A pasture growth model for use in a whole-farm dairy production model

McCall, D. G.; Bishop-Hurley, Greg

doi:10.1016/s0308-521x(02)00104-x

Cited by 81 publications

(69 citation statements)

References 34 publications

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“…Several published simulation models related to pasture growth with complex mathematical formulations (Nouvellon et al, 2000;Bonsemo and Bélanger, 2002;McCall and Bishop-Hurley, 2003) showed validation tests with similar results to those presented here. Therefore, the results obtained with this model are satisfactory, despite the simplicity of its equation formulation.…”

Section: Behavior Prediction Of Above-ground Dry Matter Accumulationsupporting

confidence: 72%

A Simulation Model of Mesophytic Perennial Grasslands

Vera

et al. 2012

Chilean J. Agric. Res.

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Grasslands are complex ecosystems and their processes are affected by soil, meteorological, and management variables. In this context, dynamic simulation models are useful to understand these processes and to design grassland use strategies. The aim of this study was to develop and validate a simulation model of perennial pasture growth based on soil and climate variables. A first approach considered that soil fertility levels were adequate; therefore, soil water availability and phytomass level were the main variables affecting pasture growth. The subroutines considered were water balance, pasture growth, and root biomass dynamics. The hypotheses regarding the functioning of the system were formulated as a group of equations which were solved numerically with a program written in Visual Basic®. Model validation was performed by statistical comparison between simulated DM and DM obtained from experiments conducted in Valdivia (39º47' S., 73º15' W; 9 m a.s.l.). In these experiments we measured DM accumulation on naturalized grassland and ryegrass (Lolium perenne L.)-white clover (Trifolium repens L.) pastures under frequent defoliation. Soil data, temperature, solar radiation, and rainfall were obtained from a meteorological station located in Valdivia. The coefficient of determination between simulated values and those measured in the experiments were higher in the DM accumulation (R 2 = 98%) simulations. When pasture was subjected to frequent defoliation, the degree of fit of the model was lower (R 2 = 60%); however, the model was able to predict the trend in the data.

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Section: Behavior Prediction Of Above-ground Dry Matter Accumulationsupporting

confidence: 72%

A Simulation Model of Mesophytic Perennial Grasslands

Vera

et al. 2012

Chilean J. Agric. Res.

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“…To describe the variety of pasture management systems available, pasture yield and nutrient contents were therefore simulated by the McCall pasture model (McCall and Bishop-Hurley, 2003) as updated by Romera et al (2009). The McCall model was parameterized and validated for U.S. pasture production as described in Appendix A.…”

Section: Crop and Pasture Production Parametersmentioning

confidence: 99%

“…When pasture was used as a feed, land use was determined based on projected yields from the pasture module. Water use included irrigation for crop production (USDA/ NASS, 2007) or pasture growth (McCall and Bishop-Hurley, 2003); drinking water was also accounted for (Meyer et al, 2006). Greenhouse gas emissions included CO 2 , N 2 O and CH 4 .…”

Section: Model Objectivesmentioning

confidence: 99%

Optimizing diet and pasture management to improve sustainability of U.S. beef production

White

Brady

Capper³

et al. 2014

Agricultural Systems

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a b s t r a c tSystem sustainability balances environmental impact, economic viability and social acceptability. Assessment methods to investigate impacts of enterprise management and consumer decisions on sustainability of beef cattle operations are critically needed. Tools of this nature are especially important given the predictions of climate variability and the dependence of beef production systems on forage availability. A model optimizing nutritional and pasture management was created to examine the environmental impact of beef production. The model integrated modules calculating cradle-to-farm gate environmental impact, diet cost, pasture growth and willingness to pay (WTP). Least-cost diet and pasture management options served as a baseline to which environmental-impact reducing scenarios were compared. Economic viability was ensured by a constraint limiting change in diet cost to less than consumer WTP. Increased WTP was associated with improved social acceptability. Model outputs were evaluated by comparing to published data. Sensitivity analysis of the WTP constraint was conducted. A series of scenarios then examined how forecasted changes in precipitation patterns might alter forage supply and opportunities to reduce environmental impact in three regions in the United States. On a national scale, single-objective optimization indicated individual reductions in greenhouse gases (GHG), land use and water use of 3.6%, 5.4% and 4.3% were possible by changing diets. Multi-objective optimization demonstrated that GHG, land and water use could be simultaneously reduced by 2.3%. To achieve this change, cow-calf diets relied on grass hay, continuously-or rotationally-grazed irrigated and fertilized pasture as well as rotationally-grazed pasture. Stocker diets used rotationally-grazed, irrigated and fertilized pasture and feedlot diets used grass hay as a forage source. The model was sensitive to consumer WTP. When alternative precipitation patterns were simulated, opportunities to decrease the environmental impact of beef production in the Pacific Northwest and Texas were reduced by precipitation changes; whereas opportunities in the Midwest improved. Economic viability, rather than biological limitations, reduced the potential to improve environmental impact under future precipitation scenarios. Decreased spring rainfall resulted in lower pasture yields and required greater use of stored forages. Related increases in diet cost reduced opportunities to appropriate funds toward investment in environmental-impact reducing pasture management strategies. The model developed in this study is a robust tool that can be used to assess the impacts of enterprise management and consumer decisions on beef production sustainability.

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“…Johnson et al 2003;McCall & BishopHurley 2003;Barrett et al 2004;Corson et al 2006) and some of these are integrated in dedicated pastoral system models such as the Whole Farm Model (McCall & Bishop-Hurley 2003) and SGS/DairyMod/EcoMod (Johnson et al 2003. These models are convenient for modelling pastoral systems, but have limited or no ability to investigate interactions with, or conversion to, other types of land use such as forestry or cropping systems (Jamieson et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Modelling the seasonal and geographical pattern of pasture production in New Zealand

Li¹,

Snow²,

Holzworth³

2011

New Zealand Journal of Agricultural Research

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The pasture growth module AgPasture was integrated into the APSIM (Agricultural Production System Simulator) simulation model, allowing pasture-based systems to be modelled in combination with other land uses at farm scale or within land use change studies. The model's predictions of pasture growth were evaluated against 32 pasture growth datasets from a diverse range of soil types and climatic zones across New Zealand. The pasture herbage accumulation simulated by the model closely matched actual measurements over varying intervals. Both predicted and measured pasture growth rate demonstrated the same seasonal pattern, including mean growth rate and inter-annual variation across measurement years. Predicted and measured annual average net herbage accumulation (NHA) on a dryland pasture was similar over 37 observation years (mean, 6.83 and 7.27 t DM/ha respectively; coefficient of variation, 29% and 27% respectively) and highly correlated (R 2 0 0.838, P B 0.0001; relative root mean squared deviation (RMSD) 0 16%). The model's prediction of annual average NHA of all simulated pastures, spanning a wide range of pasture environments, also matched the measurement data well (R 2 0 0.777, P B 0.0001; relative RMSD 0 21%). However, discrepancies between simulated and observed values occurred in some seasons and at some sites. Analysis of these discrepancies identified areas where the model could be improved by incorporating more accurate descriptions of the effects of plant development and grazing, soil temperature and the interactive effects of high temperature and soil moisture dynamics.

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A pasture growth model for use in a whole-farm dairy production model

Cited by 81 publications

References 34 publications

A Simulation Model of Mesophytic Perennial Grasslands

A Simulation Model of Mesophytic Perennial Grasslands

Optimizing diet and pasture management to improve sustainability of U.S. beef production

Modelling the seasonal and geographical pattern of pasture production in New Zealand

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