Light interactions, use and efficiency in row crop canopies under optimal growth conditions

Accurate estimates of light (Photosynthetically Active Radiation or PAR) absorption, in addition to other interactions is imperative to quantify growth, productivity, energy and water balance and other physiological and biophysical processes in any vegetative surface. Currently, a comparative assessment of light interaction patterns across row crops is lacking, especially under current levels of productivity achieved in the U.S. High Plains. Here, we continuously measured canopy light balance components at high-frequency (15 min) to characterize transmittance (R), reflectance (R), fraction of intercepted PAR (fIPAR), fraction of absorbed PAR (fAPAR), light extinction coefficients (k), and light use efficiency (LUE) comparatively across maize, soybean, sorghum and winter wheat under optimal growth conditions. While maximum fAPAR was 88-96% of incoming PAR in all crops, mean fAPAR varied from 82% in sorghum to 46% in winter wheat; while k ranged from 0.36 (winter wheat) to 0.48 (sorghum and soybean), and these differences reflect highly crop-specific signatures. Conversion factors among fIPAR and fAPAR and LUE based on either component (LUE_i and LUE_a) were quantified that were substantially different from the conventionally used values; especially during early and late growth stages. A commonly employed approach of solar-noon light sampling was evaluated, and it was found that early and late stages of crop growth experience greater potential errors (as high as 110%) under this sampling approach, and hence should be avoided. LUE_a was the highest for maize (5.3 g MJ⁻¹), followed by sorghum (4.1 g MJ⁻¹), winter wheat (4.0 g MJ⁻¹) and soybean (3.1 g MJ⁻¹). The datasets measured, analyzed and interpreted here present unprecedented quantities of biomass productivity and canopy light use parameters for four major cropping systems, and hence, should be accounted for in crop growth and productivity modeling applications to enhance predictive accuracy and robustness.