Study Reveals Flaws in Vineyard Sampling Protocols

A study published by the International Viticulture and Enology Society (IVES) and conducted by Baptiste Oger, Cécile Laurent, Philippe Vismara, and Bruno Tisseyre has highlighted the need for accurate sampling protocols in vineyards to estimate harvest production more precisely. Early estimation of the average number of grape clusters per vine is essential for planning viticultural activities, investment, and marketing strategies. However, there is currently no standardized sampling protocol for this type of estimation, leading to variations in results as each producer uses different methods. This research analyzed the impact of these differences on estimation errors to propose more effective sampling practices.

The main variations observed in current sampling protocols relate to whether missing vines are included or excluded during counting, the spatial arrangement of sampled vines (which can be grouped into different site sizes along the row), and the total number of vines sampled per field. The study found that these differences significantly influence the accuracy of the estimates, particularly in fields with a high percentage of missing vines.

The researchers recommend focusing the estimation on the number of grape clusters rather than the presence or absence of missing vines. Including missing vines as having zero clusters can skew results, as it would involve estimating two separate yield components simultaneously: the proportion of missing vines and the number of clusters per vine. Since these components have different properties, they should be estimated independently using specific protocols for each. Experimental results showed that in fields with more than 30% missing vines, considering these during sampling increased the estimation errors of cluster numbers. This emphasizes the need for separate protocols to avoid significant errors, especially when the proportion of missing vines is high.

Another key finding was the importance of spatial organization in sampling within the vineyard. Because yield components like the number of clusters per vine are often spatially structured, there is a risk of overestimating or underestimating the number of clusters if consecutive vines are sampled within a single measurement site. This occurs when an area has more or fewer clusters than the average. To reduce this risk, it is preferable to spread observations across several sites, ideally at least two or three, rather than clustering them in a single location.

The study compared six sampling protocols that distribute 12 vines across 1, 2, 3, 4, 6, or 12 sampling sites. The results showed that estimation errors were highest when vines were grouped into a single site and that these errors decreased as the vines were distributed across more sites. Error reduction was most noticeable in fields with strong spatial autocorrelation, meaning where clusters were more systematically arranged.

The final analysis focused on the relationship between estimation error, sample size, and field variability, measured using the coefficient of variation (CV). The CV is calculated by dividing the sample's standard deviation by its mean and is a standardized way to quantify value dispersion within a sample. A high CV indicates high field variability, which is associated with a higher risk of estimation error. In such cases, increasing the sample size is recommended to reduce errors.

The study showed that increasing sample size yields greater benefits when variability is high. For example, if the CV is 0.4, increasing the sample size from 5 to 13 vines reduces the upper limit of the confidence interval for estimation errors from 39% to 22%. Conversely, if the CV is 0.2, the same increase in sample size only reduces the error from 19% to 11%. This indicates that as variability decreases, increasing the sample size has a smaller impact on estimation accuracy.

Using the coefficient of variation in real-time during sampling allows growers to assess sample quality and decide whether to continue sampling based on the desired level of precision. If the CV changes with new measurements, it is necessary to adjust the sampling protocol to reflect this variation.

In conclusion, the study recommends implementing specific sampling protocols for each yield component, avoiding grouping vines at a single sampling site, and adjusting sample size according to observed field variability. Defining a uniform sample size for all fields may be counterproductive; instead, a size based on the observed heterogeneity should be used. These findings help improve the accuracy of estimating the number of clusters per vine and optimize resources dedicated to these tasks.