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Improving vineyard sampling efficiency via dynamic spatially explicit optimisation
Author(s) -
MEYERS J.M.,
SACKS G.L.,
VAN ES H.M.,
VANDEN HEUVEL J.E.
Publication year - 2011
Publication title -
australian journal of grape and wine research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.65
H-Index - 77
eISSN - 1755-0238
pISSN - 1322-7130
DOI - 10.1111/j.1755-0238.2011.00152.x
Subject(s) - sampling (signal processing) , statistics , stratified sampling , sampling design , cluster sampling , sample size determination , sample (material) , mathematics , slice sampling , poisson sampling , computer science , environmental science , markov chain monte carlo , bayesian probability , filter (signal processing) , population , chemistry , demography , chromatography , sociology , computer vision
Background and Aims: Environmental variables within vineyards are spatially correlated, impacting the economic efficiency of cultural practices and accuracy of viticultural studies that utilise random sampling. This study aimed to test the performance of non‐random sampling protocols that account for known spatial structures (‘spatially explicit protocols’) in reducing sampling requirements versus random sampling. Methods and Results: Canopy microclimate data were collected across multiple sites/seasons/training systems. Autocorrelation was found in all systems, with a periodicity generally corresponding to vine spacing. Three spatially explicit sampling models were developed to optimise the balance between minimum sample sizes and maximum fit to a known probability density function. A globally optimised explicit sampling (GOES) model, which performed multivariate optimisation to determine best‐case sampling locations for measuring fruit exposure, reduced fruit cluster sample size requirements versus random sampling by up to 60%. Two stratified sampling protocols were derived from GOES solutions. Spatially weighted template sampling (STS) reduced sampling requirements up to 24% when based on probabilistic panel weighting (PW), and up to 21% when preferentially selecting specific locations within canopy architecture (AW). Conclusions: GOES, PW STS and AW STS each reduced required sample size versus random sampling. Comparative analyses suggested that optimal sampling strategies should simultaneously account for spatial variability at multiple scales. Significance of the Study: This study demonstrates that dynamically optimised sampling can decrease sample sizes required by researchers and/or wineries.