Skip to main content
ARS Home » Plains Area » Bushland, Texas » Conservation and Production Research Laboratory » Soil and Water Management Research » Research » Publications at this Location » Publication #270583
Title: Evaluating shade effects on crop productivity in sorghum-legume intercropping systems using support vector machines

Author
item ANGADI, SANGAMESH - New Mexico State University
item Gowda, Prasanna
item RANGAPPA, UMESH - New Mexico State University
item OOMMEN, THOMAS - Michigan Technological University
item PRASAD, VARA - Kansas State University

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 8/18/2011
Publication Date: 10/18/2011
Citation: Angadi, S., Gowda, P., Rangappa, U., Oommen, T., Prasad, V.P. 2011. Evaluating shade effects on crop productivity in sorghum-legume intercropping systems using support vector machines. [abstract]. ASA-CSSA-SSSA Annual meeting, October 16-19, 2011, San Antonio, Texas. 2011 CDROM.

Interpretive Summary:

Technical Abstract: Sorghum-legume intercropping has the potential to improve forage productivity, resource use efficiency, and forage quality under irrigation in the Southern High Plains of the United States. Crop production is conversion of solar radiation into biomass and solar radiation is wasted early in the season in most annual row cropping systems. An intercropping system improves radiation interception as well as utilization of water, nutrient and other resources. As a result, system efficiency is expected to be improved early in the season; however, later competition for resources including light reduces complementarity. Many crops try to adjust to low light by changing plant architecture, biomass partitioning, and plant physiology to maintain productivity under shade. However, each species may differ in their response to shade. An ideal legume species for intercropping is the one that is least affected by shade, while significantly contributing to forage quality and quantity. If it can improve productivity under low light it is even better. Support vector machine (SVM) is one of the statistical learning algorithms based on the statistical learning theory. Support vector machine is rarely used in agronomic research for identifying and analyzing treatment effects. The main objective of the research was to study effects of shade on morphology, physiology and productivity of five diverse legumes (lablab, limabean, cowpea, pigeonpea and polebean) grown under shaded and unshaded conditions using SVM. A set of statistical relationships were developed and compared to evaluate the shade effects on morphology, physiology, and productivity to identify suitable sorghum-legume intercropping system for forage production in the Southern High Plains. Preliminary analysis of the statistical results indicates that sorghum-lablab intercropping system followed by sorghum-limabean has the potential to improve forage productivity, resource use efficiency, and forage quality under irrigation.