Grid Sampling Design Research Articles

Sustainable extraction and conservation of soil resources: New insights through Natural Gamma-ray Spectrometry based Proximal Sensing Method

Soil is one the most extracted natural raw materials. The vast expanses of fertile alluvial soils of the Indo Gangetic Plains have long remained as abundant soil resource pool for brick manufacturing and construction sectors. Unmonitored continuous removal of soil is reported to cause depletion of soil reserves, loss of soil fertility and affect crop yield. Excavation and removal of soil from isolated patches of land creates low lying and elevated degraded areas which disrupts normal crop cultivation pattern. Natural gamma-ray spectrometry (NGS) can be used as a non-destructive and rapid geophysical sensing method, for identification and delineation of areas with suitable soils. During this work brick kiln areas were visited to understand soil's availability and extraction pattern. NGS measurements of samples from soil profiles were carried out to find if gamma-ray intensities varied with soil clay content. Soil texture and plasticity of the same samples were obtained following standard testing procedures. Winkler and Plasticity charts were used to assess suitability of the soils. A strong linear relationship between gamma-ray potassium (K) intensity and clay contents of soil profile samples (R2 = 0.88) was observed. NGS based devices can be used to scan soil samples rapidly and log shallow depth boreholes in grid sampling design. The gathered spectral gamma-ray data can be then used to predict and generate high resolution 3D models of soil properties, based on which resource areas of suitable soils can be delineated for long term soil extraction without affecting cultivated areas. This will help in delineating areas restricted for soil extraction, which will not only make soil mining sustainable but also address soil conservation by setting aside large cultivated fertile soil areas untouched. Adopting NGS methods will prevent unsystematic removal of fertile soil and creation of degraded lands. This will ultimately result in efficient soil resource management.

Examining the influence of bare soil UAV imagery combined with auxiliary datasets to estimate and map soil organic carbon distribution in an erosion-prone agricultural field

Soil organic content (SOC), an indicator of soil fertility, can be estimated quickly and accurately with remote sensing (RS) datasets; however, the issue of vegetation cover on the field still remains a major concern. In order to minimize the effects of vegetation cover, studies relating reflectance spectra to SOC may require bare soil. However, acquiring satellite images devoid of vegetation is still an enormous challenge for RS techniques. This is because the area that may have been accurately predicted at a targeted date is sometimes limited since many pixels are covered by vegetation. The study goal was to assess the impact of using UAV-borne imagery coupled with auxiliary datasets, which include spectral indices (SPIs) and terrain attributes (TAs) (at 20 cm and 30 m resolution), singly or merged, to estimate and map SOC in an erosion-prone agricultural field. Both field samples and UAV imagery were acquired while the fields were bare. Using a grid sampling design, 133 soil surface samples were collected. The models used include partial least square regression (PLSR), extreme gradient boosting (EGB), multivariate adaptive regression splines (MARS), and regularised random forest (RFF). The models were evaluated using the root mean squared error (RMSE), the coefficient of determination (R2), ratio of performance to interquartile distance (RPIQ), and the mean absolute error (MAE). For prediction, the three merged datasets (R2val = 0.86, RMSEval = 0.13, MAEval = 0.11, RPIQval = 4.19) outperformed the best separate dataset (R2val = 0.82, RMSEval = 0.15, MAEval = 0.10, RPIQval = 2.08). Though all datasets detected both low and high estimates of soil SOC, the three merged datasets with EGB showed a less extreme prediction error. This study demonstrated that SOC can be estimated with high accuracy using completely bare soil UAV imagery with other auxiliary data, and it is thus highly recommended.

Spatial variability of soil chemical properties under different land-uses in Northwest Ethiopia.

The understanding of the spatial variation of soil chemical properties is critical in agriculture and the environment. To assess the spatial variability of soil chemical properties in the Fogera plain, Ethiopia, we used Inverse Distance Weighting (IDW), pair-wise comparisons, descriptive analysis, and principal component analysis (PCA). In 2019, soil samples were collected at topsoil (a soil depth of 0-20 cm) from three representative land-uses (cropland, plantation forestland, and grazing lands) using a grid-sampling design. The variance analysis for soil pH, available phosphorus (avP), organic carbon (OC), total nitrogen (TN), electrical conductivity (EC), exchangeable potassium (exchK), exchangeable calcium (exchCa), and cation exchange capacity (CEC) revealed significant differences among the land-uses. The highest mean values of pH (8.9), avP (32.99 ppm), OC (4.82%), TN (0.39%), EC (2.28 dS m-1), and exchK (2.89 cmol (+) kg-1) were determined under grazing land. The lowest pH (6.2), OC (2.3%), TN (0.15%), and EC (0.11 dS m-1) were recorded in cultivated land. The PCA result revealed that the land-use change was responsible for most soil chemical properties, accounting for 93.32%. Soil maps can help identify the nutrient status, update management options, and increase productivity and profit. The expansion of cultivated lands resulted in a significant decrease in soil organic matter. Thus, soil management strategies should be tailored to replenish the soil nutrient content while maintaining agricultural productivity in the Fogera plain.

A Novel Method for Using Small Unoccupied Aerial Vehicles to Survey Wildlife Species and Model Their Density Distribution

There is growing interest from research and conservation groups in the potential for using small unoccupied aerial vehicles (UAVs; <2 kg) to conduct wildlife surveys because they are affordable, easy to use, readily available and reliable. However, limitations such as short flight endurance, and in many situations, aviation regulations, have constrained the use of small UAVs in survey applications. Thus, there is a need to refine survey methods adapted to small UAVs that conform to standard operations within aviation law. We developed a novel survey approach based on a grid sampling design using two multirotor UAVs (Phantom 4 Pros) flying simultaneously, within visual line of sight, from our vessel base-station. We used this approach to assess the fine-scale distribution and abundance of dugongs (Dugong dugon) in the remote waters of the Pilbara, Western Australia during three field seasons across 2 years. We surveyed 64 non-overlapping survey cells in random order one or more times and obtained complete image coverage of each surveyed cell of our 31 km2 survey area. Our sampling design maximizes sampling effort while limiting survey time by surveying four cells, two at a time, from one location. Overall, we conducted 240 flights with up to 17 flights per day (mean = 14 flights per day) and could obtain complete coverage of up to 11.36 km2per day. A total of 149 dugongs were sighted within the 50,482 images which we manually reviewed. Spatially-explicit models of dugong density distribution (corrected for availability and perception bias) were produced using general additive models to identify areas more or less used by dugongs (range of corrected dugong densities across all field season = 0.002–1.79 dugongs per 0.04 km2). Dugong abundance estimates ranged from 47 individuals in June 2019 (CV = 0.17) to 103 individuals in May 2018 (CV = 0.36). Our method, which proved convincing in a real-word application by its feasibility, ease of implementation, and achievable surface coverage has the potential to be used in a wide range of applications from community-based local-scale surveys, to long-term repeated/intensive surveys, and impact assessments and environmental monitoring studies.

Exploring the optimal sampling density to characterize spatial heterogeneity of soil carbon stocks in a Karst Region

AbstractThe aim of this study is to investigate the spatial distribution of soil organic C stock and to determine the optimal number of samples required for its determination, in a small karst basin. By taking the soil organic C stock in the Houzhai River Basin, calculated on the basis of a 150 by 150 m grid sampling design as original data and selecting sample sets at five grid scales (300, 450, 600, 750, and 900 m), the optimal number of samples for different sampling distances was obtained, using geographic information system (GIS) technology and geostatistical analyses. With increasing sampling distance, the coefficient of variation of the soil organic C stocks first increased and then decreased; the spatial predictions of the soil organic C stock tended to be smoother, and the ability to represent detail was weaker. The prediction accuracy was comprehensively discussed. When the surface soil organic C stock (0–20 cm) was considered, 357 sampling points at a sampling distance of 450 by 450 m could accurately represent its spatial variability. For a depth of 0–100 cm, 200 sampling points and a sampling distance of 600 m could accurately represent the spatial variability in the soil organic C stock. The results of this study could provide data for future studies for the estimation of soil organic C stock in the karst areas of Southwest China and similar areas.

Cover Crops on Temporal and Spatial Variations in Soil Microbial Communities by Phospholipid Fatty Acid Profiling

Core Ideas Cover crops improve soil microbial biomass compared to no cover crop and influence soil microbial community structure.Soil microbial communities change both spatially and temporally.Results of the study imply that integration of CC in a regular corn–soybean rotation can improve soil quality and environmental benefits. Although cover crops (CC) are believed to play a major role in soil quality improvement, the effects of CC on microbial populations and community structure is not well understood. The objective of this study was to quantify CC effects on soil microbial biomass and community structure under a corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation. The study was conducted at the Chariton County Cover Crop Soil Health Research and Demonstration Farm (CCSH) in Missouri, USA, where CC were first established in 2012. Soils were sampled in 2016, 2017, and 2018 from the 0‐ to 10‐cm depth layer using a grid sampling design and phospholipid fatty acid (PLFA) profiles were determined. Microbial biomass and microbial community structure (total fungi, total bacteria, rhizobia, gram (−), and actinomycetes biomass), as estimated from the PLFA biomarkers, were significantly greater (P < 0.05) in the CC treatment compared to no cover crop (NCC) in 2016 and 2018 (2.4‐ and 1.7‐fold larger, respectively). Within the CC treatment, differences by soil type were also observed, finding that the silt loam soil supported greater total microbial biomass than the loam soil in 2018. Spatial distribution patterns of total microbial biomass, bacteria biomass and fungi biomass differed with time. Overall, this study demonstrated that the CC treatment affected the soil microbial community biomass and structure, which has potential environmental, production, and soil quality benefits.

Effects of wind turbine noise on the surrounding soundscape in the context of greater-prairie chicken courtship vocalizations

The development of wind energy facilities in grasslands has increased in recent years. We assessed sound levels near a 36-turbine wind energy facility in a contiguous grassland in Brown County, Nebraska, USA, using a grid sampling design. Greater prairie-chickens (Tympanuchus cupido pinnatus), a bird species of special conservation concern, occupy the landscape near the wind facility. We evaluated the variation in wind turbine noise in the landscape at 296 Hz, the average peak frequency of the critically important courtship (‘boom’) vocalization of male prairie-chickens. The levels of ambient sounds were affected by ordinal day (β = −0.0752, SE = 0.0073) and wind speed (β = 1.1679, SE = 0.0419). Sound levels (dB) at the wind facility were affected by distance to the nearest turbine (β = 0.6917, SE = 0.0111). Sound levels increased during the breeding season (ordinal day: β = 0.0166, SE = 0.0028), during the day (time of day: β = 0.0035, SE = 0.0015), and at higher wind speeds (β = 1.1929, SE = 0.0261). Sound levels were higher downwind from the facility (wind direction: β = 0.9914, SE = 0.1691) and at sites that were within the viewshed of turbines (topography: β = 0.3819, SE = 0.0824). At 100 m from the turbines, under average temporal, landscape, and wind conditions, wind turbine noise at 296 Hz was 25 dB above the ambient sound level. At 500 m from the turbines, wind turbine noise at 296 Hz was 16 dB above the ambient sound level. Our results demonstrate the variation of wind turbine noise in the soundscape and suggest a protocol for evaluating effects of wind turbine sound on sound-sensitive wildlife.

Spatial variability of soil microbial indices in common alder COMMON ALDER (Alnus glutinosa) stands using a geostatistical approach in northern Iran

Microbial indices and their spatial patterns are strongly affected by environmental factors. Spatial variability of soil properties is one of the most important causes of variability in soil microbial indices. This research was conducted in the Caspian forest to assess spatial variabilities and frequency distributions of microbial properties. Ninety soil samples were taken using a grid sampling design 40 × 40 m. Microbial indices, organic carbon, nitrogen and pH were determined. Soil variable distributions showed that microbial indices had abnormal distributions. Logarithmic transformation produced normal distribution. Spatial continuity using geostatistical (variogram) was studied and maps obtained by point kriging. The variograms revealed the presence of spatial autocorrelation. The results indicate that spatial dependence of soil microbial indices was affected by non-intrinsic factors and forest management procedures. The maps show that soil microbial indices and soil properties have spatial variability. The spatial pattern of microbial indices was correlated to organic carbon and nitrogen.

Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

With a mining-driven economy, Botswana has experienced increased geochemical exploration of minerals around existing mining towns. The mining and smelting of copper and nickel around Selibe-Phikwe in the Central Province are capable of releasing heavy metals including Pb, Fe, Mn, Co, Ni and Cu into the soil environments, thereby exposing humans, plants and animals to health risks. In this study, turning bands co-simulation, a multivariate geostatistical algorithm, was presented as a tool for spatial uncertainty quantification and probability mapping of cross-correlated heavy metals (Co, Mn, Fe and Pb) risk assessment in a semiarid Cu–Ni exploration field of Botswana. A total of 1050 soil samples were collected across the field at a depth of ~ 10 cm in a grid sampling design. Rapid elemental concentration analysis was done using an Olympus Delta Sigma portable X-ray fluorescence device. Enrichment factor, geoaccumulation index and pollution load index were used to assess the potential risk of heavy metals contamination in soils. The partially heterotopic nature of the dataset and strong correlations among the heavy metals favors the use of co-simulation instead of independent simulation in the probability mapping of heavy metal risks in the study area. The strong correlation of Co and Mn to iron infers they are of lithogenic origin, unlike Pb which had weak correlation pointing to its source in the area being of anthropogenic source. Manganese, Co and Fe show low enrichment, whereas Pb had high enrichment suggesting possible lead pollution. We, however, recommend that speciation of Pb in the soils rather than total concentration should be ascertained to infer chances of possible bioaccumulation, and subsequent health risk to human by chronic exposure.

Spatial capture-recapture model performance with known small-mammal densities.

Abundance and density of wild animals are important ecological metrics. However, estimating either is fraught with challenges; spatial capture-recapture (SCR) models are a relatively new class of models that attempt to ameliorate common challenges, providing a statistically coherent framework to estimate abundance and density. SCR models are increasingly being used in ecological and conservation studies of mammals worldwide, but have received little testing with empirical field data. We use data collected via a web and grid sampling design to evaluate the basic SCR model where small-mammal abundance (N) and density (D) are known (via exhaustive sampling). We fit the basic SCR model with and without a behavioral effect to 11 small-mammal populations for each sampling design using a Bayesian and likelihood SCR modeling approach. We compare SCR and ad hoc density estimators using frequentist performance measures. We found Bayesian and likelihood SCR estimates of density (D) and abundance (N) to be similar. We also found SCR models to have moderately poor frequentist coverage of D and N (45-73%), high deviation from truth (i.e., accuracy; D, 17-29%; N, 16-29%), and consistent negative bias across inferential paradigms, sampling designs, and models. With the trapping grid data, the basic SCR model generally performed more poorly than the best ad hoc estimator (behavior CR super-population estimate divided by the full mean maximum distance moved estimate of the effective trapping area), whereas with the trapping web data, the best-performing SCR model (null) was comparable to the best distance model. Relatively poor frequentist SCR coverage resulted from higher precision (SCR coefficients of variation [CVs] < ad hoc CVs); however D and D were fairly well correlated (r2 range of 0.77-0.96). SCR's negative relative bias (i.e., average underestimation of the true density) suggests additional heterogeneity in detection and/or that small mammals maintained asymmetric home ranges. We suggest caution in the use of the basic SCR model when trapping animals in a sampling grid and more generally when small sample sizes necessitate the spatial scale parameter (σ) apply to all individuals. When possible, researchers should consider variation in detection and incorporate individual biological and/or ecological variation at the trap level when modeling σ.

Optimal Soil Raster Unit Resolutions in Estimation of Soil Organic Carbon Pool at Different Map Scales

A proper soil raster unit resolution for grid sampling design is important to estimate the soil organic carbon (SOC) pool at certain map scales, which is related to the soil sampling density and the accuracy of the estimation. A series of raster soil unit data sets at varying resolutions were derived from different vector soil unit data sets at six map scales of 1:50,000, 1:200,000, 1:500,000, 1:1,000,000, 1:4,000,000, and 1:14,000,000 in the Tai-Lake region of China. Four indices—soil type number (STN) and area (AREA), average SOC density (ASOCD), and total SOC stocks (SOCS) of surface paddy soils—were attributed from all these vector and raster units data sets. Subjected to the four index values (IV) from parent vector unit data set, the relative variability (VIV, %) from raster unit data set was used to assess its accuracy and redundancy, which reflects uncertainty and workload of SOC estimation, respectively. Optimal raster unit resolutions were generated and suggested for each map scale’s SOC estimation, in which the soil raster unit data set can hold the same accuracy as its parent vector unit data set without any redundancy when VIV < 1% of all the four indices was assumed as criteria to the assessment. A relationship between map scale (1:x) of soil vector unit and its optimal grid resolution (y, km) was found to be: y = −8.03 × 10⁻⁶x² + 0.0256x- 0.087 (R² = 0.998, p < 0.05). The results may serve for soil unit conversion from vector to raster and soil grid sampling design at a certain map scale in the investigation of regional SOC pool.

Assessment of Nutrients and Load of Organic Carbon in Soil and Sediments Due To Flood and Heavy Irrigation of River Krishna in the Bagalkot District

Levels of chemical parameters (pH, EC, Na & Ca), Major nutrients (Organic Carbon, Available Nitrogen, Phosphorous and Potassium) and Micro nutrients ( Fe, Mn, Zn, Cu, Boron and Molybdenum) were determined in soil and sediments samples of cultivated lands around the belt of River Krishna across the Bagalkot district, Karnataka state, India. The present study aim was to evaluate some physical and chemical properties, assessment of Organic load, nutrients and their special variation in soil and sediment samples due to heavy irrigation of cultivated lands. The spatial variability and distribution of soil properties at the cultivated lands affect yield potential, hydrologic responses and transport of herbicides to soil surface. The study reveals the seasonal effect on soil and sediment properties by flood and heavy irrigation, fertilizer addition, high water table level and agriculture practices. This effect may cause positive and negative Organic load and nutrients on soil properties. For this evaluation 12 soil samples (seasonally 4 samples each) from soil surface for layer according to grid sampling design and12 sediment samples at three different seasons with 1000 to 2000 meters distance based soil and sediments variability were selected and analyzed.

Microbial biomass carbon and enzyme activities of urban soils in Beijing

To promote rational and sustainable use of soil resources and to maintain the urban soil quality, it is essential to assess urban ecosystem health. In this study, the microbiological properties of urban soils in Beijing and their spatial distribution patterns across the city were evaluated based on measurements of microbial biomass carbon and urease and invertase activities of the soils for the purpose of assessing the urban ecosystem health of Beijing. Grid sampling design, normal Kriging technique, and the multiple comparisons among different land use types were used in soil sampling and data treatment. The inherent chemical characteristics of urban soils in Beijing, e.g., soil pH, electronic conductivity, heavy metal contents, total N, P and K contents, and soil organic matter contents were detected. The size and diversity of microbial community and the extent of microbial activity in Beijing urban soils were measured as the microbial biomass carbon content and the ratio of microbial biomass carbon content to total soil organic carbon. The microbial community health measured in terms of microbial biomass carbon, urease, and invertase activities varied with the organic substrate and nutrient contents of the soils and were not adversely affected by the presence of heavy metals at p < 0.01. It was shown that the older and the biologically more stable part of city exhibited higher microbial activity levels than the more recently developed part of the city and the road areas of heavy traffic. It was concluded that the land use patterns in Beijing urban soils influenced the nature and activities of the microbial communities.

Natural disturbance dynamics in an old-growth forest: from tree to landscape

Forest structure is the result of past disturbances, regeneration establishment, competition, and mortality dynamics. Natural disturbances can create single dead tree to larger canopy gaps creating both small and large scale patterns and processes. The analysis of forest spatial pattern can provide information on these dynamics shaping forest ecosystems. Knowledge about primeval forest dynamics is relevant for their conservation and is an important reference for delineating sustainable forest management.The main aim of this research was to assess forest structure and understand spatial patterns and related disturbance processes in a mixed silver fir-Norway spruce-beech old-growth forest. The study site is the forest reserve of Lom, an old-growth forest located in Bosnia and Erzegovina. We analysed forest structure at different scales and with different approaches : a) landscape, b) forest, and c) single tree. A Kompsat-2 satellite image was used for forest canopy gaps delineation (a). The classification was based on an artificial neural network and allowed the identification of 297 canopy gaps (50-1776 m2). The size and spatial pattern of the gaps were found to be different between core area and buffer zone. Forest structure (b) in the core area was analysed applying a 120 m grid sampling design (40 plots). From this preliminary analysis we identified a stand with typical old-growth characteristics in the central part of the core area and a 1 ha intensive sampling plot was established (c). Inside the plot all trees were mapped, measured, and a core extracted to assess tree age. Spatial structure was investigated by means of Point Pattern Analysis and LISA (Local Indices of Spatial Autocorrelation). The three scales and approaches produced consistent results. The landscape approach confirmed the hypothesis that small-scale processes predominate at Lom especially within the core area of the reserve. At the forest stand level the presence of small scale disturbance/mortality processes was confirmed.

Spatial Variability of Soil Particle Size Distribution and Fractal Features in Water-Wind Erosion Crisscross Region on the Loess Plateau of China

The spatial variability of soil particle size distribution and fractal dimension (D) values play important roles in both agriculture and environment. They impact soil texture, soil quality, and soil erosion. A grid sampling design was used to collect 325 topsoil (0-20 cm) samples from the Daye watershed (0.54 km2) in the water-wind erosion crisscross region of the Loess Plateau of China. Classical and geostatistical methods were combined to determine the spatial variability of soil particle size distribution and D values. The D values in the research area ranged from 1.37 to 2.66. There was a significant sigmoidal correlation between D values and the contents of clay, silt, and sand fractions. The D values of four different land uses could be arranged in the following order: woodland > farmland > grassland > shrubland. Values of D had moderate spatial autocorrelation, and clay, silt, and sand contents had strong spatial autocorrelation in the watershed landscape. The interpolation maps showed that D values could be an efficient way to evaluate the degree of soil desertification and degradation.

Spatial variability of soil total nitrogen and soil total phosphorus under different land uses in a small watershed on the Loess Plateau, China

The spatial variability of soil total nitrogen (STN) and soil total phosphorus (STP) levels, which may be greatly affected by land use, plays an important role in both agriculture and the environment, especially with regard to soil fertility, soil quality, and water-body eutrophication. Little research has been done that addresses the spatial patterns of STN and STP under different land use types at a watershed scale. We collected 689 soil surface (0–20 cm) samples, using a grid sampling design, from the Liudaogou watershed (6.89 km 2) on the Loess Plateau of North China. Using classical statistical and geostatistical methods, we characterized and compared the spatial heterogeneities of STN and STP under different land use types (farmland, grassland, and shrubland).Concentrations of STN and STP were normally distributed with the exception of STP in grassland, and decreased in the order: farmland > grassland > shrubland. Stepwise multiple regression analysis indicated a strong relationship between STN and soil organic carbon (which was mainly controlled by plant growth and microbial activity), while STP was associated with the content of finer soil particles (which absorb P more readily and whose distribution is related to slope aspect and altitude). Both STN and STP showed moderate variability under different land use types. Nugget ratios for STN showed a moderate spatial dependence and decreased in the order: farmland > grassland > shrubland, whereas STP increased in that order and showed strong, moderate, and weak spatial dependence, respectively. The type of optimal theoretical isotropy models differed for STN and STP as well as for the land use type. We concluded that spatial patterns of STN and STP would change significantly with land use changes currently being implemented to achieve sustainable agriculture development and environmental restoration. Taking land use type into account when considering the spatial variation of STN and STP would increase the accuracy in modeling and prediction of soil nutrient status and nutrient movement at the watershed scale.

Optimised Spatial Sampling Scheme for Soil Electriclal Conductivity Based on Variance Quad-Tree (VQT) Method

The acquisition of precise soil data representative of the entire survey area, is a critical issue for many treatments such as irrigation or fertilization in precision agriculture. The aim of this study was to investigate the spatial variability of soil bulk electrical conductivity (EC b) in a coastal saline field and design an optimized spatial sampling scheme of EC b based on a sampling design algorithm, the variance quad-tree (VQT) method. Soil EC b data were collected from the field at 20 m interval in a regular grid scheme. The smooth contour map of the whole field was obtained by ordinary kriging interpolation, VQT algorithm was then used to split the smooth contour map into strata of different number desired, the sampling locations can be selected within each stratum in subsequent sampling. The result indicated that the probability of choosing representative sampling sites was increased significantly by using VQT method with the sampling number being greatly reduced compared to grid sampling design while retaining the same prediction accuracy. The advantage of the VQT method is that this scheme samples sparsely in fields where the spatial variability is relatively uniform and more intensive where the variability is large. Thus the sampling efficiency can be improved, hence facilitate an assessment methodology that can be applied in a rapid, practical and cost-effective manner.

Soil and Crop Response to Variable‐Rate Liming for Two Michigan Fields

Soil pH is known to vary spatially, making variable‐rate lime application attractive where soil acidity occurs. This study examined the efficacy of variable‐rate lime applications based on grid sampling at various scales on soil pH and the yield of corn (Zea mays L.) and soybean [Glycine max L. (Merr.)] during 3 yr. Lime was applied in two fields according to five lime management strategies: lime requirement (LR) estimated from maps interpolated from soil samples obtained from 30.5‐, 61‐, and 91.5‐m grids; LR determined for the plot; and no lime. The LR interpolations consistently underestimated and were not correlated with LR measured on each plot. Granulated lime, used to insure uniform lime application, was slow to react in the field and a lab incubation verified that it reacted much slower than agricultural lime. Changes in pH occurred in the surface 10 cm, reflecting the depth of lime incorporation by chisel plowing. Corn yield did not respond to liming in 1995 and 1996. Soybean yield in 1997 increased due to liming at the Durand field, which had a lower range of soil pH values. Normalized soybean yields at both fields followed the same linear‐plateau response to pH, with soybean yields declining below a threshold pH of about 5.9. While there is need for improvements in grid sampling design, variable lime applications under all of the grid sampling scales increased soil pH above the threshold pH for soybean in these fields, resulting in an improvement compared with whole field management.

The relation between fine root density and proximity of stems in closed Douglas-fir plantations on homogenous sandy soils : implications for sampling design

Studies have been carried out in two fully stocked, fast growing Douglas-fir plantations of the Dutch ACIFORN project in three consecutive years, to obtain information on fine root densities (Olsthoorn 1991). For the present paper, data collected in early summer 1987 were used to study the relation of fine root density and proximity to the nearest tree or the dominant tree. A large number of samples (37 in one site and 55 in another) was collected in a small plot (10 × 11 m). Two distances were measured at each sampling point: the distance to the nearest tree and the distance to the tree with a dominant crown above that point. There was large variability in fine root density in the samples. Tests with different regression models showed a distinct rooting pattern for one of the two locations. It is concluded that systematic errors in the assessment of fine root density can arise when sampling points are chosen at a constant distance from trees. For Douglas-fir, this systematic error could have been an overestimation of the fine root density by up to 10%. These systematic errors can be avoided easily, using a stratified random design or a random sampling design. When trees are spaced irregularly, a grid sampling design is also appropriate.