Explore the vast range of titles available.

The growing interest in high‐biomass sorghum (Sorghum bicolor L. Moench), hereafter referred to as sorghum, as a bioenergy feedstock in the United States requires an understanding of geographical adaptation to identify the most suitable hybrids for the Midwest. In this study, 13 sorghum hybrids (H1–H13) were evaluated for biomass yield potential in central and southern IL over two growing seasons (2022 and 2023). In addition to biomass yield, the effects of nitrogen (N) fertilization on yield, nutrient removal (N, P, and K), and feedstock composition (cellulose, hemicellulose, lignin, and soluble fractions) were determined to identify the best‐performing sorghum hybrid across environmental gradients. The experimental design was a split‐plot arrangement within a randomized complete block design with four replications at each of two locations: N rates (0 and 112 kg‐N ha−1) as a whole plot factor and 13 sorghum hybrids as a subplot factor. As a result, complex genotypes (13 hybrids) by environment (2 sites and 2 years) and management (2 N rates) interactions were observed in biomass yield. The best hybrids at both sites were H1 (ATx2932/F10702_PSL) and H13 (TX08001), which were very photoperiod sensitive (PS). These hybrids produced superior biomass yield, and they also exhibited less nutrient removal and high energy‐rich feedstock compositions (cellulose, hemicellulose, and lignin). Biomass yield potential was associated with morphological and phenological traits according to environmental conditions. Low‐yielding hybrids were short‐stature (H5 and H6) with pollinators (F10801_PSL‐3dw and F10805_PSL‐3dw) that are recessive at the Dw3 locus. Moderate PS hybrids (H7, H8, H11, and H12) that produced grain panicles at harvest showed high biomass yield plasticity and excessive nutrient removal as they accumulated high K concentrations in biomass tissues and high N and P in grain panicles. High‐biomass sorghum is a key biomass feedstock for meeting the U.S. Department of Energy's Sustainable Aviation Fuel (SAF) Grand Challenge targets. This study evaluated 13 high‐biomass sorghum hybrids to identify those with beneficial feedstock traits, specifically high yield, favorable feedstock composition, and sustainable nutrient removal across environmental gradients. Nitrogen fertilization effects on yield and composition were also assessed. The hybrids ATx2932/F10702_PSL and TX08001 emerged as top performers, combining high biomass yield with reduced nutrient removal and elevated levels of cellulose, hemicellulose, and lignin. These findings inform farmers, breeders, and policymakers in selecting elite hybrids for sustainable bioenergy production.

The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4–52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits.

Field experiments were conducted in marginal lands, i.e., sub-humid climate and saline-land (SHS) and semi-arid climate and wasteland (SAW), to evaluate ethanol potential based on the biomass yield and chemical composition of biomass type (var. GN-2, GN-4, and GN-10) and sweet type (var. GT-3 and GT-7) hybrids of energy sorghum [ (L.) Moench] in comparison with sub-humid climate and cropland (SHC) in northern China. Results showed that environment significantly ( < 0.05) influenced plant growth, biomass yield and components, and subsequently the ethanol potential of energy sorghum. Biomass and theoretical ethanol yield of the crop grown at SHS (12.2 t ha and 3,425 L ha , respectively) and SAW (8.6 t ha and 2,091 L ha , respectively) were both statistically ( < 0.001) lower than values at the SHC site (32.6 t ha and 11,853 L ha , respectively). Higher desirable contents of soluble sugar, cellulose, and hemicellulose were observed at SHS and SHC sites, while sorghum grown at SAW possessed higher lignin and ash contents. Biomass type sorghum was superior to sweet type as non-food ethanol feedstock. In particular, biomass type hybrid GN-10 achieved the highest biomass (17.4 t ha ) and theoretical ethanol yields (5,423 L ha ) after averaging data for all environmental sites. The most productive hybrid, biomass type GN-4, exhibited biomass and theoretical ethanol yields >42.1 t ha and 14,913 L ha , respectively, at the cropland SHC site. In conclusion, energy sorghum grown on marginal lands showed a very lower ethanol potential, indicating a considerable lower possibility for being used as commercial feedstock supply when compared with that grown on regular croplands. Moreover, screening suitable varieties may improve energy sorghum growth and chemical properties for ethanol production on marginal lands.

Accurate simulation of crop growth processes for predicting final yield is critical for optimizing resource management, particularly in regions with variable climates and limited resource availability. This paper proposes a novel data-driven crop model to simulate phenotypic changes during biomass sorghum growth. The model integrates a detailed physiological framework for sorghum development—tracking how phenotypes are determined by genotype, environment, management practices, and their interactions—with data-driven techniques to calibrate genotypic parameters using experimental data. Results demonstrate that the model achieves accurate biomass production predictions and successfully disentangles the effects of environmental and management factors on phenotypic development, even with limited data. This model enhances the accuracy and applicability of biomass sorghum growth and yield prediction models, offering valuable insights for precision agriculture.

Producing scalable, economically viable, low-carbon biofuels or biochemicals hinges on more efficient bioconversion processes. While microbial conversion can offer robust solutions, the native microbial growth process often redirects a large fraction of carbon to CO2 and cell mass. By integrating genome-scale metabolic models with techno-economic and life cycle assessment models, this study analyzes the effects of converting cell mass lipids to hydrocarbon fuels, and CO2 to methanol on the facility’s costs and life-cycle carbon footprint. Results show that upgrading microbial lipids or both microbial lipids and CO2 using renewable hydrogen produces carbon-negative bisabolene. Additionally, on-site electrolytic hydrogen production offers a supply of pure oxygen to use in place of air for bioconversion and fuel combustion in the boiler. To reach cost parity with conventional jet fuel, renewable hydrogen needs to be produced at less than$2.2 to $ 3.1/kg, with a bisabolene yield of 80% of the theoretical yield, along with cell mass and CO2 yields of 22 wt% and 54 wt%, respectively. The economic combination of cell mass, CO2, and bisabolene yields demonstrated in this study provides practical insights for prioritizing research, selecting suitable hosts, and determining necessary engineered production levels.

Coal is the primary fuel for most Indonesian power plants, causing high CO 2 emissions. Since the Paris Agreement prioritizes efforts to reduce CO 2 emissions, power generation companies have put great efforts into demonstrating co-firing of coal with biomass. This research is the first study to examine the synergistic effect of combining coal and sorghum during co-firing. To complete this investigation, an experimental setup was carried out utilizing a drop tube furnace equipment that creates ash deposits, followed by X-ray diffraction (XRD) analysis to acquire the crystallinity parameters and characterization. Based on the parameters of derivative thermogravimetry, R max , sample residue, root mean square of synergistic occurrence, ash deposit, insufficient crystallinity, amorphous content, and crystal structure, namely C5, C10, C15, C20, and C25 (representing the sorghum ratios of 5 10, 15, 20, and 25%, respectively), the grey relational grade analysis provides recommendations for the implementation of a mixture of coal and sorghum co-firing. The thermogravimetric analysis revealed that 10% of sorghum in the fuel mixture (namely C10) was the best recommendation for mixing with coal in terms of the activation energy (111 kJ mol −1 ) with an error deviation close to zero. Overall results from thermogravimetric to XRD analyses indicate recommendations for co-firing coal and sorghum in the sequence of C10, C15, C5, C25, and C20. Graphical abstract

The objective of this study was to evaluate sorghum hybrids associated or not associated with Azospirillum brasilense and nitrogen fertilization (N) during planting on the yield, fermentative profile, and nutritional value of the respective silages. Five sorghum hybrids (Volumax, 201813B, 201814B, 201709B, BRS716) were evaluated with three nitrogen fertilization strategies using urea (100 kg ha −1 of N) and Azospirillum brasilense, and urea (100 kg ha −1 of N)/A. brasilense in association. A randomized block design was used in a 5 × 3 factorial scheme, with five hybrids, three fertilization strategies and three replications (blocks). The useful area of each experimental unit was 3 m x 3 m. The biomass sorghum hybrids showed a dry matter (DM) production (P = 0.01) 48.31% higher than the DM production of the Volumax forage (mean of 17.49 t ha −1 of dry matter). There was no difference between the sorghum hybrid silages in the pH values (mean of 4.11; P = 0.68), gas losses (mean of 3.74% of DM; P = 0.19). The sorghum hybrids biomass 201709B and BRS 716 showed better digestible and DM productivity. Azospirillum brasilense can be used as a nitrogen fertilization strategy in partial or total replacement of urea.

Energycane and biomass sorghum are two of the most promising cellulosic energy crops in the southeastern US. Research on these two energy crops has focused mainly on biomass production, and there is a lack of knowledge on their ability to promote biodiversity and ecosystem services. This paper presents results from a comprehensive study on ground-active arthropod diversity in seven sites across five states in the southeastern US (Florida, Georgia, Louisiana, Mississippi, and Texas). Pitfall traps were deployed four times during each crop season for energycane, biomass sorghum, and a local reference conventional crop from 2020 to 2022. Arthropod abundance (individuals/(trap × day)) values were 4.9 ± 0.46, 3.7 ± 0.18, and 2.6 ± 0.16 (mean ± stderr) for conventional crops, biomass sorghum, and energycane, respectively, with a significant difference found only between conventional crops and energycane. Individuals were identified to arthropod orders, and Hill’s diversity indices were calculated based on the number of individuals in each arthropod order instead of the number of individuals in each arthropod species. Order-based arthropod richness values were 5.3, 5.2, and 4.8 for biomass sorghum, conventional crops, and energycane, with significant difference found only between biomass sorghum and energycane. There was no significant difference in the order-based Shannon diversity and Simpson diversity between the three crop types. The effective number of arthropod orders for the two energy crops decreased from 5.0 to 3.4 to 2.9 with increasing order of diversity from arthropod richness to Shannon diversity to Simpson diversity. The explained variability by environmental factors also decreased with increasing Hill’s order of diversity. The results from this study indicate no significant advantage in order-based arthropod diversity in growing biomass sorghum and energycane. This research fills a critical knowledge gap in understanding the impacts of cellulosic energy crop production on biodiversity and ecosystem services.

Producing scalable, economically viable, low-carbon biofuels or biochemicals hinges on more efficient bioconversion processes. While microbial conversion can offer robust solutions, the native microbial growth process often redirects a large fraction of carbon to CO2 and cell mass. By integrating genome-scale metabolic models with techno-economic and life cycle assessment models, this study analyzes the effects of converting cell mass lipids to hydrocarbon fuels, and CO2 to methanol on the facility’s costs and life-cycle carbon footprint. Results show that upgrading microbial lipids or both microbial lipids and CO2 using renewable hydrogen produces carbon-negative bisabolene. Additionally, on-site electrolytic hydrogen production offers a supply of pure oxygen to use in place of air for bioconversion and fuel combustion in the boiler. To reach cost parity with conventional jet fuel, renewable hydrogen needs to be produced at less than $2.2 to $3.1/kg, with a bisabolene yield of 80% of the theoretical yield, along with cell mass and CO2 yields of 22 wt% and 54 wt%, respectively. The economic combination of cell mass, CO2, and bisabolene yields demonstrated in this study provides practical insights for prioritizing research, selecting suitable hosts, and determining necessary engineered production levels.

Sorghum crop is grown under tropical and temperate latitudes for several purposes including production of health promoting food from the kernel and forage and biofuels from aboveground biomass. One of the concerns of policy-makers and sorghum growers is to cost-effectively predict biomass yields early during the cropping season to improve biomass and biofuel management. The objective of this study was to investigate if Sentinel-2 satellite images could be used to predict within-season biomass sorghum yields in the Mediterranean region. Thirteen machine learning algorithms were tested on fortnightly Sentinel-2A and Sentinel-2B estimates of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) in combination with in situ aboveground biomass yields from demonstrative fields in Italy. A gradient boosting algorithm implementing the xgbtree method was the best predictive model as it was satisfactorily implemented anywhere from May to July. The best prediction time was the month of May followed by May–June and May–July. To the best of our knowledge, this work represents the first time Sentinel-2-derived fAPAR is used in sorghum biomass predictive modeling. The results from this study will help farmers improve their sorghum biomass business operations and policy-makers and extension services improve energy planning and avoid energy-related crises.