Abstract

Remotely-sensed identification of ozone stress in crops can allow for selection of ozone resistant genotypes, improving yields. This is critical as population, food demand, and background tropospheric ozone are projected to increase over the next several decades. Visual scores of common ozone damage have been used to identify ozone-stress in bio-indicator plants. This paper evaluates the use of a visual scoring metric of ozone damage applied to soybeans. The scoring of the leaves is then combined with hyperspectral data to identify spectral indices specific to ozone damage. Two genotypes of soybean, Dwight and Pana, that have shown different sensitivities to ozone, were grown and visually scored for ozone-specific damage on multiple dates throughout the growing season. Leaf reflectance, foliar biophysical properties, and yield data were collected. Additionally, ozone bio-indicator plants, snap beans, and common milkweed, were investigated with visual scores and hyperspectral leaf data for comparison. The normalized difference spectral index (NDSI) was used to identify the significant bands in the visible (VIS), near infrared (NIR), and shortwave infrared (SWIR) that best correlated with visual damage score when used in the index. Results were then compared to multiple well-established indices. Indices were also evaluated for correlation with seed and pod weight. The ozone damage scoring metric for soybeans evaluated in August had a coefficient of determination of 0.60 with end-of-season pod weight and a Pearson correlation coefficient greater than 0.6 for photosynthetic rate, stomatal conductance, and transpiration. NDSI [R558, R563] correlated best with visual scores of ozone damage in soybeans when evaluating data from all observation dates. These wavelengths were similar to those identified as most sensitive to visual damage in August when used in NDSI (560 nm, 563 nm). NDSI [R560, R563] in August had the highest coefficient of determination for individual pod weight (R2 = 0.64) and seed weight (R2 = 0.54) when compared against 21 well-established indices used for identification of pigment or photosynthetic stress in plants. When evaluating use of spectral bands in NDSI, longer wavelengths in SWIR were identified as more sensitive to ozone visual damage. Trends in the bands and biophysical properties of the soybeans combined with evaluation of ozone data indicate likely timing of significant ozone damage as after late-July for this season. This work has implications for better spectral detection of ozone stress in crops and could help with efforts to identify ozone tolerant varieties to increase future yield.

Highlights

  • Despite regulations placed on pollutants, tropospheric ozone levels are projected to increase in polluted regions with a warming climate [1]

  • This paper evaluated the use of foliar visual scores of chlorosis and necrosis at classifying soybean ozone damage as well as identifying spectral wavelengths and bands for use in a normalized difference spectral index (NDSI) that correlates well to the visual damage

  • A visual scoring system developed for bio-indicator plants was applied to soybeans to investigate the crop’s ozone damage

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Summary

Introduction

Despite regulations placed on pollutants, tropospheric ozone levels are projected to increase in polluted regions with a warming climate [1]. High ozone concentrations during the summer months, have been shown to negatively influence crop growth and yield through impacts on leaf-level photosynthesis as well as damages to whole-canopy physiology [4]. Soybeans are among the crops that are vulnerable to tropospheric ozone concentrations, and global relative yield losses in 2008 were estimated to be between 6% and 16% [5]. Global yield losses due to ambient ozone are projected to increase, posing a threat to global food security [8]. An ozone monitor, (Model 106-L, 2BTechnologies, Boulder, CO, USA), providing 15-minute resolution ambient ozone concentration data, was present on-site. (Model 106-L, 2BTechnologies, Boulder, CO, USA), providing 15-minute resolution ambient ozone concentration data, was present on-site. Have been to be consistent with archived data at Missouri’s Department of Natural Resources. Data was for July and August and was imputed using data from nearby sites at Grant’s Farm and Southwestern missingCollege; for July locations and August wasofimputed data from nearby sites at

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