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The utility of less-common statistical methods for analyzing agricultural systems: focus on kernel density estimation, copula modeling and extreme value theory

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Abstract

A variety of statistical methods have been developed for multivariate analysis of agricultural systems. Some statistical methods are rarely used to study these systems, although they can contribute to issues such as identifying atypical farms, modeling relations among variables and describing farms with common characteristics. To address these issues, we reviewed studies that applied kernel density estimation (KDE), copula modeling and extreme value theory (EVT) to French dairy farm data. KDE helped identify joint value ranges of forage production and milk production or greenhouse gas emissions that most farms in specific French region were likely to have. Copula modeling formalized the shapes of relations among farm characteristics, while EVT distinguished production strategies and management practices of farms that produced extreme amounts of forage. The present study reviews studies that applied these three methods, recommends when to use the latter and discusses their contribution to improving the understanding of dairy farms.

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Source: Integrated Farm System Model, USDA-ARS, https://www.quantitative-plant.org/model/IntegratedFarmSystemModel

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Data availability statement

Data sharing is not applicable to this article since no new data were created in this study.

Abbreviations

EVT:

Extreme value theory

GHG:

Greenhouse gas emissions

KDE:

Kernel density estimation

cdf:

Cumulative distribution function

pdf:

Probability density function

LU:

Livestock unit

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Acknowledgements

The authors thank the French Livestock Institute (IDELE) for providing the dataset and Françoise Vertès, Samuel Le Féon and Hayo van der Werf for their careful reading of and helpful discussions about the manuscript, which helped to improve it greatly.

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The authors declare that they received no funds, grants or other financial support for preparing this manuscript.

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Authors and Affiliations

  1. INRAE, Institut Agro, SAS, 35000, Rennes, France

    Tristan Senga Kiesse & Michael S. Corson

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  1. Tristan Senga Kiesse
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  2. Michael S. Corson
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Both TSK and MSC contributed to the structure of the review. TSK wrote the first draft, and both authors revised the manuscript. Both authors read and approved the final manuscript.

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Correspondence to Tristan Senga Kiesse.

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Senga Kiesse, T., Corson, M.S. The utility of less-common statistical methods for analyzing agricultural systems: focus on kernel density estimation, copula modeling and extreme value theory. Behaviormetrika 50, 491–508 (2023). https://doi.org/10.1007/s41237-022-00190-y

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