Adaptation of C4 Bioenergy Crop Species to Various Environments within the Southern Great Plains of USA

Kim, Sumin; Kiniry, James R.; Williams, Amber S.; Meki, Norman; Gaston, Lewis A.; Brakie, Melinda R; Shadow, Alan; Fritschi, Felix B.; Wu, Yanqi

doi:10.3390/su9010089

Cited by 9 publications

(6 citation statements)

References 50 publications

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“…In previous studies, the ALMANAC model that was mostly calibrated by adjusting RUE and WUE values for different ecotypes of switchgrass could provide an accurate estimation of biomass production on different soils and with different weather scenarios [9,50]. Moreover, this model could successfully predict the productivity of these grasses on cropped soils and marginal soils, as well as in wet, normal and dry years for a location [51].…”

Section: Calculating Wue With the Almanac Modelmentioning

confidence: 99%

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A Review of Modeled Water Use Efficiency of Highly Productive Perennial Grasses Useful for Bioenergy

Kiniry

Kim²

2020

Agronomy

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Whole plant productivity is obviously the ultimate product of leaf photosynthesis and this has led to numerous efforts to relate the two. However, often with perennial grasses, plant productivity is more sink-limited than source-limited, causing the linkage between the photosynthetic rate and productivity to be weak or nonexistent. This has led to a different approach, characterizing plant productivity in terms of the efficiency of intercepted light use in producing biomass, also called radiation use efficiency. Likewise, the efficiency of the use of water to produce plant biomass, or water use efficiency, has been the object of much interest. The use of a simulation model to quantify biomass, using radiation use efficiency in parallel with a daily water balance simulation, allows for the effective calculation of water use efficiency. In this project, the process of determining radiation use efficiency with field data is described, as well as example values for highly productive perennial grasses useful for feedstock for bioenergy. In addition, values of water use efficiency for these grasses are reported and compared with other perennial grasses and common cultivated crops.Water requirements for growing switchgrass and giant miscanthus are often an issue. Without adequate water, their biomass yields can be reduced by 45%-80% of total biomass yields [10,11]. Moreover, competition for water with other users makes maximizing water use efficiency vitally important. Again, process-based simulation models are valuable tools since they simulate the dynamics of crop water use based on evaporation of soil water, leaf transpiration, weather, and dynamics of plant communities. Quantifying Photosynthetic Performance via Two Approaches: Single Leaf Photosynthesis vs. Radiation Use Efficiency (RUE)There are many plant simulation models based on single leaf photosynthesis, which is then scaled up to the whole leaf canopy. Such models, such as ORYZA for rice (Oryza sativa L.) [12,13], are based on the assumption that photosynthesis is a major driver and determinant for whole plant productivity. However, frequently grass systems have been shown to be more sink-limited than source-limited. They often rely more on the processes of tiller production, leaf development, and leaf canopy orientation.Examples:

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Section: Calculating Wue With the Almanac Modelmentioning

confidence: 99%

“…There are numerous simulation models for switchgrass and giant miscanthus plant growth [1][2][3][4][5][6][7][8][9]. These models simulate plant productivity and also can predict soil erosion and nutrient cycling.…”

Section: Generalmentioning

confidence: 99%

A Review of Modeled Water Use Efficiency of Highly Productive Perennial Grasses Useful for Bioenergy

Kiniry

Kim²

2020

Agronomy

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“…Switchgrass growth and development are affected by numerous factors, including temperature [45], rainfall [46], fertilizer inputs [47], and field location (e.g., soil type and latitude) [37,48]. To determine which variable explains most of the variation in switchgrass yields, in first phase, correlations and the importance of 14 variables for yield in switchgrass were analyzed.…”

Section: Analysis Of Factors Determining Switchgrass Yieldmentioning

confidence: 99%

“…Simulations are subjected to various management strategies [35]. ALMANAC predicts growth and biomass of switchgrass and has been validated against field data for multiple sites across the southern U.S. [36,37]. Future application of this model with switchgrass will benefit from its improved simulation of switchgrass production for biofuels in diverse environments.…”

Section: Introductionmentioning

confidence: 99%

Simulated Biomass, Climate Change Impacts, and Nitrogen Management to Achieve Switchgrass Biofuel Production at Diverse Sites in U.S.

Kim¹,

Kim

Cho³

et al. 2020

Agronomy

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Switchgrass (Panicum virgatum L.) is a C4, warm season, perennial native grass that has been strongly recommended as an ideal biofuel feedstock. Accurate forecasting of switchgrass yield across a geographically diverse region and under future climate conditions is essential for determining realistic future ethanol production from switchgrass. This study compiled a switchgrass database through reviewing the existing literature from field trials across the U.S. Using observed switchgrass data, a process-based model (ALMANAC) was developed. The ALMANAC simulation results showed that crop management had more effect on yield than location. The ALMANAC model consists of functional relationships that provide a better understanding of interactions among plant physiological processes and environmental factors (water, soil, climate, and nutrients) giving realistic predictions in different climate conditions. This model was used to quantify the impacts of climate change on switchgrass yields. Simulated lowland switchgrass would have more yield increases between Illinois and Ohio in future (2021–2050) under both Representative Concentration Pathway (RCP) 4.5 and 8.5 pathways with low N fertilizer inputs than high N fertilizer inputs. There was no significant effect of climate variability on upland simulated yields, which means that N fertilization is a key factor in controlling upland switchgrass yields under future climate conditions.

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“…To our knowledge, there are no systematic sensitivity analyses of crop models performed on a wide range of parameters for perennials. However, Kiniry et al [33] and Kim et al [29] have changed the values of a few selected crop parameters and concluded that the yield of switchgrass was sensitive to radiation use efficiency, water use efficiency, and nitrogen application.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of the APEX Model for Assessing Sustainability of Switchgrass Grown for Biofuel Production in Central Texas

Zhang

Juenger

Osorio³

et al. 2017

Bioenerg. Res.

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Crop simulation models are increasingly being used to understand the feasibility of large-scale cellulosic biofuel production along with the multi-dimensional impacts on environmental sustainability. However, how the uncertainty in model parameters impacts model performance for sustainability is unclear. In this case study, sensitivity analyses were conducted for three switchgrass sustainability metrics: total biomass production, nitrogen loss, and soil carbon change using the APEX (Agricultural Policy/Environmental eXtender) model. Fifteen out of the 45 parameters (25 crop growth (CROP) parameters and 20 additional model parameters (PARM)) were identified as influential for the three sustainability metrics for three lowland genotypes (WBC, AP13, and KAN) across two locations (Temple, TX, and Austin, TX). Our sensitivity results showed that parameter importance was not dependent on the genotypes but depended on the variables of interest, and differed only slightly between locations. Influential belowground-related CROP and PARM parameters were identified for each sustainability metric, indicating that belowground-related parameters are just as important as commonly measured aboveground CROP parameters.Further investigation of the linear or non-linear relationships and the two-way interactions between each of the individual influential parameters with the three sustainability metrics reflected the functions and characteristics within the APEX model and the interrelations among different processes. Strong interactions between the most influential parameters for total biomass, nitrogen loss, and soil carbon change also highlighted the importance of accurately setting these parameters. Identification of influential model parameters for switchgrass sustainability may help guide field measurements and provide further understanding of the interrelated processes in the APEX model. Furthermore, future field experiments can be designed to measure these influential parameters and understand the non-linear relationships identified between influential parameters and response variables. More accurate model parameterization will help improve APEX model performance and our understanding of the possible underlying physiological mechanisms.

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Adaptation of C4 Bioenergy Crop Species to Various Environments within the Southern Great Plains of USA

Cited by 9 publications

References 50 publications

A Review of Modeled Water Use Efficiency of Highly Productive Perennial Grasses Useful for Bioenergy

A Review of Modeled Water Use Efficiency of Highly Productive Perennial Grasses Useful for Bioenergy

Simulated Biomass, Climate Change Impacts, and Nitrogen Management to Achieve Switchgrass Biofuel Production at Diverse Sites in U.S.

Sensitivity Analysis of the APEX Model for Assessing Sustainability of Switchgrass Grown for Biofuel Production in Central Texas

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