Long-term evaluation of pasture production, seasonality, and variability: An application of the DairyMod pasture model for three tropical species

Abstract

Adoption of improved pastures coupled with intensified management provide quality pastures in adequate quantities and thus improve livestock productivity. While pasture modelling is imperative for exploring the performance of newer pastures, models are little used for long-term simulations of multiple tropical pastures (genotype), under varying soil, climate (environment) and pasture production systems (management). We applied the DairyMod, a biophysical model to simulate the long-term pasture production of Brachiaria ruziziensis x B. decumbens x B. brizantha ‘Brachiaria Mulato II’ (BM), Megathyrsus maximus ‘Gatton Panic’ (GP), and Chloris gayana ‘Rhodes grass cv. Reclaimer’ (RR) across major dairying regions of Sri Lanka under different management scenarios and characterize the long-term pasture growth, seasonality and spatial variability, and possible implications for dairying in Sri Lanka. Simulations of three pasture species were carried out for 16 locations (8 dry (DZ), 5 intermediate (IZ), and 3 wet zone (WZ)) over 30 years (1980–2010). Three pasture management scenarios simulated were; 1) potential pasture production system under non-limiting N and irrigation (Yp) 2) rainfed pasture production system under non-limiting N fertilizer (Yw), and 3) rainfed pasture production system under current nitrogen (N) fertilizer rate (Ya). Statistical techniques were used to identify the long-term growth rates, variability, and trends in pasture production. The long-term pasture production varied greatly among climate, species, and management scenarios. Overall, the Ya showed a seasonal cycle following the rainfall pattern, with a reduction in growth rates in dry seasons (May–September). Pasture growth rates were greater in GP at Ya, and BM at Yw and Yp while RR showed the lowest growth rate at all times. Variability of pasture growth was high in DZ (May–September) and RR has the lowest growth variability. The Yw increased the growth rate (doubled) while the Yp substantially increased (nearly tripled) the growth rate and growth pattern producing less variable pastures. Simulated growth rates suggest that GP in low-input and BM in high-input farming areas would be more suitable. Our study suggested that the BM, GP, and RR are edaphic-climatologically fit for major dairying regions in Sri Lanka and the appropriate fertilizer and irrigation management can greatly increase the herbage accumulation and availability of year-round pastures. While this study offers valuable insights, the species-specific growth pattern, growth variability, yield potential under different managements and the possible implications for herbage quality need to be sensibly considered when selecting the appropriate species.