APPLICATION OF STATISTICS IN AGRICULTURE EXTENSION: A REVIEW OF ITS RELEVANCE IN THE MALAYSIAN ARICULTURE FOOD CROP SECTOR
Kalaivanan Nadarajah1 Pirakas Athmanathan2
ABSTRAK
Sungguhpun statistik amat berguna di dalam kerja-kerja
pengembangan, ianya jarang digunakan dalam pelaporan dan proses membuat
keputusan oleh pegawai-pegawai pengembangan di Malaysia. Ini mungkin disebabkan
faktor-faktor berikut; kurang pengetahuan tentang faedah statistik, kerumitan
kerja-kerja mengumpul data, masa serta kos dan sikap. Kertas ini telah
mengenalpasti penggunaan statistik sebagai alat
pengembangan yang boleh digunakan sepanjang masa untuk mengenalpasti
isu-isu dalam pengeluaran dan memberikan
gambaran yang lebih tepat tentang hasil ladang. Pengunaan Statistik Descriptive
dicadangkan sebab dengan latihan minimum
kita dapat memastikan data yang tepat
dikutip dan dibentang kepada pihak pengurusan untuk tujuan membuat keputusan
dan mencapai perubahan yang diinginkan. Cadangan penggunaan perician Microsoft Excel,
SPSS dan Expert Systems (Perisian Site Specific Decision Making Tool) dicadangkan
bagi mendapatkan laporan kuantitatif berkaitan hasil serta faktor-faktor yang menjejaskan
kualiti hasil ladang.Cadangan dibuat untuk memperkukuhkan fungsi latihan Pengurusan
Ladang serta penggunaan statistik yang lebih luas dikalangan petani dalam usaha
mewujudkan petani bijak. Pelbagai fungsi dan kegunaan statistik turut dibincang
sebagai peringatan relevannya statistik didalam aktiviti pengembangan pertanian
masa kini.
ABSTRACT
Statistics although a useful tool in agriculture extension,
is seldom used in reporting and decision making by extension workers in
Malaysia. Reasons could be due to; lack of knowledge and awareness of its
benefits, cumbersome data collection, time and cost factor and attitude. This paper has identified specific uses of statistics as a extension
tool and calls for all extension workers to apply statistics daily to identify
issues as well as describe farm output in a sensible way. The paper suggests a
greater use of descriptive statistics so that with minimum training, accurate information is collected, presented to the
management and subsequently used to plan and direct changes. Suggestions
include the wider use of tools such as Microsoft Excel, SPSS and Expert Systems
(Site Specific Decision Making Tool) to quantitatively describe yield and crop
quality issues. It also suggests strengthening the Farm Management Training function
of the Department of Agriculture and the use of Expert Systems to make farm
level decisions based of quantitative science. The paper also suggests
encouraging farmers to explore Statistics in an effort to create knowledge
workers. The paper clearly outlines the relevance of statistics in today’s
agriculture extension.
INTRODUCTION
The agriculture sector presents volumes of data and
profiles that include; land use, yield, crop composition, fertility level, pest
level and irrigation efficiency. The Department’s pest surveillance report is
an example of the active use of statistics where pest occurrence is sampled,
composite data collected, complied and analysed for recommending pest control
measures. However, it was observed that extension workers and even commercial
farmers in most of the agriculture food sector, still shy away from using
statistics in recording and reporting performance. This has led to the
continued misreporting and inaccurate reflection of land use, crop acreage,
grade, yield levels and even yield factor issues such as pest level, fertility
level and irrigation. Agriculture agencies and even commercial farmers fail to
understand and explain quantitatively, yield gaps and unachieved income targets.
Public policy on Agriculture calls for maximum yield levels, precision farming
and knowledge farmers, therefore the use of statistics by extension workers
will only add professionalism in data collection, analysing, reporting and
recommendations. Beginning 2014, all extension workers have been ungraded to
the sub professional level and no longer operate as technicians, therefore
their ability to apply analytical tools such as statistics should no longer be
an option.
Fields of application and variables of interest
Several fields for the application of statistics by
extension workers and commercial farmers include;
- Site specific description of
yield
- Site specific description of pest occurrences
- Site specific description of irrigation/ drainage
- Site specific description of fertility
- Site specific description of disorders and other physiological problems
- Land use statistics (within the farm)
- Farm operations level; machine costs, labor costs, direct and indirect
costs estimations etc.
- Business level; sales, cash flow, costs analysis, ROI, IRR and portfolio
analysis etc.
Besides the farm level usage
of statistics, the agencies can explore performance measurement such as;
- Productivity per unit area
or time
- Productivity comparison between blocks (inter and intra)
- Productivity comparison between districts
The full use of descriptive statistics and some extent
of inferential statistics using software such as windows excel and SPSS is
sufficient to explore the above fields. Inferences drawn can be used to suggest
corrective actions.
Data collection and description
The application of statistics begins with data collection.
Often incorrect methods of data collection such as improper scale and sampling methods, lead to
wasteful reports and unreliable information. The use of the range of error in
recording data becomes useful in ensuring accuracy and reliability of data. For
example, the average weight of corn cobs per plant should be recorded as 12+0.1kg
if the smallest unit in the measuring scale is 0.1kg. Agencies must establish
safe sampling methods for data collection based on local needs. Finally, the
use of Descriptive Statistics will simplify data processing, reporting and
presenting a good quantitative report.
Central tendency; mean, mode and median
The use of mean (or average) and mode (the most
popular or frequent value) by extension workers to describe yield or other parameters
is so popular that it often misleads the management. This is because it is not a
good measurement of central tendency and also does not take into consideration
outliers or extreme values that distort the information. The mean does not give
a picture of the scatterness of the values which is required for inferences
and corrective actions. Thus, a description of the actual dispersion in any
population is recommended.
Dispersion
The simplest view of the dispersion or scatterness of
any population such as yield can be seen in a histogram of the frequency of
individual values. The histogram, pie chart and Bar chart are popularly used to
give gross information of the distribution of values. The standard deviation
can be used to check the deviation from the mean and can be used to compare two
or more samples if their mean and units are similar. The use of coefficient of
variation is strongly recommended as a common tool for the comparison of values
between populations in agriculture. Beyond descriptive statistics, extensionist
often use correlation to explain yield and its association with causal factors
such as; rainfall, variety, control methods, fertilizer and chemical inputs,
investment level, man days, machine hours etc. However, the use of correlation
will yield inaccurate interpretations if yield models are not tested for site
specific use.
ISSUES AND
DISCUSSIONS
Selection
The proper selection of farmers for training and
distribution of aid is critical for the success of the extension program.
Extension officers should minimize the predominant use of qualitative
measurements, estimations and perception to select deserving farmers. This
paper suggests the use of expert systems to analyse and select the best lists
of projects, farmers and sites for extension activities.
The system should allow the user to key in, select and
process fields such as; age, present yield, size of farm, frequency of
participation in previous trainings, income level and other features such as
the suitability of the site.
Technology Transfer
Over the years, the Department of Agriculture through
its extension services and technical support has improved basic yield levels of
most major crops such as rice, vegetable and fruits. However, to maximize yield levels, we have to
analyse statistically and present suggestions in a convincing way as yield and
grades are influenced by a multitude of factors. Secondly, in an effort to
improve grades of farm produce, technologists can use statistics to confirm
which corrective actions yield results and discard other factors that are not
significant. Analysis on the increase in the percentage (%) of grade A from one
season to the other can be attributed to several actions and the farmer needs
to be educated.
Without quantitative measurement and reporting, there
is a risk of reports based on perception as there is a tendency to explain
yield changes or even grades through opinions based on perception.
Using statistics farmers and extension agents can
together identify deviations from crop manuals and secure yield and qualities
guaranteed by researches.
Farm Management
Costs analysis is an integral part of farm management
as farming is basically another business. The investor needs to accurately
analyse the fixed and operational costs for each season and use specific cash
flow spread sheets. With the proper use of statistics farmers can ensure
accurate costs analysis and therefore choose the best crops.
Suggestions
Public policy on farmer training
should favour empowering the farmer with knowledge and reduce government
spending on farm inputs often used as an inducement to increase productivity.
Thereby, agencies can invest public funds in training and procurement of tools such as statistics software.
Both the extension worker
and the farmer need to identify significant causal factors (yield factors) and
analyse yield gaps through the use of statistics. Officers should refer to
research material presented based on data from their locality, such as the rice
yield-training correlation studies (Kalaivanan,2000). Only expert opinion will
be useful in ensuring that apt statistical methods are used and results well
interpreted for even in the simple mean and range estimations, only experts can
identify outliers.
The Department Of Agriculture as the lead extension
service provider can consider the following;
- Establish Farm Management
Training functions within its organization.
- Equip all extension
workers with tools in Basic Statistics and insists on the frequent use of
descriptive statistics in reporting, while considering exploring
Inferential Statistics.
- Establish a policy on
limiting direct farm inputs by percentage (%) so that a clear amount can
be set for training in tools like statistics.
- Besides its use in
extension work, statistics can be used in performance management and in
presenting a more convincing budget for the procurement of logistics and
fund for training from the state and federal treasury.
- Use of expert systems
to serve as decision making tools in selection of recipients for training
and inputs and to ensure adherence to crop manuals and package technology.
Conclusion
Agriculture Extension agencies can freely apply
statistics from the selection process of projects till the analysis of project
outcome to ensure farm productivity, accountability, budget efficiency and
transparency in implementation. Its application is highly relevant for the effective
delivery of all agriculture technologies.
Acknowledgment
The writers wish to thank all
extension workers of the Department of Agriculture Malaysia.
REFERENCES
[2]
Kalaivanan,N (2000) Effect Of Extension Training
On Rice Yield, MSc Report, Department of Business Studies, School of Graduate
Studies, University Utara Malaysia.
[3]
Seema J. (1981) Descriptive Statistics and Exploratory Data Analysis (pp.5-6), Indian
Agricultural Statistics Research Institute, New Delhi.
By,
AJK PERPETA
Jabatan Pertanian,
Putrajaya.
(6 Julai 2015)
