A new methodology for climate model selection and application to temperature of Europe

Altinsoy, Hamza; Kurnaz, M. Levent

doi:10.1007/s00704-021-03622-z

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…Future climate projections were derived from daily climate data extracted from a selection of models among the 38 available at two Representative Concentration Pathway (RCP), namely, RCP 4.5 and RCP 8.5, at the horizons of 2050 and 2070. Multiple General Circulation Models (GCM) share model parametrization, forcing and validation data, which can lead to the potential issues of biases in the mean and variance, overestimation of sample size, and the possibility of false correlations (Altinsoy & Kurnaz, 2021; Ruane & McDermid, 2017; Sanderson et al, 2015). To reduce these problems, we chose seven models that demonstrated relative independence and had better skills compared with historical observation data.…”

Section: Methodsmentioning

confidence: 99%

Genomic, climatic, and cultural diversity of maize landraces from the Himalayan Kingdom of Bhutan

Tamang,

Macharia,

Caproni

et al. 2024

Plants People Planet

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Societal Impact StatementBhutan is an ancient kingdom in the Himalayan range and one of the most rugged, geodiverse, and mountainous agricultural countries in the world. Historically secluded and geographically isolated, Bhutan is a hotspot for Himalayan agrobiodiversity where small‐scale agriculture supports the livelihoods of a large share of the resident population. Here, Bhutanese maize agrobiodiversity is explored to unlock its adaptation potential using genomics and participatory variety selection in combination with climate research. We show that Bhutanese traditional farmers maintain a wealth of diversity that may support the sustainable intensification of maize cropping in the Himalayas and beyond.Summary Bhutan, an ancient kingdom enshrouded in the Himalayas, hosts largely untapped agrobiodiversity that may harness genetic variation useful for adaptation to local climates and user needs. Here, we genotyped‐by‐sequencing 351 pooled samples of local maize (Zea mays L.) landraces, the entire collection of the Bhutan National Gene Bank, comparing their genomic diversity with maize from other countries in the Himalayan range. We reconstructed the adaptation of Bhutanese maize to historical and projected climates, identifying areas of future maladaptation. We then run a common garden experiment involving local smallholder farmers in a participatory evaluation of landraces' performance, aiming at the identification of quantitative trait nucleotides (QTNs) contributing to adaptation, performance, and farmers' choice. We found that Bhutanese maize agrobiodiversity is unique in the Himalayan range, and a locus on Chromosome 5 supports the differentiation of three distinct genetic clusters. We found that a portion of current genomic diversity can be associated with the Bhutanese landscape and that maize cultivation in the southwest of the country may be negatively impacted by projected climates. We also found that Bhutanese maize agrobiodiversity is large and may contribute to adaptation and improvement. A genome‐wide association study identified 117 QTNs for climatic adaptation, agronomic performance, and farmers' preferences. Our results show that Bhutanese maize landraces are a unique source of genetic agrobiodiversity for local adaptation. We found that the integration of genomics, climate science, and participatory methods can speed up the identification of genetic factors contributing to the sustainable intensification of maize cultivation in the Himalayas and beyond.

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Section: Methodsmentioning

confidence: 99%