Frequency Analysis of Yield for Delineating Yield Response Zones

The yield in any given field or management zone is a product of interaction between many soil properties and production inputs. Therefore, multi-year yield maps may give better insight into determining potential management zones. This research was conducted to develop a methodology to delineate yield response zones by using two-state frequency analysis conducted on yield maps for 3 years on two commercial corn fields near Wiggins, Colorado. A zone was identified by the number of years that yield was equal and greater than the average yield in a given year. Classes producing statistically similar yield were combined resulting in three potential yield zones. Results indicated that the variability of yield over time and space could successfully be assessed at the same time without the drawbacks of averaging data from different years. Frequency analysis of multi-year yield data could be an effective way to establish yield response zones. Seventeen percent of the field #1 consistently produced lower yield than the mean while 43 of the field produced yield over the mean. Corresponding values for field #2 were 6% and 42%.The remainder of the fields produced fluctuating yields between years. These spatially and temporally sound yield response maps could be used to identify the yield-limiting factors in zones where yield is either low or fluctuating. Yield response maps could also be helpful to delineate potential management zones with the help of resource zones such as electrical conductivity and soil maps, along with the directed soil sampling results.     

Mapping Grain Sorghum Yield Variability Using Airborne Digital Videography

Mapping crop yield variability is one important aspect of precision agriculture. Combine-mounted yield monitors are becoming widely available for measuring and mapping yields for different crops. This study was designed to assess airborne digital videography as a tool for mapping grain sorghum yields for precision farming. Color-infrared (CIR) imagery was acquired with a three-camera digital video imaging system from two grain sorghum fields in south Texas over the 1995 and 1996 growing seasons. The multispectral video data obtained during the bloom to soft dough stages of plant development were related to hand-harvested grain yields at sampling sites determined from unsupervised image classification maps of the two fields. Significant correlations were found between grain yields and the red band, the green band, and the normalized difference vegetation index (NDVI). Regression equations were developed to describe the relations between grain yields and each of the three significant spectral variables using an exponential model and two segmented models. Multiple linear regression equations were also determined to relate grain yields to the three bands and NDVI. These equations were then used to estimate grain yields at each video image pixel within each field and to generate grain yield maps. Comparisons of the estimated average yields from the regression equations with the actual yields indicated that yield estimation errors from the equations ranged from 0.0 to 10.0% in 1995 and from 0.2 to 7.3% in 1996 for field 1, and from 4.0 to 11.2% in 1995 and 6.3 to 12.5% in 1996 for field 2. Although the equations developed for one field in a given year may not apply to the same field in any other year, the practical value of these relationships is for mapping within-field grain yield variations. The results from this study showed that airborne digital videography, combined with ground sampling, regression analysis, and image processing, could be a useful approach for mapping spatial crop yield variability within fields.     

产量分布图生成系统的研究

使用Visual Basic6．0编程语言开发了YMapper产量分布图生成软件系统。研究了一种具有粗大误差数据过滤功能的局部平均插值方法，分析了产量分布图生成过程中涉及到的坐标系定义、产量数据分类与统计分析、图形配色与绘制等问题。对谷物和棉花等作物产量数据的处理结果表明，该系统能够对联合收割机测产系统记录的产量数据文件进行处理，通过插值运算将离散分布的产量数据点生成连续的产量分布图；其误差数据过滤功能能够防止产量过低和过高的粗大误差数据点参与插值运算，使产量分布图的精度得到了保证。系统能够按照用户设置的分类和着色方式，将作物产量的空间分布情况以产量数据点图或产量分布图的形式显示。并且能够对作物的产量水平进行统计分析。     

谷物联合收割机在线产量监测综述——测产传感方法、产量图重建和动力学模型

为深入了解测产方法、产量图重建和动力学模型的研究内容及关键技术,对测产方法、产量图重建、谷物流的动力学模型以及产量测量中的误差等研究成果进行梳理。重点概述了测产方法的分类,介绍了不同测产方法的原理、产量图重建涉及到的关键技术和动力学模型上取得的成果;对测产方法的试验结果和优缺点进行比较;分析了测产方法、产量图重建、水分传感器、切割宽度传感器和GPS定位装置等研究的误差来源。结果表明:1)对不同方式的测产装置进行合理的安装、校准和操作,就能使测产结果达到足够的精度,建议对不同的测产方式加强误差分析并提高试验准确度。2)产量图重建过程中的部分误差通过校准可以减小甚至消除,但基于小面积地块的产量图构建及误差研究还有待加强。3)一阶动力学模型无法确定谷物混合对产量监测的影响,建议在基于非线性组合算法和反褶积算法的动力学模型上加强研究。     

Block Estimating of Spatial Yield Data and its Uncertainty

On-the-go yield monitors have been available for both grain and bulk crops. Most of the yield monitors today provide yield measurement at a fixed time interval. Conversion of these point yield data into raster yield maps for further analysis is necessary. In this study, a data-blocking procedure is proposed to create raster yield maps from point yield data. The blocking procedure includes: (1) converting the fixed-time-interval data into fixed-distance-interval data; (2) using a moving average algorithm to estimate a cell value when there are sufficient data points within the cell; (3) using a geostatistical algorithm to estimate a cell value when there are not enough data points within the cell but values of its neighboring cells are known; and (4) calculating an uncertainty index for each cell value estimation. An example application of the yield-blocking procedure with potato harvest data in 1996 was given.     

Remedial Correction of Yield Map Data

Many yield maps exhibit systematic errors that attenuate the underlying yield variation. Two errors are dealt with in detail in this paper: those that occur when the harvester has a narrow finish to a land and those that occur when the harvester is filling up at the start of a harvest run. The authors propose methods to correct or remove erroneous data by the use of an expert filter, or alternatively use of an interpolation technique called potential mapping.     

Identifying and characterizing yield limiting factors in paddy rice using remote sensing yield maps

Identification and characterization of yield limiting factors based on multi-year yield maps is important for delineating field management zones. Multi-year yield maps were derived from satellite images of a paddy-rice (Oryza sativa L.) study site with a conventional two-cropping system in central Taiwan. Spatiotemporal yield-trend maps with consistently high, average and low yields, and inconsistent yield areas were delineated based on temporal variation and the means of the normalized yields on a per pixel basis. Soil and plant samples were collected and grouped for statistical analysis based on the derived yield-trend maps. Comparison of soil properties and rice yield components among yield classes indicated that differences in leaching loss of basal and top-dressed N fertilizers were the likely limiting factor affecting the spatial variation of yield within the study site.     

Crop type mapping using spectral–temporal profiles and phenological information

Spatially explicit multi-year crop information is required for many environmental applications. The study presented here proposes a hierarchical classification approach for per-plot crop type identification that is based on spectral–temporal profiles and accounts for deviations from the average growth stage timings by incorporating agro-meteorological information in the classification process. It is based on the fact that each crop type has a distinct seasonal spectral behavior and that the weather may accelerate or delay crop development. The classification approach was applied to map 12 crop types in a 14,000 km² catchment area in Northeast Germany for several consecutive years. An accuracy assessment was performed and compared to those of a maximum likelihood classification. The 7.1% lower overall classification accuracy of the spectral–temporal profiles approach may be justified by its independence of ground truth data. The results suggest that the number and timing of image acquisition is crucial to distinguish crop types. The increasing availability of optical imagery offering a high temporal coverage and a spatial resolution suitable for per-plot crop type mapping will facilitate the continuous refining of the spectral–temporal profiles for common crop types and different agro-regions and is expected to improve the classification accuracy of crop type maps using these profiles.     

Estimation of yield zones using aerial images and yield data from a few tracks of a combine harvester

Yield maps derived from yield mapping systems are often erroneous not only due to limitations in measuring the yield precisely but due to insufficient consideration of the requirements of yield mapping systems in practice as well. Aerial images of cultivated crop fields at an advanced growth stage frequently provide a spatial pattern similar to that of yield maps. Therefore, the possibility of generating a yield map using aerial images and measured yield data of a few tracks was examined for a period of 2 years in two fields grown with cereals. Yield zones based on Visible Atmospherically Resistant Index (VARI) values were compared with yield zones based on measured yield data of the whole field. About half of the grid cells of a field were allocated to the same yield zones irrespective of the mode of yield determination. Using the Kruskal–Wallis test, the data sub-sets of measured yield within the yield zones based on the VARI values differed significantly for all tested yield zones. As a result, the approach was successful in the case of these experimental sites.     

Predicting Cotton Lint Yield Maps from Aerial Photographs

It is generally accepted that aerial images of growing crops provide spatial and temporal information about crop growth conditions and may even be indicative of crop yield. The focus of this study was to develop a straightforward technique for creating predictive cotton yield maps from aerial images. A total of ten fields in southern Georgia, USA, were studied during three growing seasons. Conventional (true color) aerial photographs of the fields were acquired during the growing season in two to four week intervals. The aerial photos were then digitized and analyzed using an unsupervised classification function of image analysis software. During harvest, conventional yield maps were created for each of the fields using a cotton picker mounted yield monitor. Classified images and yield maps were compared quantitatively and qualitatively. A pixel by pixel comparison of the classified images and yield maps showed that spatial agreement between the two gradually increased in the weeks after planting, maintained spatial agreement of between 40% and 60% during weeks eight to fourteen, and then gradually declined again. The highest spatial agreement between a classified image and a yield map was 78%. The highest average agreement was 52% and occurred 9.9 weeks after planting. The visual similarity between the classified images and the yield maps were striking. In all cases, the dates with the best visual agreement occurred between eight and ten weeks after planting, and generally, during July for southern Georgia. This method offers great potential for offering cotton farmers early-season maps that predict the spatial distribution of yield. Although these maps can not provide magnitudes, they clearly show the resulting yield patterns. With inherent knowledge of past performance, farmers can use this information to allocate resources, address crop growth problems, and, perhaps, improve the profitability of their farm operation. These maps are well suited to be offered to farmers as a service by a crop consultant or a cooperative.     

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.     

Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability

Increased availability of hyperspectral imagery necessitates the evaluation of its potential for precision agriculture applications. This study examined airborne hyperspectral imagery for mapping cotton (Gossypium hirsutum L.) yield variability as compared with yield monitor data. Hyperspectral images were acquired using an airborne imaging system from two cotton fields during the 2001 growing season, and yield data were collected from the fields using a cotton yield monitor. The raw hyperspectral images contained 128 bands between 457 and 922 nm. The raw images were geometrically corrected, georeferenced and resampled to 1 m resolution, and then converted to reflectance. Aggregation functions were then applied to each of the 128 bands to reduce the cell resolution to 4 m (close to the cotton picker's cutting width) and 8 m. The yield data were also aggregated to the two grids. Correlation analysis showed that cotton yield was significantly related to the image data for all the bands except for a few bands in the transitional range from the red to the near-infrared region. Stepwise regression performed on the yield and hyperspectral data identified significant bands and band combinations for estimating yield variability for the two fields. Narrow band normalized difference vegetation indices derived from the significant bands provided better yield estimation than most of the individual bands. The stepwise regression models based on the significant narrow bands explained 61% and 69% of the variability in yield for the two fields, respectively. To demonstrate if narrow bands may be better for yield estimation than broad bands, the hyperspectral bands were aggregated into Landsat-7 ETM+ sensor's bandwidths. The stepwise regression models based on the four broad bands explained only 42% and 58% of the yield variability for the two fields, respectively. These results indicate that hyperspectral imagery may be a useful data source for mapping crop yield variability.     

Relationships Between Yield Monitor Data and Airborne Multidate Multispectral Digital Imagery for Grain Sorghum

Remote sensing imagery taken during a growing season not only provides spatial and temporal information about crop growth conditions, but also is indicative of crop yield. The objective of this study was to evaluate the relationships between yield monitor data and airborne multidate multispectral digital imagery and to identify optimal time periods for image acquisition. Color-infrared (CIR) digital images were acquired from three grain sorghum fields on five different dates during the 1998 growing season. Yield data were also collected from these fields using a yield monitor. The images and the yield data were georeferenced to a common coordinate system. Four vegetation indices (two band ratios and two normalized differences) were derived from the green, red, and near-infrared (NIR) band images. The image data for the three bands and the four vegetation indices were aggregated to generate reduced-resolution images with a cell size equivalent to the combine's effective cutting width. Correlation analyses showed that grain yield was significantly related to the digital image data for each of the three bands and the four vegetation indices. Multiple regression analyses were also performed to relate grain yield to the three bands and to the three bands plus the four indices for each of the five dates. Images taken around peak vegetative development produced the best relationships with yield and explained approximately 63, 82, and 85% of yield variability for fields 1, 2, and 3, respectively. Yield maps generated from the image data using the regression equations agreed well with those from the yield monitor data. These results demonstrated that airborne digital imagery can be a very useful tool for determining yield patterns before harvest for precision agriculture.     

科技进步增产的“多年平均产量移动模型”的验证

应用全国、31个省、6个典型地区和16个典型县的数据对科技进步增产的"多年平均产量移动模型"进行了系统性的验证和讨论。研究结果如下:(1)定义了用相邻10年平均单产之差表示科技进步对单产的贡献及其趋势,结论是科技进步是单产增加的主要驱动力;(2)分别定义了用每年单产与5、10、20年平均单产对比关系的概率作为短、中、长期单产稳定性的指标;稳定性研究结果表明国家级大于省级、省级大于地区级、地区级大于县级,不同省、不同地区、不同县之间稳定性差别比较大,这与境内气候的互补性和农田抗御自然灾害的能力有关;(3)就全国而言,越是经济发达的地区科技进步增产加速的时间越早;发达地区单产存在增加-下降-回升阶段,下降原因是经济快速发展初期高产农田被大量占用和(或)蔬菜、水果面积大量增加,回升原因是科技进步持续作用于中、低产田而使其单产得到稳步提高。     

Mapping regional cropping patterns by using GF-1 WFV sensor data

The successful launched Gaofen satellite no. 1 wide field-of-view(GF-1 WFV) camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study,taking the Bei'an City,Northeast China as the study area,aims to investigate the potential of GF-1 WFV images for crop identification and explore how to fully use its spectral,textural and temporal information to improve classification accuracy. In doing so,an object-based and Random Forest(RF) algorithm was used for crop mapping. The results showed that classification based on an optimized single temporal GF-1 image can achieve an overall accuracy of about 83%,and the addition of textural features can improve the accuracy by 8.14%. Moreover,the multi-temporal GF-1 data can produce a classification map of crops with an overall accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%,which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data. By comparing classification results of GF-1 data with different feature inputs,it is concluded that GF-1 WFV data in general can meet the mapping efficiency and accuracy requirements of regional crop. But given the unique spectral characteristics of the GF-1 WFV imagery,the use of textual and temporal information is needed to yield a satisfactory accuracy.     

Yield map generation of perennial crops for fresh consumption

Yield mapping technologies can help to increase the quantity and quality of agricultural production. Current systems only focus on the quantification of the harvest, but the quality has equal or greater importance in some perennial crops and impacts directly on the financial profitability. Therefore, a system was developed to quantify and relate the quality obtained in the classification line with the plants of the orchard and for decision-making. The system is comprised of hardware, which obtains the location of the harvester bag during harvesting and unloading at the unloading site, and software that processes the collected data. The cloud of real-time data contributed from the different collectors (bins) allows the construction of yield maps, considering the multi-stage harvesting system. Further, the system enables the creation of a detailed map of the plants and fruits harvested. As the harvest focuses on quality, it takes place in stages, depending on the ripening of the fruits. In addition to the yield maps, the system allows identification of the efficiency of each worker undertaking the harvest by the number of performed discharges and by the time spent. The system was developed in partnership with the Federal Technological University of Paraná and Embrapa Uva & Vinho and was tested in apple orchards in southern Brazil. Although the system was evaluated with only data from apple cultivation, monitoring the quality and quantifying other orchard fruits can positively impact the fruit sector.

     

Identifying Potential Within-Field Management Zones from Cotton-Yield Estimates

Remotely-sensed cotton yield estimates, collected mid-season over the past 11 years, were investigated to identify the degree of temporal stability exhibited in two irrigated fields on Colly Central farm, Collarenabri, NSW, Australia. In particular, the aims of the investigation were: (1) to develop stable yield zones from multi-year yield estimates derived from 11 consecutive years' mid-season Landsat TM imagery; (2) to discover the number of consecutive years of yield estimates required to give similar stable estimates of yield zones to those derived from all 11 years of available data. Results of the investigation indicate that the fields described in the study exhibit a strong degree of temporal stability. Additionally, where an assumption is made that 11 years worth of yield estimates will cluster to generate the most temporally stable regions of similarity, the mapping of clusters generated using 5 or more years will generate comparable regions of similarity with high confidence that the regions will indeed closely match those of the temporally stable 11 year estimates.     

Subfield crop yields and temporal stability in thousands of US Midwest fields

Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p?<?0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha^?1 for maize, 0.39 Mg ha^?1 for cotton, 0.34 Mg ha^?1 for soybean, and 0.59 Mg ha^?1 for wheat. The yield of the unstable zones was similar to the pixels classified as low-and-stable by the standard deviation algorithm, whereas the two-way outlier algorithm did not exhibit this bias. Furthermore, the increase in the number years of yield maps available induced a modest but significant increase in the certainty of stability classifications, and the proportion of unstable pixels increased with the precipitation heterogeneity between the years comprising the yield maps.

     

日本晴/中嘉早17重组自交系产量性状QTL定位

【目的】对产量相关性状进行多年、多环境的QTL分析,寻找能够稳定遗传的产量性状主效QTL,剖析超级早籼稻中嘉早17的高产机理,为选育高产新品种提供有用信息。【方法】以日本晴×中嘉早17构建的重组自交系群体为研究材料。筛选亲本间多态性SSR标记,对群体各家系进行基因型分析,利用Mapmarker/exp 3.0构建分子遗传连锁图谱。群体于2015—2016年,两地三季种植于杭州、海南和杭州,成熟期考察有效穗数、每穗粒数、单株产量、结实率、千粒重、粒长、粒宽和粒厚等产量相关性状。运用Windows QTL Cartographer 2.5检测产量相关性状QTL,运用QTL Network 2.2检测QTL与环境互作效应。【结果】构建的连锁图谱共包含163对SSR标记,73%的标记父母本基因型比例符合1﹕1理论分离比,23%标记显著偏分离,主要偏向父本中嘉早17,图谱总图距约1 479.4 cM,标记间平均距离约为9.08 c M。3个环境下共检测到46个QTL,分布于除第11染色体外的其他染色体上,贡献率变幅为3.78%—25.45%。共有10个QTL在3个环境下能被重复检测到,分别是控制有效穗数的qEP1、qEP2、qEP4a,控制每穗粒数的qNGPE1、qNGPE7,控制结实率的q SRT7,控制千粒重的q TGW2,控制粒长的qGL3和qGL9,控制粒宽的q GW2b;其中qEP1、qEP2、qNGPE7、qTGW2和q GW2b的增效等位来自亲本日本晴;而qEP4a、qNGPE1、qSRT7、qGL3和qGL9的增效等位来自亲本中嘉早17;除此之外,所检测到的每穗粒数、结实率、粒长和单株产量QTL中大部分增效等位基因均来自中嘉早17。产量性状与环境互作分析显示,控制每穗粒数qNGPE1和qNGPE7、控制结实率的q SRT1a和q SRT7、控制单株产量的q YPP1和q YPP7等6个QTL与环境互作效应显著或极显著。此外,在第1、2、7染色体某区段多个与产量相关的QTL成簇分布。【结论】以日本晴×中嘉早17构建的重组自交系群体连锁图谱具有丰富的多态性标记,覆盖水稻基因组的93.64%,可较好地满足水稻重要农艺性状QTL定位要求。利用该套群体检测到多个产量相关性状QTL,其中,多数控制每穗粒数、结实率、粒长和单株产量的QTL的增效等位基因均来自中嘉早17。该结果与中嘉早17的每穗粒数、结实率、单株产量、千粒重和粒长等性状显著明显优于日本晴的结果一致,这些产量增效QTL可能是中嘉早17高产、稳产的遗传基础。     

Yield prediction in apple orchards based on image processing

It has been suggested that apple ( Malus * domestica Borkh) flowering distribution maps can be used for site-specific management decisions. The objectives of this study were (i) to study the flower density variability in an apple orchard using image analysis and (ii) to model the correlation between flower density as determined from image analysis and fruit yield. The research was carried out in a commercial apple orchard in Central Greece. In April 2007, when the trees were at full bloom, photos of the trees were taken following a systematic uniform random sampling procedure. In September 2007, yield mapping was carried out measuring yield per ten trees and recording the position of the centre of the ten trees. Using this data (the measured yield of the trees and the pictures samples, representing the flower distribution), an image processing-based algorithm was developed that predicts tree yield by analyzing the picture of the tree at full bloom. For the evaluation of the algorithm, a case study scenario is presented where the error of the predicted yield was set at 18%. These results indicated that potential yield could be predicted early in the season from flowering distribution maps and could be used for orchard management during the growing season.