Abstract

The thermal time, expressed in accumulated growing degree-days (AGDD), was used as a predictor to describe and simulate the independent growth of two pasture crops, Lolium perenne L. and Bromus valdivianus Phil. Two sinusoidal models (four-parameter Logistic and Gompertz) were applied to the growth variables (total leaf blade length per tiller-LBL, and accumulated herbage mass-AHM). The nutritive value of pastures was predicted and modeled using regression equations (linear and quadratic), depending on each nutrient. Data for modeling were collected from a two-year study, in which LBL, AHM, and nutritive value variables for L. perenne and B. valdivianus pastures were measured at three-day intervals. Defoliation was determined according to the AGDD, such that the swards were defoliated at 90, 180, 270, 360, and 450 AGDD. The Logistic and Gompertz models presented similar values for the growth rate (GR) parameters, superior asymptote (A(sup)), inferior asymptote (A(inf)), and point of maximum growth (P-max). In both species, the maximum growth was 260 AGDD. The GR was similar for both species in different seasons of the year, but the maximum AHM varied, with B. valdivianus presenting a higher value (+1500 kg DM ha(-1)) than L. perenne during the spring. The regressions accurately described the nutritive value, demonstrating a positive linear relationship between the AGDD and concentrations of neutral and acid detergent fiber (NDF, ADF), an inverse linear relationship with crude protein (CP), and a quadratic relationship with metabolizable energy (ME) and water-soluble carbohydrate (WSC) concentration.