Performance of innovative cropping systems diversified with oilseeds and protein crops: identification and resolution of methodological issues, using the Syppre experimental network as a case study | OCL

Innovative Cropping Systems / Systèmes innovants de culture

Open Access

Table 3

Simplified table representing the identification key of possible analytical methods (Gomez and Gomez, 1984; Siegel and Castellan Jr, 1988; Cady, 1991; Federer, 1999; De’ath, 2002; Baayen et al., 2008; Zuur et al., 200; 9; Dagnelie, 2012; Payne, 2015) based on three generic experimental questions (column 1). This table does not enable one to answer agronomic questions on a case-by-case basis, but it does provide guidance and access to the main families of data analysis whenever possible. It is read from left to right. The user chooses at each step the line that corresponds to a given experimental situation. The proposed analyses can be undertaken only after having verified that the data meet their validity conditions. A good use of this table begins with the identification of the experiment’s statistical terms: factors, modalities, measurement scale, blocks, control.

Objective	Key 1	Key 2	Main families
Compare groups or systems	1 factor	2 groups / modalities	Comparison tests of 2 samples −According to scale of measurement and size
	1 factor	>2 groups / modalities	Comparison tests of more than two samples − According to scale of measurement and size
	>1 factor	No blocks	Model, multi-factor ANOVA type
	>1 factor	Blocks	Mixed models
Follow an evolution over time	Time factor only	No control	Graphics only (confounding effect with weather effect)
		Control	Quantitative measurements --> Longitudinal models
		Control	Qualitative measurements --> Longitudinal models --> Proportional tests
	>1 factor	Quantitative measurements	Longitudinal models
	>1 factor	Qualitative measurements	Longitudinal models Proportional test
Study the relationships between variables / indicators	No causal link	2 variables	Quantitative & ordinal measurements --> Correlation tests Nominal measurements --> Contingency coefficients --> Correspondence Analysis
	No causal link	>2 variables	Concordance test Multi-factorial analyses
	Causal link	2 variables	Quantitative measurements --> Regression
			Qualitative measurements --> Proportion comparisons (chi2) − --> Correspondence Analysis
			Mixed measurement scales --> Sample comparisons
		-->2 variables	Quantitative measurements --> Multiple regression --> Principal Component Analysis
			Qualitative measurements --> Discriminant analyses --> Multiple correspondance analysis
			Mixed measurement scales --> Model to specify --> Mixed Factor Analysis

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