Crop Parameters for Modeling Sugarcane under Rainfed Conditions in Mexico
Campo Experimental Pabellon, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias (INIFAP), km 32.5 Carr, Aguascalientes-Zacatecas, Pabellon de Arteaga 20660, Aguascalientes, Mexico
USDA, Agricultural Research Service, Grassland Soil and Water Research Laboratory, 808 E. Blackland Rd., Temple, TX 76502, USA
AgriLife Research, Blackland Research and Extension Center, 720 E. Blackland Rd., Temple, TX 76502, USA
Campo Experimental Tecoman, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias, km. 35, Carr, Colima-Manzanillo, Tecoman 28930, Colima, Mexico
Campo Experimental Rio Bravo, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias, km 61 Carr, Matamoros-Reynosa, Rio Bravo 88900, Tamaulipas, Mexico
Campo Experimental Chetumal, INIFAP, km 25.2 Carr. Chetumal-Bacalar, Ejido Juan Sarabia, Municipio Othon P. Blanco 77900, Quintana Roo, Mexico
Author to whom correspondence should be addressed.
Sustainability 2017, 9(8), 1337; https://doi.org/10.3390/su9081337
Received: 21 May 2017 / Revised: 25 July 2017 / Accepted: 27 July 2017 / Published: 31 July 2017
(This article belongs to the Section Sustainable Agriculture)
Crop models with well-tested parameters may help improve sugarcane productivity for food and biofuel generation, especially in rainfed areas where studies are scarce. This study aimed to calibrate crop parameters for the sugarcane cultivar CP 72-2086, an early-maturing cultivar widely grown in Mexico and other countries, and evaluate their adequacy in simulating sugarcane in a diverse range of rainfed conditions. For the calibration and evaluation of parameters, the ALMANAC model was used with climate, soil, management, and yield for two growing seasons from 30 farms in three regions (Northeastern Mexico, Gulf of Mexico, and Pacific Mexico). Statistical analyses were made using regression analysis and mean squared deviation and its three components, i.e., the squared bias, the lack of correlation weighted by the standard deviations, and the squared difference between standard deviations. Model simulations with a light extinction coefficient (k) 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 of 6000 to 7400 degree days (base 11 °C), harvest index of 0.9; maximum crop height of 4.0 m, and root depth of 2.0 m showed highest accuracy and captured best the magnitude of yield fluctuations with a root mean squared deviation of 7.8 Mg ha−1. The parameters were found to be reasonable to use in simulating sugarcane in diverse regions under rainfed conditions. Using a dynamic value of k (varying during the growing season) deserves further study as it may help improve crop model precision.