Introduction to Cellulosic Energy Crops

Laser, Mark; Lynd, Lee R.

doi:10.1002/9781118676332.ch1

Cited by 6 publications

(5 citation statements)

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“…Wang et al [ 43 ] found that switchgrass ash was composed of 67% SiO 2 , 12% CaO and many other mineral oxides in small concentrations. The high silica content can melt and fuse together when biomass is thermochemically pretreated [ 44 ], thereby causing problems when scaling up fuel conversion methods at the biorefinery. However, if harvested, the inorganic residue can be recycled into fertilizer [ 45 ] or a cement additive [ 43 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

Quantitative trait loci for cell wall composition traits measured using near-infrared spectroscopy in the model C4 perennial grass Panicum hallii

Milano

Payne

Wolfrum

et al. 2018

Biotechnol Biofuels

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BackgroundBiofuels derived from lignocellulosic plant material are an important component of current renewable energy strategies. Improvement efforts in biofuel feedstock crops have been primarily focused on increasing biomass yield with less consideration for tissue quality or composition. Four primary components found in the plant cell wall contribute to the overall quality of plant tissue and conversion characteristics, cellulose and hemicellulose polysaccharides are the primary targets for fuel conversion, while lignin and ash provide structure and defense. We explore the genetic architecture of tissue characteristics using a quantitative trait loci (QTL) mapping approach in Panicum hallii, a model lignocellulosic grass system. Diversity in the mapping population was generated by crossing xeric and mesic varietals, comparative to northern upland and southern lowland ecotypes in switchgrass. We use near-infrared spectroscopy with a primary analytical method to create a P. hallii specific calibration model to quickly quantify cell wall components.ResultsAsh, lignin, glucan, and xylan comprise 68% of total dry biomass in P. hallii: comparable to other feedstocks. We identified 14 QTL and one epistatic interaction across these four cell wall traits and found almost half of the QTL to localize to a single linkage group.ConclusionsPanicum hallii serves as the genomic model for its close relative and emerging biofuel crop, switchgrass (P. virgatum). We used high throughput phenotyping to map genomic regions that impact natural variation in leaf tissue composition. Understanding the genetic architecture of tissue traits in a tractable model grass system will lead to a better understanding of cell wall structure as well as provide genomic resources for bioenergy crop breeding programs.Electronic supplementary materialThe online version of this article (10.1186/s13068-018-1033-z) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Quantitative trait loci for cell wall composition traits measured using near-infrared spectroscopy in the model C4 perennial grass Panicum hallii

Milano

Payne

Wolfrum

et al. 2018

Biotechnol Biofuels

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“…Plant biomass has been important to humans for centuries because of its use for a variety of applications, including fuel for warmth and cooking, lumber and other building materials, textiles and papermaking [3]. Recently, the focus for biomass crops has been on bioenergy production, but with increasing emphasis on green chemistry endeavorsdefined as the design of chemical products and processes that reduce or eliminate the use or generation of hazardous substances [4] -the demand for biobased products such as plant-based plastics, textiles and pharmaceuticals will likely increase.…”

Section: Special Report Moore and Karlenmentioning

confidence: 99%

“…Increased production of lignocellulosic biomass crops potentially offers many environmental benefits that can contribute to the sustainability of soil resources. Some examples include: fossil fuel displacement, lower emissions of GHGs and other air pollutants, enhanced soil quality, reduced soil erosion, reduced nutrient runoff and enhanced biodiversity [3]. Demirbas, Rowe et al Anjum, and Skinner et al provide more detailed reviews and discussion of these and other potential benefits [8][9][10][11].…”

Section: Special Report Moore and Karlenmentioning

confidence: 99%

“…Increased biomass crop production, using practices such as double cropping, has the potential to promote rural economic activity within several sectors [3]. Farmers would have increased demand for their products, including crop residues from existing crops, because double cropping would ensure the land was not bare and exposed to erosive forces between crops.…”

Section: Special Report Moore and Karlenmentioning

confidence: 99%

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Double cropping opportunities for biomass crops in the north central USA

Moore¹,

Karlen

2013

Biofuels

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“…Corn, along with rice and wheat, is one of the world's three major grains, and it is a food crop that has high productivity per unit area and is widely used as snacks, forage, starch, and cooking oil [1,2]. The consumption of corn continues to increase worldwide [3]. Production was 1135 million tons in 2019, accounting for the largest proportion of food crops [4].…”

Section: Introductionmentioning

confidence: 99%

UAV, a Farm Map, and Machine Learning Technology Convergence Classification Method of a Corn Cultivation Area

Lee

Kim²,

Park

2021

Agronomy

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South Korea’s agriculture is characterized by a mixture of various cultivated crops. In such an agricultural environment, convergence technology for ICT (information, communications, and technology) and AI (artificial intelligence) as well as agriculture is required to classify objects and predict yields. In general, the classification of paddy fields and field boundaries takes a lot of time and effort. The Farm Map was developed to clearly demarcate and classify the boundaries of paddy fields and fields in Korea. Therefore, this study tried to minimize the time and effort required to divide paddy fields and fields through the application of the Farm Map. To improve the fact that UAV image processing for a wide area requires a lot of time and effort to classify objects, we suggest a method for optimizing cultivated crop recognition. This study aimed to evaluate the applicability and effectiveness of machine learning classification techniques using a Farm Map in object-based mapping of agricultural land using unmanned aerial vehicles (UAVs). In this study, the advanced function selection method for object classification is to improve classification accuracy by using two types of classifiers, support vector machine (SVM) and random forest (RF). As a result of classification by applying a Farm Map-based SVM algorithm to wide-area UAV images, producer’s accuracy (PA) was 81.68%, user’s accuracy (UA) was 75.09%, the Kappa coefficient was 0.77, and the F-measure was 0.78. The results of classification by the Farm Map-based RF algorithm were as follows: PA of 96.58%, UA of 92.27%, a Kappa coefficient of 0.94, and the F-measure of 0.94. In the cultivation environment in which various crops were mixed, the corn cultivation area was estimated to be 96.54 ha by SVM, showing an accuracy of 90.27%. RF provided an estimate of 98.77 ha and showed an accuracy of 92.36%, which was higher than that of SVM. As a result of using the Farm Map for the object-based classification method, the agricultural land classification showed a higher efficiency in terms of time than the existing object classification method. Most importantly, it was confirmed that the efficiency of data processing can be increased by minimizing the possibility of misclassification in the obtained results. The obtained results confirmed that rapid and reliable analysis is possible when the cultivated area of crops is identified using UAV images, a Farm Map, and machine learning.

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Introduction to Cellulosic Energy Crops

Cited by 6 publications

References 50 publications

Quantitative trait loci for cell wall composition traits measured using near-infrared spectroscopy in the model C4 perennial grass Panicum hallii

Quantitative trait loci for cell wall composition traits measured using near-infrared spectroscopy in the model C4 perennial grass Panicum hallii

Double cropping opportunities for biomass crops in the north central USA

UAV, a Farm Map, and Machine Learning Technology Convergence Classification Method of a Corn Cultivation Area

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