Evaluation of the DSSAT‐CANEGRO model for simulating the growth of energy cane (Saccharum spp.), a biofuel feedstock crop

Abstract

The Decision Support System for Agrotechnology Transfer for sugarcane (DSSAT-CANEGRO) was evaluated for simulating the growth of energy cane (Saccharum spp. hybrid), a perennial biofuel feedstock crop. Plant growth data collected from a field experiment conducted in the semi-arid Texas Rolling Plains was used for model calibration and validation. All model performance indicators showed good agreement between simulated and measured biomass accumulation. After calibration, model was used for simulating plant cane and ratoon biomass yield using long-term (i.e., 33-year) historic weather data under rainfed and two irrigation levels. The irrigated treatments were a) 300 mm seasonal supplementary irrigation and b) auto-irrigation to replenish soil water in the profile to field capacity. The long-term average biomass yield of plant cane (6.4 ± 0.47 Mg ha -1 ) and ratoon (14.4 ± 0.92 Mg ha -1 ) were the lowest when simulated under rainfed conditions. Supplemental irrigation with 300 mm irrigation water significantly increased yield of both crops (14.6 ± 0.53 Mg ha -1 for plant cane and 29.5 ± 1.33 Mg ha -1 for ratoon). The auto-irrigation option substantially increased the amount of water applied and resulted in an average biomass of 16.7 ± 0.24 Mg ha -1 for plant cane and 44.4 ± 0.36 Mg ha -1 for ratoon.Biomass water use efficiency was the highest for ratoon due to its longer growing season. This modeling study showed reasonable performance of DSSAT-CANEGRO for simulating energy cane growth. Irrigation experiments using historical data showed energy cane as a competitive biomass crop in the Texas Rolling Plains region. INTRODUCTIONCrop simulation models are widely used in many aspects of crop improvement due to their ability to integrate the response of genotype, environment, management practices, and their Abbreviations: CANEGRO, sugarcane crop simulation model included in the DSSAT software; CRM, coefficient of residual mass; DSSAT, Decision Support System for Agrotechnology Transfer; ET, evapotranspiration; LAI, leaf area index; MAE, mean absolute error; RMSE, root mean square error; WUE, water use efficiency; WUE B , biomass water use efficiency.

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

“…In the United States, ref. [46] assessed the feasibility of simulating sugarcane growth and estimating biomass yield for type II energy sugarcane genotypes, which are characterized by having a low sugar level (sucrose less than 6%) and very high fiber content. In the study, the authors concluded that calibrated DSSAT/CANEGRO could provide good estimations of energy sugarcane biomass (Mean Absolute Error, MAE = 2.9 ton ha −1 ; % Root Mean Square Error, %RMSE = 16.5 ton ha −1 ; Coefficient of Determination, R 2 = 0.94), and emphasized that the modeling could be improved using specific genotype data for energy sugarcane in the simulation process.…”

Pyrolysis of Energy Cane Bagasse: Investigating Kinetics, Thermodynamics, and Effect of Temperature on Volatile Products

“…Its biomass productivity is 2.5 times higher than conventional sugarcane; besides having high fiber content, these species produce a juice containing sucrose that can be used in the production of sugar [27,28]. There are two types of energy cane depending on the composition: type I has a low sucrose content (10-14%) and high fiber content (15-20%); type II has a lower sucrose content (<10%) and high fiber content (>20%) [29]. In Brazil, energy cane is obtained from the genetic crossing of the sugarcane species S. officinarum and S. Spontaneum.…”