Geostatistical disaggregation of polygon maps of average crop yields by area-to-point kriging

Brus, D.J.; Boogaard, H.L.; Ceccarelli, T.; Orton, Thomas G.; Traoré, Seydou; Zhang, M.

doi:10.1016/j.eja.2018.05.003

Cited by 11 publications

(11 citation statements)

References 27 publications

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“…For REML, the approach as described by Brus et al (2018) was applied. For MML, the posterior mode of φ was calculated using the Bayesian approach with a uniform prior for φ.…”

Section: Estimation and Prediction With Reml And MMLmentioning

confidence: 99%

“…6 for millet. Covariates for the trends as suggested by Brus et al (2018) are used: for millet no covariates, and for sorghum four covariates are shown and briefly explained in Online Resource F.…”

Section: Real Case Study: Crop Yield Datamentioning

confidence: 99%

“…Given a covariance model that is more or less accurate in terms of short-range behaviour, the conclusion is that, for data sets of sufficient size, or if a slight underestimation of prediction uncertainty is allowed, the REML approach as demonstrated by Brus et al (2018) should be sufficient. For smaller data sets with no prior information available, the most robust and in many cases best approach, albeit somewhat conservative, appeared to be MML.…”

Section: General Remarksmentioning

confidence: 99%

“…Truong et al (2014) showed that variogram uncertainty can have a substantial impact on ATPK variances. Brus et al (2018) summarised earlier work by Pardo-Igzquiza and Dowd (2001) showing that uncertainty in the variogram parameters can be quantified by the inverse Fisher matrix of the variogram parameters, but did not integrate this uncertainty in the kriging prediction uncertainty itself.…”

Section: Introductionmentioning

confidence: 99%

“…It is not uncommon in ATPK studies to have only a small data set of areal means and no available relevant expert knowledge to inform model parameters (for example Brus et al 2018). Therefore, this research aims to provide some insight into the applicability and behaviour of ATPK methods under these circumstances.…”

Section: Introductionmentioning

confidence: 99%

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Model-Based Geostatistics from a Bayesian Perspective: Investigating Area-to-Point Kriging with Small Data Sets

2019

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Area-to-point kriging (ATPK) is a geostatistical method for creating high-resolution raster maps using data of the variable of interest with a much lower resolution. The data set of areal means is often considerably smaller (< 50 observations) than data sets conventionally dealt with in geostatistical analyses. In contemporary ATPK methods, uncertainty in the variogram parameters is not accounted for in the prediction; this issue can be overcome by applying ATPK in a Bayesian framework. Commonly in Bayesian statistics, posterior distributions of model parameters and posterior predictive distributions are approximated by Markov chain Monte Carlo sampling from the posterior, which can be computationally expensive. Therefore, a partly analytical solution is implemented in this paper, in order to (i) explore the impact of the prior distribution on predictions and prediction variances, (ii) investigate whether certain aspects of uncertainty can be disregarded, simplifying the necessary computations, and (iii) test the impact of various model misspecifications. Several approaches using simulated data, aggregated real-world point data, and a case study on aggregated crop yields in Burkina Faso are compared. The prior distribution is Electronic supplementary material The online version of this article (

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“…For REML, the approach as described by Brus et al (2018) was applied. For MML, the posterior mode of φ was calculated using the Bayesian approach with a uniform prior for φ.…”

Section: Estimation and Prediction With Reml And MMLmentioning

confidence: 99%

Section: Real Case Study: Crop Yield Datamentioning

confidence: 99%

Section: General Remarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model-Based Geostatistics from a Bayesian Perspective: Investigating Area-to-Point Kriging with Small Data Sets

2019

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From Natural Resources Evaluation to Spatial Epidemiology: 25 Years in the Making

Goovaerts

2020

Math Geosci

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When in the winter of 1994, under the supervision of my post-doc adviser André Journel, I started writing “ Geostatistics for Natural Resources Evaluation ” in the bedroom of a tiny Palo Alto apartment, little did I know that 25 years later I would be conducting NIH-funded research on medical geostatistics from a lakefront office nestled in the Irish Hills of Michigan. The professional and personal path that led me to trade the mapping of heavy metal concentrations in the topsoil of the Swiss Jura for the geostatistical analysis of cancer data was anything but planned, yet André’s help and guidance were instrumental early on. Looking back, shifting scientific interest from the characterization of contaminated sites to human health made sense as the field of epidemiology is increasingly concerned with the concept of exposome, which comprises all environmental exposures (e.g., air, soil, drinking water) that a person experiences from conception throughout the life course. Although both environmental and epidemiological data exhibit space-time variability, the latter has specific characteristics that required the adaptation of traditional geostatistical tools, such as semivariogram and kriging. Challenges include: (i) the heteroscedasticity of disease rate data (i.e., larger uncertainty of disease rates computed from small populations), (ii) their uneven spatial support (e.g., rates recorded for administrative units of different size and shape), and (iii) the limitations of Euclidean metrics to embody proximity when dealing with data that pertain to human mobility. Most of these challenges were addressed by borrowing concepts developed in adjacent fields, stressing the value of interdisciplinary research and intellectual curiosity, something I learned as a fresh PhD in agronomical sciences joining André’s research group at the Stanford Center for Reservoir Forecasting in the early nineties.

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Downscaling crop production data to fine scale estimates with geostatistics and remote sensing: a case study in mapping cotton fibre quality

Tilse,

Filippi,

Whelan

et al. 2024

Precision Agric

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Purpose A generalised approach to downscale areal observations of crop production data is illustrated using cotton yield and fibre quality (length and micronaire) data which is measured as a module (areal/block) average. Methods Two features of the downscaling algorithm are; (i) to estimate spatial trends in yield and quality using regression with fine resolution predictors such as remote sensing imagery, and (ii) use area-to-point kriging (A2PK) to downscale either the observations in the absence of a useful spatial trend model or the residuals from the trend model (if useful) from areal averages. Results Correlations with remote sensing covariates were stronger for cotton fibre yield than for cotton fibre micronaire, and much stronger compared to those for cotton fibre length. Spatial trends in cotton fibre yield and micronaire could be estimated with good model quality using regression with remote sensing covariates with or without A2PK in almost all fields. Conversely, model quality was poorer for cotton fibre length and there was only a small difference in model performance between the null and trend models. When the downscaling approach was tested using fine-resolution yield observations, model performance was poorer at a fine-resolution compared to the module-resolution, which was to be expected. Conclusion This approach enables the creation of high-resolution raster maps of variables of interest with a much finer spatial resolution compared to the areal observations, and can be applied for any areal averaged crop production data in a range of broadacre and horticultural industries (e.g. sugarcane, apples, citrus). The finer spatial resolution may allow growers or agronomists to better understand the drivers of variability within fields, assess management implications, and create management plans at a higher resolution.

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Geostatistical disaggregation of polygon maps of average crop yields by area-to-point kriging

Cited by 11 publications

References 27 publications

Model-Based Geostatistics from a Bayesian Perspective: Investigating Area-to-Point Kriging with Small Data Sets

Model-Based Geostatistics from a Bayesian Perspective: Investigating Area-to-Point Kriging with Small Data Sets

From Natural Resources Evaluation to Spatial Epidemiology: 25 Years in the Making

Downscaling crop production data to fine scale estimates with geostatistics and remote sensing: a case study in mapping cotton fibre quality

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