Submitted to: Sustainability
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/27/2017
Publication Date: 7/31/2017
Citation: Baez-Gonzalez, A.D., Kiniry, J.R., Meki, M.N., Williams, J., Alvarez-Cilva, M., Ramos-Gonzalez, J.L., Magallanes-Estala, A., Zapata-Buenfiel, G. 2017. Crop parameters for modeling sugarcane under rainfed conditions in Mexico. Sustainability. 9:1337. doi:10.3390/su9081337. Interpretive Summary: Improving sugarcane productivity for food and biofuel generation requires crop models with well-tested parameters. This study aimed to calibrate the light extinction coefficient and other crop parameters for a common sugarcane type. This early-maturing sugarcane is commonly grown in Mexico and other countries. In this study we determined the best value for calculating light interception to simulate sugarcane in a diverse range of dryland conditions in Mexico. The ALMANAC model was used with input data (climate, soil, management and yield) for two growing seasons from 35 farms in the Northeast, Gulf of Mexico and Pacific sugarcane regions of Mexico. The model with new values for several plant growth parameters showed high accuracy and simulated yield fluctuations realistically. With these new parameters, the model captured the pattern of yield fluctuations. We should now be able to simulate a wide range of sugarcane types in a wide range of environments.
Technical Abstract: Crop models with well-tested parameters can improve sugarcane productivity for food and biofuel generation. This study aimed to (i) calibrate the light extinction coefficient (k) and other crop parameters for the sugarcane cultivar CP 72-2086, an early-maturing cultivar grown in Mexico and many other countries, and (ii) determine the best k value for simulating sugarcane in a diverse range of dryland conditions. The ALMANAC model was used with input data (climate, soil, management and yield) for two growing seasons from 35 farms in the Northeast, Gulf of Mexico and Pacific sugarcane regions of Mexico. Statistical analyses were made using regression analysis and mean squared deviation (MSD=RMSD2). Model simulations with a k of of 0.69, maximum leaf area index of 7.5, leaf area index decline rate of 0.3, optimal and minimum temperature for plant growth of 32°C and 11°C, respectively, potential heat units 6000 to 7400, harvest index 0.9, maximum crop height and root depth of 4.0 and 2.0 meters, respectively, showed highest accuracy and captured best the magnitude of yield fluctuations. With a k of 0.65, the model captured best the pattern of yield fluctuations. Having the lowest MSD, a k of 0.69 seems reasonable for simulating CP 72-2086 under diverse dryland conditions. In the absence of a specified k value for a sugarcane cultivar, either a k of 0.69 or a k of 0.65, the default k value in the ALMANAC model, may be used. Using a dynamic value of k (varying during the growing season) deserves further study as it may help improve crop model precision.