An optimisation model was developed to compare the profitability of different forage species on irrigated dairy farms. The model is driven by the energy and protein requirements of the milking cow. The objective of the model is to maximise income, after herd and feed costs, by selecting the area of the farm sown to particular forage species. Different forage species may require different animal production systems to optimise their profitability. In order to achieve this, the model can alter the herd size and structure, level of production, concentrate feeding regime and forage conservation and feeding on a monthly basis.Dairy cows, Modelling, Forage., Farm Management,

Gaffy, Joe

Gyles, Oliver

English

Research Papers in Economics

 
AARES 2003 
 
 
 
 
 
Economic Evaluation of Irrigated Dairy Forage Production. 
 
 
 
Joe Gaffy and Oliver Gyles 
 
 
Department of Primary Industries. Victoria 
Correspondence to joe.gaffy@nre.vic.gov.au 
 
 
 
 
 
Abstract 
An optimisation model was developed to compare the profitability of different forage species 
on irrigated dairy farms. The model is driven by the energy and protein requirements of the 
milking cow. The objective of the model is to maximise income, after herd and feed costs, by 
selecting the area of the farm sown to particular forage species. Different forage species may 
require different animal production systems to optimise their profitability. In order to achieve 
this, the model can alter the herd size and structure, level of production, concentrate feeding 
regime and forage conservation and feeding on a monthly basis. 
 
Keywords: Dairy cows; Modelling; Forage.    
Introduction 
 
A feature of the different forages and forage management options is the variation in total dry 
matter produced and the timing of this dry matter production. Valuing the total DM 
production of forage is relatively straightforward however valuing the timing of that 
production is more involved. The contribution of the forage to the daily ration of the cow and 
the value of the milk produced from that ration both play a role in the determination of 
profitability. A model is currently being tested which attempts to capture the effect of the 
timing of pasture production on the profitability of the farming system. The model does this 
by balancing metabolisable energy (ME) and crude protein (CP) supply and demand while 
keeping dietary neutral detergent fibre (NDF) below a stated threshold, on a monthly basis. 
  
 
The monthly ME, CP and NDF balance works in the following manner. Energy, protein and 
fibre can be supplied into a particular month (let’s say November) as Fodder grown in 
November, fodder grown in October and transferred into November, Fodder conserved in a 
previous month and fed in November, or purchased hay, silage and concentrate fed in 
November. The number of milking cows and their level of production, change in liveweight, 
stage of gestation and environmental factors set energy and protein requirements for the herd 
for each month. Energy protein and fibre can also leave a month as pasture transferred to the 
next month or in conserved fodder, which can be fed in a later month or sold. 
 
The model is a simulation model constructed in Excel spreadsheets and uses What’s Best! ®
1 
to optimise the results. What’s Best! is a solver and when applied to the simulation model it 
uses liner and non-linear optimisation to find the combination of inputs that produces the 
maximum return to total capital. 
 
The Herd 
 
The value of milk produced varies throughout the year and this variation is region specific. In 
northern Victoria incentives are paid to encourage farmers to produce milk in winter. To 
achieve high milk production during the winter incentive period some farmers calve a portion 
of, or even all of the herd in autumn instead of the more traditional spring calving date, 
resulting in different energy and protein requirement patterns. The profitability of different 
fodder species will therefore depend on their relationship with the calving pattern of the dairy 
system. To accommodate this, the model can be allowed to select the proportion of the herd 
calving in each of two calving periods. The user must enter production details, litres, fat and 
protein production per month, for a representative cow for each calving period. 
 
Herd size and per cow production is another variable that can be controlled by the model 
within the restrictions set by the user. When the production is varied the fat and protein 
percentages remain constant. By varying the calving pattern, herd size and production level 
the model is able to generate a wide range of dairying systems from low intensity, low per 
cow production systems to high intensity, high per cow production systems, with either split 
or seasonal calving herds. This provides the model the flexibility to find the most appropriate 
animal system for a particular fodder production system, within the limits of the infrastructure 
of the farm. By restricting these parameters the model can be used for case study analysis. 
 
The herd generates income through milk production and through profits from the livestock 
trading schedule. Each calving period has a separate livestock-trading schedule and these are 
aggregated together in the case of a split calving herd. 
 
                                                            
1 Lindo Systems Inc., Chicago  
Forages  
 
The model has the capacity to assess up to 8 forage options at a time, five permanent pasture 
and three double cropping options. The production information that the model requires for 
each on a monthly basis is DM available for consumption, digestibility, CP % and Neutral 
Detergent Fibre (NDF) content. It has been documented that dairy cows are able to select a 
diet higher in ME and CP and lower in NDF than the pasture on offer (Jacobs et al 1999). A 
study by Wales et al, (1998) found that the nutrient selection differential varied between 
different pastures. To allow for this, the model requires the user to enter selection differentials 
for each grazed fodder. 
 
The double cropping options are included in the model so that options like shaftal and maize 
can be incorporated. Monthly production data will be required for the crops if the model is 
able to graze them, alternatively the user can nominate that  a portion or all of the crops is  
conserved as hay or silage. 
 
The energy supply figures used in the model are for established pastures. During perennial 
pasture establishment the energy supplied from the pasture is reduced. In accounting for this 
the model assumes that any shortfall in energy supply must be compensated for by purchased 
supplements. The user nominates production shortfall and then the energy density and cost of 
the supplements used to replace this. The longevity of the pasture stands is also considered 
with provision made for reducing the level of production as the pasture reaches the end of its 
productive life. The shortfall is again compensated for with supplement. 
 
Fodder produced in one month can be consumed by the milking herd, conserved as hay or 
silage, or transferred as standing feed into the next month. When standing feed is transferred 
into the next month the quality of that feed is reduced. The rate of this reduction can be set for 
each month for each fodder option.  
 
 
Pasture cost 
 
Annual costs include topping, irrigation water, fertiliser, insecticide and herbicide. These 
costs are assumed to be the same each year. For the double cropping options, all the costs are 
considered annual. Non-annual costs are mainly establishment costs.  
 
Non-annual costs for establishment of perennial pastures include herbicide, cultivation and 
sowing as well as supplement costs for reduced pasture production in at the start and end of 
the pasture stands life. Oversowing is another non-annual cost common to perennial ryegrass 
pasture. In order for these non-annual costs to be incorporated into the annual cost for each 
pasture option the present value of the non-annual costs occurring after year 1 is calculated. 
This present value of future non-annual costs is added to the year 1 non-annual costs and the 
total is annualised over 7 years at a rate of 10%. This annualised cost is then added to the true 
annual costs such as irrigation and fertiliser, to give a totalled annual cost for each pasture. 
 
Different forage options will require different quantities of water for production. The user will 
set the farm water allocation and the market price for water. The model is then able to buy 
water to satisfy the needs of the farm or sell any surplus water at the market rate. By allowing 
the model to do this the value of the water use efficiency of the pastures and crops is being 
taken into consideration.    
Labour 
 
Labour requirements and costing are sometimes points of contention. In an attempt to retain 
flexibility the model requires the user to enter the operator allowance and the number of cows 
that the operator can handle on their own. The user then nominates the number of labour units 
that would be required for 100 additional cows calving in spring and for 100 additional cows 
calving in autumn, as these requirements may be different. In general the rule of thumb of 100 
cows per labour unit seems to hold true. 
 
 
Supplementation. 
 
Up to six sources of supplementary feed can be entered into the model. The user sets the level 
of wastage, purchase price and feeding cost for each supplement as well as a purchase limit 
for each of the supplements to simulate the possibility of a limited supply. What’s Best! can 
then select the timing and quantity of feeding for each within limits on daily intake set by the 
user.  
     
When a cow is grazing pasture ad libitum and is then offered 1 kg of dry matter of grain 
supplement she will consume the grain supplement and reduce her intake of pasture. This is 
referred to as substitution and the amount of pasture, in kilograms, that is forgone per 
kilogram of supplement is dependent on the quantity of supplement being fed. Substitution 
provides for the effect of diminishing returns to feeding supplements. The model assumes that 
substitution occurs according to an equation taken from Stockdale (2000). Supplementation 
varies between the seasons and with the maximum pasture intake of the cattle.  
 
The model uses a series of linear segments to approximate the substitution effects of 
supplementary feeding. This is similar to the approach used by Hulme et al (1986). The 
substitution rates presented in Table 1 are for a 550kg cow with a pasture intake of 3% of live 
weight in summer. 
 
Table 1: Substitution rates for a 550kg cow with a pasture intake of 3% of live weight in summer. 
 
% of diet as supplements  0 to 10  10 to 20  20 to 30  30 to 40  40 to 50  50 to 60  60 to 70  70 to 80  80 to 90  90 to 100 
Incremental sub. rate  0.44  0.54  0.64  0.74  0.84  0.93  1.03  1.13  1.23  1.33 
Net Substituion Rate  0.44  0.49  0.54  0.59  0.64  0.69  0.74  0.79  0.84  0.89 
 
The Net substitution rate is calculated for a cow receiving 40% of her diet as supplements in 
the following example.  
 
Net substitution rate = (10% x 0.36)+(10% x 0.46)+(10% x 0.56) + (10% x 0.66))/0.4) = 0.51  
 
The Model 
 
The model uses a series of Excel spreadsheets to enter and calculate the data that is used to 
make up a matrix which can be optimised using What’s Best. Income is generated from milk 
sales and the livestock trading schedule as well as selling water, hay and silage. Activities 
such as making hay, feeding concentrate, producing pasture or fodder crops, purchasing water 
and purchasing hay and silage contribute to variable costs along with variable herd costs. To 
optimise the model in terms of return to total capital it is reasonable to use case study data to 
fix farm size and capital value. That way the capital value of the farm and its infrastructure 
can be considered along with a realistic limit on the maximum herd size possible included. The capital value of the herd changes with the herd size according to the livestock trading 
schedule. 
 
Outputs. 
 
The first output of the model is a graph that gives the daily ration composition on a monthly 
basis. This is important in checking that the model is producing feasible answers. The model 
is designed so that four scenarios can be compared simultaneously. The “Report” worksheet 
is set up so that the user can keep track of which of the variables have been altered between 
the scenarios, the variables that are different from the baseline become highlighted. All of the 
variables that the user sets appear on this worksheet. Profitability of dairy farming systems is 
strongly linked to milk price. For this reason the model is constructed to automatically carry 
out a price sensitivity analysis when a scenario is to be tested. The “Results” worksheet 
records the changes in important model controlled variables that occur with different milk 
prices. Graphs showing the area used for each fodder option, water sales and purchases, the 
herd size and composition in terms of calving periods, silage use, hay use, annual ration per 
cow, milk production and farm income are produced for the price sensitivities for each of the 
scenarios.  
 
Future. 
 
This model is currently being tested on a dairy farm in south west Victoria in preparation for 
use in the economic analysis of a pasture grazing management experiment. The experiment in 
question looks at 6 different strategies for grazing a ryegrass based pasture so the nutritional 
value of the pasture will be similar between the strategies and the difference occurs in the 
total DM produced and the timing of the DM production. Preliminary results suggest that the 
while DM production is the more important factor in determining the most profitable strategy, 
the timing of that production also plays a role. 
 
This model has been developed as a research tool and not as a decision support tool for farm 
managers.   
 
 
 
 
Hulme DJ, Kellaway RC, Booth PJ (1986) The CAMDAIRY Model for Formulating and 
Analysing Dairy Cow Rations. Agricultural Systems 22, 81-108 
 
Jacobs JL, McKenzie FR, Ward GN (1999) Changes in the botanical composition and 
nutritive characteristics of pasture and nutrient selection by dairy cows grazing rainfed 
pastures. Australian Journal of Experimental Agriculture 39, 419-28 
 
Stockdale C (2000) Levels of pasture substitution when concentrates are fed to grazing dairy 
cows in northern Victoria. Australian Journal of Experimental Agriculture 40, 913-921 
 
Wales WJ, Doyle PT, Dellow DW (1998) Dry matter and nutrient selection by lactating cows 
grazing irrigated pastures at different pasture allowances in summer and autumn. Australian 
Journal of Experimental Agriculture 38, 451-60 

Economic Evaluation of Irrigated Dairy Forage Production.

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