Experimental Results on the Effects of Nonregular Spatial Patterns of Plants on Yield per Area

Based on several simplifying assumptions, a stochastic approach was developed which allows an estimation of the effects of nonregular spatial patterns of the distribution of individual plants on yield per area (F). In this approach, two random variables were attached to each plant: single plant yield (E) and individual space per plant (A). The latter was estimated by the area of Thiessen polygons. Yield per area was calculated theoretically by the expectation of the ratio E / A. Appropriate approximations of this expectation depend on the means (ē and ā), coefficients of variation (vE and vA) of E and A and their correlation (rEA). Yield per area can be decomposed into two additive terms: the first term gives the commonly used estimate ē/ā— or h(ā)/ā if a functional relationship between E and A is assumed: E = h(A). In this study, the two relationships E = k1 + k2 · ln A and E = A/(k3 + k4A) were used (with appropriately chosen constants k1, k2, k3, and k4). The second term in the decomposition of F can be interpreted as the effect of variable individual plant spaces on yield per area. In this paper, all theoretical concepts and results were applied to 17 experimental data sets of three cultivars of winter oilseed rape (Brassica napus L.). Single plant yields (E) and individual plant areas (A) were positively correlated with correlation coefficients from 0.64 up to 0.91. The ranges for both coefficients of variation were similar: 0.27 ≤ vE ≤ 0.65 and 0.28 ≤ vA ≤ 0.59. One obtains no significant differences in the goodness-of-fit for both tested relationships between E and A although the logarithmic relationship seems to be slightly superior. For only three data sets one obtains negative values for the percentage of the second term in the decomposition of F. This indicates an overestimation of yield per area by the commonly used estimates h(ā)/ā and ē/ā, respectively. These overestimations, however, are less than 5 %. In all other cases with positive values for the second term the yield per area is underestimated by the common estimates. For almost all data sets, however, the percentages of F which are explained by the common estimates are much larger than 90 %.     

Irrigation Water Management for Efficient Water Use in Mulched Onion

Caused by the necessarily imperfect seed placement accuracy of sowing machines and, additionally, caused by many other biotic and abiotic factors, the resulting plant stands exhibit nonregular spatial distributions of its plants. Based on several simplifying assumptions, a stochastic approach is developed which allows an estimation of the effects of nonregular spatial patterns on yield per area. In this approach, two random variables are attached to each plant: single plant yield E and individual space A . The latter is estimated by the area of Thiessen polygons. Yield per area, calculated by the expectation of the ratio E/A , can be approximately expressed dependent on the means ( Ē and Ā ) and coefficients of variation ( v _E and v _A ) of E and A and their correlation ( r _EA ). In relation to the commonly used estimate Ē/Ā for yield per area, one obtains yield decreases if v _A / v _E  <  r _EA . This inequality, however, will be usually valid in the field of applications. The theoretical approaches and results were applied to three experimental data sets for drilled seeds of winter oilseed rape ( Brassica napus L.) (plant density: 60 plants m⁻², row distance: 10 cm). These data sets are characterized by different accuracies of longitudinal distributions within rows (58 %, 101 %, 150 %): yield depression increases with an increasing variability of plant distances within rows.     

理顺操作程序,提高林地落界精度

文章概述了林地落界过程中的操作程序以及需要重点把控的技术环节和技术标准,提出了满足林地落界精度的建议和方法。     

基于泰森多边形的南京市PM2.5时空特征及其与土地利用的相关性研究

[目的]分析南京市PM2.5时空分异规律,探讨PM2.5浓度变化与土地利用的关系,为南京市生态保护和空气污染治理决策提供依据。[方法]基于南京市2013年12个月的日均PM2.5浓度数据以及2013年土地利用数据,利用泰森多边形将南京市划分为9个研究区域,以月、季、年为时间尺度,对各个研究区内的PM2.5浓度的时空特征以及与土地利用的关系进行分析。[结果]时间上,南京市PM2.5浓度冬季呈现最高,达到129.93μg/m^3,夏季最低,达到44.65μg/m^3。空间上,迈皋桥和瑞金路监测区片年均浓度最高,达到78.90和78.56μg/m^3,仙林大学城和中华门监测区片年均PM2.5浓度最低,为72.09和72.64μg/m^3。在与土地利用类型的相关性分析中,与水域用地的相关性较强,春夏秋呈现正相关,冬季呈现负相关;年均PM2.5浓度与5种土地利用类型成不同程度的负相关。[结论]南京市PM2.5浓度具有明显的时空分异规律,土地利用类型对PM2.5浓度变化具有重要影响。     

淮河流域面雨量计算方法的比较分析

利用2007年7月淮河流域常规站、自动雨量站和自动气象站逐日降雨资料,选取不同的方法,计算淮河流域15个子单元的面雨量实况,并对结果进行了对比分析。结果表明：所采取的泰森多边形法和算术平均法计算结果相差不大。同时,将自动气象站、自动雨量站以及常规站点进行合成,用合成后的站点雨量计算的面雨量与常规站计算结果进行对比,进一步分析站点密度及站点分布对流域面雨量计算的影响,结果表明不同雨量资料的计算结果存在差别。     

基于Kriging估计误差的县域耕地等级监测布样方法

为了监测耕地的质量等级,通常采取抽样调查的方法.由于空间样本间存在不独立性等原因,传统抽样方法效率低、精度不高.为此,该文提出基于Kriging估计误差的布样方法,定义了反映Kriging估计情况的统计量作为评估监测网的标准,通过分析样本量与抽样精度的变化趋势确定最优样本容量,将调整过的方形格网作为监测网的基础,在泰森多边形限制下对监测网优化增密,并选用部分标准样地作为监测点.以北京市大兴区为例对该方法进行验证,结果表明,当监测点数同为48时,该文方法均方根误差小于简单随机抽样、分层抽样以及单一使用格网布样的方法,预测总体均值的相对误差为0.07%.因此,该文方法使用较少的监测点反映县域耕地等级的分布状况和变化趋势,能够满足县域耕地等级监测的需求.     

策勒绿洲生态与灌溉用水对地下水埋深的影响

[目的]估测地下水资源变化动态并对其安全性进行识别,为策勒绿洲水资源管理及维护绿洲生态安全提供一定决策依据。[方法]分析策勒绿洲总用水需求,基于策勒河径流与地下水动态监测数据,计算绿洲用水缺口;最后,通过绿洲长期地下水位监测资料分析地下水位随时间变化规律。[结果]综合考虑生态用水背景下,策勒绿洲年均用水缺口5.20×10~6~1.15×10~7 m^3,当利用策勒绿洲地下水补足绿洲用水差额时,地下水平均埋深年均下降0.27~0.60m,在非枯水年状态下策勒绿洲地下水补给相对充足使得地下水平均水位无明显变化,只在地下水水位时空分布上有所改变,如2008—2014年绿洲地下水平均埋深虽有较大幅度波动,但基本维持在21m上下。[结论]短期来看策勒绿洲地下水埋深变化处于安全范围内,但为了保证绿洲健康可持续发展,并维持地下水埋深的稳定,当前应投入财力到水资源使用的监督和管理中去,将建设农业节水设施作为长期发展策略。     

Voronoi图-泰森多边形法在角规测树中的应用

介绍了Voronoi图—泰森多边形的概念,以及在角规测树中的实现方法。实例分析结果表明,Voronoi图—泰森多边形法比以往的算术平均法更合理,蓄积总量估测精度更高。     

塔里木盆地南缘绿洲地下水时空变异与监测点位优化——以策勒绿洲为例

[目的]揭示塔里木盆地南缘绿洲地下水时空变异特征,优化监测点位,为实现水资源的合理利用与绿洲可持续发展提供依据。[方法]基于2008—2014年地下水监测数据,首先利用地统计学方法,通过对球状、指数、高斯3种模型的系统分析,确定适宜该地区的最优模型;其次利用该模型定量分析区域地下水时空变异特征,并进行地下水监测点位优化;最后利用Thiessen多边形法计算地下水平均埋深,对绿洲地下水平均埋深变化特征进行分析。[结果]高斯模型为最优模型,并且地下水的空间异质性和连通性增强;在不影响监测精度前提下,将原有23个监测点有效减至12个,降低监测成本;绿洲地下水平均埋深变化趋于稳定。[结论]绿洲地下水尽管受绿洲扩张影响显著,但因径流补给,目前总体处于安全状况。为保障绿洲健康可持续发展,需在目前绿洲规模基础上适当控制绿洲扩张。     

土壤Hg和NDVI全局均值估计的样本点权重调整方法

样本点权重调整是统计推断样本点属性全局均值的重要手段.以北京市顺义区农业用地样本点为例,提出了一种面向全局均值估计的样本点权重调整方法——泰森多边形权重调整法.首先构建样本点类型划分规则,将样本点划分为聚集样本点、稀疏样本点和均匀样本点;其次针对不同类型样本点,分别确定样本点权重调整量和调整规则并进行样本点权重调整;最...