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1.
Process models are commonly used in soil science to obtain predictions at a spatial scale that is different from the scale at which the model was developed, or the scale at which information on model inputs is available. When this happens, the model and its inputs require aggregation or disaggregation to the application scale, and this is a complex problem. Furthermore, the validity of the aggregated model predictions depends on whether the model describes the key processes that determine the process outcome at the target scale. Different models may therefore be required at different spatial scales. In this paper we develop a diagnostic framework which allows us to judge whether a model is appropriate for use at one or more spatial scales both with respect to the prediction of variations at those scale and in the requirement for disaggregation of the inputs. We show that spatially nested analysis of the covariance of predictions with measured process outcomes is an efficient way to do this. This is applied to models of the processes that lead to ammonia volatilization from soil after the application of urea. We identify the component correlations at different scales of a nested scheme as the diagnostic with which to evaluate model behaviour. These correlations show how well the model emulates components of spatial variation of the target process at the scales of the sampling scheme. Aggregate correlations were identified as the most pertinent to evaluate models for prediction at particular scales since they measure how well aggregated predictions at some scale correlate with aggregated values of the measured outcome. There are two circumstances under which models are used to make predictions. In the first case only the model is used to predict, and the most useful diagnostic is the concordance aggregate correlation. In the second case model predictions are assimilated with observations which should correct bias in the prediction, and errors in the variance; the aggregate correlations would be the most suitable diagnostic. 相似文献
2.
Geostatistics is commonly used to describe and predict the variation of soil properties over the landscape. However, many geostatistical methods require the assumption that our observed data are a realization of a random function which is intrinsically stationarity. Under stationarity, observations of a single realization of the random function at different positions can be treated as a form of replication. There are various ways in which a random function may breach the assumption of intrinsic stationarity and numerous geostatistical techniques have been developed that are able to cope with some forms of non-stationarity. What is currently needed is a set of diagnostic tools capable of detecting and identifying when data cannot plausibly be treated as a realization of a process which is stationary in the variance.In this paper, we propose an inferential method that can identify when stationarity in the variance cannot plausibly be assumed. The basis of our approach is to obtain a model for the random function under the assumption of intrinsic stationarity. If the global dataset can be regarded as a realization of a Gaussian process (perhaps after transformation), then the global variogram is sufficient for this purpose. By using a window-based method to locally estimate variograms, we can define some statistic of homogeneity of the sample variation of the data. This allows us to obtain a sample distribution for this statistic, under the null hypothesis of intrinsic stationarity, by generating multiple realizations of the postulation random function at the original sample points using Monte Carlo methods and recomputing the statistic for each realization. We selected as statistics the interquartile ranges of: i) the spatial dependence ratio (s), the proportion c1 / (c0 + c1), ii) a distance parameter (a), which is the maximum lag over which the random function is autocorrelated for variograms like the spherical, and iii) the local variances (v; c0 + c1), where (c0) is the nugget component and (c1) the spatially structured component. We demonstrated this method using data from the large scale sampling (n = 1341 over 8248 km2) of the Florida Everglades, United States. 相似文献
3.
Spatially nested sampling and the associated nested analysis of variance by spatial scale is a well-established methodology for the exploratory investigation of soil variation over multiple, disparate scales. The variance components that can be estimated this way can be accumulated to approximate the variogram. This allows us to identify the important scales of variation, and the general form of the spatial dependence, in order to plan more detailed sampling by design-based or model-based methods. Implicit in the standard analyses of nested sample data is the assumption of homogeneity in the variance, i.e. that all variations from sub-station means at some scale represent a random variable of uniform variance. If this assumption fails then the comparable assumption of stationarity in the variance, which is an important assumption in geostatistics, will also be implausible. However, data from nested sampling may be analysed with a linear mixed model in which the variance components are parameters which can be estimated by residual maximum likelihood (REML). Within this framework it is possible to propose an alternative variance parameterization in which the variance depends on some auxiliary variable, and so is not generally homogeneous. In this paper we demonstrate this approach, using data from nested sampling of chemical and biogeochemical soil properties across a region in central England, and use land use as our auxiliary variable to model non-homogeneous variance components. We show how the REML analysis allows us to make inferences about the need for a non-homogeneous model. Variances of soil pH and cation exchange capacity at different scales differ between these land uses, but a homogeneous variance model is preferable to such non-homogeneous models for the variance of soil urease activity at standard concentrations of urea. 相似文献
4.
Darren R. Grafius Ron Corstanje Philip H. Warren Karl L. Evans Steven Hancock Jim A. Harris 《Landscape Ecology》2016,31(7):1509-1522
Context
Urbanisation places increasing stress on ecosystem services; however existing methods and data for testing relationships between service delivery and urban landscapes remain imprecise and uncertain. Unknown impacts of scale are among several factors that complicate research. This study models ecosystem services in the urban area comprising the towns of Milton Keynes, Bedford and Luton which together represent a wide range of the urban forms present in the UK.Objectives
The objectives of this study were to test (1) the sensitivity of ecosystem service model outputs to the spatial resolution of input data, and (2) whether any resultant scale dependency is constant across different ecosystem services and model approaches (e.g. stock- versus flow-based).Methods
Carbon storage, sediment erosion, and pollination were modelled with the InVEST framework using input data representative of common coarse (25 m) and fine (5 m) spatial resolutions.Results
Fine scale analysis generated higher estimates of total carbon storage (9.32 vs. 7.17 kg m?2) and much lower potential sediment erosion estimates (6.4 vs. 18.1 Mg km?2 year?1) than analyses conducted at coarser resolutions; however coarse-scale analysis estimated more abundant pollination service provision.Conclusions
Scale sensitivities depend on the type of service being modelled; stock estimates (e.g. carbon storage) are most sensitive to aggregation across scales, dynamic flow models (e.g. sediment erosion) are most sensitive to spatial resolution, and ecological process models involving both stocks and dynamics (e.g. pollination) are sensitive to both. Care must be taken to select model data appropriate to the scale of inquiry.5.
Breure T. S. Haefele S. M. Hannam J. A. Corstanje R. Webster R. Moreno-Rojas S. Milne A. E. 《Precision Agriculture》2022,23(4):1333-1353
Modern sensor technologies can provide detailed information about soil variation which allows for more precise application of fertiliser to minimise environmental harm imposed by agriculture. However, growers should lose neither income nor yield from associated uncertainties of predicted nutrient concentrations and thus one must acknowledge and account for uncertainties. A framework is presented that accounts for the uncertainty and determines the cost–benefit of data on available phosphorus (P) and potassium (K) in the soil determined from sensors. For four fields, the uncertainty associated with variation in soil P and K predicted from sensors was determined. Using published fertiliser dose–yield response curves for a horticultural crop the effect of estimation errors from sensor data on expected financial losses was quantified. The expected losses from optimal precise application were compared with the losses expected from uniform fertiliser application (equivalent to little or no knowledge on soil variation). The asymmetry of the loss function meant that underestimation of P and K generally led to greater losses than the losses from overestimation. This study shows that substantial financial gains can be obtained from sensor-based precise application of P and K fertiliser, with savings of up to £121 ha?1 for P and up to £81 ha?1 for K, with concurrent environmental benefits due to a reduction of 4–17 kg ha?1 applied P fertiliser when compared with uniform application.
相似文献6.
Breure T. S. Milne A. E. Webster R. Haefele S. M. Hannam J. A. Moreno-Rojas S. Corstanje R. 《Precision Agriculture》2021,22(1):226-248
Precision Agriculture - How well could one predict the growth of a leafy crop from reflectance spectra from the soil and how might a grower manage the crop in the light of those predictions?... 相似文献
7.
The application of expert knowledge in Bayesian networks to predict soil bulk density at the landscape scale 
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下载免费PDF全文 K. Taalab R. Corstanje T. M. Mayr M. J. Whelan R. E. Creamer 《European Journal of Soil Science》2015,66(5):930-941
This paper investigates the use of expert knowledge as a resource for digital soil mapping. To do this, three models of topsoil soil bulk density (Db) were produced: (i) a random forest model formulated and cross‐validated with the limited data available (which served as the benchmark), (ii) a naïve Bayesian network (BN) where the conditional probabilities that define the relations between Db and explanatory landscape variables were derived from expert knowledge rather than data and (iii) a ‘hierarchical’ BN where model structure was also defined by expert knowledge. These models were used to generate spatial predictions for mapping topsoil Db at a landscape scale. The results show that expert knowledge‐based models can identify the same spatial trends in soil properties at a landscape scale as state‐of‐the‐art mapping algorithms. This means that they are a viable option for soil mapping applications in areas that have limited empirical data. 相似文献
8.
R. Corstanje G. J. D. Kirk M. Pawlett R. Read R. M. Lark 《European Journal of Soil Science》2008,59(6):1260-1270
Quantitative predictions of ammonia volatilization from soil are useful to environmental managers and policy makers and empirical models have been used with some success. Spatial analysis of the soil properties and their relationship to the ammonia volatilization process is important as predictions will be required at disparate scales from the field to the catchment and beyond. These relationships are known to change across scales and this may affect the performance of an empirical model. This study is concerned with the variation of ammonia volatilization and some controlling soil properties: bulk density, volumetric water content, pH, CEC, soil pH buffer power, and urease activity, over distances of 2, 50, 500, and >2000 m. We sampled a 16 km × 16 km region in eastern England and analyzed the results by a nested analysis of (co)variance, from which variance components and correlations for each scale were obtained. The overall correlations between ammonia volatilization and the soil properties were generally weak: –0.09 for bulk density, 0.04 for volumetric water content, –0.22 for CEC, –0.08 for urease activity, –0.22 for pH and 0.18 for the soil pH buffer power. Variation in ammonia volatilization was scale‐dependent, with substantial variance components at the 2‐ and 500‐m scales. The results from the analysis of covariance show that the relationships between ammonia volatilization and soil properties are complex. At the >2000 m scale, ammonia volatilization was strongly correlated with pH (–0.82) and CEC (–0.55), which is probably the result of differences in parent material. We also observed weaker correlations at the 500‐m scale with bulk density (–0.61), volumetric water content (0.48), urease activity (–0.42), pH (–0.55) and soil pH buffer power (0.38). Nested analysis showed that overall correlations may mask relationships at scales of interest and the effect of soil variables on these soil processes is scale‐dependent. 相似文献
9.
Darren R. Grafius Ron Corstanje Gavin M. Siriwardena Kate E. Plummer Jim A. Harris 《Landscape Ecology》2017,32(9):1771-1787
Context
Connectivity is fundamental to understanding how landscape form influences ecological function. However, uncertainties persist due to the difficulty and expense of gathering empirical data to drive or to validate connectivity models, especially in urban areas, where relationships are multifaceted and the habitat matrix cannot be considered to be binary.Objectives
This research used circuit theory to model urban bird flows (i.e. ‘current’), and compared results to observed abundance. The aims were to explore the ability of this approach to predict wildlife flows and to test relationships between modelled connectivity and variation in abundance.Methods
Circuitscape was used to model functional connectivity in Bedford, Luton/Dunstable, and Milton Keynes, UK, for great tits (Parus major) and blue tits (Cyanistes caeruleus), drawing parameters from published studies of woodland bird flows in urban environments. Model performance was then tested against observed abundance data.Results
Modelled current showed a weak yet positive agreement with combined abundance for P. major and C. caeruleus. Weaker correlations were found for other woodland species, suggesting the approach may be expandable if re-parameterised.Conclusions
Trees provide suitable habitat for urban woodland bird species, but their location in large, contiguous patches and corridors along barriers also facilitates connectivity networks throughout the urban matrix. Urban connectivity studies are well-served by the advantages of circuit theory approaches, and benefit from the empirical study of wildlife flows in these landscapes to parameterise this type of modelling more explicitly. Such results can prove informative and beneficial in designing urban green space and new developments.10.