Farming system profitability and impacts of commodity price risk

Take home messages

  • Large gaps in profitability are possible between the best and worst systems – differences of $92-494/ha per year were found between systems at each site
  • Intensity is the major factor driving good/poor economic performance of the farming system - more so than crop choice. Matching intensity to environmental potential seems to be the most important lever to optimise farming system profitability
  • Increasing crop intensity increased costs and risks, but potentially higher crop income wasn’t realised over the dry run of seasons and hence has produced lower gross margins than more conservative systems
  • Lower crop intensity had lower system gross returns, but because of lower inputs and costs may achieve a more favourable return on investment at lower risk when there are limited planting opportunities. These systems have achieved lower gross margins than the baseline system in all but one comparison
  • Increasing legume frequency has the potential to capitalise on favourable legume prices but using long-term prices has rarely exceeded gross margins of baseline systems
  • Increasing nutrient supply incurred higher costs and required favourable seasonal conditions to increase grain yields and gross margins – this rarely occurred over the experimental years (excluding Trangie 2016 and Emerald 2017 where significant crop responses were obtained)
  • Systems involving crops with higher price variability (e.g. pulses, cotton) had limited downside risk but increased upside opportunities of higher economic returns. Even when comparing recent and long-term grain prices, the relative profitability ranking of systems rarely changed
  • Selecting a crop system is a long-term decision with unknown future yield and prices, hence choose systems that maximise system productivity and resilience, rather than responding to current commodity prices.

Introduction

Leading farmers in the Australian northern grains region (NGR) perform well in terms of achieving the yield potential of individual crops. However, the performance of the overall system is harder to measure and less frequently well considered. The key factors appear to relate to issues occurring across the crop sequence such as poor weed management, disease and pest losses, sub-optimal fallow management and cropping frequency. Similarly, farming systems are threatened by the emerging challenges of increasing herbicide resistance, declining soil fertility and increasing soil-borne pathogens, all of which require responses to maintain total system productivity. Questions are emerging about how systems should evolve to integrate practices that:

  • Efficiently capture and utilise rainfall particularly for high-value, low-stubble crops
  • Reduce costs of production and the likelihood of climate-induced risk
  • Respond to declining chemical, physical and biological fertility
  • Improve crop nutrition and synchrony of nutrient supply
  • Suppress or manage crop pathogen populations
  • Reduce weed populations and slow the onset, prevalence and impact of herbicide resistance
  • The price risk of individual crops and the impact on systems’ economic returns.

Because of the multi-faceted nature of these challenges, an important need is for a farming systems research approach that develops an understanding of how various practices or interventions come together. This requires quantifying synergies or trade-offs and investigate the impact on whole-of-system productivity, risk, economic performance and sustainability.

As a result, research was initiated in 2014 to identify the key limitations, consequences and economic drivers of farming systems in the NGR. The aim is to assess the impacts of modifying farming system on multiple attributes (e.g. nutrients, water, pathogens, soil health, and economics) across multiple sites.  Experiments were established at seven locations and a large factorial experiment at Pampas near Toowoomba with locally relevant systems being studied at six regional centres across central and southern Qld (Emerald, Billa Billa, Mungindi) and northern NSW (Spring Ridge, Narrabri and Trangie).

Assessing how changes to the farming systems alter profitability is critical. This paper examines the economic performance of different modifications that we have tested in combination with commodity price risk. This will help quantify the costs or benefits of changing the farming system and the trade-offs for the different cropping intensities and nutrient strategies.

System modifications being tested

We used a set of farming system strategies across our site locations within the NGR. These strategies resulted in different cropping systems per location, based on the environmental (climate & soil) conditions.  Below we outline the common set of farming system strategies employed across the farming systems experimental sites over the past 4.5 years.

  • Baseline– an approximation of current best management practice in each district against which each of the system modifications are compared: involves only dominant crops used in the district; sowing crops on a moderate soil water threshold (i.e. 50-60% full profile) to approximate moderately conservative crop intensities (often 0.75-1 crop per year); and fertilising to median crop yield potential
  • High crop intensity – aims to increase the proportion of rainfall transpired and reduce unproductive loses by increasing the proportion of time that crops are growing; this is implemented by reducing the soil water threshold required to trigger a planting opportunity (e.g. 30% full profile) so that cropping intensity is increased relative to the baseline
  • Low crop intensity systems – this aims to minimise risk by only growing crops when plant available soil water approaches full (i.e. > 80% full) before a crop is sown and higher value crops are used when possible. This requires longer fallows and will lower crop intensity relative to the baseline
  • High legume frequency – crop choice is dictated to have every second crop as a legume across the crop sequence and uses high biomass legumes (e.g. fababean) when possible.
  • High crop diversity – a greater set of crops are used with the aim of managing soil-borne pathogens and weed herbicide resistance risk through crop rotations. This implemented by growing 50% of crops resistant to root lesion nematodes (preferably 2 in a row) and 2 alternative crops are required before the same crop is grown in the crop sequence
  • High nutrient supply - increasing the fertiliser budget for each crop based on 90% of yield potential rather than the baseline of 50% of yield potential.

At several sites there are also some additional, locally relevant systems implemented. These include:

  • Higher fertility systems (Billa Billa and Emerald) where the high nutrient supply system is also complimented with the additions of a large amount of organic amendments with the aim of boosting background soil fertility. The aim is to see if this can be maintained when used in combination with the higher nutrient input strategy, as well as the economic outcome.
  • Integrated weed management systems (Emerald). The system has implemented combinations of agronomic management options particularly focussed on summer grass weeds (e.g. feather-top Rhodes grass) such as higher levels of crop competition and use of multiple herbicide modes of action.
  • Two low-intensity systems have been implemented at Mungindi, one involving only grain crops and the other implementing cotton in the rotation when conditions are appropriate.

Finally, at the core experimental site at Pampas, each of these system modifications are being tested in a factorial where some modifications are combined, with the four overarching themes being: mixed opportunity, intensive, summer-cropping, and winter-cropping dominant.

Quantifying system profitability and commodity price risk

Over the 4.5 experimental years we have collected data on crop grain yields, the total inputs of machinery operations, fertilisers, seed, herbicides and other pesticides for each cropping sequence. This allows us to calculate the accumulated income (sum of grain yield x price for all crops in the sequence) and total gross margins (income minus costs) for each of the cropping systems deployed at each location (Table 2 and Table 3). Prices for inputs of fertilisers, herbicides, other pesticides and seed were based on market prices at purchase for each input. Costs for operations differed by crop to reflect different contract rates or machinery requirements, but fertiliser applications ($8/ha) and each spraying operation ($3/ha) were held constant. All grain yields were corrected to 12% moisture irrespective of harvest moisture levels. We have used consistent prices for each commodity and inputs across all locations to avoid introducing discrepancies in the data.

In this research we used the key metric of “total gross margins” to compare system profitability per hectare across environments and cropping systems over the whole period (4.5 years). It should be noted that gross margins do not include overhead, or other fixed costs associated with the farming enterprise, as these are likely to vary significantly from farm to farm and region to region.

Initially we have calculated these system gross margins using 10-year median commodity price over the period 2008-2017 (adjusted for inflation, transportation, grading or bagging costs) (Table 1). However, to explore the impact that variability in commodity prices may play on the relative profitability of different crop sequences we have then calculated the gross margin across a full set of combinations of prices for each crop commodity that may have been received over the past 10 years. We also calculate the specific gross margin for each crop system using commodity prices received over the last 3 years (see Table 1) to see the actual economic outcome during the experimental period and where they fell within the range of possible outcomes.

Table 1. Market commodity prices and farm gate prices used for calculating system gross margins for each crop grown across the farming systems experiments.

  

Barley

Wheat (APH)

Wheat (Durum)

Canola

Chickpea

Fababean

Fieldpea

Sorghum

Maize

Mungbean

Sunflower

Cotton (Lint + seed – 40% turnout)*

Port Prices ($/t)

10-yr median

258

309

339

543

544

422

375

261

321

950

749

1267

3-yr average

254

287

317

518

831

419

364

255

325

1151

905

1243

Transportation costs ($/t)

40

40

40

40

40

40

40

40

40

40

40

40

Grading and bagging costs ($/t)

0

0

0

0

0

0

0

0

0

242

0

137

Farm Gate Prices ($/t)

10-yr median

218

269

299

503

504

382

335

221

281

667

709

1090

3-yr average

214

247

277

478

791

379

324

215

285

869

865

1066

* Cotton price calculated per tonne of bolls harvested assuming 40% is lint and 60% is seed

Commodity prices can be driven by the volatility of local and international demand and supply. Depending on the commodity, annual prices offered can be greatly different to the median price (Figure 1). These price ranges can be used to estimate the future possible range of prices. In figure 1, sorghum, wheat and maize had the lowest median price and lowest variance in price over the ten years. Therefore, even when the price is close to the quartiles (P=0.25 & 0.75 on the y-axis) the price is relatively unchanged (x-axis). Whereas chickpea, mungbean and sunflower median price is high and highly variable. For example, the 3-year average prices are 22-57% higher than the median price.

This line graph shows the probability distribution of annual average farm gate price of commodities (2008-2017) in the northern grain production region adjusted for inflation to 2017. The lowest annual price in this ten-year period is shown at P=0 on the y-axis and the highest price is at P=1. We used the 10-year median (P=0.5) prices as the expected price for our long-term economic analysis and compare this to the 3-year average price (2015-2017) (shown in red). Cotton price are given as $/bale (including lint and seed).Figure 1. The probability distribution of annual average farm gate price of commodities (2008-2017) in the northern grain production region adjusted for inflation to 2017. The lowest annual price in this ten-year period is shown at P=0 on the y-axis and the highest price is at P=1. We used the 10-year median (P=0.5) prices as the expected price for our long-term economic analysis and compare this to the 3-year average price (2015-2017) (shown in red). Cotton price are given as $/bale (including lint and seed).

Economic performance of farming systems

As would be expected the total income and gross margins varied substantially across all sites, owing to the difference in rainfall, and hence crop productivity, and input costs required (Table 2 and Table 3). While we have used a common approach and assumptions for calculating total income, costs and gross margin returns across all sites, care should be taken when comparing the economic performance between sites. There are large yield, income and cost differences incurred between sites, due to differences in environmental (climate & soil) conditions, starting nutrient levels and weed status, which greatly influence the gross margin outcome between sites. For this reason, we focus mainly on comparing the economic outcomes between systems at the same site.

The difference between the best and worst system gross margins per location

Within each experimental comparison there was a significant gap between the best and the worst cropping system (Table 2 and Table 3). The difference between the highest grossing and lowest grossing system over the 4.5 experimental years (in $/ha/yr) was $410 at Billa Billa, $359 at Emerald, $269 at Mungindi, $296 at Narrabri, $176 at Spring Ridge, $169 at Trangie on red soil and $232 on grey soil, $285 for the mixed opportunity systems at Pampas, $332 for summer rotation systems at Pampas, and $494 for winter rotation systems at Pampas.  The differences amongst rotations have declined over the past year due to the drought conditions limiting planting opportunities and hence total income has remained constant in most systems.

The best (or worst) system at each location was also not consistent. At most regional sites (except Emerald), the baseline cropping system designed to replicate current best management practice in a district performed the best or as well as any altered system. At Emerald, the high fertility systems performed the best, $118/ha/yr higher than the baseline. At Spring Ridge, the higher-legume system was the only system that resulted in higher economic returns of $60/ha/yr. If the lucerne crop had not been successful in the year of planting, then the baseline system would have been the best performing crop on grey soil.  Amongst the Pampas systems, the gross margin returns of the baseline systems was exceeded by systems with higher legume frequency or crop diversity by $9 and $31/ha/yr, respectively over the experimental period.

Across all comparisons, the systems that produced the lowest gross margins were those where cropping intensity was altered. Higher crop intensity achieved the lowest gross margin at Billa Billa, Emerald, Spring Ridge and lower crop intensity the lowest GM at Mungindi (grain), Narrabri (ignoring crop diversity), and Pampas (mixed opportunity and winter themes). What this means is that getting cropping intensity wrong for your environment is a major driver of suboptimal system performance.

At Trangie, the ley pasture system resulted in higher returns of $71 and $140/ha/yr than the baseline system for the experimental period for the red and grey soil, respectively. The success of this system was due to good establishment of a lucerne crop in the early wetter years of the experiment, which has survived over the experimental period with periodic harvests. Whereas other cropping systems could not establish crops due to poor soil moisture. Overall, this highlights that there is a significant difference in the profitability of farming systems within a particular situation.

System modification effects on economics

While there was significant variation in the relative performance of different system modifications across sites, there were several consistent impacts from some of the system modifications.

  • Higher nutrient strategy increased input costs significantly due to the higher fertiliser inputs to meet the crop nutrient budget that matched crop yield potential. Across all sites (except Emerald and Trangie red soil), this increased system costs and reduced total gross margins by $80-$610 per ha over the crop sequence (or $18-$136 /ha/yr). So far, we have seen few yield or economic responses to this higher nutrient supply approach (except Trangie – red soil and Emerald), so this reduced gross margins compared to the baseline, and resulted in lower return on costs at most sites.
  • Higher crop diversity has not significantly altered the costs of the production system, though there are some notable site differences (Table 2). The performance of the alternative crops at each location has been the central driver of how these systems have performed relative to the baseline. Across the regional sites gross margins were between $296 and $1334 less over the whole crop sequence ($66-296/ha/yr lower) than the baseline system. At Pampas diversifying the cropping system has consistently exceeded the returns of the baseline crop sequence by between $138 and $987/ha over the 4.5 years ($31-219/ha/yr higher).
  • Higher legume frequency systems have increased the variable costs of production in most cases, mainly due to higher costs for pesticides. While the Emerald and Spring Ridge sites there were marginally higher gross margins ($60-68/ha/yr) than the baseline, because of these higher costs they have a lower return on variable costs (ROVC) (Table 2 and Table 3).
  • Lower crop intensity systems generally incurred lower costs but this was not universal across all sites; 5 of the 8 lower intensity systems had lower costs than the baseline with the 3 sowing cotton having similar or slightly higher costs. Despite the more conservative approach of waiting until the soil profile was full to sow a crop, this did not necessarily increase the outlay required to run such a system. At most sites, the maximum cash outlay required in the low intensity system was similar to the baseline, and in some cases lower (e.g. Spring Ridge). It would be expected that lower intensity systems would have lower costs and therefore may have higher ROVC than the baseline system, but this was not the case for all regional sites apart from Spring Ridge and Trangie red soil. And it is expected under more favourable conditions the baseline system would have had higher ROVC than the low intensity system. At Pampas, only the summer lower intensity system offered high ROVC, but this was not driven by savings in costs but rather higher income.
  • Higher intensity systems did not increase total crop income at any of the sites and typically brought about an increase in costs, so that net returns were generally lower and the ROVC was dramatically lower. This highlights the risks associated with these systems. That is, over the relative dry run of years, these systems were working harder but not smarter than a more conservative cropping system. The high intensity system was up to $410/ha/yr behind the baseline system at Billa Billa, and even at the higher rainfall sites (Pampas, Spring Ridge, and Emerald) it was >100/ha/yr behind the baseline.

Table 2. Total revenue generated, costs of production (fertilisers, seed, operations, chemicals), gross margins (GM), the gap of a system to that the highest system GM per site, returns on variable costs (RVOC, ratio of income to costs), and the maximum cash outlay over the 4.5 years for each farming system tested at each of the 7 regional locations across the northern grains region.

Site

System

Total income ($/ha)

Total costs ($/ha)

Total GM ($/ha)

Gap from best ($/ha/yr)

ROVC

Max. cash outlay ($/ha)

Billa Billa

Baseline

3901

839

3062

0

4.7

-317

Higher nutrient

3872

1055

2817

-54

3.7

-326

Higher fertility

3590

1003

2587

-106

3.6

-321

Higher legume

3597

1017

2581

-107

3.5

-306

Crop diversity

3010

923

2087

-217

3.3

-352

Higher intensity

2360

1144

1217

-410

2.1

-513

Lower intensity

2305

852

1453

-358

2.7

-341

Emerald

Baseline

3787

1492

2295

-118

2.5

-417

Higher nutrient

4090

1534

2556

-60

2.7

-422

Higher fertility

4352

1528

2824

0

2.8

-417

Higher legume

4115

1512

2603

-49

2.7

-395

Higher intensity

2913

1706

1207

-359

1.7

-395

Integrated Weed man.

4031

1972

2059

-170

2.0

-532

Mungindi

Baseline

1590

643

947

0

2.5

-290

Higher nutrient

1504

909

595

-78

1.7

-313

Higher legume

1495

727

768

-40

2.1

-290

Crop diversity

669

537

132

-181

1.2

-351

Lower intensity (cotton)

1297

752

545

-89

1.7

-297

Lower intensity (grain)

375

638

-263

-269

0.6

-310

Narrabri

Baseline

2569

1023

1546

0

2.5

-354

Higher nutrient

2265

1329

936

-136

1.7

-486

Higher legume

2049

928

1121

-94

2.2

-354

Crop diversity

1439

1227

212

-296

1.2

-633

Higher intensity

2687

1177

1510

-8

2.3

-507

Lower intensity

1707

797

910

-141

2.1

-451

Spring Ridge

Baseline

3294

2166

1128

-60

1.5

-593

Higher nutrient

3363

2730

633

-170

1.2

-974

Higher legume

3403

2006

1398

0

1.7

-712

Crop diversity

2992

2160

832

-126

1.4

-593

Higher intensity

2563

1960

604

-176

1.3

-731

Lower intensity

2525

1480

1045

-78

1.7

-827

Trangie – red

Baseline

1845

1021

824

-16

1.8

-324

Higher nutrient

2337

1444

894

0

1.6

-426

Higher legume

1853

1049

804

-20

1.8

-363

Crop diversity

1431

1049

382

-114

1.4

-363

Lower intensity

1605

737

868

-6

2.2

-442

Trangie-grey

Baseline

1217

713

504

0

1.7

-251

Higher nutrient

963

873

91

-92

1.1

-380

Higher legume

1119

821

299

-46

1.4

-302

Crop diversity

953

816

137

-82

1.2

-302

Lower intensity

761

567

195

-69

1.3

-289

Table 3. Total revenue generated, costs of production (fertilisers, seed, operations, chemicals), gross margins (GM), the gap of a system to that the highest system GM per site, returns on variable costs (RVOC, ratio of income to costs), system WUE ($ gross margin/mm water use) and the maximum cash outlay achieved over 3.5 years for each farming system tested the core experimental site at Pampas across mixed opportunity, summer-dominated or winter-dominated cropping systems.

System modification

Total Income ($/ha)

Total Costs ($/ha)

Total GM ($/ha)

Gap from best ($/ha/yr)

ROVC

Max. cash outlay ($/ha)

Mixed opportunity

Baseline

4409

885

3524

-31

5.0

-326

Higher nutrient

4623

1223

3400

-58

3.8

-418

Higher legume

4678

1032

3647

-3

4.5

-358

Crop diversity

4665

1003

3662

0

4.7

-314

Crop div. + nutrient

4371

1394

2977

-152

3.1

-491

Higher leg. + diversity

4398

978

3420

-54

4.5

-346

Lower intensity

3382

1002

2380

-285

3.4

-632

Higher intensity

Baseline

4266

1218

3049

-9

3.5

-308

Higher nutrient

4358

1608

2750

-75

2.7

-358

Higher legume

4105

1332

2773

-70

3.1

-334

Crop diversity

4085

1081

3004

-19

3.8

-296

Crop div. + nutrient

3977

1665

2312

-172

2.4

-471

Higher leg. + diversity

4222

1134

3088

0

3.7

-328

Summer

Baseline

3196

724

2472

-261

4.4

-382

Higher nutrient

3329

938

2392

-278

3.6

-426

Higher legume

3073

921

2152

-332

3.3

-441

Crop diversity

4170

906

3264

-85

4.6

-578

Crop div. + nutrient

4197

1227

2970

-150

3.4

-650

Higher leg. + diversity

4206

1048

3158

-108

4.0

-593

Lower intensity

4351

705

3645

0

6.2

-317

Winter

Baseline

3775

863

2913

-219

4.4

-445

Higher nutrient

3570

1064

2506

-310

3.4

-479

Higher legume

4323

815

3508

-87

5.3

-237

Crop diversity

4598

698

3900

0

6.6

-237

Crop div. + nutrient

4252

1162

3090

-180

3.7

-430

Higher leg. + diversity

4420

739

3680

-49

6.0

-220

Lower intensity

2444

767

1678

-494

3.2

-441

Cross-site analysis of system profitability

While there are several interesting differences between different farming systems at each experimental location, here we examine across the full range of sites how modifications to the farming system that were common across several sites (i.e. higher nutrient, higher legumes, crop diversity, higher intensity, lower intensity) have influenced the economic performance compared to the baseline at each site. This was done by calculating the system total gross margins ($ GM/ha) and the return on variable costs (ROVC) ratio as a proportion of that achieved in the baseline (Figure 2). Hence, the baseline achieves a value of 1.0, and systems achieving 0.8 have a 20% lower value and systems achieving 1.2 have a 20% higher value for these economic metrics.

Across the various sites there are some variable and some consistent results in terms of the relative performance of the farming systems.

  • Higher nutrient supply achieved a lower system total gross margin most sites (7 of 10 comparisons), due to the higher costs associated with supplying nutrients to satisfy a 90th percentile crop yield rather than fertilising for the median yield. Only at Emerald and Trangie red-soil did we observe a positive yield response to additional nutrient supply and hence this is the only location where system gross margins increased. However, the return on investment was similar at 20-30% lower ROVC ratios. At Mungindi the additions of more nutrient reduced grain yield and crop income in one year and added significantly to the costs of this system.  We may expect this result under the challenging seasonal conditions we have experienced and with better seasonal conditions it might be expected to realise the benefits of such a strategy.
  • Increasing legume frequency achieved 20-40% lower total gross margins at Billa Billa, Mungindi, Narrabri, and Trangie red-soil, at other sites gross margins were either higher or similar to system total gross margins in the baseline system. At Pampas the winter-legumes achieved 20% higher and the summer 13% lower gross margins than the baseline system. However, interestingly all ROVC ratios were within ±20% of the baseline system.
  • Increase crop diversity resulted in 20-80% lower gross margins across all regional sites relative to the baseline system. However, at Pampas, diversity increased the summer and winter legume systems gross margins by 32%; the opportunity system was similar to the baseline.  Few sites had significant soil-borne disease issues at the initiation of the study and hence rotational benefits have not yet been observed. The exception was Pampas where there have been rotation benefits for subsequent crops. This demonstrates that there can be significant costs or risks associated with implementing alternative crops to address weed or pathogen issues.
  • Increased crop intensityhad significantly lower total gross margin at all sites relative to the baseline system, with 20-30% lower total gross margins at Pampas. These systems also have higher costs and hence the return on investment is typically lower.
  • Lower crop intensity systemshave achieved 40-70% lower system total gross margins over the 4.5 years at most locations. However, it also resulted in 47% higher gross margins in the summer system at Pampas and returns were similar to the baseline at the Spring Ridge site.

This column graph shows relative system profitability of different farming systems as a ratio of the baseline system (i.e. 1 equals the baseline, higher is better and lower is worse) at 7 regional sites and under 3 different seasonal crops at the Core site (Pampas). Gross margin as a proportion of the baselineThis column graph shows the relative system profitability of different farming systems as a ratio of the baseline system (i.e. 1 equals the baseline, higher is better and lower is worse) at 7 regional sites and under 3 different seasonal crops at the Core site (Pampas). The return on variable costs (ROVC) ratio relative to the baseline system. Figure 2. Relative system profitability of different farming systems as a ratio of the baseline system (i.e. 1 equals the baseline, higher is better and lower is worse) at 7 regional sites and under 3 different seasonal crops at the Core site (Pampas). Top shows the gross margin as a proportion of the baseline and the bottom the return on variable costs (ROVC) ratio relative to the baseline system.

Impact of commodity price variability

The previous section has been based on the 10-year median commodity prices; however, as indicated by figure 1 some commodity prices can be more volatile than others. Therefore, the possible range of total gross margins for each system will be affected by the combination of commodities it produces. There is little correlation between the prices received for the different commodities here, i.e. the price of wheat does not affect the price of chickpeas.

Figure 3 and figure 4 show the system total gross margins using different combinations of crop grain prices for each of the trial sites and production systems at each site. On these figures, the median (P=0.5) total gross margin values are shown with the black dot and are the same as those presented in the above tables – correlating to 10-year median commodity prices. The values furthest to the left are the lowest probable GM and furthest to the right are the higher GM. The lines show the full range of combinations using the range of grain prices over the past 10 years, and the red dot is the system gross margin using the average price over the past 3 years. For example, at Billa Billa with the 10-year median commodity price for the baseline system total gross margins were $3062/ha (Table 2) and this could be as low as $2490/ha (when all commodity prices of that system are low) and as high as $4092/ha (when all commodity prices are high). Based on the last 3-year average price the returns of the baseline system would have been $3393/ha. Comparing this point, there is a 73% chance of getting lower returns in the future; or 27% chance of higher compared to historical prices. Higher legume prices in recent year has resulted in the baseline and higher legume systems to have above average returns at Billa Billa. Whereas lower sorghum and wheat prices has resulted in the other systems having below average returns.

It is notable that based on total gross margins, the ranking of systems rarely changes when using both the 10-year median commodity price and the actual price over the last 3-years for Billa Billa, Emerald, Mungindi, Narrabri and Trangie red-soil (Figure 3). For Mungindi, even when the higher crop diversity system had high prices (P=0.8) it still did not do better than the lower intensity system with low prices (P=0.2). At Spring Ridge the 10-year median commodity price ranked higher crop diversity ($832/ha; P=0.5) above higher intensity ($604/ha; P=0.5); however, based on the last 3-year average price the higher intensity ($1045/ha; P=0.8) was greater than the higher diversity ($652/ha; P=0.35). The ranking of systems at Trangie red soil also changed slightly with the 3-year pricing, however the baseline, higher legume and lower intensity systems offer similar gross margins and price risk for P=0 to 1.0. This information provides greater understanding of the risk and relative profitability as affected by grain prices associated with different systems.

This collection of seven line graphs shows the distribution of total gross margins over 4.5 years calculated using the range of historical commodity prices for each farming system tested at regional locations across the NGR (Figure 1). The total gross margins with the lowest set of grain prices are shown where P=0 on and the highest combination of grain prices is shown where P=1. The median (P=0.5) total gross margins are the equivalent of our median price assumptions (shown in black dot), and the total gross margin using the 3-year average price (2015-2017) is in redFigure 3. The distribution of total gross margins over 4.5 years calculated using the range of historical commodity prices for each farming system tested at regional locations across the NGR (Figure 1). The total gross margins with the lowest set of grain prices are shown where P=0 on and the highest combination of grain prices is shown where P=1. The median (P=0.5) total gross margins are the equivalent of our median price assumptions (shown in black dot), and the total gross margin using the 3-year average price (2015-2017) is in red.

At Pampas, variability in commodity prices would create significant differences in relative profitability amongst the different farming systems. Under the mixed opportunity systems, the  higher crop diversity offers the highest expected outcome, and when all commodity prices are down (P=0.0) or high (P=1.0) it is still expected to outperform the other systems by offering higher total gross margins during the experimental period (Figure 4). Therefore, it had the highest returns and least risk of all the systems at that location with those 4.5 year climatic conditions. This was also the case for the winter-dominant cropping theme.

For the summer dominant system, 70% (P=0.7) of the time the lower intensity system benefited from better commodity prices; and 30% (1-0.7) of the time the higher diversity + legume system returned higher total gross margins due to favourable commodity prices. With the higher intensity theme, the median returns and variation of all cropping systems where similar - apart from higher legume. The latter had an 80% chance of offering lower total gross margins 80% of the time, with far lower returns with low prices (P=0.0) and even with high prices (P=1.0) they were only marginally better than the other cropping systems.

This collection of four line graphs shows the distribution of total gross margins over 4.5 years calculated using the range of historical commodity prices for each farming system tested at the core experimental site, Pampas (Figure 1). The total gross margins with the lowest set of grain prices are shown where P=0 on and the highest combination of grain prices is shown where P=1. The median (P=0.5) total gross margins are the equivalent of our median price assumptions (shown in black dot), and the total gross margin using the 3-year average price (2015-2017) is in red.Figure 4. The distribution of total gross margins over 4.5 years calculated using the range of historical commodity prices for each farming system tested at the core experimental site, Pampas (Figure 1). The total gross margins with the lowest set of grain prices are shown where P=0 on and the highest combination of grain prices is shown where P=1. The median (P=0.5) total gross margins are the equivalent of our median price assumptions (shown in black dot), and the total gross margin using the 3-year average price (2015-2017) is in red.

Acknowledgements

The research undertaken as part of this project (CSA00050, DAQ00192) is made possible by the significant contributions of growers through both trial cooperation and the support of the GRDC, the author would like to thank them for their continued support. We would also specifically like to thank all the farm and field staff contributing to the implementation and management of these experiments, the trial co-operators and host farmers.

Contact details

Andrew Zull
Qld. DAF
PO Box 102, Toowoomba Qld, 4350
Ph: 0417 126 941
Email: andrew.zull@daf.qld.gov.au

Lindsay Bell
CSIRO
PO Box 102, Toowoomba Qld, 4350
Ph: 0409 881 988
Email: Lindsay.Bell@csiro.au

GRDC Project code: DAQ00192, CSA00050