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