A Simulation Model of Mesophytic Perennial Grasslands
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 simula...
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| Autores principales: | , , , |
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| Lenguaje: | English |
| Publicado: |
Instituto de Investigaciones Agropecuarias, INIA
2012
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| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392012000300013 |
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| Sumario: | 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 (39°47’ S., 73°15’ 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 (R² = 98%) simulations. When pasture was subjected to frequent defoliation, the degree of fit of the model was lower (R² = 60%); however, the model was able to predict the trend in the data. |
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