Haemonchosis risks in New Zealand under changing climates


One shapefile of New Zealand showing estimates for the risk of haemonchosis at 0.05 degree resolution under historical and future climates.
Historical results are averaged from 1981 to 2000.
Future results are averaged over 20 year time frames from 2041 to 2060 and 2081 to 2100, and are based on multiple General Circulation Models (BCC-CSM1.1, CESM1-CAM5, GFDL-CM3, GISS-E2-R, HadGEM2-ES and NorESM1-M) for four different Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0 and RCP8.5).


Date: September 2023 Version: v1

Owner: AgResearch

Contact: Christian Sauermann (AgResearch)

Preview Image


Dataset attributes

Spatial extent New Zealand's North Island, South Island and Stewart Island
Spatial resolution 0.05 degrees
Temporal extent 1981-2000, 2041-2060 and 2081-2100
Temporal resolution past, mid- and end century in 20 year time frames
Evaluation method (Validation) Expert assessment
Evaluation result (Numeric)
Evaluation result (Categorical)
Uncertainty method
Uncertainty data format (Numeric) None
Uncertainty data format (Categorical)



New Zealand farmers face threats from a number of animal health issues which can be described as ‘occasional-acute’ i.e., when outbreaks occur, they are intense, causing severe clinical disease, but outbreaks are sporadic and only occur in some locations and when specific climatic and/or biophysical conditions are met. One such health issue is haemonchosis, which is caused by the highly pathogenic intestinal nematode Haemonchus contortus and is historically restricted to the North Island of New Zealand.

A simple model (Leathwick, 2013) was used to estimate the risk of haemonchosis in New Zealand for past and future climate scenarios, using published response estimates of free-living Haemonchus contortus stages to temperature. The development of the egg, first and second larval stages of the parasite is modelled as a single process, i.e. without differentiation of individual pre-infective stages. Two rate functions describe the progression of individuals through the pre-infective stages to the infective larvae on an hourly basis, a development rate and a survival rate. The model calculated the percentage of H. contortus eggs successfully developing to infective stage larvae over a 30 day period (= one month) using maximum and minimum daily temperatures.

The cumulative model output for September, October and November was used as a proxy for the spring increase in parasite population with a spatial resolution of 0.05 degrees. The results for the 1981-2000 period were related to expert knowledge to establish risk levels for the likelihood of haemonchosis related health issues i.e.,

  • 1 = H. contortus present;
  • 2 = occasional outbreak, but unlikely;
  • 3 = regular outbreaks;
  • 4 = high probability of outbreaks.
These risk levels were then transferred to the model outputs for the future climate scenarios to estimate the impact of changing climate conditions.

The risk of haemonchosis in New Zealand was estimated for past and future climate scenarios, i.e.

predicted under four Representative Concentration Pathways (RCP), i.e.
  • a scenario with high mitigation resulting in a peak and decline before 2100 (RCP2.6),
  • two increasing scenarios of stabilization without overshoot after 2100 (RCP4.5 and RCP6.0), and
  • a high emission scenario with continuous rise during the 21st century (RCP8.5).

    Fitness for purpose / limitations

    This table indicates whether the dataset is suitable for different types of questions at different scales.

    Note: Users should carefully consider their purpose as this dataset may not be suitable.

    Operational Absolute Relative Screening/scoping
    Block/farm No No No No
    Multi-farms(5+) No No Maybe Maybe
    Catchment Maybe No Maybe Maybe
    National/regional Maybe No Maybe Yes
    Caveat(s) The estimated risk of haemoncosis is based on a simplistic model, which would benefit from further refinement. The probability and severity of an Haemonchus contortus outbreak is also highly dependent on the management strategies implemented at an individual farm level.

Data and Resources