Growth curve of a tropical mixed-grass pasture in a hot and humid climate

Background. Pasture growth rate (PGR, kg/ha/day) depends on climatic and management practices. However, studies on the influence of the environment on pasture production and productivity of dry matter are scarce in tropical, hot, and humid regions of México. Objective. To estimate the pasture growth curve using time and climatic variables. Methodology. We related, through nonlinear models, the accumulated growth of a pasture composed of native grasses mixed with exotic grasses, using time and the variables temperature and day length as independent variables, the latter integrated into a single variable called thermal photo units (PTU). We estimated the daily growth rates of five divisions; from these, the forage yields for ten days until completing 29 periods. The best-fit models had the largest coefficients of determination and the lowest Akaike’s information criterion. Results. The model that best described the relationship between cumulative yield (Y) and cumulative growing days (X) was reciprocal-quadratic: y = x/(0.097535 – 0.0000881x + 0.0000006810x2) with R2Adj, of 0.9988 and an AICC of 222.6. The model that best described the relationship between the accumulated performance and the accumulated PTU was rational: y = (- 317.8 + 1.594x + 0.00001307x2)/(1 + 0.001059x + 0.00000001964x2), with R2Adjt.=0.9985 and AICC=233.4. Likewise, a two-segment model showed a close fit. The logarithmic model described the first segment: y1 = -2268 + 417.2*(ln(x)), when y2 =1079.3e0.00003932X, if x > 8415; with R2Adj. = 0.9975 and AICc = 245.1. The value 8415 PTU was when the first derivative of both models coincided. Implications. The information generated is useful because it allows grazing system adjustment concerning the correct stocking rate application and designing more efficient grazing rotations. Conclusions. The conversion of growth rates to accumulated yield for ten-day periods produced a smooth curve that allowed fitting high-precision nonlinear models to predict forage accumulation from time and climatic variables.