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ARS Home » Plains Area » Bushland, Texas » Conservation and Production Research Laboratory » Soil and Water Management Research » Research » Publications at this Location » Publication #331838

Title: Allometric method to estimate leaf area index for row crops

item Colaizzi, Paul
item Evett, Steven - Steve
item Brauer, David - Dave
item HOWELL, TERRY - Retired ARS Employee
item TOLK, JUDY - Retired ARS Employee
item Copeland, Karen

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/22/2016
Publication Date: 3/23/2017
Citation: Colaizzi, P.D., Evett, S.R., Brauer, D.K., Howell, T.A., Tolk, J.A., Copeland, K.S. 2017. Allometric method to estimate leaf area index for row crops. Agronomy Journal. 109(3):1-12.

Interpretive Summary: Leaf area index (LAI), plant leaf area per unit ground area, is very important in the agricultural and ecological sciences, but is difficult to measure directly. LAI can be estimated indirectly using other plant measurements, such as individual leaf length and width, but such data are also often impractical to obtain. Scientists at the USDA-ARS, Bushland, Texas developed a new method to estimate LAI for row crops. The method uses growing degree days, canopy height, and plant population, which are easily obtained and more widely available in. The scientists tested the method using existing LAI measurements of corn, cotton, grain sorghum, and soybean and the method could estimate LAI with good accuracy. The method will make LAI estimates more practical and widely available compared with previous methods, and this will enhance the usefulness of large agricultural and ecological datasets.

Technical Abstract: 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 Fourier series was developed to calculate LAI, where input variables were cumulative growing degree days (CGDD), canopy height (hc), and plant population (plt). Fourier series functions are periodic and may have better transferability, and CGDD, hc, and plt are 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.), grain sorghum (Sorghum bicolor L.), and soybean (Glycine max L.) at USDA-ARS, Bushland, Texas, USA. Fourier series 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 0.66 to 0.88, model indices of agreement from 0.70 to 0.86, root mean square errors from 0.69 to 0.92, mean absolute errors from 0.49 to 0.70, and mean bias errors from -0.40 to 0.32. The new allometric method can mitigate missing or sparse LAI data, which will enhance the value of large ecological datasets.