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Development of an algorithm for relating pasture nitrogen status to yield response curves

journal contribution
posted on 2023-05-03, 22:05 authored by Iris Vogeler, Rogerio Cichota
Determination of optimum nitrogen (N) fertilisation rates which maximise pasture growth is challenging due to variability in plant requirements and likely near future supply by the soil in both time and space. Remote sensing can be used for mapping the N nutrition status of plants and to rapidly assess the spatial variability within a field. An algorithm is, however, lacking which relates the N status of the plants to yield response curves. To develop such an algorithm a simulation study was done using the Agricultural Production Systems Simulator (APSIM). The simulations were done for an irrigated ryegrass pasture in the Canterbury region of New Zealand. Nitrogen fertiliser was applied monthly at different rates, ranging from 0 to 150 kg N/ha. To obtain a range of different pasture N concentrations a total of 1456 different fertilisation rules were set up. These were run for 20 consecutive years giving a total of 29120 combinations of pasture N contents and pasture growth responses for each month. The analysis focused on November (spring), a month with generally vigorous growth. A three dimensional surface response function, based on the Mitscherlich yield response function was developed. This function was used to determine required N fertilisation rates, which achieve 90% of the maximum yield, based on the pasture N content. At low pasture N contents of 25 g/kg the required fertilisation rate was estimated as 130 kg N/ha. At much higher pasture N contents of 40 g/kg only 60 kg N/ha was required to obtain the same yield, reflecting the much higher supply of N by the soil.


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© 2017 John Wiley & Sons Ltd


  • English

Does this contain Māori information or data?

  • No



Journal title

Grass and Forage Science




Vogeler, I., & Cichota, R. (2017). Development of an algorithm for relating pasture nitrogen status to yield response curves. Grass and Forage Science, 72(4), 734–742. doi:10.1111/gfs.12284


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