GRAZPLAN: Decision support systems for Australian grazing enterprises. III. Pasture growth and soil moisture submodels, and the GrassGro DSS

Moore, Andrew D.; Donnelly, J. R.; Freer, M.

doi:10.1016/s0308-521x(97)00023-1

Cited by 195 publications

(93 citation statements)

References 27 publications

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“…When precipitation changes negatively impacted pasture growth, more stored forage was required and environmental impact tended to increase. Future analyses should move to a more precise spatial scale and utilize more precise pasture simulation models (Graux et al, 2011;Johnson et al, 2003;Moore et al, 1997;Riedo et al, 1998) to accurately simulate heterogeneity to help identify ideal management practices across a varied landscape. An additional area for future research could be modeling the long-term response of pastures to variation in climate and grazing management system.…”

Section: Trends In Precipitation Impacts Across Regionsmentioning

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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Section: Trends In Precipitation Impacts Across Regionsmentioning

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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“…Agricultural system simulation models have been increasingly used to assist with the evaluation of the effects of environmental and management changes on pastoral system performance, particularly the likely economic and environmental outcomes (Moore et al 1997;Clark et al 2000;Johnson et al 2003). Whole pastoral system models enable farm management options to be explored, which, in turn, may assist farmers to adapt to future economic and environmental changes.…”

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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“…Simulations of each experiment were constructed using the GRAZPLAN pasture and livestock models 317 Moore et al 1997) linked to the APSIM soil water and soil nutrient cycling models 318 (Holzworth et al 2014) Cranbrook sites were taken from the modelling study of Ojeda et al (2016), which also used the APSIM 327 soil water balance model. Local soil data for some sites such as the Tamworth experiments in particular 328 were limited, which introduces uncertainty into model predictions from the outset.…”

Section: Simulation Of the Experimental Data Sets 316mentioning

confidence: 99%

Modelling of lucerne (Medicago sativa L.) for livestock production in diverse environments

et al. 2017

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A number of models exist to predict lucerne (Medicago sativa L.) dry matter production; however most 30 of these models do not adequately represent the ecophysiology of the species to predict daily growth 31 rates across the range of environments in which it is grown. Since it was developed in the late 1990s 32 the GRAZPLAN model has not been updated to reflect modern genotypes and has not been widely 33 validated across the range of climates and farming systems in which lucerne is grown in modern times. 34Therefore the capacity of GRAZPLAN pasture growth model to predict lucerne growth and 35 development was assessed. This was done by re-estimating values for some key parameters based on 36 information in the scientific literature. The improved GRAZPLAN model was also assessed for its 37 capacity to reflect differences in the growth and physiology of lucerne genotypes with different winter 38 activity. Modifications were made to GRAZPLAN to improve its capacity to reflect changes in 39 phenology due to environmental triggers such as short photoperiods, declining low temperatures, 40 defoliation and water stress. Changes were also made to the parameter governing the effect of vapour 41 pressure VPD on the biomass-transpiration ratio and therefore biomass accumulation. Other 42 developments included the representation of root development and partitioning of canopy structure, 43 notably the ratio of leaf to stem dry matter. Data from replicated field experiments across Australia 44 were identified for the purpose of model validation. These data were broadly representative of the range 45 of climate zones, soil types and farming systems in which lucerne is used for livestock grazing. 46Validation of predicted lucerne growth rates was comprehensive due to the plentiful data. Across a 47 range of climate zones, soils and farming systems there was an overall improvement in the capacity to 48 simulate the pasture dry matter production, with a reduction in the mean prediction error of 0.33 and 49 the root mean square deviation of 9.6 kg/ha/d. Validation of other parts of the model was restricted 50 however as information relating to plant roots, soil water, plant morphology and phenology were 51 limited. This study has highlighted the predictive power, versatility and robust nature of GRAZPLAN 52 to predict the growth, development and nutritive value of perennial species such as lucerne. 53 4

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GRAZPLAN: Decision support systems for Australian grazing enterprises. III. Pasture growth and soil moisture submodels, and the GrassGro DSS

Cited by 195 publications

References 27 publications

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

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

Modelling of lucerne (Medicago sativa L.) for livestock production in diverse environments

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