粗颗粒土壤坡面侵蚀泥沙颗粒特征

为揭示粗颗粒土壤坡面侵蚀机理,采用湖北通城县、江西赣县、福建长汀县、广东五华县4个样地的4种粗颗粒土壤（分别定义为TCA、GXA、CTA、WHA）进行室内模拟降雨试验,研究粗颗粒土壤坡面侵蚀过程及侵蚀泥沙颗粒组成的变化规律。结果表明：（1）4种土壤的地表径流随着降雨时间的增长呈现出先增加后递减并趋于稳定的规律;（2）4种土壤的侵蚀特征存在差异,土壤侵蚀速率表现为WHA>TCA>GXA>CTA;（3）4种土壤的侵蚀泥沙中颗粒分布百分比大小均为砂粒>黏粒>粉粒>砾石。不同土壤侵蚀泥沙富集率表现出明显差异;（4）水流功率与土壤侵蚀速率的相关性显著,用幂函数可以准确描述其关系。在表达式中引入土壤黏粒含量、砾石含量后模型更加可靠（D_r=0.001ω^1.163C_l^-4.069,R²=0.82;D_r=0.003ω^1.149G_r^-1.934,R²=0.84）,提高了模型预测土壤侵蚀速率的精度,在实际应用中具有更广的适应范围与现实价值。     

利用正交信号校正消除水分影响的银川平原土壤盐分高光谱反演

高光谱遥感是监测土壤盐渍化的重要手段之一，但野外光谱反射率易受土壤水分的影响，导致盐分监测精度难以保证。为有效消除水分因素，提高土壤含盐量反演精度，该研究以银川平原盐渍化土壤为研究对象，以野外土壤光谱反射率（reflectance，Ref）和实测土壤含盐量为数据源，分析不同含水率的土壤光谱特征，将反射率经过一阶微分（first derivative of reflectance，FDR）、正交信号校正（orthogonal signal correction，OSC）和一阶微分-正交信号校正（first derivative of reflectance - orthogonal signal correction，FDR-OSC）变换，分析各光谱数据与含盐量、含水率的相关性，确定最佳“除水”方法，然后基于支持向量机（support vector machine，SVM）建立土壤含盐量反演模型。结果表明：1）含水率与土壤光谱反射率呈反比，光谱在1 430、1 950、2 200 nm附近存在吸收带，1 950 nm附近为最主要吸收波段，且存在向长波漂移的现象。2）光谱数据与含水率相关性由强到弱的顺序为：Ref、OSC、FDR、FDR-OSC；与含盐量相关性由强到弱的顺序为：FDR-OSC、FDR、OSC、Ref。3）基于FDR-OSC“除水”的SVM含盐量模型决定系数R_c²、R_p²和相对分析误差（relative prediction deviation，RPD）分别达到0.952、0.960和5.04，具有极强的拟合和反演能力。研究结果可为银川平原及同类地区土壤含盐量的精准监测提供科学依据。     

黄土高原典型退耕草地植被特征对土壤入渗过程的影响

植被恢复过程可显著影响土壤入渗性能。通过选取黄土高原典型草地白羊草和铁杆蒿草地,设置不同种植密度（5,10,15,20,25,30株/m²）和采用人工模拟降雨试验（60 mm/h,60 min）,系统研究了草地植被特征对土壤入渗过程的影响。结果表明：（1）种植密度增加可显著延缓产流,不同种植密度下白羊草草地和铁杆蒿草地初始产流时间分别为0.76~5.74,0.87~2.08 s,且随盖度、根系生物量和有机质的增加呈幂函数增加（R²≥0.18,p<0.05）。（2）不同种植密度下白羊草草地的平均入渗速率、稳定入渗速率、入渗总量分别为0.47~0.82,0.46~0.82 mm/min和7.12~11.84 mm,铁杆蒿草地分别为0.38~0.67,0.35~0.60 mm/min和5.70~10.07 mm。当种植密度为20株/m²时,土壤入渗各参数均最大;平均入渗速率、稳定入渗速率、入渗量总量、入渗系数（K）随土壤有机质的提高呈幂函数增大（R²≥0.26,p<0.01）,衰减系数（α）随生物结皮盖度呈降低趋势（p>0.05）。（3）白羊草草地具有较高的根系生物量、生物结皮盖度和有机质含量,其初始产流时间、平均入渗速率、稳定入渗速率、入渗量总量及入渗系数（K）均不同程度高于铁杆蒿草地,衰减系数（α）低于铁杆蒿草地,土壤入渗性能较强。总体而言,对于典型退耕草地,土壤入渗总量（A）可表征为根系生物量密度（RMD）和土壤有机质（SOC）的拟合函数（A=2.77×RMD^0.149 SOC^0.614,R²=0.663,NSE=0.653）。研究结果可为黄土高原退耕草地生态水文过程和植被建设提供数据来源和理论依据。     

HDXRF法农田土壤镉测定结果准确度评价与精准校正模型构建

以甘肃白银某污灌区重金属污染农田土壤为研究对象，对影响高精度便携式X荧光光谱（HDXRF）法总镉（Cd_T）测定精度的主要因素进行了筛选，分别研究了土壤水分、有机质类型与含量、土壤类型对HDXRF法Cd_T测定的影响，并采用相对误差（RE）、相对标准偏差（RSD）和决定系数（R²）对测定结果的准确度和精密度进行了评价。结果表明：HDXRF法Cd_T测定的RE≤10%、RSD≤10%、R²>0.99，符合农田土壤环境质量监测技术规范和美国环境保护署标准的准确度和精密度规定。HDXRF法Cd_T测定结果随着土壤水分含量的增加呈指数衰减趋势，衰减方程为y=0.803e^–1.3284x，衰减系数（μ_ω）为–1.328 4，R²为0.984 5。HDXRF法Cd_T测定结果与有机质含量呈显著的负相关关系（r=–0.955），且腐殖酸（HA）比泥炭（Peat）对测定结果的影响更大，HA与测定结果的校正方程为y=–1.555x +0.780，R²为0.934 4。土壤类型对HDXRF法Cd_T测定结果存在一定的影响，相对于红壤和水稻土，灰钙土的测定结果与传统实验室分析方法测定结果更接近。总之，虽然HDXRF法Cd_T测定结果受多种因素影响，但通过校正模型校正，其校正结果的可靠性能够满足Cd污染农田土壤精准调查需求。     

中国农耕区土壤有机质含量及其与酸碱度和容重关系

对我国农耕区土壤有机质区域变化及其与酸碱度和容重关系进行系统分析,为耕地地力提升和改善土壤结构提供支撑。基于国家级耕地长期定位监测点913个,统计分析全国及7大区域（东北NE、华北NC、西北NW、长江中游MYR、长三角YRD、华南SC、西南SW）耕层土壤有机质含量、酸碱度及容重变化特征。结果表明,全国农耕区耕层土壤有机质含量平均值为22.4~24.8 g/kg。其中有机质含量中等偏低的监测点位占比达72.5%。不同区域耕层土壤有机质含量差异显著（p<0.05）,MYR耕层土壤有机质含量显著高于其他6个区域。全国农耕区耕层土壤pH和容重平均分别为（6.90±1.20）,（1.30±0.15） g/cm³。不同土壤利用方式对土壤有机质、酸碱度及容重产生影响。水田耕层土壤有机质含量显著高于旱地,旱地耕层土壤pH和容重则显著高于水田。亚当斯方程和指数函数分别推荐拟合土壤容重对有机质含量响应关系（R²=0.09,RMSE=0.17,n=759）,以及土壤pH对土壤有机质含量响应（R²=0.16,RMSE=1.24,n=886）。全国农耕区耕层土壤有机质含量总体中等偏低,呈现出东南向西北依次降低趋势。土壤pH及容重与土壤有机质呈现显著的负相关关系。亚当斯模型及指数方程能较好地拟合土壤容重及pH对有机质的响应关系,可用于非线性插值法补充土壤容重及pH缺失值。     

三峡库区紫色砂岩不同发育程度土壤入渗特征及其影响因素

为探究典型母岩不同发育程度土壤入渗特征,在三峡库区王家桥小流域采用圆盘入渗仪测定新成土（S1）、雏形土（S2）、淋溶土（S3）3种紫色砂岩不同发育程度土壤入渗过程,探究入渗过程的影响因素,并用3种常用模型进行入渗过程模拟,分析比较这些模型的适宜性。结果表明：（1）不同发育程度间土壤理化性质差异显著。雏形土和淋溶土土壤容重较新成土分别增加10.71%,19.50%,土壤总孔隙度分别降低8.79%,18.69%,通气孔隙度分别降低67.40%,8.16%,土壤黏粒含量分别增加10.01%,38.36%,砂粒含量分别减小8.09%,48.29%,土壤有机质分别增加2.88%,8.68%。（2）不同发育程度土壤间初始入渗率、平均入渗率、饱和导水率均表现为新成土>雏形土>淋溶土,雏形土、淋溶土平均入渗率及饱和导水率分别是新成土的0.99,0.58和0.89,0.83倍。（3）不同发育程度土壤理化性质差异对土壤入渗具有显著影响,土壤入渗速率与总孔隙度、毛管孔隙度、通气孔隙度、砂粒含量呈正相关关系,与容重、黏粒含量、有机质含量呈负相关关系。（4） Horton模型对紫色砂岩不同发育程度土壤入渗过程拟合效果最优（R²=0.942）,Kostiakov模型次之（R²=0.858）,Philip模型拟合效果较差（R²=0.832）。通过观测与模拟不同发育程度土壤入渗过程,研究结果可为流域土壤水分运移规律探究提供有益借鉴。     

东北黑土坡耕地土壤化学计量特征变化对侵蚀的响应

[目的] 探究土壤侵蚀对土壤养分含量及其化学计量比的影响,对于加深认识黑土区坡耕地质量退化过程及防控具有重要意义。[方法] 选择典型黑土区克山县开垦100多年的直形坡和开垦50多年的凸形坡为研究对象,根据¹³⁷Cs示踪技术估算坡耕地土壤侵蚀速率,定量分析土壤侵蚀与土壤有机碳(SOC)、全氮(TN)、全磷(TP)含量及生态化学计量比的关系。[结果] (1)利用¹³⁷Cs示踪法得到坡面整体的年平均侵蚀速率为4 428.56 t/(km²·a),直形坡和凸形坡侵蚀速率平均值分别为3 284.53,5 884.59 t/(km²·a),侵蚀总量分别为3.21×10⁵,2.94×10⁵ t/km²。(2)坡面整体碳氮比(C/N)与SOC呈极显著正相关(p<0.01),碳磷比(C/P)与SOC呈极显著正相关(p＜0.01),氮磷比(N/P)与TP呈极显著负相关(p＜0.01)。直形坡SOC、TN、C/N、C/P和N/P均极显著小于凸形坡养分含量和化学计量比值(p＜0.01),TP含量大于凸形坡TP含量(p＜0.01)。(3)坡面土壤侵蚀空间分布特征与土壤有机碳、全氮及全磷的空间分布具有一致性。坡面土壤侵蚀量与SOC、TN、TP、C/P均呈极显著负相关(p<0.01),与C/N呈显著负相关(p<0.05),与N/P呈负相关但相关性不显著(p>0.05)。[结论] 土壤侵蚀导致坡面土壤SOC、TN和TP在坡面再分布,影响土壤养分化学计量比,造成坡面养分流失严重。     

基于不同风蚀模型的区域土壤风蚀变化及影响因素研究——以内蒙古自治区为例

基于遥感数据、气象数据等,利用RWEQ模型和风蚀预报模型对内蒙古自治区2000—2017年土壤风蚀进行评估并分析其驱动因素。结果表明：（1） RWEQ模型（R²=0.85,P<0.01）和风蚀预报模型（R²=0.43,P<0.01）的预测值与¹³⁷Cs示踪技术风蚀的值具有较好的相关性,其中RWEQ模型预测精度更好。（2）时间上,RWEQ和风蚀预报模型模拟的结果均表明2000—2017年内蒙古自治区土壤风蚀呈下降的趋势,下降趋势分别为0.73,1.18 t/（hm²·a）,2个模型模拟的土壤风蚀模数在2011年均达到最低值。空间上,2000—2017年,2个模型的模拟结果均表明内蒙古自治区土壤风蚀以微度和轻度侵蚀为主,其中剧烈侵蚀在整个研究区的占比较小（RWEQ 1.79%,风蚀预报模型5.45%）,主要分布于北方风沙区的西南部。趋势上,89.74%（RWEQ）和72.05%（风蚀预报模型）的土壤风蚀模数呈下降趋势,其中显著降低的区域主要分布于北方风沙区的巴丹吉林沙漠和乌兰布和沙漠。（3）大风天数对土壤风蚀具有显著影响,随着大风天数的增多,土壤风蚀呈显著上升趋势,植被覆盖度和降水量的增长在一定程度上可抑制土壤风蚀的进程。     

蒸渗仪测定滴灌枣树不同时间尺度下腾发强度特征

为精确测定、准确模拟阿克苏地区滴灌枣树腾发过程,基于大型称重式蒸渗仪测定枣树全生育期逐时及逐日腾发强度（ET）,利用水量平衡方程、PM公式及经典统计原理,分析不同时间尺度下叶面积指数（LAI）、气象因素[温度（I）、风速（V）、净辐射（R_n）]、表层土壤含水率（W）与枣树腾发强度的相关关系并建立预测模型。结果表明：枣树日内腾发强度呈单峰型变化趋势,夜间变化幅度较小且腾发贡献率低。枣树全生育期逐日腾发强度变化呈先增大后减小的趋势,花期的腾发强度最大,为4.42 mm·d^-1;全生育期腾发总量为640.83 mm,其中花期和果实生长发育期耗水量占比较大,分别为38.61%和32.72%。在小时和日时间尺度上,影响腾发强度的主要因素不完全相同,且影响程度有所差异。综合考虑各影响因素,以萌芽期、花期、果实发育期为基础,分别建立以小时、日尺度下估算腾发强度的经验模型ET₁（h）=0.153+0.004T+0.012V+0.176R_n+0.002W+0.067LAI、ET₂（d）=-3.325+0.081T+0.163R_n+0.069W+2.089LAI,拟合度R²均在0.7以上,以果实发育期与成熟期数据对模型进行检验,纳什效率系数分别达0.63、0.80。经偏相关检验,冠层净辐射（R_n）对两种尺度的腾发强度均影响最显著,因此以枣树全生育期数据量为基础,仅建立冠层净辐射（R_n）与腾发强度的回归模型ET₁（h）=-0.063 3R_n²+0.361 2R_n—0.003 7、ET₂（d）=-0.018 3R_n²+0.684 7R_n–1.642 1,R²分别为0.704 7与0.743 6,可满足缺少数据支撑情况下的腾发过程估算。这些模型明确了阿克苏地区滴灌枣树腾发机制及影响程度,可为水分管理精准化提供计算基础。     

晋西黄土区典型林分枯落物层水文生态特性研究

选择山杨栎类次生林（以下简称次生林）、刺槐林、侧柏林、油松林为研究对象，通过样地调查，结合室内浸泡方法，对比分析枯落物（未分解层、半分解层）的水文特征指标，研究典型林分枯落物层水文生态特性。结果表明：（1）枯落物厚度为3.93~4.95 cm，刺槐林最大，油松林最小；蓄积量为次生林最大（19.28 t/hm²），侧柏林（18.03 t/hm²）和刺槐林（17.57 t/hm²）次之，油松林最小（14.73 t/hm²），未分解层蓄积量小于半分解层。（2）枯落物最大持水量（率）为30.92~61.31 t/hm²（197%~320%），次生林最大，依次为刺槐林、侧柏林，最小为油松林。（3）枯落物有效拦蓄存在显著差异（P>0.05），表现为次生林（31.29 t/hm²） > 刺槐（22.20 t/hm²） > 侧柏（18.19 t/hm²） > 油松（13.94 t/hm²），有效拦蓄率为107%~173%。（4）在浸水2 h内，枯落物持水量和吸水速率变化以次生林与刺槐林最为迅速，半分解层较未分解层变化迅速；持水过程中，两者与时间分别呈对数函数（R²>0.89）和幂函数关系（R²>0.99）。在4种林地中，次生林林下枯落物水文生态潜力最优，油松纯林最差，表现为次生林 > 刺槐 > 侧柏 > 油松。刺槐是除次生林外的3种人工林中最优林种。建议研究区内合理优化恢复树种配置，以提高水文生态功能。     

Use of percentage and milliequivalent expressions in plant composition studies

Abstract

Corn yields and leaf samples vere obtained from experimental plots receiving various rates and combinations of N, P and K. Yields were regressed on leaf N, P, K, Ca and Mg as independent variables expressed in milliequivalents per 100 grams and percentages in three regression models. The fit of two models was shown to be equivalent regardless of method of expressing the independent variables. For the other model the choice of milliequivalents per 100 grams or percentages determines a unique function.     

Developing Pedotransfer Functions for Predicting FC and PWP

Using pedotransfer functions (PTF) is a useful way for field capacity (FC) and permanent wilting point (PWP) prediction. The aim of this study was to model PTF to estimate FC and PWP using regression tree (RT) and stepwise multiple linear regressions (SMLR). For this purpose, 165 and 45 soil samples from UNSODA and HYPRES datasets were used for development and validation of new PTFs, respectively. %Clay, geometric mean diameter (dg), and bulk density (BD) were selected as predictor variables due to the highest correlation and lowest multicollinearity. The results showed that clay percentage with W* = 0.89 and dg with W* = ?0.57 were the most effective variables to predict PWP and FC, respectively. The RT method had a better performance (R² = 0.80, ME = ?0.002 cm³cm^?3, RMSE = 0.05 cm³cm^?3 for FC and R² = 0.85, ME = 0.003 cm³cm^?3, RMSE = 0.03 cm³ cm^?3 for PWP) than SMLR in estimation of FC and PWP.     

黑龙江省黑土区拉林河流域土壤侵蚀强度评价方法比较

为了保护水土资源、改善生态环境,进行区域土壤侵蚀强度评价,以黑龙江省黑土区拉林河流域为研究区,选取坡度、坡向、土壤类型、土地利用状况和标准化植被指数等5项评价指标,分别采用逻辑回归和广义回归神经网络模型,在ArcGIS平台上进行土壤侵蚀强度评价。应用受试者工作特征曲线对2种方法的评价结果进行对比。结果表明:逻辑回归模型和广义回归神经网络模型的受试者工作特征曲线下面积值分别为0.857和0.881,与实际的土壤侵蚀强度情况基本吻合;2种模型的评价结果可以相互校验,广义回归神经网络模型评价结果的精度较高。     

Soil erodibility and its prediction in semi-arid regions

ABSTRACT

Pedotransfer functions (PTFs) have been used to save time and cost in predicting certain soil properties, such as soil erodibility (K-factor). The main objectives of this study were to develop appropriate PTFs to predict the K-factor, and then compare new PTFs with Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE) K-factor models. The K-factor was measured using 40 erosion plots under natural rainfall in Simakan Watershed, an area of 350 km² in central of Iran. The Regression Tree (RT) and Multiple Linear Regression (MLR) were used to develop PTFs for predicting the K-factor. The result showed that the mean of measured K was 0.01 t h MJ^?1 mm^?1. The mean K value predicted by USLE and RUSLE was 2.08 and 2.84 times more than the measured K, respectively. Although calcium carbonate was not considered in the original USLE and RUSLE K-factors, it appeared in the advanced PTFs due to its strong positive significant impact on aggregate stability and soil infiltration rate, resulting in decreased K-factor. The results also showed that the RT with R² = 0.84 had higher performance than developed MLR, USLE and RUSLE for the K estimation.     

Evaluation of a local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution

Apparent electrical conductivity of soil (ECa) is a property frequently used as a diagnostic tool in precision agriculture, and is measured using vehicle‐mounted proximal sensors. Crop‐yield data, which is measured by harvester‐mounted sensors, is usually collected at a higher spatial density compared to ECa. ECa and crop‐yield maps frequently exhibit similar spatial patterns because ECa is primarily controlled by the soil clay content and the interrelated soil moisture content, which are often significant contributors to crop‐yield potential. By quantifying the spatial relationship between soil ECa and crop yield, it is possible to estimate the value of ECa at the spatial resolution of the crop‐yield data. This is achieved through the use of a local regression kriging approach which uses the higher‐resolution crop‐yield data as a covariate to predict ECa at a higher spatial resolution than would be prudent with the original ECa data alone. The accuracy of the local regression kriging (LRK) method is evaluated against local kriging (LK) and local regression (LR) to predict ECa. The results indicate that the performance of LRK is dependent on the performance of the inherent local regression. Over a range of ECa transect survey densities, LRK provides greater accuracy than LK and LR, except at very low density. Maps of the regression coefficients demonstrated that the relationship between ECa and crop yield varies from year to year, and across a field. The application of LRK to commercial scale ECa survey data, using crop yield as a covariate, should improve the accuracy of the resultant maps. This has implications for employing the maps in crop‐management decisions and building more robust calibrations between field‐gathered soil ECa and primary soil properties such as clay content.     

影响大豆NDVI的气象因素多元回归分析

针对太阳辐射、大气温度、空气湿度和风速等气象因素对大豆归一化植被指数(normalized difference vegetation index,NDVI)在每天不同时间的影响,提高大豆NDVI的监测精度。该研究采用Green Seeker手持式光谱仪对大豆苗期、花荚期和成熟期3个主要生育阶段的NDVI值以小时为单位进行连续监测,并收集测量时的太阳辐射、大气温度、空气湿度和风速等气象数据,采用偏最小二乘法、逐步回归和岭回归方法,建立不同气象因素对大豆NDVI值影响的回归模型,并分析其定量关系。结果表明,影响大豆不同生育期NDVI变化的主要气象因素为太阳辐射和大气温度,风速和空气湿度的影响较小,可以忽略不计。经对3种模型进行预测精度评价后得出,岭回归模型的预测精度最佳,其在3个阶段的预测均方根误差(RMSE)分别为0.034、0.018和0.016,决定系数(R2)分别为0.820、0.908和0.934,其次为逐步回归法,偏最小二乘法的预测精度最低。     

Pedotransfer functions for predicting bulk density of coastal soils in East China

Soil bulk density(BD) is an important physical property and an essential factor for weight-to-volume conversion. However, BD is often missing from soil databases because its direct measurement is labor-intensive, time-consuming, and sometimes impractical, particularly on a large scale. Therefore, pedotransfer functions(PTFs) have been developed over several decades to predict BD. Here, six previously revised PTFs(including five basic functions and stepwise multiple linear regression(SMLR)) and t...     

A vulnerability analysis of the temperate forests of south central Chile

Areas of the landscape that are priorities for conservation should be those that are both vulnerable to threatening processes and that if lost or degraded, will result in conservation targets being compromised. While much attention is directed towards understanding the patterns of biodiversity, much less is given to determining the areas of the landscape most vulnerable to threats. We assessed the relative vulnerability of remaining areas of native forest to conversion to plantations in the ecologically significant temperate rainforest region of south central Chile. The area of the study region is 4.2 million ha and the extent of plantations is approximately 200 000 ha. First, the spatial distribution of native forest conversion to plantations was determined. The variables related to the spatial distribution of this threatening process were identified through the development of a classification tree and the generation of a multivariate, spatially explicit, statistical model. The model of native forest conversion explained 43% of the deviance and the discrimination ability of the model was high. Predictions were made of where native forest conversion is likely to occur in the future. Due to patterns of climate, topography, soils and proximity to infrastructure and towns, remaining forest areas differ in their relative risk of being converted to plantations. Another factor that may increase the vulnerability of remaining native forest in a subset of the study region is the proposed construction of a highway. We found that 90% of the area of existing plantations within this region is within 2.5 km of roads. When the predictions of native forest conversion were recalculated accounting for the construction of this highway, it was found that approximately 27 000 ha of native forest had an increased probability of conversion. The areas of native forest identified to be vulnerable to conversion are outside of the existing reserve network.     

基于实验室高光谱数据的大围山森林土壤氧化铁全量反演

氧化铁是土壤中含铁矿物的主体,是土壤发育和土壤分类最明显和最有用的指标之一。本文以湖南省大围山森林土壤为研究对象,通过实验室化学成分测定和光谱采集,在光谱预处理及组合变换基础上,采用相关性分析筛选土壤氧化铁全量的敏感波段,并分别建立多元逐步回归和偏最小二乘回归反演模型。结果表明:不同土壤光谱曲线趋势基本一致,均形似陡坎,且在420～580 nm波段,土壤氧化铁全量与光谱反射率呈负相关关系;不同的光谱数据变换方式可以提高光谱与氧化铁全量的相关性,Savitzky-Golay(S-G)平滑和去包络线相结合优于其他预处理方法;土壤氧化铁全量的特征波段主要为392、427、529、523、549、559、565、570、994和1040nm,偏最小二乘回归模型比多元逐步回归模型具有更好的稳定性,适合于快速估算红黄壤区森林土壤氧化铁全量。     

玉米倒伏胁迫影响因子的空间回归分析

为指导玉米新品种的推广,采用回归模型分析玉米主产区倒伏胁迫空间分布成因。该文用多元逐步线性回归法筛选黄淮海夏播玉米区的倒伏胁迫的决定因素,比较普通最小二乘法线性回归模型和地理加权回归模型的结果,以确定倒伏胁迫及其决定因素是否存在空间非平稳性和空间依赖性。结果表明：在探索倒伏的空间异质性时,地理加权回归模型显著优于普通最小二乘法线性回归模型;日降水量是玉米倒伏胁迫的主要环境成因,且倒伏程度随日降水量增加而加重;土壤含氮量、留苗密度和日平均风速与倒伏的关系随空间位置而发生正负向变化,因地制宜的分析倒伏成因才能客观有效的指导农民种植生产。