Logistic modelling of smallholder livestock farmers’ adoption of tree-based fodder technology in Zimbabwe

Based on field data collected from 131 small scale dairy farmers that were randomly selected from four agro-ecological zones, this study assessed the potential of adoption of fodder bank technology as a means for improving livestock production and income generation for smallholder farmers in Zimbabwe. Using a logit modelling approach, it also identified the drivers of adoption of the technology by analysing the influence of household characteristics and ecological factors on farmers’ decision to adopt the technology. The model correctly predicted 75% of observed adoption and non-adoption by farmers. Results reveal that dairy herd size, land holding size, membership of dairy association and agro-ecological potential are the key factors influencing farmers’ adoption of fodder bank. Age, sex, household size and educational level of farmers play lesser role. Male and female farmers were equally likely to take up and practice fodder bank if they are given equal access to information and incentives. The study recommends farmer-led extension approaches where farmers who possess certain key characteristics should constitute the initial group for disseminating information regarding the technology in rural communities. The results highlight the importance of access to dairy product markets as a driver for the adoption of fodder banks. It is recommended that forging a strategic partnership with the Dairy Development Programme (DDP) will offer high potential for enhancing the scaling up of the adoption and impact of fodder bank technology in the country.Livestock Production/Industries, Research and Development/Tech Change/Emerging Technologies,

Application of multi-layer extreme learning machine for efficient building energy prediction

Building energy efficiency is vital, due to the substantial amount of energy consumed in buildings and the associated adverse effects. A high-accuracy energy prediction model is considered as one of the most effective ways to understand building energy efficiency. In several studies, various machine learning models have been proposed for the prediction of building energy efficiency. However, the existing models are based on classical machine learning approaches and small datasets. Using a small dataset and inefficient models may lead to poor generalization. In addition, it is not common to see studies examining the suitability of machine learning methods for forecasting the energy consumption of buildings during the early design phase so that more energy-efficient buildings can be constructed. Hence, for these purposes, we propose a multilayer extreme learning machine (MLELM) for the prediction of annual building energy consumption. Our MLELM fuses stacks of autoencoders (AEs) with an extreme learning machine (ELM). We designed the autoencoder based on the ELM concept, and it is used for feature extraction. Moreover, the autoencoders were trained in a layer-wise manner, employed to extract efficient features from the input data, and the extreme learning machine model was trained using the least squares technique for a fast learning speed. In addition, the ELM was used for decision making. In this research, we used a large dataset of residential buildings to capture various building sizes. We compared the proposed MLELM with other machine learning models commonly used for predicting building energy consumption. From the results, we validated that the proposed MLELM outperformed other comparison methods commonly used in building energy consumption prediction. From several experiments in this study, the proposed MLELM was identified as the most efficient predictive model for energy use before construction, which can be used to make informed decisions about, manage, and optimize building design before construction

Logistic modelling of smallholder livestock farmers’ adoption of tree-based fodder technology in Zimbabwe

Based on field data collected from 131 small scale dairy farmers that were randomly selected from four agro-ecological zones, this study assessed the potential of adoption of fodder bank technology as a means for improving livestock production and income generation for smallholder farmers in Zimbabwe. Using a logit modelling approach, it also identified the drivers of adoption of the technology by analysing the influence of household characteristics and ecological factors on farmers’ decision to adopt the technology. The model correctly predicted 75% of observed adoption and non-adoption by farmers. Results reveal that dairy herd size, land holding size, membership of dairy association and agro-ecological potential are the key factors influencing farmers’ adoption of fodder bank. Age, sex, household size and educational level of farmers play lesser role. Male and female farmers were equally likely to take up and practice fodder bank if they are given equal access to information and incentives. The study recommends farmer-led extension approaches where farmers who possess certain key characteristics should constitute the initial group for disseminating information regarding the technology in rural communities. The results highlight the importance of access to dairy product markets as a driver for the adoption of fodder banks. It is recommended that forging a strategic partnership with the Dairy Development Programme (DDP) will offer high potential for enhancing the scaling up of the adoption and impact of fodder bank technology in the country

Spousal desertion and coping strategies among women with cervical cancer in Nigeria: a schematic framework for wellbeing

Background: Women that are not in good health cannot contribute to sustainable development but effective coping during and after sickness could enhance their contributions to development. Objective: The study examined the coping strategies among women with cervical cancer in different marital context in Nigeria Methods: In-depth interview from eight women survivors and patients of cervical cancer in two distinct marital contexts characterised by the presence or absence of husband from two states of Nigeria. Data were analysed using content analytic procedures and premised upon relationship-focused coping strategy theory.Results: The study identified basic coping strategies as seeking support from religious organisations and adjustment of sexual lifestyle.Conclusion: The study concluded that women’s coping strategies on cervical cancer varied according to marital context. The authors recommend counselling, increasing cervical cancer risk awareness and husband-wife support, especially during life-threatening sicknesses to engender quick recovery and improved well-being for sustaining women contributions to development