Modelling soil nitrogen mineralisation in semi-mature pine stands of South Africa to identify nutritional limitations and to predict potential responses to fertilisation

Abstract

& Key Message Estimates of available nitrogen (based on topsoil nitrogen mineralisation rates) in semi-mature stands of Pinus radiata (D Don.) as well as pure species and hybrids of Pinus elliottii (Engelm.) in South Africa display a strong, significant, negative relationship with growth response to applied fertilisers in field experiments. This inexpensive technique can be incorporated in a fertiliser decision support system to minimise wasteful fertiliser applications. & Context South African mid-rotation pine stands respond with high variability to nitrogen fertiliser supplements in the presence of phosphorus. & Aims We aimed to find a screening tool for stands that are responsive to fertilisation. & Methods The volume growth response relative to control plots (3 years after fertilisation with 200 kg ha −1 N in the presence of 100 kg ha −1 P) was determined across 14 pine fertiliser experiments in South Africa. These values were correlated to soil and other properties for each site and also to estimates of N availability and N mineralisation rates using the Soil Nitrogen Availability Predictor (SNAP) model. & Results The relative volume growth response to fertilisation ranged with a factor from 0.05 to 0.96. No single result from standard soil analyses showed a strong positive or negative correlation with this growth response. However, highly significant negative correlations were observed between an estimate of N availability based on topsoil aerobic nitrogen mineralisation rates and the relative volume growth responses to fertilisation. & Conclusion The estimate of N availability derived from incubation studies may assist us to predict the magnitude of the response to N fertilisation (in the presence of P) with a fair degree of accuracy. The accuracy can be improved if the N availability estimate from aerobic incubation is used with other variables (pH and soil C content) in a multivariate regression to predict relative yield response to fertilisation.