Table 6

Main methodological issues and solutions to answer the question “Are there conflicts between performance indicators?”.

Methodological issues Solutions Actions
Should we use data collected at the plot level or at the block level? Plot is the preferred statistical unit because it is the unit on which the measurements/observations were made and the most reliable for studying correlations.

How to study discrepancies between performance indicators? Analyze non causal relationships between many variables. Multifactorial analyses are therefore appropriate. Correlation analysis and Principal Component Analysis (PCA) in our case, since all indicators are quantitative.

How to select variables/indicators that will be active variables in a multifactorial analysis? Clearly draw the distinction between result variables/indicators (dependent variables) and explanatory variables/indicators (independent variables) describing: (i) cropping practices and (ii) the production situation.
Remove from the dataset all redundant and unrelated indicators used in the analysis.

How can we easily identify discrepancies and concordances between indicators when an increase in a performance indicator can mean either an improvement or a deterioration of the considered performance depending on the specific situation? Transform indicators whose increase indicate a lower performance by adding a minus sign. Rename the transformed indicators. We have added a minus sign in front of indicator values whose increase reveals a poorer performance. The names of these indicators begin with “m.”

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