ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Construction of prediction models for maize grain quality components
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
Abstract:
To investigate the impact of meteorological conditions on the key quality components (protein, fat, starch, and amino acids) of maize grains, stepwise regression-based quality prediction models were constructed based on interval sowing tests and tested. The values calculated by the models were converted into quality grades and compared with the actual grades. The results showed that all stepwise regression-based prediction models passed the significance test, establishing a relationship between grain quality and meteorological factors from tasseling to milk stage and from milk stage to maturity. The linear relationship between grain quality and meteorological factors was quantified. The results of the model test and forecasting test indicated that the mean absolute percentage errors for all four quality components were less than 15 % and the predictions for starch and protein were closer to the actual values compared to those for fat and amino acids. The actual and predicted contents of grain quality components (regardless of cropping system) were converted into grades for validation. For protein, fat, and starch, the combined proportion of samples with predicted grades matching or within one grade of actual grades exceeded 90 % (reaching 100 % for starch), and was 76.67 % for amino acids. The predicted grades aligned well with the actual grades, indicating that the prediction models exhibited high accuracy and can be used for forecasting and evaluating grain quality. The findings can offer an objective and quantitative basis for optimizing environmental resource utilization to improve maize quality and for informing maize ecological zoning.
Keywords:
Project Supported:
National Key R＆D Program of China (2022YFD2001003)，Science and Technology Development Fund of CAMS (2024KJ011)
Clc Number:
Mobile website
