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Record W2278146213

Evaluation of switchgrass, M. x giganteus, and sorghum as biomass crops: Effects of environment and field management practices

2011· article· en· W2278146213 on OpenAlexaboutno aff
Matthew Maughan

Bibliographic record

VenueIllinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignEnergy Biosciences InstituteU.S. Department of Energy
KeywordsPanicum virgatumEcotypeAgronomyBiomass (ecology)Growing seasonEnvironmental scienceBioenergyFertilizerBiofuelBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

Switchgrass (Panicum virgatum L.), Miscanthus x giganteus (M. x giganteus), and sorghum (Sorghum bicolor L.) have been proposed as potential bioenergy feedstock crops. This study evaluates how these crops performs in different environments under different crop management practices, particularly nitrogen (N) fertilizer rates. Chapter 1 provides the rationale of this research and a general discussion of the unique characteristics of these three crops. In Chapter 2, an extensive database of switchgrass biomass yields from 106 sites and 45 field studies in eastern two thirds of the USA and southeastern Canada is evaluated using descriptive statistics, and using a random coefficients model. Switchgrass has been researched extensively in North America as a biomass crop and data reported since the 1990???s reveal large variability in dry biomass yields which are related to multiple environment and field management practices. This analysis describes switchgrass biomass N response, and shows that in addition to N fertilizer rate the most important factors affecting switchgrass dry biomass yields are growing region, spring precipitation, growing season, ecotype, and harvest timing. Chapter 3 remarks that studies reporting M. x giganteus dry biomass yields to date in the USA are few in number and little information is available to suggest a suitable growing region. This study investigates M. x giganteus in four Midwest and Atlantic Coast environments under three N rates. Establishment success, plant growth, morphology, and dry biomass yields were evaluated and results reveal no response to N rate during the establishment years, large biomass yield differences among environments, and decreased yield when the crop experienced a combination of high heat and dry conditions. Chapter 4 introduces two types of sorghum, forage sorghum and biomass sorghum (referred to as energy sorghum) which have been proposed as crops with high biomass production potential although prior to this study no research had evaluated these sorghum types grown for biomass in IL. This field study evaluated two forage sorghum and two energy sorghum hybrids in four IL environments under different N rates. Measurements of morphology and crop growth were measured throughout the growing season, and dry biomass yields revealed significant differences between the two sorghum types. The energy sorghum hybrids achieved the greatest biomass yields in each environment with the effects of environment and N rate affecting the biomass yields. The results of these studies provide valuable information for stakeholders, producers, and scientists regarding the impact of environment and management practices on biomass yields of switchgrass, M. x giganteus, and sorghum. It is necessary that these factors be evaluated prior to making decisions as to which crop species and which cultivar or hybrid to plant in a given location. In most cases, no regional recommendations for species selection and N fertility rates are adequate and most field management practices must be made on a site-by-site basis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.233
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2011
Admission routes1
Has abstractyes

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