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Record W2374570789

Balanced nutrition and crop production practices for the study of grain sorghum nutrient partitioning and closing yield gaps

2016· dissertation· en· W2374570789 on OpenAlexaboutno aff
Brandon McHenry

Bibliographic record

VenueK-State Research Exchange (Kansas State University) · 2016
Typedissertation
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
Fundersnot available
KeywordsSorghumClosing (real estate)AgronomyYield (engineering)NutrientCropGrain yieldProduction (economics)Crop productionAgricultural engineeringEnvironmental scienceMathematicsAgricultural scienceGeographyAgricultureEngineeringBiologyBusinessEconomicsPhysicsEcology
DOInot available

Abstract

fetched live from OpenAlex

Mid-west grain sorghum (Sorghum bicolor (L.) Moench) producers are currently obtaining much lower than attainable yields across varying environments, therefore, closing yield gaps will be important. Yield gaps are the difference between maximum economic attainable yield and current on-farm yields. Maximum economic yield can be achieved through the optimization of utilizing the best genotypes and management practices for the specific site-environment (soil-weather) combination. This research project examines several management factors in order to quantify complex farming interactions for maximizing sorghum yields and studying nutrient partitioning. The factors that were tested include narrow row-spacing (37.5 cm) vs. standard wide row-spacing (76 cm), high (197,600 seeds haˉ¹) and low (98,800 seeds haˉ¹) seeding rates, balanced nutrient management practices including applications of NPKS and micronutrients (Fe and Zn), crop protection with fungicide and insecticide, the use of a plant growth regulator, and the use of precision Ag technology (GreenSeeker for N application). This project was implemented at four sites in Kansas during 2014 (Rossville, Scandia, Ottawa, and Hutchinson) and 2015 (Topeka, Scandia, Ottawa, Ashland Bottoms) growing seasons. Results from both years indicate that irrigation helped to minimize yield variability and boost yield potential across all treatments, though other factors affected the final yield. In 2014, the greatest significant yield difference under irrigation in Rossville, KS (1.32 Mg haˉ¹) was documented between the ‘low-input’ versus the ‘high-input’ treatments. The treatment difference in grain sorghum yields in 2014 was not statistically significant. In 2014, the Ottawa site experienced drought-stress during reproductive stages of plant development, which resulted in low yields and was not influenced by the cropping system approach. In 2015 the treatments were significant, and in Ottawa, narrow row spacing at a lower seeding rate maximized yield for this generally low-yielding environment (<6 Mg haˉ¹) (treatment two at 6.26 vs. treatment ten at 4.89 Mg haˉ¹). Across several sites, including Rossville, Hutchinson, Scandia, Topeka, and Ashland, a similar trend of narrow row spacing promoting greater yields has been documented. Additionally, when water was not limiting sorghum yields (i.e., under irrigation), a balanced nutrient application and optimization of production practices did increase grain sorghum yields (‘high-input’ vs. ‘low-input’; the greatest difference was seen in 2014 in Rossville, 1.2 Mg haˉ¹, and in 2015 in Ashland, 1.98 Mg haˉ¹). In the evaluation of nutrient uptake and partitioning in different plant fractions, there was variability across all site-years which did not always follow the same patterns as the yield, however, the low-input treatment was shown to have significantly lower nutrient uptakes across all the nutrients evaluated (N, P, K, S, Fe, Zn) and across most fractions and sampling times. The objectives of this project were to identify management factors that contributed to high sorghum yields in diverse environments, and to investigate nutrient uptake and partitioning under different environments and crop production practices.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.124
GPT teacher head0.339
Teacher spread0.215 · 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 designBench or experimental
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
Published2016
Admission routes1
Has abstractyes

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