Growth Analysis and Simulation Approach for Sugarcane Genotypes Grown Under Different Growing Seasons

Abstract

Selecting the appropriate genotypes for various growing seasons and environments could improve sugarcane productivity, and the decision support system for agrotechnology transfer (DSSAT) program offers a tool to decrease time and resources for this process. The objectives of this study were to evaluate the performances of four sugarcane genotypes (Phukeaw 1 (PK1), Phukeaw 2 (PK2), MPT09-296, and Khon Kaen (KK3)) for planting dates of 1 Mar 2020 and 15 Dec 2020, and to calibrate and validate the genetic coefficients of four sugarcane genotypes for the Canegro model. Four sugarcane genotypes were evaluated using a randomized complete block design (RCBD) with four replications in two growing periods from 2020 to 2021 in Chaiyaphum, Thailand. Soil characteristics before planting, crop traits, weather data, and management data were recorded as input for model calibration to determine the genetic coefficients. Low temperatures and solar radiation were associated with low biomass accumulation rates when planting on 15 Dec 2020. KK3 appeared to be a good genotype in terms of total and stalk dry weights at the final harvest. On the 1 Mar 2020 planting date, KK3 showed the highest value of CGR from 4 to 6 MAP and 8–10 MAP, except for the 15 Dec 2020 planting date, where it achieved the highest value of CGR only from 4 to 6 MAP. The calibration results generally showed good accuracy between simulated and observed sugarcane traits, as indicated by d-stat values above 0.8 for all sugarcane genotypes. For model validation using an independent data set, the results revealed fair to good agreement between simulated and observed values for total stalk dry weight, stalk height, and sucrose percentage. This suggests the model’s potential as a tool for evaluating sugarcane genotype performance across different growing seasons.