Establishment of Perennial Groundcovers for Maize-Based Bioenergy Production Systems

Bartel, Cynthia A.; Banik, Chumki; Lenssen, Andrew W.; Moore, Kenneth J.

doi:10.31274/farmprogressreports-180814-1661

Cited by 4 publications

(21 citation statements)

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“…This is unsurprising, given the greater uncertainty that exists with root measurements than aboveground biomass measurements (Ordóñez et al., 2018) as well as uncertainties in modeling root biomass (Ebrahimi‐Mollabashia et al., 2019). These results indicate an enhanced sensitivity to the PGC in the simulated maize grain yield over the maize grain‐yield values recorded in our field study, even with the rapid post‐suppression recovery of the PGC observed in our field study (Bartel et al., 2017b). The enhanced effect of PGC biomass production in the simulation on end of season maize biomass is likely attributable to how well we can represent the spatial distribution of the crops within the model.…”

Section: Resultssupporting

confidence: 61%

“…It is important to note that our model simulation involved one maize hybrid. A significant maize hybrid by PGC species interaction was reported for several measured maize parameters in maize with intercropped PGC (Bartel et al., 2017b), documenting the disparity in maize germplasm response to PGC species. For example, a yield‐stable maize hybrid may have better yield response to an aggressive PGC than other maize varieties; conversely, maize germplasm that responds positively to enhanced nutrient availability will be advantageous when intercropped with adequately suppressed or summer dormant perennial grass species.…”

Section: Resultsmentioning

confidence: 90%

“…The experiment design and many of the materials and methodologies used in this study were similar or identical to those included in related studies by Bartel et al. (2017a, 2017b). A 3 site‐year study was conducted in successive years, 2014, 2015, and 2016 for maize, maize following maize (MM), and maize following 2 yr of maize (MMM) sequences, respectively, at the Agronomy and Agricultural Engineering Sorenson Research Farm (Boone), 11.9 km southeast of Boone, IA (42°0′N; 93°44′W).…”

Section: Methodsmentioning

confidence: 99%

“…While the integration of PGC into an annual row‐crop system can mitigate the degradation of natural resources associated with certain production practices, reported maize grain yield from PGC systems is inconsistent, emphasizing the need for system refinement prior to deployment. Maize grain yield in a conventional system was reported as greater than maize grain yield with competitive intercropped PGC (Adams, Pallas, & Dawson, 1970; Bartel et al., 2017a, 2017b; Carreker, Box, Dawson, Beaty, & Morris, 1972; Flynn, Moore, Singer, & Lamkey, 2013; Robertson, Lundy, Prine, & Currey, 1976). However, it was elsewhere reported that maize grain yield was similar in both systems (Wiggans, Singer, Moore, & Lamkey, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Pleasant, Burt, & Otis, 1987), as PGC competition with the maize crop is less than in PGC post‐establishment years. Key system aspects contributing to these treatment effects and requiring further investigation include PGC and maize hybrid compatibility (Beale & Langdale, 1964), groundcover biomass accumulation, suppression effectiveness and duration (Elkins, Vandeventer, Xapusta, & Anderson, 1979; Elkins, Frederking, Marashi, & McVay, 1983), and general system resiliency (Bartel et al., 2017b).…”

Section: Introductionmentioning

confidence: 99%

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Modeling perennial groundcover effects on annual maize grain crop growth with the Agricultural Production Systems sIMulator

et al. 2020

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The inclusion of perennial groundcover (PGC) in maize (Zea mays L.) production offers a tenable solution to natural resources-related concerns associated with conventional maize; however, insight into system management and key information gaps is needed to guide future research. We therefore extended the Agricultural Production Systems sIMulator (APSIM) to an annual and perennial intercrop by integrating annual and perennial APSIM modules. These were parameterized for Kentucky bluegrass (Poa pratensis L.) or creeping red fescue (Festuca rubra L.) as PGC using a 3-yr dataset. Our objectives for this intercropping modeling study were to: (a) simultaneously model a PGC and annual cash crop using APSIM software; (b) utilize APSIM to understand interactive processes in the maize-PGC system; and (c) utilize the calibrated model to explore both production and environmental benefits via scenario modeling. For the first objective, the integrated model successfully predicted maize total aboveground biomass (relative root mean square error [RRMSE] of 13-27%) and PGC above-and belowground tissue N concentration (RRMSE of 11-18%). The calibrated model captured observed trends in PGC biomass accumulation and soil nitrate. For the second objective, model analysis showed that competition for light was the primary PGC-related maize yield-penalty factor, while water and N affected maize yield later in the growing season. In the third objective, we concluded that effective PGC suppression produces minimal maize-yield loss and significant environmental benefits; conversely, poor groundcover suppression may produce unfavorable environmental consequences and decrease maize grain yield. Effective PGC suppression is key for long-term system success.

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Section: Resultssupporting

confidence: 61%

Section: Resultsmentioning

confidence: 90%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling perennial groundcover effects on annual maize grain crop growth with the Agricultural Production Systems sIMulator

et al. 2020

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Evaluating cool‐season grass species as potential perennial groundcover for maize production

et al. 2022

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Removal of crop residue poses negative consequences for soil health, nutrient leaching, and runoff. Perennial groundcovers (PGC) provide a method of maintaining soil health while allowing the sustainable intensification of maize (Zea mays L.) systems for biofuel production by providing groundcover for several years. However, previous studies have only evaluated a few varieties of PGC. We conducted a 2‐yr study to evaluate the compatibility of cool‐season turfgrass PGCs under maize. Maize was interplanted into 20 accessions of Kentucky bluegrass (Poa pratensis L.), Sandberg bluegrass (Poa secunda J. Presl), and red fescue (Festuca rubra L.) in a randomized complete block design in Boone, IA. Data on maize grain yield, maize grain quality, and ground coverage of PGC were collected. There were no differences in maize grain yield across species in 2017. In 2018, maize grain yield under red fescue was 1.0, 1.8, and 2.1 Mg ha–1 less than Kentucky bluegrass, Sandberg bluegrass, or the no‐grass control, respectively. Maize under red fescue produced 2.3, 4.8, and 3.9 g kg–1 more protein than Kentucky bluegrass, Sandberg bluegrass, and the no‐grass control, but produced 878 L ha–1 and 758 L ha–1 less ethanol than the no‐grass control and Sandberg bluegrass, respectively. In both years, Kentucky bluegrass groundcover declined approximately 22%, while Sandberg bluegrass groundcover remained below 10%. Red fescue maintained groundcover over 22% in 2017 and 34% in 2018. These results indicate that maize under Kentucky bluegrass and Sandberg bluegrass can perform similarly to conventional maize.

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Photothermal controls of vegetative dormancy in Poa secunda

et al. 2022

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Background Summer vegetative dormancy is a desirable trait in cool‐season grasses when they are interplanted with annual crops. Sandberg bluegrass (Poa secunda J. Presl.) shows summer dormancy, but the environmental cues that control dormancy remain unknown. Methods A controlled environment study using temperature and day length combinations of 32.2°C/15 h, 26.6°C/14 h, 21.1°C/13 h, and 15.5°C/12 h was conducted with P. secunda accessions PI232347, PI639272, and PI232348, and ‘Audubon’ red fescue as a nondormant control to determine the optimum treatment for dormancy induction. A second study using treatments of 26.6°C/14 h, 21.1°C/13 h, and 15.5°C/12 h was conducted to determine the thresholds for dormancy release. A third study used a factorial experiment with two temperatures (32.2°C and 15.5°C) and two day lengths (15 and 12 h) to differentiate between temperature and day length effects on dormancy induction. Results Of the four temperature and day length combinations, all except for 15.5°C/12 h resulted in dormancy by the end of 6 weeks, with 32.2°C/15 h inducing dormancy in only 17 days. Of the three treatments for dormancy release, 15.5°C/12 h broke dormancy the fastest in all accessions and released the most number of plants from dormancy. Considerable variation existed between accessions for the speed of dormancy release in the 21.1°C/13 h and 26.6°C/14 h treatments. The third study showed that temperature is the primary inducer for summer dormancy, while longer day length may promote dormancy under inductive temperatures. Conclusions This study identified the optimum photothermal for induction and release of summer dormancy in P. secunda, which will help future studies in elucidating the mechanism of summer dormancy.

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Establishment of Perennial Groundcovers for Maize-Based Bioenergy Production Systems

Cited by 4 publications

References 0 publications

Modeling perennial groundcover effects on annual maize grain crop growth with the Agricultural Production Systems sIMulator

Modeling perennial groundcover effects on annual maize grain crop growth with the Agricultural Production Systems sIMulator

Evaluating cool‐season grass species as potential perennial groundcover for maize production

Photothermal controls of vegetative dormancy in Poa secunda

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