Predicting genotypic differences in irrigated sugarcane yield using the Canegro model and independent trait parameter estimates

Abstract

Abstract Crop models have the potential to support plant breeding by predicting genotype response to environmental factors, and identifying desirable genetic traits for improved crop performance. The study tested whether the Canegro sugarcane model can predict genotypic differences in stalk dry mass (SDM) yields observed in field trials using independently derived genetic trait information. Other objectives included the estimation of three trait parameters (TP) for selected genotypes, and assessing their role in determining genotypic differences in SDM yields. Phenotyping was conducted in a well-watered pot trial at Mount Edgecombe, South Africa comprising 14 genotypes. Gross photosynthate produced per unit of intercepted photosynthetically active radiation under ideal conditions (PARCEo) was estimated from leaf level photosynthetic efficiency (A) and stomatal conductance (gs). Thermal time from shoot emergence to the start of stalk elongation (CHUPIBASE) was estimated from measurements of leaf number. Maximum fraction of aerial dry biomass growth partitioned to stalks (STKPFMAX) was estimated from the measured stalk fraction of aerial biomass at harvest. Values of PARCEo (A) and PARCEo (gs) differed significantly between genotypes with a range of 47% and 67% of the mean, respectively. CHUPIBASE values also differed significantly between genotypes and showed a range of 23% of the mean. STKPFMAX values did not differ significantly between genotypes and showed the least variation with a range of 17% of the mean. The Canegro model predicted SDM yields and rankings well (r = 0.90**) for nine genotypes grown in well-watered field trials at Pongola, South Africa, using these independent estimates of PARCEo (A), CHUPIBASE and STKPFMAX values. The overestimation of the observed genotypic range in SDM yields were corrected by dynamically scaling leaf level photosynthetic efficiency using fractional sunlit leaf area. The reliable prediction of genotype performance was mostly ascribed to the impact of PARCEo. The extent of genetic variation in PARCEo found in the relatively small number of genotypes for well-watered crops, suggest that sugarcane improvement could be enhanced by screening breeding populations for high values of this trait. The study provided proof of concept that realistic sugarcane models could be used for identifying key traits (in this case PARCEo) and their ideal values (in this case as high as possible), and therefore could be used to assist in defining sugarcane breeding targets.