Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 5/26/2009
Publication Date: N/A
Citation: N/A Interpretive Summary:
Technical Abstract: It is desirable to be able to predict above ground biomass production indirectly, without extensive sampling or destructive harvesting. Leaf area index (LAI) is the amount of leaf surface area per ground area and is an important parameter in ecophysiology. As LAI increases, the photosynthetically active surface area per unit ground area increases, such that the fraction of photosynthetically active radiation intercepted by the canopy (fiPAR) and the biomass production of the canopy increase with increasing LAI until an optimal LAI is reached. The relationship, as described by Beer’s Law, between leaf angle, LAI, and fiPAR allow researchers to calculate LAI without measuring it directly. We examined variability of fiPAR measurements taken by an AccuPAR LP-80 ceptometer based on three possible measurement techniques. Predicted LAI values were compared to measured LAI values for miscanthus, switchgrass, and four cultivars of buffelgrass. Among buffelgrass cultivars, the fiPAR measurement method used biased results (P = 0.005). Measurements of switchgrass and miscanthus showed measurement method × nutrient addition interaction effects (P = 0.02), which were apparently driven by differences in LAI due to nutrient addition. At low LAI values, results from the three fiPAR measurement methods were distinguishable; as LAI increased, the results of the three methods tended to converge, suggesting that experimental error associated with ceptometer deployment method decreases as LAI increases. We conclude that researchers interested in measuring fiPAR of canopies with low LAI values should carefully consider the fiPAR determination method employed. Accurate LAI prediction and associated above ground biomass estimates are particularly important with the emergent interest in developing lands for biofuel production based on yield predictions.