Exploring the spatial relations between cereal yield and soil chemical properties and the implications for sampling

Abstract. In general, agricultural management has focused on differences between fields or on the gross differences within them. Recent developments in agricultural technology, yield mapping, Global Positioning Systems and variable rate applications, have made it possible to consider managing the considerable variation in soil and other properties within fields. This system is known as precision agriculture. More precise management of fields depends on a better understanding of the factors that affect crop input decisions. This paper examines the spatial variation in crop yield, soil nutrient status and soil pH within two agricultural fields using geostatistics. The observed properties vary considerably within each field. The relation between yield and the measured soil properties appears to be weak in general. However, the range of spatial correlation for yield, shown by the variogram, is similar to that of the soil chemical properties. In addition the latter changed little over two years. This suggests that information on the scale of variation of soil chemical properties can be derived from yield maps, which can also be used as a guide to a suitable sampling interval for soil properties.     

Assessment of soil texture class on agricultural fields using ECa,Amber NDVI,and topographic properties

Soil texture (ST) is relatively stable over time, although it may change due to erosion, clay eluviation, and other processes. Soil texture affects soil quality, productivity and management. Therefore, indirect, accurate methods for assessing of soil texture classes (STCs) are needed in agricultural practice. A study was performed on four production fields in northern and central Poland to compare the fitting performance of STC models based on apparent electrical conductivity (ECa), topographic properties (elevation, slope gradient and wetness index) and Amber NDVI measurements. One common and accurate indicator of STCs was not found for all study fields. On average, ECa was most accurate in indicating areas of different STCs within the fields, but it tended to overestimate the size of sandy areas on loamy fields and vice versa. The accuracy of STC assessment using ECa measurements may be biased due to imperfect soil drainage, high elevations, which increase evaporation and STC variation with depth. STC assessment using Amber NDVI measurements may be useful, particularly on flat and sandy fields, but the results are affected by the same factors as ECa, and additionally by crop growth stages and by the weather conditions in the period preceding the measurements. Despite the good quantitative results of the STC assessment by elevation (one field) and by the topographic wetness index (another field), both terrain attributes failed to accurately indicate the distribution of some STC areas within each field. Therefore, in landscapes developed from deposits of the last glaciation relevant ST differences might not sufficiently be detected by the analysis of terrain attributes alone. The selection of STC predictors and evaluation of the assessment quality must consider both the quantitative indicators such as correlation and determination coefficients describing relationships between ST and ECa, NDVI and topography and percentage of a field area with accurately indicated STC and the distribution of areas with different STCs within a field. The use of ECa, NDVI values, and topographic properties for STC assessment may be useful in reducing costs of soil sampling and analysis, but cannot replace it.     

田间尺度下测定土壤有效K、P的采样方法

Field nutrient distribution maps obtained from the sutdy on soil variations within fields are the basis of precision agriculture,The quality of these maps for maagement depends on the accuracy of the predicted values,which depends on the initial sampling.to Produce reliable predictions efficently the minimal sampling size and combination should be decided firstly,which could avoid the misspent funds for field sampling work.A 7.9 hectare silage field close to the Agricultural Research Institute at Hillaborough,Northern Irealnd,was selected for the study,Soil samples were collected from the field at 25m intervals in a rectangular grid to provide a database of selected soil propoerties.Different data combinations were subsequently abstracted from this database for comparison purposes,and ordinary krging used to produce interpolated soil maps.These prediced data groups were compared using least significant difference(LSD) test method.The results showed that the 62 sampling sizes of triangle arrangement for soil available K were sufficient to reach the required accuracy.The triangular sample combination proved to be superior to a rectangular one of similar sample size.     

Soil-geographical regionalization as a basis for digital soil mapping: Karelia case study

Recent development of digital soil mapping (DSM) allowed improving significantly the quality of soil maps. We tried to make a set of empirical models for the territory of Karelia, a republic at the North-East of the European territory of Russian Federation. This territory was selected for the pilot study for DSM for two reasons. First, the soils of the region are mainly monogenetic; thus, the effect of paleogeographic environment on recent soils is reduced. Second, the territory was poorly mapped because of low agricultural development: only 1.8% of the total area of the republic is used for agriculture and has large-scale soil maps. The rest of the territory has only small-scale soil maps, compiled basing on the general geographic concepts rather than on field surveys. Thus, the only solution for soil inventory was the predictive digital mapping. The absence of large-scaled soil maps did not allow data mining from previous soil surveys, and only empirical models could be applied. For regionalization purposes, we accepted the division into Northern and Southern Karelia, proposed in the general scheme of soil regionalization of Russia; boundaries between the regions were somewhat modified. Within each region, we specified from 15 (Northern Karelia) to 32 (Southern Karelia) individual soilscapes and proposed soil-topographic and soil-lithological relationships for every soilscape. Further field verification is needed to adjust the models.     

结合多等级代表性采样和模糊隶属度的区域土壤制图方法

High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two steps: soil sampling and soil mapping. Because sampling over a large area is costly, efficient sampling strategies are required. A multi-grade representative sampling strategy, which designs a small number of representative samples with different representative grades to depict soil spatial variations at different scales,could be a potentially efficient sampling strategy for regional soil mapping. Additionally, a suitable soil mapping approach is needed to map regional soil variations based on a small number of samples. In this study, the multi-grade representative sampling strategy was applied and a fuzzy membership-weighted soil mapping approach was developed to map soil sand percentage and soil organic carbon(SOC) at 0–20 and 20–40 cm depths in a study area of 5 900 km2 in Anhui Province of China. First, geographical sub-areas were delineated using a parent lithology data layer. Next, fuzzy c-means clustering was applied to two climate and four terrain variables in each stratum. The clustering results(environmental cluster chains) were used to locate representative samples. Evaluations based on an independent validation sample set showed that the addition of samples with lower representativeness generally led to a decrease of root mean square error(RMSE). The declining rates of RMSE with the addition of samples slowed down for 20–40 cm depth, but fluctuated for 0–20 cm depth. The predicted SOC maps based on the representative samples exhibited higher accuracy, especially for soil depth 20–40 cm, as compared to those based on legacy soil data. Multi-grade representative sampling could be an effective sampling strategy at a regional scale. This sampling strategy, combined with the fuzzy membership-based mapping approach, could be an optional effective framework for regional soil property mapping. A more detailed and accurate soil parent material map and the addition of environmental variables representing human activities would improve mapping accuracy.     

How good is a reconnaissance soil map for agronomic purposes?

Abstract. Information about the soil fertility status in irrigated ricelands at regional scales (1:50 000–1:250 000) is commonly not contained in classical soil maps. To assess the agronomic suitability of two different reconnaissance soil maps, we conducted a detailed soil survey in the Nueva Ecija province, Philippines. Soil samples were collected from 384 farmers' fields, and soil properties were measured for topsoil and subsoil samples. For most soil properties, a soil map made in 1940 (1:125 000) had within-map unit variances that were smaller than the total variance, whereas a new soil map of 1992 (1:50 000) did not significantly reduce the within-class variance. In both soil maps, classification into mapping units accounted for 0–40% of the variance of 14 agronomically important soil properties and large within-map unit variabilities were found. Underlying strategies of classical soil survey supported the partition of variance for relatively stable soil properties, such as soil texture, CEC, and organic matter. If reconnaissance soil maps are used in quantitative land evaluation studies, existing maps require upgrading by adding quantitative information about relevant soil properties and their within-map unit variability The sampling demand for upgrading a reconnaissance soil map was large, but pedotransfer functions can be used as cost-saving tools. Measures of soil nutrient status were highly variable within all mapping units and differences among farmers were much greater than the differences between soil types. Therefore, nutrient management in the study region should be based on individual field or farm recommendations rather than on soil-map based recommendations.     

土壤水盐空间变异尺度效应的研究

水文学和土壤学中的尺度问题是目前水土科学研究的前沿课题之一。该文利用空间信息科学——地质统计学、根据设计的各种田间网格，在一维和二维(平面)空间中初步研究了黄河河套平原长胜试验区中小尺度的土壤水分和盐分随采样尺度所表现出的空间变异(结构性)的某些规律，表明采样尺度的划分和选取与水分和盐分的空间变异性大小有密切关系，尺度效应的研究对于指导农业技术研究中野外采样系统设计、节省外业调查的工作量及科学地进行内业计算、评估和揭示农业工程中具有地学特征的区域性自然规律有重要作用。     

土壤制图中多等级代表性采样与分层随机采样的对比研究

采样设计是土壤地理研究中备受关注的重要问题。本文以区域尺度土壤属性制图为例,将多等级代表性采样与经典采样中的分层随机采样进行对比研究。以安徽宣城研究区的表层砂粒含量为目标要素,采集数量均为59个的两套样点,设计不同数量(46、58和59)的样点分组,采用两种制图方法进行制图并利用独立验证点进行评价。结果表明:1)无论是采用多元线性回归方法还是基于环境相似度的制图方法,在同等样点数量下,利用代表性样点所得土壤图精度均高于利用随机样点所得精度,并且利用少量代表性样点(46个)所得土壤图精度也高于利用多量随机样点(59个)所得精度;2)随着代表性较低样点的增加,土壤制图精度基本有一个提高的趋势,而采用随机样点所得土壤图的精度波动较大。因此,可认为多等级代表性采样方法是一种可用于区域尺度土壤调查的有效采样方法,且比分层随机采样高效、稳定。     

Surface and profile soil moisture spatio-temporal analysis during an excessive rainfall period in the Southern Great Plains,USA

In this work we analyze the temporal stability of soil moisture at the field and watershed scales in the Little Washita River Experimental Watershed (LWREW), as part of the remote sensing Cloud and Land Surface Interaction Campaign (CLASIC07) during June 2007 in south-central Oklahoma. Temporal stability of surface and profile soil moisture data were investigated for 20 LWREW soil moisture measurement stations. In addition, daily surface and profile soil moisture measurements were obtained in four 800 m by 800 m fields (remote sensing footprint), including two rangeland sites and two winter wheat fields. The work aimed to analyze the temporal stability of soil moisture at the watershed and field scale and to identify stations within the watershed, as well as locations within each field, that were representative of the mean areal soil moisture content. We also determined the relationship between sites found to be temporally stable for surface soil moisture versus those determined stable for average profile soil moisture content. For the unusually wet experimental period, results at the watershed scale show that LWREW stations 133 and 134 provided stable underestimates, while stations 132 and 154 provided stable overestimates of the watershed mean at all depths. In addition, station 136 had very high non-zero temporal stability at the 25 cm and 45 cm depths indicating that it could be used as representative watershed site provided a constant offset value is used to acquire a watershed mean soil water content value. In general, the deeper depths exhibited higher soil moisture spatial variability, as indicated by the higher standard deviations. At the field scale, measured average profile soil moisture was higher in the winter wheat fields than the rangeland fields with the majority of the winter wheat depth intervals having high non-zero temporal stability. Field scale temporal stability analysis revealed that 4 of the 16 sampling sites in the rangeland fields and 3 of the 16 sampling sites in the winter wheat fields either under or overestimated the field means in the 0–5 and 0–60 cm depth intervals. Field sites considered temporally stable for the surface soil moisture were not stable for the profile soil moisture, except for the LW45 field where two sites were stable at both the surface and profile soil moisture. This finding is significant in terms of soil moisture ground-truth sampling for calibrating and validating airborne remotely sensed soil moisture products under extremely wet conditions. In addition, identification of temporally stable sites at the watershed and field scales in the LWREW provide insight in determining future measurement station locations and field scale ground sampling protocol, as well as providing data sets for hydrologic modeling.     

Characterization of soil conditions at the field level for soil specific management

Soil variability within fields is well documented by a variety of sources: soil survey, soil testing, and crop yields. Technology is emerging to vary field management procedures as the machinery traverses fields. This new concept can increase farm profitabily and reduce environmental pollution. Operational methods of soil characterization includes US National Cooperative Soil Survey maps and databases, various methods of field soil sampling, aerial photography and remote sensing techniques, and some soil sensors. Although some techniques have been used for many years, there is still a strong need to improve precision and procedures of methods. Soil specific management is a high technology type management. Easy-to-use soil characterization tools must be developed and incorporated into decision aid systems to insure a successful adoption of this emerging technology.     

土地混合使用制度下土壤硝态氮分布的地理空间制图研究

Mapping the spatial distribution of soil nitrate-nitrogen (NO₃-N) is important to guide nitrogen application as well as to assess environmental risk of NO₃-N leaching into the groundwater. We employed univariate and hybrid geostatistical methods to map the spatial distribution of soil NO₃-N across a landscape in northeast Florida. Soil samples were collected from four depth increments (0-30, 30-60, 60-120 and 120-180 cm) from 147 sampling locations identified using a stratified random and nested sampling design based on soil, land use and elevation strata. Soil NO₃-N distributions in the top two layers were spatially autocorrelated and mapped using lognormal kriging. Environmental correlation models for NO₃-N prediction were derived using linear and non-linear regression methods, and employed to develop NO₃-N trend maps. Land use and its related variables derived from satellite imagery were identified as important variables to predict NO₃-N using environmental correlation models. While lognormal kriging produced smoothly varying maps, trend maps derived from environmental correlation models generated spatially heterogeneous maps. Trend maps were combined with ordinary kriging predictions of trend model residuals to develop regression kriging prediction maps, which gave the best NO₃-N predictions. As land use and remotely sensed data are readily available and have much finer spatial resolution compared to field sampled soils, our findings suggested the effcacy of environmental correlation models based on land use and remotely sensed data for landscape scale mapping of soil NO₃-N. The methodologies implemented are transferable for mapping of soil NO₃-N in other landscapes.     

土壤制图中土壤类型配色模型构建与应用

在全国1∶5万土壤图集制图中,土壤类型的配色既需表现土类等高级类型的分布特征,也要表现土属等较低级类型的差别。我国土壤低级类型众多,且1∶5万基本比例尺图幅达2万余幅,采用传统人工设色方法进行土壤制图,不仅效率低,而且难以保持图幅间土壤颜色的协调一致性。针对这一技术难题,本研究采用图幅间相似配色方法和人机交互的设计思想,通过建立1个多层级管理色库、人工设置土壤类型的Q配色单元及其多个近似色系(色组),建立了Q配色单元的避让选色和区域土壤特征分析等5个组件模型,构建了土壤类型配色模型(SCO-Model)。该模型在大比例尺土壤制图中不仅反映了区域土壤的总体分布特征,也表达了土壤类型间的差异,特别是实现了大比例尺土壤制图中土壤类型的快速智能配色,大大提高了制图效率。     

Comparing mapping approaches at subcatchment scale in northern Thailand with emphasis on the Maximum Likelihood approach

This study compares different soil mapping approaches in three different petrographic areas in order to test their suitability for regional mapping in northern Thailand. Sampling was based on transects or grid-based randomization. Maps were created based on expert knowledge (eye fitting) or using Classification Tree (CART algorithm) or the Maximum Likelihood approach. In addition, local knowledge-based-soil maps were created. Validation was performed using soil reference maps and independent sampling points. The mapping approaches based on transects and grid-based randomization showed a very high correspondence with the respective reference soil map and a very high degree of matching with independent sampling points. Both methods are best suited for sub-watershed scale. Mapping larger areas is difficult due to the inaccessibility of the mountainous regions. The soil maps based on Maximum Likelihood showed a high correlation with the respective reference soil maps and the individual sampling points. Maximum Likelihood maps and Classification Tree maps showed similar levels of accuracy. The Maximum Likelihood approach is applicable to upscaling procedures; therefore, a calibration area is required which represents the target area. Local knowledge-based-soil mapping is very cheap and fast, but is restricted to village areas where classification often varies even within a village. Despite this, local knowledge is very useful for soil reconnaissance surveys, as well as to acquire an overview of the major distribution of soils and their properties. Upscaling of local knowledge due to its inherent inconsistency is not realistic.     

How representatively can we sample soil mineral nitrogen?

Spatial distribution of soil mineral‐N content (N_min) is a scale‐variant process. Precision farming assumes knowledge about the spatial distribution of N_min. Moreover, sampling in management zones is based on the assumption of spatial dependence between sampling points. In the present study, variability structure of N_min and the sources of variability were investigated. Within an agricultural landscape, N_min was investigated across a field in a nested design over four consecutive years. Temporally unstable structure of individual nests require a sampling with several nests in the field. In the investigated field, 35%–49% of the total variability derived from small‐scale variability observed at spatial distances of <5 m and from sampling and analytical errors. Differences between 10 and 26 kg N ha^–1 for the soil depth increment 0–60 cm can be expected. Uncertainty due to analytical errors were in the order of 5–10 kg N ha^–1 for a 0–60 cm layer.     

Dynamics of soil protozoa using a direct count method

We adapted a direct count method for obtaining counts of active protozoa that was not overly time consuming. Soil samples from an agricultural field were examined at 1- to 3-day intervals three times through the year. The three sampling periods represented different weather conditions. At each sampling event, fresh soil samples were extracted upon return to the laboratory for protozoa. These were enumerated at the microscope without prior culture, in soil–water suspension dilutions. We describe a procedure that allowed all samples to be processed in a few hours. Our results suggest there is good reproducibility and agreement between samples collected on the same day. Our data resolve differences between days as soil conditions changed slowly with drying or wetting. This procedure is suitable for describing species active at the time of sampling. Unlike the ‘most probable number’ procedure that relies on cultivable species, it is less prone to enumerating excysting individuals, and it provides better resolution between sampling dates, with a relatively low number of samples.     

Small scale digital soil mapping in Southeastern Kenya

Digital soil mapping techniques appear to be an interesting alternative for traditional soil survey techniques. However, most applications deal with (semi-)detailed soil surveys where soil variability is determined by a limited number of soil forming factors. The question that remains is whether digital soil mapping techniques are equally suitable for exploratory or reconnaissance soil surveys in more extensive areas with limited data availability. We applied digital soil mapping in a 13,500 km² study area in Kenya with the main aim to create a reconnaissance soil map to assess clay and soil organic carbon contents in terraced maize fields. Soil spatial variability prediction was based on environmental correlation using the concepts of the soil forming factors equation. During field work, 95 composite soil samples were collected. Auxiliary spatially exhaustive data provided insight on the spatial variation of climate, land cover, topography and parent material. The final digital soil maps were elaborated using regression kriging. The variance explained by the regression kriging models was estimated as 13% and 37% for soil organic carbon and clay respectively. These results were confirmed by cross-validation and provide a significant improvement compared to the existing soil survey.     

半干旱沙区土类/亚类的遥感调查制图方法

传统土壤调查制图存在低时效性、低精度等问题。为了解决半干旱沙区土壤遥感调查制图问题,该文以科尔沁左翼后旗为例,基于野外实地调查和专家知识分析了半干旱沙区土壤类型分布特征与环境因素之间的关系,并探讨了基于多时相Landsat8 OLI影像数据的半干旱沙区土类/亚类遥感调查制图方法。结果表明:利用多时相Landsat8 OLI影像数据提取的归一化差异水体指数(modified normalized difference water index,MNDWI)、盐分指数(salt index,SI)、归一化差异湿度指数(normalized difference moisture index,NDMI)、归一化差异植被指数(normalized difference vegetation index,NDVI)等环境信息,可实现对沼泽土、盐碱土、草甸土、风沙土及其亚类等半干旱沙区主要土壤类型的遥感调查制图。应用本文提出的半干旱区土类/亚类遥感调查制图方法对科左后旗进行土壤遥感调查制图和精度验证,总体精度约为72.84%,Kappa系数为0.667 8。该方法可为半干旱沙区数字土壤调查制图提供思路和参考。     

Computerized soil data used in agricultural water planning, Denmark

Abstract. A nationwide soil database system has been established containing soil maps, analytical data, and soil classification. The system has been widely used in agricultural planning at county and national level. This paper describes the basic data used for the soil database system, and the exploitation of this system in agricultural water planning. The principles for calculating the irrigation need at county level are given and also those for nationwide mapping of the potential need for drainage. Mapping of potentially acid sulphate soils, which has given rise to legislation on drainage of wetlands, is described, and the future use of the soil database system in agricultural water planning is discussed.     

农田土壤颗粒尺寸分布分维及颗粒体积分数的空间变异性

盐渍土膜下滴灌过程中的土壤理化性质存在空间变异性,对灌溉管理有一定影响。该文根据3个不同尺度下土壤采样及颗粒尺寸分布（PSD）测定结果,探讨了土壤PSD分维和颗粒体积分数之间的定量关系,分析了各尺度和综合尺度下土壤PSD分维和颗粒体积分数的经典统计特征和地质统计特征,根据颗粒体积分数的半方差函数计算了不同尺度颗粒体积分数的分形维,并做出了等值线图。研究表明,土壤PSD分维与黏粒的体积分数呈正相关关系,黏粒体积分数增大引起PSD分维增加的原因在于细颗粒的比表面积比粗颗粒的大;各尺度土壤PSD分维及颗粒体积分数均无强变异特征;不同尺度下土壤PSD分形维的半方差函数中块金和基台值均非常低;50 m尺度土壤PSD分维和黏粒体积分数的等值线图具有类似的集中度和走势,而砂粒体积分数和粉粒体积分数则具有相反的集中度和走势。该研究说明土壤PSD分维和颗粒体积分数都有一定的尺度依赖性。