Can the development of drought tolerant ideotype sustain Australian chickpea yield?

Identification of agro-physiological traits of lentil that reduce risks of drought

Ideotype breeding is an essential approach for selection of desired combination of plant traits for testing in crop growth model for potential yield gain in specific environments and management practices. Here we parameterized plant traits for untested lentil cultivars for the APSIM-lentil model in phenology, biomass, and seed yield. We then tested these against independent data and applied the model in an extrapolated analysis (i) to assess the impact of drought on productivity across different rainfall environments; (ii) to identify impactful plant traits and (iii) to design new lentil ideotypes with a combination of desirable traits that mitigate the impact of drought, in the context of various agronomic practices across a wide range of production environments. Desirable phenological and physiological traits related to yield were identified with RUE having the greatest effect on yield followed by HI rate. Leaf size significantly affected seed yield (p< 0.05) more than phenological phases. The physiological traits were integrated into four ideotype designs applied to two baseline cultivars (PBA Hallmark XT and PBA Jumbo2) providing eight ideotypes. We identified a combination of genetic traits that promises a yield advantage of around 10% against our current cultivars PBA Hallmark XT and PBA Jumbo2. Under drought conditions, our ideotypes achieved 5 to 25% yield advantages without stubble and 20 to 40% yield advantages with stubble residues. This shows the importance of genetic screening under realistic production conditions (e.g., stubble retention in particular environments). Such screening is aided by the employment of biophysical models that incorporate both genetic and agronomic variables that focus on successful traits in combination, to reduce the impact of drought in the development of new cultivars for various environments. Stubble retention was found to be a major agronomic contributor to high yield in water-limiting environments and this contribution declined with increasing growing season rainfall. In mid- and high-rainfall environments, the key drivers of yield were time of sowing, physiological traits and soil type. Overall, the agronomic practices, namely, early sowing, residue retention and narrow row spacing deceased the impact of drought when combined with improved physiological traits of the ideotypes based on long term climate data.

A kabuli chickpea ideotype

The concept of 'crop ideotype' is coined as a desirable plant model expected to better perform for seed yield, oils and other useful characteristics when developed as a cultivar, and it consists of two major approaches, namely, (i) 'defect elimination', that is, integration of disease resistance to a susceptible genotype from a resistant genotype and (ii) 'selection for yield' by improving yield after crosses between desirable parents. For consideration of these approaches, here we introduced an ideotype in kabuli chickpea (Cicer arietinum L.) which is high-yielding, extra-large-seeded, and double- or multi-podded, has high plant height and imparipinnate-leafed traits, and is heat tolerant and resistant to ascochyta blight [Ascochyta rabiei (Pass.) Labr.], which causes considerable yield losses, via marker-assisted selection. F3 and F4 lines were evaluated for agro-morphological traits divided into six classes, namely, (i) imparipinnate-leafed and single-podded progeny, (ii) imparipinnate-leafed and double-podded progeny, (iii) imparipinnate-leafed and multi-podded progeny, (iv) unifoliolate-leafed and single-podded progeny, (v) unifoliolate-leafed and double-podded progeny, (vi) unifoliolate-leafed and multi-podded progeny. F3:4 lines having 100-seed weight ≥ 45 g and double- or multi-podded traits were additionally assessed for resistance to ascochyta blight using molecular markers including SCY17590 and CaETR-1. Superior lines having higher values than their best parents were determined for all studied traits indicating that economic and important traits including yield and seed size in chickpea could be improved by crossing suitable parents. Imparipinnate-leafed and multi-podded plants had not only the highest number of pods and seeds per plant but also the highest yield. On the other hand, imparipinnate-leafed and single podded progeny had the largest seed size, followed by imparipinnate-leafed and double-podded progeny. Multi-podded plants produced 23% more seed yield than that of single-podded plants, while multi-podded plants attained 7.6% more seed yield than that of double-podded plants. SCY17590 and CaETR-1 markers located on LG4 related to QTLAR2 and QTLAR1 were found in 14 lines among 152 F3:4 lines. Six superior lines were selected for being double- or multi-podded, imparipinnate-leafed, suitable for combine harvest, heat-tolerant, and resistant to ascochyta blight, and having both of two resistance markers and extra-large seeds as high as 50-60 g per 100-seed weight. Resistance alleles from two different backgrounds for resistance to ascochyta blight were integrated with double- or multi-podded kabuli chickpea lines having high yield, extra-large seeds, high plant height, imparipinnate-leaves and high heat tolerance, playing a crucial role for future demands of population and food security. These approaches seem to be applicable in ideotype breeding for other important crop plants.

Modelling the effects of cold temperature during the reproductive stage on the yield of chickpea (Cicer arietinum L.)

During the reproductive stage, chilling temperatures and frost reduce the yield of chickpea and limit its adaptation. The adverse effects of chilling temperature and frost in terms of the threshold temperatures, impact of cold duration, and genotype-by-environment-by-management interactions are not well quantified. Crop growth models that predict flowering time and yield under diverse climates can identify combinations of cultivars and sowing time to reduce frost risk in target environments. The Agricultural Production Systems Simulator (APSIM-chickpea) model uses daily temperatures to model basic crop growth but does not include penalties for either frost damage or cold temperatures during flowering and podding stages. Regression analysis overcame this limitation of the model for chickpea crops grown at 95 locations in Australia using 70 years of historic data incorporating three cultivars and three sowing times (early, mid, and late). We modified model parameters to include the effect of soil water on thermal time calculations, which significantly improved the prediction of flowering time. Simulated data, and data from field experiments grown in Australia (2013 to 2019), showed robust predictions for flowering time (n = 29; R² = 0.97), and grain yield (n = 22; R² = 0.63-0.70). In addition, we identified threshold cold temperatures that significantly affected predicted yield, and combinations of locations, variety, and sowing time where the overlap between peak cold temperatures and peak flowering was minimal. Our results showed that frost and/or cold temperature-induced yield losses are a major limitation in some unexpected Australian locations, e.g., inland, subtropical latitudes in Queensland. Intermediate sowing maximise yield, as it avoids cold temperature, late heat, and drought stresses potentially limiting yield in early and late sowing respectively.