Effluent Management at Tokanui
Achieving higher production with better environmental management tools
David Houlbrooke and others are developing ways of making smarter decisions using the information gathered on Tokanui Research Dairy Farm for tasks like effluent irrigation, pugging management and fertilizer choice and application. These go under the generic name of ‘Environment Management Tools’.
Newly developed tools will inform farm staff of how to make the best decisions. This item concentrates on different irrigation methods for different soil types.
Tokanui was developed as the AgResearch dairy research farm during 2009 and set up with two effluent storage ponds and a reticulated effluent distribution to about 30 paddocks containing 30 hydrants.
Different irrigation applicators can be connected to these, such as low application rate pods and travelling irrigators.
After the solids have settled, effluent from the first pond can be applied to the low-risk land (free-draining soil) with travelling irrigators.
If irrigation conditions are not suitable, effluent water goes into the second storage pond from which it is drawn out to be recycled into washdown water for two farm dairies, a large feed pad and a stand-off pad. Alternatively it can be reticulated out to low application rate irrigation pods, delivering 4mm/hour.
The objectives of the project for developing the smart environment management system are:
• To ensure that the various components of the effluent management system are matched to the soil and landscape risk features for Tokanui farm.
• To use environmental data (soil and climate information) to guide where and when effluent should be applied.
• To instrument the effluent storage pond to guide appropriate decision-making around when storage or land application is most appropriate.
• To automate the above to minimise labour requirements and human-induced error.
Tokanui has been classified into three different land management units based on soil types and topography:
1. Flat land – Otorohanga alephanic, deep, well-drained soil derived from volcanic ash.
2. Sloping Otorohanga well-drained soil.
3. Lowland, heavy Punui silt loam, poorly drained.
These different land management units (LMUs) require different effluent irrigation programmes:
1. Low-risk category – flat and undulating, well-drained soil on which any irrigation tool can be used to apply irrigation at any time, except when raining.
2. High-risk Category One – sloping, well-drained soil where only low application rate sprinklers should be used for deferred irrigation (an application depth which is smaller than the soil water deficit).
3. High-risk Category Two – poorly draining soils, requiring deferred irrigation. Able to use any irrigation tool but a travelling irrigator requires application depth to be less than soil water deficit before safely applied.
The high-risk Category One LMU is currently being irrigated with a low application rate pod system, applying 4mm/hr. The travelling irrigators for the other two LMUs have a long-arm and short-arm design to achieve a more even spread pattern and settings on the irrigators provide a range of ground speeds and therefore application depths.
The project aims to deliver the decision process for effluent irrigation to farm staff as management options using mobile phone texts or email delivery. However for stage one of this project, farm staff have to manually record application depths and paddocks chosen (using the tools software programme) based on the tools guidance.
The effluent from the first pond has default nutrient concentrations of 500mg/litre for nitrogen (N) and 200mg/litres for potassium (K).
The pre-requisite information for the decision management tool is the different application depths for travelling and low-rate sprinklers at different application times, the volume of daily farm effluent generated, the pond storage capacity, the field capacity for the three LMUs, the soil water content for the LMUs (based on Aquaflex tapes) and the default pond nutrient concentrations (above).
In the second stage of the Tokanui project the researchers propose to add in-pond height sensors and forecasted weather predictions in order to fine tune the smart decision rules. They also want to provide proof of placement by GPS mapping the movement of the two irrigation systems or integrating the Tokanui system with an existing commercial supplier of this service.
At stage three they would like to remove the human factor from the management of farm effluent by automating (as much as possible) the placement of effluent on the Tokanui farm. This is most likely to be possible using the low rate system given sufficient pods to cover large areas of the effluent block without the need for regular shifting.