A method to position a simple strip trial to improve trial efficiency and maximise the value of vineyard variability for decision-making

Abstract

The main difficulties grapegrowers and consultants face in obtaining robust trial results include time and labour to collect data and land variability that confounds trial results. Spatial approaches that use whole-field designs, sensing technologies and geostatistical analysis enable more efficient data collection and account for the impact of spatial variation on crop responses while generating statistically robust results. However, the practical application of these approaches for vineyard trials requires affordable automation of measurements of viticultural variables and access to skills for geostatistics. A strip approach has been developed to simplify experimentation by allowing the farmer to use a single crop row to trial and analyse data in a spreadsheet. However, guidance is needed as to how to position trial strips in a vineyard block to reveal likely treatment effects across the entire block. Here, we investigated using a covariate to a response variable of interest to position a strip trial to infer treatment effects beyond the trial strip. Strip trials were simulated for two experiments: one comparing three treatments for vineyard floor management on grape yield and another comparing two spray programs for powdery mildew control. Useful covariates for yield or mildew severity were determined using correlation analyses. Trial results were analysed using a moving pairwise comparison of treatments and a moving average of the covariates. Simulated trial strips that incorporated a range of variation in a useful covariate close to that encountered in the whole block showed how yield or mildew severity varied with the covariates along the strips. Importantly, such results provided information about likely crop responses in other parts of the block according to variation in the covariates, thus contributing to better-informed decision-making. Compared to whole-field approaches, this strip approach is more efficient and simpler for growers to implement.

Weather Variables for Within-Vineyard Awareness of Botrytis Risk

“…Such patterns are moderated by features of the land, vine row orientation, and canopy architecture that in turn infuence temperature, relative humidity, surface wetness duration, and wind speed. Recent work to simplify and improve the efciencies of onvineyard trials also points to the potential to estimate BBR severity from a single vineyard row that represents most of the variance in this response variable [66]. Metadata and sample data sets from this study should aid standardised implementation of algorithms in digital applications that will beneft in the future from remote sensing of BBR severity and machine learning approaches.…”