230
Views
11
CrossRef citations to date
0
Altmetric
Original Articles

Mineral Leaf Composition of Sweet Sorghum in Relation to Biomass and Sugar Yields under Different Nitrogen and Salinity Conditions

, , , , , , & show all
Pages 2376-2388 | Received 30 Dec 2010, Accepted 14 Mar 2012, Published online: 20 Sep 2012
 

Abstract

Diagnosing nutrient insufficiencies or toxicities in sorghum through foliar analysis is still unusual and mainly used for grain sorghum. The influences of the combinations of four nitrogen (N) rates with three sodium chloride (NaCl) rates on the leaf N, phosphorus (P), calcium (Ca), magnesium (Mg), potassium (K), and sodium (Na) concentrations of sweet sorghum [Sorghum bicolor (L.) Moench ssp. saccharatum], cropped for ethanol production, and on biomass and sugar yields were evaluated in three consecutive years of an experiment established on a Eutric Fluvisol equipped with a trickle irrigation system (“triple emitter source”). The relationships among leaf nutrient concentrations, dry matter, and sugar yields were also examined. Nitrogen, much more than salinity, affected leaf nutrient concentration, stem dry weight, and sugar yield. Leaf N concentration was the best indicator for predicting sugar production of sweet sorghum.

Acknowledgements

This work was funded by the Fundação para a Ciência e a Tecnologia (FCT), under the framework of the Project “Optimization of Nitrogen Fertilization According to the Salt Content of Irrigation Water” (2007–2010).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 408.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.