Resultados de búsqueda - "learning"

Does trust in the extension source improve learning outcomes? Experimental evidence from Southern Africa por Ngoma, Hambulo, Mulungu, Kelvin H., Manyanga, Mark, Simutowe, Esau, Mhlanga, Blessing, Nyagumbo, Isaiah, Chimonyo, Vimbayi G. P., Thierfelder, Christian

Materias: “…learning…”
Enlace del recurso

AgroTutor Academy: annual report por Kiragi, Pratibha Suresh

Materias: “…learning…”
Enlace del recurso

Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data por Kebede, G., Mudereri, B.T., Abdel-Rahman, E.M., Mutanga, O., Landmann, T., Odindi, J., Motisi, N., Pinard, F., Tonnang, H.E.Z.

Materias: “…machine learning…”
Enlace del recurso

Prediction of pulse suitability in rice fallow areas using fuzzy AHP-based machine learning methods in Eastern India por Sahoo, Satiprasad, Singha, Chiranjit, Govind, Ajit

Materias: “…marchine learning…”
Enlace del recurso

Detecting cumulative effects of inputs within the flexible production function framework through LASSO shrinkage estimation: Implications for potassium fertilizer use in India por Takeshima, Hiroyuki, Kishore, Avinash

Materias: “…machine learning…”
Enlace del recurso

IITA Monitoring, Evalation, Learning, and Impact Assessment (MELIA) Unit Refreshed Strategy 2024 - 2030 por Webber, H.

Materias: “…learning…”
Enlace del recurso

Foodgravity: understand food flows using classic gravity model and explainable artificial intelligence techniques por Dusabe, B.

Materias: “…machine learning…”
Enlace del recurso

The effect of teacher training and community literacy programming on teacher and student outcomes por Chimbutane, Feliciano, Karachiwalla, Naureen, Herrera-Almanza, Catalina, Leight, Jessica, Lauchande, Carlos

Materias: “…learning…”
Enlace del recurso

Web mapping and monitoring tools por Nelson, A.

Materias: “…e-learning…”
Enlace del recurso

Demand intelligence: an extended descriptor report for sub-indicators por Sartas, M., Konlambigue, M.

Materias: “…machine learning…”
Enlace del recurso

Simultaneous adoption of integrated soil fertility management technologies in the Chinyanja Triangle, Southern Africa por Mponela, Powell, Kassie, Girma T., Tamene, Lulseged D.

Materias: “…learning…”
Enlace del recurso

Redes neuronales para la identificación de enfermedades en el cultivo de tomate (Solanum lycopersicum L.) en Honduras por Erazo V., Jorge D., Uema H., Ken S.

Materias: “…Machine Learning…”
Enlace del recurso

Desarrollo de un módulo e-learning en Pago por Servicios Ambientales por Murillo M., Oscar I.

Materias: “…e- learning…”
Enlace del recurso

Scaling up climate services for smallholder farmers: Learning from practice por Huysen, Tiff van, Hansen, James, Tall, Arame

Enlace del recurso

Effect of fruit and host fly species on the associative learning by Fopius arisanus por Monsia, A., Mègnigbèto, G.S.B., Gnanvossou, D., Karlsson, M.F.

“…We studied associative learning of Fopius arisanus (Hymenoptera: Braconidae) and tested its range of learning in natural and conditional hosts and host fruits, i.e. …”
Enlace del recurso

Learning to pay by phone: MOBIS, Ensibuuko's mobile-money app in Uganda por Otim, Gerald, Tokwiny, Alfred

Enlace del recurso

Experience capitalization: We learn the most from the challenges we face por Macuiane, Messias A.

Enlace del recurso

Facilitating conditions for farmer learning behaviour in the student-to-farmer university outreach por Kalule, S.W., Sseguya, H., Ongeng, D., Karubanga, G.

“…Structural equation modelling was used to analyse how faculty supervision support to students in combination with other facilitating conditions affect the formation of intentions for learning and actual farmer learning behaviour. Findings: Faculty supervision support in the student-to-farmer outreach was found to significantly influence formation of intentions for learning (β = 0.380; t = 5.263; P < .01) and actual farmer learning behaviour (β = 0.182; t = 2.081; P < .05). …”
Enlace del recurso

Knowledge management in action: Lessons learned in repackaging phase 1 results por Schuetz, Tonya, Brakel, Martin L. van, Dassenaike, L., Victor, Michael

Enlace del recurso

A mobile-based deep learning model for cassava disease diagnosis por Ramcharan, A., McCloskey, P., Baranowski, K., Mbilinyi, N., Mrisho, L., Ndalahwa, M., Legg, James P., Hughes, D.P.

Enlace del recurso