Allometric Method to Estimate Leaf Area Index for Row Crops
2017
Colaizzi, Paul D. | Evett, Steven R. | Brauer, David K. | Howell, Terry A. | Tolk, Judy A. | Copeland, Karen S.
CORE IDEAS: A new allometric method was developed to estimate LAI for row crops. Good agreement resulted for four crops over multiple seasons. Best agreement resulted using GDD, plant population, and canopy height. Leaf area index (LAI) is critical for predicting plant metabolism, biomass production, evapotranspiration, and greenhouse gas sequestration, but direct LAI measurements are difficult and labor intensive. Several methods are available to measure LAI indirectly or calculate LAI using allometric methods (i.e., exploiting relationships between LAI and more easily measured plant variables), but these depend on other measurements not widely available, and have limited transferability to different seasons. A new allometric method using a log normal function was developed to calculate LAI. Input variables were normalized cumulative growing degree days (CGDD), canopy height (CH), and plant population (PP), which were usually more widely available in crop production datasets. Destructive LAI measurements were obtained over multiple growing seasons for corn (Zea mays L.), cotton (Gossypium hirsutum L.), sorghum (Sorghum bicolor L.), and soybean [Glycine max (L.) Merr.] at USDA‐ARS, Bushland, TX. Log normal functions were calibrated to LAI measurements from a single season of each crop, and tested using independent LAI measurements from all remaining crop seasons. For all crops, discrepancies between calculated and measured LAI resulted in coefficients of determination from 0.23 to 0.85, model indices of agreement from 0.52 to 0.84, root mean square errors from 0.76 to 1.4, mean absolute errors from 0.57 to 1.2, and mean bias errors from ‐0.46 to 0.60. The new allometric method can mitigate missing or sparse LAI data, which will enhance the value of large ecological datasets.
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