Crop improvement aims to produce high yielding genotypes for target environments. Crop models simulate yield formation as the outcome of a series of low-level processes, driven by environmental (E) variables and regulated by genetic (G) traits. There is potential for crop models to aid sugarcane breeding, by identifying desirable genetic traits for target environments. The objective of this study was to evaluate existing concepts of G and E control of plant processes for explaining crop development, growth and yield, using an international growth analysis dataset.
Crop development, growth and yield were monitored in the plant and 1st ratoon crops for seven cultivars (N41, R570, CP88-1762, HoCP96-540, Q183, ZN7 and NCo376) grown under well-watered conditions at La Mare (Reunion Island, France), Pongola (South Africa (RSA), Chiredzi (Zimbabwe), and Belle Glade (Florida, USA). Weather data were collected and environmental conditions characterized for each experiment. Derived process-level phenotypic parameters, based on concepts from four sugarcane growth simulation models (DSSAT-Canegro, Mosicas, APSIM-Sugar and Canesim), were calculated from observations and used to (1) evaluate current understanding of E drivers of sugarcane growth and development processes, and (2) identify and quantify G control at a process level.
Final yields showed significant E and GxE variation; dry above-ground biomass and stalk yields were highest in La Mare and lowest in Pongola. Cultivar rankings in stalk dry mass for the common cultivars (N41, R570, CP88-1762) varied significantly between Es. Significant E variation in phenotypic parameters describing germination, tillering and timing of the onset of stalk growth (OSG) revealed shortcomings in the underlying simulation concepts. Significant G variation was found for germination rate, leaf appearance rate and canopy development rate per unit thermal time (TT), and maximum radiation use efficiency, indicating strong G control of the associated underlying processes.
Solar radiation was found to influence tillering rate per unit TT, and TT to OSG, challenging the current theory of TT as the sole driver of these processes.
By explaining more of the E variation, more stable and accurate G-specific model parameters can be defined and evaluated. This is anticipated to lead to less GxE confounding of modelled processes, and hence crop models that are better-equipped for supporting sugarcane crop improvement.