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Using productivity indices for mapping efficient crop regions

Harpal S Mavi

NSW Agriculture 161 Kite Street, Orange NSW 2800
Phone 02 6391 3637 Fax 02 6391 3767
Email: harpal.mavi@agric.nsw.gov.au

Abstract

Statistical analysis of spatial and temporal crop data is an effective tool in understanding the logic behind existing cropping pattern. Such analysis and comparison throws light on the misfits in the landuse pattern and the gaps between production performance and potential. It is a simple and handy tool for bringing adjustments in landuse pattern and for evolving strategies for the development of the resources to the potential. Based on productivity indices, this study makes an assessment of relative suitability of different Shires of New South Wales for major crops grown in the State.

Introduction

Analysis of climate and crop production statistics in the different regions of the state can provide a handy tool in understanding the soundness of the existing cropping systems. Such an analysis also throws light on the misfits in cropping patterns and gaps in production performance and potential to serve as a basis for developing resources and technologies and for evolving risk management strategies, fulfil national requirements and achieve production targets.

The area sown under different crops expressed as percentage of gross cropped area in each administrative unit are usually utilised to give a good description of inter-regional differences and to demarcate homogeneous crop regions. Another approach is to study relative yield and relative spread of each crop on district and regional basis to define the crop regions. The best index of the suitability of an area for a particular crop is its relative yield as well as its stability from year to year as judged from co-efficient of variability (CV) regardless the area under the crop (Singh et al., 1995). Such quantitative information on spatial and temporal variability in crop production can be assessed through crop production models and geographical information systems.

Approach

Area and production data of major crops of NSW were collected from the Australian Bureau of Statistics for a ten years (1987-88 to 1996-97) period. Crop yield was calculated for each year on Shire basis. That was analysed to work out the relative yield and coefficient of variation in the yield with methods:

Relative Yield of Crop X

=

Coefficient of Variability of Crop X

=

The relative yield and CV for each crop in each shire was classified as follows:

Criteria

High

Medium

Low

Relative Yield

Above 120%

80-120%

Below 80%

CV

Threshold varied from crop to crop on the basis of the range in values and frequency of their distribution

Nine combinations of relative yield and CV are arranged in a 3x3 two-way table for each crop indicating the shires in each . These were grouped in 5 categories.

No

Category

Productivity level

Efficiency level

1

Shires with low yield and low/medium/high CV

Low productivity

Inefficient

2

Shires with medium yield and medium/high CV

Medium unstable productivity

Less efficient

3

Shires with medium yield and low CV

Medium stable productivity

Moderately efficient

4

Shires with high yield and medium/high CV

High unstable productivity

Potentially efficient

5

Shires with high yield and low CV

High stable productivity

Efficient

Base on these criteria, production efficiency of six major crops grown across NSW is mapped and presented in Figure 1.

The performance of a crop can be interpreted against the ecological requirements of the crop – climatic, soil conditions and irrigation development. Climate is the major factor that regulates and determines the growth and development of crop plants. The excess or deficiency of climatic elements exerts a negative influence on plant production.

In this study, interpretation of crop efficiency is attempted only in terms of climatic conditions. To interpret the productivity in terms of climate, monthly rainfall data for 10 years corresponding to crop data was collected for the representative stations of each shire. Monthly rainfall data from Australian Rainman (Australian Rainman 3.3, 2000) were analysed to work out the average crop season rainfall and its coefficient of variability. Temperature data were collected from the Bureau of Meteorology’s publication ‘Climatic Averages, Australia (BoM, 1988).

Production efficiency indicators of 4 major crops along with corresponding seasonal rainfall, its variability and temperature conditions at certain critical crop stages are presented in Tables 1 to 4.

Table 1. Wheat

Shire

Production efficiency

Relative yield

(%)

Yield

CV

(%)

Seasonal rainfall

(mm)

*Av. Max. T.

Sept.- Oct.

Walgett

Cobar

Coonamble

Gunnedah

Forbes

Narrandera

Leeton

Coolamon

Wagga

Young

Low

Low

Medium unstable

Medium unstable

Medium stable

Medium stable

High unstable

High unstable

High stable

High stable

66

56

80

113

109

113

122

130

126

145

44

21

43

32

29

26

32

32

28

30

258

196

252

326

331

311

336

341

378

454

30.9

30.3

28.3

27.9

26.3

26.0

25.5

24.9

23.5

24.6

* Average max. temperature exceeded in 14 % of the years

Table 2. Barley

Shire

Production efficiency

Relative yield

(%)

Yield

CV

(%)

Seasonal rainfall
(mm)

*Av. Max. T.

Sept.- Oct

Walgett

Warren

Inverell

Dubbo

Wakool

Junee

Tamworth

Cootamundra

Leeton

Cowra

Low

Low

Medium unstable

Medium unstable

Medium stable

Medium stable

High unstable

High unstable

High stable

High stable

63

76

101

88

86

117

131

127

126

122

32

32

30

39

25

17

49

37

24

20

258

271

328

323

239

328

321

424

336

384

30.9

28.5

30.0

26.7

25.3

24.9

26.4

24.3

25.5

25.1

* Average max. temperature exceeded in 14 % of the years

Table 3. Sorghum

Shire

Production efficiency

Relative yield

(%)

Yield CV

(%)

Seasonal rainfall (mm)

*Av. Max. T.

February

Brewarrina

Walgett

Narrabri

Moree Plains

Bingara

Forbes

Warren

Gunnedah

Coonabarabran

Carrathool

Hay

Murrumbidgee

Narromine

Low

Low

Low

Low

Medium unstable Medium unstable

Medium unstable

Medium stable

Medium stable

High unstable

High unstable

High unstable

High stable

49

57

72

69

82

105

111

105

108

142

179

140

122

80

46

28

17

33

48

36

20

18

58

74

47

22

184

234

310

341

78

220

214

371

426

164

143

147

219

34.7

34

33.1

33

33.4

31.6

2.3

31.3

30.6

32.7

32.6

31.5

30.9

*Average max. temperature exceeded in 14 % of the years.

Table 4. Cotton

Shire

Production efficiency

Relative yield (%)

Yield

CV (%)

Seasonal rainfall mm)

Rainfall

CV (%)

Inverell

Bingara

Moree

Bogan

Narromine

Brewarrina

Bourke

Walgett

Narrabri

Low

Low

Medium unstable

Medium unstable

Medium stable

High unstable

High unstable

High stable

High stable

76

72

107

111

119

130

137

135

121

63

48

23

31

14

31

28

12

10

452

378

341

233

219

184

186

234

310

15

30

26

43

40

40

37

38

38

References

Australian Bureau of Statistics. 2001. Agriculture Census Data, NSW, 1987-88 to 1996-97.

Bureau of Meteorology. 1988. Climatic Averages Australia. Melbourne, 532 pp.

Singh, Mukhtiar; Dhingra, K.K. and Dhillon, M. S. 1995. Efficient Crop Zones of India based on Productivity Indices. (Personal Communication).

Queensland Department of Primary Industries. 2000. Australian Rainman 3.3.

Fig. 1 Crop Productivity in New South Wales

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