Embedding gender in conservation agriculture R4D in Sub-Saharan Africa: Relevant research questions

This resource examines the impact of gender relations on Conservation Agriculture (CA) adoption and adoption mechanisms. It offers a set of research questions to help integrate gender considerations into CA research for development, including on topics such as gender dynamics at the household and community level, minimum tillage, crop rotation and diversification, residue management, and knowledge networks

Enhancing the gender-responsiveness of your project's technical farmer training events

This resource sets out simple suggestions for ensuring that women as well as men feel included in training events, are fully informed about technological options, learn effectively, and have the confidence to implement what they have learned. Over time, if their experience has been successful, they should be able to build on the training course to innovate by themselves in response to their needs and changes in the wider environment

Strengthening women in wheat farming in India: Old challenges, new realities, new opportunities

This resource provides guidance for scientists, researchers, and rural advisory services in wheat-based systems on how to better target women in all communities and how to improve inclusion for everyone. It builds on 12 case studies conducted across India’s wheat belt under CRP WHEAT. It discusses how norms are shifting in parts of rural India to accommodate open recognition of women as farmers and managers of wheat and as adopters of associated technologies, including zero tillers, combine harvesters, and improved varieties of wheat

Leaving no one behind: Supporting women, poor people, and indigenous people in wheat-maize innovations in Bangladesh

This guidance note for scientists and research teams acknowledges the complexity of marginalization processes and provides recommendations for making sure no one is left behind. It draws on GENNOVATE findings from a community in Bangladesh where the indigenous Santals, Bengali Muslims, and Hindus live and work together

Integration of gender considerations in climate-smart agriculture R4D in South Asia: Useful research questions

Aimed at researchers working with climate-smart agriculture in South Asia, this resource suggests a set of issues to consider in relation to the integration of gender in climate-smart agricultural research for development. Climate change often exacerbates the problems and inequities that poor rural women face. The feminization of agriculture underscores the need to ensure that both men and women are able to learn about, try out, take up, and benefit from improved agricultural technologies, including climate-smart practices

Challenging gender myths: Promoting inclusive wheat and maize research for development in Nepal

This technical note provides research evidence debunking four gender myths: 1) men are the main decision makers; 2) women don’t do much in wheat and maize; 3) women don’t innovate; and 4) women lack resources for innovation. Data is drawn from six GENNOVATE Nepal case studies in the Myagdi, Chitwan, Rupandehi, and Jajarkot Districts. The resource posits that understanding and working with women in wheat- or maize-related innovation processes will help to improve the design and relevance of innovations

Gender and innovation processes in maize-based systems

This MAIZE report offers a panorama of the gender dimensions of local agricultural innovation processes in the context of maize-based farming systems and livelihoods

SOCIAL RELATIONS AND SEED TRANSACTIONS AMONG SMALLSCALE MAIZE FARMERS IN THE CENTRAL VALLEYS OF OAXACA, MEXICO; PRELIMINARY FINDINGS

This paper explores social arrangements associated with seed transactions among small-scale maize farmers in the Central Valleys of Oaxaca, Mexico, a centre of crop genetic diversity. A formal seed distribution system has yet to develop in the region and when seed loss occurs, farmers are faced with costs and difficulties identifying, locating, and obtaining seed of desired varieties. For these reasons, it was hypothesized that there were strong incentives for collective action among farmers to facilitate seed supply. The study found, however, no evidence of collective action with regards to seed supply in the three study communities-San Pablo Huitzo, San Lorenzo Albarradas, Santa Ana Zegache. Instead, farmers acquired seed using a variety of networks of social relations and different types of seed transactions. The results suggest that seed flow among farmers in the Central Valleys of Oaxaca is a complex process of negotiation and reciprocity, influenced by a variety of agroecological, socioeconomic, and cultural factors.Farm Management,

Data Resource: The National Pupil Database (NPD)

Introduction: The National Pupil Database (NPD) is a record-level administrative data resource curated by the UK government's Department for Education that is used for funding purposes, school performance tables, policy making, and research. Processes Data are sourced from schools, exam awarding bodies, and local authorities who collect data on an on-going basis and submit to the Department for Education either termly or yearly. Data contents NPD contains child-level and school-level data on all pupils in state schools in England (6.6 million in the 2016/17 academic year). The primary module is the census, which has information on characteristics and school enrolment. Other modules include alternative provision, exam attainment, absence and exclusions. Data from children's social care are also available on children referred for support and those who become looked after. Children's records are linkable across different modules and across time using a nationally unique, anonymised child-level identifier. Linkage to external datasets has also been accomplished using child-level identifiers. Conclusions The NPD is an especially valuable data resource for researchers interested in the educational experience and outcomes of children and young people in England. Although limited by the fact that children in private schools or who are home schooled are not included, it provides a near-complete picture of school trajectories and outcomes for the majority of children. Linkage to other datasets can enhance analyses and provide answers to questions that would otherwise be costly, time consuming and difficult to find

DECODE: Data-driven Energy Consumption Prediction leveraging Historical Data and Environmental Factors in Buildings

Energy prediction in buildings plays a crucial role in effective energy management. Precise predictions are essential for achieving optimal energy consumption and distribution within the grid. This paper introduces a Long Short-Term Memory (LSTM) model designed to forecast building energy consumption using historical energy data, occupancy patterns, and weather conditions. The LSTM model provides accurate short, medium, and long-term energy predictions for residential and commercial buildings compared to existing prediction models. We compare our LSTM model with established prediction methods, including linear regression, decision trees, and random forest. Encouragingly, the proposed LSTM model emerges as the superior performer across all metrics. It demonstrates exceptional prediction accuracy, boasting the highest R2 score of 0.97 and the most favorable mean absolute error (MAE) of 0.007. An additional advantage of our developed model is its capacity to achieve efficient energy consumption forecasts even when trained on a limited dataset. We address concerns about overfitting (variance) and underfitting (bias) through rigorous training and evaluation on real-world data. In summary, our research contributes to energy prediction by offering a robust LSTM model that outperforms alternative methods and operates with remarkable efficiency, generalizability, and reliability.Comment: 11 pages, 6 figures, 6 table