Search Results - "data science"

Context-dependent agricultural intensification pathways to increase rice production in India by Hari S. Nayak, McDonald, Andrew J., Kumar, Virender, Craufurd, Peter Q., Dubey, Shantanu Kumar, Nayak, Amaresh Kumar, Parihar, Chiter Mal, Panneerselvam, Peramaiyan, Poonia, Shishpal, Fantaye, Kindie Tesfaye, Malik, Ram K., Urfels, Anton, Gautam, Udham Singh, Silva, João Vasco

“…With emerging large-n agronomic datasets and data science methods, pathways for narrowing yield gaps can be identified that provide actionable insights into where and how cropping systems can be sustainably intensified. …”
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Herramientas informáticas para el estudio de los recursos genéticos by Gómez-Godínez, Lorena J., Calvillo Aguilar, Francisco F., Ponce de León, Arturo V., Khoury, Colin K., Diaz, Maria V.

“…La ciencia de datos (data science), ha permitido el modelado y caracterización de los recursos genéticos. …”
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Digital sequence information and plant genetic resources: Global policy meets interoperability by Manzella, Daniele, Marsella, Marco, Jaiswal, Pankaj, Arnaud, Elizabeth, King, Brian

“…The development and adoption of common agricultural data standards are key to assuring good stewardship, but face several challenges, including limited awareness about standards compliance; lagging data science capacity; emphasis on data collection rather than reuse; and limited fund allocation for data and standards management. …”
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Unleashing the potential of underutilized datasets to improve agricultural decision-making through comprehensive data analysis: An example of rice crop manager (RCM) dataset by Gakhar, Shalini, Sharma, Sheetal

“…In this paper, we explore the role of data science in modern agriculture, highlighting the importance of big data analytics, geospatial technology, and machine learning. …”
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IRRI's Digital and Data Strategy (Framework) by Gimena, Joy Marie

“…These activities will ultimately seek to create impact by improving advisory support services (e.g., Analytics, Geo-Spatial, Climate Credit Modeling, Data Science, Bioinformatics), targeted research for higher yields and farmer resiliency, incubate more cohesive research, and drive data and model validation with partners.…”
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Distribución espacial de la vulnerabilidad futura bajo escenario de cambio climático. Serie 3. Análisis municipal para Corredor Seco hondureño. by Gonzalez Rodriguez, Carlos Eduardo, Le Coq, Jean-François, Schiek, Benjamin E., Prager, Steven D.

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Data-driven approaches can harness crop diversity to address heterogeneous needs for breeding products by Etten, Jacob van, Sousa, Kauê de, Cairns, Jill E., Dell’Acqua, Matteo, Fadda, Carlo, Güereña, David T., Heerwaarden, Joost van, Assefa, Teshale, Manners, Rhys, Müller, Anna, Pè, Mario Enrico, Polar, Vivian, Ramírez Villegas, Julián Armando, Solberg, Svein Øivind, Teeken, Béla, Tufan, Hale Ann

“…A continued investment in this area should fill remaining gaps and seize opportunities, including i) supporting genebanks to play a more active role in linking with farmers using data-driven approaches; ii) designing low-cost, appropriate technologies for phenotyping; iii) generating more and better gender and socioeconomic data; iv) designing information products to facilitate decision-making; and v) building more capacity in data science. Broad, well-coordinated policies and investments are needed to avoid fragmentation of such capacities and achieve coherence between domains and disciplines so that crop diversity management systems can become more effective in delivering benefits to farmers, consumers, and other users of crop diversity.…”
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Importancia del empoderamiento de la mujer en la nutrición infantil: Estudio de caso Guatemala by Jerez S., Catheryne B.

“…Se utilizó el programa “Statistical Software for Data Science (Stata versión 17.0®)”, empleando modelos de regresión para explicar la relación entre estos factores, con el fin de que sirvan de herramienta para guiar la toma de decisiones en los proyectos de desarrollo en Guatemala. …”
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AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data by Devare, Medha, Aubert, Céline, Benites Alfaro, Omar Eduardo, Pérez Masias, Ivan Omar, Laporte, Marie-Angélique

“…With the advent of powerful data science capabilities, predictive, empirical approaches are possible that operate over large data pools to discern patterns. …”
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Advanced spatial analytics for policy support: Use cases from One CGIAR by Song, Chun, Cenacchi, Nicola, Chamberlin, Jordan, Diao, Xinshen, Gebrekidan, Bisrat, Ghosh, Aniruddha, Gonzalez, Carlos, Gotor, Elisabetta, Guo, Zhe, Lenaerts, Bert, Mbabazi, Gloria, Mishra, Abhijeet, Mkondiwa, Maxwell, Mwungu, Chris, Otieno, Felix, Pede, Valerian, Petsakos, Athanasios, Robertson, Richard D., Thomas, Tim, Wanjau, Agnes, Yego, Francis, You, Liangzhi, Zhou, Shuang

“…It demonstrates how spatial analytics, a fast-evolving field that sits at the intersection of economics, public policy, geography, and data science, can provide actionable policy insights. …”
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Molecular insights into quality and authentication of sheep meat from proteomics and metabolomics by Zhang, Renyu, Pavan, Enrique, Ross, Alastair, Deb-choudhury, Santanu, Dixit, Yash, Mungure, Tanyaradzwa, Realini, Carolina, Cao, Mingshu, Farouk, Mustafa

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What can we learn from the ecophysiology of plants inhabiting extreme environments? From ‘sherplants’ to ‘shercrops’ by Flexas, Jaume, Fernie, Alisdair R., Usadel, Björn, Alonso-Forn, David, Ardiles, Victor, Ball, Marilyn C., Ballesteros, Daniel, Bravo, Leon, Brodribb, Tim J., Carriquí, Marc, Ergo, Veronica Vanesa, Gago, Jorge

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Digital data and tools for managing agriculture: focusing on earth observation data and climate change by Ghosh, Surajit, Mallick, A., Dawn, A., De Sarkar, K., Chowdhury, A., Kour, S., Ghosh, A., Holmatov, Bunyod

“…The discourse was further enriched by an expert panel discussion on the ‘Role of Digital Tools in Mitigating Climate Change Effects on Agriculture’, an intensive brainstorming session entitled ‘Data Science and Sustainability Challenge’ and a showcase of research presentation by the participants. …”
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Aplicación de computadora para estimar el porcentaje de área foliar con imágenes digitales by Landero Valenzuela, Nadia, Lara Viveros, Francisco Marcelo, Aguado Rodríguez, Graciano Javier

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HBeeID : a molecular tool that identifes honey bee subspecies from diferent geographic populations by Donthu, Ravikiran, Marcelino, Jose A. P., Giordano, Rosanna, Tao, Yudong, Weber, Everett, Avalos, Arian, Band, Mark, Akraiko, Tatsiana, Chen, Shu‑Ching, Reyes, Maria P, Hao, Haiping, Ortiz‑Alvarado, Yarira, Cuf, Charles A., Pérez Claudio, Eddie, Soto‑Adames, Felipe, Smith‑Pardo, Allan H., Meikle, William G., Evans, Jay D., Giray, Tugrul, Abdelkader, Faten B., Allsopp, Mike, Ball, Daniel, Morgado, Susana B., Barjadze, Shalva, Correa‑Benitez, Adriana, Chakir, Amina, Báez, David R., Chavez, Nabor H. M., Dalmon, Anne, Douglas, Adrian B., Fraccica, Carmen, Fernández‑Marín, Hermógenes, Galindo Cardona, Alberto, Guzman‑Novoa, Ernesto, Horsburgh, Robert, Kence, Meral, Kilonzo, Joseph, Kükrer, Mert, Le Conte, Yves, Mazzeo, Gaetana, Mota, Fernando, Muli, Elliud, Oskay, Devrim, Ruiz‑Martínez, José A., Oliveri, Eugenia, Pichkhaia, Igor, Romane, Abderrahmane, Guillen Sanchez, Cesar, Sikombwa, Evans, Satta, Alberto, Scannapieco, Alejandra Carla, Stanford, Brandi, Soroker, Victoria, Velarde, Rodrigo A., Vercelli, Monica, Huang, Zachary

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Role of digital platforms in maintaining the value chains of vegetable, root and tuber crops functional in the face of Covid-19. Research Report by Ayuya, O.I., Adongo, S., Wanjohi, L., Rajendran, S.

“…Strong focus of the digital platforms in providing unique products and solutions in ever-changing environment is key in keeping pace with consumers dynamic needs, key among them Enhance interactiveness between agricultural digital solutions and clients e.g. chatbot Integrating youth entrepreneurs and higher education students to support development of ICT related agri solutions e.g., accessible database by use of USSD code for vendors to easily authenticate the quality of the produce as well as data science. This will facilitate creation of robust learning curve to trigger development of marketable and viable agricultural digital services.…”
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Caracterización de los sistemas de pastoreo de pequeños rumiantes en el sur del Tolima by Galeano, Lina, Gómez, Mario, Gómez, Julio

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Evaluación de modelos de aprendizaje automático para la predicción de Boro en suelos Andisoles de Nariño-Colombia by Álvarez Sánchez, David, López Estrella, Xilena, Manso Ordoñez, Eduar, López Rivera, Laura, Rodriguez Valenzuela, Jeison

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