淄博优势玉米监测区土壤砷元素空间变异性研究

采用地统计技术研究了淄博优势玉米监测区土壤砷元素空间变异性,结果表明,淄博优势玉米监测区土壤砷元素监测数据呈对数正态分布,其经验半变异函数存在明显的各向异性,在北偏东70°方向上连续性最强。应用指数模型拟合经验半变异函数,采用普通克里格法预测了砷元素的空间分布状况,研究表明,本区域砷元素存在明显的空间异质性,污水灌溉是引起土壤砷元素空间变异性最主要的因素。     

干旱荒漠区污水灌溉山地园林土壤养分的空间变异特征

以乌鲁木齐市雅玛里克山污水灌溉区为例,采用地统计学方法研究了干旱荒漠区山地园林污灌区土壤中的6种土壤养分指标全N、全P、全K、速效N、速效P、速效K的空间变异特征.结果表明,6种土壤养分指标的变异系数在8.34%～39.68%之间,全N、全P、速效P服从正态分布,全K、速效N、速效K服从对数正态分布.所有养分的块金值与基台值之比都在25%以下,说明结构性因素控制这些养分的含量分布,导致样点之间的空间自相关作用强.土壤养分的相关距变化范围为19.1～116.7 m.因为灌溉污水的影响,强矿化作用使得全N、速效N在质地粗轻的山坡含量较高,而氨挥发则造成全N、速效N在质地细重的谷地含量较低,偏弱碱性的污水加剧了 P的固定,使研究区内全P、速效P含最整体偏低,但同时偏弱碱性的污水促进了K的释放,使速效K含量明显提高.污灌能促进速效养分的释放,使养分之间具有一定相关关系.     

Spatial interpolation of orchard soil pH using soil type and planting duration as auxiliary information

Intensified field management in orcahrds has resulted in significant and widespread acidification in the soils. However, effectively mapping the spatial patterns of soil pH aiming to support ecological management is impeded by its large variotions across soil types and planting durations. Kriging methods were used to integrate soil type and planting duration information for effective mapping of orchard soil pH in a case study in orchards of the Northeast Jiaodong Peninsula, East China. A total of 1 472 surface soil samples were collected, and the planting duration of each sampled orchard was acquired to generate a planting duration map via Voronoi tessellations. The performance of five kriging methods was compared, namely, ordinary kriging (OK), OK combined with soil type (OK_ST), OK combined with planting duration (OK_PD), cokriging combined with soil type and planting duration (OCK_STPD), and OK combined with soil type and planting duration (OK_STPD). Results showed that soil pH declined significantly with increasing planting duration and exhibited moderate spatial variability over the study area. Soil type and planting duration both had significant influence on the spatial distribution of soil pH. The OCK_STPD and OK_STPD methods showed better prediction efficiency than OK, OK_ST, or OK_PD. With regard to the predicted maps of soil pH, the OCK_STPD and OK_STPD methods highly reflected local variations associated with soil type and planting duration, but the OK method was poorly representative. Categorical soil type and planting duration information may be used as ancillary information to improve the mapping quality of orchard soil pH. The OCK_STPD and OK_STPD methods were practical and efficient methods for interpolating orchard soil pH in the study area. The resultant high-quality soil pH maps can contribute to improved site-specific management in the orchards.     

昌黎黄金海岸国家级自然保护区土壤理化性质空间分布及影响因素

【目的】探究昌黎黄金海岸国家级自然保护区土壤理化性质空间分布特征,为建立保护区植被生态系统的良性循环提供科学参考。【方法】采用 1 000 m×1 000 m 网格取样的方法确定 57 个采样点,分别采集0~10 cm 和 10~20 cm 土壤样品进行理化性质测定,利用克里金插值 法进行空间制图,根据相关分析和方差分析探讨影响因素。【结果】保护区土壤理化性质水平分异明显,距海越远,部分理化性质指标值越高;土壤质地主要分为砂土、壤质砂土、砂质壤土及粉壤土四类,其中砂土占 70% 左右,砂粒含量达到 85%~100%;七里海实验区西部为盐土,全盐量为 5.22 g/kg,沿海以非盐化和轻度盐化为主。各指标间显著相关,其中土壤容重与含水量、养分指标呈显著负相关;除容重外,土壤含水量与其他 6 种指标之间呈显著正相关;4 种养分指标之间互为正相关关系,pH 值与有效磷、速效钾及全盐量呈显著正相关。此外,土壤理化性质分布格局受距海岸线距离、土地利用类型、土壤类型等因素的影响。其中距海岸线距离因素所起作用较大;耕地土壤含水量和养分含量显著高于林地、草地和未利用地;与潮土和滨海盐土相比,滨海风沙土的土壤容重较高,但含水量、有机质含量、全盐量和 pH 值较低。【结论】七里海实验区西部树种选择应以耐盐碱植物为主,陆域缓冲区、陆域核心区南部和金沙湾实验区应选择耐旱、抗风的良种壮苗。     

Spatial analysis of long‐term effects of different tillage practices based on penetration resistance

Measuring penetration resistance (PR) is a common technique for evaluating the effects of field management on soils. This study focuses on the effects of long‐term tillage on the spatial distribution of PR, comparing reduced and conventional tillage (CT) practices. The study site, located in Lower Saxony (Germany), has been subdivided into three plots, with one plot having been managed conventionally, whereas reduced tillage (RT) practices have been applied to the other two. In total, PR was measured at 63 randomly selected points. The PR data were stepwise interpolated using kriging with external drift. Core samples have been taken at 20 additional sites. The results show significant differences in PR between the different tillage practices. Within the conventionally managed plot, PR ranges to 2.3 MPa less in the topsoil than under RT. However, measured saturated hydraulic conductivity and amount of biopores at the depth of 30–35 cm are significantly greater under RT, indicating improved soil properties under RT. Comparisons between the headlands (HL) and the inner field point out the effects of intense field traffic in the HL, where maximum PR values of about 6 MPa have been measured. The spatial prediction of PR values show that long‐term effects of different tillage practices result in clearly structured patterns between CT and RT and the HL. Combining extensive PR measurements and point measurements of additional soil properties supports an adequate interpretation of PR data and can lead to fieldwide derivation of soil functions influenced by field management.     

Mapping crop diseases using survey data: The case of bacterial wilt in bananas in the East African highlands

Globally, crop diseases result in significant losses in crop yields. To properly target interventions to control crop diseases, it is important to map diseases at a high resolution. However, many surveys of crop diseases pose challenges to mapping because available observations are only proxies of the actual disease, observations often are not normally distributed and because typically convenience sampling is applied, leading to spatially clustered observations and large areas without observations. This paper addresses these challenges by applying a geostatistical methodology for disease incidence mapping. The methodology is illustrated for the case of bacterial wilt of banana (BWB) caused by Xanthomonas campestris pv. musacearum in the East African highlands. In a survey using convenience sampling, 1350 banana producing farmers were asked to estimate the percentage yield loss due to bacterial wilt. To deal with the non-normal distribution of the data, the percentages were classified into two binary variables, indicating whether or not the disease occurred and whether or not the yield loss was severe. To improve the spatial prediction of disease incidence in areas with low sampling density, the target variables were correlated in a logistic regression to a range of environmental variables, for which maps were available. Subsequently, the residuals of the regression analysis were interpolated using simple kriging. Finally, the interpolated residuals were added to the regression predictions. This so-called indicator regression kriging approach resulted in continuous maps of disease incidence. Cross-validation showed that the method yields unbiased predictions and correctly assesses the prediction accuracy. The geostatistical mapping is also more accurate than conventional mapping, which uses the mean of observations within districts as the predicted value for all locations within the district, although the accuracy improvement is not very large. The maps were also spatially aggregated to district level to support regional decision-making. The analysis showed that the disease is widespread on banana farms throughout the study area and can locally reach severe levels.     

利用地统计学技术预测流域尺度土壤团聚体稳定性及团聚体结合有机碳含量空间分布

The association of organic carbon with secondary particles (aggregates) results in its storage and retention in soil. A study was carried out at a catchment covering about 92 km2 to predict spatial variability of soil water-stable aggregates (WSA), mean weight diameter (MWD) of aggregates and organic carbon (OC) content in macro- (> 2 mm), meso- (1-2 mm), and micro-aggregate (< 1 mm) fractions, using geostatistical methods. One hundred and eleven soil samples were collected at the 0-10 cm depth and fractionated into macro-, meso-, and micro-aggregates by wet sieving. The OC content was determined for each fraction. A greater percentage of water-stable aggregates was found for micro-aggregates, followed by meso-aggregates. Aggregate OC content was greatest in meso-aggregates (9 g kg?1), followed by micro-aggregates (7 g kg?1), while the least OC content was found in macro-aggregates (3 g kg?1). Although a significant e?ect (P = 0.000) of aggregate size on aggregate OC content was found, however, our findings did not support the model of aggregate hierarchy. Land use had a significant e?ect (P = 0.073) on aggregate OC content. The coe?cients of variation (CVs) for OC contents associated with each aggregate fraction indicated macro-aggregates as the most variable (CV = 71%). Among the aggregate fractions, the micro-aggregate fraction had a lower CV value of 27%. The mean content of WSA ranged from 15% for macro-aggregates to 84% for micro-aggregates. Geostatistical analysis showed that the measured soil variables exhibited di?erences in their spatial patterns in both magnitude and space at each aggregate size fraction. The relative nugget variance for most aggregate-associated properties was lower than 45%. The range value for the variogram of water-stable aggregates was almost similar (about 3 km) for the three studied aggregate size classes. The range value for the variogram of aggregate-associated OC contents ranged from about 3 km for macro-aggregates to about 6.5 km for meso-aggregates. Kriged maps of predicted WSA, OC and MWD for the three studied aggregate size fractions showed clear spatial patterns. However, a close spatial similarity (co-regionalization) was observed between WSA and MWD.     

Geospatial modeling of surface soil texture of agricultural land using fuzzy logic,geostatistics and GIS techniques

ABSTRACT

Soil texture is a key controlling factor of soil properties and its functions include water and nutrient holding capacity, retention of pollutants, root development, soil biodiversity, and biogeochemical cycling. From the geotechnical standpoint, it is interesting to analyze the soil texture in regions due to its relation with the infiltration and runoff processes and, consequently, the effect on erosion processes. The purpose of this study is to present a methodology that provides the soil texture spatial variation by using Fuzzy logic theory and geostatistical technique in Geographic Information System (GIS) platform. A total of 140 soil samples were taken from topsoil (0–30 cm) in the study area located in the north of Guilan Province, the southern coast of Caspian Sea, Northern Iran. The soil textural classes were converted to numerical values (fuzzy values) using the fuzzy logic concept. The fuzzy values were spatially interpolated by ordinary kriging method such that the fitted model on experimental semi-variogram was exponential with moderate structure. The results showed the accuracy of soil texture predictive map was acceptable according to the values of normalized root-mean-square error for train data set (0.182) and test data set (0.179). The knowledge of the spatial variability of soil properties such as the soil texture can be an important tool for land-use planning in order to reduce the potential soil losses during rainy seasons. The results indicated that the integration of fuzzy logic, geostatistics, and GIS can improve the interpolation process.     

Comparison of euphausiid population size estimates obtained using replicated acoustic surveys of coastal inlets and block average vs. geostatistical spatial interpolation methods

Because of their intense patchiness, euphausiid spatial distributions and stock sizes are often assessed using echosounder surveys. However, statistical error bars appropriate for individual survey results are not well known. We quantified these by examining the statistical repeatability of acoustic estimates of total euphausiid biomass within two enclosed fjords adjoining the Strait of Georgia, British Columbia. Within each inlet, paired and replicated `mirror image' zig-zag survey tracks provided sets of closely spaced backscatter profiles along the survey lines. Local stock density (biomass per unit area) was calculated by vertical integration across the euphausiid scattering layer. Total inlet population size was then estimated by horizontal interpolation and integration of the local measurements, both by block averaging and by geostatistical interpolation (kriging). By assuming no change in true population biomass over the short time interval separating replicate surveys, we could then estimate statistical precision by analysis-of-variance comparison among replicate survey grids. For the partial surveys (one or the other half of the mirror-image paired grid) multiplicative error bars were about a factor of 1.5 for Jervis Inlet and 1.7 for Saanich Inlet. Use of the full surveys (both parts of the mirror-image pair, roughly doubling the number of measurements in each estimate) reduced the error to about factor of 1.35 for Jervis but only to about 1.65 for Saanich. Statistical precision was similar for the block average vs. kriging interpolation and integration methods, however, kriging provided additional useful information about spatial pattern within the inlets.