Resultados de búsqueda - "learning"

Use of low cost near‑infrared spectroscopy, to predict pasting properties of high quality cassava flour por Abubakar, M., Wasswa, P., Masumba, E., Ongom, P., Mkamilo, G., Kanju, E., Abincha, W., Edema, R., Sichalwe, K., Tukamuhabwa, P., Kayondo, S., Rabbi, I., Kulembeka, H.

“…In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740–1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated. …”
Enlace del recurso

A crop-specific and time-variant spatial framework to characterize production environments: a case study for rainfed wheat in Ethiopia por Gelagay, H.S., Leroux, L., Tamene, Lulseged D., Chernet, M., Blasch, G., Tibebe, D., Abera, W., Sida, T., Tesfaye, K., Corbeels, M., Silva, J.V.

“…METHODSAn ensemble machine learning approach built upon time-series satellite images and environmental data was used for crop type mapping while pixel- and object-based clustering algorithms were used to delineate dynamic ASUs from two temporal perspectives: annual ASUs for the 2021 and 2022 growing seasons to assess short-term dynamism, and ASUs from aggregated data (2016 – 2022) to capture long-term variations in the production environment.4. …”
Enlace del recurso

Leveraging state of the art computer vision models for tree monitoring in silvopastoral systems por Ruiz-Hurtado, Andres Felipe, Cardoso, Juan Andres

“…According to the model’s capabilities, the methodology involved applying either model full training, transfer learning, out-of-the-box inference, or fine-tuning. …”
Enlace del recurso

Is PROGRESA working? summary of the results of an evaluation by IFPRI por Skoufias, Emmanuel, McClafferty, Bonnie

“…In brief, the findings of IFPRI’s evaluation are that after just three years, the poor children of Mexico in the rural areas where PROGRESA is currently operating are more likely to enroll in school, are eating more diversified diets, getting more frequent health care and learning that the future may look quite different from the past. …”
Enlace del recurso

Co-Creation with FarMoRe: Collaborative training for agroecological transition por Tran, Nguyen Bao, Dittmer, Kyle M, Cuong, Ong Quoc, Trang, Vu Hong, Nelson, Katherine

“…Participants will gain digital literacy skills and learn how to use FarMoRe to monitor and improve farming activities, align practices to the "One Must Do, Five Reductions" (1M5R) package, track greenhouse gas (GHG) emissions, and foster collaboration and co-creation among farmers, field agents, and agricultural management officers. …”
Enlace del recurso

Increasing sorghum yields for smallholder farmers in Mali: the evolution towards a context-driven, on-farm, gender-responsive sorghum breeding program por Isaacs, Krista, Weltzien-Rattunde, Eva, Some, Tiare Hermann Williams, Abdoulaye Diallo, Bocar Diallo, Mamourou Sidibé, vom Brocke, Kirsten, Bakary Samake, Nebié, Baloua, Weltzien-Rattunde, Henry Frederick

“…This case study details (a) more than a decade of sorghum breeding activities and research that led to (b) the inclusion of women in participatory plant breeding, culinary tests, and large-scale participatory selection in on-farm trials, reaching hundreds of women each year and (c) iterative co-learning processes to develop preferred sorghum varieties and increase sorghum yields on men and women’s fields. …”
Enlace del recurso

"The reality in the DRC is just not the reality in Rwanda" - How context factors affect transdisciplinary research projects por Parli, R., Byamungu, M., Fischer, M., Kantengwa, S., Kintche, K., Konlambique, M., Lieberherr, E., Six, J., Wilde, B., Spath, L.

“…Further, we found that TD projects may achieve learning and social effects by tackling certain gaps in action resources. …”
Enlace del recurso

Goal-setting Capacity Scale por Measures for Advancing Gender Equality (MAGNET) Initiative

“…In terms of timing, they can be short-term (e.g., saving money for a small household purchase, learning a new skill) or long-term (e.g., saving money for a large household purchase, achieving some level of education). …”
Enlace del recurso

Small groceries in Viet Nam por de Brauw, Alan, The Anh, Dao, Tho, Pham Thi Hanh

“…As a result, what they sell could help play a role in improving the diets of Viet Nam’s population. To focus on learning more about small groceries, this note makes use of two datasets. …”
Enlace del recurso

National Mainstreaming Workshop on Climate Risk Management in Agricultural Extension (CRMAE) for Ethiopia por Belay, Berhanu, Bulo, Million, Ambaw, Gebermedihin, Amha, Yosef, Demissie, Teferi, Solomon, Dawit

“…The updated curricula, incorporating CKPs into Occupational Standards, Teacher Training, and Learning Manuals (TTLM), have been submitted to MoLS for implementation. …”
Enlace del recurso

MELIA Results 2024: evaluation of AICCRA interventions in Senegal on access to and use of climate-smart agriculture (CSA) and climate information services (CIS) por Thiaw, Moussa, Diouf, Abdoul Mouhamed, Said, Shakira Hachim, Josiane, Kengne, Prosper, Houessionon, Edorh, Theopile, Worou, Omonlola Nadine, Wane, Abdrahmane

“…Key findings emphasize the potential for scaling CSA and CIS use through enhanced bundling strategies, gender-responsive policies, and peer learning networks. Future interventions should focus on expanding access to underutilized climate services, improving financial support mechanisms, and leveraging community-driven knowledge transfer systems to maximize resilience and sustainability in agricultural practices.…”
Enlace del recurso

Evaluating climate change impacts and seasonal dynamics in Senegal to predict crop yields and develop early warning signals por Panjwani, Shweta, Jampani, Mahesh, Amarnath, Giriraj, Sambou, Mame Henriette Astou

“…We used the best-fit combinations of rainfall and NDVI and machine-learning models to predict crop yield for the upcoming season for major crop growing districts, with an accuracy (R2) ranging from 0.6 to 0.8 and a one-month lag to the harvest period. …”
Enlace del recurso

Insights on social equity and climate adaptation from low-income rural Filipinos por Petesch, Patti, Fisher, Eleanor, Echavez, Chona, Hellin, Jon, Montejo, Michael Lou, Rockwell, Perry Henry, Salcedo, Sonica, Ebenstål Almeida, Olivia

“…Participatory action learning approaches work to maximize the opportunities and minimize the risks because they are locally led and—with facilitation support and other resources of external partners—grounded in the knowledge and experiences of what works according to poor and marginalized women and men on the frontlines of climate change.…”
Enlace del recurso

Value chain research and development: The quest for impact por Donovan, Jason, Stoian, Dietmar

“…Funders can help by requiring grantees to critically reflect on past action, by providing “safe spaces” for sharing such reflections, and by engaging in joint learning with practitioners and researchers.…”
Enlace del recurso

Evaluations of CGIAR Science Groups: Synthesis on Quality of Science por Jill Lenné, SG Evaluation Teams

“…To dive deeper into the findings and recommendations: • Download the full report: Access detailed insights, lessons learned, and actionable steps to drive high-quality research and innovation aligned with the SDGs. • Visit the Science Group evaluations portal: Explore comprehensive evaluations and reports on Quality of Science. • Explore the QoS Resource Hub: Access tools, guidelines, and resources to support research excellence and impactful innovation.…”
Enlace del recurso

Farmer and Extension Staff Training on Agroecological Farming Practices por Dube, Farayi, Magaisa, Alec, Moyo, Martin

“…Facilitate knowledge exchange and peer learning through interactive sessions and discussions among farmers and extension staff 4. …”
Enlace del recurso

Analysis of antibiotic use and access to drugs among poultry farmers in Kenya por Muloi, Dishon M., Kasudi, Mitchelle R., Murungi, Maurice K., Ibayi, Eugine L., Kahariri, S., Karimi, C., Korir, Max, Muasa, B., Mwololo, D., Ndanyi, R., Ndungi, R., Njiru, J., Omani, R., Owada, R., Omulo, S., Azegele, A., Fèvre, Eric M.

“…We also collected antibiotic sales and geolocation data from 321 veterinary drug stores in Nakuru and Kilifi counties, representing areas with high and low-intensity poultry production, respectively. Using a machine learning framework, we predicted farm-level antibiotic use based on collected demographic and production traits. …”
Enlace del recurso

Responses of surface runoff and soil water-erosion to changes in seasonal land cover and rainfall intensity; the case of Shilansha Watershed, Rift Valley Basin of Ethiopia por Gedle, Assefa, Rientjes, T., Haile, Alemseged Tamiru, Mekuria, Wolde, Hallett, P., Smith, J.

“…This study assessed these effects using the event-based physically based distributed open-source Limburg Soil Water Erosion Model (OpenLISEM), incorporating local field data and multi-sensor satellite data processed with machine learning techniques. New Hydrological Insights: During the fallow season, simulated surface runoff and total soil loss were 9.7 % and 47 % larger than the growing season and 0.9 % and 42 % larger than the harvest season, respectively. …”
Enlace del recurso

Ghana: Systematic analysis of world market and domestic production shocks por Mukashov, Askar, Pauw, Karl, Jones, Eleanor, Thurlow, James

“…A country-specific, economywide Computable General Equilibrium (CGE) model then simulates the impacts of these shocks on both total and sector-specific economic outcomes, deriving changes in poverty and undernourishment for each shock scenario. Finally, machine learning techniques are applied to obtain metrics for the relative im portance of different risk factors. …”
Enlace del recurso

Management and agricultural water productivity improvement in India and the Ganges Basin por Sharma, Bharat, Sikka, Alok, Lautze, Jonathan F., Mabhaudhi, T.

“…A matrix approach was employed to compile and review the DSS with broader segregation under (i) crop and farm-based decision support systems, (ii) DSS based on artificial intelligence, enhanced machine learning, fuzzy multi-criteria decision making, and knowledge systems, (iii) DSS for real-time operation of micro-irrigation systems, (iv) DSS for management of tanks and reservoirs for water-deficit regions, and (v) DSS for improving water productivity under canal commands and conjunctive management of surface and groundwater resources. …”
Enlace del recurso