Genetic basis and adaptive implications of temperature-dependent and temperature-independent effects of drought on chickpea reproductive phenology.

Water deficit often hastens flowering of pulses partially because droughted plants are hotter. Separating temperature-independent and temperature-dependent effects of drought is important to understand, model, and manipulate phenology. We define a new trait, drought effect on phenology (DEP), as the difference in flowering time between irrigated and rainfed crops, and use FST genome scanning to probe for genomic regions under selection for this trait in chickpea (Cicer arietinum). Owing to the negligible variation in daylength in our dataset, variation in phenology with sowing date was attributed to temperature and water; hence, genomic regions overlapping for early- and late-sown crops would associate with temperature-independent effects and non-overlapping genomic regions would associate with temperature-dependent effects. Thermal-time to flowering was shortened with increasing water stress, as quantified with carbon isotope composition. Genomic regions on chromosomes 4-8 were under selection for DEP. An overlapping region for early and late sowing on chromosome 8 revealed a temperature-independent effect with four candidate genes: BAM1, BAM2, HSL2, and ANT. The non-overlapping regions included six candidate genes: EMF1, EMF2, BRC1/TCP18, BZR1, NPGR1, and ERF1. Modelling showed that DEP reduces the likelihood of drought and heat stress at the expense of increased likelihood of cold stress. Accounting for DEP would improve genetic and phenotypic models of phenology.

Relationships of frequencies of extreme low temperatures with grain yield of some Australian commercial chickpea cultivars.

In this study, we examined the relationships between extremes of low temperatures and chickpea yield in 12 field experiments conducted at six sites in the subtropical environment of southeast Queensland (SEQ) from 2014 to 2019. Three commercial chickpea cultivars, PBA-Boundary, PBA-HatTrick and PBA-Seamer, were grown in all the experiments. Cultivars PBA-Pistol, PBA-Monarch and Kyabra were also included in three of these experiments conducted in 2015. In these experiments, the crop experienced a total of 8 to 41 frosts (minimum temperature < = 0 °C), 2 to 41 pre-flowering frosts, 2 to 19 frosts during the critical period, 0 to 13 frosts and 2 to 71 low-temperature days (< = 15 °C) after flowering. The mean yield, which varied from 1 to 3 t/ha, was negatively related to post-flowering frosts (r = - 0.74, p < 0.01) and low-temperature days (r = - 0.76, p < 0.01), and positively related to pre-flowering frosts (r = 0.67, p < 0.05). Each post-flowering frost was associated with a 5% decrease and a low-temperature day with a 1% decrease in yield. The cultivar × site interaction was significant only in the three experiments with six commercial cultivars. This interaction was most likely due to an increase in the sensitivity range with additional cultivars, as indicated by frost damage scores and their relationships with yield. The results imply that extreme low-temperature events after flowering could negatively impact chickpea yield in SEQ and similar subtropical environments. Overcoming these effects through management and breeding should increase and stabilise chickpea yield.

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.

Can pigeonpea hybrids negotiate stresses better than inbred cultivars?

Pigeonpea [Cajanus cajan (L.) Millsp.] is an important rainfed pulse crop of tropics and sub-tropics, and during its long growth cycle of 6-9 months it encounters a number of biotic and abiotic stresses. The recently developed CMS-based pigeonpea hybrids have demonstrated large gains in yield and stability over the traditional inbred cultivars. In this review, the authors argue that the heterosis expressed in traits like seed germination, radicle growth, root biomass production and moisture retention during water stress confers advantages to hybrid plants in negotiating a few abiotic and biotic stresses in much better way than pure line cultivars.

Accounting for soil moisture improves prediction of flowering time in chickpea and wheat.

Matching crop phenology to environment is essential to improve yield and reduce risk of losses due to extreme temperatures, hence the importance of accurate prediction of flowering time. Empirical evidence suggests that soil water can influence flowering time in chickpea and wheat, but simulation models rarely account for this effect. Adjusting daily thermal time accumulation with fractional available soil water in the 0-60 cm soil layer improved the prediction of flowering time for both chickpea and wheat in comparison to the model simulating flowering time with only temperature and photoperiod. The number of post-flowering frost events accounted for 24% of the variation in observed chickpea yield using a temperature-photoperiod model, and 66% of the variation in yield with a model accounting for top-soil water content. Integrating the effect of soil water content in crop simulation models could improve prediction of flowering time and abiotic stress risk assessment.