Physiological drivers of responses of grains per m2 to environmental and genetic factors in wheat

Abstract

Most of the required increases in food production over the next decades are expected to be achieved through increases in crop yield. As wheat is essential for food security it is worrying that its yield gains over the last two decades were small. To achieve further yield increases it is critical to continue increasing number of grains per unit area (GN m -2 ), the trait best related to yield. In this context, it is relevant to identify the main determinants of GN m -2 in response to genetic and environmental factors as well as the trade-offs between them. In the present study we compiled a large database across the literature to analyse the relative importance of components when affected by genetic or environmental factors, producing small or large changes in GN m -2 and its components, either numerical (the number of spikes per m 2 , SN m -2 ; and the number of grains per spike, GN spike -1 ) or physiological (spike dry weight at anthesis, SDWa; and fruiting efficiency, FE) determinants. The database included 367 papers published in: (i) Field Crop Research (FCR), (ii) European Journal of Agronomy (EJA), (iii) Crop Science (CS) and (iv) Crop and Pasture Science (CPS, formerly Australian Journal of Agricultural Research) between 1990 and 2020. The complete dataset was split into classes, depending on the source of experimental variation, environment or genotype and was normalised to remove the differences between experiments and determine the environmental and genotypic effects within each experiment. Normalised data showed that the responsiveness of GN m -2 was similarly explained by changes in both SN m -2 and GN spike -1 , but in terms of physiological components SDWa was more relevant than FE for explaining the variations in GN m -2 . Considering the numerical components of GN m -2 genotypic and environmental factors modified more GN spike -1 than in SN m -2 . On the other hand, physiological components were differently modified by genotype and environment: for genotypic effects FE was more critical than SDWa and the other way around for environmental factors. A trade-off between numerical and physiological components was observed although was greater between physiological than between numerical components. • We quantified physiological/numerical components (and trade-offs) of grains/m -2 in wheat. • We recognised differences depending on magnitude and source of variation (genetic/environmental). • Grains/spike -1 explained better grains/m -2 than spikes/m -2 , particularly for genotypes. • Genotypes affected more fruiting efficiency than spike-dry-weight-at-anthesis, environments the opposite. • Trade-offs seemed more relevant for physiological than for numerical components.