Ultraviolet-B radiation in a row-crop canopy: an extended 1 -D model

Abstract

A decrease in stratospheric ozone may result in a serious threat to plants, since biologically active short-wavelength ultraviolet-B (UV-B 280–320 nm) radiation will increase even with a relatively small decrease in ozone. Numerous investigations have demonstrated that the effect of UV-B enhancements on plants includes reduction in grain yield, alteration in species competition, susceptibility to disease, and changes in plant structure and pigmentation. To determine the physiological effects on plants of any increases in UV-B radiation, the irradiances at the potential sensitive plant surface need to be known. A number of radiative transfer models exist but because of the importance of sky diffuse radiation to the global UV-B irradiance, models designed to estimate photosynthetically active radiation or total solar radiation may not accurately model the UV-B. This paper compares spatially and temporally averaged measurements of the UV-B canopy transmittance of a relatively dense maize canopy (sky view: 0.27°) to the estimations of two one-dimensional models differing mainly in the handling of sky radiance. The model that considered the distribution of sky radiance tended to underestimate the canopy transmittance, the model that assumed an isotropic sky radiance distribution tended to overestimate the canopy transmittance. However, the assumption concerning the sky radiance distribution accounted for only about 0.01 of the model error. Consequently, the sky radiance distribution is probably not important in modeling such dense crop canopies. The model that overestimated transmittance and had the generally larger errors, a modified Meyers model, used the assumption of uniform leaf angle distribution, whereas in the other model, designated the UVRT model, leaf angle distributions were estimated by sample measurements. Generally this model would be satisfactory in describing the statistically average UV-B irradiance conditions in the canopy. This model may also be applied to other dense plant canopies including forests.