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Introduction: In the quest to improve the understanding of climate change impacts on elements of the atmospheric, physical, and life systems, scientists are challenged by the scarcity and uneven distribution of grounded data. Through their long history of interaction with the environment, Indigenous Peoples and local communities have developed complex knowledge systems that allow them to detect impacts of climate change in the local environment. The study protocol presented here is designed 1) to inventory climate change impacts on the atmospheric, physical, and life systems based on local knowledge and 2) to test hypotheses on the global spatial, socioeconomic, and demographic distribution of reported impacts. The protocol has been developed within the framework of a project aiming to bring insights from Indigenous and local knowledge systems to climate research (https://licci.eu). Methods: Data collection uses a mixed-method approach and relies on the collaboration of a team of 50 trained partners working in sites where people's livelihood directly depend on nature. The data collection protocol consists of two steps. Step 1 includes the collection of secondary data (e.g., spatial and meteorological data) and site contextual information (e.g., village infrastructure, services). Step 1 also includes the use of 1) semi-structured interviews (n = 20-30/site) to document observations of environmental change and their drivers and 2) focus group discussions to identify consensus in the information gathered. Step 2 consist in the application of a household (n from 75 to 125) and individual survey (n from 125 to 175) using a standardized but locally adapted instrument. The survey includes information on 1) individual and household socio-demographic characteristics, 2) direct dependence on nature, 3) household's vulnerability, and 4) individual perceptions of climate change impacts. Survey data are entered in a specifically designed database. Expected results: This protocol

The high concentration of arsenic (As) in rice grains, in a large proportion of the rice growing areas, is a critical issue. This study explores the feasibility of conventional (QTL-based) marker-assisted selection and genomic selection to improve the ability of rice to prevent As uptake and accumulation in the edible grains. A japonica diversity panel (RP) of 228 accessions phenotyped for As concentration in the flag leaf (FL-As) and in the dehulled grain (CG-As), and genotyped at 22,370 SNP loci, was used to map QTLs by association analysis (GWAS) and to train genomic prediction models. Similar phenotypic and genotypic data from 95 advanced breeding lines (VP) with japonica genetic backgrounds, was used to validate related QTLs mapped in the RP through GWAS and to evaluate the predictive ability of across populations (RP-VP) genomic estimate of breeding value (GEBV) for As exclusion. Several QTLs for FL-As and CG-As with a low-medium individual effect were detected in the RP, of which some colocalized with known QTLs and candidate genes. However, less than 10% of those QTLs could be validated in the VP without loosening colocalization parameters. Conversely, the average predictive ability of across populations GEBV was rather high, 0.43 for FL-As and 0.48 for CG-As, ensuring genetic gains per time unit close to phenotypic selection. The implications of the limited robustness of the GWAS results and the rather high predictive ability of genomic prediction are discussed for breeding rice for significantly low arsenic uptake and accumulation in the edible grains.