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Record W2560254290 · doi:10.2134/csa2015-60-10-3

Mixtures of bioenergy species yield as well as monocultures

2015· article· en· W2560254290 on OpenAlexaboutno aff
Madeline Fisher

Bibliographic record

VenueCSA News · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsnot available
Fundersnot available
KeywordsPolycultureMonocultureBioenergyPerennial plantCellulosic ethanolAgronomyBiomass (ecology)AgroforestryBiofuelEnvironmental scienceEcologyBiologyAquacultureFishery

Abstract

fetched live from OpenAlex

For those hoping to sow some diversity back into the Corn Belt with dedicated bioenergy crops, mixtures of native prairie species seem like an obvious choice. Farmers, though, need to manage biofuels crops for maximum yields and returns. So for them, simple monocultures of switchgrass fertilized with nitrogen may be best. A study in the September–October 2015 issue of Agronomy Journal (http://bit.ly/1NusPO2) now suggests the options aren't quite so black or white. In a seven-year experiment spanning nine locations in Minnesota and North Dakota, an eight-species mixture, or polyculture, of perennial grasses and legumes produced as much biomass as switchgrass monocultures when plots weren't fertilized. And yields of another polyculture—an assemblage of four grasses—rivaled those of switchgrass when nitrogen was added. Although the bioenergy industry has taken a hit recently from plummeting oil and gas prices, a few commercial cellulosic ethanol plants are now in operation–notably the POET-DSM plant in Emmetsburg, IA. In the meantime, many Midwest farmers are interested in restoring native prairie vegetation for non-commercial reasons, says Jacob Jungers, the study's lead author. But they also can't neglect the bottom line. “We've spoken to producers who want to manage a polyculture because it provides wildlife habitat and other ecosystem services. But they also want to make sure it's an economically feasible system that will produce some level of biomass to make a return,” explains the University of Minnesota postdoctoral research fellow. “So we hope the results from this study can help inform that decision-making process.” Although it remains controversial, the hypothesis that systems with higher plant diversity are more productive is well supported by ecological research. In a seminal 2006 paper, for example, University of Minnesota ecologist David Tilman reported that on an abandoned farm with unproductive soils, diverse mixtures of prairie grasses and forbs produced 238% more bioenergy, on average, than monocultures of switchgrass or other species. However, Tilman's study was “a classical plant community ecology experiment,” not an agricultural one, says Jungers, who worked on the project as an undergrad—leaving many unconvinced that a real bioenergy production system would yield similar results. In 2006, Jungers’ advisers Don Wyse and Craig Sheaffer decided to put this to the test. In true agronomic fashion, their experiment compared yields of mixtures and monocultures across a wide range of soil, temperature, and other growing conditions. They tested the effects of adding nitrogen; selected biofuels species—such as Canada wild rye and big blue stem—that are relatively cheap and easy to establish; and harvested plant biomass at the end of each season just like producers would. They also collected data for seven years. “Natural succession takes place in any polyculture, whether it's managed or not, and we didn't know how management would steer the succession,” Jungers explains. A particular question was how adding nitrogen might alter the mixtures, since previous research has documented the loss of species with fertilization. Most of the time, switchgrass monocultures were the star performers, the team found. What they didn't expect is that an eight-species mixture of grasses and legumes would generate as much biomass as switchgrass in unfertilized plots: 5.1 Mg/ha–1 on average. With nitrogen fertilizer, switchgrass yields jumped to 6.8 Mg/ha–1. But even then, a fertilized, four-species grass mixture kept pace, producing 6.4 Mg/ha–1, on average. In contrast to previous work, nitrogen fertilizer also didn't reduce diversity over time—even in polycultures with as many as 12 and 24 species. The reason, Jungers believes, is that biomass was harvested each season. Additions of fertilizer tend to favor species that can take up nitrogen quickly and efficiently, allowing them to outcompete and eventually exclude other plants. “However, when you harvest, you're giving another set of species an advantage,” he says—specifically those that flourish in the extra light that biomass removal lets in. Put another way, harvesting “resets” the system each year, helping ensure no one species can take over. All in all, the findings suggest that while managing agricultural lands for greater diversity and ecological functions will never be easy, it's more feasible than people once thought. “Natural phenomena do still play out”—and can be leveraged—in plant communities subjected to harvesting, fertilizing, and other management practices, Jungers says. Still, even he admits to being surprised—and relieved, he adds with a laugh, since he wants to make a career in agroecology. “All the work that's been done in the past, the basic ecology, is holding up, even though we're manipulating these systems.” Photos courtesy of Jacob Jungers.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.999

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)0.0020.000

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 head0.049
GPT teacher head0.231
Teacher spread0.182 · 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.

Study designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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