Three approaches to data analysis were compared to describe competitive interactions between wheat and Italian ryegrass. Replacement series were performed using the two species at total densities of 100, 200, and 400 plants/ m2, and separate monoculture experiments for each species at densities from 33 to 800 plants/m2. Approaches to data analysis included: 1) conventional analysis of replacement series experiments, 2) development of synthetic no-interaction responses from monoculture experiments for comparison with results from mixtures, and 3) responses of the reciprocal yield of individual plants to variation in densities of the two species. Wheat was the superior competitor to ryegrass; however, the three approaches varied in ability to quantify this competitive relationship. The conventional replacement series analysis was least sensitive in describing the influences of either density or proportion on the plant association. The synthetic no-interaction approach provided the most detailed analysis of the influence of proportion on the species interaction. The reciprocal yield approach provided the simplest and most sensitive analysis of the joint influences of density and proportion. The latter approach also provided the most quantitative analysis of the influence of density on the species interaction. Plant density and species proportion are important variables for interpreting the process of plant competition.

Plant competition studies designed to quantify interference between species provide valuable information on competitive interactions and on the effects of agronomic practices on those interactions. The effect of each species' density on the growth of itself and on the growth of the other species is quantified in a series of regression models. Traditionally, the models' regression coefficients have been combined in a series of ratios to quantify relative competitive ability and niche differentiation. Coefficients that are negative (positive interference—facilitation, mutualism) or zero (neutral interference or nonsignificant coefficient) do not lend themselves well to ratio-based methodology because of sign cancellation or undefined values, respectively. As a result, ratio-based methodology is limited to using only positive coefficients (negative interference—amensalism, competition). Rather than using ratios, the absolute-log method uses addition and subtraction of coefficients converted to a pseudologarithmic scale, thus allowing for use of coefficients with values that are negative or zero. As a result, the absolute-log method can be used to quantify relative competitive ability and niche differentiation involving all types of interference—negative, positive, and neutral. The absolute-log method includes an optional statistical procedure constructing confidence intervals for the estimates of relative competitive ability and niche differentiation.