Meta-analysis of the effects of management factors on Miscanthus×giganteus growth and biomass production

Miguez, Fernando E.; Villamil, María B.; Long, Stephen P.; Bollero, Germán A.

doi:10.1016/j.agrformet.2008.03.010

Cited by 161 publications

(140 citation statements)

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“…Although experimental trials have produced yield estimates for cellulosic bioenergy sources at a variety of locations (Heaton et al 2004, Miguez et al 2008, Wang et al 2010 and models have been used to estimate yields across the United States (Davis et al 2011;Miguez et al in press), uncertainty remains as to the actual yields that will be achieved in agriculturalscale settings and how these vary regionally. In our analysis, yield uncertainty commonly resulted in overlapping confidence intervals for f B or GHGV NF (Figs.…”

Section: Discussionmentioning

confidence: 99%

“…We assumed a conversion efficiency of 310 L ethanol/Mg dry matter for cellulosic ethanol and 420 L ethanol/Mg grain for corn ethanol (Perrin et al 2009, Somerville et al 2010. Substantial variation in yield was driven by a range of factors including geographic location and management practices (Scurlock et al 2002, Heaton et al 2004, Miguez et al 2008, Wang et al 2010). Our analysis considered both average and maximum yield estimates for each bioenergy crop.…”

Section: Methodsmentioning

confidence: 99%

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Biofuels on the landscape: Is “land sharing” preferable to “land sparing”?

Anderson‐Teixeira

Duval

Long

et al. 2012

Ecological Applications

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Abstract. Widespread land use changes, and ensuing effects on ecosystem services, are expected from expanding bioenergy production. Although most U.S. production of ethanol is from corn, it is envisaged that future ethanol production will also draw from cellulosic sources such as perennial grasses. In selecting optimal bioenergy crops, there is debate as to whether it is preferable from an environmental standpoint to cultivate bioenergy crops with high ecosystem services (a ''land-sharing'' strategy) or to grow crops with lower ecosystem services but higher yield, thereby requiring less land to meet bioenergy demand (a ''land-sparing'' strategy). Here, we develop a simple model to address this question. Assuming that bioenergy crops are competing with uncultivated land, our model calculates land requirements to meet a given bioenergy demand intensity based upon the yields of bioenergy crops. The model combines fractional land cover of each ecosystem type with its associated ecosystem services to determine whether land-sharing or land-sparing strategies maximize ecosystem services at the landscape level. We apply this model to a case in which climate protection through GHG regulation-an ecosystem's greenhouse gas value (GHGV)-is the ecosystem service of interest. Our results show that the relative advantages of land sparing and land sharing depend upon the type of ecosystem displaced by the bioenergy crop; as the GHGV of the unfarmed land increases, the preferable strategy shifts from land sharing to land sparing. Although it may be preferable to replace ecologically degraded land with high-GHGV, lower yielding bioenergy crops, average landscape GHGV will most often be maximized through highyielding bioenergy crops that leave more land for uncultivated, high-GHGV ecosystems. Although our case study focuses on GHGV, the same principles will be applicable to any ecosystem service whose value does not depend upon the spatial configuration of the landscape. Whenever bioenergy crops have substantially lower ecosystem services than the ecosystems with which they are competing for land, the most effective strategy for meeting bioenergy demand while maximizing ecosystem services on a landscape level is one of land sparing: focusing simultaneously on maximizing the yield of bioenergy crops while preserving or restoring natural ecosystems.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Biofuels on the landscape: Is “land sharing” preferable to “land sparing”?

Anderson‐Teixeira

Duval

Long

et al. 2012

Ecological Applications

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“…These models are often applied to describe plant height, weight, leaf area index, or seed germination as a function of time, N application rate, herbicide dose, etc. (e.g., Gan et al, 1996;Miguez et al, 2008). Sigmoid equations are also used as 0-1 modifiers in processbased models to incorporate moisture availability or soil pH, etc., effects on soil N transformation processes (e.g., McGechan and Wu, 2001) and also as a switch-off function in studies assessing plant photoperiodic sensitivity (e.g., Amaducci et al, 2008).…”

Section: Group Ii-sigmoid Curvesmentioning

confidence: 99%

Nonlinear Regression Models and Applications in Agricultural Research

2015

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In data analysis, we often ask the following questions:Which is the best model to describe our data? Which is the best statistical index to judge the goodness of fit? How do we choose among competing models? There are no simple answers to these questions. Here we attempt to provide agronomists with a general framework on how to approach these questions appropriately. Our specific objectives are: (i) to provide a succinct overview of nonlinear models and to develop a guideline to understand the family of functions used in agricultural applications; (ii) to indicate techniques to modify nonlinear models and how to cope with multiple nonlinear models; (iii) to discuss key methodological issues on parameter estimation, model performance, and comparison; and (iv) to demonstrate step-by-step analysis of experimental data using a nonlinear regression model. The structure follows the flow diagram in Fig. 1. We start with the definition of nonlinear regression models and discuss their main advantages and disadvantages. Then we present 77 nonlinear functions (including those in supplemental tables) with references to applications in agriculture. We offer an updated overview of methodologies to fit models, choose starting values, assess goodness of fit, select the best models, and evaluate residuals. Finally, we reanalyze experimental data on biomass growth with time (Danalatos et al., 2009). NONLINEAR REGRESSION MODELS DefinitionIn general, statistical models used in agricultural applications can be described with the following notation:where y is the response variable, f is the function or model, x are the inputs, q denotes the parameters to be estimated, and e is the error. Each parameter can be evaluated for whether it is linear or not: if the second derivative of the function with respect to a parameter is not equal to zero, then the parameter is nonlinear. Thus a given function ( f ) can have a mix of linear and nonlinear parameters. Why Should We Use Nonlinear Models?The main advantages of nonlinear models are parsimony, interpretability, and prediction (Bates and Watts, 2007). In general, nonlinear models are capable of accommodating a vast variety of mean functions, although each individual nonlinear model can be less flexible than linear models (i.e., polynomials) in terms of the variety of data they can describe; however, nonlinear models appropriate for a given application can be more parsimonious (i.e., there will be fewer parameters involved) and more easily interpretable. Interpretability comes from the fact that the parameters can be associated with a biologically meaningful process. For example, one of the most widely used nonlinear models is the logistic equation (Eq. [2.1] in Table 1). This model describes the pervasive S-shaped growth curve. The ABSTRACT Nonlinear regression models are important tools because many crop and soil processes are better represented by nonlinear than linear models. Fitting nonlinear models is not a single-step procedure but an involved process that requires careful examinati...

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“…Isenhart, 2000;Bullard, 2001;Heaton et al, 2008;Thomas et al, 2014). Although best management practices of M. × giganteus have been studied for many areas in both the United States and Europe (Miguez et al, 2008;Xue et al, 2015), its production in the United States is limited. However, in 2012 the USDA implemented a national program through the Farm Service Agency called the Biomass Crop Assistance Program (BCAP 3 ) to provide financial assistance to growers seeking to establish and produce biomass feedstocks (USDA-Farm Service Agency, 2012).…”

Section: Introductionmentioning

confidence: 99%

Impact of rhizome quality on Miscanthus establishment in claypan soil landscapes

Randall

Yost

Kitchen

et al. 2016

Industrial Crops and Products

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Thousands of eroded-soil hectares in the U.S. Midwest have been planted to Miscanthus × giganteus as an industrial or bioenergy crop in recent years, but few studies on factors affecting crop establishment have been performed on these soils. The objective of this study was to quantify how both rhizome quality and depth of soil from the surface to the first argillic horizon (or depth to claypan (DTC1)) affected M. × giganteus establishment. Rhizome quality (i.e., mass, length, diameter, viable buds, score), emergence, growth, and winter survival were measured on rhizomes planted in 2013 at Columbia and 2014 at Centralia, Missouri on clay loam soils with a range of DTC. Rhizome emergence and early tillering slightly increased as DTC increased, but these effects on growth diminished as the season progressed. Rhizome emergence and growth were more influenced by some metrics of rhizome quality; the odds of a rhizome emerging increased by 25 and 40% with each 1 cm and 1 bud increase in rhizome length and active bud count, respectively. Furthermore, late tiller counts, basal circumference, and end-of-season biomass increased as rhizome length and mass increased. Winter survival could not be estimated as well as emergence, but the odds of survival across sites increased by 5% with each 1 cm increase in rhizome length. When DTC was categorized as soil erosion class or landscape position, only the backslope at Centralia caused greater M. × giganteus growth than other positions. These findings demonstrate the resiliency of M. × giganteus for early growth and establishment on even the most degraded parts of the claypan soil landscape and indicate that propagating larger rhizomes will improve establishment. a b s t r a c tThousands of eroded-soil hectares in the U.S. Midwest have been planted to Miscanthus × giganteus as an industrial or bioenergy crop in recent years, but few studies on factors affecting crop establishment have been performed on these soils. The objective of this study was to quantify how both rhizome quality and depth of soil from the surface to the first argillic horizon (or depth to claypan (DTC 1 )) affected M. × giganteus establishment. Rhizome quality (i.e., mass, length, diameter, viable buds, score), emergence, growth, and winter survival were measured on rhizomes planted in 2013 at Columbia and 2014 at Centralia, Missouri on clay loam soils with a range of DTC. Rhizome emergence and early tillering slightly increased as DTC increased, but these effects on growth diminished as the season progressed. Rhizome emergence and growth were more influenced by some metrics of rhizome quality; the odds of a rhizome emerging increased by 25 and 40% with each 1 cm and 1 bud increase in rhizome length and active bud count, respectively. Furthermore, late tiller counts, basal circumference, and end-of-season biomass increased as rhizome length and mass increased. Winter survival could not be estimated as well as emergence, but the odds of survival across sites increased by 5% with each 1 cm increase in ...

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Meta-analysis of the effects of management factors on Miscanthus×giganteus growth and biomass production

Cited by 161 publications

References 36 publications

Biofuels on the landscape: Is “land sharing” preferable to “land sparing”?

Biofuels on the landscape: Is “land sharing” preferable to “land sparing”?

Nonlinear Regression Models and Applications in Agricultural Research

Impact of rhizome quality on Miscanthus establishment in claypan soil landscapes

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