Process-based simple model for simulating sugarcane growth and production

Abstract

Dynamic simulation models can increase research effi ciency and improve risk management of agriculture. Crop models are still little used for sugarcane (Saccharum spp.) because the lack of understanding of their capabilities and limitations, lack of experience in calibrating them, diffi culties in evaluating and using models, and a general lack of model credibility. This paper describes the biophysics and shows a statistical evaluation of a simple sugarcane processbased model coupled with a routine for model calibration. Classical crop model approaches were used as a framework for this model, and fi tted algorithms for simulating sucrose accumulation and leaf development driven by a source-sink approach were proposed. The model was evaluated using data from fi ve growing seasons at four locations in Brazil, where crops received adequate nutrients and good weed control. Thirteen of the 27 parameters were optimized using a Generalized Likelihood Uncertainty Estimation algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index, stalk and aerial dry mass, and sucrose content, using bias, root mean squared error, modeling effi ciency, correlation coeffi cient and agreement index. The model well simulated the sugarcane crop in Southern Brazil, using the parameterization reported here. Predictions were best for stalk dry mass, followed by leaf area index and then sucrose content in stalk fresh mass. IntroductionDynamic simulation models can increase research effi ciency by allowing the analyst to search for strategies and analyze system performance, improve risk management, and interpret fi eld experiments that deal with crop responses to soil, management, genetic or environmental factors (Keating et al., 1999).Sugarcane (Saccharum spp.) is of major social and economic importance in Brazil. Worldwide, there have been several models developed specifi cally for sugarcane crop simulation (Pereira and Machado, 1986;Jones et al., 1989;Langellier and Martine, 2007;Keating et al., 1999;Thorburn et al., 2005;Inman-Bamber, 1991;Singels et al., 2008).Some of the physiological development and growth parameters that appear in the functions vary among sugarcane cultivars, meaning that they have to be estimated from data in order to predict growth and yield. Some of the parameters cannot be measured directly in typical experiments; instead, they have to be estimated based on data that are measured in experiments. Recent literature contains relatively little work on parameter estimation for crop models (Ahuja and Ma, 2011; Makowski et al., 2006). Makowski et al. (2006) point out the importance of raising the quality of calibration in crop models with automatic procedures for parameter adjustment. This would help ensure that the data are always used appropriately and in the same way for parameter estimation (Wallach et al., 2001), but such procedures are not available for direct use with existing sugarcane models.A new sugarcane model was developed ...

Effect of soil straw cover on evaporation, transpiration, and evapotranspiration in sugarcane cultivation

“…The quantitative understanding of the influence of straw on the water availability of soil, the evaporation of soil water, and the transpiration of sugarcane crop can aid growers on making decision. This knowledge could be useful to be included in process based crop models (Marin and Jones, 2014).…”

Sugarcane productivity simulation under different planting times by DSSAT/CANEGRO model in Alagoas, Brazil

“…It was observed that the dry matter productivity simulated by the model also showed higher errors for ratoon crop, although most of the results are within the acceptable error (± 20%). Singels et al (2008), Marin et al (2011), Marin and Jones (2014) reported high variations in the estimate of this parameter in the modeling. Although satisfactory, results show a slight deficiency in the fresh and dry matter yield simulation model in ratoon crop and that can still be adjusted.…”

“…Dynamic crop growth models are tools used to assist in this planning. They allow greater understanding of the crop and its behavior in different management scenarios and climate, assisting in decision making (Thorp et al, 2008;Marin and Jones, 2014;Carvalho et al, 2015).…”

Energy conversion efficiency in sugarcane under two row spacings in northeast of Brazil

“…Thus, the solar radiation balance in the plant canopy in these different agricultural management is fundamental to understanding the growth and development processes (Sinclair & Muchow, 1999;Ferreira Junior et al, 2014b;Wang et al, 2015) and the application of models that use components of this balance as input variable ( Liu & Bull, 2001;Marin & Jones, 2014). Solar radiation intercepted (or absorbed) by the plant canopy is the most important component for growth analysis, but to estimate this radiation, it is necessary to know the leaf area index (LAI) and the extinction coefficient (k) (Silva & Costa, 2012).…”