Modelling pasture and animal production.

Rickert, K. G.; Stuth, Jerry W.; McKeon, Greg; Mannetje, L. ‘t; Jones, R. M.

doi:10.1079/9780851993515.0029

Cited by 43 publications

(59 citation statements)

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“…This is described in Equation [20], which is similar to the results reported by Rickert et al (2000) and shown in Figure 2b. …”

Section: Grassland Transpirationsupporting

confidence: 81%

“…When the soil water content is higher than the permanent wilting point, evaporation occurs at a rate equal to potential evaporation (Ep, ). This variable depends on the reference evapotranspiration (Eo, mm d -1 ) and effective soil cover (COB ef ) expressed as a fraction between 0 and 1 (Rickert et al, 2000). When soil water is lower than the permanent wilting point, evaporation is reduced by the relative water content in the layer (WDIv), which is referred to as residual water content estimated at one third of the permanent wilting point (Campbell, 1997).…”

Section: Methodsmentioning

confidence: 99%

“…Effective soil cover depends on the green (COL v ) and dead DM cover, including litter (COL m ). Variables expressed in fractional values between 0 and 1 are related to the following equations (Rickert et al, 2000):…”

Section: Methodsmentioning

confidence: 99%

“…mechanisms, respectively (Rickert et al, 2000). The function that restricts grassland growth with temperature effects is:…”

Section: Figure 2 (A) Growth Restriction Function For Green Dry Mattmentioning

confidence: 99%

“…To estimate the actual grassland growth rate (TG, kg DM ha -1 d -1 ), TGMAX should be adjusted by the weighted value of three multipliers (with observed values ranging from 0 -1), which are related to daily mean values of solar radiation (FSR), mean air temperature (FRCTEMP), and degree of soil water stress (FRCWD) (Rickert et al, 2000). The resulting product, called GROWTHINDEX is further weighted by a relative factor which depends on root biomass (ROOTSCALAR) and reflects the effect of this component on the shoot biomass growth rate (Hudson et al, 2001):…”

Section: Grassland Transpirationmentioning

confidence: 99%

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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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“…This is described in Equation [20], which is similar to the results reported by Rickert et al (2000) and shown in Figure 2b. …”

Section: Grassland Transpirationsupporting

confidence: 81%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…mechanisms, respectively (Rickert et al, 2000). The function that restricts grassland growth with temperature effects is:…”

Section: Figure 2 (A) Growth Restriction Function For Green Dry Mattmentioning

confidence: 99%

Section: Grassland Transpirationmentioning

confidence: 99%

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A Simulation Model of Mesophytic Perennial Grasslands

Vera

et al. 2012

Chilean J. Agric. Res.

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An integrated pasture biomass and beef cattle liveweight predictive model under weather forecast uncertainty: An application to Northern Australia

Masoud

Hsieh

Helmstedt

et al. 2023

Food and Energy Security

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Spatially explicit modelling of the impact of climate changes on pasture production in the North Island,New Zealand

2007

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To assess the potential impact of climate changes on pasture production in the North Island, New Zealand, eight climate scenarios of increased temperature and increased (or decreased) rainfall were investigated by integrating a polynomial regression model for pasture production with a Geographic Information System (GIS). The results indicated that the climate change scenarios assuming an increase in temperature by 1-2°C and a rainfall change by −20 to +20% would have a very significant impact on pasture production with a predicted pasture production variation from −46.2 to +51.9% compared with the normal climate from 1961-1990. Increased temperature would generally have a positive effect on pasture production in the south and southeast of the North Island, and increased rainfall would have a positive effect in the central, south and southeast of the North Island and a negative effect in the north of the North Island. The interaction of decreased rainfall and increased temperature would have a negative impact for the whole North Island except some central areas with high rainfall. Relevant management practices for coping with potential climate change are discussed.

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Modelling pasture and animal production.

Cited by 43 publications

References 0 publications

A Simulation Model of Mesophytic Perennial Grasslands

A Simulation Model of Mesophytic Perennial Grasslands

An integrated pasture biomass and beef cattle liveweight predictive model under weather forecast uncertainty: An application to Northern Australia

Spatially explicit modelling of the impact of climate changes on pasture production in the North Island,New Zealand

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