Open-path Fourier transform infrared spectroscopy (OP-FTIR) has often been used to measure hazardous or trace gases from hot point sources (e.g. volcano, industrial, or agricultural facilities) but seldom used to measure greenhouse gases (GHGs) from field-scale sources (e.g. agricultural soils). Closed-path mid-IR laser-based N₂O, nondispersive-IR CO₂ analysers, and OP-FTIR were used to measure concentrations of N₂O and CO₂ at a maize cropping system during 9–19 June 2014. To measure N₂O and CO₂ concentrations accurately, we developed a quantitative method of N₂O∕CO₂ analysis that minimized interferences from diurnal changes of humidity and temperature. Two chemometric multivariate models, classical least squares (CLS) and partial least squares (PLS), were developed. This study evaluated various methods to generate the single-beam background spectra and different spectral regions for determining N₂O and CO₂ concentrations from OP-FTIR spectra. A standard extractive method was used to measure the actual path-averaged concentrations along an OP-FTIR optical path in situ, as a benchmark to assess the feasibilities of these quantitative methods. Within an absolute humidity range of 5000–20 000 ppmv and a temperature range of 10–35 ^∘C, we found that the CLS model underestimated N₂O concentrations (bias $=-\mathrm{4.9}\pm \mathrm{3.1}$ %) calculated from OP-FTIR spectra, and the PLS model improved the accuracy of calculated N₂O concentrations (bias $=\mathrm{1.4}\pm \mathrm{2.3}$ %). The bias of calculated CO₂ concentrations was $-\mathrm{1.0}\pm \mathrm{2.8}$ % using the CLS model. These methods suggested that environmental variables potentially lead to biases in N₂O and CO₂ estimations from OP-FTIR spectra and may help OP-FTIR users avoid dependency on extractive methods of calibrations.

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R² as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

Open-path Fourier transform infrared spectroscopy (OP-FTIR) is susceptible to environmental variables which can become sources of errors for gas quantification. In this study, we assessed the effects of water vapour, temperature, path length, and wind speed on quantitative uncertainties of nitrous oxide (N₂O) and carbon dioxide (CO₂) derived from OP-FTIR spectra. The presence of water vapour in spectra underestimated N₂O mole fractions by 3 % and 12 %, respectively, from both lab and field experiments using a classical least squares (CLS) model when the reference and sample spectra were collected at the same temperature (i.e. 30 ^∘C). Differences in temperature between sample and reference spectra also underestimated N₂O mole fractions due to temperature broadening and the increased interferences of water vapour in spectra of wet samples. Changes in path length resulted in a non-linear response of spectra and bias (e.g. N₂O and CO₂ mole fractions were underestimated by 30 % and 7.5 %, respectively, at the optical path of 100 m using CLS models). For N₂O quantification, partial least squares (PLS) models were less sensitive to water vapour, temperature, and path length and provided more accurate estimations than CLS. Uncertainties in the path-averaged mole fractions increased in low-wind conditions (<2 m s⁻¹). This study identified the most common interferences that affect OP-FTIR measurements of N₂O and CO₂, which can serve as a quality assurance/control guide for current or future OP-FTIR users.

Miscanthus × giganteus (Miscanthus) is a warm-season perennial grass grown for bioenergy feedstock production. Nitrogen (N) fertilizer management is crucial for the sustainability of Miscanthus production. In our two-year study (2018 and 2019), we investigated the role of vegetation indices (VIs) in evaluating N fertilization (0 N, 56 N, 112 N, and 168 N kg ha⁻¹) impacts on Miscanthus biomass yield and stand health. The flight campaigns were conducted early, middle, and late during the summer growing season. Among the VIs, mid-summer growing season NDRE provided the best prediction of fresh biomass (R² = 0.87 and 0.97) and dry biomass (R² = 0.89 and 0.97) in 2018 and 2019, respectively. The VIs generally showed that it was possible to distinguish between 0 N and 168 N treatments, but neither 0 N and 56 N kg ha⁻¹ nor 112 N and 168 N kg ha⁻¹ could be separated. The results from this study highlight the importance of moderate application of N (112 kg N ha⁻¹) in improving and maintaining the stand health and biomass yield of Miscanthus over time and suggest that mid-summer growing season VIs, NDRE in particular, can be useful for assessment of Miscanthus stand health and biomass yield.

Perennial grass mixtures established on Conservation Reserve Program (CRP) lands can be an important source of feedstock for bioenergy production. This study aimed to evaluate management practices for optimizing the quality of bioenergy feedstock and stand persistence of grass‐legume mixtures under diverse environments. A 5‐year field study (2008–2012) was conducted to assess the effects of two harvest timings (at anthesis vs after complete senescence) and three nitrogen (N) rates (0, 56, 112 kg N ha⁻¹) on biomass chemical compositions (i.e., cell wall components, ash, volatiles, total carbon, and N contents) and the feedstock energy potential, examined by the theoretical ethanol yield (TEY) and the total TEY (i.e., the product of biomass yield and TEY, L ha⁻¹), of cool‐season mixtures in Georgia and Missouri and a warm‐season mixture in Kansas. The canonical correlation analysis (CCA) was used to investigate the effect of vegetative species transitions on feedstock quality. Although environmental variations (mainly precipitation) greatly influenced the management effect on chemical compositions, the delayed harvest after senescence generally improved feedstock quality. In particular, the overall cell wall concentrations and TEY of the warm‐season mixtures increased by approximately 7%. Additional N supplies improved the total TEY of both mixtures by ~1.6–4.2 L ha⁻¹ per 1.0 kg N ha⁻¹ input but likely lowered the feedstock quality, particularly for the cool‐season mixture. The cell wall concentrations of cool‐season mixture reduced by approximately 3%–6%. The CCA results indicated that the increased legume compositions (under low N input) likely enhanced lignin but reduced ash concentrations. This field research demonstrated that with proper management, grass‐legume mixtures on CRP lands can provide high‐quality feedstock for bioenergy productions.