Yield gaps - how much yield potential is left behind by better growers on the Central West and why? Identifying areas to capture lost yield and profit

Take home messages

  • Average wheat yields between 2000 and 2014 for the central west (1.4 t/ha) are 2.2 t/ha below the water-limited yield potential (Yw) for dryland wheat. On average, this is costing growers $550/ha
  • A national survey of 232 growers found a median yield gap of 36% for wheat grown in 2016. There was a 49% difference between average yield gaps of the top half compared with the bottom half of those surveyed.
  • The national average N fertiliser rate of 45 kg N/ha restricts yield to 60% of Yw. For Nyngan this rate restricts yield to 88% of Yw and for Condobolin to 72%. Failure to control weeds during the summer fallow could restrict yield to 74% of Yw nationally, 60% of Yw in Nyngan and 84% in Condobolin. A two week delay in sowing wheat restricts yields nationally to 93% of Yw, 92% of Yw in Nyngan and 82% in Condobolin.
  • An emerging practice of sowing early (before 26 April) with a late maturing variety and flexible N fertiliser application is expected to have the potential to increase the yield frontier by 30% where this can be implemented.
  • This emerging best practice has the potential to increase financial returns in the central west from about $760/ha to about $1,650/ha. This emerging practice has been proven in many sites south of Dubbo and should be further investigated in local fields by growers, consultants and researchers.

Introduction

It is well known that Australia’s growers are among the best in the world. So why is the yield gap in the central west subregion 61% of the yield potential? Between 2000 and 2014 average annual wheat yield (Ya) was 1.4 t/ha while the water–limited yield potential (Yw) was 3.5 t/ha. This means that there is a potential yield gap of 2.2 t/ha or $550 per ha (@250 $/t) that is not being realised (www.yieldgapaustralia.com.au).

We ask why such a substantial yield gap exists and why some growers achieve their yield potential while others do not. We examined this in three different ways:

  1. A grower survey that investigated how farms with large yield gaps differ from farms with low yield gaps by relating yield gaps to grower characteristics, farm characteristics and farm management practices.
  2. A simulation study that examined the impact of sub-optimal management practices at 50 weather stations spanning the Australian grain zone.
  3. An economic (risk-adjusted profit) analysis that explored the results from the simulation study.

Grower survey

The survey aimed to comprehensively examine farm management practices as well as farm and farmer characteristics that may contribute to the wheat yield gaps in Australia. Using the GRDC customer relation database we conducted telephone interviews of 232 wheat producers from 14 contrasting local areas (SA2s; roughly equivalent to a shire) in the Australian grain zone (Figure 1).

Figure 1 is a map of Australia showing locations of surveyed local statistical areas with contrasting average relative yields. Figure 1. Locations of surveyed local statistical areas (SA2s) with contrasting average relative yields. The relative yield of wheat (% of water-limited yield potential) is indicated by the red-yellow colour gradient. The white borders shows the GRDC sub-regions of the Australian grain zone

The average participants’ age was 51 years old (SD = 11, ranging from 20 to 89 years in age), with an average of 31 years (SD = 13) of experience in growing crops. Among the participants, there were only 10 female producers (4%). Seventeen participants (7%) identified as corporate farms while the rest identified as family farms. Thirty three participants (14%) owned or managed other farms in locations more than 50 km apart. The average cropping land area was 2,149 hectares (SD = 2,073). The total area cropped by participants was 0.5 million hectares, or about 2% of Australia’s cropped area.

Each farm’s yield gap was calculated by comparing their reported wheat yield in 2016 against the calculated water-limited yield potential, simulated under best management practices for their three dominant soil types, using weather data from all stations in their postcode. All farms were ranked according to their relative yields (Y% = 100 X Ya/Yw). The median relative yield was 64% and this value was used as a cut-off for dividing the respondents into two equal sized groups: the high relative yield (=small yield gap) group (mean Y% = 96%; SD = 20%) and the low relative yield (= large yield gap) group (mean Y% = 47%; SD = 12%). Hence, an average yield gap of 49% exists between these two groups. All survey responses were analysed to determine if there were significant differences in how the high and low relative yield groups responded.

The results revealed significant differences between farms with smaller yield gaps and those with greater yield gaps in relation to farming management, farm characteristics, and grower characteristics. Australian farms with smaller yield gaps (high relative yield) are more likely to be smaller holdings (high relative yield: Mean = 1886 ha, SD = 1993 vs low relative yield: Mean = 2395 ha, SD = 2127; p = .061), growing less wheat (high relative yield: Mean = 743 ha, SD = 880 vs low relative yield: M = 1171 ha, SD = 1111.2; p = .001) on more favourable soil types. These growers are more likely to apply considerably more N fertiliser to their wheat crop (Table 1), to grow a greater variety of crops, to soil-test a greater proportion of their fields, to have less area affected by herbicide-resistant weeds, and to be early adopters of new technology. They are less likely to grow wheat following either cereal crops or a pasture (Table 1). They are more likely to use and trust a fee-for-service agronomist, and to have a university education.

Table 1. Preceding crops before wheat crop and average nitrogen applied

 

% of farms

Nitrogen application

 

High relative yield group
(%)

Low relative yield group
(%)

High relative yield group
M (SD)
(kg N/ha)

Low relative yield group
M (SD)
(kg N/ha)

A cereal crop

37***

65***

79 (51)*

57 (42)*

A canola crop

44

48

116 (146)**

58 (45)**

A pulse crop

62

53

75 (61)***

42 (34)***

A pasture phase

22***

44***

64 (58)**

30 (33)**

Note. The asterisk symbol indicates the statistical significance level of the differences between high and low relative yield groups, * p < .05, ** p < .01, *** p < .001.

Simulation study

We conducted a simulation study on the impact of sub-optimal management practices at 50 weather stations that span the whole grain zone (Figure 2). A benchmark “best management practice” was defined by: zero tillage with clean fallows and stubble retained; a non-limiting supply of nitrogen to the crop; sowing at 150 plants/m² was activated between 26 April and 15 July with 30 mm of plant available water (PAW) and a 15 mm cumulative rain event occurred over any 3 consecutive days. Table 2 shows the average national impact, relative to water-limited yield potential (Yw), of selected sub-optimal management practices.

Nationally, the average rate of N applied to grain crops is 45 kg N/ha (Angus and Grace, 2017). This one practice is sufficient to account for a 40% yield gap. Even at double that rate, a 23% yield gap remains. Frost and heat stress accounted for yield losses of between 16% and 25% of Yw depending on the function used (two versions of the Bell et al., 2015 function were used due to uncertainty about the function’s parameters). Failure to control weeds during the summer fallow could account for up to a 26% yield loss; delayed sowing accounted for a 7% yield loss and low seedling density for an 8% yield loss. Any grower who is still practising conventional tillage could be missing out on 33% of their yield potential. Other factors that contribute to the yield gap, not included in simulations, include biotic stresses such as plant diseases, insects and other pests, in-crop weeds and extreme weather events (e.g. floods, strong winds and hail).

Figure 2 is a map of Australian grains regions showing fifty high quality weather stations and their distribution in Australia’s cropping zone Figure 2. Fifty high quality weather stations and their distribution in Australia’s cropping zone

Table 2. Impacts of management factors (treatments 2-7) and of frost and heat stress (treatments 9 & 10) on
water-limited yield potential (Yw).

No

Treatment

Australian Grain Zone

Nyngan

Condobolin

Mean (t/ha)

SD (t/ha)

Y% (%)

Mean (t/ha)

Y% (%)

Mean (t/ha)

Y% (%)

1

Yw (water-limited yield)

4.28

0.92

100

2.78

100

3.44

100

2

Seedling density (50 plants/m2)

3.78

1.10

88

2.68

96

3.11

90

3

Late sowing (2 week delay)

3.97

1.04

93

2.57

92

2.82

82

4

Summer weeds

3.18

1.17

74

1.66

60

2.89

84

5

Conventional tillage

2.86

1.08

67

2.35

84

2.18

63

6

N fertiliser (45 kgN/ha)

2.57

0.44

60

2.44

88

2.47

72

7

N Fertilizer (90 kgN/ha)

3.30

0.96

77

2.67

96

2.80

82

9

Frost and Heat

3.15

1.00

74

1.83

66

2.29

67

10

Frost and Heat 2 (moderate impact)

3.60

0.95

84

2.24

81

2.75

80

Two of the 50 stations of Figure 2 were located less than 100 km from Narromine and their results are summarised alongside the national results in Table 2. In Nyngan, which had a 15 year average Yw of 2.78 t/ha using the ‘best management’ parameters, the impact of only applying 45 kg N/ha was to restrict yields to 88% of Yw; failure to control weeds during the summer fallow could restrict yield to 60% of Yw; and a two week delay in sowing restricts yield to 92% of Yw; frost and heat stress accounted for yield losses of between 19% and 34% of Yw, depending on the function used. In Condobolin, which had a 15 year average Yw of 3.44 t/ha, the impact of only applying 45 kg N/ha was to restrict yields to 72% of Yw; failure to control weeds during the summer fallow could restrict yield to 84% of Yw; and a two week delay in sowing restricted yield to 82% of Yw; frost and heat stress accounted for yield losses of between 20% and 33% of Yw, depending on the function used.

We investigated some emergent management rules based on the idea of early sowing and matching the earlier time of sowing with slower maturing varieties that will best exploit the longer growing season as well as the need for new N fertiliser rules that will allow crops to fully exploit the additional yield potential due to the longer growing season. We found that in most locations the ideal sowing date was earlier than 26 April with a late maturing cultivar. This “emergent Yw” treatment, with a 15 year national average water-limited yield potential of 6.0 t/ha, had a 30% yield advantage over Yw and should be considered as the new yield frontier. While frost and heat stress reduce the yield potential of both Yw and the new simulated yield frontier, the advantage of the new treatment is slightly enhanced when frost and heat stress are taken into account. The advantage of early sowing combined with later maturing (slower developing) varieties is consistent with recently published field and simulation work for sites from Dubbo south to Victoria, and west to SA and WA (Flohr et al., 2017) but will require flexible additional application of N fertiliser to meet crop N requirements when seasonal conditions are right (e.g. seasons like 2016).

Risk-adjusted profit

Growers generally do not seek to maximise yield but rather to maximise their profit.  However, growers are also generally averse to risk, meaning that profits should be adjusted for yield and price risk via a measure of certainty equivalent. The certainty equivalent represents the smallest amount of certain money a farmer is willing to receive to forgo an uncertain profit, and can be calculated as the difference between average profit and a risk premium (e.g. Hardaker et al., 2004; Monjardino et al., 2015). When typical costs were built in to allow profit and risk-adjusted profit to be calculated for a risk-neutral and a moderate risk-averse context, respectively (Figure 3), we found that despite the emergent Yw treatment providing the highest water-limited yield potential, the higher costs and risks associated with the additional N required meant that both maximum profit and maximum risk-adjusted profit were achieved by the optimised time of sowing by variety (OptTOS+var) treatment.

Figure 3 is a column and line graph showing wheat yield and net returns achieved by average site practice, water-limited yield potential, yield-maximizing practice, and profit-maximizing and risk-adjusted profit maximizing practices that are OptTOS+Var treatments at a medium yielding site at Enabba, WA.Figure 3. Wheat yield (t/ha) (orange bars) and net returns ($/ha) (red line) achieved by average site practice, water-limited yield potential (Yw), yield-maximizing (Ymax) practice (emergent Yw), and profit-maximizing (Pmax) and risk-adjusted profit maximizing (RAPmax) practices that are OptTOS+Var treatments at a medium yielding site (Enabba, WA).

Conclusions

There is ample room for central western growers to close the yield gap by adopting flexible non-limiting N fertiliser practices, by timely sowing and by controlling fallow weeds. For those growers who have already closed the exploitable yield gap by consistently achieving over 80% of Yw, simulation analysis suggests that the yield frontier can be raised by sowing wheat earlier than 26 April with slow maturing varieties. For middle yielding parts of Australia’s grain zone, this practice has the potential to lift the production frontier by 19% and significantly improve risk-adjusted profitability. This finding needs to be fully evaluated in local field experiments.

Acknowledgements

The research undertaken as part of this project is made possible by the significant contributions of growers through both trial cooperation and the support of the GRDC. The authors would like to thank them for their continued support. We gratefully acknowledge the expert advice provided by Jan Edwards and Alan Umbers of GRDC as well as by Pam Watson, CEO of Down To Earth Research who conducted the interviews. We wish to thank Dr Jeremy Whish who reviewed an earlier draft of this paper.

References

Angus, J.F., Grace, P.R., 2017. Nitrogen balance in Australia and nitrogen use efficiency on Australian farms. Soil Res. 55, 435-450.

Bell, L.W., Lilley, J.M., Hunt, J.R., Kirkegaard, J.A., 2015. Optimising grain yield and grazing potential of crops across Australia's high-rainfall zone: a simulation analysis. 1. Wheat. Crop & Pasture Science 66, 332-348.

Flohr, B.M., Hunt, J.R., Kirkegaard J.A., Evans J.R., 2017. Water and temperature stress define the optimal flowering period for wheat in south-eastern Australia. Field Crops Res. 209, 108-119.

Hardaker, B.J., Huirne, R.B.M., Anderson, J.R., Lien, G., 2004. Coping with Risk in Agriculture, second ed. CABI Publishing, Oxford.

Monjardino, M., McBeath, T., Ouzman, J., Llewellyn, R., Jones, B., 2015. Farmer risk-aversion limits closure of yield and profit gaps: A study of nitrogen management in the southern Australian wheatbelt. Agric. Sys. 137, 108-118.

Contact details

Zvi Hochman
CSIRO Agriculture and Food
Ph: 07 3214 2234
Email: zvi.hochman@csiro.au

GRDC Project code: CSA00055