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Chilean Journal of Agricultural Research
Instituto de Investigaciones Agropecuarias, INIA
ISSN: 0718-5820
EISSN: 0718-5839
Vol. 72, No. 3, 2012, pp. 388-396
Bioline Code: cj12060
Full paper language: English
Document type: Research Article
Document available free of charge

Chilean Journal of Agricultural Research, Vol. 72, No. 3, 2012, pp. 388-396

Castellaro G., Giorgio; Aguilar G., Claudio; Vera I., Raúl & Morales S., Luis


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 check for this species in other resources L.)-white clover ( Trifolium repens check for this species in other resources 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.

Grassland simulation models, mesophytic grasslands

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