A geostatistical analysis of the spatial variation of soil mineral nitrogen and potentially available nitrogen within an arable field.

The technology for site-specific applications of nitrogen (N) fertilizer has exposed a gap in our knowledge about the spatial variation of soil mineral N, and that which will become available during the growing season within arable fields. Spring mineral N and potentially available N were measured in an arable field together with gravimetric water content, loss on ignition, crop yield, percentages of sand, silt, and clay, and elevation to describe their spatial variation geostatistically. The areas with a larger clay content had larger values of mineral N, potentially available N, loss on ignition and gravimetric water content, and the converse was true for the areas with more sandy soil. The results suggest that the spatial relations between mineral N and loss on ignition, gravimetric water content, soil texture, elevation and crop yield, and between potentially available N and loss on ignition and silt content could be used to indicate their spatial patterns. Variable-rate nitrogen fertilizer application would be feasible in this field because of the spatial structure and the magnitude of variation of mineral N and potentially available N.

A geostatistical analysis of soil, vegetation, and image data characterizing land surface variation

The elucidation of spatial variation in the landscape can indicate potential wildlife habitats or breeding sites for vectors, such as ticks or mosquitoes, which cause a range of diseases. Information from remotely sensed data could aid the delineation of vegetation distribution on the ground in areas where local knowledge is limited. The data from digital images are often difficult to interpret because of pixel-to-pixel variation, that is, noise, and complex variation at more than one spatial scale. Landsat Thematic Mapper Plus (ETM+) and Satellite Pour l'Observation de La Terre (SPOT) image data were analyzed for an area close to Douna in Mali, West Africa. The variograms of the normalized difference vegetation index (NDVI) from both types of image data were nested. The parameters of the nested variogram function from the Landsat ETM+ data were used to design the sampling for a ground survey of soil and vegetation data. Variograms of the soil and vegetation data showed that their variation was anisotropic and their scales of variation were similar to those of NDVI from the SPOT data. The short- and long-range components of variation in the SPOT data were filtered out separately by factorial kriging. The map of the short-range component appears to represent the patterns of vegetation and associated shallow slopes and drainage channels of the tiger bush system. The map of the long-range component also appeared to relate to broader patterns in the tiger bush and to gentle undulations in the topography. The results suggest that the types of image data analyzed in this study could be used to identify areas with more moisture in semiarid regions that could support wildlife and also be potential vector breeding sites.

Geostatistical analysis of rainfall

Rainfall can be modeled as a spatially correlated random field superimposed on a background mean value; therefore, geostatistical methods are appropriate for the analysis of rain gauge data. Nevertheless, there are certain typical features of these data that must be taken into account to produce useful results, including the generally non-Gaussian mixed distribution, the inhomogeneity and low density of observations, and the temporal and spatial variability of spatial correlation patterns. Many studies show that rigorous geostatistical analysis performs better than other available interpolation techniques for rain gauge data. Important elements are the use of climatological variograms and the appropriate treatment of rainy and nonrainy areas. Benefits of geostatistical analysis for rainfall include ease of estimating areal averages, estimation of uncertainties, and the possibility of using secondary information (e.g., topography). Geostatistical analysis also facilitates the generation of ensembles of rainfall fields that are consistent with a given set of observations, allowing for a more realistic exploration of errors and their propagation in downstream models, such as those used for agricultural or hydrological forecasting. This article provides a review of geostatistical methods used for kriging, exemplified where appropriate by daily rain gauge data from Ethiopia.

Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products

Global NDVI data are routinely derived from the AVHRR, SPOT-VGT, and MODIS/Terra earth observation records for a range of applications from terrestrial vegetation monitoring to climate change modeling. This has led to a substantial interest in the harmonization of multisensor records. Most evaluations of the internal consistency and continuity of global multisensor NDVI products have focused on time-series harmonization in the spectral domain, often neglecting the spatial domain. We fill this void by applying variogram modeling (a) to evaluate the differences in spatial variability between 8-km AVHRR, 1-km SPOT-VGT, and 1-km, 500-m, and 250-m MODIS NDVI products over eight EOS (Earth Observing System) validation sites, and (b) to characterize the decay of spatial variability as a function of pixel size (i.e. data regularization) for spatially aggregated Landsat ETM+ NDVI products and a real multisensor dataset. First, we demonstrate that the conjunctive analysis of two variogram properties – the sill and the mean length scale metric – provides a robust assessment of the differences in spatial variability between multiscale NDVI products that are due to spatial (nominal pixel size, point spread function, and view angle) and non-spatial (sensor calibration, cloud clearing, atmospheric corrections, and length of multi-day compositing period) factors. Next, we show that as the nominal pixel size increases, the decay of spatial information content follows a logarithmic relationship with stronger fit value for the spatially aggregated NDVI products (R2 = 0.9321) than for the native-resolution AVHRR, SPOT-VGT, and MODIS NDVI products (R2 = 0.5064). This relationship serves as a reference for evaluation of the differences in spatial variability and length scales in multiscale datasets at native or aggregated spatial resolutions. The outcomes of this study suggest that multisensor NDVI records cannot be integrated into a long-term data record without proper consideration of all factors affecting their spatial consistency. Hence, we propose an approach for selecting the spatial resolution, at which differences in spatial variability between NDVI products from multiple sensors are minimized. This approach provides practical guidance for the harmonization of long-term multisensor datasets.

Towards more precise sugar beet management based on geostatistical analysis of spatial variability within fields

Within-field variation in sugar beet yield and quality was investigated in three commercial sugar beet fields in the east of England to identify the main associated variables and to examine the possibility of predicting yield early in the season with a view to spatially variable management of sugar beet crops. Irregular grid sampling with some purposively-located nested samples was applied. It revealed the spatial variability in each sugar beet field efficiently. In geostatistical analyses, most variograms were isotropic with moderate to strong spatial dependency indicating a significant spatial variation in sugar beet yield and associated growth and environmental variables in all directions within each field. The Kriged maps showed spatial patterns of yield variability within each field and visual association with the maps of other variables. This was confirmed by redundancy analyses and Pearson correlation coefficients. The main variables associated with yield variability were soil type, organic matter, soil moisture, weed density and canopy temperature. Kriged maps of final yield variability were strongly related to that in crop canopy cover, LAI and intercepted solar radiation early in the growing season, and the yield maps of previous crops. Therefore, yield maps of previous crops together with early assessment of sugar beet growth may make an early prediction of within-field variability in sugar beet yield possible. The Broom’s Barn sugar beet model failed to account for the spatial variability in sugar yield, but the simulation was greatly improved when corrected for early canopy development cover and when the simulated yield was adjusted for weeds and plant population. Further research to optimize inputs to maximise sugar yield should target the irrigation and fertilizing of areas within fields with low canopy cover early in the season.

Geostatistical analysis of the spatial distribution of Rotylenchulus reniformis on cotton cultivated under crop rotation

The spatial distribution of Rotylenchulus reniformis on cotton cultivated in crop rotation with sorghum-peanut-velvetbean was studied using geostatistics. The experimental field, which had been continuously cropped with cotton for 20 years, comprised two 32 x 48 m-grids, each divided in sixty-four 4 x 6 in sampling plots. For all crops, 300 cm(3) soil samples were taken at the center of each plot at crop germination (Pi) and again at harvest (Pf), from which the numbers of nematodes were determined. The results revealed that the spatial distribution of R. reniformis was highly aggregated and with the aid of geostatistical techniques the nematode intensities were mapped and the risk areas accurately identified. Consequently, geostatistics is here considered a useful tool for planning nematode control strategies, particularly in precision agriculture.

Using geostatistical analysis to evaluate the presence of Rotylenchulus reniformis in cotton crops in Brazil: Economic implications

In recent years, the productivity of cotton in Brazil has been progressively decreasing, often the result of the reniform nematode Rotylenchulus reniformis. This species call reduce crop productivity by up to 40%. Nematodes can be controlled by nematicides but, because of expense and toxicity, application of nematicides to large crop areas may be undesirable. In this Work. a methodology using geostatistics for quantifying the risk of nematicide application to small crop areas is proposed. This risk, in economic terms, can be compared to nematicide cost to develop an optimal strategy for Precision Farming, Soil (300 cm(3)) was sampled in a regular network from a R. reniformis-infested area that was a cotton monoculture for 20 years. The number of nematodes in each sample was counted. The nematode number per volume of soil was characterized using geostatistics, and 100 conditional simulations were conducted. Based on the simulations, risk maps were plotted showing the areas where nematicide should be applied in a Precision Farming context. The methodology developed can be applied to farming in countries that ale highly dependent on agriculture, with useful economic implications.

Comparing the performance of geostatistical models with additional information from covariates for sewage plume characterization

In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the Mat`ern models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.

Diagnostic techniques applied in geostatistics for agricultural data analysis

The structural modeling of spatial dependence, using a geostatistical approach, is an indispensable tool to determine parameters that define this structure, applied on interpolation of values at unsampled points by kriging techniques. However, the estimation of parameters can be greatly affected by the presence of atypical observations in sampled data. The purpose of this study was to use diagnostic techniques in Gaussian spatial linear models in geostatistics to evaluate the sensitivity of maximum likelihood and restrict maximum likelihood estimators to small perturbations in these data. For this purpose, studies with simulated and experimental data were conducted. Results with simulated data showed that the diagnostic techniques were efficient to identify the perturbation in data. The results with real data indicated that atypical values among the sampled data may have a strong influence on thematic maps, thus changing the spatial dependence structure. The application of diagnostic techniques should be part of any geostatistical analysis, to ensure a better quality of the information from thematic maps.

Multivariate analysis and geostatistics of the fertility of a humic rhodic hapludox under coffee cultivation

The spatial variability of soil and plant properties exerts great influence on the yeld of agricultural crops. This study analyzed the spatial variability of the fertility of a Humic Rhodic Hapludox with Arabic coffee, using principal component analysis, cluster analysis and geostatistics in combination. The experiment was carried out in an area under Coffea arabica L., variety Catucai 20/15 - 479. The soil was sampled at a depth 0.20 m, at 50 points of a sampling grid. The following chemical properties were determined: P, K+, Ca2+, Mg2+, Na+, S, Al3+, pH, H + Al, SB, t, T, V, m, OM, Na saturation index (SSI), remaining phosphorus (P-rem), and micronutrients (Zn, Fe, Mn, Cu and B). The data were analyzed with descriptive statistics, followed by principal component and cluster analyses. Geostatistics were used to check and quantify the degree of spatial dependence of properties, represented by principal components. The principal component analysis allowed a dimensional reduction of the problem, providing interpretable components, with little information loss. Despite the characteristic information loss of principal component analysis, the combination of this technique with geostatistical analysis was efficient for the quantification and determination of the structure of spatial dependence of soil fertility. In general, the availability of soil mineral nutrients was low and the levels of acidity and exchangeable Al were high.

Correlating soil porosity and respective geological unit in Paraíba do sul Valley, Brazil: a geostatistical methodology proposal

This manuscript aims proposing a methodology for correlating soil porosity to the respective geological units using geostatistical analysis techniques, including interpolation data by kriging. The site studied was in Lorena municipality, Paraíba do Sul Valley, southeastern Brazil. Specifically all studies were carried out within an area of 12 km2 located at Santa Edwirges farm. The database comprehended 41 soil samples taken at different geological and geomorphologic units at three different depths: surface, 50 cm and 100 cm depth. The geostatistical analyses results were correlated to a geological mapping specifically elaborated for the site. This mapping accounts for two different geological formations and a geological contact characterized by a shearing zone. The results indicate the existence of a significant relationship between the soil porosity and the respective geological units. The studies revealed that the residual soils from weathered granitic rocks tend to have higher porosities than the residual soils from weathered biotite gneiss rocks, while the soil porosity within the shearing zone is relatively un-sensitive to the respective geological formation. The spatial patterns observed were efficient to evaluate the relationship between the soil porosity, geology unit and the and geomorphology showing a good potential for correlating with others soil properties such as hydraulic conductivity, soil water retention curves and erosion potentials.

Bayesian Geostatistical Design

This paper describes the use of model-based geostatistics for choosing the optimal set of sampling locations, collectively called the design, for a geostatistical analysis. Two types of design situations are considered. These are retrospective design, which concerns the addition of sampling locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing optimal positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model parameter values are unknown. The results show that in this situation a wide range of inter-point distances should be included in the design, and the widely used regular design is therefore not the optimal choice.

Watershed scale temporal stability of soil water content

The recognition of temporally stable locations with respect to soil water content is of importance for soil water management decisions, especially in sloping land of watersheds. Neutron probe soil water content (0 to 0.8 m), evaluated at 20 dates during a year in the Loess Plateau of China, in a 20 ha watershed dominated by Ust-Sandiic Entisols and Aeolian sandy soils, were used to define their temporal stability through two indices: the standard deviation of relative difference (SDRD) and the mean absolute bias error (MABE). Specific concerns were (a) the relationship of temporal stability with soil depth, (b) the effects of soil texture and land use on temporal stability, and (c) the spatial pattern of the temporal stability. Results showed that temporal stability of soil water content at 0.2 m was significantly weaker than those at the soil depths of 0.6 and 0.8 m. Soil texture can significantly (P<0.05) affect the stability of soil water content except for the existence of an insignificant difference between sandy loam and silt loam textures, while temporal stability of areas covered by bunge needlegrass land was not significantly different from those covered by korshinsk peashrub. Geostatistical analysis showed that the temporal stability was spatially variable in an organized way as inferred by the degree of spatial dependence index. With increasing soil depth, the range of both temporal stability indices showed an increasing trend, being 65.8-120.5 m for SDRD and 148.8-214.1 m for MABE, respectively. This study provides a valuable support for soil water content measurements for soil water management and hydrological applications on sloping land areas. (C) 2010 Elsevier B.V. All rights reserved.

Spatio-temporal variability behavior of land surface soil water content in shrub- and grass-land

The knowledge of the relationship between spatial variability of the surface soil water content (theta) and its mean across a spatial domain (theta(m)) is crucial for hydrological modeling and understanding soil water dynamics at different scales. With the aim to compare the soil moisture dynamics and variability between the two land uses and to explore the relationship between the spatial variability of theta and theta(m), this study analyzed sets of surface theta measurements performed with an impedance soil moisture probe, collected 136 times during a period of one year in two transects covering different land uses, i.e., korshinsk peashrub transect (KPT) and bunge needlegrass transect (BNT), in a watershed of the Loess Plateau, China. Results showed that the temporal pattern of theta behaved similarly for the two land uses, with both relative wetter soils during wet period and relative drier soils during dry period recognized in BNT. Soil moisture tended to be temporally stable among different dates, and more stable patterns could be observed for dates with more similar soil water conditions. The magnitude of the spatial variation of theta in KPT was greater than that in ENT. For both land uses, the standard deviation (SD) of theta in general increased as theta(m) increased, a behavior that could be well described with a natural logarithmic function. Convex relationship of CV and theta(m) and the maximum CV for both land uses (43.5% in KPT and 41.0% in BNT) can, therefore, be ascertained. Geostatistical analysis showed that the range in KPT (9.1 m) was shorter than that in BNT (15.1 m). The nugget effects, the structured variability, hence the total variability increased as theta(m) increased. For both land uses, the spatial dependency in general increased with increasing theta(m). 2011 Elsevier B.V. All rights reserved.

Variabilidade espacial e temporal de atributos químicos do solo em povoamento de eucalipto

Nos anos mais recentes, observa-se aumento na adoção das técnicas de silvicultura de precisão em florestas plantadas no Brasil. Os plantios de eucalipto ocorrem preferencialmente em áreas com baixa fertilidade de solo e consequentemente baixa produtividade. Logo, para otimizar ao máximo a produção, é necessário saber o quanto essa cultura pode produzir em cada local (sítio). Objetivou-se aplicar uma metodologia que utiliza técnicas de estatística, geoestatística e geoprocessamento, no mapeamento da variabilidade espacial e temporal de atributos químicos do solo cultivado com eucalipto, em área de 10,09 ha, situada no sul do estado do Espírito Santo. Os atributos químicos da fertilidade do solo estudados foram: fósforo (P), potássio (K), cálcio (Ca) e magnésio (Mg), no ano da implantação do povoamento do eucalipto, em 2008, e três anos após, em 2011. O solo foi amostrado em duas profundidades, 0-0,2 m e 0,2-0,4 m, nos 94 pontos de uma malha regular, com extensão de 33 x 33 m. Os dados foram analisados pela estatística descritiva e, em seguida, pela geoestatística, por meio do ajuste de semivariogramas. Diferentes métodos de interpolação foram testados para produzir mapas temáticos mais precisos e facilitar as operações algébricas utilizadas. Com o auxílio de índices quantitativos, realizou-se uma análise geral da fertilidade do solo, por meio da álgebra de mapas. A metodologia utilizada neste estudo possibilitou mapear a variabilidade espacial e temporal de atributos químicos do solo. A análise variográfica mostrou que todos os atributos estudados apresentaram-se estruturados espacialmente, exceto para o atributo P, no Ano Zero (camada 0-0,2 m) e no Ano Três (ambas as camadas). Os melhores métodos de interpolação para o mapeamento de cada atributo químico do solo foram identificados com a ajuda gráfica do Diagrama de Taylor. Mereceram destaque, os modelos esférico e exponencial nas interpolações para a maioria dos atributos químicos do solo avaliados. Apesar de a variação espacial e temporal dos atributos estudados apresentar-se, em média, com pequena variação negativa, a metodologia usada mostrou variações positivas na fertilidade do solo em várias partes da área de estudo. Além disso, os resultados demonstram que os efeitos observados são majoritariamente em função da cultura, uma vez que não foram coletadas amostras de solo em locais adubados. A produtividade do sítio florestal apresentou-se com tendências semelhantes às variações ocorridas na fertilidade do solo, exceto para o magnésio, que se mostrou com tendências espaciais para suporte de elevadas produtividades, de até 50 m3 ha-1 ano-1. Além de mostrar claramente as tendências observadas para as variações na fertilidade do solo, a metodologia utilizada confirma um caminho operacional acessível para empresas e produtores florestais para o manejo nutricional em florestas plantadas. O uso dos mapas facilita a mobilização de recursos para melhorar a aplicação de fertilizantes e corretivos necessários.