Modelling the effects of soil organic content and pH on the yield responses of tea to nitrogen fertilizer

CONTEXT: Sustained high yields of tea rely on the supply of nitrogen (N) from soil reserves, typically maintained by N fertilisation from inorganic or organic sources.OBJECTIVE: This paper describes how soil N levels, including the effects of soil organic content and pH, were developed and incorporated into a crop yield simulation model called CUPPA-Tea.METHODS: The nitrogen dynamics are presented in terms of i) the initial nitrogen stocks, ii) the addition of nitrogen to the system, iii) the uptake, use and loss of nitrogen by tea plants, and iv) nitrogen flows within the soil. CUPPA-Tea was then calibrated and validated using measured tea yields from Tanzania and Kenya.RESULTS AND CONCLUSIONS: After integrating a wide range of nitrogen algorithms, the model explained 79% of the variation in annual yields within a nitrogen and irrigation experiment in Tanzania and a fertilizer experiment in Kenya. The slope of the relationship was 0.84 and 0.73 respectively, the root mean square error was 660 kg ha(-1) and 507 kg ha- 1, and the modelling efficiency was 0.77 and 0.75 respectively. The model predicted that in the absence of N application, tea yields would be higher from a site with a high rather than a low soil organic content. By contrast, at high levels of mineral N application, the yield response in the model was not sensitive to the soil organic content. Hence within the model, a site in Tanzania with a low soil organic content of 1.6% showed a greater yield response to applied mineral N than a site in Kenya where the soil organic carbon was 4.0%. The model also predicted small losses of N from the cropping system through denitrification and leaching due to the acidic soil conditions (pH < 4.5) and an assumed tea rooting depth between 300 and 500 cm. In Tanzania, irrigation was predicted to result in around 10% higher nitrogen uptake than under unirrigated conditions.SIGNIFICANCE: The use of the CUPPA-Tea model can be useful in supporting decision making and improving the accuracy of tea yield estimates, as well as predictions of N fate within the soil-plant-atmosphere continuum.