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Record W4414889013 · doi:10.3390/agronomy15102352

Yield Potential of Silage Sorghum: Cultivar Differences in Biomass Production, Plant Height, and Tillering Under Contrasting Soil Conditions in Central Europe

2025· article· en· W4414889013 on OpenAlexfundno aff
Lenka Porčová, Nicole Frantová, Michal Rábek, Ivana Jovanović, Vladimír Smutný, Michal Řiháček, Eva Mrkvicová

Bibliographic record

VenueAgronomy · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
FundersMendelova Univerzita v BrněMinistry of Agriculture - Saskatchewan
KeywordsSorghumCultivarBiomass (ecology)Yield (engineering)ProductivitySweet sorghumSilageTrait

Abstract

fetched live from OpenAlex

We conducted a three-year field study to evaluate the above-ground biomass yield, plant height, and tillering capacity of eight Sorghum bicolor (L.) Moench varieties under two contrasting soil conditions (heavy clay soil and sandy soil) with different water retention. At the Field Experimental Station Žabčice of Mendel University in Brno, Czech Republic, we assessed yield performance and yield stability across years and environments. We applied standard agronomic practices and recorded detailed soil and climatic data. Significant differences were found among varieties and between locations in terms of plant height and tillering. KWS SOLE showed the most stable yield (11.80–15.63 t ha−1), while LATTE, KWS TARZAN, and KWS HANNIBAL achieved the highest average yields (up to 20.16 t ha−1). Plant height showed a strong positive correlation with biomass yield. This relationship underscores plant height as a valuable trait for selecting sorghum varieties with improved productivity and drought resilience. Variations in tillering capacity and environmental conditions also significantly influenced yield outcomes, highlighting the complex interaction between genotype and environment. These findings offer practical insights for cultivar selection and breeding strategies that aim to enhance the performance of sorghum varieties under the variable climatic conditions of Central Europe.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.218
Threshold uncertainty score0.201

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.204
Teacher spread0.186 · 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 teacher head, 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

Citations2
Published2025
Admission routes1
Has abstractyes

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