Wheat blast, caused by the ascomycetous fungus Pyricularia oryzae Triticum lineage (PoTl), is mainly controlled by fungicide use, but resistance to the main fungicide groups—sterol demethylase (DMI), quinone outside (QoI), and succinate dehydrogenase inhibitors (SDHI)—has been reported in Brazil. In order to rationalize fungicide inputs (e.g., choice, timing, dose-rate, spray number, and mixing/alternation) for managing wheat blast, we describe a new monitoring tool, enabling the quantitative measurement of pathogen’s inoculum levels and detection of fungicide resistance alleles. Wheat blast airborne spores (aerosol populations) were monitored at Londrina in Paraná State, a major wheat cropping region in Brazil, using an automated high-volume cyclone coupled with a lab-based quantitative real-time PCR (qPCR) assay. The objectives of our study were as follows: (1) to monitor the amount of PoTl airborne conidia during 2019–2021 based on DNA detection, (2) to reveal the prevalence of QoI resistant (QoI-R) cytochrome b alleles in aerosol populations of wheat blast, and (3) to determine the impact of weather on the dynamics of wheat blast aerosol populations and spread of QoI resistant alleles. PoTl inoculum was consistently detected in aerosols during the wheat cropping seasons from 2019 to 2021, but amounts varied significantly between seasons, with highest amounts detected in 2019. High peaks of PoTl DNA were also continuously detected during the off-season in 2020 and 2021. The prevalence of QoI resistant (QoI-R) cytochrome b G143A alleles in aerosol populations was also determined for a subset of 10 PoTl positive DNA samples with frequencies varying between 10 and 91% using a combination of PCR-amplification and SNP detection pyrosequencing. Statistically significant but low correlations were found between the levels of pathogen and the weather variables. In conclusion, for wheat blast, this system provided prior detection of airborne spore levels of the pathogen and of the prevalence of fungicide resistance alleles.

Ecuador, a country with distinct coastal (CO), highland (HL), and Amazon (AM) regions that are characterized by unique climatic, ecological, and socio-economic features is highly vulnerable to climate change. This study focuses on these three regions, highlighting their individual importance in the broader context of Ecuador's climate vulnerability. Utilizing dynamically downscaled data from the Regional Climate Model (RCM), we generated precipitation and air temperature projections for the period 2070–2099 under three different climate change scenarios. We indicate projected temperature increases across all three regions: mean temperature increases for the CO, HL and AM regions are of 1.35, 1.55, and 1.21°C, respectively. Each year, the largest temperature increases are predicted for the third quarter (June–August), with the smallest increases predicted for the last quarter (December–February). Precipitation patterns show varied changes, with CO exhibiting a positive mean daily change, in contrast to a mean negative change in the AM region. These region-specific projections underscore the differential impacts of climate change within Ecuador and highlight the necessity for tailored adaptation measures. The study's novel approach, focusing on distinct regional impacts within a single nation, offers valuable insights for policymakers, aiding in the development of effective, region-specific climate change mitigation and adaptation strategies. This targeted approach is crucial to address unique challenges faced by different regions, thereby supporting national resilience strategies.