As with many regions in the world, southern Africa strongly relies on seasonal rains for livelihoods, ecosystems, food and water security, therefore understanding rainfall changes is vital for adaptation planning and climate service practitioners. Future changes in rainfall over southern Africa are highly uncertain, with different global climate models projecting wetter, drier and shifted rainfall seasons. Taking an ensemble average suggests poor model agreement on the direction of change and therefore a low mean change that is not statistically robust. Ensemble averages also blur boundaries between different sources of uncertainty and obscure significant aspects of rainfall over this region that we have a better (or at least reasonable) understanding and confidence about. Analysis focussing on large ensemble averages often does not account for the significant internal variability in the climate, which we find to be very large for this region.We will show results from a recently published study (Kennedy-Asser et al., Climatic Change, 2026), using over 200 different global climate model simulations from CMIP5, CMIP6 and UKCP18 (global runs used for UK Climate Projections), highlighting how important internal variability has been in the past and continues to be in the future for southern Africa. All models are imperfect, each have strengths, weaknesses and significant biases that prevent us from fully constraining potential futures. By reframing analysis around temporal variability, we show there is better model agreement on scenarios of low change, where the future remains within the range of historic variability, than there is on significant change towards wetting or drying in future.In addition, by analysing models individually, it is possible to construct Climate Process Chains and Climate Storylines that explain the mechanisms behind simulated responses and plausibly justify the divergent model signals. We will present results from an in-depth analysis of outputs from multiple ensemble members across four CMIP6 models that show contrasting futures for this region (CanESM5, CNRM-ESM2-1, HadGEM3-GC31-MM, IPSL-CM6A-LR). We explore linkages between regional rainfall and Indian Ocean sea surface temperatures, pressure systems, ENSO teleconnections and changes in the Angola Low, and demonstrate how these changes could result in wetting, drying or delayed rainfall seasons. Framing the analysis in this way highlights some important climate states and drivers that may be indicative of future change in seasonal rainfall in one direction or another.This research is part of an interdisciplinary project, SALIENT (https://www.climatebristol.org/projects/salient/), combining climate science and modelling findings presented here with insights from risk communication research and structured expert judgement. Novel insights on the interdisciplinary research process and of policy relevance will also be presented.

In 2023 Somalia saw the largest-ever scale of anticipatory action (AA) linked to a seasonal forecast. $24m was spent on preparedness and early response, based on forecasted flooding driven by El Niño and a positiveIn 2023 Somalia saw the largest-ever scale of anticipatory action (AA) linked to a seasonal forecast. $24m was spent on preparedness and early response, based on forecasted flooding driven by El Niño and a positiveIn 2023 Somalia saw the largest-ever scale of anticipatory action (AA) linked to a seasonal forecast. $24m was spent on preparedness and early response, based on forecasted flooding driven by El Niño and a positiveIn 2023 Somalia saw the largest-ever scale of anticipatory action (AA) linked to a seasonal forecast. $24m was spent on preparedness and early response, based on forecasted flooding driven by El Niño and a positive Indian Ocean Dipole. 188 flood-related deaths occurred; 90% fewer than 1997, the last time similar climatic conditions were seen. Here we reflect on the role AA played in reducing mortality. While we document significant progress in forecasting, early warning and early action, we also note that exposure to extreme rainfall was substantially lower than in 1997 - and thus cannot confidently attribute the lower mortality to AA. Thus whilst 2023 was an AA milestone to be celebrated, it is a dress rehearsal for the threat posed by 1997-level rainfall, an event made more likely with climate change. With a strong El Niño forecast in 2026, building on 2023 is critical; we make recommendations here.

Post-flowering heatwaves impact individual grain weight (IGW) and grain quality of wheat. Here, the effect of post-flowering heatwaves on IGW, yield and grain protein content (GPC) were examined in 25 wheat genotypes grown in irrigated field trials across locations, seasons and sowing dates. The photoperiod-extension method (PEM) was employed with measurements on individual spikes to synchronize flowering among genotypes, ensuring heat impacts were assessed at similar developmental stages. Adjacent to most PEM trials, genotypes were cultivated in machine-harvested field plots to evaluate crop performance using a more conventional screening method. Heat stress significantly reduced IGW and increased GPC in PEM trials, with impacts not as clearly discernible in conventional plots. Across genotypes and traits, heat responses (slope of the reaction norms) were negatively correlated with trait values observed in non-stressed conditions (intercept). The level of heat stress each genotype could endure before reaching critical IGW (30 mg), yield (250 g m-2) or GPC (15%) thresholds revealed high genotypic variability and highly tolerant genotypes. The findings provide insights for improving heat responses in crop models and enhancing genotype selection for climate-resilient breeding.

In many African countries, the response to climate change is obstructed by a lack of accessible and usable information, such as localised flood maps. Compounding this, current disaster risk management systems often fail to account for context-specific drivers of social vulnerability and environmental risks, crucial for enhancing social resilience to flood impacts. This paper captures the community-based narratives of flood risk in Lusaka, Zambia. Using a well-established network from the Future Resilience for African Cities And Lands (FRACTAL) group, a cross-disciplinary approach of natural and social sciences to support decision-making for flood resilience is presented as the Participatory Climate Information Distillation for Urban Flood Resilience in Lusaka (FRACTAL-PLUS) project. Local flood inundation maps were created using global rainfall and GIS datasets and then analysed across two interactive “Learning Labs” with local stakeholders. Historical observations and lived experiences were distilled from the learning labs into three community-based social narratives of flood risk. These narratives were used to calibrate the flood maps with insights from Lusaka’s stakeholders using Natural Language Processing (NLP) and Text Network Analysis (TNA). The narrative-informed flood maps provide a dynamic entry point for enhancing stakeholder engagement by discussing social vulnerability to floods and climate change, highlighting future challenges and opportunities forresilience planning. The outputs demonstrate the value of convening stakeholders to discuss these topics in asustainable setting for addressing the interdisciplinary challenges of climate resilience,offering abenchmark for better use ofavailable resources andenabling a swift evaluation of needs and measures for resilience building.

Urban populations face increasing vulnerability to extreme heat events, particularly in rapidly urbanising Global South cities where environmental exposure intersects with socioeconomic inequality and limited healthcare access. This study quantifies heat vulnerability across Johannesburg, South Africa, by integrating high-resolution environmental data with socio-economic and health metrics across 135 urban wards. We examine how historical urban development patterns influence contemporary vulnerability distributions using principal component analysis and spatial statistics. Environmental indicators (Land Surface Temperature (LST), vegetation indices, and thermal field variance) were combined with socioeconomic and health variables (including indicators on crowded dwellings and healthcare access, self-reporting of chronic diseases) in a comprehensive vulnerability assessment. Principal Component Analysis revealed three primary dimensions explaining 56.6% (95% CI: 52.4–60.8%) of the total variance: urban heat exposure (31.5%), health status (12.8%), and socio-economic conditions (12.3%). Built-up areas showed weak but significant correlations with heat indices (ρ = 0.28, p < 0.01), while higher poverty levels demonstrated moderate positive correlations with LST (ρ = 0.41, p < 0.001). The spatial analysis identified significant clustering of vulnerability (Global Moran's I = 0.42, p < 0.001), with distinct high-vulnerability clusters in historically disadvantaged areas. Alexandra Township showed the highest vulnerability(HVI score: 0.87, LST: 29.8 °C ± 0.4 °C, NDVI: 0.08 ± 0.02), with factors characterising the high vulnerability in that area including limited healthcare access and extreme heat exposure. Northern suburbs formed a significant low-vulnerability cluster (Mean HVI = 0.23 ± 0.07, p < 0.001), benefiting from greater vegetation coverage and better healthcare access. These findings demonstrate how historical planning decisions continue to shape contemporary environmental health risks, with vulnerability concentrated in areas of limited healthcare access and high extreme heat exposure. Results suggest the need for targeted interventions that address both environmental and social dimensions of heat vulnerability, particularly focusing on expanding healthcare access in identified hotspots and implementing community-scale green infrastructure in high-risk areas. This study provides an evidence-based framework for prioritising heat-resilience initiatives in rapidly urbanising Global South cities while highlighting the importance of addressing historical inequities in urban adaptation planning.

Better understanding genotype by environment interaction (GxE) can help breeding for better adapted varieties. Envirotyping for environmental water status was applied to assist interpretation of GxE interactions for wheat yield in multi-environment trials conducted in drought-prone Australian environments. Genotypes from a multi-reference parent nested association mapping (MR-NAM) population were tested in 10 trials across the Australian wheatbelt. Genotype yield and phenology were measured in all trials, while traits associated with the stay-green phenotype were assessed for a subset of 5 trials. Envirotyping was conducted by characterizing water stress experienced by genotypes at each trial using crop modelling. Envirotyping facilitated the understanding of GxE interactions by explaining 75, 67, and 66 % of the genotypic variance for yield in severe water-limited (ET3), mild terminal water-stress (ET2), and water-sufficient (ET1) environments, respectively. Yield and stay-green were negatively correlated with flowering time in most trials. However, when focusing on genotypes flowering at similar times within a trial, no significant correlation was found between yield and flowering. Importantly stay-green traits remained significantly correlated with yield. Stay-green traits such as delayed onset of senescence and slower senescence rate benefited yield by 0.2-1.1 t ha-1 across environments, highlighting the breeding potential for stay-green traits in both water-sufficient and water-limited environments. Hence, sustaining green leaf area during grain filling helped to enhance yield. Envirotyping to better understand GxE interactions for yield, coupled with screening for traits exhibiting superior adaptive mechanisms, are powerful assets in assisting plant breeders to select more effectively drought adapted genotypes.

Losses due to frost undermine the financial sustainability of growing spring wheat, which is often managed by the late sowing of crops. While late sowing may reduce frost risk, it can compromise yields due to increasing risks of heat and drought stress later in crop development. We tested a novel targeted frost index insurance cover called the heating degree day temperature minimum call option, which allows farmers to plant earlier and increases their chances of attaining higher yields while also financially protecting them in case of a frost event. The potential value of the insurance was investigated using crop simulation modelling. Based on the integration of the Agricultural Production Systems sIMulator modelling framework and index insurance structures for 22 frost-affected farms over 40 years in Australia’s wheat growing regions, we (i) determined the optimal sowing date and potential yield benefits, (ii) estimated the yield impact of frost for crops sown on that date , and (iii) examined the utility of index-based frost insurance options that may financially protect farmers from frost risk if they sow on the optimal date, assuming they plant and insure their crops every year. On all farms modelled, gains were made on those sowed on the optimal date. The use of the targeted frost index where frost occurred regularly helped secure increased returns and therefore benefits. The targeted integration of the frost index insurance with optimal cropping dates, especially for frost-prone regions, may be an important strategy for reducing financial impacts and enhancing income stability.

Crop variety trials increasingly incorporate high-throughput phenotyping (HTP) data, such as normalized difference vegetation index (NDVI), collected over time. In addition to HTP data, marker or pedigree information may be available to account for genetic structure. These datasets require spatio-temporal modelling, which poses challenges when integrating spatial, temporal, and genetic components in a single analysis. A two-stage modelling approach was adopted. In the first stage, data for each plot at each time point were adjusted to remove design and spatial effects. In the second stage, the adjusted plot-level effects were used for temporal modelling. Seven temporal modelling strategies were evaluated: natural cubic smoothing splines including reduced-rank models, B-splines, P-splines, and adaptive variants of B- and P-splines with two forms. These methods were applied at both the plot and genetic levels at the second stage of analysis. The analysis focused on NDVI measurements recorded across 19 time points, representing a stay-green trait. Model comparison using the Akaike Information Criterion (AIC) indicated that adaptive B-splines provided the best fit across both levels of analysis. This suggests that adaptive spline-based methods offer improved flexibility and accuracy for modelling longitudinal HTP data in crop trials.

Introduction African cities, particularly Abidjan and Johannesburg, face challenges of rapid urban growth, informality and strained health services, compounded by increasing temperatures due to climate change. This study aims to understand the complexities of heat-related health impacts in these cities. The objectives are: (1) mapping intraurban heat risk and exposure using health, socioeconomic, climate and satellite imagery data; (2) creating a stratified heat–health forecast model to predict adverse health outcomes; and (3) establishing an early warning system for timely heatwave alerts. The ultimate goal is to foster climate-resilient African cities, protecting disproportionately affected populations from heat hazards.Methods and analysis The research will acquire health-related datasets from eligible adult clinical trials or cohort studies conducted in Johannesburg and Abidjan between 2000 and 2022. Additional data will be collected, including socioeconomic, climate datasets and satellite imagery. These resources will aid in mapping heat hazards and quantifying heat–health exposure, the extent of elevated risk and morbidity. Outcomes will be determined using advanced data analysis methods, including statistical evaluation, machine learning and deep learning techniques.Ethics and dissemination The study has been approved by the Wits Human Research Ethics Committee (reference no: 220606). Data management will follow approved procedures. The results will be disseminated through workshops, community forums, conferences and publications. Data deposition and curation plans will be established in line with ethical and safety considerations.

Durum wheat (Triticum durum Desf.) breeding programs face many challenges surrounding the development of stable varieties with high quality and yield. Therefore, researchers and breeders are focused on deciphering the genetic architecture of biotic and abiotic traits with the aim of pyramiding desirable traits. These efforts require access to diverse genetic resources, including wild relatives, germplasm collections and mapping populations. Advances in accelerated generation technologies have enabled the rapid development of mapping populations with significant genetic diversity. Here, we describe the development of a durum Nested Association Mapping (dNAM) population, which represents a valuable genetic resource for mapping the effects of different alleles on trait performance. We created this population to understand the quantitative nature of drought-adaptive traits in durum wheat. We developed 920 F6 lines in only 18 months using speed breeding technology, including the F4 generation in the field. Large variation in above- and below-ground traits was observed, which could be harnessed using genetic mapping and breeding approaches. We genotyped the population using 13,393 DArTseq markers. Quality control resulted in 6,785 high-quality polymorphic markers used for structure analysis, linkage disequilibrium decay, and marker-trait association analyses. To demonstrate the effectiveness of dNAM as a resource for elucidating the genetic control of quantitative traits, we took a genome-wide mapping approach using the FarmCPU method for plant height and days to flowering. These results highlight the power of using dNAM as a tool to dissect the genetics of durum wheat traits, supporting the development of varieties with improved adaptation and yield.

Wheat is highly sensitive to elevated temperatures, particularly during pollen meiosis and early-to-mid grain filling. The impact of heat stress greatly depends on the plant developmental stage. Thus, germplasm ranking for heat tolerance in field trials may be confounded by variations in developmental phase between genotypes at the time of heat events. A photoperiod-extension method (PEM) was developed allowing screening of 35 diverse genotypes at matched developmental phase despite phenological variations. Paired trials were conducted to compare the new PEM against conventional field screening in plots. In the PEM, plants were sown in single rows or small plots. Artificial lighting was installed at one end of each row or plot to extend day length, inducing a gradient of flowering times with distance from the lights. Individual stems or plot quadrats of each genotype were tagged at flowering. Late-sown plants received more heat shocks during early to mid grain filling than earlier sowings, suffering reductions in both individual grain weight (IGW) and yield. IGW was reduced by 1.5 mg for each additional post-flowering day with temperature > 30°C. Significant genotypic differences in heat tolerance ranking were observed between PEM versus conventional plot screening. Strong correlations between trials experiencing similar degree of heat were found both for IGW and for total grain weight with the PEM either with individual-stem tagging (e.g. average r of 0.59 and 0.54, respectively for environments with moderate postflowering heat) or quadrat tagging ( r of 0.53 and 0.47). However, correlations for IGW and yield in these environments were either poor or negative for conventional trials (e.g. average r of 0.11 and 0.12, respectively for environments with moderate postflowering heat). Accordingly, a PCA grouped genotypes consistently for heir performance across environments with similar heat stress in PEM trials but not in conventional trials. In this study, most consistent genotype ranking for heat tolerance was achieved with the PEM with tagging and harvesting individual spikes at matched developmental phase. The PEM with quadrat sampling provided slightly less consistent rankings but appears overall more suitable for high-throughput phenotyping. The method promises to improve the efficiency of heat tolerance field screening, particularly when comparing genotypes of different maturity types.

Soil sodicity is a major constraint to seedling emergence and crop production, potentially reducing plant growth due to physical and chemical constraints. Studying responses to ion imbalances may help identify genotypes tolerant to chemical constraints in sodic soils, thereby improving productivity. We evaluated the performance of four wheat (Triticum aestivum L.) genotypes in solutions with five sodium adsorption ratios (SARs) ranging from 0 to 60. For all four genotypes, seedling emergence and shoot dry matter (DM) decreased significantly with increasing SARs. A significant positive correlation was observed between Ca concentration in roots as well as both root and shoot DM for all genotypes. At SAR values > 20, the more tolerant genotype (EGA Gregory) displayed higher Ca concentrations in root tissues, whereas the more sensitive genotype (Baxter) exhibited Na-induced Ca deficiency. Thus, the selection of genotypes that are able to accumulate Ca in roots in sodic conditions may be a useful trait for selecting genotypes tolerant of soils with high ESP values. However, for soils that restrict plant growth at ESP (SAR) values of 6–10%, it is likely that growth is restricted by physical constraints rather than by a Na-induced Ca deficiency.

National and sub-national actors are grappling with how to urgently move from climate change commitments to widespread actions that drastically reduce the risks posed by a changing climate and greenhouse gas emissions driving this change in tandem with addressing socio-economic inequalities. A pivotal challenge is how to cohere and sequence interventions in light of competing priorities, changing risk profiles and deep structural inequalities. Ideas of characterising and transitioning to climate-resilient development (CRD) pathways, and away from the historically carbon-intensive, climate-vulnerable and highly inequitable development pathways, are gaining traction in science and policy domains. But how do these ideas get operationalised in practice? Especially in contexts where many people's basic needs remain unmet, much of what happens is unplanned and unregulated, and access to public decision-making processes is limited. This paper reviews published applications of adaptation and CRD pathways approaches, focussing on those undertaken in Global South contexts. The review reflects on how issues of (in)equity are foregrounded and addressed when working with marginalised and powerful groups to identify risk thresholds, assess and prioritise options and confront unsettling trade-offs and lock-ins. Particular attention is given firstly to how scientific climate information pertaining to various time-and spatial scales is woven together with lived experiences and traditional forms of knowledge. Secondly, the institutional capacities that are needed to transition from maladaptive to more CRD pathways are considered. Building networks of intermediaries to work across social groups, sectors, disciplines and scales, fostering trust and creating opportunities for transformative action, emerges as key to realising equitable CRD pathways.

Soil sodicity is a major constraint to seedling emergence and crop production, potentially reducing plant growth due to physical and chemical constraints. Examining responses to ion imbalances may assist in the identification of genotypes tolerant to chemical constraints in sodic soils and improve productivity. We evaluated the performance of four wheat (Triticum aestivum. L) genotypes in solutions with five sodium adsorption ratios (SARs) ranging from 0 to 60. For all four genotypes, seedling emergence and shoot dry matter (DM) decreased significantly with increasing SAR. A significant positive correlation was observed between Ca concentration in roots, as well as both root and shoot DM for all genotypes. At SAR values >20, a more tolerant genotype (EGA Gregory), had higher Ca concentrations in root tissues, while a more sensitive genotype (Baxter) exhibited Na-induced Ca deficiency. Thus, selection for genotypes that are able to accumulate Ca in roots in sodic conditions may be a useful trait for selecting genotypes tolerant of soils with high ESP values. However, for soils that restrict plant growth at ESP (SAR) values of 6-10%, it is likely that growth is restricted by physical constraints rather than by a Na-induced Ca deficiency.

Yield losses of bread wheat due to crown rot can be more severe when drought conditions occur during the grain-filling period. Root architecture characteristics are important for soil exploration and below-ground resource acquisition and are essential for adaptation to water-limited environments. Traits such as root angle, length and density have been strongly associated with acquisition efficiency and contribute to yield stability of the crop. The impact of crown rot pathogens on wheat root architecture is poorly understood. We examined differences in root angle, length and number, as well as dry root weight of the crown rot-susceptible bread wheat cultivar, Livingston inoculated with one of two crown rot pathogens Fusarium culmorum or Fusarium pseudograminearum in a transparent-sided root observation chamber. Significant adverse impacts on plant health and growth were revealed by visual discolouration of the leaf sheaths; fresh and dry shoot weight; leaf area of the oldest and the youngest fully expanded leaf and leaf number. Values of most recorded root system measurements were reduced when inoculated with either F. culmorum or F. pseudograminearum. In contrast, root angle was increased in the presence of F. culmorum but was not significantly changed by F. pseudograminearum. The development of whiteheads and grain losses in bread wheat caused by crown rot have previously been associated with blockages of the vascular systems. The method employed here was able to identify differences in the pathogen impacts on roots, which were not detected using previous systems. This research indicates that in the presence of F. culmorum and F. pseudograminearum infection, not only reductions in the size and biomass of the shoot system but also changes in the length, biomass and architecture of the root system could play an important role in yield loss.

Due to the climate change and an increased frequency of drought, it is of enormous importance to identify and to develop traits that result in adaptation and in improvement of crop yield stability in drought-prone regions with low rainfall. Early vigour, defined as the rapid development of leaf area in early developmental stages, is reported to contribute to stronger plant vitality, which, in turn, can enhance resilience to erratic drought periods. Furthermore, early vigour improves weed competitiveness and nutrient uptake. Here, two sets of a multi-reference nested association mapping (MR-NAM) population of bread wheat ( Triticum aestivum ssp. aestivum L.) were used to investigate early vigour in a rain-fed field environment for 3 years, and additionally assessed under controlled conditions in a greenhouse experiment. The normalised difference vegetation index (NDVI) calculated from red/infrared light reflectance was used to quantify early vigour in the field, revealing a correlation ( p < 0.05; r = 0.39) between the spectral measurement and the length of the second leaf. Under controlled environmental conditions, the measured projected leaf area, using a green-pixel counter, was also correlated to the leaf area of the second leaf ( p < 0.05; r = 0.38), as well as to the recorded biomass ( p < 0.01; r = 0.71). Subsequently, genetic determination of early vigour was tested by conducting a genome-wide association study (GWAS) for the proxy traits, revealing 42 markers associated with vegetation index and two markers associated with projected leaf area. There are several quantitative trait loci that are collocated with loci for plant developmental traits including plant height on chromosome 2D (log 10 ( P ) = 3.19; PVE = 0.035), coleoptile length on chromosome 1B (–log 10 ( P ) = 3.24; PVE = 0.112), as well as stay-green and vernalisation on chromosome 5A (–log 10 ( P ) = 3.14; PVE = 0.115).

Climate change and solar geoengineering have different implications for drought. Climate change can “speed up” the hydrological cycle, but it causesgreater evapotranspiration than the historical climate because of higher temperatures. Solar geoengineering (stratospheric aerosol injection), on the other hand, tends to “slow down” the hydrological cycle while reducing potential evapotranspiration. There are two common definitions of drought that take this into account; rainfall-only (SPI) and potential-evapotranspiration (SPEI). In different regions of Africa, this can result in different versions of droughts for each scenario, with drier rainfall (SPI) droughts under geoengineering and drier potential-evapotranspiration (SPEI) droughts under climate change. However, the societal implications of these different types of drought are not clear. We present a systematic review of all papers comparing the relationship between real-world outcomes (streamflow, vegetation, and agricultural yields) with these two definitions of drought in Africa. We also correlate the two drought definitions (SPI and SPEI) with historical vegetation conditions across the continent. We find that potential-evapotranspiration-droughts (SPEI) tend to be more closely related with vegetation conditions, while rainfall-droughts (SPI) tend to be more closely related with streamflows across Africa. In many regions, adaptation plans are likely to be affected differently by these two drought types. In parts of East Africa and coastal West Africa, geoengineering could exacerbate both types of drought, which has implications for current investments in water infrastructure. The reverse is true in parts of Southern Africa. In the Sahel, sectors more sensitive to rainfall-drought (SPI), such as reservoir management, could see reduced water availability under solar geoengineering, while sectors more sensitive to potential-evapotranspiration-drought (SPEI), such as rainfed agriculture, could see increased water availability under solar geoengineering. Given that the implications of climate change and solar geoengineering futures are different in different regions and also for different sectors, we recommend that deliberations on solar geoengineering include the widest possible representation of stakeholders.

Plants grown on sodic soils can suffer from macronutrient deficiencies, such as calcium (Ca), magnesium (Mg), and potassium (K), reducing health and growth. Nutrient concentrations in plant tissue could potentially provide a signal to identify cultivars tolerant to sodic conditions. However, conventional approaches to diagnosing crop nutrient and chlorophyll status involve determining total elemental content in plant tissues. These methods are time-consuming, tedious, and expensive, requiring destructive sampling of plant parts and complex laboratory analyses. Here, we propose a novel approach using hyperspectral sensing to determine macronutrient and chlorophyll variations/deficiencies of 18 different wheat genotypes grown in moderately sodic (MS) and highly sodic (HS) soil conditions in north-eastern Australia. Canopy reflectance was measured using a handheld spectroradiometer close to flowering to compute red edge spectral indices, such as normalized difference red edge index (NDRE), red edge inflection point (REIP), and red edge chlorophyll index (Cl-rededge). Plant Ca, Mg, and K concentrations were also measured by destructive sampling of young mature leaves followed by laboratory analysis. The maximum first derivative of reflectance spectra for 18 wheat genotypes were observed at 722-728 nm and 719-725 nm for the MS and HS site, respectively and was used to determine REIP for the genotypes using a four-point linear interpolation method. Ca and Mg had a significant positive association with both REIP and NDRE, with Ca more closely correlated than either Mg or K. REIP was more closely associated with Ca (R-2 = 0.72; RMSE=0.02 for the MS site and R-2 = 0.57; RMSE=0.02 for the HS site) than NDRE. This suggests that REIP has a great potential to detect structural variations of wheat genotypes in sodic soil environment. Furthermore, Ca was also significantly (p < 0.0001) and positively correlated with Cl-rededge at both sites with R-2 = 0.53 and 0.51 for the MS and HS site. This suggests that plant structural variations in sodic soil can regulate leaf chlorophyll concentration and, in turn, photosynthetic activities. Overall, results demonstrate that hyperspectral sensing can be efficiently used to detect plant Ca, Mg, and chlorophyll concentrations. The study improves understanding of genotypic nutrient variation for tolerance to different levels of sodic soil conditions using optical properties of plant structure and can be beneficial to the plant science community for developing new approaches to study plant physiology. Crown Copyright (C) 2022 Published by Elsevier B.V.& nbsp;

As climate change increases the frequency and intensity of extreme weather events, governments and civil society organizations are making large investments in early warning systems (EWS) with the aim to avoid death and destruction from hydro-meteorological events. Early warning systems have four components: (1) risk knowledge, (2) monitoring and warning, (3) warning dissemination and communication, and (4) response capability. While there is room to improve all four of these components, we argue that the largest gaps in early warning systems fall in the latter two categories: warning dissemination/communication and response capability. We illustrate this by examining the four components of early warning systems for the deadliest and costliest meteorological disasters of this century, demonstrating that the lack of EWS protection is not a lack of forecasts or warnings, but rather a lack of adequate communication and lack of response capability. Improving the accuracy of weather forecasts is unlikely to offer major benefits without resolving these gaps in communication and response capability. To protect vulnerable groups around the world, we provide recommendations for investments that would close such gaps, such as improved communication channels, impact forecasts, early action policies and infrastructure. It is our hope that further investment to close these gaps can better deliver on the goal of reducing deaths and damages with EWS.

Key message A powerful QTL analysis method for nested association mapping populations is presented. Based on a one-stage multi-locus model, it provides accurate predictions of founder specific QTL effects. Abstract Nested association mapping (NAM) populations have been created to enable the identification of quantitative trait loci (QTL) in different genetic backgrounds. A whole-genome nested association mapping (WGNAM) method is presented to perform QTL analysis in NAM populations. The WGNAM method is an adaptation of the multi-parent whole genome average interval mapping approach where the crossing design is incorporated through the probability of inheriting founder alleles for every marker across the genome. Based on a linear mixed model, this method provides a one-stage analysis of raw phenotypic data, molecular markers, and crossing design. It simultaneously scans the whole-genome through an iterative process leading to a model with all the identified QTL while keeping the false positive rate low. The WGNAM approach was assessed through a simulation study, confirming to be a powerful and accurate method for QTL analysis for a NAM population. This novel method can also accommodate a multi-reference NAM (MR-NAM) population where donor parents are crossed with multiple reference parents to increase genetic diversity. Therefore, a demonstration is presented using a MR-NAM population for wheat ( Triticum aestivum L.) to perform a QTL analysis for plant height. The strength and size of the putative QTL were summarized enhancing the understanding of the QTL effects depending on the parental origin. Compared to other methods, the proposed methodology based on a one-stage analysis provides greater power to detect QTL and increased accuracy in the estimation of their effects. The WGNAM method establishes the basis for accurate QTL mapping studies for NAM and MR-NAM populations.