Quantifying genotypic and environmental factors affecting potato canopy growth
There is a high degree of variation in potato yield which may contribute to the current UK yield plateau. There is a strong correlation between light intercepted, total biomass produced and biomass partitioned to the tubers as yield. Variation in potato canopy growth can be analysed using non-destructive measurements and the size of the canopy determines radiation interception. Proportion of soil covered by green leaves (percentage of ground covered, GC) is a simple proxy for light intercepted. Two empirical models were compared for ability to summarize GC throughout the growing season. Both models showed a similar ability to describe GC, but differed in output and ease of interpretation, so the simpler, more descriptive model was selected for use in further canopy quantification. The effects of planting date, nitrogen rate, cultivar and stem density were examined in two multi-year experiments. Planting date had a strong effect on early growth, though subsequent growth was less sensitive to temperature. Leaf production and canopy duration varied with cultivar determinacy. Cultivar and nitrogen fertilizer rate determined the potential for branch and branch leaf production (and therefore canopy longevity), altering distribution of leaf area index (LAI) within the canopy, but duration of growth was determined by planting date, which when delayed, shortened the season. The effects of stem density were most noticeable early in the season when higher stem density resulted in faster canopy expansion and earlier canopy closure. Branch production was reduced at high stem densities, but total LAI varied little. Canopy quantification was also used to analyse historical data and across 20 cultivars, decreasing duration of early canopy expansion was the only universal response to increasing stem density. By quantifying the growth and maintenance of experimental and commercial crop canopies, causes of variation in light interception and subsequent yield can be identified. ‘Best agronomic practice’ can then be identified, enabling targeted changes to reduce the variability in yields and improve resource use efficiency. Additionally, this greater understanding of canopy development will refine existing potato yield models, enabling more specific and more accurate predictions. Future yield models should include stem density to better predict early season canopy growth and cultivar determinacy to predict interactions between canopy production and season length.
Potato agronomy, Canopy quantification, Canopy growth, Curve fitting, Cultivar determinacy, Planting date, Nitrogen rate, Stem density
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