James M. Fedders

Individual fitness versus whole‐crop photosynthesis:solar tracking tradeoffs in alfalfa

Despite the optimism of some molecular biologists, natural selection among the wild ancestors of crops is unlikely to have missed simple genetic improvements that would consistently have enhanced individual fitness. Tradeoff‐free opportunities for further improvement of crop traits like photosynthetic efficiency or drought tolerance may therefore be elusive. Opportunities linked to acceptable tradeoffs may be abundant, however. Tradeoffs between individual competitiveness and the collective productivity of plant communities (e.g. those linked to height) have been key to past increases in yield potential. Solar tracking by leaves could involve such tradeoffs, if photosynthetic benefits to tracking leaves are outweighed by increased shading of leaves lower in the canopy. This hypothesis was tested using rotation in the horizontal plane to disrupt solar tracking in alfalfa. In sparse canopies, solar tracking increased net canopy photosynthesis, but rarely by more than 3%. As leaf area increased, solar tracking tended to decrease net canopy photosynthesis, despite edge effects in our 1‐m2 artificial communities, which probably exaggerated net photosynthetic benefits of tracking. Computer modeling suggested that the season‐long effects of solar tracking on community productivity can be negative. Solar tracking may have persisted, nonetheless, because individuals whose leaves track the sun increase shading of competitors.

Risk analysis, analysis of variance: getting more from our data.

Analysis of variance (ANOVA) and regression are common statistical techniques used to analyze agronomic experimental data and determine significant differences among yields due to treatments or other experimental factors. Risk analysis provides an alternate and complimentary examination of the same data by determining yield probabilities for each treatment or factor. We generated and analyzed a synthetic data set to illustrate that data with similar means, as determined by ANOVA, can have markedly different probability distributions due to differences in standard deviations. We then applied the techniques to data from a five-year yield trial of twelve Bermudagrass cultivars. ANOVA detected significant year-by-cultivar interactions while risk analysis illustrated differences among the cultivars in yield stability and in the probabilities of achieving specific yield goals. Together, ANOVA and risk analysis provide a more complete view of the data that facilitates technical transfer of experimental results to producers and other end-users.