No abstract

Extreme weather events threaten food security, yet global assessments of impacts caused by crop waterlogging are rare. Here we first develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a farming systems model to discern changes in global crop waterlogging under future climates. Third, we develop avenues for adapting cropping systems to waterlogging contextualised by environment. We find that yield penalties caused by waterlogging increase from 3–11% historically to 10–20% by 2080, with penalties reflecting a trade-off between the duration of waterlogging and the timing of waterlogging relative to crop stage. We document greater potential for waterlogging-tolerant genotypes in environments with longer temperate growing seasons (e.g., UK, France, Russia, China), compared with environments with higher annualised ratios of evapotranspiration to precipitation (e.g., Australia). Under future climates, altering sowing time and adoption of waterlogging-tolerant genotypes reduces yield penalties by 18%, while earlier sowing of winter genotypes alleviates waterlogging by 8%. We highlight the serendipitous outcome wherein waterlogging stress patterns under present conditions are likely to be similar to those in the future, suggesting that adaptations for future climates could be designed using stress patterns realised today.

Climate change will drive increased frequencies of extreme climatic events. Despite this, there is little scholarly information on the extent to which waterlogging caused by extreme rainfall events will impact on crop physiological behaviour. To improve the ability to reliably model crop growth and development under soil waterlogging stress, we advanced the process-basis of waterlogging in the farming systems model Agricultural Systems Production Systems sIMulator. Our new mathematical description of waterlogging adequately represented waterlogging stress effects on the development, biomass and grain yield of many commercial Australian barley genotypes. We then used the improved model to examine how optimal flowering periods (OFPs, the point at which long-term abiotic stresses are minimal) change under historical and future climates in waterlogging-prone environments, and found that climate change will reduce waterlogging stress and shift forward OFP (26 d earlier on average across locations). For the emissions scenario representative concentration pathway 8.5 at 2090, waterlogging stresses diminished but this was not enough to prevent substantial yield reduction due to increasingly severe high temperature stress (−35% average reduction in yield across locations, genotypes and sowing dates). It was shown that seasonal waterlogging stress patterns under future conditions will be similar to those occurring historically. Yield reduction caused by waterlogging stress was 6% and 4% on average across sites under historical and future climates. To adapt, both genotypic and management adaptations will be required: earlier sowing and planting waterlogging tolerant genotypes mitigate yield penalty caused by waterlogging by up to 26% and 24% under historical and future climates. We conclude that even though the prevalence of waterlogging in future will diminish, climate change and extreme climatic events will have substantial and perverse effects on the productivity and sustainability of Australian farms.

No abstract

Waterlogging has increasingly become one of the major constraints to maize productivity in some maize production zones because it causes serious yield loss. Bulked segregant RNA-seq (BSR-seq) has been widely applied to profile candidate genes and map associated Single Nucleotide Polymorphism (SNP) markers in many species. In this study, 10 waterlogging sensitive and eight tolerant inbred lines were selected from 60 maize inbred lines with waterlogging response determined and preselected by the International Maize and Wheat Improvement Center (CIMMYT) from over 400 tropical maize inbred lines. BSR-seq was performed to identify differentially expressed genes and SNPs associated with waterlogging tolerance. Upon waterlogging stress, 354 and 1094 genes were differentially expressed in the tolerant and sensitive pools, respectively, compared to untreated controls. When tolerant and sensitive pools were compared, 593 genes were differentially expressed under untreated and 431 genes under waterlogged conditions, of which 122 genes overlapped. To validate the BSR-seq results, the expression levels of six genes were determined by qRT-PCR. The qRT-PCR results were consistent with BSR-seq results. Comparison of allelic polymorphism in mRNA sequences between tolerant and sensitive pools revealed 165 (normal condition) and 128 (waterlogged condition) high-probability SNPs. We found 18 overlapping SNPs with genomic positions mapped. Eighteen SNPs were contained in 18 genes, and eight and nine of 18 genes were responsive to waterlogging stress in tolerant and sensitive lines, respectively. Six alleles of the 18 originated from tolerant pool were significantly up-regulated under waterlogging, but not those from sensitive pool. Importantly, one allele (GRMZM2G055704) of the six genes was mapped between umc1619 and umc1948 on chromosome 1 where a QTL associated with waterlogging tolerance was identified in a previous research, strongly indicating that GRMZM2G055704 is a candidate gene responsive to waterlogging. Our research contributes to the knowledge of the molecular mechanism for waterlogging tolerance in maize.