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Record W2790449322 · doi:10.2134/csa2018.63.0310

Maize Yield Potential

2018· article· hu· W2790449322 on OpenAlexaboutno aff
Tracy Hmielowski

Bibliographic record

VenueCSA News · 2018
Typearticle
Languagehu
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsAgronomySowingBiologyCropIrrigationAgricultureYield (engineering)PEST analysisPlant breedingHorticultureEcology

Abstract

fetched live from OpenAlex

Plant breeders have been enhancing desirable traits in crops like flavor, yield, and pest resistance since the birth of agriculture. Some of these transformations have dramatically changed the appearance of crops. One example is maize. Breeding practices have taken maize from a grass with little seed production to the top grain crop produced worldwide. Even with the high-yielding maize varieties available, there is great demand for maize to be used in food products, animal feed, and biofuel. Increasing yield potential is a combination of genetics and management practices. Planting timing and spacing, the application of fertilizer, irrigation, and weather conditions throughout a season interact with genotypes. For maize, there are two main traits that breeders can focus on to increase maize production without increasing the number of acres planted. The first is increasing the number of kernels or kernel size of plants so that individual plants produce more grain. The second is to increase the density tolerance of plants, enabling farmers to decrease the spacing between plants and rows, such that more maize can be planted on existing cropland. In recent history, density tolerance appears to have been the focus of breeding efforts. In the 1950s, planting densities of 30,000 plants/ha were the maximum, whereas currently, farmers plant up to 80,000 plants/ha. Maize breeder and geneticist Elizabeth Lee, from the University of Guelph, points out that while density tolerance may have received most attention, other traits were also improved upon. One example is the stay-green trait, where plants have an extended growth period even in dry conditions. “Stay green is basically a balancing of source and sink,” Lee explains. “The amount of carbon fixed during the grain period has been improved, which results in better stay green.” However, at the same time, kernel size and number have shown limited change and have not contributed to increased maize yields. Lee and co-authors recently published an article in Crop Science (http://bit.ly/2Et6Nvh) to investigate if there is potential for breeders to increase both kernel size and number (referred to in the article as yield potential) as well as the density tolerance in future hybrids. In this article, the researchers present the results of a series of experiments using commercially available hybrids as a starting point. “The experimental hybrids tested represented the four possible combinations of density tolerance and yield potential,” Lee explains. The researchers grew these hybrids in a range of densities over multiple years. Across hybrids and experimental conditions, they observed differences in maize yields and further investigated how yield was impacted by harvest index (ratio of grain yield to total biomass) and kernel set efficiency (number of kernels at harvest per gram of dry matter fixed per day at silking). The researchers explain that the harvest index was not correlated with yield potential. This applied to genotypes with and without density tolerance. Kernel set efficiency was also investigated. Genotypes with high density tolerance had a static kernel set efficiency, regardless of the density they were planted at. From these results, the researchers concluded that the genetic and biological mechanisms that control these two traits are distinct. The results were not a surprise to the team. “We knew that historically there were hybrids known as flex-ear hybrids that could take advantage of situations where there was a reduced plant stand,” Lee says. These flex-ear hybrids can produce a greater yield than fixed-ear varieties with fewer plants, so the research team had a hunch that the genes for kernel size and number were present and flexible in the commercial germplasm available. “Genotypes possessing both attributes were easily identified in a segregating population, which is also evidence that genetic variation is still present for yield potential in the commercial germplasm pool,” the authors state in the article. Lee says that these results are “applicable to environments where drought and high temperatures are not the main limitations of yield, which are Canada and the traditional U.S. Corn Belt in most growing seasons.” The U.S. and Canada are major producers of maize, and while it has not been confirmed that maize has reached a yield plateau in North America, yield increases may be slowing. Research suggests that solar brightening is responsible for 27% of maize yield gains from 1984 to 2013. Additionally, the yield gap has narrowed to less than 25% between observed and potential maize yields. If maize is nearing a plateau, this research will be important information for maize breeders as it demonstrates that breeders can focus on increasing kernel size or number without altering density tolerance to provide a way to continue to increase maize yields to meet future demands. Check out the Crop Science article, “Maize Yield Potential and Density Tolerance” at: http://bit.ly/2Et6Nvh.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.998

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)0.0170.002

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 head0.033
GPT teacher head0.234
Teacher spread0.201 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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