Economic performance and system water-use-efficiency of farming systems

Take home messages

  • Large gaps in profitability are possible between the best and worst systems – differences of $200-700 per year were found between systems at each site
  • Intensity is the major factor driving good/poor economic performance of the farming system - more so than crop choice. Matching intensity to environmental potential seems to be the most important lever to optimise farming system profitability
  • Increasing crop intensity increased costs and risks, and either reduced or equalled the system water use efficiency (WUE) of baseline systems across all sites over the experimental period
  • Lower crop intensity had lower system WUE and gross returns, but because of lower inputs and costs may achieve a more favourable return on investment at lower risk. These systems had similar profitability under lower rainfall conditions but were suboptimal in more favourable environments
  • Increasing legume frequency can achieve similar profitability and system WUE, especially if nutrient balance differences were considered, but often had higher production costs
  • Increasing crop diversity and growing alternative crops as a means of managing diseases or weeds had significant costs at many sites, but in some locations was able to increase or equal system profitability. These systems were more favourable at locations with more available rainfall
  • Increasing nutrient supply incurred higher costs and hence, rarely increased system profitability, but if costs of system nutrient balance systems were attributed (i.e. nutrient export – inputs), similar or higher system WUE ($/mm water use) were achieved.
  • We found that a system water use efficiency of $2.50 of crop income/mm of rainfall over the cropping sequence is achievable and could be used to benchmark current farming systems.

Introduction

Leading farmers in Australia’s northern grains region 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. Analysis suggests that fewer than one third of crop sequences achieve more than 80% of their potential water use efficiency despite having adequate nitrogen fertiliser inputs (Hochman et al. 2014). The key factors appear not to be related to in-crop agronomy but to the impact of crop rotations and are thought 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:

  • Maximise capture and utilisation of rainfall particularly when using high-value, low-residue 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.

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, quantifies synergies or trade-offs and shows how these interventions impact on whole-of-system productivity, risk, economic performance and sustainability of farming systems.

As a result, research was initiated in 2014 to identify the key limitations, consequences and economic drivers of farming systems in the northern region; to assess farming systems and crop sequences that can meet the emerging challenges; and test the impacts of modifications of the farming system on multiple attributes (e.g. nutrients, water, pathogens, soil health, and economics) across multiple sites.  Experiments were established at seven locations; a large factorial experiment at Pampas near Toowoomba, and 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 the profitability and efficiency of the farming system is critical. This paper examines the economic performance of different modifications that we have tested. This will help quantify the costs or benefits of changing the farming system to deal with a particular issue (e.g. weeds or disease issue), and the trade-offs for the different cropping intensities and nutrient strategies.

In this research we used the key metric of “system water use efficiency” to compare system productivity or profitability per mm of rain across environments and cropping systems. Most agronomists and farmers would be familiar with the concept of crop water use efficiency (i.e. kg grain yield/mm crop water use) for comparing how efficiently crops under different management or environments perform. However, for comparing the cropping system as a whole across multiple years with different crops, a different approach is required. This also needs to account for both rainfall capture and loss during the fallow over a sequence of crops, the differences in the inputs required, as well as the productivity of different crops which may be influenced both positively, or negatively, by previous crops in the sequence or rotation. Hence, in the farming systems project we have been evaluating the system WUE as the $ gross margin return per mm of system water use (i.e. rain minus the change in soil water content) over the period of interest.

A formula stating that system WUE equals the sum of yield multiplied by price less the variable costs divided by the sum of rainfall and the change in soil water.

System modifications

Across these projects a common set of farming system strategies were used to examine how changes in the farming system aimed at addressing particular challenges impact on multiple aspects of the farming system. These different farming system strategies are not predetermined and hence play out differently in different locations, based on the environmental (climate & soil) conditions at that location.  Below we outline the common set of farming system modifications employed across the farming systems experimental sites over the past 3.5 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 system modifications being implemented. These include higher fertility treatments 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. At Emerald, a system aimed at implementing an integrated weed management package is included.  This tests the implications of using 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. At Mungindi, two low intensity systems have been implemented, 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. These are also being tested across rotations spanning those employed across the northern region, either winter-crop focussed, mainly summer crops, or a mix of both which is driven entirely by soil water.

Economic calculations

Over the 3.5 experimental years of experiments conducted for each system we have collected data on the grain yields of crops, the total inputs of fertilisers, seed, herbicides and other pesticides, and operations. This allows us to calculate the cash-flow, accumulated income (sum of grain yield x price for all crops in the sequence) and gross margins (income minus costs) for each of the cropping systems deployed at each location (Table 4 and 5). We have used consistent prices for each commodity and inputs across all locations to avoid introducing discrepancies in the data (Table 1). All grain yields were corrected to 12% moisture irrespective of harvest moisture levels. Grain commodity prices used were based on inflation corrected average grain prices for each crop over the past 10 years.

Table 1. Commodity prices (10-year average) for each crop grown across the farming systems experiments

Crop

$/t grain#

Barley

218

Wheat (durum & APH)

269

Canola

503

Chickpea

504

Fababean

382

Fieldpea

350

Sorghum

221

Maize

281

Mungbean

667

Sunflower

700

Cotton

1090 ($480/bale lint)

#farm gate price with grading & additional harvesting costs already deducted

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. It should be noted we have not attempted to correct for 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.

Cropping sequence deployed

Tables 2 (core site) and 3 (regional sites) show the diversity and differences in crop sequences that have been deployed across the various farming systems at each experimental location over the first 3.5 years of the farming systems experiments.  These tables are intended as a guide for interpreting the subsequent analysis of profitability across these various systems, and what differences in crop sequence are associated with those. This is also relevant for subsequent papers in this series presenting results on the nutrient use and balance and soil water dynamics across the various farming systems.

Table 2. Summary of crop sequences deployed across the 3.5 years of the experiment (winter – WIN, summer – SUM, year season started) across the various farming systems at the core site at Pampas. Crop abbreviations: W – wheat, Cp – Chickpea, Fb – Fababean, Fp – Fieldpea, Cn – Canola, Dm – Durum wheat, Mg – Mungbean, Sg – Sorghum, Mz – Maize, Ct – Cotton, Sf – Sunflower, (m) – millet cover crop

System

Win15

Sum15

Win16

Sum16

Win17

Sum17

Win18

Sum18

Mixed Opportunity

Baseline

W

x

x

Sg

Cp

x

x

Sg

Higher nutrient

W

x

x

Sg

Cp

x

x

Sg

Higher legume

Fb

x

x

Sg

Cp

x

x

Sg

Crop diversity

Cn

x

x

Sg

Cp

x

x

Ct

Crop div. + nutrient

Cn

x

x

Sg

Cp

x

x

Ct

Higher leg. + diversity

Fp

x

x

Sg

Cp

x

x

Ct

Lower intensity

W

x

x

Ct

W

x

x

x

Higher intensity

Baseline

W

Mg

x

Sg

Cp

Sg

x

Sg

Higher nutrient

W

Mg

x

Sg

Cp

Sg

x

Sg

Higher legume

Fb

Mg

x

Sg

Cp

Sg

x

Mg

Crop diversity

Cn

Mg

x

Sg

Dw

Sf

x

Sg

Crop div. + nutrient

Cn

Mg

x

Sg

Cp

Sf

x

Sg

Higher leg. + diversity

Fp

Mg

x

Sg

Cp

Sf

x

Mg

Summer

Baseline

W

x

x

Mz

x

Sg

x

x

Higher nutrient

W

x

x

Mz

x

Sg

x

x

Higher legume

Fb

x

x

Mz

x

Mg

x

x

Crop diversity

W

x

x

Ct

x

Sg

x

x

Crop div. + nutrient

W

x

x

Ct

x

Sg

x

x

Higher leg. + diversity

Fb

x

x

Ct

x

Mg

x

x

Lower intensity

x

Mz

x

Mg

x

x

x

Ct

Winter

Baseline

W

x

Cp

x

W

x

x#

x

Higher nutrient

W

x

Cp

x

W

x

x

x

Higher legume

Fb

x

W

x

Cp

x

x

x

Crop diversity

Cn

x

Dm

x

Cp

x

x

x

Crop div. + nutrient

Cn

x

Dm

x

Cp

x

x

x

Higher leg. + diversity

Fb

x

Dm

x

Fp

x

x

x

Lower intensity

W

x

x

x

Cp

(m)

x

x

# no sowing opportunities occurred within the acceptable window in this season

Table 3. Summary of crop sequences deployed across the 3.5 years of the experiment (winter – WIN, summer – SUM, year season started) across all regional sites for the different farming systems. Crop abbreviations: W – wheat, B – Barley, Cp – Chickpea, Fb – Fababean, Fp – Fieldpea, Cn – Canola, Dm – Durum wheat, Mg – Mungbean, Sg – Sorghum, Ct – Cotton, Sf – Sunflower, (lower case) indicates terminated crop.

Site

System

Win15

Sum15

Win16

Sum16

Win17

Sum17

Win18

Billa Billa

Baseline

W

x

B

x

W

x

Cp

Higher nutrient

W

x

B

x

W

x

Cp

Higher fertility

W

x

B

x

W

x

Cp

Higher legume

W

x

Fb

Mg

x

Sg

Cp

Crop diversity

W

x

Fp

Sg

x

x

Cn

Higher intensity

W

Mg

x

Sg

W

Sg

x

Lower intensity

W

x

x

Sg

x

x

W

Emerald

Baseline

W

x

Cp

x

W

Sg

x

Higher nutrient

W

x

Cp

x

W

Sg

x

Higher fertility

W

x

Cp

x

W

Sg

x

Higher legume

Cp

x

W

x

Cp

Sg

x

Higher intensity

W

Mg

W

x

W

Sg

x

IWM

W

x

Cp

x

W

Sg

x

Mungindi

Baseline

W

x

Cp

x

(w)

x

W

Higher nutrient

W

x

Cp

x

(w)

x

W

Higher legume

W

x

Cp

x

(w)

x

Cp

Crop diversity

x

Sf

x

Sg

x

x

Dm

Lower intensity (cotton)

W

x

x

Ct

x

x

W

Lower intensity (grain)

x

Sg

x

x

(w)

x

Cp

Narrabri

Baseline

W

x

Cp

x

B

x

x

Higher nutrient

W

x

Cp

x

B

x

x

Higher legume

W

x

Fb

x

W

x

x

Crop diversity

W

x

Fp

x

Cn

x

x

Higher intensity

W

x

Cn

x

W

x

x

Lower intensity

W

x

x

Ct

(b)

x

x

Spring Ridge

Baseline

W

x

Cp

x

W

x

x

Higher nutrient

W

x

Cp

x

W

x

x

Higher legume

W

x

Fb

x

W

x

x

Crop diversity

W

x

Fp

x

W

x

x

Higher intensity

W

x

x

Sg

Cp

x

x

Lower intensity

W

x

x

x

x

Ct

x

Trangie

Baseline

W

x

W

x

B

Higher nutrient

  

W

x

W

x

B

Higher legume

  

W

x

Cp

x

W

Crop diversity

  

W

x

Cp

x

Fp

Lower intensity

  

W

x

x

x

B

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 (Tables 4 & 5). 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 cost differences incurred between sites, due to differences in starting nutrient levels and weed status, which greatly influence the GM outcome between sites. For this reason, we focus mainly on comparing the economic outcomes between systems at the same site.

Best and worst system gross margins

Within each experimental comparison there was a significant gap between the best and the worst cropping system (Table 4 & 5). The difference between the highest grossing and lowest grossing system over the 3.5 experimental years (in $/ha/yr) was $550 at Billabilla, $304 at Emerald, $214 at Mungindi, $434 at Narrabri, $210 at Spring Ridge, $329 for the mixed opportunity systems at Pampas, $348 for summer rotation systems at Pampas, and $766 for winter rotation systems at Pampas.  Overall, this highlights that there is a significant difference in the profitability of farming systems within a particular situation.

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 legume and High fertility systems performed the best, $150/ha/yr. higher than the baseline. Amongst the Pampas systems, the gross margin returns of the baseline systems was exceeded by systems with higher crop diversity or high legume frequency by $120-$380 per year 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 Billabilla, Emerald, Spring Ridge and lower crop intensity the lowest GM at Narrabri, Pampas and Mungindi. What this means is that getting cropping intensity wrong for your environment is a major driver of suboptimal system performance.

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, this increased system costs by $150-$300 per ha over the crop sequence (or $50-$100 per year). So far we have seen few yield or economic responses to this higher nutrient supply approach (except Emerald), so this reduced gross margins compared to the baseline, and resulted in lower return on costs at most sites.
  • Higher crop diversityhas 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 $223 and $1430 less over the whole crop sequence ($64-$400 per year lower). At Pampas diversifying the cropping system has consistently exceeded the returns of the baseline crop sequence by between $372 and $1180 of the 3.5 years ($106-$340/year higher).
  • Higher legume frequency systems have increased the variable costs of production in most cases, mainly due to higher costs for pesticides. While in several locations these systems achieved similar or higher GM to the baseline, because of these higher costs they have a lower return on costs in most cases.
  • 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).
  • Higher intensitysystems did not increase total crop income at any of the regional sites as expected and typically brought about an increase in costs, so that net returns were generally lower and the return on costs was dramatically lower. This highlights the risks associated with these systems. At Pampas, there was an increase in total crop income from increasing crop intensity of $500-$900 over the experimental period ($140-$300), but costs also increased which diminished the benefit to GM to less than $150/ha/yr.

Benchmarks for system WUE

The data generated here could provide a useful benchmark for farmers and advisers to compare their own current production system performance against.  As mentioned above, the costs of production are likely to vary significantly across different situations, based on soil nutrient status, weed burdens, and operating costs.  For this reason, examining potential total income per mm may be helpful to assess system productivity.  Across all sites and systems, the maximum achieved income was $3.0 /mm, but a benchmark of $2.50/mm would be an achievable target at most locations (i.e. 80% of the potential).

Table 4. Total revenue generated, costs of production (fertilisers, seed, operations, chemicals), gross margins (GM), returns on variable costs (RVOC, ratio of income to costs), system WUE ($ gross margin/mm water use) and the maximum cash outlay achieved over the 3.5 years for each farming system tested at each of the 5 regional locations across the northern grains region.

Site

System

Total income ($/ha)

Total costs ($/ha)

Total GM ($/ha)

ROVC

Syst. WUE ($ GM/mm)

Max. cash outlay ($/ha)

Billa Billa

Baseline

3946

672

3274

5.9

2.26

-284

Higher nutrient

3942

878

3065

4.5

2.16

-293

Higher fertility

3579

826

2753

4.3

1.91

-289

Higher legume

3606

853

2753

4.2

2.08

-306

Crop diversity

3176

758

2419

4.2

1.83

-257

Higher intensity

2288

973

1315

2.4

0.93

-513

Lower intensity

2287

597

1690

3.8

1.29

-298

Emerald

Baseline

3013

1341

1673

2.3

0.91

-449

Higher nutrient

3278

1383

1895

2.4

1.06

-454

Higher fertility

3537

1373

2164

2.6

1.20

-449

Higher legume

3409

1201

2209

2.8

1.20

-352

Higher intensity

2549

1404

1146

1.8

0.64

-365

Integrated Weed management

3307

1360

1947

2.4

1.08

-449

Mungindi

Baseline

1581

573

1008

2.8

0.89

-271

Higher nutrient

1496

840

657

1.8

0.58

-297

Higher legume

1487

654

833

2.3

0.75

-271

Crop diversity

634

378

256

1.7

0.23

-274

Lower intensity (cotton)

1287

680

607

1.9

0.54

-286

Lower intensity (grain)

371

366

5

1.0

0.00

-266

Narrabri

Baseline

3260

780

2480

4.2

1.36

-307

Higher nutrient

3263

916

2348

3.6

1.29

-354

Higher legume

2902

718

2184

4.0

1.19

-286

Crop diversity

1959

910

1049

2.2

0.58

-431

Higher intensity

3304

878

2427

3.8

1.34

-381

Lower intensity

1740

778

962

2.2

0.61

-395

Spring Ridge

Baseline

3248

1381

1867

2.4

1.56

-840

Higher nutrient

3083

1449

1634

2.1

1.37

-840

Higher legume

3388

1512

1875

2.2

1.54

-971

Crop diversity

3041

1396

1644

2.2

1.38

-855

Higher intensity

2531

910

1621

2.8

1.36

-431

Lower intensity

3130

773

2357

4.0

1.58

-578

Table 5. Total revenue generated, costs of production (fertilisers, seed, operations, chemicals), gross margins (GM), 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)

ROVC

Syst. WUE ($ GM/mm)

Max. cash outlay ($/ha)

Mixed opportunity

Baseline

3466

769

2697

4.51

1.51

-263

Higher nutrient

3555

1106

2448

3.21

1.36

-355

Higher legume

3976

919

3057

4.33

1.71

-296

Crop diversity

3736

667

3069

5.60

1.73

-252

Crop div. + nutrient

3699

1058

2641

3.50

1.49

-426

Higher leg. + diversity

3153

723

2430

4.36

1.36

-283

Lower intensity

3753

953

2800

3.94

1.65

-516

Higher intensity

Baseline

3940

994

2945

3.96

1.66

-255

Higher nutrient

3889

1385

2504

2.81

1.39

-313

Higher legume

4208

1139

3070

3.70

1.72

-289

Crop diversity

4411

828

3583

5.33

2.04

-221

Crop div. + nutrient

4268

1411

2857

3.02

1.64

-406

Higher leg. + diversity

3994

977

3017

4.09

1.69

-262

Summer

Baseline

3167

649

2518

4.88

1.44

-366

Higher nutrient

3386

863

2522

3.92

1.46

-410

Higher legume

3725

883

2842

4.22

1.60

-422

Crop diversity

4330

831

3499

5.21

2.02

-477

Crop div. + nutrient

4664

1153

3511

4.05

2.03

-549

Higher leg. + diversity

4818

1010

3807

4.77

2.19

-489

Lower intensity

2835

471

2364

6.02

1.37

-228

Winter

Baseline

3775

698

3077

5.41

1.90

-312

Higher nutrient

3746

884

2862

4.24

1.77

-330

Higher legume

4667

741

3926

6.29

2.28

-237

Crop diversity

4807

549

4257

8.75

2.44

-237

Crop div. + nutrient

4295

1020

3275

4.21

2.01

-430

Higher leg. + diversity

4580

664

3915

6.89

2.22

-220

Lower intensity

2444

601

1844

4.07

1.07

-411

System WUE adjusted for nutrient balance

One of the complications with comparisons across the various sites here is that there were significant differences in starting soil N status which greatly influenced the need for fertiliser inputs and hence costs at those sites. For example, at Billabilla there was a large amount of mineral N at the start of the experiment (> 300 kg N/ha), and hence for the first 3 years no N fertilisers were needed to satisfy crop nutrient budgets. Hence, this site had significantly lower N fertiliser costs which arbitrarily biases the system WUE (GM$/mm). Similarly, accounting for differences in system nutrient export or balance will help to better define the real cost of the farming system. In an attempt to adjust for these differences, in Table 6 we have adjusted the system WUE to take into consideration the different nutrient balances across sites and some systems. This reduces the system WUE (GM$/mm) of sites which have exploited a high soil mineral N and adjusts for differences in P application relative to P export across sites.

What this shows is that across sites the differences in system WUE (GM$/mm) between the baseline and higher nutrient or higher legume systems is diminished once these factors are considered. Hence, taking into consideration the impacts of the farming system on the natural resources (in this case nutrients) can significantly alter the relative profitability of different farming systems over the long-term. This clearly shows that if the costs of nutrients exported from the farming system are accounted for, and not treated as an externality, it demonstrates the value of systems aimed at maintaining long-term soil fertility.

Table 6. Gross margin return per mm under baseline, and systems with higher nutrient, higher legume frequency and higher intensity when corrected for site and system differences in nutrient balance (i.e. change in soil mineral N, net P balance (export – applied) and K removal). Nutrients were valued at $1.3/kg N, $2.5/kg P, $0.9/kg K.

Site

Baseline

High nutrient

High legume

High intensity

Pampas - Opportunity

1.28

1.16

1.45

1.40

Pampas - Summer

1.18

1.25

1.38

Pampas - Winter

1.61

1.52

1.99

Billa Billa

1.98

1.99

1.85

0.70

Emerald

0.84

1.01

1.12

0.62

Narrabri

1.31

1.27

1.12

 

Spring Ridge

1.50

1.36

1.45

 

Mungindi

0.89

0.62

0.72

 

Cross-site analysis of system WUE

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 WUE ($ GM/mm) and the return on investment (i.e. income:cost ratio) as a proportion of that achieved in the baseline. 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 10% lower system WUE (GM$/mm) at most sites, due to the higher costs associated with supplying nutrients to satisfy a 90th percentile crop yield rather than fertilising for the median yield. Only at Emerald did we observe a positive yield response to additional nutrient supply and hence this is the only location where system WUE was increased – though return on investment was similar. At Mungindi the additions of more nutrient reduced grain yield and hence income in one year and added significantly to the costs of this system.  We may expect this result with only good seasonal conditions expected to realise the benefits of such a strategy.
  • Increasing legume frequencyachieved either higher or similar system WUE (GM$/mm) to the baseline across most sites. However, interestingly the return-on-investment for these systems was lower in most cases owing to higher costs for growing legumes.
  • Increasing crop diversitywas either equally or more profitable than the baseline system at Spring Ridge and Pampas across all crop rotation systems (summer, winter and opportunity). However, at all other locations system WUE (GM$/mm) was reduced by 20-70% through implementing more diverse crop rotations. Few sites had significant soil-borne disease issues at the initiation of the study and hence rotational benefits have not yet been observed. The exception was Pampas where there have been rotation benefits for subsequent crops. This demonstrates that there can be significant costs or risks associated with implementing alternative crops to address weed or pathogen issues.
  • Increased crop intensityhas only achieved a slightly higher systems WUE (GM$/mm) at Pampas while at most locations there has been significant downsides. These systems also have higher costs and hence the return on investment is typically lower.
  • Lower crop intensity systemshave also achieved lower system WUE (GM$/mm) at most locations, and lower than most other system modifications. The exceptions are where a sufficiently high value crop has been grown (e.g. cotton) that has offset the longer fallows required. However, because of the lower inputs and costs associated with these systems they achieve much more favourable return on investment often equal to other system modifications.

This is two column graphs showing the relative system water use efficiency (i.e. $ GM/mm) of modifying farming systems compared to the baseline at 5 regional sites and under 3 different seasonal crops at the Core site (Pampas). Across the various sites there are some variable and some consistent results in terms of the relative performance of the farming systems.

Figure 1. Relative system water use efficiency (i.e. $ GM/mm) of modifying farming systems compared to the baseline at 5 regional sites and under 3 different seasonal crops at the Core site (Pampas).

In order to explore any environmental response of economic performance of the different farming systems, in Figure 2 we plot across all experimental sites the relationships between total system water use and economic returns over the experimental period. These plots demonstrate several important findings.

Firstly, as expected the revenue or income generated increased as the amount of water available increased. That is, the locations that received the lowest rainfall over the 3.5 years of the experiment (Mungindi and Spring Ridge) had lower total income and lower sequence GM. However, this relationship did not continue to increase as the amount of rainfall increased, reaching a maximum at around 1500-1600 mm. This may suggest that the systems that used more rainfall than this failed to convert the additional rainfall effectively into higher incomes or gross returns. Secondly, it can be seen that different farming systems responded differently for their return per mm of water use across the various environments.

  • The low intensity systems had a very flat relationship – they achieved similar income and system GM (GM$/mm) to the other systems under dryer conditions, but as the amount of water available increased they fell below the other systems. This indicates that these system are favourable under lower rainfall situations, but less so under more favourable conditions.
  • Figure 6 shows that systems with increased crop diversity and higher legume frequency had an advantage over the baseline in locations that had more available rainfall. This is shown by the triangles (crop diversity) and squares (higher legume) exceeding the filled circles (baseline) consistently at locations that used > 1600 mm of rainfall over the experimental period. This suggests that there is likely to be greater benefit from employing these system modifications in more favourable conditions than in lower rainfall environments, where risks for alternative break crops or legumes are higher.

This is two line/scatter graphs showing relationships across sites between total system water use (rain - change in soil water) and sequence, total income and sequence total income ($/ha) (top), and sequence gross margin GM ($/ha) (bottom) over 3.5 years between different farming systems modifications – baseline (black circles), increasing crop diversity (grey triangles), increasing legume frequency (squares) and low intensity (hollow circles). Firstly, as expected the revenue or income generated increased as the amount of water available increased. That is, the locations that received the lowest rainfall over the 3.5 years of the experiment (Mungindi and Spring Ridge) had lower total income and lower sequence GM. However, this relationship did not continue to increase as the amount of rainfall increased, reaching a maximum at around 1500-1600 mm. This may suggest that the systems that used more rainfall than this failed to convert the additional rainfall effectively into higher incomes or gross returns. Secondly, it can be seen that different farming systems responded differently for their return per mm of water use across the various environments.

Figure 2. Relationships across sites between total system water use (rain - change in soil water) and sequence, total income and sequence total income ($/ha) (top), and sequence gross margin GM ($/ha) (bottom) over 3.5 years between different farming systems modifications – baseline (black circles), increasing crop diversity (grey triangles), increasing legume frequency (squares) and low intensity (hollow circles).

Conclusions

The economic performance of the farming system integrates many of the various factors that may influence their short and long-term productivity (water use efficiency, nutrient inputs and balance, yield responses to crop rotation). Across all farming systems sites, several of the modified farming systems could achieve similar or even greater profits, however this was not consistent across all sites. That is, in many cases there are options to address particular challenges (e.g. soil-borne diseases or weeds, nutrient rundown) that can be profitable. However, in some locations the options seem much more limited, particularly where risky climatic conditions (or challenging soils) limit the reliability of alternative crops in the farming system. The results here provide a snapshot in time over only a 3.5 year period. The longer term impacts of some of these farming systems strategies may yet to be fully realised and hence, some consideration of these results against this longer-term view is also required.

References

Hochman Z, Prestwidge D and Carberry PS (2014). Crop sequences in Australia’s northern grain zone are less agronomically efficient than the sum of their parts. Agricultural Systems 129, 124-132.

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

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

GRDC code: DAQ00192, CSA00050

GRDC Project code: DAQ00192, CSA00050