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Random Forests for Global and Regional Crop Yield Predictions

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  1. Helder Fraga & Teresa R. Freitas & Marco Moriondo & Daniel Molitor & João A. Santos, 2024. "Determining the Climatic Drivers for Wine Production in the Côa Region (Portugal) Using a Machine Learning Approach," Land, MDPI, vol. 13(6), pages 1-16, May.
  2. Jaturong Som-ard & Savittri Ratanopad Suwanlee & Dusadee Pinasu & Surasak Keawsomsee & Kemin Kasa & Nattawut Seesanhao & Sarawut Ninsawat & Enrico Borgogno-Mondino & Filippo Sarvia, 2024. "Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms," Land, MDPI, vol. 13(9), pages 1-19, September.
  3. Florian Schierhorn & Max Hofmann & Taras Gagalyuk & Igor Ostapchuk & Daniel Müller, 2021. "Machine learning reveals complex effects of climatic means and weather extremes on wheat yields during different plant developmental stages," Climatic Change, Springer, vol. 169(3), pages 1-19, December.
  4. Li, Siyi & Wang, Bin & Feng, Puyu & Liu, De Li & Li, Linchao & Shi, Lijie & Yu, Qiang, 2022. "Assessing climate vulnerability of historical wheat yield in south-eastern Australia's wheat belt," Agricultural Systems, Elsevier, vol. 196(C).
  5. Che-Hao Chang & Jason Lin & Jia-Wei Chang & Yu-Shun Huang & Ming-Hsin Lai & Yen-Jen Chang, 2024. "Hybrid Deep Neural Networks with Multi-Tasking for Rice Yield Prediction Using Remote Sensing Data," Agriculture, MDPI, vol. 14(4), pages 1-21, March.
  6. Silva, J.F. & Santos, J.L. & Ribeiro, P.F. & Marta-Pedroso, C. & Magalhães, M.R. & Moreira, F., 2024. "A farming systems approach to assess synergies and trade-offs among ecosystem services," Ecosystem Services, Elsevier, vol. 65(C).
  7. Devkota, Mina & Yigezu, Yigezu Atnafe, 2020. "Explaining yield and gross margin gaps for sustainable intensification of the wheat-based systems in a Mediterranean climate," Agricultural Systems, Elsevier, vol. 185(C).
  8. Puyu Feng & Bin Wang & De Li Liu & Hongtao Xing & Fei Ji & Ian Macadam & Hongyan Ruan & Qiang Yu, 2018. "Impacts of rainfall extremes on wheat yield in semi-arid cropping systems in eastern Australia," Climatic Change, Springer, vol. 147(3), pages 555-569, April.
  9. Kouame, Anselme K.K. & Bindraban, Prem S. & Kissiedu, Isaac N. & Atakora, Williams K. & El Mejahed, Khalil, 2023. "Identifying drivers for variability in maize (Zea mays L.) yield in Ghana: A meta-regression approach," Agricultural Systems, Elsevier, vol. 209(C).
  10. Ahmed, Moiz Uddin & Hussain, Iqbal, 2022. "Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan," Telecommunications Policy, Elsevier, vol. 46(6).
  11. Chen, Kefei & O'Leary, Rebecca A. & Evans, Fiona H., 2019. "A simple and parsimonious generalised additive model for predicting wheat yield in a decision support tool," Agricultural Systems, Elsevier, vol. 173(C), pages 140-150.
  12. Shine, P. & Scully, T. & Upton, J. & Murphy, M.D., 2019. "Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine," Applied Energy, Elsevier, vol. 250(C), pages 1110-1119.
  13. Sakshi Balyan & Harsita Jangir & Shakti Nath Tripathi & Arpita Tripathi & Tripta Jhang & Praveen Pandey, 2024. "Seeding a Sustainable Future: Navigating the Digital Horizon of Smart Agriculture," Sustainability, MDPI, vol. 16(2), pages 1-21, January.
  14. Li Fan & Shibo Fang & Jinlong Fan & Yan Wang & Linqing Zhan & Yongkun He, 2024. "Rice Yield Estimation Using Machine Learning and Feature Selection in Hilly and Mountainous Chongqing, China," Agriculture, MDPI, vol. 14(9), pages 1-18, September.
  15. Martin Kuradusenge & Eric Hitimana & Damien Hanyurwimfura & Placide Rukundo & Kambombo Mtonga & Angelique Mukasine & Claudette Uwitonze & Jackson Ngabonziza & Angelique Uwamahoro, 2023. "Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize," Agriculture, MDPI, vol. 13(1), pages 1-19, January.
  16. van der Velde, M. & Nisini, L., 2019. "Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015," Agricultural Systems, Elsevier, vol. 168(C), pages 203-212.
  17. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2022. "Exploring the weather-yield nexus with artificial neural networks," Agricultural Systems, Elsevier, vol. 196(C).
  18. Paudel, Dilli & Boogaard, Hendrik & de Wit, Allard & Janssen, Sander & Osinga, Sjoukje & Pylianidis, Christos & Athanasiadis, Ioannis N., 2021. "Machine learning for large-scale crop yield forecasting," Agricultural Systems, Elsevier, vol. 187(C).
  19. Barlin O. Olivares & Andrés Vega & María A. Rueda Calderón & Edilberto Montenegro-Gracia & Miguel Araya-Almán & Edgloris Marys, 2022. "Prediction of Banana Production Using Epidemiological Parameters of Black Sigatoka: An Application with Random Forest," Sustainability, MDPI, vol. 14(21), pages 1-18, October.
  20. Banda, Enid & Rafiei, Vahid & Kpodo, Josué & Nejadhashemi, A. Pouyan & Singh, Gurjeet & Das, Narendra N. & Kc, Rabin & Diallo, Amadiane, 2024. "Millet yield estimations in Senegal: Unveiling the power of regional water stress analysis and advanced predictive modeling," Agricultural Water Management, Elsevier, vol. 291(C).
  21. Keach Murakami & Seiji Shimoda & Yasuhiro Kominami & Manabu Nemoto & Satoshi Inoue, 2021. "Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-19, October.
  22. Xu Zhang & Guangsheng Chen & Lingxiao Cai & Hongbo Jiao & Jianwen Hua & Xifang Luo & Xinliang Wei, 2021. "Impact Assessments of Typhoon Lekima on Forest Damages in Subtropical China Using Machine Learning Methods and Landsat 8 OLI Imagery," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
  23. Sawadogo, Alidou & Dossou-Yovo, Elliott R. & Kouadio, Louis & Zwart, Sander J. & Traoré, Farid & Gündoğdu, Kemal S., 2023. "Assessing the biophysical factors affecting irrigation performance in rice cultivation using remote sensing derived information," Agricultural Water Management, Elsevier, vol. 278(C).
  24. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2021. "Estimation of the Farm-Level Yield-Weather-Relation Using Machine Learning," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317075, German Association of Agricultural Economists (GEWISOLA).
  25. Sumin Park & Haemi Park & Jungho Im & Cheolhee Yoo & Jinyoung Rhee & Byungdoo Lee & ChunGeun Kwon, 2019. "Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-23, October.
  26. Qingchuan Zhang & Zihan Li & Wei Dong & Siwei Wei & Yingjie Liu & Min Zuo, 2023. "A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different Environments," IJERPH, MDPI, vol. 20(5), pages 1-17, February.
  27. Mohsen Shahhosseini & Guiping Hu, 2020. "Machine Learning Models for Corn Yield Prediction A Survey of Literature," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 25(3), pages 80-83, July.
  28. Timsina, Jagadish & Dutta, Sudarshan & Devkota, Krishna Prasad & Chakraborty, Somsubhra & Neupane, Ram Krishna & Bishta, Sudarshan & Amgain, Lal Prasad & Singh, Vinod K. & Islam, Saiful & Majumdar, Ka, 2021. "Improved nutrient management in cereals using Nutrient Expert and machine learning tools: Productivity, profitability and nutrient use efficiency," Agricultural Systems, Elsevier, vol. 192(C).
  29. Britta L. Schumacher & Emily K. Burchfield & Brennan Bean & Matt A. Yost, 2023. "Leveraging Important Covariate Groups for Corn Yield Prediction," Agriculture, MDPI, vol. 13(3), pages 1-18, March.
  30. Indy Man Kit Ho & Anthony Weldon & Jason Tze Ho Yong & Candy Tze Tim Lam & Jaime Sampaio, 2023. "Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement," IJERPH, MDPI, vol. 20(10), pages 1-15, May.
  31. Indy Man Kit Ho & Kai Yuen Cheong & Anthony Weldon, 2021. "Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-27, April.
  32. Héctor García-Martínez & Héctor Flores-Magdaleno & Roberto Ascencio-Hernández & Abdul Khalil-Gardezi & Leonardo Tijerina-Chávez & Oscar R. Mancilla-Villa & Mario A. Vázquez-Peña, 2020. "Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles," Agriculture, MDPI, vol. 10(7), pages 1-24, July.
  33. Yiliang Kang & Yang Wang & Yanmin Fan & Hongqi Wu & Yue Zhang & Binbin Yuan & Huijun Li & Shuaishuai Wang & Zhilin Li, 2024. "Wheat Yield Estimation Based on Unmanned Aerial Vehicle Multispectral Images and Texture Feature Indices," Agriculture, MDPI, vol. 14(2), pages 1-15, January.
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