Why does sugarcane (Saccharum sp. hybrid) grow slowly?

Allison, J. C. S.; Pammenter, N. W.; Haslam, R. J.

doi:10.1016/j.sajb.2007.04.065

Cited by 47 publications

(52 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…5), representing the late decrease in stem elongation rate related to the lower specifi c leaf N content, which likely depresses the photosynthetic potential (Allison et al, 2007;Inman-Bamber et al, 2008); LI is the proportion of radiation interception able to be captured by the canopy, as a function of LAI and the extinction coeffi cient (EXTCOEF); PRATIO is the relative effect ; SWSP is the effect of soil-water stress on photosynthesis (below described in Eq. 18), with SWSP = 1 meaning no stress, and SWSP = 0 meaning maximum stress.…”

Section: Crop Componentsmentioning

confidence: 99%

Process-based simple model for simulating sugarcane growth and production

Marin

Jones

2014

Sci. agric. (Piracicaba, Braz.)

View full text Add to dashboard Cite

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 ...

show abstract

Section: Crop Componentsmentioning

confidence: 99%

Process-based simple model for simulating sugarcane growth and production

Marin

Jones

2014

Sci. agric. (Piracicaba, Braz.)

View full text Add to dashboard Cite

show abstract

“…As a C 4 plant of warm, productive habitats, sugarcane should also have a high productive potential; however, this possibility has been questioned (Bull & Glasziou 1975;Allison et al 2007; Chapter 21 of this book). Annual productivity of sugarcane is less than that of the highly productive C 4 plant Pennisetum purpureum (Napier grass) due largely to slow early growth of young sugarcane plants (Allison et al 2007).…”

Section: Notable C 3 Crops (Global Production Rank In Parentheses)mentioning

confidence: 99%

“…Annual productivity of sugarcane is less than that of the highly productive C 4 plant Pennisetum purpureum (Napier grass) due largely to slow early growth of young sugarcane plants (Allison et al 2007). These differences relate to relatively low allocation of resources to leaf and tiller production in young sugarcane (Allison et al 2007) and not to photosynthetic pathway per se.…”

Section: Notable C 3 Crops (Global Production Rank In Parentheses)mentioning

confidence: 99%

Photosynthesis in Sugarcane

Sage

Peixoto

Sage

2013

Sugarcane: Physiology, Biochemistry, and Functional Biology

View full text Add to dashboard Cite

Sugarcane and related species utilize the NADP malic enzyme type of C 4 photosynthesis, which confers superior radiation-, nitrogen-, and wateruse efficiencies relative to C 3 plants in the warm environments where sugarcane thrives. By being able to concentrate CO 2 around Rubisco, sugarcane avoids limitations due to photorespiration and exhibits superior photosynthesis over C 3 plants at low CO 2 levels and warm temperatures (> 30 • C). At current atmospheric CO 2 levels, reported photosynthetic capacities of sugarcane are often low relative to other cultivated C 4 species and frequently are equivalent to photosynthesis rates in C 3 crops such as wheat and rice. These reduced photosynthetic capacities are present in older sugarcane plants because leaf nitrogen values become depleted as cumulatively more tissues are produced, and high carbohydrate levels may downregulate photosynthesis. This is particularly apparent when growers limit late nitrogen applications to boost sugar production. When photosynthesis is measured in young plants with cumulatively low leaf area, or in plants where leaf nitrogen is maintained by regular or heavy nitrogen applications, then sugarcane photosynthetic capacities are typical of C 4 plants and higher than observed in most C 3 species at current CO 2 levels. In leaves with reduced N, photosynthesis in sugarcane could become less efficient, because an imbalance between the C 3 and C 4 metabolic cycles may promote leakage of bundle sheath CO 2 out of the leaf. These observations indicate that photosynthesis in sugarcane could be improved by creating new varieties that either maintain high leaf nitrogen or enhance photosynthetic nitrogen-use efficiency to match that of productive C 4 crops such as maize.

show abstract

“…This reflects the relative slow initial growth and establishment of sugarcane, a phenomenon also highlighted by Alexander (1985) and Allison et al (2007). Once the canopy and stalk population is established, conventional sugarcane varieties N19 and N31 were able to maintain higher growth rates, partly due to the fact that they were not limited by flowering and self-trashing as with 04G0073 and Napier grass.…”

mentioning

confidence: 81%

Water and radiation use efficiency of sugarcane for bioethanol production in South Africa, benchmarked against other selected crops

Olivier

Singels

Eksteen

2015

South African Journal of Plant and Soil

View full text Add to dashboard Cite

There are indications that high-fibre sugarcane genotypes may produce more biomass and use resources more efficiently than conventional sugarcane cultivars.The objective of this research was to gather quantitative information on resource use for selected conventional and high-fibre sugarcane genotypes and benchmark it against other bioethanol crops. Although conventional sugarcane initially grew slower than sorghum and Napier grass, it produced very high biomass (about 70 t ha -1 ) and theoretical ethanol (first-and second-generation) yields (about 27 kL ha ), outperforming all other crops except sorghum. The contribution of cellulosic ethanol to total ethanol yield varied hugely, from 89% for the high-fibre sugarcane hybrid to about 48% for conventional sugarcane, to as low as 14% for sugar beet. The highfibre sugarcane hybrid grew faster initially and produced more biomass at eight months (56 t ha -1 vs. 45 t ha -1 ) than the conventional types, but then flowered, reducing its growth rates markedly thereafter. It was also less sensitive to mild drought conditions. Results suggest that cellulosic ethanol production could be a feasible option that could be incorporated into conventional or biomass sugarcane production systems.Keywords: bioethanol crops, biomass, high-fibre sugarcane, stalk fibre composition, theoretical ethanol yield IntroductionThere is increasing interest in renewable energy, including biofuel from crops.Bioethanol can be produced from the fermentation of soluble sugars in the storage organs of feedstock crops, while 2 nd generation lignocellulose technology enables the production of ethanol from cell-wall sugars extracted from plant fibre (Ragauskas et al. 2006). This will greatly enhance the potential ethanol output from feedstock crops and address concerns regarding ethanol production from food crops in high potential production areas.Potential bioethanol crops include maize, switchgrass, Miscanthus, sugarcane, sugar beet, sorghum and poplar. Compared to other crops sugarcane has abundant potential for producing high biomass yield (Alexander, 1985) and consequently high bioethanol yields from sugars in the juice (Renouf et al. 2008) and form leaf and stalk fibre (Waclawovsky et al. 2010, de Souza et al. 2013. Energy cane that produce high biomass rather than high sucrose yield and use natural resources more efficiently, are currently in development (Tew and Cobill 2008). These genotypes 2 could possibly be used for biomass production in marginal production areas where resource levels are low, such as low rainfall areas or areas with poor soils.Very little quantitative information on radiation and water use or crop productivity is available for high-fibre sugarcane types in South Africa. Waclawovsky et al. (2006) quote commercial maximum yields of 29 t ha -1 of dry biomass and puts forward a theoretical maximum of 177 t ha -1. Alexander (1985) hypothesizes that sugarcane yields can be increased two fold by using high-fibre cane and managing water and nitrogen to maximize biomass growth an...

show abstract

Why does sugarcane (Saccharum sp. hybrid) grow slowly?

Cited by 47 publications

References 26 publications

Process-based simple model for simulating sugarcane growth and production

Process-based simple model for simulating sugarcane growth and production

Photosynthesis in Sugarcane

Water and radiation use efficiency of sugarcane for bioethanol production in South Africa, benchmarked against other selected crops

Contact Info

Product

Resources

About