Modelling the effects of calving spread, calving date and management on the profitability of a mid Canterbury dairy farm : A dissertation submitted in partial fulfilment of the requirements for the degree of Bachelor of Agricultural Science (Honours)

The objective of this study is to assess the effects of reproductive management and calving spread on feed demand and to investigate the interaction of these factors in terms of feed supply per cow, per cow production and stocking rate in terms of their effects on productivity and profitability. The study is in two parts; the first analyses the impact of changing reproduction management and calving spread on feed demand. The effects of these factors will be represented in terms of the energy demands which are associated with the cows requirements for a modelled reproductive cycle, and lactation season. The second part uses a feed optimisation model to maximise profitability by selecting the most appropriate feeding regime and stocking rate, given the feed demand for an average cow as generated in the first analysis. Three calving spreads, two cow production levels, and two farm management levels were analysed by running a series of 12 scenarios based on these factors in a Linear Program Model. The objective of this model was to maximise the gross profit. This was achieved by the model manipulating the feed demand by selecting a stocking rate, and amounts of supplementary feeds which will maximise farm gross profit. The results from the modelled illustrated that to maximise gross income, the management practices that should be adopted require; • High per cow milk yield. • A lower stocking rate in comparison to other scenarios modelled. • A tight calving spread. • An early calving date. • The use of nitrogen to increase pasture yield in the early and mid spring. • Extensive use of brought in silage in late lactation. In retrospect, the model appeared to simulate the real world situation well, especially in the high fann management sector. To incorporate, low management, and average management into the model accurately, significant adaptations will need to be made to the model