Chapter Three - Application of grazing land models in ecosystem management: Current status and next frontiers
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)
- et al.
Simulation of sandsage-bluestem forage growth under varying stocking rates
Rangel. Ecol. Manag.
(2010) Modeling human decisions in coupled human and natural systems: review of agent-based models
Ecol. Model.
(2012)- et al.
Evaluation of GPFARM for simulation of forage production and cow-calf weights
Rangel. Ecol. Manag.
(2005) - et al.
Strategic and tactical prediction of forage in northern mixed-grass prairie
Rangel. Ecol. Manag.
(2006) - et al.
GrazeGro: a European herbage growth model to predict pasture production in perennial ryegrass swards for decision support
Eur. J. Agron.
(2005) - et al.
A case study of the potential environmental impacts of different dairy production systems in Georgia
Agr. Syst.
(2012) - et al.
Integrated crop–livestock systems in Australian agriculture: trends, drivers and implications
Agr. Syst.
(2012) - et al.
Impacts of soil damage by grazing livestock on crop productivity
Soil Tillage Res.
(2011) - et al.
Evaluating nitrogen taxation scenarios using the dynamic whole farm simulation model FASSET
Agr. Syst.
(2003) - et al.
Simulation of residual effects and nitrate leaching after incorporation of different ley types
Eur. J. Agron.
(2005)