Ovine Observer

Identifying high-value tactical livestock decisions on a mixed enterprise farm in a variable environment

Introduction

Australia is renowned for its climate variation that includes droughts and floods that cause significant production and profit variability (Trompf et al. 2014; Laurie et al. 2019; Feng et al. 2022). This variation can be challenging for farmers (Heberger 2011), particularly for livestock farmers who must adhere to animal-welfare standards. In mixed-farming systems, livestock and associated pasture production complement cropping activities by utilising crop residues, providing disease and pest breaks, providing weed-management options and improving labour and machinery use efficiency during the year. As such, livestock and pasture production are key components of many farm businesses and farming systems in Australia.

To handle climate variation, farmers can alter their ‘big-picture’ strategic management to set up a more versatile and diversified enterprise mix of their farm business (Azam-Ali 2007; Kandulu et al. 2012). However, Kandulu et al. (2012) suggested that, in many locations, a sole focus on diversification does not wholly mitigate the financial effects of climate variation. An alternative management method is to implement short-term tactical adjustments in response to unfolding conditions (Anderson et al. 2020).

Tactical management is most valuable within systems where farmers have a wide portfolio of tactics for use in response to an external change (Cowan et al. 2013). This is the case in mixed-farming systems (Young et al. 2022). In mixed crop and livestock businesses, farmers can adjust enterprise allocation, their interactions and relevant tactics to better suit unfolding climate conditions. Furthermore, as outlined by Young et al. (2023a), tactical management generates opportunities to boost farm profit and/or avoid losses. However, the large array of possible tactics within mixed-enterprise farm systems can complicate management, especially when combined with the changing and evolving nature of farming systems.

In this paper, we apply a whole-farm optimisation model that, first, represents year-to-year variation and, second, includes an extensive array of tactical management options tailored to that variation. The model is used to identify and quantify optimal tactical livestock management for different weather-years.

Key findings

• A ‘set and forget’ management approach is far from optimal.
• Managing farming systems dynamically in response to unfolding weather conditions is highly profitable, increasing the expected profit by $128,000 (16%).
• The economic value of implementing an additional tactic varies, in the 8 scenarios tested in this paper, an additional tactic was worth between $7,704 and $53,171.

Materials and methods

Model description

This study employed the Australian farm-optimisation model (AFO) to analyse a representative mixed enterprise farm located in the Great Southern region of Western Australia. The model represents the economic and biological detail of a farming system, including modules for rotations, crops, pastures, sheep, crop residues, supplementary feeding, machinery, labour and finance. Furthermore, it includes land heterogeneity by considering enterprise rotations on a range of soil classes/land-management units (LMU) (Figure 1).

Weather variation can be represented in multiple ways using AFO. Importantly, based on the findings of Young et al. (2023a) and Young et al. (2023b), we adopt the four-stage single-sequence stochastic program with recourse (4-SPR) model, housed within AFO, which represents the farm system with multiple states where each state represents a different weather-year that can have separate inputs to reflect different prices and weather conditions. Full descriptions of the AFO model description are available in the full paper.

Overview of the farm system

AFO was calibrated to represent a typical farm in the medium-rainfall zone of the Great Southern region of Western Australia. The Great Southern region in Western Australia is characterised by winter-dominant rainfall (400–650 mm) and a 6-month growing season that supports a mix of cropping and livestock enterprises. Weather variance in the region was approximated by 8 discrete states of nature (weather-years) (see Table 1). The model represents a typical 2,130 ha farm that includes 3 LMUs (Table 2). Other key features of the modelled farm are shown in Table 3. The standard prices for wool, meat and grain used in the analysis were based on the 70th percentile prices received over the past 13.5 years for wool, 18.5 years for meat and 14 years for grain (Source: Mecardo 2023). A full description of the AFO inputs is available in the full paper.

 

Table 1: Summary information for each weather-year represented in the Kojonup version of the AFO model.

Code for weather-year	Definition of each weather-year	Probability of occurrence (%)	Growing season rainfall	Crop yield scalar
z1	Early break with follow up rains and a good spring.	24	447	1.2
z2	Early break with follow up rains and a poor spring.	20	346	1.0
z3	Early break that turns out to be a false break, but is followed by a good spring.	8	416	1.22
z4	Early break that turns out to be a false break and is followed by a poor spring.	4	294	0.87
z5	Medium break with follow-up rains and a good spring.	14	448	1.05
z6	Medium break with follow-up rains and a poor spring.	16	392	0.83
z7	Late break with follow-up rains and a good spring.	4	477	0.95
z8	Late break with follow-up rains and a poor spring.	10	337	0.65
Yield scalar is the relationship between yield in the given weather-year and the average yield. This was calculated using the output of APSIM modelling using Kojonup climate and soil data from 1970 to 2019. Early break (i.e. start of the growing season): before 5 May; medium break: between 5 May and 25 May; late break: after 25 May. Good spring, above the median (86 mm) rainfall for September and October; poor spring, below the median rainfall. False break, pasture feed on offer reaches 500 kg/ha, followed by 3 weeks of no growth.

 

Table 2: LMU definitions for a typical farm in the Great Southern region of Western Australia.

Soil class	Description	Arable (%)	Grazing area (ha)
Deep sands	Deep sands, but not waterlogged. Over mottled clay.	100	150
Sandy gravels	Gravels and sandy gravels down to 50 cm over clay or gravelly clay.	80	1230
Sandy loams	Sandy loam, loamy sand over clay rock outcropping in landscape.	80	750

 

Table 3: Key features of the modelled farm.

Farm size (ha)	2130
Time of lambing	‘Spring’ lambing (lambing starts mid-July)
Pregnancy scanning management	Scanning for pregnancy status only
Sheep liveweight	Nutrition profile is optimised by AFO
Sheep genetics	Medium-frame Merino
Standard reference weight (kg)	55
Fibre diameter (μ)	20
Canola yield (t/ha)A
Roundup-ready	2.6
Standard (non-GM)	2.2
Wheat yield (t/ha)A	4.5
Barley yield (t/ha)A	5.0
Oat yield (t/ha)A	4.5
Hay yield (t/ha)A	8.0
Lupin yield (t/ha)A	2.5
Faba bean yield (t/ha)A	3.0

A Reported yield is on LMU 4 (best-performing areas of the farm) in a canola–cereal or pulse–cereal rotation weighted across all weather-years.

Tactics Comparison

Using this model, we investigated the economic significance of 5 key livestock management tactics. These included:

1. Sale quantity and timing – additional classes of sheep can be sold or retained in response to the unfolding years condition.
2. Pasture area and rotation – the area of pasture can be adjusted based on the time of break and pasture can be established in paddocks with different land-use histories that affect germination (e.g. continuous pasture has a higher germination than pasture following multiple years of crop).
3. Grazing management – depending on the unfolding year, stock can follow different grazing management (e.g. pasture can be deferred for longer in weather-years where pasture growth is limiting).
4. Crop grazing – crops can be grazed early in the growing season when pasture is limiting, or to allow pasture to be deferred.
5. Stock nutrition profile – animals can gain more weight in a good year and lose more weight in a poor year.

To understand the value of each tactic, we compared the profitability of the farm with the tactic versus a farm with a ‘minimal’ level of the tactic. A minimal level of each tactic was used as the comparison because it is impossible for a farmer not to change some part of their management in response to changing conditions among years.

Results

Value of tactics and strategic impact

Dynamically managing farming systems in response to unfolding weather conditions increases expected profit by $128,000 (16%) (Table 4). Tactical management has a large impact in early break years that have no follow-up rain (z2 and z3) (Table 4). This is largely because in the Great Southern region of Western Australia, false breaks do not affect crop production (Table 1). However, pasture production during the false-break period is significantly reduced. Thus, tactical adjustments have the potential to significantly boost profit in those years.

A farm managed with a full complement of tactics has a different overall strategy from a farm managed with minimal tactics. For example, with tactics, the optimal overall stocking rate is increased by 30% (Table 5). Thus, the change in profit reported in Table 4 is not necessarily a reflection of the importance of including tactics in a given weather-year. For example, other results (not included here) show that the value of tactics in z7 is $90,000 (105%). Utilising tactics in z7 (a poor weather-year) allows the profit to remain similar, while the strategic stocking rate is increased.

Table 4: Weather-year profit (AUD) with full tactics versus minimal tactics.

Weather-year	Full tactics (×1000)	Minimal tactics (×1000)	Change (×1000) (% in parentheses)
Expected	$904A	$776A	$128 (16%)A
z0	$1,345	$1,164	$181 (16%)
z1	$990	$872	$118 (14%)
z2	$1,068	$767	$301 (39%)
z3	$370	$106	$264 (250%)
z4	$931	$876	$56 (6%)
z5	$624	$527	$98 (19%)
z6	$836	$778	$59 (8%)
z7	$187	$183	$4 (2%)
MinimumA	$186B	$105B	$81 (44%)B
Maximum	$1,344B	$1,164B	$180 (13%)B

A Weighted average of weather-years.

B Minimum and maximum profit across the weather-years.

 

Table 5: Summary of farm strategy with full tactics and minimal tactics.

Tactic	Full tactics	Minimal tactics
Profit (×1000)	$903.5	$775.7
Stocking rate (dry-sheep equivalents/winter grazed area, DSE/ha)	18.6	14.3
Supplement fed (t)	937.3	829.8
Pasture area (%)	35.6	39.2
Cereal area (%)	39.4	38.7
Canola area (%)	25.0	22.1

Key tactical decisions

In early break years, it is optimal to increase the canola area by up to 55% and in late-break years, it is optimal to decrease canola area by 55% (Table 6). All the tactical-rotation adjustments occur on the productive soils (LMU 3 and LMU 4). Sandy soils (LMU 2) are never tactically adjusted and always remain in continuous pasture (Table 6). The difference in rotation selection based on the presence or absence of follow-up rains in early breaks shows that in years with an early break, it is optimal to delay the rotation decision on a proportion of the area until follow-up rains are received. The results in this paper report only the changes in land-use area on each soil type. However, the adjustments are fine-tuned based on the rotation history. This is accounted for in AFO, but, for simplicity, we have not reported the full rotation changes.

 

Table 6: Optimal land-use choice on each LMU for each weather-year.

Weather-year	Pasture (ha)			Cereal (ha)			Canola (ha)
	LMU2	LMU3	LMU4	LMU2	LMU3	LMU4	LMU2	LMU3	LMU4
ExpectedA	150A	108A	500A	0A	720A	119A	0A	402A	130A
z0	150	95	424	0	537	100	0	598	227
z1	150	95	424	0	537	100	0	598	227
z2	150	107	475	0	612	209	0	511	67
z3	150	107	475	0	612	209	0	511	67
z4	150	132	620	0	920	84	0	178	46
z5	150	132	620	0	920	84	0	178	46
z6	150	99	506	0	956	182	0	175	62
z7	150	99	506	0	956	182	0	175	62
Minimal tacticsB	64	498	271	86	424	315	0	308	164

A Weighted average of weather-years.

B All weather-years are the same without tactics.

Under minimal tactics, all pasture is grazed at a similar intensity and all paddocks have a similar level of Feed on Offer (FOO). Optimal management employs rotational grazing, and grazing low-FOO paddocks lightly to maximise growth (see full paper). In early break weather-years, it is optimal to graze pastures heavily early and then defer them by grazing crops.

The optimal level of crop grazing correlates with the break of season timing, where early break seasons have the highest level of crop grazing (Table 7). After an establishment period, crops can be grazed. However, it is optimal to further delay grazing to increase relative availability of the feed. At low FOO levels, the relative availability of pasture is low, which reduces intake and nutritive feed values for sheep. At low nutritive value, the yield penalty outweighs the value of grazing. Hence, in late-break and false-break years, some of the crop available for consumption is not grazed (Table 7). Crop grazing is economical even in favourable weather-years because the stocking rate is increased, which outweighs the negative impact of yield loss.

Table 7: Tonnes of crop grazing in each weather-year.

Weather-year	Crop consumed (t)	Available proportion consumed (%)
ExpectedA	386A	85A
z0	543	100
z1	543	100
z2	395	89
z3	395	89
z4	329	100
z5	329	100
z6	4	4
z7	4	4
Minimal tacticsB	0	–

A Weighted average of weather-years.

B All weather-years are the same without tactics.

Most sales that differ based on weather-year conditions are related to stock less than 18 months of age. Additionally, there are some smaller tactical sales of sheep that include the oldest age group of ewes. Adjusting only the youngest and oldest age group of animals allows the breeding strategy to remain constant, suggesting that destocking of ewes in a poor year is not profitable because of the opportunity cost caused by being understocked in the subsequent years.

The farm strategy (minimal tactics) is to sell the heavy proportion of wethers at 8 months of age and the remainders after the second shearing at 18 months of age (Figure 2). With tactical management included, the general strategy is similar. However, in years with a false break or a poor spring; a large proportion of the wethers are sold after shearing at 5.5 months of age. In years with a false break, a greater proportion of wethers are sold at 8 months of age.

Note: There is a gap in the graph at 8 and 20 months, which is the beginning of the next weather-year, at which point all weather-years have the same opening numbers and they can then diverge again.

Implementation of the short-term tactical management increases the optimal winter stocking rate (Table 8), while reducing supplement fed per dry sheep equivalent (DSE) in 5 of 8 weather-years (Table 8).

Table 8: Winter stocking rate in each weather-year and supplement fed in each weather-year with full tactics versus minimal tactics.

Weather-year	Stocking rate (DSE/Winter grazed Ha)	Full tactics		Minimal tactics
		Total (t)	kg/DSE	Total (t)	kg/DSE
ExpectedA	18.6A	1053A	80A	963A	82A
z0	21.1	1173	87	1065	90
z1	21.1	889	69	620	53
z2	18.3	1010	78	1255	107
z3	18.3	1614	122	1989	171
z4	15.3	898	68	795	68
z5	15.3	1011	77	963	83
z6	18.0	1095	84	782	67
z7	18.0	1173	87	1065	90
Minimal tacticsB	14.3B	NA	NA	NA	NA

Discussion

Australia’s variable climate results in the need to manage its dryland farming systems dynamically to maximise profitability. This paper utilises a current up-to-date farm-optimisation model, to identify the optimal complement of tactical adjustments to apply and their associated profitability. The findings indicated that managing farming systems dynamically in response to unfolding weather conditions is highly profitable, increasing the expected profit by 16% (Table 4). This concurs with the few previous studies that have examined the mixed-enterprise farming system of Western Australia. From a farmer’s point of view, the key message from all these studies is that a ‘set and forget’ management approach is far from optimal. However, the value of implementing the optimal tactical management will vary among producers, depending on their current management.

The implementation of tactics can potentially be complex. Farmers must consider that as they implement tactics into their system, their underlying strategy must also be adjusted (Table 5). The added complexity of each category of tactic being made up of many sub options means that the farm manager must be skilled to identify the type of unfolding season and implement the correct tactic (monitoring tools such as Pastures from Space™ may assist farmers in identifying the weather-year being experienced). In this analysis, there was no economic cost incurred for the additional skill required to implement tactics. However, it may warrant consideration. Additionally, no cost has been attributed to the potential need to store additional inputs to facilitate management adjustments. Given these factors and each farmer's unique circumstances, they may want to implement a subset of the available tactical options. The economic value of implementing an additional tactic varies depending on the complement of tactics being applied (Table 6). However, in the 8 scenarios, an additional tactic was worth between $7,704 and $53,171. This indicates that a farmer can improve farm profitability and by implementing only a subset of the available tactics.

This study was based on a ‘typical’ farm in the Great Southern region of Western Australia. Like the study region, many farming regions within Australia have significant weather variation. Therefore, we expect that implementing tactical management can increase profit will be applicable to a range of farm systems, provided those regions have variation in weather among years. The value of different tactics is likely to vary across regions and farms, so results need to be implemented with care. However, the modelling method could be used to generate customised results.

Conclusion

Short-term adjustments to the overall farm strategy in response to unfolding weather conditions can result in substantial improvements in expected profit on dryland mixed-enterprise farms in the Great Southern region of Western Australia by approximately 16%. Benefits stem, first, from capitalising on knowledge about the profitability of different decision tactics tailored to the unfolding weather conditions. Second, the benefits accrue from more optimally selecting the underlying farm management strategy of the farm business. Deterministic models and even stochastic models that do not include activities for tactical adjustments miss this key feature of the system and may incorrectly identify optimal activities.

Full Paper

Acknowledgements

Declaration of funding. The authors thank the Department of Primary Industries and Regional Development, WA for financial support through the Sheep Industry Business Innovation project.

Acknowledgements. The authors thank Katelyn Bruinsma for final edits. This paper forms part of the PhD thesis of Michael Young (2023).

Data availability. All data used in this paper have been referenced and is publicly available. The model/code used for this paper can be licensed to others on request.