No abstract

The objective of this study was to improve the McCall herbage growth model. A more mechanistic senescence function was incorporated, replacing an empirical senescence function. The new function is based on leaf lifespan (LLS), measured in thermal time, which is characteristic of a given grass species, and can be measured independently or sourced from the literature. Other complementary changes were also incorporated, the major ones being to incorporate soil water infiltration constraints and to improve seasonal predictions in winter based on greater photosynthetic efficiency of leaves at low light intensity. The existing and new versions of the model were each calibrated against five time series of herbage growth data using the first half of each time series. The second half of each time series was used for validation, along with an independent dataset. respectively). Most goodness-of-fit indicators also improved for the independent dataset (e.g. ρ c = 0.708 versus 0.787, for existing and new versions, respectively). It is concluded that the new improved version of the model can be confidently used as a replacement for the existing version.

Factors that optimize milk production from Northeast United States and New Zealand grazing systems are compared using a linear programming model. The objective function maximized gross margin per hectare of land farmed. The experimental design compared the optimum characteristics of each system over a range of milk prices. The Northeast has a shorter grazing season and lower cropping costs than New Zealand. The optimum pasture area was 49% of the farm for Northeast systems. Gross margins declined rapidly above 55% or below 36% pasture area. The optimum stocking rate was 1.13 cows/ha, or 2.3 cows/ha of pasture. Optimum per cow production was higher for Northeast [7105 kg of fat-corrected milk (FCM)] than New Zealand (5710 kg of FCM) systems. This was related to lower grain relative to milk prices in the Northeast. New Zealand, all-pasture systems gave the lowest cost per unit of milk but also gave the lowest gross margin across all milk price scenarios. The best use of purchased feed in New Zealand systems was to support increased stocking rate rather than per cow production. Optimum grazing management practices were similar for supplemented New Zealand and Northeast systems. All-pasture New Zealand systems are characterized by short lactations and long autumn rotations to transfer pasture in situ for winter feeding. Higher costs per unit of milk produced will be an inevitable consequence of maximizing gross margin at high milk prices in New Zealand systems.

A computer program called Stockpol is described. It is a biological model designed for decision support applications on pastoral farms. Individual farm scenarios are defined in terms of component subfiles which define stock (numbers and performance), land (area, pasture growth rates and land use), prices and constants. Physical and financial reports are available for individual scenarios, and for comparisons among scenarios. Once defined, scenarios are tested for biological feasibility by calculating if there is enough pasture cover on the farm at all times to meet animal requirements for targetperformance levels. Policies for biologically unfeasible farms can be automatically modified if necessary. Stockpol can be used to analyse long-term policy changes or short-term feed budgets, but it is not suitabIe for paddock-level feed budgeting. Keywords sheep, beef, pasture growth, pasture cover, feed budget, biological feasibility, prices, profits, computer model

Parallels exist in the recent developments of dairy systems in the Northeast United States and New Zealand because of greater use of pasture grazing and feed supplements, respectively. Lessons can be learned from each system. However, major differences exist between the regions in the patterns of pasture production, the costs of supplementary feed, and milk prices. These differences affect the optimum use of feed. In this paper, a linear programming model developed to determine optimum feeding strategies for dairy systems in each country is presented. The model optimizes grazing management (rotation lengths) and the conservation of pasture subject to constraints on their use. Other feed resources include N fertilizer, grain, corn silage, and alfalfa silage. All feeds are represented in energy terms. The substitution of pasture intake by grain and forage supplements is included, and cow performance can be optimized by choosing from 73 seasonal calving herds that vary in calving date, lactation length, and daily milk production. The model predicts that marginal responses to grain feeding are between 1.35 and 1.8 kg of milk/kg of grain dry matter supplement, well within the range of responses reported in the literature. Evaluation of the model against data from nine grazing system treatments in New Zealand and two in Pennsylvania showed that model predictions averaged +3% (New Zealand) and +0.04% (Northeast) of measured milk production. The model could be used with confidence to study systems in both the Northeast United States and New Zealand.