Knowledge about pasture growth rates is crucial for optimizing forage use efficiencies in intensively managed pasture and silage-based dairy systems, enabling optimized cutting/grazing times for high yields with high forage quality. The aim of this study was to parameterise a simple model, GrasProg, for predicting pasture growth in an intensively managed dairy production system under a cut-and-carry management. For this, pasture crop-growth rates were measured over a period of two years (2016 and 2017) at five contrasting sites in Schleswig-Holstein, Northern Germany. The pastures received nitrogen (N) fertilizer at a rate of 280 kg N ha⁻¹ and were cut on a four-week interval. Average annual dry matter (DM) yields ranged from 10.9 to 11.6 t/ha for the three different locations. The DM accumulation simulated by GrasProg matched actual measurements over the varying intervals well (R² = 0.65; RMSE = 49.5 g DM m⁻²; and NSE = 0.44). Two model parameters were adjusted within the vegetation period, namely, the relative growth rate, a proxy of the number of generative tillers, and the initial biomass at the start of each growth period, a proxy for the tillering density. Both predicted and measured pasture growth rates showed the same typical seasonal pattern, with high growth rates in spring, followed by decreasing growth rates to the end of the vegetation period. These good calibration statistics, with adjusting of only two model parameters, for the different sites and different climatic conditions mean that GrasProg can be used to identify optimum grazing or cutting strategies, with optimal yield and forage quality.

High doses of nitrogen (N) fertiliser input on permanent pastures are crucial in terms of N surplus and N losses. Quantitative analyses of the response of plant functional traits (PFT) driving crop growth rate (CGR) under low N input are lacking in frequently defoliated pastures. This study aimed to understand the significance of PFTs for productivity and N uptake in permanent grasslands by measuring dynamics in tiller density (TD), tiller weight (TW), leaf weight ratio (LWR), leaf area index (LAI), specific leaf area (SLA), as well as leaf N content per unit mass (LNC_m) and per unit area (LNC_a) in perennial ryegrass (Lolium perenne)-dominated pastures, in a simulated rotational grazing approach over two consecutive growing seasons. Annual N application rates were 0, 140 and 280 kg N ha⁻¹. The phenological development of perennial ryegrass was the main driver of CGR, N uptake and most PFTs. The effect of N application rate on PFTs varied during the season. N application rate showed the greatest effect on TD, LAI and, to a lesser extent, on SLA and LNC_m. The results of this study highlight the importance of TD and its role in driving CGR and N uptake in frequently defoliated permanent pastures.

Studies evaluating the complexity of models, which are suitable to simulate grass growth at regional scales in intensive grassland production systems are scarce. Therefore, two different grass growth models (GrasProg1.0 and APSIM) with different complexity and input requirements were compared against long-term observations from variety trials with perennial ryegrass (Lolium perenne) in Germany and Denmark. The trial sites covered a large range of environmental conditions, with annual average temperatures ranging from 5.9 to 10.3 °C, and annual rainfall from 536 to 1154 mm. The sites also varied regarding soil type, which were for modelling categorised into three different groups according to their plant available water (PAW) content: light soils with a PAW of 60 mm, medium soils with a PAW of 80 mm, and heavy soils with a PAW of 100 mm. The objective was to investigate whether the simple model performed equally well with the given low number of inputs, namely climate and PAW group. Evaluation statistics showed that both models provided satisfactory results, with root mean square errors for individual cuts ranging from 0.59 to 1.28 t dry matter ha⁻¹. The model efficiency (Nash–Sutcliffe efficiency) for the separate cuts were also good for both models, with 81% of the sites having a positive Nash–Sutcliffe efficiency value with GrasProg1.0, and 72% with APSIM. These results reveal that without detailed site-specific descriptions, the less complex GrasProg1.0 model can be incorporated into a simple decision support tool for optimising grassland management in intensive livestock production systems.