Traditional agroecological knowledge and practices:The drivers and opportunities for adaptation actions in the northern region of Ghana

Agroecological practices are promoted as a more proactive approach than conventional agriculture to achieving a collective global response to climate change and variability while building robust and resilient agricultural systems to meet food needs and protect the integrity of ecosystems.There is relatively limited evidence on the key traditional agroecological knowledge and practices adopted by smallholder farmers,the factors that influence smallholder farmers’decision to adopt these practices,and the opportunities it presents for building resilient agricultural systems.Using a multi-scale mixed method approach,we conducted key informant interviews(n=12),focus group discussions(n=5),and questionnaire surveys(N=220)to explore the traditional agroecological knowledge and practices,the influencing factors,and the opportunities smallholder farmers presented for achieving resilient agricultural systems.Our findings suggest that smallholder farmers employ a suite of traditional agroecological knowledge and practices to enhance food security,combat climate change,and build resilient agricultural systems.The most important traditional agroecological knowledge and practices in the study area comprise cultivating leguminous crops,mixed crop-livestock systems,and crop rotation,with Relative Importance Index(RII)values of 0.710,0.708,and 0.695,respectively.It is reported that the choice of these practices by smallholder farmers is influenced by their own farming experience,access to market,access to local resources,information,and expertise,and the perceived risk of climate change.Moreover,the results further show that improving household food security and nutrition,improving soil quality,control of pest and disease infestation,and support from NonGovernmental Organizations(NGOs)and local authorities are opportunities for smallholder farmers in adopting traditional agroecological knowledge and practices for achieving resilient agricultural systems.The findings call into question the need for stakeholders and policy-makers at all levels to develop capacity and increase the awareness of traditional agroecological knowledge and practices as mechanisms to ensure resilient agricultural systems for sustainable food security.

An overview of proven Climate Change Vulnerability Assessment tools for forests and forest-dependent communities across the globe：a literature analysis

Climate Change Vulnerability Assessment（VA） tools for forest ecosystems and forest-dependent communities are important for making decisions and understanding the impact of climate change on both social and natural systems.However,the tools are poorly coordinated,making it difficult for policymakers to carry out VAs properly.The aim of this study was to analyze VA literature worldwide to find representative case studies in terms of methods and tools applied and which have been successful in performing VAs on forests and forest-dependent communities.All successful VA studies analyzed had common characteristics such as significant funding,data availability and technical capacity.An additional characteristic was the development of an integrated approach that considered the vulnerability of both ecosystems and communities by combining qualitative and quantitative methods.Community members and relevant stakeholders were significantly involved in a participatory process that concluded with the identification of adaptation measures.The case studies also revealed how policymakers need to choose suitable methods and tools to undertake efficient assessment of vulnerabilities.They need to consider several aspects of the VA process such as subject matter,availability of resources,time and scale.

Predicting the clothing insulation through machine learning algorithms:A comparative analysis and a practical approach

Since indoor clothing insulation is a key element in thermal comfort models,the aim of the present study is proposing an approach for predicting it,which could assist the occupants of a building in terms of recommendations regarding their ensemble.For that,a systematic analysis of input variables is exposed,and 13 regression and 12 classification machine learning algorithms were developed and compared.The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain.Outdoor temperature at 6 a.m.,indoor air temperature,indoor relative humidity,comfort temperature and gender were the most relevant features for predicting clothing insulation.When comparing machine learning algorithms,decision tree-based algorithms with Boosting techniques achieved the best performance.The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.