Submitted to: Agronomy Journal
Publication Type: Peer reviewed journal
Publication Acceptance Date: 7/2/2012
Publication Date: 8/22/2012
Publication URL: http://handle.nal.usda.gov/10113/55243
Citation: Nielsen, D.C., Miceli-Garcia, J.J., Lyon, D.J. 2012. Canopy cover and leaf area index relationships for wheat, triticale, and corn. Agronomy Journal. 104:1569-1573. Interpretive Summary: Cropping systems models are valuable tools for assessing management and weather effects on crop production. The recently developed model AquaCrop from the United Nations Food and Agriculture Organization is useful for assessing crop production under varying temperature and water availability conditions. In order to calibrate and validate the model simulated canopy cover (as a measure of crop growth and development) must be matched against field measured values. But canopy cover measurements have not been routinely taken in field studies, whereas leaf area index measurements have been taken. ARS scientists at Akron, CO generated mathematical relationships between leaf area index and canopy cover for winter wheat, corn, and triticale. The relationships had the form of exponential rise to a maximum value and will be useful for completing modeling studies with data sets in which leaf area index has been recorded. Additionally, the relationship can also be used as a low cost method to predict leaf area index from canopy cover measurements made with an inexpensive digital camera.
Technical Abstract: The AquaCrop model requires canopy cover (CC) measurements to define crop growth and development. Some previously collected data sets that would be useful for calibrating and validating AquaCrop contain only leaf area index (LAI) data, but could be used if relationships were available relating LAI to CC. The objective of this experiment was to determine relationships between LAI and CC for corn (Zea mays L.), winter wheat (Triticum aestivum L.), and spring triticale (X Triticosecale rimpaui Wittm.) grown under dryland or very limited irrigation conditions. The LAI and CC data were collected during 2010 and 2011 at Akron, CO and Sidney, NE from two crop rotations. LAI data were obtained with a plant canopy analyzer and CC data were determined from point analysis of digital photographs taken above the crop canopy. Strong relationships were found between LAI and CC that followed the exponential rise to a maximum form. The relationship for corn was similar to a previously published relationship for LAI less than 2 m2 m-2, but predicted lower CC for greater LAI. Relationships for wheat and triticale were similar to each other. Predicted values of CC for both wheat and triticale were greater than CC predicted for corn at any given LAI value. The relationships defined in this paper will be useful for making additional existing data sets available for modeling with AquaCrop and can also be used as a low cost method to predict LAI from CC measurements made with an inexpensive digital camera.