Using optimal combination method and in situ hyperspectral measurements to estimate leaf nitrogen concentration in barley

Leaf nitrogen concentration (LNC), a good indicator of nitrogen (N) status in crops, is of special significance to diagnose nutrient stress and guide N fertilization in fields. Due to non-destructive and quick detectability, hyperspectral remote sensing plays a unique role in detecting LNC in crops. Barley, especially malting barley, is very demanding for N nutrition and requires timely monitoring and accurate estimation of N concentration in barley leaves. Hyperspectral techniques can help make effective diagnosis and facilitate dynamic regulation of plant N status. In this study, canopy reflectance spectra (between 350 and 1 050 nm) from 38 typical barley fields were measured as well as the corresponding LNC in Hailar Nongken, China’s Inner Mongolia Autonomous Region in July, 2010. Existing spectral indices that are considered to be good indicators for assessing N status in crops were selected to estimate LNC in barley. In addition, the optimal combination (OC) method was tested to extract the sensitive indices and first-order spectral derivative wavebands that are responsible for variation of leaf N in barley, and expected to develop some combination models for improving the accuracy of LNC estimates. The results showed that most of the selected indices (such as NPCI, PRI and DCNI) could adequately describe the dynamic changes of LNC in barley. The combined models based on OC performed better in comparison with the individual models using either spectral indices or first-order derivatives and the other methods (such as PCA). A combined model that integrated the first-order derivatives from five wavebands with OC performed well with R ² of 0.82 and RMSE of 0.50 for LNC in barley. This good correlation with ground measurements indicates that hyperspectral reflectance and the OC method have good potential for assessing N status in barley.     

不同收获时期对啤酒大麦品质的影响

本试验以啤酒大麦的植株外观特征为依据,划分了四个不同收获时期。初步研究了不同收获时期对啤酒大麦品质的影响。结果表明,在收获适期内,收获时间越早,籽粒色泽越浅,蛋白质含量越低,种子发芽率越高。种皮厚度与收获时间的早晚无关。千粒重呈单峰曲线变化。以第Ⅲ收获期最高。适时早收可提高干粒重、发芽率,降低蛋白质含量和达到理想的色泽。在我地,啤酒大麦的最佳收获期应在本试验的第Ⅱ与第Ⅲ收获期之间,相当于蜡熟中期与蜡熟末期之间。     

Study of spring barley grain protein qualitative composition under conditions in the Middle Volga Region

Quantitative and qualitative protein composition peculiarities and amino-acid content variability in spring barley grains of different usage (for malting, feeding, and cereal consumption) depending upon the barley varietal characteristics and weather features are studied.     

基于模型与GIS的小麦籽粒品质空间差异分析

 【目的】基于小麦籽粒品质预测模型和GIS技术,探索主要籽粒品质指标区域模拟与空间变异分析的方法。【方法】首先利用文献资料对已有小麦籽粒品质预测模型进行验证和评价,并基于江苏省40个生态点2000-2003年逐日气象数据和5个生态点、6个品种类型的小麦田间试验数据,对籽粒品质预测模型进行先计算后插值(calculate first,interpolate later,CI)和先插值后计算(interpolate first,calculate later,IC)两种升尺度方法的研究与评价;在此基础上进行小麦籽粒蛋白质含量、湿面筋含量和沉降值3项籽粒品质指标的区域模拟;最后,运用GIS和地统计学方法,分析江苏省3项籽粒品质指标的空间变异,获取3项籽粒品质指标的空间分布栅格图。【结果】IC方法的模拟精度较高,3项籽粒品质指标在不同地点、不同品种上的模拟值与实测值的RMSE基本小于20%;江苏省3项小麦籽粒品质指标在最大变程7.16 km范围内显著相关,表现为东西经向和南北纬向变异较大的各向异性分布;空间栅格图能直观显示3项籽粒品质指标的空间分布以及纬度和经度方向的变异趋势。【结论】利用基于IC的小麦籽粒品质模型升尺度方法进行区域籽粒品质模拟和空间变异分析是可行的,为小麦籽粒品质的生态变异研究提供了参考。     

Application of Probability Analysis to Assess Nitrogen Supply to Grain Crops in Northern Australia

Grain yield and protein of cereal crops in northern Australia provide a useful indicator of the supply of available nitrogen (N) to the crop. Our intention was to utilize this principle on a site-specific basis through an associated probabilistic framework to identify the likelihood that grain yield was limited by N supply. Yield and protein data were taken at harvest from sorghum, wheat and barley crops near Dalby, southern Queensland, in 1999. Considerable variation was found in grain yield for the three crops, but less so for grain protein. Frequency–response relationships, derived from historical multiple N field experiments, were applied to identify areas where grain yield was limited by N supply. This approach indicated that there was a 60% or higher likelihood that plant-available N was yield-limiting for 17%, 23%, and 26% of the area sown to sorghum, wheat and barley, respectively. These areas were not necessarily those where crop yield was relatively low. Calculation of N removal and N supply, using N transfer efficiency relationships, verified that those areas with a high likelihood of response to N had considerably lower supplies of N compared to other areas. The application of probability analysis offers a unique strategy to identify within-field areas where N supply could be yield-limiting, and provides a rationale for predicting the spatial variation and likely range of N supplies for successive seasons.     

Assessing the potential of an algorithm based on mean climatic data to predict wheat yield

The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3–4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days.     

施磷对啤酒大麦产量及品质的影响研究

采用土壤养分状况系统研究法,研究了磷对啤酒大麦产量、品质及土壤养分平衡的影响.结果表明,在平衡N、K及微量元素等养分后,施磷可提高啤酒大麦产量,磷用量在300 kg/hm2以下时,啤酒大麦产量随着磷用量的增加而增加,磷用量为300 kg/hm2时,产量达最高,为7 345 kg/hm2;施磷可改善啤酒大麦的品质,提高啤酒大麦的千粒重,降低啤酒大麦籽粒中的粗蛋白含量,磷用量在225 kg/hm2以下时,千粒重随着磷用量的增加而增加,籽粒粗蛋白含量随着磷用量的增加而下降.     

Evaluating high resolution SPOT 5 satellite imagery to estimate crop yield

High resolution satellite imagery has the potential to map within-field variation in crop growth and yield. This study examined SPOT 5 satellite multispectral imagery for estimating grain sorghum yield. A 60 km × 60 km SPOT 5 scene and yield monitor data from three grain sorghum fields were recorded in south Texas. The satellite scene contained four spectral bands (green, red, near-infrared and mid-infrared) with a 10-m spatial resolution. Subsets were extracted from the scene that covered the three fields. Images with pixel sizes of 20 and 30 m were also generated from the individual field images to simulate coarser resolution satellite imagery. Vegetation indices and principal components were derived from the images at the three spatial resolutions. Grain yield was related to the vegetation indices, the four bands and the principal components for each field, and for all the fields combined. The effect of the mid-infrared band on estimates of yield was examined by comparing the regression results from all four bands with those from the other three bands. Statistical analysis showed that the 10-m, four-band image and the aggregated 20-m and 30-m images explained 68, 76 and 83%, respectively, of the variation in yield for all the fields combined. The coefficient of determination between yield and the imagery increased with pixel size because of the smoothing effect. The inclusion of the mid-infrared band slightly improved the R ² values. These results indicate that high resolution SPOT 5 multispectral imagery can be a useful data source for determining within-field yield variation for crop management.     

73份优异啤酒大麦种质资源主要麦芽品质性状的评价

以73份国内外啤用大麦种质资源为材料,分析了5个主要麦芽品质指标(麦芽浸出率、糖化力、α-氨基氮、库尔巴哈值、蛋白质)的遗传多样性及其相关.结果表明,73份大麦基因型麦芽品质性状间具有不同程度的变异,不同大麦基因型的5个主要品质指标均具有很高的遗传多样性指数,且5个品质性状之间存在不同程度的复杂相关.动态聚类分析将73份大麦基因型分成3类,不同类型分别适合于不同用途.研究结果可为啤酒大麦种质资源的多元化利用提供基础.     

Canopy reflectance, thermal stress, and apparent soil electrical conductivity as predictors of within-field variability in grain yield and grain protein of malting barley

Three consecutive crops of malting barley grown during 2002–2004 on clay-loam on a Swedish farm (59°74’ N, 17°00’ E) were monitored for canopy reflectance at growth stages GS32 (second node detectable) and GS69 (anthesis complete), and the crops were sampled for above ground dry matter and nitrogen content. GPS-positioned unfertilised plots were established and used for soil sampling. At harvest, plots of 0.25 m² were cut in both fertilised and unfertilised plots, and 24 m² areas were also harvested from fertilised barley. The correlations between nine different vegetation indices (VIs) from each growth stage and yield and grain protein were tested. All indices were significantly correlated (at 5% level) with grain yield (GY), and protein when sampled at GS69 but only four when sampled at GS32. Three variables (the best-correlated vegetation index sampled at GS32; an index for accumulated elevated daily maximum temperatures for the grain filling period, and normalised apparent electrical conductivity (ECa) of the soil) were sufficient input in the final regressions. Using these three variables, it was possible to make either one multivariate (PLS) regression model or two linear multiple regression models for grain yield (GY) and grain protein, with correlation coefficients of 0.90 and 0.73 for yield and protein, respectively.     

Early-season crop type mapping using 30-m reference time series

Early-season crop type mapping could provide important information for crop growth monitoring and yield prediction, but the lack of ground-surveyed training samples is the main challenge for crop type identification. Although reference time series based method(RBM) has been proposed to identify crop types without the use of ground-surveyed training samples, the methods are not suitable for study regions with small field size because the reference time series are mainly generated using data set with low spatial resolution. As the combination of Landsat data and Sentinel-2 data could increase the temporal resolution of 30-m image time series, we improved the RBM by generating reference normalized difference vegetation index(NDVI)/enhanced vegetation index(EVI) time series at 30-m resolution(30-m RBM) using both Landsat and Sentinel-2 data, then tried to estimate the potential of the reference NDVI/EVI time series for crop identification at early season. As a test case, we tried to use the 30-m RBM to identify major crop types in Hengshui, China at early season of 2018, the results showed that when the time series of the entire growing season were used for classification, overall classification accuracies of the 30-m RBM were higher than 95%, which were similar to the accuracies acquired using the ground-surveyed training samples. In addition, cotton, spring maize and summer maize distribution could be accurately generated 8, 6 and 8 weeks before their harvest using the 30-m RBM; but winter wheat can only be accurately identified around the harvest time phase. Finally, NDVI outperformed EVI for crop type classification as NDVI had better separability for distinguishing crops at the green-up time phases. Comparing with the previous RBM, advantage of 30-m RBM is that the method could use the samples of the small fields to generate reference time series and process image time series with missing value for early-season crop classification; while, samples collected from multiple years should be further used so that the reference time series could contain more crop growth conditions.     

发展我国啤用大麦生产的初步设想

本文分析了世界啤用大麦生产、消费和品种使用状况及我国啤用大麦生产和育种所存在的主要问题，提出了发展我国啤用大麦生产的初步设想，即加强啤用大麦应用基础研究，选定宜种啤用大麦区域，实行啤用大麦品种有偿使用制，建立啤用大麦育种、生产、消费三位一体体系。     

Estimation of surface soil organic matter using a ground-based active sensor and aerial imagery

Active canopy sensors are currently being studied as a tool to assess crop N status and direct in-season N applications. The objective of this study was to use a variety of strategies to evaluate the capability of an active sensor and a wide-band aerial image to estimate surface soil organic matter (OM). Grid soil samples, active sensor reflectance and bare soil aerial images were obtained from six fields in central Nebraska before the 2007 and 2008 growing seasons. Six different strategies to predict OM were developed and tested by dividing samples randomly into calibration and validation datasets. Strategies included uniform, interpolation, universal, field-specific, intercept-adjusted and multiple-layer prediction models. By adjusting regression intercept values for each field, OM was predicted using a single sensor or image data layer. Across all fields, the uniform and universal prediction models resulted in less accurate predictions of OM than any of the other methods tested. The most accurate predictions of OM were obtained using interpolation, field-specific and intercept-adjusted strategies. Increased accuracy in mapping soil OM using an active sensor or aerial image may be achieved by acquiring the data when there is minimal surface residue or where it has been excluded from the sensor’s field-of-view. Alternatively, accuracy could be increased by accounting for soil moisture content with supplementary sensors at the time of data collection, by focusing on the relationship between soil reflectance and soil OM content in the 0–1 cm soil depth or through the use of a subsurface active optical sensor.     

2012年9月18—19日辽宁省桓仁地区暴雨天气个例分析

利用2012年9月18—19日常规观测资料、卫星资料、雷达、数值预报以及区域自动气象站资料对2012年9月18—19日桓仁地区大范围强降水的形成和演变特征进行分析，得出此次大暴雨过程是副热带、西风带、热带系统共同作用的结果，为及时总结预报经验，提高暴雨预报服务的准确性和及时性提供了参考。     

基于WheatGrow和CERES模型的区域小麦生育期预测与评价

【目的】研究小麦生长模型在区域应用中的关键技术,并利用WheatGrow和CERES-Wheat两套模型模拟区域小麦生育期,以检验和评价模型区域应用方法的有效性。【方法】首先利用薄盘样条法（thin plate spline,TPS）对各站点逐日气象数据进行空间插值,得到研究区域气象要素表面数据;其次利用TPS方法对各站点历史多年小麦播种期进行空间插值,并将插值后的结果进行多年平均得到研究区域播种期表面数据;进一步将Markov Chain Monte Carlo（MCMC）方法与生长模型相结合,利用典型站点历史多年小麦生育期实测数据,估算出典型站点的品种参数,并将其作为各省份的代表性生态型品种参数;最后将生成的气象要素和播种期表面数据以及生态型品种参数等输入到WheatGrow和CERES-Wheat模型中,并以栅格为单元进行研究区域小麦生育期的模拟,进一步结合不同站点历史年份的生育期观测资料,检验和评价模型区域应用方法的有效性,并量化区域生育期模拟结果的不确定性。【结果】两个模型在区域尺度上的生育期预测效果均较好,区域尺度上拔节期、抽穗期、成熟期的预测值和观测值之间的R2分别为0.85、0.87和0.86（WheatGrow）,0.87、0.85和0.82（CERES）;RMSE分别为9.6、7.2和6.3 d（WheatGrow）,9.4、7.8和6.6 d（CERES）。另外,WheatGrow模型对抽穗期和成熟期区域预测的准确度略高于CERES-Wheat,但由品种参数导致的区域模拟结果的不确定性也相对较高。【结论】通过气象数据和小麦播期数据的TPS插值技术结合MCMC方法的品种参数估计技术,将基于机理的生育期模型拓展到区域尺度,较好地预测区域小麦生育期,量化了由品种参数导致模拟结果的不确定性,研究结果可为进一步量化中国小麦主产区的区域生产力提供技术支撑。     

Algorithms for sensor-based redistribution of nitrogen fertilizer in winter wheat

Several methods were developed for the redistribution of nitrogen (N) fertilizer within fields with winter wheat (Triticum aestivum L.) based on plant and soil sensors, and topographical information. The methods were based on data from nine field experiments in nine different fields for a 3-year period. Each field was divided into 80 or more subplots fertilized with 60, 120, 180 or 240 kg N ha⁻¹. The relationships between plot yield, N application rate, sensor measurements and the interaction between N application and sensor measurements were investigated. Based on the established relations, several sensor-based methods for within-field redistribution of N were developed. It was shown that plant sensors predicted yield at harvest better than soil sensors and topographical indices. The methods based on plant sensors showed that N fertilizer should be moved from areas with low and high sensor measurements to areas with medium values. The theoretical increase in yield and N uptake, and the reduced variation in grain protein content resulting from the application of the above methods were estimated. However, the estimated increases in crop yield, N-uptake and reduced variation in grain protein content were small.     

Comparison of QuickBird Satellite Imagery and Airborne Imagery for Mapping Grain Sorghum Yield Patterns

Timely and accurate information on crop conditions obtained during the growing season is of vital importance for crop management. High spatial resolution satellite imagery has the potential for mapping crop growth variability and identifying problem areas within fields. The objectives of this study were to use QuickBird satellite imagery for mapping plant growth and yield patterns within grain sorghum fields as compared with airborne multispectral image data. A QuickBird 2.8-m four-band image covering a cropping area in south Texas, USA was acquired in the 2003 growing season. Airborne three-band imagery with submeter resolution was also collected from two grain sorghum fields within the satellite scene. Yield monitor data collected from the two fields were resampled to match the resolutions of the airborne imagery and the satellite imagery. The airborne imagery was related to yield at original submeter, 2.8 and 8.4 m resolutions and the QuickBird imagery was related to yield at 2.8 and 8.4 m resolutions. The extracted QuickBird images for the two fields were then classified into multiple zones using unsupervised classification and mean yields among the zones were compared. Results showed that grain yield was significantly related to both types of image data and that the QuickBird imagery had similar correlations with grain yield as compared with the airborne imagery at the 2.8 and 8.4 m resolutions. Moreover, the unsupervised classification maps effectively differentiated grain production levels among the zones. These results indicate that high spatial resolution satellite imagery can be a useful data source for determining plant growth and yield patterns for within-field crop management.     

Selection of ancillary data to derive production management units in sweet corn (<Emphasis Type="Italic">Zea Mays</Emphasis> var. rugosa) using MANOVA and an information criterion

In production systems where high-resolution harvest data are unavailable there is often a reliance on ancillary information to generate potential management units. In these situations correct identification of relevant sources of data is important to minimize cost to the grower. For three fields in a sweet corn production system in central NSW, Australia, several sets of high-resolution data were obtained using soil and crop canopy sensors. Management units were derived by k-means classification for 2–5 classes using three approaches: (1) with soil data, (2) with crop data and (3) a combination of both soil and crop data. Crop quantity and quality were sampled manually, and the sample data were related to the different management units using multivariate analysis of variance (MANOVA). The corrected Akaike information criterion (AICc) was then used to rank the different sources of data and the different orders of management units. For irrigated, short-season sweet corn production the management units derived from the crop canopy sensor data explained more variation in key harvest variables than management units derived from an apparent soil electrical conductivity (EC_a) survey or a mixture of crop and soil sensor data. Management units derived from crop data recorded just prior to side-dressing outperformed management units derived from data recorded earlier in the season. However, multi-temporal classification of early and mid-season crop data gave better results than single layer classification at any time. For all three fields in this study, a 3- or 4-unit classification gave the best results according to the information criterion (AICc). For growers interested in adopting differential management in irrigated sweet corn, investment in a crop canopy sensor will provide more useful high-resolution information than that in a high-resolution EC_a survey.