Spatial variation, bias and experimental design in agronomic field trials: a case study of a farming systems trial in the Western Cape province, South Africa
Date
2017-12
Authors
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Journal ISSN
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Publisher
Stellenbosch : Stellenbosch University
Abstract
ENGLISH SUMMARY: Detecting small statistically significant changes in an observed parameter (e.g. – yield) in response to
applied treatments within field trials is a major challenge for modern agriculture, since we continue
looking for smaller increments where spatial variability (noise) may obscure the sought trend. A reliable
detection of change can significantly reduce the length of some long-term trials required for a trend
confirmation.
This study assesses the effects of spatial variation in yield, terrain and soil properties within a long-term
field trial on the possible outcome of results. The experiment was set up at the Langgewens research farm
of the Western Cape Department of Agriculture, South Africa as an extension of the long-term trial
initiated by the provincial government. In year 1 (2015) the whole field was planted to wheat as a
uniformity trial to assess the magnitude of productivity, variation and spatial trends within the 12 ha
experimental site. A yield monitor mounted on a combine harvester and remote sensing techniques using
drone surveys were used to study the variability in wheat growth/yield, and topographic parameters
within the field trial area. Soil profiles positioned along the field perimeter were interpolated into a soil
map to understand some of the reasons for yield variability. The relationship between topographic
parameters and wheat yield was significantly more important in the drier year (2015) than in 2016. Results
from a random forest regression model indicated that the use of spectral vegetation indices NDVI and
SAVI, in combination with topographic parameters can be used to accurately predict wheat yield in dry
and in wet years (R2 = 0.974 and R2= 0.987 respectively).
Wheat growth and productivity during the uniformity trial showed high variation within the trial area
(1.46±0.61 t·ha-1) with a bimodal distribution largely determined by variation in topography and soil types.
The first step in trial layout optimization was the exclusion of a small low-yielding area on the eastern
margin retaining the bimodal distribution, but slightly increasing the mean and significantly reducing the standard deviation (1.68±0.46 t·ha-1). The reduced-area experimental setup comprises 120 plots
combining three main 10-year crop rotation treatments starting at a different year of rotation resulting in
k=30 x n=4 trial for each year and finally combining into k=3 x n=40 at the end of the trial period.
Soil core samples at a 0-5cm depth (120 in total) were analysed for some key soil parameters. The variation
in routinely analysed soil properties across the field within the 0-5cm layer, where the most changes may
be expected as a result of applied treatments collectively showed no effect on wheat productivity. The
only exception was the resistance measurements, which indicated a high degree of leaching negatively
correlated with wheat production.
The trial was further optimized for means by complete randomization of the trial setup by placing three
treatment replicates in the high-yielding block and the fourth replicate in the lower-yielding part of the
field. This resulted in a relatively uniform distribution of means and standard deviations across the trial.
Such setup also allowed the possibility of replication reduction to n=3 (or n=30 at the end of the trial) by
excluding the lower-yielding section. The supposition that reduction of replications under the
circumstances may lower the difference was supported by power analysis. The power analysis of the 2015
yield has shown that three replications within a more uniform field allow for detection of smaller changes
(160 kg·ha-1) as opposed to highly variable conditions with 4 replications (314 kg·ha-1).
A different approach – optimization for within-treatment standard deviation was realized by quantifying
and accounting for the spatial trend. The combination of replications into ranked groups results in
significant reduction of within-group standard deviation and, subsequently smaller detectable
differences. This approach should be further investigated in the time-series analysis as the current
experiment progresses.
Description
Thesis (MScAgric)--Stellenbosch University, 2017.
Keywords
Statistics -- Interpretations, UCTD, Soil surveys -- Statistical method, Soil surveys -- Experiments, Corn -- Field experiments, Experimental design