Simulation of biomass and sugar accumulation in sugarcane using a process-based model

Liu, De Li; Bull, T. A.

doi:10.1016/s0304-3800(01)00372-6

Cited by 53 publications

(48 citation statements)

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“…The sum of all RWF i for the entire profi le should be equal to 1. Because the distribution of sugarcane root length is similar to an exponential pattern (Liu and Bull, 2001;Laclau and Laclau, 2009), RWF values were estimated using the approach proposed by Jones et al (1991) using an exponential geotropism constant equal to 2. (13) where L i is the soil layer depth i, S maxdepth is the maximum root depth in the soil profi le assumed in the simulations, and n is the number of soil layers.…”

Section: Crop Componentsmentioning

confidence: 99%

Process-based simple model for simulating sugarcane growth and production

Marin

Jones

2014

Sci. agric. (Piracicaba, Braz.)

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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 ...

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Section: Crop Componentsmentioning

confidence: 99%

Process-based simple model for simulating sugarcane growth and production

Marin

Jones

2014

Sci. agric. (Piracicaba, Braz.)

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“…Em geral, esses modelos correlacionam os elementos ambientais com processos fisiológicos e com a produção das culturas, o que é possível em virtude da evolução do conhecimento dos processos fisiológicos vegetais e dos sistemas computacionais. A maioria dos modelos de crescimento considera a radiação solar como a variável principal de entrada (Keating et al, 1999;Liu & Bull, 2001;Scarpari & Beauclair, 2009), uma vez que esta variável é a fonte de energia para a fixação do CO 2 atmosférico. Porém apenas uma porcentagem da radiação solar é utilizada na fotossíntese, que corresponde à parte da energia da faixa do espectro solar entre os comprimentos de onda de 400 a 700 nanômetros (nm).…”

Section: Introductionunclassified

“…Alguns modelos, como exemplo, o QCANE (Liu & Bull, 2001;Singels et al, 2005), se preocupam com o detalhamento das relações dos fatores ambientais com os processos fisiológicos. Neste modelo o processo de crescimento da cultura inclui estimativas da fotossíntese e a respiração, que são funções do desenvolvimento da cultura e das variações dos elementos ambientes.…”

Section: Introductionunclassified

Crescimento e fotossíntese de cana-de-açúcar em função de variáveis biométricas e meteorológicas

Junior¹,

Souza

Lyra

et al. 2012

Rev. bras. eng. agríc. ambient.

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R ESU M OO crescimento da cana-de-açúcar pode ser obtido por modelos biofísicos em que a fotossíntese bruta (FB) é obtida em função da radiação solar. O objetivo do trabalho é avaliar variedades de canas-deaçúcar em regime irrigado em relação à radiação fotossinteticamente ativa interceptada (RFA INT ) e a estimativa da FB acumulada. Para isto, conduziu-se um estudo na Universidade Federal de Alagoas, entre 2008 e 2009, com variedades de cana RB. Foram realizadas medidas biométricas, variáveis de produção e dos elementos meteorológicos. A irradiância fotossintética (RFA) interceptada foi obtida pela diferença entre RFA e RFA transmitida (RFA T ). A RFA T foi determinada pela Lei de Beer. Na estimativa da FB diária usou-se uma integração numérica, com uma abordagem trapezoidal. As variáveis de produção tiveram correlações com a RFA INT acumulada e com a FB acumulada durante o ciclo. A média da irradiação solar global diária do período chuvoso da região (maio -agosto) foi igual a 14,9 MJ m -2. A variedade RB92579 teve os maiores variáveis de produção, como também maiores RFA interceptada e FB acumuladas no ciclo, devido à sua maior capacidade de rebrotação e conversão de energia em fotoassimilados.Palavras-chave: radiação fotossintética, modelos de crescimento, Saccharum spp. Growth and photosynthesis of sugarcane based on biometric and meteorological variablesA B ST R A C T Sugarcane growth can be obtained by biophysical models in which gross photosynthesis (GP) is obtained as a function of solar radiation. This work aims to evaluate sugarcane varieties under irrigation in relation to intercepted photosynthetic active radiation (PARint) and the estimated accumulative GP. To achieve that, a study was conducted at the Federal University of Alagoas during 2008 and 2009, with RB sugarcane varieties. Biometric measurements, production variables and meteorological elements were made. The intercepted photosynthetic irradiance (PAR) was obtained by the difference between PAR and transmitted PAR (PAR T ), which was determined by Beer's Law. The daily GP was estimated numerically by the trapezoidal approach. The production variables had correlations with accumulated PARint and accumulated GP during the crop cycle. The average global solar radiation in the region for rainy season (May-August) was 14.9 MJ m -2 . The variety RB92579 had the highest production variables as well as higher intercepted PAR and accumulated GP in the cycle due to its greater capacity for regrowth and energy conversion in photoassimilate.

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“…From Scientific point of view chronological age of sugarcane is not a reliable guide to determine cane maturity alone [10]. Therefore, other factors such as varieties, weather conditions, and soil type may have more direct bearing on the real maturity of canes than the crop age [11]. The current sugar production of the Ethiopian Sugar Industry covers only 60% of the annual demand for domestic Consumption while the deficient is imported from abroad.…”

Section: Introductionmentioning

confidence: 99%

Determining Optimum Harvest Age of Sugarcane Varieties on the Newly Establishing Sugar Project in the Tropical Areas of Tendaho, Ethiopia

Mengistu¹

2014

Adv Crop Sci Tech

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Field experiment was conducted to determine the optimum maturity of the major sugarcane varieties (Saccharium officinarium L.) with high sucrose content and sugar yield. Six levels of harvest ages (10,12,14,16,18 and 20 months) and four major varieties N-14, NCO-334, CO-680 and B52-298 which cover 90% of the area were used in a completely randomized block design with 6x4x3 factorial treatment arrangements. All data's were collected at the end of each level of harvest ages. Analysis of variance (ANOVA) showed that harvest age significantly influenced quality parameters (brix, pol, purity and ERS) and yield parameters (plant height, cane yield and sugar yield) (P<0.001). The important parameters of maximizing sugar yield and net revenue in relation to harvest date and crop age is expressed by t/ha/month as an index of time value of sugarcane crop. Considering the time value, increase in harvest age showed a negative impact on brix, pol, estimated recoverable sucrose, cane yield and sugar yield in the tropical area of Ethiopia. As a result high sugar yield was recorded at the early harvesting ages 12 and 14 months. However, optimum sugar yield was recorded on 12 months harvest age with economically acceptable marginal rates of return 178.13%. Therefore, adjusting harvest age to 12 months for the major sugarcane varieties was economically recommended to obtain optimum sugar yield with efficient time use at the tropical areas of Tendaho.

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Simulation of biomass and sugar accumulation in sugarcane using a process-based model

Cited by 53 publications

References 53 publications

Process-based simple model for simulating sugarcane growth and production

Process-based simple model for simulating sugarcane growth and production

Crescimento e fotossíntese de cana-de-açúcar em função de variáveis biométricas e meteorológicas

Determining Optimum Harvest Age of Sugarcane Varieties on the Newly Establishing Sugar Project in the Tropical Areas of Tendaho, Ethiopia

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