Documentation for the Manukit DST

The mānukit-DST calculates the likely costs and environmental benefits of establishing a mānuka or kānuka-based ecosystem adjacent to a waterbody in an agricultural environment. The mānukit-DST also provides a list of species that are likely suitable for the site.

If the mānukit-DST indicates that planting is feasible, then a site assessment should be carried out before planting: the mānukit-DST is not intended as a replacement for expert advice on individual sites. As such, the input parameters have been kept as simple as possible so that they may be entered by a layperson.

The mānukit-DST is not a static tool; rather it is under continual development incorporating the latest studies on the interactions of mānuka and kānuka with their environment. Descriptions of the various process in the mānukit-DST

A selection of native plants suitable for riparian planting have been categorised for their tolerance to wet conditions, drought, cold and salt spray / salinity. Depending on the conditions, some or all of these species could be included as patches in the Leptospermum scoparium or Kunzea robusta ecosystem.

The following secondary species are currently included in the mānukit-DST: Carex secta, Carpodetus serratus, Coprosma lucida, C. repens, C. robusta, C. propinqua, Cordyline australis, Dacrocarpus dacrydiodes, Dodonaea viscosa, Griselinea littoralis, Hebe stricta / salisifolia, Hoheria sexstylosa, H. anguistifolia, Melicytus ramiflorus, Myrsine australis, Myoporum laetum, Olearia virgate, O. paniculata, Ozothamnus leptophyllus, Phormium tenax, Pittosporum crassifolium, P. eugenoides, P. tenuifolium, Plagianthus regius, Podocarpus totara, Pseudopanax crassifolius.

K. robusta is better adapted to dry conditions than L. scoparium. Aridity is calculated from the annual rainfall and the maximum available water in the soil (Table 1). If the average annual rainfall is less than 1000 mm or the maximum available water content is less than 101 then the conditions are assessed as dry, favouring K. robusta. K. robusta is favoured if there is salt spray or salinity. These conditions are overridden if there is already L. scoparium present in the environment. Runoff is calculated as rainfall x runoff coefficient. The runoff coefficient, Rc is:

Rc = 0.8 x (0.6 / Maximum Infiltration Rate) x (0.5 + Slope / 200)

This formula was derived from reports by FAO (2017) and El-Hassanin et al. (1993). Note that a more accurate calculation of runoff requires rainfall intensity data, which would be difficult for the layperson to obtain. The Maximum Infiltration Rate is derived from the soil texture (Table 1). Table 1. Maximum infiltration rate and available water content as a function of soil texture (after CCC 2011; Saxton et al. 2006; and Dane & Toppe 2002).

Soil texture (USDA)	Maximum infiltration rate (mm/hr)	Available water content (mm H2O / m of soil)
Sand	230	50
Loamy Sand	60	70
Sandy Loam	22	100
Sandy Clay Loam	3	100
Loam	13	140
Sandy Clay	1.2	110
Silt loam	7	200
Silt	5	240
Clay loam	2	140
Silty clay loam	2	100
Silty clay	1	140
Clay	0.6	120

Under the vegetation, the infiltration rate is assumed to increase by 10 mm/hr, which consequently decreases the runoff coefficient. The calculations also incorporate runoff from the unplanted block into the planted block, resulting in increased water entering the planted block. Sediment transport to the waterbodies is a function of the runoff and the slope. If the area is unfenced then there is an increase in sediment due to animal trampling.

Phosphorous entering the waterways is a function of the sediment entering the waterways. In unfenced waterways, some phosphorous directly enters the waterway as a result of direct defecation into the water. The amount of phosphrous in livestock manures was taken from Buckley and Makotoff (2004). Clearly, manure P concentration will depend on the feedstock of the animal – however, this information is unlikely to be available, which would make the DST difficult to parameterise.

The amount of faeces produced by animals was taken from the Queensland Government (2017) and other sources. Table 2 shows the Faeces production of various livestock.

Stock type	Faeces per year (kg dry matter)
Cattle (225 kg)	646
Cattle (340 kg)	969
Cattle (450 kg)	1291
Dairy (450 kg)	1765
Dairy (650 kg)	2475
Goats	86
Horse	969
Llama	344
Sheep	86
Swine	90

The DSS assumes 1x10e8 cfu of E.coli per gram of faeces. There will be 90% mortality before the E.coli reach the waterway. If animals are allowed access to waterways, then 10% of the faeces will enter the waterway directly. It is assumed that each metre of L. scoparium or K. robusta will kill 80% of the E.coli.

References:

Buckley K and Makortoff M (2004). Phosphorous in livestock manures. http://www.farmwest.com/node/1077

Dane JH, Toppe GC eds. (2002). Methods of soil analysis part 4 physical methods. Soil Science Society of America, Inc. Madison, Wisconsin, USA.

CCC (2011). Waterways, Wetlands and Drainage Guide. Part B. Christchurch City Council.

El-Hassanin AS, Labib TM, Gaber EI (1993). Effect of vegetation cover and land slope on runoff and soil losses from the watersheds of Burundi. Agriculture, Ecosystems and Environment 43,301-308.

FAO (2017). Rainfall –runoff analysis. (http://www.fao.org/docrep/u3160e/u3160e05.htm)

Fang H, Sun L, Tang Z (2014). Effects of rainfall and slope, soil erosion and rill development: an experimental study using two loess soils. Hydrological Processes 29(11), 2649-2658.

Queensland Government (2017). Manure production data. Department of Agriculture and Fisheries. https://www.daf.qld.gov.au/environment/intensive-livestock/cattle-feedlots/managing-environmental-impacts/manure-production-data

Saxton KE, Rawls WJ (2006). Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Science Society of America Journal 70,1569-1578.

Wang L, Mankin KR, Marchin GL (2004). Survival of fecal bacteria in dairy cow manure. Americal Society of Agricultural Engineers 47(4): 1239-1246.