Key factors in the use of Agricultural Extension Services by women farmers in Babati District, Tanzania : the role of societal gender norms

Agricultural Extension Services (AES) – defined here as a system of services providing advice, information and training to farmers – are critical for enhancing agricultural productivity and development in Tanzania. Women farmers often face particular constraints to using AES, and consequently have lower levels of access on average than their male counterparts. The constraints women farmers face comprise a range of practical, institutional and norm-based factors. Improving women farmers’ access to and use of AES requires identifying and understanding these constraints and exploring how AES can be designed and delivered to overcome them. In this thesis, I explore women (and men) farmers’ access to and use of AES in two villages in Babati District, Tanzania and identify the critical factors affecting this, with a particular focus on the role of societal gender norms. I also investigate if and how gender is considered within current AES services and explore perceptions of AES practitioners about women farmers as users of AES. Finally, I consider opportunities for (women) farmers to shape AES and how AES may be delivered to better meet their needs. The study is based on empirical data collected during six weeks of fieldwork in Tanzania in March and April 2017. Findings are from group interviews, in-depth individual interviews and observations. The thesis is informed by a liberal feminist perspective and I draw on theory around social norms and institutions, gender norms and relations, and knowledge systems to explore my empirical findings. I find that women farmers’ AES needs and preferences often differ from men farmers’ and that there are multiple factors that affect their willingness and ability to use AES. I argue that many, if not most, of these factors are rooted in societal gender norms. Critically, I also find that current measures within AES to target women farmers do not comprehensively address gender norms and there is an apparent lack of gender capacity amongst institutions and staff involved in providing AES. I conclude that in order to effectively deliver to women farmers and contribute to agricultural development, AES should involve efforts to address the multifaceted ways in which societal gender norms affect AES use and delivery

Velocity at maximal oxygen uptake best predicts 3 km race time in collegiate distance runners

Purpose: There is a lack of scientific investigation into the predictors of 3 km race performance in collegiate distance runners. The purpose of this investigation was to determine what physiological variables best predict 3 km race time in a group of collegiate distance runners. Methods: Twenty-one endurance trained runners (11 men, 10 women) volunteered for this investigation. Running economy (RE) and maximal oxygen uptake (VO2max) testing were conducted within 9 ± 6 days of the race in a single session. All participants ran in a 3 km race at an NCAA sanctioned track meet. Pearson’s product moment correlations were performed between 3 km race time and velocity at VO2max (vVO2max), relative VO2max, RE at 9.7, 11.3, 12.9, and 14.5 km•hr-1 and percent of VO2max. A stepwise multiple regression was performed with 3 km race time as the dependent variable and independent variables of vVO2max, VO2max, RE9.7, RE11.3, RE12.9, RE14.5. Results: The results revealed that vVO2max was the best predictor of 3 km race performance in a heterogeneous group of collegiate distance runners (R2=0.90). For the men, vVO2max remained the best predictor of 3 km race performance (R2=0.49). For the women, the best predictors of 3 km performance were vVO2max and VO2max (R2=0.97). Conclusions: Distance coaches should consider emphasizing vVO2max as a primary factor in training to improve 3 km race performance and conversely, the pace achieved in a 3-km race is a good predictor of vVO2max

11. Some aspects of enhancing natural pest control

Expert assessors Barbara Smith, Game and Wildlife Conservation Trust, UK Tony Harding, Rothamsted Research, UK Anthony Goggin, Linking Environment and Farming (LEAF), UK Felix Wackers, BioBest/University of Lancaster, Belgium/UK Melvyn Fidgett, Syngenta, UK Michael Garratt, University of Reading, UK Michelle Fountain, East Malling Research, UK Phillip Effingham, Greentech Consultants, UK Stephanie Williamson, Pesticides Action Network, UK Toby Bruce, Rothamsted Research, UK Andrew Wilby, Univ..

What works in conservation? Using expert assessment of summarised evidence to identify practices that enhance natural pest control in agriculture.

This paper documents an exercise to synthesize and assess the best available scientific knowledge on the effectiveness of different farm practices at enhancing natural pest regulation in agriculture. It demonstrates a novel combination of three approaches to evidence synthesis-systematic literature search, collated synopsis and evidence assessment using an expert panel. These approaches follow a logical sequence moving from a large volume of disparate evidence to a simple, easily understandable answer for use in policy or practice. The example of natural pest regulation in agriculture was selected as a case study within two independent science-policy interface projects, one European and one British. A third funder, a private business, supported the final stage to translate the synthesized findings into a useful, simplified output for agronomists. As a whole, the case study showcases how a network of scientific knowledge holders and knowledge users can work together to improve the use of science in policy and practice. The process identified five practices with good evidence of a benefit to natural pest regulation, with the most beneficial being 'Combine trap and repellent crops in a push-pull system'. It highlights knowledge gaps, or potential research priorities, by showing practices considered important by stakeholders for which there is not enough evidence to make an assessment of effects on natural pest regulation, including 'Alter the timing of pesticide application.' Finally, the process identifies several important practices where the volume of evidence of effects on natural pest regulation was too large (>300 experimental studies) to be summarised with the resources available, and for which focused systematic reviews may be the best approach. These very well studied practices include 'Reduce tillage' and 'Plant more than one crop per field'.Elements of this work were funded by the European Commission under its 7th framework programme (BiodiversityKnowledge project, Grant number: 265299), the Natural Environment Research Council (NERC)'s Knowledge Exchange Programme on Sustainable Food Production (Grant code: NE/K001191/1) and Waitrose plc. L.V.D is funded by NERC under the Biodiversity and Ecosystem Service Sustainability (BESS) Programme (Grant code: NE/K015419/1).This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Springer

Cell Mates

Catalog for the exhibition Cell Mates held at the Seton Hall University Walsh Gallery, June 3 - July 18, 2013. Curated by Jeanne Brasile and Lisbeth Murray. Includes the essays The Art of Science and the Science of Art by Jeanne Brasile and Incubating Hybrid Art by Lisbeth Murray. Includes color illustrations

The Cool Farm Biodiversity metric: an evidence-based online tool to report and improve management of biodiversity at farm scale

Halting biodiversity loss and achieving food security are both aims of the United Nations 2030 Agenda for Sustainable Development, but there is complex interplay between them. Agriculture drives biodiversity loss, but biodiversity provides benefits to agriculture. There is substantial potential to develop ‘win-win’ solutions for biodiversity and people within productive farmland, by boosting wildlife that can be supported, whilst maintaining yield and other services. To achieve this, farmers need to be able to assess the impacts of their management on biodiversity at farm scale. While suitable tools exist to drive improvement in biodiversity management, none incorporates evidence on the effectiveness of specific management practices. In this study we present the Cool Farm Biodiversity metric, which generates a farm-scale action-based biodiversity management assessment, scored using expert judgements and expert assessment of experimental evidence. The metric is designed to be biome-specific, so it responds to conservation aims, ecosystem processes and farming systems in particular biomes. To demonstrate that the metric is responsive to changes in farm management, we present an example of use on a large arable farm from the temperate forest biome

The Cool Farm Biodiversity metric: An evidence-based online tool to report and improve management of biodiversity at farm scale

Halting biodiversity loss and achieving food security are both aims of the United Nations 2030 Agenda for Sustainable Development, but there is complex interplay between them. Agriculture drives biodiversity loss, but biodiversity provides benefits to agriculture. There is substantial potential to develop ‘win-win’ solutions for biodiversity and people within productive farmland, by boosting wildlife that can be supported, whilst maintaining yield and other services. To achieve this, farmers need to be able to assess the impacts of their management on biodiversity at farm scale. While suitable tools exist to drive improvement in biodiversity management, none incorporates evidence on the effectiveness of specific management practices. In this study we present the Cool Farm Biodiversity metric, which generates a farm-scale action-based biodiversity management assessment, scored using expert judgements and expert assessment of experimental evidence. The metric is designed to be biome-specific, so it responds to conservation aims, ecosystem processes and farming systems in particular biomes. To demonstrate that the metric is responsive to changes in farm management, we present an example of use on a large arable farm from the temperate forest biome