Do higher lambing percentages result in higher profits and if not, what is the optimal percent?
What are the factors in lambing% that result in less despite more?
Farmers are routinely being told to improve their performance in order to survive; the main theme appears to be one of increased production and often of a single factor such as lambing percent. Such recommendations ignore the relationships that apply to farm production and profit.
Let’s list just a few of the obvious relationships:
- There are only brief periods where straight line responses apply in agriculture. Invariably responses decline as more is added (“The Law of Diminishing Returns”).
- There are physical limits to animal intake. If higher production is required, larger animals and/or higher quality feed needs to be offered and eaten.
- Intake limits may impose problems at crucial times in the biological cycle. Many ewes carrying twins and triplets cannot eat enough to maintain themselves in late pregnancy due to the compression of the rumen area by the growing foetuses. This reduces intake in a period when pasture quality and quantity are also limiting. Add poor weather to this mix and the overall stress leads to metabolic and low-energy induced deaths.
- Individual multi-birth lambs are lighter. Small animals lose body heat more rapidly than larger ones. Small lambs have a much reduced chance of survival in cold, wet weather. Wind compounds this lethal mix.
- Lower birth weight lambs do not grow as fast as their larger counterparts. The slower an animal grows, the higher the maintenance proportion of the feed consumed. Maintenance requirements must be covered before any growth occurs.
Comparative analysis and benchmarks neither account for, nor distinguish between these facts and this failure has led to some extraordinary statements in the farming press. Benchmarks and KPI’s have undermined the basic biological and production economic facts that describe livestock systems. Environmental constraints mean that any comment made about farm systems is reliant on identifying how single events “ripple” through individual farm systems. Assessing the biological implications is difficult even without adding economic implications to the mix.
The blunt fact is that most commentators neither have the experience nor the analytical horsepower to make the statements they do.
Some industries have overcome dynamic production factors by combining their known relationships into analytical computer models. Agriculture has relied on spreadsheets.
These invariably present a series of inflexible outcomes, predetermined by input.
In real life, the ability to adjust input as prices and production alter with economic and environmental (or market) conditions, defines how efficiently resources are utilised.
This requires an ability to integrate biological constraints, diminishing returns and economics at each point in the decision and implementation process. In other words -to be informed on risk and flexible in application.
Newer technologies (www.grazingsystems.co.nz) have enabled fully integrated systems modelling which calculates best use of available resources from any set of pasture, stock and price variables. By adding the manager’s practical experience of the farm to the model input, the ability to predict outcomes from the farm as a system becomes a reality.
The following examples show how this combination progresses.
By using multiple runs and allowing choice of best allocation of resources within each model run (by allowing substitution to occur as diminishing returns occur) the model shows what can and cannot be achieved using a range of lambing percentages. This particular farm is modelled to demonstrate the effect of pasture quality and ewe fertility on profit contribution.
The selected “Runs” illustrate how the quality of resource (in this case pasture quality) can change “possible and profitable” to “struggle and loss”. This type of modelling pinpoints constraints and can be used to assess future change strategies, their costs and benefit.
Runs 1-4 use “dairy quality” pastures and show how the impact of increasing lambing percent flows through a system, impacting on ewe numbers, lamb weights and sale policies.
Feed quality and quantity will vary between and within years. These results are for a particular pattern.
As weight of ewe and lambing percent increases, the number of ewes able to be wintered reduces and the time and manner of sale also changes. It is not as simple as “more lambs equals more money”. Even with high quality feed and performance levels, 185% lambing will prove difficult (Run 4). High survival and the difference between store and schedule prices will be crucial for this profit to be achieved. (The replacement rate, losses and number of years the ewe lasts in the flock can also be varied and has a large effect on the final $ figure. If high production ewes have a lower longevity than lower producing ewes, this can be modelled and narrows the margin between the two systems even more.)
Run 5 shows a more normal level of feed quality for hill country farms (10.5MJME/kg DM). Profit from high lamb percentages drops due to poor liveweight gains. Depending on LWG, smaller lambs at birth may consume up to 30% more feed to finish at the same weight and at a much later date. They eat feed that could otherwise be used on more profitable (faster LWG or different species) animals, or used to winter more ewes.
To achieve best results, lambs must grow rapidly and be sold soon enough to ensure ewe weight and pasture cover are at required levels before winter. Run 6 achieves this as most lambs are singles where milk and feed combine to provide higher energy levels and fast LWG.
Farm systems are ever changing. They require constant observation, analysis and adjustment. Any change will ripple through the whole system, often with unforeseen results. In this case the ripple effect has been modelled using GSL which links the relationships to produce a dynamic whole farm effect. As with real life, the results are often unexpected but can be easily interpreted using an extensive reporting function.
The ability to rapidly model real systems emphatically shows:
- Each farm will have a different set of performance levels to achieve best results in terms of profit.
- Each farm has a unique combination of resources that will produce the best results.
- Averaged industry standards are of minimal value for planning future change as they assume straight line responses and therefore cannot define the point at which additional resource becomes unprofitable.
Those who quote the successful rearing of twins and triplets in the UK should also note that the ewes there are predominantly large, are fed concentrates prior to lambing (which largely overcomes energy intake limits), lose up to 20% bodyweight milking (due to high pre-lamb body condition), are normally lambed indoors and often their lambs have the benefit of “creep” concentrate feeds. None of these apply to hill country in New Zealand.
More information on Individual Farm Systems Modelling?
Barrie Ridler firstname.lastname@example.org
Abstract: Defining Optimal Lambing Percentages.
Do higher lambing percentages result in higher profits and if not, what is the optimal percent? What are the factors in lambing% that result in less despite more?