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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