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All Member States are expected to put in place sustainable institutional arrangements for implementation of the United Nations Framework Convention for Climate Change (UNFCCC) and the Paris Agreement, and to describe these in their reporting transparency obligations. Institutional arrangements provide the mechanism by which work gets visioned, planned, coordinated, implemented, monitored and reported on and are therefore fundamental to implementing the UNFCCC and the Paris Agreement. Without sustainable institutional arrangements, implementation of Nationally Determined Contributions (NDCs) will be jeopardised. Consequently, it is important to clearly identify what constitutes institutional arrangement, both from the perspective of reporting of what is in place and what is optimal for just transitions to achieve UNFCCC and Paris Agreement goals. The Consultative Group of Experts (CGE) and UNFCCC recommend five separate components for describing institutional arrangements for Member States to support their measurement, verification and reporting (MRV), as well as for climate action transparency and support. These components are: i) organization mandates; (ii) expertise; (iii) data flows; (iv) systems and tools; and (v) stakeholder engagement. In this study, we propose that describing institutional arrangements is akin to describing the components of an organisation (e.g. a government, ministry or agency). The Star Organisational Model, with its five inter-linked components of strategy, structure, processes, people and rewards is a useful additional approach for defining what constitutes institutional arrangements. Establishing sustainable institutional arrangements is challenging for developing countries, and yet critical for implementing the UNFCCC-Paris Agreement at country level. By applying the Star model to six developing countries to ascertain its usefulness for reporting on institutional arrangements related to MRV, we conclude that the Star Model offers a valuable additional method for more effectively delineating and documenting institutional arrangements, including describing existing organisational arrangements and envisioning ideal setups, in the context of the UNFCCC and Paris Agreement agendas. [ABSTRACT FROM AUTHOR]

Northern corn leaf blight (NCLB), caused by Setosphaeria turcica , is a major fungal disease affecting maize production in sub-Saharan Africa. Utilizing host plant resistance to mitigate yield losses associated with NCLB can serve as a cost-effective strategy. In this study, we conducted a high-resolution genome-wide association study (GWAS) in an association mapping panel and linkage mapping with three doubled haploid (DH) and three F3 populations of tropical maize. These populations were phenotyped for NCLB resistance across six hotspot environments in Kenya. Across environments and genotypes, NCLB scores ranged from 2.12 to 5.17 (on a scale of 1–9). NCLB disease severity scores exhibited significant genotypic variance and moderate-to-high heritability. From the six biparental populations, 23 quantitative trait loci (QTLs) were identified, each explaining between 2.7% and 15.8% of the observed phenotypic variance. Collectively, the detected QTLs explained 34.28%, 51.37%, 41.12%, 12.46%, 12.11%, and 14.66% of the total phenotypic variance in DH populations 1, 2, and 3 and F3 populations 4, 5, and 6, respectively. GWAS, using 337,110 high-quality single nucleotide polymorphisms (SNPs), identified 15 marker–trait associations and several putative candidate genes linked to NCLB resistance in maize. Joint linkage association mapping (JLAM) identified 37 QTLs for NCLB resistance. Using linkage mapping, JLAM, and GWAS, several QTLs were identified within the genomic region spanning 4 to 15 Mbp on chromosome 2. This genomic region represents a promising target for enhancing NCLB resistance via marker-assisted breeding. Genome-wide predictions revealed moderate correlations with mean values of 0.45, 0.44, 0.55, and 0.42 for within GWAS panel, DH pop1, DH pop2, and DH pop3, respectively. Prediction by incorporating marker-by-environment interactions did not show much improvement. Overall, our findings indicate that NCLB resistance is quantitative in nature and is controlled by few major-effect and many minor-effect QTLs. We conclude that genomic regions consistently detected across mapping approaches and populations should be prioritized for improving NCLB resistance, while genome-wide prediction results can help incorporate both major- and minor-effect genes. This study contributes to a deeper understanding of the genetic and molecular mechanisms driving maize resistance to NCLB. [ABSTRACT FROM AUTHOR]

Genotype × environment (GxE) interaction effects are one of the major challenges in identifying cultivars with stable performance across agri-environments. In this study we analysed GE interactions to identify quinoa (Chenopodium quinoa) cultivars with high and stable yields under different soil moisture regimes, representing control conditions, waterlogging and drought. Waterlogging and drought treatments were artificially induced using normoxia, a combination of hypoxia-normoxia, and 10% PEG (Polyethylene glycol) under hydroponic growth conditions, respectively. Both waterlogging and drought conditions significantly reduced the plant height (PH), number of leaves (NoL) and number of branches (NoB), stem diameter (SD), leaf area (LA) and dry weight (DW) of quinoa genotypes. The genotype, water regime, and genotype by water regime effects all significantly affected the measured quinoa traits. Based on the additive main effects and multiplicative interaction (AMMI) model for DW, the genotypes G18, Puno, Q4, 2-Want, Puno, Real1 x Ruy937 and Titicaca were found to exhibit tolerance and were stable across water regimes. A second-stage evaluation was conducted to test genotype × environment interaction effects in crop production field trials, selecting two contrasting seasons based on soil moisture conditions involving a diverse set of genotypes (58 varieties in total). Our results demonstrate significant variations in both growth and yield among the quinoa genotypes across the cropping seasons. The GGE analysis for grain yield indicate that field conditions matched to G × E under hydroponic experimental conditions and the cultivars G18, Q1, Q4, NL-3, G28, 42-Test, Atlas and 59-ALC were classified within a range of high productivity. Our findings provide a basis for understanding the mechanisms of wide adaptation, while identifying germplasm that enhances the water stress tolerance of quinoa cultivars at early growth stages. [ABSTRACT FROM AUTHOR]