Lodging is a major constraint to increasing the global productivity of maize (Zea Maize L.). The objectives of this paper are to: i) describe a model for stem and root lodging in maize, ii) calibrate the anchorage strength component of the model, iii) evaluate the model's applicability by assessing its capacity to explain effects of crop husbandry on lodging risk and iv) investigate the potential to further develop the lodging model to predict lodging risk at an early enough growth stage for tactical agronomic action to minimise lodging risk. The study involved a multidisciplinary collaboration between crop scientists, wind engineers and geospatial scientists in the UK and China. Three field experiments with plant population density and nitrogen (N) fertiliser rate treatments were conducted in the UK and China to develop and test the lodging model. Plant characteristics associated with lodging were measured in the experiments after flowering. An existing model of cereal anchorage strength that uses the spread of the root plate as its primary input was demonstrated to be applicable for maize and calibrated for this crop species. The lodging model's predictions of the effects of plant population and N fertiliser on lodging risk were consistent with published observations. The lodging model calculated that increasing the plant population significantly reduced the anchorage and stem failure wind speeds in all experiments, thus increasing the risk of lodging. This effect was primarily due to increased plant population reducing the spread of the root plate and the stem strength. Changes in N fertiliser had a smaller effect on the lodging associated plant characters. A sensitivity analysis showed that stem failure wind speed was influenced most by variation in stem strength and root failure wind speed was influenced most by variation in the spread of the root plate. This study has shown that the leaf area index measured at leaf 4, 6 or 8 stages is a good indicator of a crop's future risk of lodging, which demonstrates the potential to develop the model into a practical tool for predicting lodging risk in time for tactical agronomic decisions to be made during the crop's growing period. © 2020 Elsevier B.V.
This is the author’s version of a work that was accepted for publication in Field Crops Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Field Crops Research, 262, 2021 DOI: 10.1016/j.fcr.2020.108037
