Biology subjects

Sadras, V. O.

Publications and source records attributed to Sadras, V. O..

3 recordsLinked to original sources

bioRxiv2024

Linking phenology, harvest index and genetics to improve chickpea grain yield

Phenology is critical to crop adaptation. We grew 24 chickpea genotypes in 12 environments to analyse: the environmental and genotypic drivers of phenology; associations between phenology and yield; and phenotypes associated with allelic variants of three flowering related candidate loci: CaELF3a; a cluster of three FT genes on chromosome 3; and a region on chromosome 4 with an orthologue of the floral promoter GIGANTEA. A simple model with 3 genotype-specific parameters explained the differences in flowering response to daylength. Environmental factors causing flower abortion, such as low temperature and radiation and high humidity, led to a longer flowering-to-podding interval. Late podding associated with poor partition to grain, limiting yield in favourable environments. Sonali, carrying the early allele of Caelf3a (elf3a), was generally the earliest to set pod, had low biomass but the highest harvest index. Genotypes combining the early variants of GIGANTEA and FT orthologues FTdel, where a deletion in the intergenic region of FTa1-FTa2 was associated with slow development, usually featured early reproduction and high harvest index, returning high yield in favourable environments. We emphasise the importance of pod set, rather than flowering, as a target for breeding, agronomic, and modelling applications. HighlightThis paper analyses the environmental and genetic controls of chickpea phenology and its effects on grain yield, in a multi-environment trial including 24 genotypes with varying combinations of flowering related genes.

plant biology↗

bioRxiv2024

The causal arrows - from genotype, environment and management to plant phenotype - are double headed

Unidirectional, cause-and-effect arrows are drawn from genotype (G), environment (E), and agronomic management (M) to the plant phenotype in crop stands. Here we focus on the overlooked bidirectionality of these arrows. The phenotype-to-genotype arrow includes increased mutation rates in stressed phenotypes, relative to basal rates. From a developmental viewpoint, the phenotype modulates gene expression returning multiple cellular phenotypes with a common genome. From a computational viewpoint, the phenotype influences gene expression in a process of downward causation. The phenotype-to-environment arrow is captured in the process of niche construction, which spans from persistent and global (e.g., photosynthetic archaea and cyanobacteria that emerged [~]3.4 billion years ago created the oxygen-rich atmosphere that enabled the evolution of aerobic organisms and eukaryotes) to transient and local (e.g., lucerne tap root constructs soil biopores that influence the root phenotype of the following wheat crop). Research on crop rotations illustrates but is divorced of niche construction theory. The phenotype-to-management arrow involves, for example, a diseased crop that triggers fungicide treatments. Making explicit the bidirectionality of the arrows in the G x E x M model allows to connect crop improvement and agronomy with other, theoretically rich scientific fields. HighlightIn the G x E x M model, the plant phenotype is not only influenced by but also influences G, E and M.

ecology↗

bioRxiv2022

Benchmarking water-limited yield potential and yield gaps of Shiraz in the Barossa and Eden Valleys

Background and AimsVineyard performance is impacted by water availability including the amount and seasonality of rainfall and evapotranspiration and irrigation volume. We benchmarked water-limited yield potential (Yw), calculated yield gaps as the difference between Yw and actual yield, and explored the underlying environmental and management causes of these gaps. Methods and ResultsThe yield and its components in two sections of 24 Shiraz vineyards was monitored during three vintages in the Barossa zone (GI). The frequency distribution of yield was L-shaped, with half the vineyards below 5.2 t ha-1, and an extended tail of the distribution that reached 24.9 t ha-1. The seasonal ratio of actual crop evapotranspiration and reference evapotranspiration was below 0.48 in 85% of cases, with a maximum of 0.65, highlighting a substantial water deficit in these vineyards. A boundary function relating actual yield and seasonal rainfall was fitted to quantify Yw. Yield gaps increased with increasing vine water deficit, quantified by the carbon isotope composition in the fruit. The yield gap was smaller with higher rainfall before budburst, putatively favouring early-season vegetative growth and allocation to reproduction, and with higher rainfall between flowering and veraison, putatively favouring fruit set and berry growth. The gap was larger with higher rainfall and lower radiation between budburst and flowering. The yield gap increased linearly with vine age between 6 and 33 yr at a rate of 0.3 t ha-1 yr-1. The correlation between yield gap and yield components ranked bunch weight {approx} berries per bunch > bunch number > berry weight; the minimum to close the yield gap was 185,000 bunches ha-1, 105 g bunch-1, 108 berries bunch-1 and 1.1 g berry-1. ConclusionsWater deficit and vine age were major causes of yield gaps. Winter irrigation provides an opportunity to improve productivity. The cost of dealing with older, less productive vines needs to be weighed against the rate of increase in yield gap with vine age. Significance of the StudyA boundary function to estimate water-limited yield potential returned viticulturally meaningful yield gaps and highlighted potential targets to improve vineyard productivity.

plant biology↗