Effects of crop acreage and aggregation level on price-yield correlations

R. Finger

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)


Purpose – The purpose of this paper is to analyze the effects of data aggregation and farm-level crop acreage on the level of natural hedge, i.e. the level of price-yield correlations, which is an important issue in risk modeling and management. Design/methodology/approach – Swiss FADN data for five crops covering the period 2002-2009 are used to estimate price-yield correlations at the farm- as well as on an aggregated level. Tobit regressions are used to estimate empirical relationships between the level of natural hedge and the underlying crop acreage. Findings – Price-yield correlations differ significantly between farm- and aggregated-level. More specifically, the natural hedge observed at the farm-level is much smaller, i.e. correlations are closer to zero. Taking correlations from aggregated levels thus leads to an underestimation of farm-level revenue variability. Furthermore, it is found that larger farms have a stronger natural hedge. For instance, a 1 percent increase in area under maize and intensive barley leads to a change in the correlation by 20.02 and 20.08, respectively. Practical implications – The natural hedge is often approximated with correlations observed at more aggregated levels, e.g. the county level. The results show that this implies errors in risk assessment and modeling as well as insurance applications. Thus, farm-level estimates should be used. The here presented relationship between price-yield correlations and farm-level crop acreage can be used to derive better information on levels of the natural hedge. Originality/value – Even though the effects of data aggregation on price-yield correlations have been discussed in earlier research, this paper is the first to also account for on-farm effects of underlying crop acreage on levels of natural hedge. It is found that this simple relationship can be useful in risk management and modeling applications.
Original languageEnglish
Pages (from-to)436-455
JournalAgricultural Finance Review
Issue number3
Publication statusPublished - 2012

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