3. Producción
Browse
5 results
Search Results
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dramatically increased accessibility and decreased cost-per-person impacts are needed for scaling IPM in Africa(NLM (Medline), 2022-12-01)While traditional scaling for integrated pest management (IPM) in Africa requires the movement of expert trainers from village to village, these efforts are often costly, time-inefficient, hampered by distance, and became impossible under COVID-19's movement restrictions (despite tremendously increased public need for IPM-scaling knowledge). One solution to this dilemma is IPM-scaling, usable by a diversity of development actors expending limited or few resources, to deliver critical information to large numbers of people with systems-approach information and communication technologies. This paper describes one such systems-approach scaling platform, Scientific Animations Without Borders, which effectively elicited end-user solution-adoption and decreased unit costs over increasing scales in three African countries during COVID-19. How to scale game-changing IPM insights 'off the shelf' and 'into people's hands in the field' is also discussed. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Large-scale rollout of extension training in Bangladesh: Challenges and opportunities for gender-inclusive participation(Public Library of Science, 2022-07-01)Despite the recognized importance of women’s participation in agricultural extension services, research continues to show inequalities in women’s participation. Emerging capacities for conducting large-scale extension training using information and communication technologies (ICTs) now afford opportunities for generating the rich datasets needed to analyze situational factors that affect women’s participation. Data was recorded from 1,070 video-based agricultural extension training events (131,073 farmers) in four Administrative Divisions of Bangladesh (Rangpur, Dhaka, Khulna, and Rajshahi). The study analyzed the effect of gender of the trainer, time of the day, day of the week, month of the year, Bangladesh Administrative Division, and venue type on (1) the expected number of extension event attendees and (2) the odds of females attending the event conditioned on the total number of attendees. The study revealed strong gender specific training preferences. Several factors that increased total participation, decreased female attendance (e.g., male-led training event held after 3:30 pm in Rangpur). These findings highlight the dilemma faced by extension trainers seeking to maximize attendance at training events while avoiding exacerbating gender inequalities. The study concludes with a discussion of ways to mitigate gender exclusion in extension training by extending data collection processes, incorporating machine learning to understand gender preferences, and applying optimization theory to increase total participation while concurrently improving gender inclusivity. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Agricultural education in Africa using YouTube multilingual animations: A retrospective feasibility study assessing costs to reach language-diverse populations(Public Library of Science, 2024-04-01)There is a critical need for widespread information dissemination of agricultural best practices in Africa. Literacy, language and resource barriers often impede such information dissemination. Culturally and linguistically localized, computer-animated training videos placed on YouTube and promoted through paid advertising is a potential tool to help overcome these barriers. The goal of this study is to assess the feasibility of reaching language-diverse populations in Africa using this new type of information dissemination channel. As a case study, cost estimates were obtained for YouTube ad campaigns of a video to prevent postharvest loss through safe food storage using sanitized jerrycan containers. Seventy-three video variants were created for the most common 16 languages in Ghana, 35 languages in Kenya, and 22 languages in Nigeria. Using these videos, campaigns were deployed country wide or focused on zones of influence that represent economically underdeveloped regions known to produce beans suitable for jerrycan storage. Using data collected from YouTube ad campaigns, language-specific models were created for each country to estimate how many viewers could be reached per US dollar spent. Separate models were created to estimate the number of viewers who watched 25% and 75% of the video (most of video without end credits), reflecting different levels of engagement. For language campaigns with both country wide and zone of influence areas of deployment, separate region-specific models were created. Models showed that the estimated number of viewers per dollar spent varied considerably amongst countries and languages. On average, the expected number of viewers per dollar spent were 1.8 (Range = 0.2-7.3) for 25% watched and 0.8 (Range = 0.1-3.2) for 75% watched in Ghana, 1.2 (0.2-4.8) for 25% watched and 0.5 (Range = 0.1-2.0) for 75% watched in Kenya, and 0.4 (Range = 0.2-1.3) for 25% watched and 0.2 (Range = 0.1- 0.5) for 75% watched in Nigeria. English versions of the video were the most cost-effective in reaching viewers in Ghana and Nigeria. In Kenya, English language campaigns ranked 28 (country wide) and 36 (zones of influence) out of 37 analyzed campaigns. Results also showed that many local language campaigns performed well, opening the possibility that targeted knowledge dissemination on topics of importance to local populations, is potentially cost effective. In addition, such targeted information dissemination appears feasible, even during regional and global crises when in-person training may not be possible. In summary, leveraging multilingual computer-animations and digital platforms such as YouTube shows promise for conducting large-scale agricultural education campaigns. The findings of the current study provides the justification to pursue a more rigorous prospective study to verify the efficacy of knowledge exchange and societal impact through this form of information dissemination channel. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine-Supported Decision-Making to Improve Agricultural Training Participation and Gender Inclusivity(Public Library of Science, 2023-05-01)Women comprise a significant portion of the agricultural workforce in developing countries but are often less likely to attend government sponsored training events. The objective of this study was to assess the feasibility of using machine-supported decision-making to increase overall training turnout while enhancing gender inclusivity. Using data obtained from 1,067 agricultural extension training events in Bangladesh (130,690 farmers), models were created to assess gender-based training patterns (e.g., preferences and availability for training). Using these models, simulations were performed to predict the top (most attended) training events for increasing total attendance (male and female combined) and female attendance, based on gender of the trainer, and when and where training took place. By selecting a mixture of the top training events for total attendance and female attendance, simulations indicate that total and female attendance can be concurrently increased. However, strongly emphasizing female participation can have negative consequences by reducing overall turnout, thus creating an ethical dilemma for policy makers. In addition to balancing the need for increasing overall training turnout with increased female representation, a balance between model performance and machine learning is needed. Model performance can be enhanced by reducing training variety to a few of the top training events. But given that models are early in development, more training variety is recommended to provide a larger solution space to find more optimal solutions that will lead to better future performance. Simulations show that selecting the top 25 training events for total attendance and the top 25 training events for female attendance can increase female participation by over 82% while at the same time increasing total turnout by 14%. In conclusion, this study supports the use of machine-supported decision-making when developing gender inclusivity policies in agriculture extension services and lays the foundation for future applications of machine learning in this area. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Investment modeling for scalable agricultural learning(Public Library of Science, 2026-03-01)With the rise of information and communication technologies, localized farmer training can be transformed into scalable strategies applicable across diverse communities, cultures, and languages. However, the economic value of these approaches and the factors shaping their returns remain underexplored. This study presents a general framework for evaluating the economic impact of scalable agricultural learning initiatives, using multilingual instructional animations and YouTube dissemination as a case study. Systems modeling was used to simulate potential returns, assess key drivers of impact, and estimate the number of farmers required for economic viability. Sensitivity analysis shows that returns are most influenced by the cost to inform an individual, adoption rates, and income gains, and to a lesser degree, technique-sharing rates and adoption costs. When existing educational content is adapted and its lifespan extended, learning initiatives can be economically viable with few targeted farmers, making the linguistic adaption into minority or rarer languages an economically viable option. The wide variation in returns across scenarios highlights the importance of tailoring models to specific contexts to obtain more precise estimates of economic impact. These findings underscore the value of adaptable and durable learning materials and suggest that future research-for-development (R4D) investments could benefit from systems modeling to identify and prioritize high-impact agricultural solutions.1