6 pages, Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world’s most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations’ 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centered around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good.
21 Pages, While farmers sell their crops, middlemen provide a linkage between them, markets and buyers. Middlemen have good knowledge of working conditions of markets and have access to agricultural market information. Due to poor access to markets and agricultural market information by smallholders, there is a feeling that middlemen benefit more while farmers sell their crops. Good access to markets and market information may help farmers bypass middlemen while selling crops and thus benefit more. Thus, it is best to improve the informational capabilities (ICs) of farmers in agricultural marketing. Thus, this research measured ICs of farmers accessing market information, through a program NINAYO, while selling their crops. The research utilized the informational, psychological, social, and economic dimensions of the empowerment framework in identifying capability indicators to formulate survey questions. Data were collected from smallholders in six regions in Tanzania. The analysis utilized measures of life satisfaction and results showed that about half of the variation in the dependent variable, satisfaction with capabilities, was explained by the model. Backward elimination analysis confirmed that life satisfaction is multidimensional. Robustness test confirmed a positive relationship between satisfaction and capabilities. Overall, results confirmed ICs are multidimensions, their improvement empowers farmers in agricultural marketing.
10 pages., he impact of mobile money services in sub-Saharan Africa have been largely recognised. However, empirical studies are principally lacking on the factors influencing the decision to own a mobile phone (first hurdle), register with mobile money (second hurdle) and the intensity of use of mobile money services (third hurdle). This study examined the determinants of the mobile phone ownership, drivers of registration (participation) of mobile money services, and the intensity of use of mobile money services in rural Ghana by employing the triple hurdle approach. The first and second hurdle were analysed using the logit model while quasi-poisson regression was used to analyse the third hurdle. The analysis from the cross-sectional data showed that the decision to own a mobile phone was driven by household size, marital status, the farm size, access to electricity, income status and the type of occupation engaged, whereas the decision to register with mobile money was influenced by the age, educational status, marital status, household size, farm size and the type of occupation engaged in by the household head. The intensity of usage of mobile money services was influenced by the age of the household head, higher educational level, marital status of the household head, household and farm size as well as the distance of the household heads from the mobile money agent which directly influences the intensity of use of mobile money services by household heads. The study recommends that strategies that promote access to electricity and occupation in the formal sector or both farming and trading in the rural communities should be promoted. Furthermore, policy attention should focus on location, farmers and farm characteristics.
8 pages, The use of digital technologies in agriculture offers various benefits, such as site-specific application, better monitoring, and physical relief. The handling of these technologies requires a specific skill set. Therefore, the question arises of when and how farm managers learn about digital technologies. Aiming to analyse the current situation, the present research investigated the role that digital technologies play in vocational training for future farm managers. Taking the example of farm management information systems (FMIS), the present study also analysed various predictors of adoption, including the effect of training. To investigate these research questions, an online survey among teachers and students of the farm management vocational programme across Switzerland was conducted in the spring of 2021. In total, 150 individuals participated, 41 of whom were teachers. Participants answered questions about the learning content in the farm management programme and their perception of digital technologies in general. Students further reported whether they already had a farm they would be managing in the future and how they perceived FMIS. The results indicate that both teachers and students are convinced that digital technologies play an important role in agriculture and will gain more importance in the future. A substantial part of 43% of the students who participated indicated that they had learned neither about digital technologies during their basic agricultural training nor the subsequent farm management programme. In terms of FMIS, 51% of the student sample indicated that they had never heard about FMIS during their agricultural training. While having learned about FMIS was not a significant predictor for adoption, gender, perceived ease of use, and intention to use more digital technologies in the future significantly predicted the adoption of FMIS. The paper concludes that, to support the adoption of digital technologies and FMIS specifically, training for future farm managers should focus on how to operate an FMIS to increase the perceived ease of use of this technology.
18 pages, This study examines factors that appear to contribute to farmers’ adoption and discontinuation of poly house technology for off-season vegetable production. We collected cross-sectional survey data from a sample of 151 households in Kaski district, Nepal during October 2018. The data are analyzed using Heckman’s two stage sample selection model. The study reveals that the family members report being engaged in nonfarm sector that there is an increased probability of discontinuation of poly house technology. Farmers may be diverting their labor towards nonfarm activities that result in higher returns to labor and different risks. At the same time, the results indicate that farmers who did not receive training on vegetable production were more likely to discontinue poly house technology. It was also found that increasing farmers’ engagement with marketing activities increased the likelihood of farmers to continue poly house technology and increase household income. The provision of continued technical support (e.g., training), input supply (e.g., seeds, fertilizers) and market information are essential to sustain the adopted technologies. The study sheds light on the sustainability of technology adoption by underpinning the importance of extension services for longer-term adoption. We believe that the combined effect of various technologies would be associated with sustained adoption of the improved off-season technologies. This provides a new direction to operationalize farmer-oriented policies in agricultural extension and helps in devising programs for sustained adoption of technology.
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 107 Document Number: C10126
Notes:
search from AgEcon., American Agricultural Economics Association Annual Meeting, August 2-5, 1998, Salt Lake City, Utah. 5 pages; Adobe Acrobat PDF 18K bytes, Selected Paper Session SP - 6R Adoption of Technology in Developing Countries Abstract/Description: These
papers move beyond the questions of who adopts technologies to ask how preferences for characteristics (of maize in Mexico or cattle in Burkina Faso) affect adoption and how technical change differentially affects semi-subsistence farmers and how it affects productivity and yield variability. Modeling the Impacts of Soil Conservation on Productivity and Yield Variability: Evidence From a Heteroskedastic Switching Regression Gerald Shively, Purdue University Selecting Genetic Traits for Cattle Improvement: Preservation of Disease Resistant Cattle in Africa Kouadio Tano, University of Abidjan; Merle Faminow, University of Manitoba Variety Characteristics and the Land Allocation Decisions of Farmers in a Center of Maize Diversity Melinda Smale, Maricio Bellon, and Alfonso Aguirre The Distributional Impacts of Farm Policy in Semi-subsistence Agriculture Garth Holloway and Nermin Akyil, AERI