,对选取的Landsat 8影像数据根据NDVI最大值进行合成,然后进行特征构建,...