Elsevier

Advances in Agronomy

Volume 158, 2019, Pages 173-215
Advances in Agronomy

Chapter Three - Application of grazing land models in ecosystem management: Current status and next frontiers

https://doi.org/10.1016/bs.agron.2019.07.003Get rights and content

Abstract

Grazing land models can assess the provisioning and trade-offs among ecosystem services attributable to grazing management strategies. We reviewed 12 grazing land models used for evaluating forage and animal (meat and milk) production, soil C sequestration, greenhouse gas emission, and nitrogen leaching, under both current and projected climate conditions. Given the spatial and temporal variability that characterizes most rangelands and pastures in which animal, plant, and soil interact, none of the models currently have the capability to simulate a full suite of ecosystem services provided by grazing lands at different spatial scales and across multiple locations. A large number of model applications have focused on topics such as environmental impacts of grazing land management and sustainability of ecosystems. Additional model components are needed to address the spatial and temporal dynamics of animal foraging behavior and interactions with biophysical and ecological processes on grazing lands and their impacts on animal performance. In addition to identified knowledge gaps in simulating biophysical processes in grazing land ecosystems, our review suggests further improvements that could increase adoption of these models as decision support tools. Grazing land models need to increase user-friendliness by utilizing available big data to minimize model parameterization so that multiple models can be used to reduce simulation uncertainty. Efforts need to reduce inconsistencies among grazing land models in simulated ecosystem services and grazing management effects by carefully examining the underlying biophysical and ecological processes and their interactions in each model. Learning experiences among modelers, experimentalists, and stakeholders need to be strengthened by co-developing modeling objectives, approaches, and interpretation of simulation results.

Introduction

Grazing lands, including range and pasture lands, are essential for animal production (meat, milk, and fiber) systems worldwide (Derner et al., 2017a), and sustain the world population of nearly 8 billion through the provision of high-quality dietary protein (Snow et al., 2014). They are also critical for a variety of regulating, cultural, and supporting ecosystem services desired by society (Havstad et al., 2007; Yahdjian et al., 2015). Grazing lands provide key habitats for wildlife and biodiversity (Fuhlendorf and Engle, 2001; West, 1993), as well as opportunities for maintaining and enhancing soil health (Derner et al., 2018). Growing demand for livestock products is producing an imperative for sustainable intensification of livestock agriculture that attempts to reconcile increased production with long-term environmental stewardship (Spiegal et al., 2018).

The multiple purposes of grazing lands mean that land managers are faced with difficult decision-making processes associated with complex production and conservation problems (Boyd and Svejcar, 2009) in intertwined social-ecological systems (Lubell et al., 2013; Wilmer et al., 2018). These complex problems vary in time and space, which require process-based understanding of the problem(s), adaptive management and coordination of management and research (Boyd and Svejcar, 2009). Changing climates will affect forage supply and its quality for livestock (Augustine et al., 2018; Ghahramani and Moore, 2016) and require greater adaptive capacity, with enhanced decision-making skills, which integrates biophysical, social, and economic considerations (Derner et al., 2017b).

Grazing land managers need decision-making tools to cope with seasonal, annual, and inter-annual variability of weather, variable production, and commodity market price fluctuations (Derner et al., 2012; Kipling et al., 2016a, Kipling et al., 2016b; Kragt and Robertson, 2014; Moore, 2014; von Lampe et al., 2014). Grazing land models can assist managers with decision-making through evaluation of alternative management strategies under current as well as projected conditions, including from predicted climate change (Bunting et al., 2016; Fullman et al., 2017; Kalaugher et al., 2017; Moore and Ghahramani, 2014; Snow et al., 2014). Modeled output of multiple ecosystem services from alternative management scenarios can provide valuable insight into tradeoffs, but challenges remain for decision-making at spatial and temporal scales relevant to land managers (de Groot et al., 2010; Derner et al., 2012; Nelson et al., 2009). Increasing utility of models in grazing lands for decision-making and adaptive management is possible when goals associated with decision-making are matched with the complexity of the model (Derner et al., 2012).

Previous reviews of grazing land models have identified gaps related to modeling feed intake and rumen function of grazing animals, plant diversity, grazing animal-forage interactions, and animal growth (Brilli et al., 2017; Bryant and Snow, 2008, Cavalli et al., 2019; Del Prado et al., 2013; Kipling et al., 2016a, Kipling et al., 2016b; Snow et al., 2014). However, these reviews have not addressed applications of grazing land models for multiple ecosystem services, assessing trade-offs among these services and long-term sustainability considerations. Our objective was to review grazing land models (Table 1), based on a literature search of 12 commonly-used models, for simulating the impacts of grazing management on the ecosystem services of forage production, animal production, plant diversity, soil carbon (C) sequestration, and nitrogen (N) losses (Fig. 1) in both extensively managed rangelands and intensively managed grasslands. We first review the grazing land models for applications to increase understanding of the ecosystem for: (1) plant-animal interactions, (2) animal-animal interactions, (3) forage production, (4) animal production, and (5) natural resources and production tradeoffs. Second, we review the models for application to short- and long-term decision-making. Third, we conclude with a next frontier for grazing land models to improve their utility for land managers.

Section snippets

Model applications for systems understanding

Among the 12 mechanistic models in Table 1 and 2, DayCent and ALMANAC have no animal component, and APEX's cow-calf production is yet to be tested. APSIM is combined with GRAZPLAN for animal simulation. For vegetative production, APSIM, DairyNZ WFM, DayCent, and PaSim simulate the vegetation cover (or sward) as a single-plant community (although a percentage of legumes can be set to simulate symbiotic nitrogen fixation). Most models have daily time step and run at field scale. However, SAVANNA

Model application for prediction and decision support

In addition to systems understanding, mechanistic models have the capability to predict systems behavior under different conditions through the integrated biophysical processes embedded in the models. Given the empirical nature of the models, each must be calibrated to some extent under certain assumptions before transferring to different soil, climate, and management conditions. To develop a model application for prediction and decision support purposes, users need to: (1) select a model or

Multi-location and multi-model comparison

Model predictions vary considerably across locations due to differences in plant-animal-environment interactions. Likewise, simulation results obtained from multiple models for the same dataset vary greatly due to differing approaches and underlying model assumptions. One of the most extensive comparison across locations using a single model was conducted with PaSim in Europe. Gottschalk et al. (2007) studied the uncertainty of PaSim in simulating net ecosystem exchange at four sites in France,

Identified knowledge gaps

We identified several knowledge gaps that could improve the simulation of ecosystem processes (Table 3). First, improvements on forage production simulation are needed (Ehrhardt et al., 2018). Simulations of root dynamics, root carbon storage for regrowth, and rooting depth need to be improved due to the importance of roots in water and nutrient uptake (Descheemaeker et al., 2014b; Qi et al., 2012; Robertson et al., 2015; White and Snow, 2012; Zilverberg et al., 2017). Considerable errors may

Conclusion

Based on a literature search of 12 models to simulate dynamics of grazing lands, most applications are at the field scale, with a few on the farm or ranch level. The models evaluate alternative management practices on vegetation and animal production as well as environmental impacts under current and projected climate conditions. Several knowledge gaps are identified to improve forage and animal simulation, including plant phenology, root growth, forage quality, and animal grazing efficiency.

References (209)

  • P.C. Beukes et al.

    Evaluation of a whole-farm model for pasture-based dairy systems

    J. Dairy Sci.

    (2008)
  • P.C. Beukes et al.

    Using a whole farm model to determine the impacts of mating management on the profitability of pasture-based dairy farms

    Anim. Reprod. Sci.

    (2010)
  • P.C. Beukes et al.

    Improving production efficiency as a strategy to mitigate greenhouse gas emissions on pastoral dairy farms in New Zealand

    Agric. Ecosyst. Environ.

    (2010)
  • P.C. Beukes et al.

    Estimating greenhouse gas emissions from New Zealand dairy systems using a mechanistic whole farm model and inventory methodology

    Anim. Feed Sci. Technol.

    (2011)
  • P.C. Beukes et al.

    The relationship between milk production and farm-gate nitrogen surplus for the Waikato region, New Zealand

    J. Environ. Manage.

    (2012)
  • P.C. Beukes et al.

    The performance of an efficient dairy system using a combination of nitrogen leaching mitigation strategies in a variable climate

    Sci. Total. Environ.

    (2017)
  • C.S. Boyd et al.

    Managing complex problems in rangeland ecosystems

    Rangel. Ecol. Manage.

    (2009)
  • L. Brilli et al.

    Review and analysis of strengthens and weaknesses of agro-ecosystem models for simulating C and N fluxes

    Sci. Total Environ.

    (2017)
  • E.L. Bunting et al.

    Utilization of the SAVANNA model to analyze future patterns of vegetation cover in Kruger National Park under changing climate

    Ecol. Model.

    (2016)
  • P. Calanca et al.

    Simulating the fluxes of CO2 and N2O in European grasslands with the pasture simulation model (PaSim)

    Agric. Ecosyst. Environ.

    (2007)
  • X. Chang et al.

    Simulating effects of grazing on soil organic carbon stocks in Mongolian grasslands

    Agric. Ecosyst. Environ.

    (2015)
  • R. Cichota et al.

    Modelling nitrogen leaching from overlapping urine patches

    Environ. Modell. Software

    (2013)
  • Confalonieri

    CoSMo: a simple approach for reproducing plant community dynamics using a single instance of generic crop simulators

    Ecol. Model.

    (2014)
  • M.S. Corson et al.

    Evaluating warm-season grass production in temperate-region pastures: a simulation approach

    Agr. Syst.

    (2007)
  • M.S. Corson et al.

    Adaptation and evaluation of the integrated farm system model to simulate temperate multiple-species pastures

    Agr. Syst.

    (2007)
  • M.B. Coughenour

    A mechanistic simulation analysis of water use, leaf angles, and grazing in east African graminoids

    Ecol. Model.

    (1984)
  • R.S. de Groot et al.

    Challenges in integrating the concept of ecosystem services and values in landscape palnning, management, and decision making

    Ecol. Complex.

    (2010)
  • A. Del Prado et al.

    Whole-farm models to quantify greenhouse gas emissions and their potential use for linking climate change mitigation and adaptation in temperate grassland ruminant-based farming systems

    Animal

    (2013)
  • J.D. Derner et al.

    Livestock as ecosystem engineers for grassland bird habitat in the western Great Plains of North America

    Rangel. Ecol. Manag.

    (2009)
  • J.D. Derner et al.

    Opportunities for increasing utility of models for rangeland management

    Rangel. Ecol. Manag.

    (2012)
  • J.D. Derner et al.

    Soil health as a transformational change agent for US grazing lands management

    Rangel. Ecol. Manag.

    (2018)
  • K. Descheemaeker et al.

    Effects of climate change and adaptation on the livestock component of mixed farming systems: a modelling study from semi-arid Zimbabwe

    Agr. Syst.

    (2018)
  • A.V. Di Vittorio et al.

    Development and optimization of an agro-BGC ecosystem model for C4 perennial grasses

    Ecol. Model.

    (2010)
  • J.R. Donnelly et al.

    GRAZPLAN: decision support systems for Australian grazing enterprises. I. Overview of the GRAZPLAN project and a description of the MetAccess and LambAlive DSS

    Agric. Systems

    (1997)
  • J.J. Drewry

    Natural recovery of soil physical properties from treading damage of pastoral soils in New Zealand and Australia: a review

    Agric. Ecosyst. Environ.

    (2006)
  • A. Edirisinghe et al.

    Spatio-temporal modelling of biomass of intensively grazed perennial dairy pastures using multispectral remote sensing

    Int. J. Appl. Earth Obs. Geoinf.

    (2012)
  • Q.X. Fang et al.

    Modeling weather and stocking rate effects on forage and steer production in northern mixed-grass prairie

    Agr. Syst.

    (2014)
  • C.A. Fensterseifer et al.

    On the number of experiments required to calibrate a cultivar in a crop model: the case of CROPGRO-soybean

    Field Crop Res.

    (2017)
  • J.K. Foy et al.

    Evaluation of the upgraded spur model (spur2.4)

    Ecol. Model.

    (1999)
  • M. Freer et al.

    GRAZPLAN: decision support systems for Australian grazing enterprises. II. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS

    Agric. Systems

    (1997)
  • T.J. Fullman et al.

    Predicting shifts in large herbivore distributions under climate change and management using a spatially-explicit ecosystem model

    Ecol. Model.

    (2017)
  • P. Fust et al.

    Integrating spatio-temporal variation in resource availability and herbivore movements into rangeland management: RaMDry-An agent-based model on livestock-feeding ecology in a dynamic heterogeneous, semi-arid environment

    Ecol. Model.

    (2018)
  • E. Gellesch et al.

    Grassland experiments under climatic extremes: reproductive fitness versus biomass

    Environ. Exp. Bot.

    (2017)
  • A. Ghahramani et al.

    Impact of climate changes on existing crop-livestock farming systems

    Agr. Syst.

    (2016)
  • F. Ginaldi et al.

    Interoperability of agronomic long term experiment databases and crop model intercomparison: the Italian experience

    Eur. J. Agron.

    (2016)
  • P. Gottschalk et al.

    The role of measurement uncertainties for the simulation of grassland net ecosystem exchange (NEE) in Europe

    Agric. Ecosyst. Environ.

    (2007)
  • A.L. Graux et al.

    Ensemble modelling of climate change risks and opportunities for managed grasslands in France

    Agric. For. Meteorol.

    (2013)
  • P. Gregorini et al.

    Screening for diets that reduce urinary nitrogen excretion and methane emissions while maintaining or increasing production by dairy cows

    Sci. Total Environ.

    (2016)
  • T. Guo et al.

    Response of semi-arid savanna vegetation composition towards grazing along a precipitation gradient—the effect of including plant heterogeneity into an ecohydrological savanna model

    Ecol. Model.

    (2016)
  • T. Guo et al.

    The role of landscape heterogeneity in regulating plant functional diversity under different precipitation and grazing regimes in semi-arid savannas

    Ecol. Model.

    (2018)
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