About the study site

The study site comprises Okepuha, Pongaroa Station and Taharoa Trust on the Māhia Peninsula, an area of about 11,400 ha. Native shrubs were planted at Pongaroa station.


Trials on native shrubs and soil monitoring were conducted at Māhia.


Native shrub trials

Key messages

  • Survival of native shrubs is reduced by hot dry conditions in the 1st summer after planting.
  • Coprosma species had high survival at this site whereas other species suffered more than 50% losses.
  • Controlling animal pests such as goats is essential when establishing native shrubs.

What was trialled

  • Seven species were evaluated: Hoheria populnea (Houhere), Pittosporum crassifolium (Karo), Griselinia littoralis (Pāpāuma), Coprosma robusta (Karamū), Coprosma repens (Taupata), Melicytus ramiflorus (Māhoe), Pseudopanax arboreus (Whauwhaupaku) and a shrub willow (Salix schwerinii) (‘Kinuyanagi’).
  • Small plot trials (15 plants in each plot with four replicates) were used to assess the establishment and early growth of each species, and the nutritional characteristics of foliage and fine stems (<5 mm diameter).
  • Shrubs were planted in rows with 1.5 m spacings between rows and 1.5 m between plants within rows to achieve a plant density of 4,400 plants/ha at 100% survival. Shrubs were planted on slopes of 20-30 degrees.
  • All plants were trimmed to 40 cm height prior to planting.
  • Weed control was achieved by spraying glyphosate around shrubs 8 weeks post planting using a knapsack sprayer.
  • At the end of their 1st summer, shrubs were assessed for survival and growth.

Key findings

  • Survival of native shrubs at Mahia ranged greatly depending on species, with the lowest survival observed in Mahoe (38%) and highest in Taupata (100%).
  • Early growth (eight months post planting) was influenced by species. Coprosma species (Karamū and Taupata) had the greatest height and diameter growth increments over the time period.
  • The Mahia trial was badly damaged by goats and was discontinued because very few live plants remained in June 2021.


Soil monitoring

What was achieved

  • A wireless sensor network was established that enabled some of the first daily farm scale mapping of soil properties in NZ hill country. These maps can be used to drive forage yield models and help inform decision-making on pasture management.

What was trialled

  • The trial investigated the potential for modelling the distribution of soil temperature and moisture in hill country landscapes at high spatial and temporal resolution. The spatial resolution of existing widely-available soil temperature and moisture data is too coarse to provide useful information at farm scale in hill country, and is unable to account for the influence of topography in these landscapes.
  • A wireless sensor network (WSN) was installed at Māhia in September 2020. The WSN consisted of twenty sensors installed in the soil at 30 cm depth. The sensors were distributed across the farm in a way that accounted for topographic variation in elevation, aspect (the direction a hillslope faces) and the potential for water to accumulate (strongly influenced by slope gradient).
  • The sensors were configured to measure soil temperature and soil moisture at hourly intervals and report measurements back to a cloud database via the cellular network. On the farm, LoRa (Long Range) technology was used to communicate between components of the WSN.
  • Statistical models were fit to the soil temperature and moisture data in order to relate those soil properties to other topographic variables including elevation, aspect and slope. The models were used to predict soil temperature and moisture across the farm at 30 m resolution at daily intervals across a generic model year.

Key findings

  • The WSN performed well.
  • As expected, sensor data revealed that north-facing slopes tended to be warmer than south-facing slopes, which reflects the influence of topography. Interestingly, soils on north-facing slopes warmed from the winter minimum temperature through an arbitrary threshold of 10°C about 45 days faster than soils on south-facing slopes (about 19 days versus 64 days in 2021).
  • The soil temperature model performed very well, but the soil moisture model performed relatively poorly. The difference in performance is due in part to differences in predictability between soil temperature and soil moisture, the former varying more smoothly and more regularly over time than the latter.
  • Soil moisture predictions derived from the model should be interpreted with caution, but should be good enough to provide a broad indication of when soils are near to capacity versus when they are near wilting point.
  • It is expected that model performance should improve with a longer time-series of data, and better sensor calibration.