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Sheep and beef - economic indicators

Description

Estimates of Revenue, Expenses, Operating Profit, Labour, and regional flow on impacts (GDP, Household income, and Labour) associated with dryland Sheep and beef in a location.

These are based on the average of 2019-2021 Beef and Lamb NZ Farm Survey.

 

Date: May 2024 Version: v1

Owner: LWP Ltd

Contact: Simon Harris, LWP Ltd

 

Link to report / paper

Harris, S., Cichota, R., Lilburne, L., and Fraser, C. 2024. Predicting the productive potential of land for pastoral sheep and beef farming in New Zealand. Submitted May 2024

Sheep and beef - Indicators and additional information

 

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Dataset attributes

Spatial extent National. Data provided only for locations suitable for intensive farming (slope <15 degrees, altitude < 600m)
Spatial resolution National scale data allocated to 5km climate grid and FSL / Smap polygons
Temporal extent Average 2019-21
Temporal resolution Annual
Evaluation method (Validation) Cross validation (k-fold (k=10) partitioning)
Evaluation result (Numeric) See Harris et al (2024)
Evaluation result (Categorical)
Uncertainty method Medium reliability
Uncertainty data format (Numeric)
Uncertainty data format (Categorical)

 

Methodology

Data is based on industry monitoring data 2019-2021 Beef + Lamb NZ Sheep and Beef Farm Survey.

Multipliers from a regional IO table were used to calculate the indirect and induced effects of the expenditure and houshold income, to generate Total GDP, Total HHI, and Total FTE/10 ha.

 

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 Maybe
Multi-farms(5+) No No No Yes
Catchment No Maybe Yes Yes
National/regional Yes Yes Yes Yes
Caveat(s) Data is based on national regression of modelled pasture against revenue, with other indicators derived as relationships with revenue. The regression had a R2 of 0.34 so there is considerable unexplained variability in farm performance. It should also be noted that the data applies only to dryland farms, and where modelled pasture growth was 10% below or above the modelled pasture data for the regression dataset the pasture data was truncated to that value so that predictions were only made within a reasonable range of the observed dataset. It should be noted that actual values could be higher or lower than those provided here.

Data and Resources