Integrating Multiscale Geospatial Analysis for Monitoring Crop Growth, Nutrient Distribution, and Hydrological Dynamics in Large-Scale Agricultural Systems

Akanbi, Olatunde D.; Bhuvanagiri, Deepa C.; Barcelos, Erika I.; Nihar, Arafath; Gonzalez Hernandez, Brian; Yarus, Jeffrey M.; French, Roger H.

doi:10.1007/s41651-023-00164-y

Cited by 9 publications

(2 citation statements)

References 83 publications

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“…Fitting the same model to multiple datasets and extracting parameters specific for each dataset is becoming common when dealing with biological and epidemiological data. Examples range from the estimation of specific parameters across different genotypes [1], cell lines [2], patients [3,4], and health district or country-specific parameters [5,6]. The number of individual datasets can increase up to hundreds of thousands when linking geostatistical maps and pathogen transmission models [7][8][9] or combining remote sensing data with crop growth models [10,11].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference for Multiple Datasets

Retkute,

Thurston,

Gilligan

2024

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Estimating parameters for multiple datasets can be time consuming, especially when the number of datasets is large. One solution is to sample from multiple datasets simultaneously using Bayesian methods such as adaptive multiple importance sampling (AMIS). Here, we use the AMIS approach to fit a von Mises distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from 3D meteorological data. A posterior distribution of parameters can help to characterise the uncertainties in wind trajectories in a form that can be used as inputs for predictive models of wind-dispersed insect pests and the pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. The novelty of our study is in testing the performance of the method on a very large number of datasets (>11,000). Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.

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Section: Introductionmentioning

confidence: 99%

Bayesian Inference for Multiple Datasets

Retkute,

Thurston,

Gilligan

2024

Stats

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show abstract

“…Fitting the same model to multiple datasets and extracting parameters specific for each dataset is becoming common when dealing with biological and epidemiological data. Examples range from estimation of specific parameters across different genotypes [1], cell lines [2]; patients [3,4], health districts or country specific parameters [5,6]. The number of individual datasets can increase up to hundreds of thousands when linking geostatistical maps and disease transmission models [7][8][9]; or combining remote sensing data with crop growth models [10,11].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference for Multiple Datasets

Retkute,

Thurston,

Gilligan

2024

Preprint

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Estimating parameters for multiple datasets can be time consuming, especially when the number of datasets is large. One solution is to sample from multiple datasets simultaneously using Bayesian methods such as adaptive multiple importance sampling (AMIS). Here we use the AMIS approach to fit a von Misses distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from a 3D meteorological data. The primary objective is to characterise the uncertainties in wind trajectories in a form that can be used as inputs for predictive models of wind-dispersed insect pests and pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.

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