不同叶形大豆品种产量性状边际效应指数分析

高产大豆品种不仅要求单株产量高,而且又适宜密植的品种。以两种叶形的10个品种（系）为材料,从边际效应指数角度,研究了不同叶形大豆品种叶部性状与产量性状的边际效应指数及其相互关系,期望筛选出耐密型大豆品种。结果表明：长叶品种的叶长、叶宽、叶面积、单株荚数、单株粒数和单株产量的边际效应指数均比圆叶品种的小,适于密植;单株产量边际效应指数与叶部性状的边际效应指数相关不显著,与单株荚数和单株粒数的边际效应指数呈极显著正相关,与百粒重的边际效应指数呈显著负相关。长叶大豆品种绥农14的单株产量边际效应指数最小,属于耐密型品种。     

大豆不同品种（系）性状与产量关系的研究

本试验以１９９５～１９９６年的１０个大豆品种（系）为试材,利用相关分析。多元线性回归分析、偏相关分析、通径分析等统计方法,对大豆２３个性状进行统计分析,以期找出各性状间及其与产量间的相互关系,为大豆育种及高产栽培提供理论依据,研究结果表明,对单株粒数贡献最大的是主茎二粒荚、三粒荚和分枝二粒荚。对产量贡献最大的是株高、Ｃｏｕ荚、单株粒数、主茎节数和分枝数。欲提高产量时,对植株的高度以直接选择和间接选     

大豆地理远缘材料杂种优势的遗传研究

以三组不同地理来源的9个亲本组配二类18个杂交组合。研究结果表明:大豆品种间杂交一代的杂种优势普遍存在;不同性状、组合间有明显差异。单株粒重的优势最强,地理远缘组合的大部分性状超亲优势率均大于相同地理来源组合。选择地理上远缘且生态类型上有差异的双亲进行组配,杂种一代的产量超亲优势最强。遗传相关分析结果表明:F_1的产量与产量因素中的单株粒数、单株荚数呈极显著遗传正相关;与每荚粒数、百粒重相关不显著。对产量因素与产量进行通径分析表明:单株粒数对产量的直接作用大于单株荚数,是杂种一代产量优势最主要的贡献因素。对 F_1的分枝、百粒重性状与双亲均值、小值亲本、大值亲本的关系进行通径分析结果表明:分枝数以双亲均值对 F_1的直接影响最大;百粒重以大值亲本对 F_1的直接影响最大。     

中美不同年代大豆品种农艺性状的比较

选用地理纬度相似的中国辽宁省、美国俄亥俄州新近育成的大豆品种,分别和中国辽宁省20世纪20年代老品种进行了农艺性状的比较研究。结果表明:中美大豆品种主要农艺性状遗传改进的共有趋势是主茎荚数、单株荚数、3粒和4粒荚率、单株粒数、单株粒重、百粒重、粒茎比、收获指数明显增加。株高、主茎节数、节间长度和分枝数显著降低。美国新品种的单株荚数、单株粒数和粒茎比的增加幅度明显高于同期的中国新品种。而中国新品种的百粒重显著高于美国新品种。施肥水平对不同年代大豆品种主要农艺性状也有一定影响,且美国新品种在高肥条件下比中国新品种表现出更高的增产潜力。     

SN—924酶激活剂在大豆上的应用效果分析

在大豆上应用ＳＮ－９２４酶激活剂，可加快幼苗生长，增加叶片叶绿素含量和光合速率；单株结荚率提高１０％，产量增加１２％。     

大豆卷叶螟生物学特性的初步研究

大豆卷叶螟(Sylepta ruralis Scopoli)主要分布于辽宁、山东、内蒙古等地区,在我省以沈阳、丹东等地区发生较为严重。国外日本、朝鲜也有此虫发生的报导。大豆卷叶螟是大豆的重要食叶性害虫,幼虫将叶片横卷成筒状,尤以大豆开花结荚盛期为害较显著,由于营养器官受到破坏,常引起大量落花落荚,或豆粒秕缩,质量降低。在严     

膜下滴灌棉花氮磷肥料施用效果研究

氮肥随水施用可显著提高棉花单株铃数、单铃重和籽棉产量,促进棉花对氮、磷素的吸收,尤其在壤土棉田的效果较明显。随水施用磷肥对棉花单株铃数影响不大,籽棉产量较不施磷肥处理提高8.80～17.40%,其在壤土棉田上的增产效果优于粘土棉田。磷肥随水滴施可提高氮、磷肥料利用率,与基施相比氮肥利用率提高4.85～12.34%,磷肥利用率提高36.75～45.88%,且在壤土棉田上的施用效果优于粘土棉田。     

不同播期对黑河38大豆生长动态及产量的影响

引进黑龙江省大豆品种黑河38,对不同播期处理的产量性状、生育期结构、叶面积指数消长、生物产量积累动态、器官平衡等进行了比较。结果表明：该品种在沈阳地区种植,随着播期延迟,生育日数逐渐缩短,最大叶面积指数下降、单株生物产量的积累减少。各播期间单株粒数、百粒重、小区产量差异显著,单位面积产量以6月9日播种最高,迟至7月11日播种,仍可获得较高产量,可作为春小麦或马铃薯下茬利用。以黑河、沈阳两地同期播种相比较,产量和品质差异较大。     

地形和土壤属性对大豆产量的影响

正确理解作物产量的变异性特征及其影响因素是对农田地块进行科学管理的基础。地块的地形特征是导致作物产量变异形成的主要因素。本文选择代表不同地形特征的洼地农田和岗地农田为研究对象,分析地形对土壤属性和大豆产量的影响以及它们之间的相互关系。研究结果显示,洼地农田与岗地农田的土壤属性和大豆产量均存在显著性差异(P<0.05)。洼地农田的土壤养分含量较高,但土壤结构较差,其平均重量直径(MWD)和几何平均直径(GMD)显著小于岗地农田,质量分形维数(Dm)显著大于岗地农田;在两类农田地块中,大豆产量的差别较大,洼地农田的大豆产量仅为岗地农田的62%。相关性分析表明,大豆产量与土壤结构稳定性和地块高程呈极显著正相关(P<0.01);土壤有机碳和土壤养分对大豆产量没有显著影响。     

大豆经济系数试验研究初报

一、试验目的作物经济系数的高低是决定作物产量高低的重要因素之一.在作物总生产力(即生物产量,粮谷作物和油料作物一般不计算根)高,经济系数也高的情况下,单位面积籽粒产量也必然是高的。提高作物经济系数已经成为作物育种的重要目标和作物高产栽培的重要环节。为了     

腐殖酸液肥对大豆产量和品质的影响

基于腐殖酸液肥在蔬菜生产的良好表现，为了拓宽该肥料的使用范围。进行了盆栽大豆试验。结果表明：腐液肥单独施用及与化肥配合施用都可以显提高大豆产量，分别比对照增产43.84%和53.01%；还可以显提高大豆籽粒中的粗脂肪含量，比等养分量的化肥处理分别高出1.51%和1.59%，但对大豆籽粒粗蛋白含量有一定的负影响。     

Application of thematic mapper data to corn and soybean development stage estimation

A model, utilizing direct relationship between remotely sensed spectral data and the development stage of both corn and soybeans has been proposed and published previously (Badhwar and Henderson, 1981; and Henderson and Badhwar, 1984). This model was developed using data acquired by instruments mounted on trucks over field plots of corn and soybeans as well as satellite data from Landsat. In all cases, the data was analyzed in the spectral bands equivalent to the four bands of Landsat multispectral scanner (MSS). In this study the same model has been applied to corn and soybeans using Landsat-4 Thematic Mapper (TM) data combined with simulated TM data to provide a multitemporal data set in TM band intervals. All data (five total acquisitions) were acquired over a test site in Webster County, Iowa from June to October 1982. The use of TM data for determining development state is as accurate as with Landsat MSS and field plot data in MSS bands. The maximum deviation of 0.6 development stage for corn and 0.8 development stage for soybeans is well within the uncertainty with which a field can be estimated with procedures used by observers on the ground in 1982.     

Field size,length, and width distributions based on LACIE ground truth data

A representative subset of a stratified random sample of LACIE (Large Area Crop Inventory Experiment) segments which are 5 nmi x6 nmi in size were ground truthed and were used to derive field size, length, and width distributions for winter wheat, spring wheat, corn, soybeans, water, and “all crops” for areas in nine states in the U. S. Great Plains and one state in the Corn Belt. Field sizes for spring wheat and soybeans appeared log-normally distributed whereas the other crops and “all crops” did not fit the log-normal distribution well. The modal field size was near 10 acres for most crops studied. Winter wheat, spring wheat, and corn were found to have field width modes near 90 m and soybeans had a mode at 200 m. About 25% of all fields were found to be more narrow than 100 m. Field length modes were found at 400, 800, and 1600 m (I mi) due to the section line road system in the agricultural midwest and the homesteading of 160-acre farms (800 m x 800 m). Based on these field size distributions and a simple theoretical model it was estimated that fields of corn, soybeans, winter wheat, and spring wheat have Landsat MSS pixels which are on the average 40% pure (i.e., 40% of all pixels contain a single generic class), and that this will increase to 75% at the thematic mapper resolution.     

Identification and area estimation of agricultural crops by computer classification of LANDSAT MSS data

LANDSAT Multispectral Scanner (MSS) data covering a three-county area in northern Illinois were classified using computer-aided techniques as corn, soybeans, or “other.” Recognition of test fields was 80% accurate. County estimates of the area of corn and soybeans agreed closely with those made by the USDA. Results of the use of a priori information in classification, techniques to produce unbiased area estimates, and the use of temporal and spatial features for classification are discussed. The extendability, variability, and size of training sets, wavelength band selection, and spectral characteristics of crops were also investigated.     

LANDSAT MSS crop classification performance as a function of scene characteristics

In order to utilize remote sensing fully to inventory crop production, it is important to identify and quantify the effects of the factors that affect the accuracy of LANDSAT classifications. The objective of this study was to investigate the effect of scene characteristics involving crop, soil and weather variables on the accuracy of LANDSAT classifications of corn and soybeans. Segments of multitemporally registered LANDSAT MSS data from two key acquisition periods sampling the U.S. Corn Belt were classified using a Gaussian maximum likelihood classifier. Field size had a strong effect on classification accuracy with small fields tending to have low accuracies even when the effect of mixed pixels was eliminated. Other scene characteristics accounting for variability in classification accuracy included proportions of corn and soybeans, crop diversity index, proportion of all field crops, soil order, soil drainage class, percentage of slope, long-term average soybean yield, maximum yield, relative position of the segment in the Corn Belt, weather and crop development stage.     

Land cover-based optimal deconvolution of PALS L-band microwave brightness temperatures

An optimal deconvolution (ODC) technique has been developed to estimate microwave brightness temperatures of agricultural fields using microwave radiometer observations. The technique is applied to airborne measurements taken by the Passive and Active L and S band (PALS) sensor in Iowa during Soil Moisture Experiments in 2002 (SMEX02). Agricultural fields in the study area were predominantly soybeans and corn. The brightness temperatures of corn and soybeans were observed to be significantly different because of large differences in vegetation biomass. PALS observations have significant over-sampling; observations were made about 100 m apart and the sensor footprint extends to about 400 m. Conventionally, observations of this type are averaged to produce smooth spatial data fields of brightness temperatures. However, the conventional approach is in contrast to reality in which the brightness temperatures are in fact strongly dependent on land cover, which is characterized by sharp boundaries. In this study, we mathematically deconvolve the observations into brightness temperature at the field scale (500-800 m) using the sensor antenna response function. The result is more accurate spatial representation of field-scale brightness temperatures, which may in turn lead to more accurate soil moisture retrieval.     

Soil moisture mapping and AMSR-E validation using the PSR in SMEX02

Field experiments (SMEX02) were conducted to evaluate the effects of dense agricultural crop conditions on soil moisture retrieval using passive microwave remote sensing. Aircraft observations were collected using a new version of the Polarimetric Scanning Radiometer (PSR) that provided four C band and four X band frequencies. Observations were also available from the Aqua satellite Advanced Microwave Scanning Radiometer (AMSR-E) at these same frequencies. SMEX02 was conducted over a three-week period during the summer near Ames, Iowa. Corn and soybeans dominate the region. During the study period the corn was approaching its peak water content state and the soybeans were at the mid point of the growth cycle. Aircraft observations are compared to ground observations. Subsequently models are developed to describe the effects of corn and soybeans on soil moisture retrieval. Multiple altitude aircraft brightness temperatures were compared to AMSR-E observations to understand brightness temperature scaling and provide validation. The X-band observations from the two sensors were in reasonable agreement. The AMSR-E C-band observations were contaminated with anthropogenic RFI, which made comparison to the PSR invalid. Aircraft data along with ancillary data were used in a retrieval algorithm to map soil moisture. The PSR estimated soil moisture retrievals on a field-by-field comparison had a standard error of estimate (SEE) of 5.5%. The error reduced when high altitude soil moisture estimates were aggregated to 25 km resolution (same as AMSR-E EASE grid product resolution) (SEE ∼ 2.85%). These soil moisture products provide a validation of the AMSR retrievals. PSR/CX soil moisture images show spatial and temporal patterns consistent with meteorological and soil conditions. The dynamic range of the PSR/CX observations indicates that reasonable soil moisture estimates can be obtained from AMSR, even in areas of high vegetation biomass content (∼ 4-8 kg/m2).     

Use of landsat-derived temporal profiles for corn-soybean feature extraction and classification

Using the multitemporal multispectral data acquired by Landsat satellites and a physical model describing this behavior, new features that are crop specific have been derived. The new feature space is two-dimensional irrespective of the number of Landsat observations. A feasibility study, over 40 sites, has been performed to classify the segment pixels into those of corn, soybeans, and others using these new features and a linear classifier. The results compare very favorably with other existing methods. The results also indicate where additional accuracy gains can be made.     

Monitoring corn and soybean crop development with hand-held radiometer spectral data

Red and photographic infrared data were collected with a hand-held radiometer under a variety of conditions at 4- to 12-day intervals throughout the growing season and were used to monitor corn and soybean growth and development. The normalized difference transformation was used to effectively compensate for the variation in irradiational conditions. With these data, plotted against time, green-leaf biomass dynamics were compared between the crops. By this approach, based entirely upon spectral inputs, the crop canopies were nondestructively monitored. Five spectral stages were defined and were related to crop development for corn and soybeans.     

An evaluation of radar as a crop classifier

Results of an investigation to specify the parameters of a space-borne imaging radar for use in crop identification are discussed. The study relied upon scaltering data acquired with a groundbased radar which were degraded to simulate the performance of a system similar to the proposed Space Shuttle Orbiter Imaging Radar. Data acquired from fields sown in corn, milo, soybeans, wheat and alfalfa were employed. The results of this study suggest that for best classification accuracy, a K-band (approximately 14 GHz), dual polarized system viewing fields at an off nadir angle in the 40° to 60° range should be employed. However it is emphasized that to attain classification accuracies exceeding 90%, multi-date acquisition is required. As best as can be determined, four target revisits at an interval of ten days is adequate for 90% accuracy.