Soil apparent conductivity measurements for planning and analysis of agricultural experiments: a case study from Western-Thailand
In experimental trials, the success or failure of agricultural improvements is commonly evaluated on the agronomic response of crops, using proper experimental designswith sufficient statistical power. Since fine-scale variability of the experimental site can reduce statistical power, efficiency gains in the experimental design can be achieved if this variation is known and used to design blocking, or some proxy variable is used as a covariate.
Near-surface geophysical techniques such as electromagnetic induction (EMI),which describes subsurface properties non-invasively by measuring soil apparent conductivity (ECa), may be one source of this information. The motivation of our study was to investigate the effectiveness of EMI-derived ECa measurements for planning and analysis of agricultural experiments. ECa and plant height measurements (the response variable) were taken from an agroforestry experiment in Western Thailand, and their variability was quantified to simulate multiple realizations of ECa and the residuals of the response variable from treatment means. These were combined to
produce simulated data from different experimental designs and treatment effects. The simulated data were then used to evaluate the statistical power by detecting three orthogonal contrasts among the treatments in the original experiment. We considered three experimental designs, a simple random design (SR), a complete randomized block design (CRB), and a complete randomized block design with spatially adjusted blocks on plot means of ECa (CRB
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