Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in Mexico
Authors:
- Diego GómezDGDiego Gómez
- Pablo SalvadorPSPablo Salvador
- Julia SanzJSJulia Sanz
- José Luis CasanovaJCJosé Luis Casanova
- 1 April 2021
- journal article
- research article
- Published by Elsevier in Agricultural and Forest Meteorology
- Vol. 300
Abstract
No abstract availableKeywords
This publication has 39 references indexed in Scilit:
- Recent advances in sensing plant diseases for precision crop protectionEuropean Journal of Plant Pathology, 2012
- Crop yield forecasting on the Canadian Prairies using MODIS NDVI dataAgricultural and Forest Meteorology, 2011
- Linkages among climate change, crop yields and Mexico–US cross-border migrationProceedings of the National Academy of Sciences, 2010
- Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemble-based probabilistic projectionAgricultural and Forest Meteorology, 2009
- Partial least squares regression as an alternative to current regression methods used in ecologyOikos, 2009
- Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approachEuropean Journal of Agronomy, 2009
- Operational maize yield model development and validation based on remote sensing and agro‐meteorological data in KenyaInternational Journal of Remote Sensing, 2007
- World Map of the Köppen-Geiger climate classification updatedMeteorologische Zeitschrift, 2006
- Comparing support vector machines with Gaussian kernels to radial basis function classifiersIEEE Transactions on Signal Processing, 1997
- Generalized Linear ModelsJournal of the Royal Statistical Society. Series A (General), 1972