Chlorophyll values as a measure of genetic variation of switchgrass (Panicum virgatum L.) populations under cool temperate climate conditions

Liatukas, Žilvinas; Lemežienė, N.; Butkutė, B.; Cesevičienė, Jurgita; Dabkevičienė, G.

doi:10.13080/z-a.2015.102.021

Cited by 6 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, GNDVI showed a stronger correlation with chlorophyll contents across the plants examined than NDVI, which utilizes the NIR and red bands [59]. Switchgrass aboveground biomass is accumulated through vegetative growth or elongation of stems from emergence in the spring through flowering, with the growth period (green-up to anthesis) dependent upon geographic location (temperature and daylength) and ecotype [74][75][76]. During the vegetative growth period, plant chlorophyll content increases [75].…”

Section: Discussionmentioning

confidence: 99%

“…Switchgrass aboveground biomass is accumulated through vegetative growth or elongation of stems from emergence in the spring through flowering, with the growth period (green-up to anthesis) dependent upon geographic location (temperature and daylength) and ecotype [74][75][76]. During the vegetative growth period, plant chlorophyll content increases [75]. This pattern was observed in the Iowa, Brighton, and Nebraska sites with green-up observed in imagery to occur in late April/early May and continue into June and July, which corresponded to the measured chlorophyll con-tent in the field during this time.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

Hamada

Zumpf

Cacho

et al. 2021

Land

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

Hamada

Zumpf

Cacho

et al. 2021

Land

View full text Add to dashboard Cite

show abstract

“…There are many different options for harvest timing and frequency, with the two main timings being at peak yield (at anthesis) at the end of the growing season, when the crop can be harvested green, and after completion of senescence, when the crop is dry. For bioenergy and for optimal stand longevity, the dry fully senesced crop is harvested in the spring of the following year (Liatukas et al, 2015) plants to harden for the winter (Douglas et al, 2009). The harvest time also affects the nutrient composition; delaying the harvest from late November to early March can result in reduced ash production and less potassium in the biomass.…”

Section: Dry Matter Repartitionmentioning

confidence: 99%

The development of a new crop growth model SwitchFor for yield mapping of switchgrass

et al. 2022

View full text Add to dashboard Cite

Switchgrass is a promising energy crop has the potential to mitigate global warming and energy security, improve local ecology and generate profit. Its quantitative traits, such as biomass productivity and environmental adaptability, are determined by genotype‐by‐environment interaction (GEI) or response of genotypes grown across different target environments. To simulate the yield of switchgrass outside its original habitat, a genotype‐specific growth model, SwitchFor that captures GEI was developed by parameterising the MiscanFor model. Input parameters were used to describe genotype‐specific characteristics under different soil and climate conditions, which enables the model to predict the yield in a wide range of environmental and climate conditions. The model was validated using global field trail data and applied to estimate the switchgrass yield potentials on the marginal land of the Loess Plateau in China. The results suggest that upland and lowland switchgrass have significant differences in the spatial distribution of the adaptation zone and site‐specific biomass yield. The area of the adaption zone of upland switchgrass was 4.5 times of the lowland ecotype's. The yield difference between upland and lowland ecotypes ranges from 0 to 34 Mg ha−1. The weighted average yield of the lowland ecotype (20 Mg ha−1) is significantly higher than the upland type (5 Mg ha−1). The optimal yield map, generated by comparing the yield of upland and lowland ecotypes based on 1 km2 grid locations, illustrates that the total yield potential of the optimal switchgrass is 61.6–106.4 Tg on the marginal land of the Loess Plateau, which is approximately twice that of the individual ecotypes. Compared with the existing models, the accuracy of the yield prediction of switchgrass is significantly improved by using the SwitchFor model. This spatially explicit and cultivar‐specific model provides valuable information on land management and crop breeding and a robust and extendable framework for yield mapping of other cultivars.

show abstract

“…The chlorophyll content is an important feature of this plant to tolerate different temperature conditions [29,30]. Switchgrass with a C 4 photosynthesis scheme is a high-yielding bioenergy crop and is a valuable source of sugars for bioethanol production [23, 31 -33].…”

Section: Introductionmentioning

confidence: 99%

Efficiency of Switchgrass (Panicum virgatum L.) Cultivation in the Ukrainian Forest-Steppe Zone and Development of Its New Lines

Rakhmetova¹,

Vergun²,

Kulyk³

et al. 2020

TOASJ

View full text Add to dashboard Cite

Background: Switchgrass (Panicum virgatum L.) is a promising C4-photosynthetising perennial grass and an important energy crop. It is resistant to drought, cold and winter frost, endures flooding and shows high performance at minimal energy consumption for cultivation. Methods: The purpose of the work was to develop and introduce high-yielding genotypes of switchgrass and develop effective cultivation technologies for the Forest-Steppe zone of Ukraine. The objective of the study was to evaluate the introduction potential of switchgrass, to establish biologically productive parameters and energy values of different genotypes and the effectiveness of cultivation techniques in the specific geo-climatic zone of Ukraine. To achieve the objectives, a wide range of introductory, biological-morphological, breeding-biotechnological, and agronomic methods have been used. Results: The gene pool of P. virgatum was collected (33 accessions), ten of which have been investigated, including both introduced genotypes and newly developed breeding lines. The bio-morphological, ecological features, the biochemical composition of plants have been evaluated. The productivity of the above-ground biomass and seeds, as well as the energy value of plants, has been evaluated. High-performance forms with specified yield parameters, biomass sugars, total energy and biofuel output per unit area have been determined. Conclusion: A new variety of P. virgatum (cv. Zoriane) has been introduced for cultivation. Among the accessions of newly developed genotypes, the most productive lines have been identified. New efficient technologies have been developed to increase the yield potential of switchgrass as a source of raw materials for biofuel production.

show abstract

Chlorophyll values as a measure of genetic variation of switchgrass (Panicum virgatum L.) populations under cool temperate climate conditions

Cited by 6 publications

References 21 publications

Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

The development of a new crop growth model SwitchFor for yield mapping of switchgrass

Efficiency of Switchgrass (Panicum virgatum L.) Cultivation in the Ukrainian Forest-Steppe Zone and Development of Its New Lines

Contact Info

Product

Resources

About