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A database was constructed comprising records from 2255 pasture phosphorus (P), potassium (K) and sulphur (S) field trials, of which 1799 included one or several rates of P. Subsets of this data were selected based on predetermined criteria to define the relationships between relative pasture production and available soil P (0-75 mm, Olsen P in µg P cm -3 soil)-the P production functions-for the major soil groups in New Zealand. These relationships, and their 95% confidence intervals, were defined using Bayesian statistics. For those soil groups for which there was sufficient data, the production functions were well defined and gave reasonably precise estimates of the relative pasture yield for a given Olsen P. the relative pasture production is most likely (P < 0.05) to be in the range 88-94% at Olsen P 25 and 98-100% at Olsen P 50. The shape of the production functions was similar for all soil groups-the relative pasture production increased with increasing Olsen P up to an asymptote-except the pumice soils and the podzols, which showed irregularities. The production function for the podzols was also flatter. There was good agreement between the empirically derived production functions and those generated from a dynamic P model. The Olsen P level required to achieve 97% maximum production was estimated for all soil groups. These ranged from 10 to 45 depending on soil group. The critical Olsen P levels were related to the soil anion storage capacity (ASC, a laboratory measure of P buffer capacity) and to soil volume weight (g cm -3 of sieved and dried soil), although not strongly. The field measured P buffer capacity (ΔP F )-the amount of soluble fertiliser P (kg P ha -1 ) required above maintenance to increase the Olsen P (0-75 mm) level by 1 unit-was estimated for selected trials. There was reasonable agreement between these estimates and those derived from the P model (ΔP M ), and these results indicated that ΔP decreases with increasing Olsen P. The results imply that factors other than those related to soil chemical properties affect the relationship between soil P and pasture production. The factors which determine the relationship between pasture production and soil P are defined and discussed. These were assigned to two categories: those factors which affect the ability of the soil to supply P for plant uptake and those that affect the ability of the plant to acquire soil P. It is concluded that further progress towards improving our ability to predict pasture responses to fertiliser P will depend on quantifying the latter effects. Based on these results and the development of a dynamic P model, an econometric P model was developed for New Zealand pastures which enables consultants to quantify the likely agronomic, financial and investment effects of any given fertiliser strategy on a given farm or block within a farm. This was not previously possible but is essential for the sustainable use of P fertilisers in pastoral farming.

The original publication is available at www.springerlink.com Key words: Agriculture, Dicyandiamide, Global positioning system, Nitrification inhibitor AbstractIn this study we review recent studies where dycandiamide was used as a nitrification inhibitor to reduce both N 2 O emissions from urine patches and nitrate leaching from pasture systems, and which led to the development of a commercial product for use on farmland. On average, emissions of N 2 O and nitrate leaching were reduced by 72% and 61% respectively. This study then demonstrates how a mitigation tool can be accounted for in the Intergovernmental Panel on Climate Change's inventory methodology when constructing an inventory of New Zealand's agricultural soil N 2 O emissions. The current New Zealand specific emission factors for EF1 (0.01), EF3 PRP (0.01) and Frac LEACH (0.07) are amended to values of 0.0058, 0.0058 and 0.0455. Examples are also given, based on OVERSEER™ models, of the implications of farm management scenarios on N 2 O inventories and total greenhouse 1 gas production when using a N 2 O mitigation tool; CO 2 equivalents kg -1 milk solid decreased from 14.2 to as little as 11.7, depending on the management scenario modelled.

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