AgResearch
Browse

Linking Land Use Capability Classes and APSIM to estimate pasture growth for regional land use planning

Download (1.61 MB)
journal contribution
posted on 2023-05-03, 11:46 authored by Iris Vogeler, Rogerio Cichota, Josef Beautrais
Investigation of land use and management changes at regional scales require the linkage of farm system models with land resource information, which for pastoral systems includes forage supply. The New Zealand Land Resource Inventory (LRI) and associated Land Use Capability (LUC) database includes estimates of the potential stock carrying capacity across the country, which can be used to derive estimates of average annual pasture yields. Farm system models and decision support tools, however, require information on the seasonal patterns of pasture growth. To generate such pasture growth curves (PGCs) the Agricultural Production Systems Simulator (APSIM) was used, with generic soil profiles based on descriptions of LUC classes, to generate PGCâ s for three regions of New Zealand. Simulated annual pasture yields were similar to the estimates of annual potential pasture yield in the LRI spatial database, and also provided information on inter-annual variability. Simulated PGCs generally agreed well with measured long-term seasonal pasture growth patterns. The approach can be used to obtain spatially discrete estimates of seasonal pasture growth patterns across New Zealand for use in farm system models and for assessing the impact of management practices and climate change on the regional sustainability.

History

Rights statement

© CSIRO 2015

Language

  • English

Does this contain Māori information or data?

  • No

Publisher

CSIRO

Journal title

Soil Research

ISSN

1838-675X

Citation

Vogeler, I., Cichota, R., & Beautrais, J. (2016). Linking Land Use Capability Classes and APSIM to estimate pasture growth for regional land use planning. Soil Research, 54(1), 94-110. doi:10.1071/SR15018

Usage metrics

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC