Abstract:
Poor management of nitrogen (N) fertilisers and water in agro-ecosystems reduces yield, quality and N-use efficiency, and leads to pollution. The objective of this study was to improve irrigation and N management for planted pastures through adaptive management with simple tools and modelling. Field experiments were conducted in 2007 and 2008 at Cedara (KwaZulu Natal) and Hatfield (Gauteng) using annual ryegrass as a case study under a range of N and irrigation application strategies. Collected data sets were also used to calibrate and validate the SWB-Pro (simple) and SWB-Sci (detailed) model versions. After validation, the model was used to develop irrigation calendars and strategies, and estimate irrigation requirements for annual ryegrass. The highest forage yields were produced when N application rates ranged between 30 to 60 kg N ha-1 for each growth cycle, except for the first 2-3 growth cycles when there was high soil N carryover from the previous season. The current farmers’ recommendation (fixed N application rate of 50 kg ha-1 per growth cycle) maximised biomass but reduced pasture quality. Adaptive strategies based on nitrate concentration in wetting front detectors at different depths, reduced fertiliser N application by 28–32% and reduced potentially leachable residual soil N, while improving forage quality without yield reduction. The rate 30-40 kg N ha-1 per growth cycle provided a compromise between forage yield and quality. The SWB model performed well in simulating ryegrass growth, leaf area index, forage yield, root zone soil water deficit, daily evapotranspiration, biomass N uptake and soil nitrate. Site specific and monthly variable irrigation calendars were developed using the SWB-Pro model, for four major milk producing areas of South Africa. The simpler monthly irrigation calendars can be used in the absence of irrigation monitoring tools or more accurate site specific calendars. The SWB-Pro model requires relatively few and simple inputs. However, irrigation monitoring/scheduling with the aid of real time modelling or measurements is better than calendars developed using the SWB-Pro model with long-term historical weather data. The SWB-Sci model showed ways of improving water use efficiency using ‘room for rain’ and ‘mildly deficit irrigation’ approaches in high rainfall areas. Scenario modelling demonstrated that the best management strategy of achieving maximum yield together with low N leaching is by integrating N and water management. This integrated management can be based on the wetness of the soil and nitrate concentration in the deep root zone using wetting front detectors. The model can be used to generate monitoring protocols such as depth of wetting front detector placement and selecting N thresholds to be used for adaptive management. Setting approximate thresholds for wetting depth and nitrate concentration is a first step in implementing an adaptive management strategy. However, the challenge is to find monitoring tools which allow effective implementation of the strategy. In this study, the wetting front detector proved to be a robust, on-farm water and nitrate monitoring tool which is relatively simple and cost effective. Should it become widely adopted, farmers are expected to improve these thresholds as more experience is gained. The SWB model could also be used to evaluate alternative thresholds for adaptive N and water management.
