Developmental Morphology and Biomass Yield of Upland and Lowland Switchgrass Ecotypes Grown in Iowa

Aurangzaib, Muhammad; Moore, Ken; Lenssen, Andrew W.; Archontoulis, Sotirios; Heaton, Emily A.; Fei, Shuizhang

doi:10.3390/agronomy8050061

Cited by 8 publications

(6 citation statements)

References 49 publications

(66 reference statements)

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“…The variable yield responses shown in the harvests are a natural occurrence in switchgrass growth and development throughout the growing season. Inconsistent trends in switchgrass yield, such as those seen here, have been noted in other studies as well [47][48][49].…”

Section: Discussionsupporting

confidence: 86%

Nutrient Dynamics in Switchgrass as a Function of Time

2020

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There are wide variations in fertilizer recommendations for switchgrass (Panicum virgatum L.) as biofuel feedstock or forage. Inconsistent yield responses to fertilization are common. Nutrient translocation, from aboveground leaves and stems to belowground roots, is a contributor to variable, and sometimes the absence of, fertilizer response. A field study evaluated how major nutrients are cycled within switchgrass during the growing season. Aboveground (AG) and belowground (BG) biomasses were harvested and analyzed separately for yield (AG) and nutrient concentrations (AG and BG). Maximum yields were 26.3 (2008), 17.5 (2009) and 29.3 (2010) Mg ha−1 until senescence. In all years, the N concentration of the AG biomass decreased as the season progressed (p < 0.0001). The belowground biomass N concentration increased over time in 2008 and 2010 (p < 0.05). Phosphorus (P) and potassium (K) displayed similar trends to nitrogen concentration. Temporal changes of other nutrients in the AG and BG biomass concentrations were inconsistent and varied compared with N. The dynamics of the macronutrients suggested translocation from AG to BG as the switchgrass matured. Nutrients stored in roots can be beneficial for regrowth in the following growing season, which may reduce response to fertilizers. A greater understanding of nutrient cycling and harvest timing is needed to better manage different switchgrass production systems.

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Section: Discussionsupporting

confidence: 86%

Nutrient Dynamics in Switchgrass as a Function of Time

2020

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“…The present screening trials, assessing the productive performance of upland and lowland switchgrass varieties grown in Greece and Italy, permitted direct comparisons between the upland and lowland ecotypes, which are still not extensively reported in the literature, as recently highlighted by [40]. Differences between the two ecotypes were evident only in Italy, with the lowlands leading to improved productive performance, even if the lack of replicates makes this result in need of further confirmation.…”

Section: Discussionmentioning

confidence: 67%

Long-Term Productivity of Thirteen Lowland and Upland Switchgrass Ecotypes in the Mediterranean Region

et al. 2020

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Switchgrass (Panicum virgatum L.) has been identified in the USA as an ideal biomass crop, in relation to its wide environmental suitability, mainly linked to the availability of both upland and lowland ecotypes, allowing the possibility of growing this species in most of the North American region. Switchgrass is conventionally grown for forage, but more recently, it has been considered as a model biofuel crop. Early European studies on switchgrass as a bioenergy crop started in the late 1990s, when a multi-location field trial was established in Greece (Aliartos) and Italy (Ozzano) to compare the productivity of 13 switchgrass genotypes, including upland (Carthage, Blackwell, Caddo, CIR, Forestburg, SU 94-1, Summer) and lowland (Alamo, Kanlow, Pangburn, SL 93-2, SL 93-3, SL94-1) genotypes. The scope was to identify the most suitable ecotype within each environment and, possibly, the best performing variety. The trials lasted 17 years (1998–2014) in Greece and 13 years (1998–2010) in Italy. While in Italy the trial was rainfed and unfertilized, in Greece, where the soil was marginal, drip irrigation was always applied, and the plots were fertilized regularly. The biomass yields in Greece, as averages across the 17 years, were similar for the lowland and upland varieties (11.5 vs. 11.1 Mg ha−1, respectively), while in Italy, as averages across the 13 years, the differences were relevant: 15.4 vs. 11.3 Mg ha−1 for lowland and upland, respectively. Alamo (lowland) was the most productive variety, both in Greece and Italy, with average annual yields of 12.7 and 16.6 Mg ha−1, respectively; CIR in Greece (10.1 Mg ha−1) and Forestburg in Italy (9.1 Mg ha−1) (both upland) were the least productive genotypes. The present results demonstrate the good suitability of switchgrass as biomass crop for the Mediterranean climate. Despite the very marginal soil (i.e., very shallow and with a sandy texture) in the Greek trial, the application of regular fertilization and irrigation produced biomass yields above 11 Mg ha−1 (grand mean) in the present 17-year-long study.

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“…The maximum LAI value 12 of Alamo is also used in the ALMANCE model (Behrman et al, 2014;Kiniry et al, 1996;Sanderson et al, 1996). The statistical maximum LAI value of CIR 6.57 is similar to the 6.64 found in Iowa, USA (Aurangzaib et al, 2018). Maximum LAI for Alamo of 12 and 7 for CIR is parameterized in the SwitchFor model.…”

Section: Leaf Area Indexmentioning

confidence: 79%

“…The DD factor describes the relationship between LAI incensement along with the DDs during leaf expansion in the SwitchFor model. It was calculated based on measurements from field trials in Iowa (42°0′41″ N, 93°44′34″ W) (Aurangzaib et al, 2018 ), where daily LAI of the Alamo and CIR was recorded. Together with daily temperature from the National Centers for Environmental Information, linear regression was made between LAI and DD0 and the results showed DD factor is 0.004 for Alamo and 0.006 for CIR (Figure S4 ).…”

Section: Methodsmentioning

confidence: 99%

“…Switchgrass is a perennial C4 energy crop that displays tremendous diversity in morphology and growth habitat due to genetic diversity (Aurangzaib et al, 2018 ). The genotypes of switchgrass are categorized as either upland or lowland ecotypes, based on their habitat preference, morphological characteristics, and ploidy level (Brunken, 1975 ; Porter, 1966 ).…”

Section: Introductionmentioning

confidence: 99%

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The development of a new crop growth model SwitchFor for yield mapping of switchgrass

et al. 2022

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Switchgrass is a promising energy crop has the potential to mitigate global warming and energy security, improve local ecology and generate profit. Its quantitative traits, such as biomass productivity and environmental adaptability, are determined by genotype‐by‐environment interaction (GEI) or response of genotypes grown across different target environments. To simulate the yield of switchgrass outside its original habitat, a genotype‐specific growth model, SwitchFor that captures GEI was developed by parameterising the MiscanFor model. Input parameters were used to describe genotype‐specific characteristics under different soil and climate conditions, which enables the model to predict the yield in a wide range of environmental and climate conditions. The model was validated using global field trail data and applied to estimate the switchgrass yield potentials on the marginal land of the Loess Plateau in China. The results suggest that upland and lowland switchgrass have significant differences in the spatial distribution of the adaptation zone and site‐specific biomass yield. The area of the adaption zone of upland switchgrass was 4.5 times of the lowland ecotype's. The yield difference between upland and lowland ecotypes ranges from 0 to 34 Mg ha−1. The weighted average yield of the lowland ecotype (20 Mg ha−1) is significantly higher than the upland type (5 Mg ha−1). The optimal yield map, generated by comparing the yield of upland and lowland ecotypes based on 1 km2 grid locations, illustrates that the total yield potential of the optimal switchgrass is 61.6–106.4 Tg on the marginal land of the Loess Plateau, which is approximately twice that of the individual ecotypes. Compared with the existing models, the accuracy of the yield prediction of switchgrass is significantly improved by using the SwitchFor model. This spatially explicit and cultivar‐specific model provides valuable information on land management and crop breeding and a robust and extendable framework for yield mapping of other cultivars.

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Developmental Morphology and Biomass Yield of Upland and Lowland Switchgrass Ecotypes Grown in Iowa

Cited by 8 publications

References 49 publications

Nutrient Dynamics in Switchgrass as a Function of Time

Nutrient Dynamics in Switchgrass as a Function of Time

Long-Term Productivity of Thirteen Lowland and Upland Switchgrass Ecotypes in the Mediterranean Region

The development of a new crop growth model SwitchFor for yield mapping of switchgrass

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