黑龙江精准农业示范区土壤养分空间变异研究

以面积为129 hm^2农田为采样区,使用Ag132 DGPS仪定位,网格法采集0～20 cm耕层土壤样品,实验室测定土壤碱解氮、速效磷、速效钾和有机质的含量.应用传统统计学方法和空间分析方法对土壤养分变异进行了研究.结果表明,土壤养分性质均存在着空间变异性,速效磷、速效钾和有机质变异相对较大,碱解氮变异亦小;土壤养分性质均存在半方差结构,分别拟合Exponential、Gaussian、Pentaspherical、Gaussian模型;碱解氮、速效磷、速效钾和有机质均显示中等的空间相关性.     

Can Landform Stratification Improve Our Understanding of Crop Yield Variability?

Farmers account for yield and soil variability to optimize their production under mainly economic considerations using the technology of precision farming. Therefore, understanding of the spatial variation of crop yield and crop yield development within arable fields is important for spatially variable management. Our aim was to classify landform units based on a digital elevation model, and to identify their impact on biomass development. Yield components were measured by harvesting spring barley (Hordeum vulgare, L.) in 1999, and winter rye (Secale cereale, L.) in 2000 and 2001, respectively, at 192 sampling points in a field in Saxony, Germany. The field was stratified into four landform units, i.e., shoulder, backslope, footslope and level. At each landform unit, a characteristic yield development could be observed. Spring barley grain yields were highest at the level positions with 6.7 t ha⁻¹ and approximately 0.15 t ha⁻¹ below that at shoulder and footslope positions in 1999. In 2000, winter rye harvest exhibited a reduction at backslope positions of around 0.2 t ha⁻¹ as compared to the highest yield obtained again at level positions with 11.1 t ha⁻¹. The distribution of winter rye grain yield across the different landforms was completely different in 2001 from that observed in 2000. Winter rye showed the highest yields at shoulder positions with 11.1 t ha⁻¹, followed by the level position with 0.5 t ha⁻¹ less grain yield. Different developments throughout the years were assumed to be due to soil water and meteorological conditions, as well as management history. Generally, crop yield differences of up to 0.7 t ha⁻¹ were found between landform elements with appropriate consideration of the respective seasonal weather conditions. Landform analysis proved to be helpful in explaining variation in grain yield within the field between different years.     

关中平原区土壤5种微量元素的空间变异及分布评价

为了揭示黄土高原地区农田土壤微量元素空间变异规律及分布特征,选择陕西省扶风县揉谷乡新集村为试验区,在219 hm2耕地上用网格法采集364个耕层(0~20 cm)土壤样品,对其5种微量元素B、Fe、Mn、Zn和Cu有效含量的空间变异进行地统计学分析,并结合Kriging插值结果对其不同级别含量的分布状况进行评价。结果表明,土壤有效B、Fe、Mn、Zn和Cu的理论半方差模型分别符合线性、指数、球状、指数和线性模型,有效B和Mn具有中等的空间相关性,有效Cu、Fe和Zn具有强烈的空间相关性;5种微量元素最大相关距离依次为:B、Cu>Zn>Mn>Fe,最大相关距离为648~1 855.1 m;5种微量元素表现出条带状和斑块状空间分布;有效Fe、Mn、Zn和Cu的含量分布与土壤水分有关,有效B的分布与土壤酸碱度有关。     

农田钾素空间变异插值模型研究--以江西省泰和县苏溪养分监测村为例

对江西省泰和县苏溪养分监测村的120hm^2的农田进行土壤采样，运用逆距离加权插值、田块平均值以及农田土壤养分空间变异插值模型对该区域的土壤样品进行插值分析，研究结果表明农田土壤养分空间变异插值模型较好地反映了土壤养分空间的分布。     

土壤饱和水力传导度的空间变异性

土壤性质的空间变异性对于土壤改良和作物高产都是重要的。本文就土壤饱和水力传导度的空间变异性、孔性数值与饱和水力传导度的相关分析和用大孔隙度标定饱和水力传导度的空间变异性进行了研究。研究结果表明土壤饱和水力传导度的半方差图体现了该土性的空间结构;土壤饱和水力传导度主要通过大孔隙度来表征,二者的关系可以用直线函数和幂函数的关系式来表示;可以从大孔隙度的分布导出和表征饱和水力传导度标定因子的频率分布,作为他和水力传导度的空间分布的度量。     

四川盆地近40年来的降水特征分析

对四川盆地22个站点1958—2000年43a来的降水特征的分析表明：盆地内降水的相对变率比较小,降水相对比较稳定,但是盆地内的降水有较强的分布不均匀性,盆地西部近40a来降水的长期变化有明显减少的趋势。盆地内降水异常的1个显著特征就是盆地西部与东部的反位相分布,盆地内暴雨发生频率在时空分布上都有相似的特征。     

基于GIS的地统计学方法在土壤科学中的应用

地统计学应用于土壤科学中,并结合GIS(Geographic Information System,GIS)技术探索其空间分布特征及其变异规律已为越来越多的学者所推崇。半方差函数提供了一个定量工具,可以将土壤某一性质的变异与成土因子和成土过程联系起来,使人们加深对土壤的作用后果的理解,而克立格可以对未采样区的区域化变量的取值进行无偏最优估计。通过介绍基于GIS的地统计学方法在土壤水盐空间变异、养分空间分布特征、重金属污染调查等方面的研究,进一步说明了地统计学在未来土壤科学发展中广阔的应用前景。     

Multi-time scale analysis of sugarcane within-field variability: improved crop diagnosis using satellite time series?

Within-field spatial variability is related to multiple factors that can be time-independent or time-dependent. In this study, our working hypothesis is that a multi-time scale analysis of the dynamics of spatial patterns can help establish a diagnosis of crop condition. To test this hypothesis, we analyzed the within-field variability of a sugarcane crop at seasonal and annual time scales, and tried to link this variability to environmental (climate, topography, and soil depth) and cropping (harvest date) factors. The analysis was based on a sugarcane field vegetation index (NDVI) time series of fifteen SPOT images acquired in the French West Indies (Guadeloupe) in 2002 and 2003, and on an original classification method that enabled us to focus on crop spatial variability independently of crop growth stages. We showed that at the seasonal scale, the within-field growth pattern depended on the phenological stage of the crop and on cropping operations. At the annual scale, NDVI maps revealed a stable pattern for the two consecutive years at peak vegetation, despite very different rainfall amounts, but with inverse NDVI values. This inversion is linked with the topography and consequently to the plant water status. We conclude that (1) it is necessary to know the crop growing cycle to correctly interpret the spatial pattern, (2) single-date images may be insufficient for the diagnosis of crop condition or for prediction, and (3) the pattern of vigour occurrence within fields can help diagnose growth anomalies.

Ground-Sensor Soil Reflectance as Related to Soil Properties and Crop Response in a Cotton Field

Bare soil reflectance from airborne imagery or laboratory spectrometers has been used to infer soil properties such as soil texture, organic matter, water content, salinity and crop residue cover. However, the relation of soil properties to reflectance data often varies with soil type and conditions and surface reflectance may not be representative of the conditions in the root zone. The objectives of this study were to assess the soil reflectance data obtained by ground-based sensors and to model soil properties in the root zone as a function of surface soil reflectance and plant response. Ground-based sensors were used to simultaneously monitor soil and canopy reflectance in the visible and near-infrared (VNIR) along six rows and in two growth stages in a 7 ha cotton field. The reflectance data were compared to soil properties, leaf nutrients and biomass measured at 33 sampling positions along the rows. Brightness values of the blue and green bands of soil reflectance were better correlated to soil water content, particulate organic matter and extractable potassium and phosphorus, while those in the red and NIR bands were correlated to soil carbonate content, total nitrogen, electrical conductivity and foliar nutrients. The correlation of red soil reflectance with canopy reflectance was significant and indicated an indirect inverse relationship between soil fertility and plant stress. The integration of surface soil reflectance and plant response variables in a multiple regression model did not substantially improve the prediction of soil properties in the root zone. However, crop nutrient status explained a significant portion of the spatial variability of soil properties related to nitrification processes when soil reflectance did not. The implication of these findings to agricultural management is discussed.