The extent of intraspecific variation in trait‒environment relationships is an open question with limited empirical support in crops. In organic agriculture, with high environmental heterogeneity, this knowledge could guide breeding programs to optimize crop attributes. We propose a three-dimensional framework involving crop performance, crop traits, and environmental axes to uncover the multidimensionality of trait‒environment relationships within a crop. 

We modeled instantaneous photosynthesis (A_sat) and water-use efficiency (WUE) as functions of four phenotypic traits, three soil variables, five carrot (D. carota) varieties, and their interactions in a national participatory plant breeding program involving a suite of farms across Canada. We used these interactions to describe the resulting 12 trait‒environment relationships across varieties.  

We found one significant trait‒environment relationship for A_sat (taproot tissue density‒soil phosphorus), which was consistent across varieties. For WUE, we found that three relationships (petiole diameter‒soil nitrogen, petiole diameter‒soil phosphorus, and leaf area‒soil phosphorus) varied significantly across varieties. As a result, WUE was maximized by different combinations of trait values and soil conditions depending on the variety.  

Our three-dimensional framework supports the identification of functional traits behind the differential responses of crop varieties to environmental variation and thus guides breeding programs to optimize crop attributes from an eco-evolutionary perspective. 

We modeled instantaneous photosynthesis (A_sat) and water-use efficiency (WUE) as functions of four phenotypic traits, three soil variables, five carrot (D. carota) varieties, and their interactions in a national participatory plant breeding program involving a suite of farms across Canada. We used these interactions to describe the resulting 12 trait‒environment relationships across varieties.  

The experimental design resulted in 405 carrot plants sampled in a nested design with the following structure: 9 farms, 5 plots within each farm (i.e. one for each variety), 3 subplots within each plot, and 3 individual plants within each subplot. However, given the logistics of our field campaign, five physiological measurements were not possible within the optimal measurement timeframe. We therefore excluded these five plants from our dataset (representing ~1% of the total sample size). After transformation and standardization (see text), we identified and removed 2 outliers (extremely high leaf area values), leading to a total of 398 sampling units (plants) for subsequent analyses. The dataset presented here contains information for these 398 sampling units and will be sufficient for reproducing the results in the corresponding article. A key explaining the name and units of each variable is included in the file.