Bayesian inference of spatially correlated random parameters for on-farm experiment

Cao, Zhanglong; Stefanova, Katia; Gibberd, Mark; Rakshit, S.

doi:10.1016/j.fcr.2022.108477

Cited by 3 publications

(2 citation statements)

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“…2.2 Spatially correlated treatment parameters Cao et al (2022) implemented the Bayesian hierarchical model with spatially correlated random parameters on large OFE strip trials. Here, for the simulation study, we use the same model to generate the simulated data.…”

Section: Basic Statistical Modelmentioning

confidence: 99%

“…The truly localised estimation at each point on the grid is unknown and the optimum treatment response continuously varies spatially on the field. Cao et al (2022) implemented a Bayesian approach of spatially correlated random parameters for large systematic OFE strip trials. They assumed a quadratic response in the model, consisting of a global component and a local spatially varying component.…”

Section: Introductionmentioning

confidence: 99%

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Optimal design for on-farm strip trials -- systematic or randomised?

Cao¹,

Grose²,

Brown³

et al. 2022

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There is no doubt on the importance of randomisation in agricultural experiments by agronomists and biometricians. Even when agronomists extend the experimentation from small trials to large on-farm trials, randomised designs predominate over systematic designs. However, the situation may change depending on the objective of the on-farm experiments (OFE). If the goal of OFE is obtaining a smooth map showing the optimal level of a controllable input across a grid made by rows and columns covering the whole field, a systematic design should be preferred over a randomised design in terms of robustness and reliability. With the novel geographically weighted regression (GWR) for OFE and simulation studies, we conclude that, for large OFE strip trials, the difference between randomised designs and systematic designs are not significant if a linear model of treatments is fitted or if spatial variation is not taken into account. But for a quadratic model, systematic designs are superior to randomised designs.

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Section: Basic Statistical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%