Classification of maize environments using crop simulation and geographic information systems

By C. Löffler, Jun Wei, T. Fast, Joe Gogerty, S. Langton, M. Bergman, Bob Merrill, Mark Cooper. 2005

156 citations recorded by the source corpus.

Subfields: Agronomy and Crop Science, Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/1790bdf3de4c1a8bfb3442db7da897b849388001

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Citations in the corpus

The U.S. drought of 2012 in perspective: A call to action (2013, methodology): “The effort utilized an environmental classification system to describe the impact of genotype by environmental interactions for yield in multienvironment trials which improved predictability of cultivar performance (Löffler et al., 2005).”
Plant adaptation to climate change—opportunities and priorities in breeding (2012, methodology): “2000), maize (Löffler et al. 2005), and wheat (Chenu et al.”
Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction (2014, background): “In all three examples (Chapman et al. 2000; Löffler et al. 2005; Chenu et al. 2011; Figs 7, 8), the challenges associated with conducting METs in a finite number of years to predict genotype trait performance in the TPE, or at least the following sequence of years, are emphasised.”
Industry-Scale Evaluation of Maize Hybrids Selected for Increased Yield in Drought-Stress Conditions of the US Corn Belt (2015, methodology): “To implement this classification we used the maize crop model embedded in the EnClass (Dupont Pioneer) system (Löffler et al., 2005).”
Evaluation of drought tolerance of some corn (Zea mays L.) hybrids in Iran. (2010, unknown): “Among various abiotic and biotic stress factors, drought is an important cause of genotype by environmental interactions in maize across years, locations (Löffler et al., 2005; Setimela et al., 2005) and most likely within individual fields (Bruce et al.”
Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials (2008, methodology): “In a practical sense, LoZer et al. (2005) showed that these types of indices (modelled for heat in their case) could assist in the interpretation of GEI in commercial breeding programmes.”