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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Precision viticulture and advanced analytics. A short review.

Luis G Santesteban¹

¹Dpt. Agronomy, Biotechnology and Food Science, Univ. P. Navarra, 31006 Pamplona, Spain.

|January 7, 2019

Summary

Precision viticulture (PV) optimizes vineyard management by addressing spatial variability for sustainability. Integrating advanced analytics with PV can enhance grower benefits and deepen understanding of grape composition factors.

Keywords:

Collaborative research Grape New technologies Vitis vinifera L., wine

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Area of Science:

Viticulture
Agricultural Science
Data Science

Background:

Precision viticulture (PV) emerged in the 1990s to enhance vineyard sustainability by managing spatial variability.
Despite PV's growth, advanced analytics integration remains underexplored, limiting its potential benefits and insights.
Understanding vineyard internal factors influencing grape composition is crucial, especially when external variables are constant within a field.

Purpose of the Study:

To highlight the potential of integrating advanced analytics into precision viticulture practices.
To explore how this integration can improve the economic and environmental sustainability of vineyards.
To foster a deeper understanding of the factors affecting grape composition through data-driven insights.

Main Methods:

Review of current precision viticulture strategies.
Exploration of advanced analytical techniques applicable to viticultural data.
Identification of research gaps in the synergy between PV and analytics.

Main Results:

Advanced analytics offer significant potential to enhance PV by providing more accurate evaluations of its benefits.
Integration can lead to a more profound understanding of internal vineyard factors influencing grape composition.
Current research inadequately leverages advanced analytics for PV optimization.

Conclusions:

Combining precision viticulture with advanced analytics is key to unlocking greater economic and environmental sustainability in vineyards.
This integration promises novel insights into grape composition determinants.
Further research is needed to fully exploit the synergistic potential of PV and advanced analytics.