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Stocking rate

‘Stocking rate’ : on matching animal intake demand to feed supply

Below we consider:

i) Stocking rate : best seen as an emergent property?

ii) Constraints on-farm

iii) Current farm best-practice was derived from ‘first principles’ based on vegetation state

iv) Stocking rate: effects of nitrogen fertiliser N input, animal class, and supplements

v) Stocking rate: effects of nitrogen fertiliser N input, animal class, and summer irrigation

vi) Stocking rate : time course following a change in stock numbers is revealing

 

Stocking rate : best seen as an emergent property?

‘Stocking rate’ is widely seen as a ‘simple’ management ‘input‘ or ‘driving‘ variable, but matching animal numbers, and their intake demands (per ha) to the rate of growth of fresh forage (per ha), in practice, is a continual and far from simple task. This is to the extent that in any attempt to understand how the grassland system functions, it is arguably more appropriate to see ‘stocking rate’ as an ‘output‘, that is as an emergent property, dependent upon weather, climate, season, fertiliser use, supplements, irrigation.. and not least animal class and physiological state.

Throughout the analyses on this web site, we treat ‘stocking rate’ as an emergent property (an ‘output’), in situations were we can be assured the number of animals, and so their variable demand for intake, is matched precisely to the rate of supply of ‘feed’ (whether fresh forage or from external sources).  To do this we use a management algorithm in which animal numbers are dynamically adjusted to maintain an optimal mean vegetation state (see papers and ‘clarifications’) as a basis for consistently ruling out ‘under-utilisation’ or ‘over-utilisation’. This uniquely allows us to understand the effects of each management variable and their interactions, clearly and systematically.

Some examples of emergent stocking rates, in relation to major management variables are shown below.

 

Constraints on-farm

This does not deny that, to a farmer, or someone setting up an experimental field trail, the choice of how many animals to manage, is one of the first decisions made. Various means, and largely observation-based decision support models, are then widely used to back-calculate what the requirements are in terms of fresh forage (eg grass) growth/supply, fertiliser N inputs, supplements, irrigation, to satisfy the demands for intake, from that decision on stocking rate. But these back-calculations cannot, and do not, take account of what the outcome will be, dynamically, and in to the future, of making any one set of decisions, notably on how the choice of stocking rate would have itself affected the rate of plant growth (and so fresh forage supply) that could be achieved. The back-calculations cannot consider how any mismatch on one ‘day’ will alter the whole set of circumstances and so outcomes, from ‘the next’.

Analyses of the consequences (dynamic outcomes) of using different stocking rates, across a range of fixed eg fertiliser nitrogen inputs; or of the consequences of choosing different fertiliser N inputs, across a range of fixed  stocking rates, are essential for understanding,  given the dynamic interactions between the two. Examples of these, and the principles and understanding of over- and under-utilisation, have been published widely (eg see papers [links] and the references therein).

But systematic analyses are often criticised on the grounds ‘a farmer wouldn’t do that’ (that is wouldn’t change stocking rate without altering nitrogen inputs, and vice versa).  This is a conundrum. Practical field trials and farm data arise from circumstances where multiple effects are confounded, to the extent they ‘cloud’ attempts to derive principles for how pastures respond to other major inputs, when using empirical (ex-farm) data alone (see Motu).

But we can make progress simply.

 

Current farm best-practice was derived from first principles

Vegetation mean state is well recognised as being central to the function of the system, not least because it drives photosynthesis, N uptake into animal feed, and likewise animal intake behaviour and responses.  Long ago this led to management guidelines for grazing systems, based on vegetation state (in many guises). Current recommendations in New Zealand are that farmers should first and foremost make best use of fresh forage, using such management guidelines, before contemplating adding eg supplements or other inputs.

The approach we adopt, then, is both essential for clarity in understanding, and our outputs reflect what a farmer would achieve using current management best-practice.

 

Stocking rate: effects of nitrogen fertiliser N input, animal class, and supplements

 

Because the two graphs below depict sustainable solutions (‘dynamic steady states’), we know they represent the situations where the stocking rate was matched sustainably to the supply of forage and feed. Hence, though the graphs are drawn with ‘stocking rate’ as an emergent property (an ‘output’ on a y axis), one can  read ‘across’ and ‘down’ from any value of stocking rate (on the y axis), to the x axis, to establish what fertiliser N inputs, irrigation etc was required to sustain that stocking rate. This is tantamount to a back calculation that does encompass the many complex dynamic interactions.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sustainable  stocking rates, in relation to nitrogen fertiliser N input (x axis), as affected by supplementary feeding. The solid line depicts ‘meat’ systems (cattle or sheep in general), and the dashed lines depict ‘dairy’ systems (which could equally be either cattle or sheep lactation based, but here the numbers are for cattle-sized stock). The points (symbols) along each line are the solutions for each of a range of fertiliser N input rates (of 15, 30, 60, 150, 300, and 600 kgN/ha/year). For ‘dairy’, responses are shown for unsupplemented (open triangle, ‘L’) and for two levels of supplementation: either 4 kg DM per animal per day, with 2% N content, and a 50% substitution by animals for fresh forage (solid diamond); or 8 kg DM per animal per day, with 2% N content and a 50% substitution rate (solid squares). All outputs here are receiving adequate rainfall, and based on the met data and soil type for Ruakura, NZ.  All are the long-term sustainable rates and states predicted by the model. All are based on stocking rate being optimally matched to the rate of supply of fresh forage (see ‘clarifications’).

The use of supplements had greatest effect in increasing the number of animals,  and hence yield of products per ha, at low fertiliser N inputs, than at higher fertiliser N inputs. This is because the N in supplements boosts plant growth more when the system is overall N deficient, (the system is N-limited).

For detailed analysis of the effects of supplementation on the C and N cycles, see supplements.

 

Stocking rate: effects of nitrogen fertiliser N input, animal class, and summer irrigation

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sustainable  stocking rates, in relation to nitrogen fertiliser N input (x axis) and the use of irrigation. The solid line depicts ‘meat’ systems (cattle or sheep in general), and the dashed lines depict ‘dairy’ systems (which could equally be either cattle or sheep lactation based, but here the animal numbers are for cattle-sized stock). The points (symbols) along each line are the solutions for each of a range of fertiliser N input rates (of 15, 30, 60, 150, 300, and 600 kgN/ha/year). All outputs here are based on the met data and soil type for Winchmore, NZ, a site which unlike Ruakura (above) has a characteristic water deficiency during three months in summer. Hence the unirrigated example is labelled ‘dry’; the next case ‘wetter’ is where extra water has been input during the critical summer months, in practice some 50% addition to the natural rainfall; and ‘wet’ is where some 100% water has been added, hence a doubling of water input relative to rainfall, during these critical summer months.

All results are the long-term sustainable rates and states predicted by the model. All are based on stocking rate being optimally matched to the rate of supply of fresh forage (see ‘clarifications’).

The use of irrigation, here to relieve a water deficiency that limited photosynthesis during critical months in summer, substantially increased the number of animals that could be sustained, whether for ‘dairy’  or ‘meat’.

The use of irrigation to increase stocking rate and the yield of products had major consequences on multiple components of the C and N cycles, and for further analysis, see irrigation.

 

Stocking rate : time course following change is revealing

The graphs below clearly demonstrate how, while stocking rate may be widely seen as a simple driving variable, this is far from being the case. Indeed seeing animals as a driving force in the C and N cycle, notably for N releases, can be misleading. The outcome of a simple change in stocking rate can be highly counter-intuitive. This is because animals are NOT a source of N to the grassland system (as is widely perceived) but a component of the internal recycling of N inputs from external sources. (For published explanation see ‘clarifications’).

The graphs show changes over time in the intake of DM (with is of course both C and N) per ha by animals, the amount of C sequestered; the total N releases to the environment; and a focus on nitrous oxide emissions, following a simple change in stocking rate alone.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The time-course is shown (above) for a pasture system sustained throughout at a given SR (solid line at 26 dry sheep per ha), compared with that where SR was increased to 40 (dot-dash line) or decreased to 20 (dashed line), in all cases making no change in fertilizer inputs.

In this example, an increase in stock numbers per ha did lead in the short term to an increase in the total rate of release of N to the environment, see (c), reflecting how a greater amount of the N input and cycling in the system, was in the form of urine (in particular) and dung, and so in a form subject to greater relative losses (than if cycled eg as senescent plant material). Likewise there was a shorter term boost in the rate of nitrous oxide emissions (d). However, these effects are transient. The increase in stock numbers, and greater releases of N, together lead to a run down of N in the system (if, as here, N inputs are not increased). This can be seen in the run down in the amount of N sequestered (see (e) below) and in the amount of C sequestered, see (b). Ultimately, the total losses of N cannot sustainably remain greater than the input of N to the system, and so losses (as in (c) ) must re-approach the same level in all cases, namely to equal the rate of N inputs (here 150kg N/ha/year).

With a greater number of animals present, the system remains intrinsically ‘leakier’, but the outcome of this, longer term, is that a lower amount of N and C are sequestered. Hence in effect, the increase in the release of N to the environment, seen initially following the increase in stock numbers, reflects the release of N from sequestered N in (predominantly) the soil.

The impacts of changes in SR per se are not only transient, but can be counter-intuitive. In (d) for example, the sustainable rate of nitrous oxide emissions, at the raised stocking rate, is actually lower, than that seen where the stocking rate had remained unchanged.

The issue is not pedantic (or of semantics). Faling to be precise about what are the real drivers of how the system responds, is important for ensuring a policy for action (eg mitigation) does not have unintended, unforeseen, consequences, longer term.

It is a change in fertilizer (or other) N input,  if/when this is associated with SR increases, and not the change in SR per se, which creates sustained changes in N emissions.

 

 

 

 

 

 

 

 

 

 

Hurley Pasture Model and Edinburgh Forestry Model A Massey Site