Abstract
Eastern cottonwood (Populus deltoides W. Bartram ex Marshall) and hybrid poplars are well-known bioenergy crops. With advances in tree breeding, it is increasingly necessary to find economical ways to identify high-performing Populus genotypes that can be planted under different environmental conditions. Photosynthesis and leaf nitrogen content are critical parameters for plant growth, however, measuring them is an expensive and time-consuming process. Instead, these parameters can be quickly estimated from hyperspectral leaf reflectance if robust statistical models can be developed. To this end, we measured photosynthetic capacity parameters (Rubisco-limited carboxylation rate (Vcmax), electron transport-limited carboxylation rate (Jmax), and triose phosphate utilization-limited carboxylation rate (TPU)), nitrogen per unit leaf area (Narea), and leaf reflectance of seven taxa and 62 genotypes of Populus from two study plantations in Mississippi. For statistical modeling, we used least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA). Our results showed that the predictive ability of LASSO and PCA models was comparable, except for Narea in which LASSO was superior. In terms of model interpretability, LASSO outperformed PCA because the LASSO models needed 2 to 4 spectral reflectance wavelengths to estimate parameters. The LASSO models used reflectance values at 758 and 935 nm for estimating Vcmax (R2 = 0.51 and RMSPE = 31%) and Jmax (R2 = 0.54 and RMSPE = 32%); 687, 746, and 757 nm for estimating TPU (R2 = 0.56 and RMSPE = 31%); and 304, 712, 921, and 1021 nm for estimating Narea (R2 = 0.29 and RMSPE = 21%). The PCA model also identified 935 nm as a significant wavelength for estimating Vcmax and Jmax. Therefore, our results suggest that hyperspectral leaf reflectance modeling can be used as a cost-effective means for field phenotyping and rapid screening of Populus genotypes because of its capacity to estimate these physicochemical parameters.
Highlights
Among the five taxa groups, deltoides × P. nigra L. (D×N) had 31% lower Vcmax than D×T; D×D had 32% lower Jmax than D×T; and D×D had 31% lower triose phosphate utilization-limited carboxylation rate (TPU) than D×T
The nitrogen per unit leaf area (Narea) model had the smallest R2 probably because Narea was weakly correlated with photosynthetic capacity parameters (Table 1) suggesting that a large portion of N in leaves was not involved in photosynthesis, and might not generate a strong spectral signal
We found that published vegetation indices did not perform better than our models because our models, the least absolute shrinkage and selection operator (LASSO) models, had the highest R2 and the lowest root mean square error (RMSE) for estimating photosynthetic capacity parameters and Narea in our Populus trees (Table 3)
Summary
Production of bioenergy through direct combustion [1] and/or the manufacture of liquid biofuels [2] can reduce the consumption of fossil fuels and associated emissions [3]. In the USA, bioenergy contributed about 7.3% to the energy sector in 2019, which could increase to an additional 18–55% by 2050 [4]. To bolster this increasing trend, short rotation woody crops (SRWCs) can be planted as a biomass feedstock for bioenergy production [5, 6]. In the USA, Populus feedstock production for bioenergy makes up a small portion of land use, but there is the potential for large-scale plantations for bioenergy in the future because Populus is a promising bioenergy crop. The biomass yield of Populus genotypes can reach 7.5 to 15.2 Mg/ha/year [13] Due to their rapid growth and high biomass productivity, the United States Department of Energy classifies Populus as a potential SRWC for bioenergy production [14]. Populus feedstocks have desirable attributes needed for producing ethanol and other biofuels, such as high cellulose content, moderate lignin and hemicellulose content, and low ash and extractives [26]
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