Effects of sodium on potassium nutrition in three tropical root crop species

The potassium (K) nutrition and high K requirement of tropical root crops may be affected by their sodium (Na) status, as has been observed in a number of plant species. Solution culture was used to study the effects of K and Na supplies in tannia [Xanthosoma sagittifolium (L.) Schott.], sweetpotato [Ipomoea batatas (L.) Lam.] and taro [Colocasia esculenta (L.) Schott]. At low K supply, Na ameliorated symptoms of K deficiency and increased growth in tannia, and to a lesser extent in sweetpotato, but not in taro. None of the species responded to Na at adequate K supply. Differences in response to Na were attributed to differences in Na translocation to plant tops. At maximum Na supply, the Na concentration in index leaves averaged 1.82% in tannia, 0.205% in sweetpotato, and 0.0067% in taro. An increase in the supply of Na resulted in a shift in the critical K concentration for deficiency (i.e., 90% of maximum yield) in index leaves from 2.9% to 1.2% in tannia, and from 4.8% to 2.5% in sweetpotato. The critical K concentration in taro was 3.3%, irrespective of Na supply. To overcome the problem in tannia and sweetpotato of determining the critical concentration relevant to a leaf sample of unknown K status, a relationship was established for each species relating the critical K concentration to the concentration of Na in the index leaves.

Trait physiology and crop modelling to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. We explore whether a crop growth and development modelling framework can link phenotype complexity to underlying genetic systems in a way that strengthens molecular breeding strategies. We use gene-to-phenotype simulation studies on sorghum to consider the value to marker-assisted selection of intrinsically stable QTLs that might be generated by physiological dissection of complex traits. The consequences on grain yield of genetic variation in four key adaptive traits – phenology, osmotic adjustment, transpiration efficiency, and staygreen – were simulated for a diverse set of environments by placing the known extent of genetic variation in the context of the physiological determinants framework of a crop growth and development model. It was assumed that the three to five genes associated with each trait, had two alleles per locus acting in an additive manner. The effects on average simulated yield, generated by differing combinations of positive alleles for the traits incorporated, varied with environment type. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages with gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies. We simulated a marker-assisted selection (MAS) breeding strategy based on the analyses of gene effects. When marker scores were allocated based on the contribution of gene effects to yield in a single environment, there was a wide divergence in rate of yield gain over all environments with breeding cycle depending on the environment chosen for the QTL analysis. It was suggested that knowledge resulting from trait physiology and modelling would overcome this dependency by identifying stable QTLs. The improved predictive power would increase the utility of the QTLs in MAS. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate QTLs.