Search Results - "Machine Learning"

Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India by Nayak, Harisankar, Silva, ‪João Vasco, Parihar, Chiter Mal, Krupnik, Timothy J., Sena, Dipaka Ranjan, Kakraliya, S., Jat, Hanuman Sahay, Sidhu, Harminder Singh, Sharma, Parbodh Chander, Jat, Mangi Lal, Sapkota, Tek Bahadur

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Developing timely and actionable drought forecasts for the Limpopo River Basin by Montes, Carlo

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Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications by Amjath-Babu, T.S., López Ridaura, Santiago, Krupnik, Timothy J.

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Untangling the effect of soil quality on rice productivity under a 16-years long-term fertilizer experiment using conditional random forest by Garnaik, Saheed, Samant, Prasanna Kumar, Mandal, Mitali, Mohanty, Tushar Ranjan, Dwibedi, Sanat Kumar, Patra, Ranjan Kumar, Mohapatra, Kiran Kumar, Wanjari, Ravi H., Sethi, Debadatta, Sena, Dipaka Ranjan, Sapkota, Tek Bahadur, Nayak, Jagmohan, Patra, Sridhar, Parihar, Chiter Mal, Nayak, Harisankar

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Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier by Schulthess, Urs, Rodrigues, Francelino, Taymans, Matthieu, Bellemans, Nicolas, Bontemps, Sophie, Ortíz Monasterio, Jose Iván, Gerard, Bruno G., Defourny, Pierre

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Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands by Irvin, Jeremy, Zhou, Sharon, McNicol, Gavin, Lu, Fred, Liu, Vincent, Fluet-Chouinard, Etienne, Ouyang, Zutao, Knox, Sara Helen, Lucas-Moffat, Antje, Trotta, Carlo, Papale, Dario, Vitale, Domenico, Mammarella, Ivan, Alekseychik, Pavel, Aurela, Mika, Avati, Anand, Baldocchi, Dennis, Bansal, Sheel, Bohrer, Gil, Campbell, David I., Jiquan Chen, Chu, Housen, Dalmagro, Higo J., Delwiche, Kyle B., Desai, Ankur R., Euskirchen, Eugénie, Feron, Sarah, Goeckede, Mathias, Heimann, Martin, Helbig, Manuel, Helfter, Carole, Hemes, Kyle S., Hirano, Takashi, Iwata, Hiroki, Jurasinski, Gerald, Kalhori, Aram, Kondrich, Andrew, Lai, Derrick Y.F., Lohila, Annalea, Malhotra, Avni, Merbold, Lutz, Mitra, Bhaskar, Ng, Andrew, Nilsson, Mats B., Noormets, Asko, Peichl, Matthias, Rey Sanchez, A. Camilo, Richardson, Andrew D., Runkle, Benjamin R.K., Schäfer, Karina V.R., Sonnentag, Oliver, Stuart-Haëntjens, Ellen, Sturtevant, Cove, Ueyama, Masahito, Valach, Alex C., Vargas, Rodrigo, Vourlitis, George L., Ward, Eric J., Wong, Guan Xhuan, Zona, Donatella, Alberto, Ma.Carmelita R., Billesbach, David P., Celis, Gerardo, Dolman, Han, Friborg, Thomas, Fuchs, Kathrin, Gogo, Sébastien, Gondwe, Mangaliso J., Goodrich, Jordan P., Gottschalk, Pia, Hörtnagl, Lukas, Jacotot, Adrien, Koebsch, Franziska, Kasak, Kuno, Maier, Regine, Morin, Timothy H., Nemitz, Eiko, Oechel, Walter C., Oikawa, Patricia Y., Ono, Keisuke, Sachs, Torsten, Sakabe, Ayaka, Schuur, Edward A.G., Shortt, Robert, Sullivan, Ryan C., Szutu, Daphne J., Tuittila, Eeva-Stiina, Varlagin, Andrej, Verfaillie, Joseph G., Wille, Christian, Windham-Myers, Lisamarie, Poulter, Benjamin, Jackson, Robert B.

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Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy by Silva, ‪João Vasco, Heerwaarden, Joost van, Reidsma, Pytrik, Laborte, Alice G., Tesfaye, Kindie, Ittersum, Martin K. van

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Mapping major land use types in Nandi County using remote sensing and machine learning by Guo, Zhe, Walter, Kibet

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Drought vulnerability of central Sahel agrosystems: a modelling-approach based on magnitudes of changes and machine learning techniques by Hanade Houmma, I., El Mansouri, L., Gadal, S., Faouzi, E., Touré, A.A., Garba, M., Imani, Y., El-Ayachi, M., Hadria, R.

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Small reservoirs in the northern regions of Ghana and their vulnerability to drying by Siabi, Ebenezer K., Akpoti, Komlavi, Zwart, Sander J.

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Crop Monitoring Using Smartphone Based Near-Surface Remote Sensing: Ground Pictures of Wheat and Auxiliary Data from Northern India by International Food Policy Research Institute, Ghent University, University of Manchester

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Site-specific fertilizer recommendation using data driven machine learning enhanced wheat productivity and resource use efficiency by Liben, Feyera, Abera, Wuletawu, Chernet, Meklit Tariku, Ebrahim, Mohammed, Tilaye, Amsalu, Erkossa, Teklu, Degefie, Tibebe Degefie, Mponela, Powell, Kihara, Job, Tamene, Lulseged D.

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Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning by Adesokan, M., Otegbayo, B., Alamu, E.O., Olutoyin, M.A., Maziya-Dixon, B.

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Predicting aflatoxin risk in maize using machine learning and satellite data in East and Southern Africa by Gachoki, Stella, Muthoni, Francis, Mahuku, George S., Atehnkeng, J., Tripathi, Leena

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Hybrid-AI and Model Ensembling to Exploit UAV-Based RGB Imagery: An Evaluation of Sorghum Crop’s Nitrogen Content by Hammouch, Hajar, Patil, Suchitra, Choudhary, Sunita, El-Yacoubi, A. Mounim, Masner, Jan, Kholová, Jana, Anbazhagan, Krithika, Vanek, Jirí, Qin, Huafeng, Stoces, Michal, Berbia, Hassan, Jagarlapudi, Adinarayana, Chandramouli, Magesh, Mamidi, Srinivas, Prasad, K.V.S.V., Baddam, Rekha

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Advancing an internet research agenda for CGIAR Focus Climate Security by Carneiro, Bia, Tucci, Giulia, Taiwo, Ibukun, Olomu, Rukeme

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In-season crop-type mapping in Kenya using Sentinel-2 imagery by Li, Hui, Guo, Zhe, Di, Liping, Guo, Liying, Zhang, Chen, Lin, Li

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Underlying drivers of deforestation in the Peruvian amazon and the highland region of the Andes by Juarez, H., Pradel, W., Navarrete, C., Gutiérrez, D., Hualla, V., Vanegas, M., Sylvester, J., Castro, A.

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Mapping mechanization suitability using machine learning, presence data and spatial co-variates: Methodological approach by Adhikari, Subash, Koirala, Pankaj, Kafle, Sagar, Mponela, Powell

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Excellence in Agronomy (EiA) product - CG Labs by Excellence in Agronomy

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