Looking Into Agricultural Statistics: Experiences from ... · whatsoever on the part of the...

Looking Into Agricultural Statistics : Experiences from Asia

and the Pacific

The designations employed and the presentation of material in this publication do not imply the expression of any opinion

whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area of its authorities, or concerning the delimitation of its frontiers or boundaries.

The opinions expressed in signed articles are those of the authors and do not necessarily represent the opinion of the United Nations.

WORKING PAPER 29

Looking Into Agricultural Statistics: Experiences from Asia

and the Pacific

J. A. Colwell CGPRT Centre Regional Co-ordination Centre for Research and Development of Coarse Grains, Pulses, Roots and Tuber Crops in the Humid Tropics of Asia and the Pacific

Table of Contents

Page ............................................................................................................................ List of Tables vii ... ........................................................................................................................ List of Figures V I I I ...

............................................................................ ......................... List of Appendix Tables .. ~ I I I

............................................................................................................................ List of Boxes ix Foreword .................................................................................................................................. xi

... Acknowledgements ................................................................................................................ X I I I

1 . Introduction ...................................................................................................................... 1

. ......................................................................... 2 Uses and Users of Agricultural Statistics 3 2.1 Statistical needs in centrally planned and market economies ........................................ 3 2.2 Government as a user of agricultural statistics ............................................................. 5 2.3 The private sector as users of agricultural statistics ....................................................... 7 2.4 Agricultural statistics and national accounts .................................................................. 8

3 . Issues in Defining Agricultural Statistics Needs .......................................................... 1 1 3.1 Agricultural data items .................................................................................................. 1 1 3.2 Statistical concepts and definitions ................... .. .................................................. 12

3.2.1 Defining the agricultural sector ............................................................................ 12 3.2.2 Defining the statistical unit ............................... ... .......................................... 13 3.2.3 Defining data items ............................................................................................... 14

3.3 Frequency and geographical level of data ..................................................................... 15 .................................................................... 3.4 Types of statistical presentations required 16

3.5 Classifications .................... .. ...................................................................................... 18 3.5.1 Classification of crops ........................................................................................ 18 3.5.2 Land use classification .......................................................................................... 19 3.5.3 Other classifications ............................................................................................ 19

. . . 3.6 R e l ~ a b ~ l ~ t y of data .......................................................................................................... 20 3.7 Level and movement estimates ................................................................................. 21

..................................................................................................... 3.8 Timing of data needs 22 3.9 Specifying output requirements for a census or survey ............................................. 23 3.10 Framework for planning of the agricultural statistics system ..................................... 26

4 . Sources of Data for Agricultural Statistics . Reporting Systems .................................. 29

5 . Sources of Data for Agricultural Statistics . Agricultural Censuses ............................ 35 5.1 Comparison of censuses and sample surveys ................... .... ............................... 36 5.2 World Census of Agriculture Programme ..................................................................... 37 5.3 Case study . National Sample Census of Agriculture, Nepal. 199192 ......................... 38

........................................................................................................ 5.3.1 Background 38 . .

5.3.2 Statlst~cal design ......................... ............... ........................................................ 39 5.3.3 Data collected ....................................................................................................... 40 5.3.4 Organization of field operations ................... ... ............................................... 40 5.3.5 Computer processing system development ....................................................... 41

........................... 5.3.6 Data entry .. ............................................................................. 4 1 ....................................................................... 5.3.7 Edit checking .......................... .. 42

5.3.8 Output tables .......................... .. ......................................................................... 42 ..................................................................................................... 5.3.9 Census reports 44

5.3.10 Census analysis ................................................................................................ 44 5.4 Use of agricultural censuses for policy analysis and research .................... .. ............. 45 5.5 Case study - analysis of small farms, Nepal ............................................................... 48 5.6 Other uses of agricultural censuses ........................... ............. ..................................... 51

6 . Sources of Data for Agricultural Statistics . Sample Surveys ...................................... 6.1 Random and non-random sampling ............................................................................

........................... 6.2 Sampling methods used in agricultural surveys .. ............................ 6.3 Agricultural sample surveys - case studies .................................................................. 6.4 Crop cutting surveys .....................................................................................................

........................................................................................................... 6.5 Sampling errors 6.6 Popular misconceptions about sample surveys ......................................................... 6.7 Problems in introducing sample surveys in countries in transition ...............................

7 . Additional Topics ................... .. ................................................................................... 69 7.1 Reporting problems in agricultural censuses and surveys ........................................... 69 7.2 Early warning information systems ............................................................................ 71 7.3 The rounding problem ......................... ... .................................................................. 72 . . . . 7.4 Statistical organ~zation .................................................................................................. 73

8 . References .......................................................................................................................... 77

Appendix National Sample Census of Agriculture. Nepal. 1991192 Output Tables for Analysis of

.............................................................................................................................. Farm Size 79

List of Tables

Page

Chapter 3 Table 3 . 1 Classification of agricultural data items .................................................................. 12 Table 3 . 2 Livestock numbers ('000) by type for selected provinces . Lao PDR. 1995 .............. 17 Table 3 . 3 Number of households owning each livestock type ('000) for selected provinces.

Lao PDR. 1995 .................................................................................................... 17 Table 3 . 4 Average livestock herd size by type for selected provinces . Lao PDR. 1995 ........... 17 Table 3 . 5 Number and percent of cattle owners by number of cattle owned - selected

provinces. Lao PDR. 1995 .................................................................................... 18 Table 3 . 6 Number of livestock owners ('000) by number of animals and farm area . four

province total. Lao PDR. 1995 .................................................................................. 18 Table 3 . 7 Land use by agricultural holdings. Nepal. 199 1192 ............................................... 19 Table 3 . 8 Distribution of farms by farm size - Oudomxay and Champasack Provinces .

Lao PDR. 1994 ....................................................................................................... 21 Table 3 . 9 Area. production and yield of maize. Nepal. 199019 1 to 1992193 ............................ 21 Table 3.10 Example of outline needs for crop statistics in a typical agricultural statistics

system ................................................................................................................... 27

Chapter 5 Table 5 . 1 List of output tables for National Sample Census of Agriculture . Nepal . 199 1192 .... 43 Table 5 . 2 Evaluation of the role of women in agriculture . issues highlighted by agricultural

census ..................................................................................................................... 46 Table 5 . 3 Study of the problems of small farms . issues highlighted by agricultural census .... 47 Table 5 . 4 Analysis of cassava production . issues highlighted by agricultural census ............. 48 Table 5 . 5 Analysis of small farms in Nepal . problems identified by agricultural census and

possible policy measures ......................................................................................... 50

Chapter 6 Table 6 . 1 Comparison of estimates of wet season rice yield (tonsha) . selected provinces .

Cambodia ............................................................................................................... 61 Table 6 . 2 Sampling errors on selected items, agricultural census. Nepal. 1991192 .................. 62

Chapter 7 Table 7 . 1 Crop area (ha) by seasonivariety and province ................................................... 72 Table 7 . 2 Crop area ('000 ha) by seasodvariety and province (rounded) ................................ 73 Table 7 . 3 Crop area ('000 ha) by seasodvariety and province (rounded and corrected) .......... 73

List of Figures

Page Chap t e r 3 Figure 3 . 1 Example output table specifications ................................................................... 24 Figure 3 . 2 A poorly designed questionnaire ................... .. .................................................... 25

List of Appendix Tables

Page Table A 1 Area and fragmentation of holdings. Nepal. 196 1162 to 199 1192 ............................ 79 Table A 2 Area of holdings . development regions and ecological belts. 1991192 ................... 79 Table A 3 Distribution of land holdings by size of holding. Nepal. 1991192 ............................ 79 Table A 4 Percent of land holdings by type of tenure and size of holding. Nepal. 1991192 ...... 80 Table A 5 Area of land holdings by type of land and size of holding. Nepal. 1991192 ............. 80 Table A 6 Land holdings with irrigated land by size of holding. Nepal. 1991192 ..................... 80 Table A 7 Number of holdings with temporary crops ('000) by crop type and size of

holding. Nepal. 198 1182 and 199 1192 ..................................................................... 81 Table A 8 Distribution of temporary crop area by crop type and size of holding. Nepal.

1991192 .................................................................................................................. 81 Table A 9 Cropping intensity by size of holding. Nepal. 1991192 ........................................... 81 Table A 10 Number of holdings with permanent crops ('000) by crop type and size of

holding. Nepal. 1991192 ........................................................................................ 82 Table A 1 1 Rice . wheat and maize growers: use of selected inputs by size of holding. Nepal.

199 1192 .................................................................................................................. 82 Table A 12 Percent of land holdings using agricultural equipment by type of equipment

and size of holding. Nepal. 1 99 1192 ........................................................................ 83 Table A 13 Number of holdings with livestock and livestock numbers by main livestock

type and size of holding. Nepal. 199 1192 ............................................................ 83 Table A 14 Number and percent of holdings with agricultural credit by source of credit

and size of holding. Nepal. 1991192 ........................................................................ 83 Table A 15 Percent of holders by age and size of holding. Nepal. 199 1192 ............................... 84 Table A 16 Holders by work status and size of holding. Nepal. 199 1192 ................................... 84 Table A 17 Farm population aged 10 years and above by labour force status. sex and size of

holding. Nepal. 1991192 ..................................................................................... 84

List of Boxes

Page Chapter 2 Box 2. 1 Solving the food supply problem with the aid of statistics ......................................... 6

. . Box 2. 2 Data disseminat~on ........................ ... .......................................... ................ 8

Chapter 3 Box 3. 1 Defining a 'farm' .......................................... 14 Box 3. 2 Rice production statistics. Cambodia: what accuracy is needed? ................................ 20 Box 3. 3 Output tables and questionnaire design 25

Chapter 4 . .

Box 4. 1 Do administrative sources provide good area stat~st~cs? ............................................. 3 1 Box 4. 2 Reliability of rice production statistics in Viet Nam ............................................ 3 1 Box 4. 3 An improved reporting system for crop statistics in Cambodia 33

Chapter 5 Box 5. 1 Unit record data and confidentiality ... ........................... 45

Chapter 6 Box 6. 1 Costing of policy options: implications of sampling errors 63 Box 6. 2 A brief lesson on sample design ................................................................................ 63

Foreword

The past decade has seen rapid changes in the economies of countries of the Asia and Pacific region. Central planning has given way to market economics in many countries. These changes have provided challenges for statistical systems, especially for agriculture. Previous sources of statistical information have often been lost through the freeing up of markets and the change to private ownership of land and livestock. Also, as the government's role in managing the economy has changed, the information needed for decision-making has also changed.

This working paper looks at agricultural statistics in the Asia and Pacific region, with emphasis on the effects of economic reforms. The data needs for agricultural planning and policy making in centrally planned and market systems are examined and issues for consideration in planning agricultural data collections are discussed. Various data collection methods, including sample surveys, agricultural censuses and "reporting systems", are also examined.

The working paper has been prepared with three types of reader in mind. First, it should help users of agricultural statistics to focus on what are their statistical needs for agricultural planning and policy making, and how statistics should feed into decision-making processes. Second, it should provide senior officials responsible for managing government statistical programmes with a greater awareness of the issues involved in developing a statistical system, the strengths and weaknesses of alternative data collection approaches, and how to provide an effective statistical service. Third, it should alert the statistical practician to problems and issues that arise in data collection work.

The working paper is not intended to be a textbook on agricultural censuses and surveys. Nor does it provide a step by step instruction on designing and canying out censuses and surveys. The aim is to go beyond the technical treatment of censuses and surveys found in textbooks to look at practical issues and problems involved in planning and implementing agricultural statistics systems, with particular reference to countries of the Asia and Pacific region.

The working paper has been prepared by J. A. Colwell, who has worked on statistical projects for F A 0 and other organizations in different countries of the region.

Haruo Inagaki Director CGPRT Centre

Acknowledgements

This working paper is a summation of my experiences in statistical work undertaken over many years in countries of the Asia and Pacific region. including Australia, Malaysia, Bangladesh, Bhutan, Myanmar, Nepal, Mongolia, Cambodia and Lao PDR. Thanks are due to all those with whom I worked in statistical offices and agricultural departments for their help in leading me to a better appreciation of the problems faced in statistical practice.

My work over nearly six years with F A 0 has provided an important basis for the content of the working paper. Special thanks are due to FAO, and in particular its Statistics Division in Rome, which has been working to improve agricultural statistics throughout the region and has provided me with the opportunity to participate in that work.

Thanks are also due to Taco Bottema of the CGPRT Centre for his conception of the project and his support during the preparation of the working paper.

Canberra, Australia July 1997

J. A. Colwell

xiii

1. Introduction

Planners and policy makers around the world constantly criticise statistics. 'The statistics do not meet our needs'. 'the data are not accurate enough', or 'information is never available when it is needed' are common complaints. One also hears 'the statistics are interesting. but what we really need is ...', and 'the data are not useful because they are only available at the provincial level, not the village level'.

Who is at fault? Are statisticians designing statistical systems without regard to user needs, or are they not providing sufficient help for users to make good use of the available data? Or is it that the user has not accurately described the data needs, has unreasonable expectations of the statistics, or perhaps is just unhappy that the statistics do not give the 'right' results?

Statisticians often fail to recognize that the collection of statistics is not an end in itself. Statistics are collected for a purpose, namely, to provide planners, policy makers, businesses, researchers, traders, farmers and the general population with information to help make decisions. Statisticians are rarely users of their own statistics: they produce the statistics for others. To. provide an effective statistical service to the user, the statistician must understand who needs the statistics, why the statistics are needed, and how the statistics will feed into the decision making processes. Too often, statistical systems fail to provide useful data because the statistical end- product and its uses are not well-understood.

The statistician is often negligent in failing to 'sell' the statistics to potential users. Much can be achieved by improving the presentation of statistics in statistical reports through graphs, and commentary and analysis of data, to bring statistics to the attention of users. This is especially important for agricultural censuses. A country may spend millions of dollars on an agricultural census to provide a large amount of data which could keep planners, policy analysts and researchers busy for years. Yet the data are often not widely used because users are unaware of their importance and how they can be used.

Some statisticians are also guilty of hindering the use of statistical information. Statistics are sometimes seen as 'confidential', with restrictions placed on the access to data, especially for the private sector. Fortunately, appreciation of the benefits of greater statistical openness is increasing. Access to information leads to better decision making, whether it be in the public or private sector, which is in the interest of the whole community.

What can users do to ensure that statistics better meet their needs? Often, the users do not have a good understanding of their own requirements for information. Data needs are often communicated to the statistician in terms such as, 'data on fertilizer usage'. What exactly does this mean? Is this data on chemical fertilizer, organic fertilizer or both? Does the user want the quantity of fertilizer used or the numbers of farmers using the fertilizer? Are data on fertilizer usage for each crop required or just for the farm as a whole? In the absence of clear specifications, statisticians make their own judgements and users often find, after the event, that the data do not meet their needs.

Users tend to focus too much attention on the questions to be asked in a survey rather than on the output to be produced. These are two different things. In a farm employment survey, one will not ask each person, 'are you employed?', or 'are you unemployed?'. Instead, a sequence of questions about the person's work activities will be asked, from which the person's employment status can be determined. What the user should be telling the statistician is not: 'change this question to ...', or 'add a question to ask ...'. It is much more useful to inform the statistician that: 'the survey should provide data on the number of unemployed persons, classified into the following age groups ..., for each province'.

Chapter 1

A problem commonly confronting statisticians in their dealings with users is the perception that statistics are 'easy'. There is often the expectation that, if one decides on Monday that statistics are needed, a questionnaire can be designed on Tuesday. the data collected on Wednesday, and the results made available on Friday. Alternatively, a letter may be written to the district agricultural office with the request, 'please report the average farm income in your district'. Why the statistician needs six months to prepare for the survey and three months to get results out is not easily understood. The statistician may not be an agricultural specialist and therefore his or her ability to handle agricultural data may be questioned.

Hastily conceived and conducted data collections do not yield useful statistical information. Development time is required to establish data needs. determine statistical concepts and defmitions. design and test questionnaires, train enumerators, and design and select the sample. Computer processing and checking of data also take time. The 'letter' approach to data collection does not work because information is often not known to the office concerned (how can a district office report income for the whole district?) and instructions on how to report the data are not provided (results will not be meaningful if each district reports income on a different basis).

As to whether the statistician is qualified to produce agricultural statistics, it is often not understood that, to obtain good statistics. one needs to use sound statistical techniques and practices. Statisticians are trained in these techniques and practices; agronomists and economists are generally not. Statistical work requires technical specialists in various fields such as sample design, data processing, field organization and questionnaire design.

Sometimes, users are unrealistic in their demands for data. One cannot expect the statistician to provide rice production statistics before the harvest is even completed. The data may well be needed at that time, for example, for certain planning or budgeting purposes. If so, the data could be provided as a forecast. based on area planted information, with final statistics to become available at a later date.

The typical user also has an excessive shopping list of statistical needs. Users need to carefully think through their data needs before a survey is carried out, rather than just collect everything that seems to be interesting. The collection of data comes at a cost. At a time when statistical agencies around the world are facing increasing funding problems, the need to ensure that scarce statistical resources are most efficiently used is paramount. For example:

Is it really important to have annual crop production data for each district? Why are the data needed? What policy actions or decisions depend on this information? Cost of production data may be needed every year, but is an annual survey necessary given that the structure of farm costs does not change very quickly over time? Why is it necessary to run a farm income survey when related data on household assets are already available from another survey? If resources are available to conduct either a milk production survey or a monthly price data collection, which one is more important?

This working paper looks in detail at the issues raised in these introductory paragraphs in the context of planning the collection of agricultural data in countries of the Asia and Pacific region. We begin in Chapter 2 by examining who needs agricultural statistics and why they need these data. Chapter 3 looks at the issues for consideration in defming what data need to be collected to meet user requirements. Issues such as the frequency. geographical breakdown. timeliness and reliability of the data are discussed. In the next three chapters, we look at the three main approaches used to collect agricultural data: Chapter 4 examines the collection of statistics through reports provided by agricultural field workers or local officials; Chapter 5 discusses agricultural censuses; and Chapter 6 reviews the collection of agricultural data through sample surveys. Finally, Chapter 7 presents some additional topics concerning statistical organization, data collection and data presentation.

2. Uses and Users of Agricultural Statistics

Agricultural statistics have many uses. Governments need statistics to monitor the performance of the agricultural sector, make policy decisions and plan development programmed international organizations need information to monitor agricultural conditions and assess needs or assistance. Research institutes need data for research and analysis. Private businesses need -.formation to help in their commercial operations. And last, but not least, farmers need information to help make decisions about farm operations.

The most important user of agricultural statistics is the government. To understand the government's agricultural statistics needs, we need to understand how the agricultural sector works in particular:

• What is the role of the government in managing the agricultural sector and in what ways can it intervene in the activities of the sector?

• What are the main goals of the government for the agricultural sector? • What are the main policy issues and problems in the agricultural sector? • What are the government's decision making processes in agricultural policy and planning? • How are statistics used in decision making?

The answers to these questions are largely determined by the type of economic system in place.

2.1 Statistical needs in centrally planned and market economies

By the 1950s, about one third of the world's population lived under socialist economic systems (World Bank 1996, p. 1). Various central planning models were used for organizing the agricultural production sector. Most of these were characterized by government involvement in determining the demand for agricultural output, setting output targets, overseeing farm operations, providling agricultural inputs, procuring and distributing agricultural products, and price setting. In some countries, agriculture was collectivized, with state farms or co-operatives operating under the product control of the government. Elsewhere, ownership of land and livestock remained in private hands, but was subject to certain government controls.

In a market economy, the government has a different role. The individual farmers themselves make all farm decisions, such as what to plant, when to plant, etc. The government will usually not be directly involved in the production or distribution of goods, its role being more one creating favorable circumstances for the agricultural sector to function most efficiently.

The decisions required to be made by governments in centrally planned and market farmers are quite different. In a centrally planned economy, the government needs to make decision such as: `this year, 1,000 ha of wheat will be planted on State Farm A'; `Cooperative B will supply the state butter factory with 10,000 liters of milk each year'; or à tractor will be supplied to State Farm C to help with land preparation'. Under a market system, the decisions are more likely to be such things as: èxtension workers will visit the field to encourage farmers to plant more wheat'; `markets will be established to help increase milk supply'; or ìmport controls will be lifted to help encourage the use of tractors'.

The statistics needed to support such decisions are quite different under the two economic systems. To illustrate, let us consider the above `tractor' decisions. In a centrally planned economy, the Ministry of Agriculture may own all farm machinery, which it makes available to agricultural

Chapter 2

4

enterprises as required. To do this effectively, it needs detailed operational information on each agricultural enterprise. Weekly (or even daily) crop reports, detailing the status of different farm activities (land preparation, planting, harvesting, etc.), expected dates of such activities, weather conditions, crop conditions, etc., are needed. Fuel price and fuel availability should also be monitored.

In a market economy, the job of the Ministry of Agriculture will usually not be to provide tractors to farmers, but to encourage farmers to take advantage of the benefits of greater farm mechanization. Weekly crop reports are not required for this. What is needed is information to monitor the use of farm machinery (how many farmers use different machinery, what is the stock of such machinery, etc.) and to assess usage amongst different groups (small and large farms, farms in each province, etc.). Data comparing the efficiency of farms with and without farm machinery are also needed.

Since the late 1980s, many socialist countries have introduced market reforms, which have changed the way the agricultural sector is organized and managed. Governments often found that existing statistical systems, which had been established to meet the requirements of the central planners, no longer provided the information needed to manage a more open economy. Data on farm operations were needed in less detail and less frequently than before. Data on farm finances were still of interest, but it was no longer necessary to do a detailed annual audit of each agricultural enterprise. With the market reforms, there was more emphasis on market-related data such as prices. Monitoring of international trends including prices, crop conditions and consumer preferences also became more important. In many countries, the economic changes also led to social problems and a greater need for statistics to measure such things as poverty and income distribution (World Bank 1996, p. 67).

Usually, the economic reforms also led to deterioration in the overall quality of statistics. Existing sources of data often disappeared. With the expansion of the unofficial economy and high inflation, it became very difficult to accurately measure output. In Russia, it has been estimated that the decline in national accounts between 1990 and 1994 was overstated by 12 percentage points (World Bank 1996, p. 19). The statistical problems accompanying transition are discussed further in Chapters 4 and 6.

Case study - Mongolia

Agriculture is one of the most important sectors of the Mongolian economy, with livestock herding being the dominant activity. After the establishment of the Mongolian People's Republic, the government introduced a collectivized system of agriculture, based on the Soviet model. B\ 1988, there were 255 livestock co-operatives, 70 state farms and 34 other agricultural enterprises. The agricultural sector, along with other sectors of the economy, was managed on a planned basis. The Ministry of Food and Agriculture in Ulaan Baatar set prices and production targets for state procurement, based on the national economic plans provided by the National Development Board. Members of co-operatives were permitted to privately own a small number of livestock for subsistence needs (Mongolian Academy of Sciences 1990).

An extensive agricultural statistics reporting system was established. Each agricultural enterprise supplied an enormous amount of detailed operational data. Information on livestock births, losses and production were provided monthly, and crop data were reported fortnightly during the growing season. Detailed annual financial statements were also provided by each agricultural enterprise. A fast information system' was also established to provide information on a day-to-day basis; crop plantings, for example, were reported every three days.

In 1990, the government introduced market reforms throughout the economy. The state enterprises were converted to private ownership. Business units were established to manage the large-scale agricultural operations, and ownership of livestock transferred from cooperatives to individual herdsmen. The marketing and pricing of agricultural produce were gradually freed up.

Uses and Users of Agriculture Statistic

5

The dismantling of the state enterprises led to a breakdown of the statistical reporting system and it was necessary to rely on assessments of local officials (see Chapter 4). The volume if statistical reporting was reduced. Steps have been taken to improve the statistical system: the collection of livestock production data through sample surveys of herdsmen has been tested; and surveys for the collection of price and other market-related data are being developed.

One problem in redefining data needs was in reorienting the policy and planning role of the ministry of Food and Agriculture. There was also disquiet felt by some about forsaking statistical formation that had 'always' been available, even when it was no longer relevant to the new economic environment.

2.2 Government as a user of agricultural statistics

A useful starting point for planning an agricultural statistics system to meet the needs of the government is to examine what the main goals and priorities are for the development of the agricultural sector and what issues and problems are likely to influence development planning and policy making.

In most developed countries, agricultural productivity is high and food is cheap and plentiful. Over-production of agricultural commodities is common and this often leads to declining farm incomes and a shift of population from rural to urban areas. The main priorities for governments are usually problems related to low farm prices, restructuring of agricultural industries, and export promotion. On the nutrition front, the problem is more likely to be people eating too much, rather than too little (US Bureau of the Census 1968).

The situation is different in developing countries. High population growth rates and low farm productivity often result in food shortages. Agricultural land is often in short supply, rural poverty is chronic, and transport and communication facilities are weak. The policy priorities and objectives for the agricultural sector are often stated in a country's national development plan (e.g., 'Nepal NPC 1992). Some typical priorities in developing countries of the Asia and Pacific region are to:

• ensure food security for the country's population; • increase farm incomes and reduce income inequalities; • improve the nutritional status of the rural population; • improve crop yields; • increase the area of land under irrigation; • improve the supply and distribution of fertilizers and seeds; • diversify crops to supply domestic and export markets; • achieve sustainability in the use of land and water resources by controlling deforestation and

providing alternatives to shifting cultivation; • ensure rural women are provided with the opportunity to participate in economic activities;

and • develop agribusiness potential.

What types of actions can a government, operating under market conditions, take to help achieve the policy objectives identified above? We have already noted that farm level decisions are made by farmers, not the government, and so the government cannot just decide, for example, that farmers will grow more fruit crops to supply export markets. Efforts to impose such decisions on farmers are usually not successful and can provoke much resentment. Farmers must be persuaded that the actions advocated are in their interests.

Instead of direct action to influence farm level decisions, governments seek to create the right conditions (legal, institutional, economic, etc.) for the desired outcome to be achieved and to

Chapter 2

6

provide technical support to help reach that goal. To help achieve the aim of `diversifying crops to supply domestic and export markets', various measures could be considered:

• undertake research into the suitability of alternative crops, export market opportunities and domestic consumption patterns;

• implement macro-economic policies favorable to export industries, such as freeing up prices, removing barriers to exports, encouraging foreign investment, and adopting suitable exchange rate and interest rate policies;

• use taxation measures, such as relief from land tax for orchard plantations, to promote crop diversification;

• improve the country's infrastructure, including roads and bridges, to help the movement of goods to markets;

• provide marketing assistance and establish wholesale and retail market facilities to help farmers market their products; and

• provide extension services to help farmers introduce new crops. Box 2.1 solving the food supply problem with the aid of statistic

Ensuring an adequate supply of food for its population is one of the most urgent problems faced by many governments. For most countries of South and South East Asia, the production of rice, the staple food crop, is of foremost importance. Let us consider how a government goes about assessing the rice production and food supply in the country. Is the production of rice sufficient? If not, what is the deficit, why has it occurred, and what can be done about it?

To examine this issue, one first needs a time series of the basic rice crop statistics, showing trends in rice area, production and yield. Is rice production declining? If so, is it because less land is being cropped or are yields declining? If yields are declining, the statistics will need to be further examined to understand why~ this is happening. Data on fertilizer usage might show that fewer farmers are using fertilizers. This might be because the supply of fertilizer is insufficient. The question is whether imports or local production of fertilizers has declined or prices have increased? Perhaps the statistics indicate that yields are low in certain provinces or amongst certain types of farms, such as small farms. If crop area is declining, the statistics will show whether it is happening in certain provinces or across the whole country, and whether it is because farm sizes are getting smaller or that there are fewer farmers.

To explore whether there is a rice surplus or deficit, one needs to examine food supply and food needs. To estimate how much rice is available for food, the rice production data need to be adjusted using data on post-harvest losses, milling losses, and the use of grain for seed and other purposes. Rice exports and imports and food stocks also need to be considered. To estimate the amount of rice required to feed the population, data on population, population growth rates and food consumption patterns are needed.

From this diagnosis, the government will obtain a description of the problem to be addressed. For example, the analysis might show that rice production is 5% less than what is needed for food self-sufficiency and will need to increase by 2.3% a year to keep up with population growth. It might further reveal that rice production has declined because of, say, three factors: the deteriorating crop yields in certain provinces; the decline in fertilizer usage; and the low productivity of small farms.

The results of this analysis will lead to a consideration of different policy approaches to bring about changes in the factors identified as causing the problem. Reducing the price of fertilizers, for example, b% lowering taxes could be one option considered. The consequences of this action can be assessed by analyzing farm income, farm management and crop yield data. What will be the farmer's response to the price reduction? What improvement will there be in rice yields with increased use of fertilizers? Statistics also help in costing alternative policy actions; the cost of the fertilizer proposal can be determined using data on the quantity of fertilizer used.

Other types of government intervention include: construction of irrigation or other facilities to promote increased agricultural output; dissemination of market information to farmers and traders to help them make informed market decisions; action to promote the purchase or distribution of farm inputs; and the provision of facilities for the distribution of emergency food in times of crop losses


7

How are statistics used to help make the decisions that lead to these actions? Statistics help, in the first instance, in identifying whether there is a problem requiring action. If so, the statistics will quantify the problem and help to explain why it has arisen. Statistics will also suggest ways in which the problem can be resolved and help to evaluate the effects of different policy options. Finally, statistics help in the monitoring and evaluation of policy actions and programs.

Statistics are also used for various types of agricultural research and statistical analysis undertaken to support government policy making. For example, a statistical model can be developed to determine what cropping systems are most suited to certain areas (Shinawatra 1988). Using statistics on prices, yield and production costs (seeds, labour, fertilizer, imputed land rent, imputed family labor); the gross margin (value of production less costs) can be calculated for each alternative crop. Using a linear programming model, the optimum crop mix - for a given amount of land, capital and labor - to maximize gross margins can be determined. Different price scenarios can also be examined.

2.3 The private sector as users of agricultural statistics

Farmers make day-to-day decisions on all aspects of farm operations, including: what crops to plant, when to plant them, what varieties to use, how much seed to use, whether to use fertilizers, how much fertilizer to apply, when to harvest, where to sell produce, and at what price t, sell produce.

Farmers in developed countries produce for export or domestic markets and their decision making will be driven by normal economic considerations. They are supplied with much statistical formation through the media and farmer associations and this becomes an essential element in efficient farm management and operations. A wheat farmer in Australia is aware of general economic conditions, crop conditions, weather, prices, the results of agricultural research studies, and factors affecting export markets such as crop conditions in other wheat producing countries, exchange rates, wheat stocks, etc. They may even trade in wheat prices or exchange rates on the futures market as protection against future uncertainty.

Farmers in developing countries are usually less concerned with concepts of maximizing profitability than with providing subsistence for the family and surpluses to meet any emergencies. Their main source of information is the government network, namely extension workers, village officials, and sometimes radio and television. The information provided is usually in the form of advice on planting of different crops or crop varieties, use of inputs, etc.

One of the most important information needs of farmers in developing countries, and one that can directly influence their decision making, is data on prices and other market factors (FAO 1996c). With such data, farmers can make informed decisions on what to plant; when to schedule their harvest; and when, how, and at what price to sell their produce. If Cambodian farmers learn that prices in Phnom Penh have increased today, they can bring their produce to the Phnom Penh markets tomorrow. In newly emerging market economies, farmers are often not familiar with the operations of markets or not aware of current prices, and as a result, can be disadvantaged in their dealings with traders. After 1990, many Mongolian herdsmen, with their newly privatized herds, experienced problems in adapting to free market conditions after a lifetime of socialist controls. A -market information system, providing regular price and other market data issued through the media and other means, can help farmers to overcome this problem. Many countries have given priority to establishing such market information systems.

Businesses also need statistics. Food wholesalers, retailers and traders need information on prices and market conditions in the same way as farmers do. A Cambodian rice trader, aware that rice prices are higher in Battambang than in Phnom Penh, can transport rice to Battambang to meet the additional demand there. Agricultural suppliers also need information to identify market opportunities and develop marketing strategies.

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the additional demand there. Agricultural suppliers also need information to identity market opportunities and develop marketing strategies.

Large private companies are often quite sophisticated users of statistics. A company planning a major investment in an orchard plantation will need to assess the economic viability of the project through analysis of production, prices, domestic consumption trends, export potential, and international factors.

The perception that official statistics are collected for governments, not for private businesses and individuals, is still widespread. This view is mistaken. Statistics help farmers, traders and others make better decisions; this certainly benefits the individuals themselves, but it also benefits the community as a whole. With access to regular price data, both farmers and traders can make informed market decisions which help in stabilizing prices and offsetting shortages.

Box 2. 2 Data dissemination. To ensure that statistics are widely used, an effective method of data dissemination is needed. Often,

statistics are not formally released and it is difficult for users to access the data. Instead of being presented in a statistical report, the statistics are on a piece of paper (often with hand amendments) in the statistician', drawer (somewhere), available for sighting on request (if the statistician hasn't gone on leave and the statistic, can be found). There may be more than one set of figures (the statistics may have originally been calculated, incorrectly but the earlier data have not been discarded). The technical department may have another set, or sets, of figures (data may have been subject to further amendments based on political or technical considerations). In these circumstances, the statistics that users get depends on who they see and when they see them. Statistics must be released formally and systematically. The responsibility for releasing statistics must be assigned to a single agency.

The most common means of release of statistics is through printed publications. Other forms of data dissemination are becoming popular, especially microfiche, diskettes, CD-ROMS, and on-line services such as the Internet. These are not only cheaper but often provide data in a more convenient form for the user. Online statistical services provide users with immediate access to the statistics rather than having to wait for the physical delivery of a statistical report.

Statistical publications should be directed at both the casual and serious user. The presentation of commentary and graphical presentations, highlighting the main results, can help bring the statistics to the attention of the casual user. However, detailed statistical tabulations are needed by those interested in serious policy analysis and research.

2.4 Agricultural statistics and national accounts

Agricultural statistics, along with statistics from all other sectors, are used in the compilation of what are the most important economic statistics for national policy and planning, namely the national accounts. National accounts statistics present a statement of the overall economic position of the country. One of the most important national accounting measures is the Gross Domestic Product (GDP), which measures the total value of all goods and services produced within the country. GDP is usually measured in `current price' and `constant price' terms. The constant price data are based on price levels in a certain base year and are used to measure real changes over time, taking out the effects of price change. The year-to-year change in constant price GDP provides a measure of the overall rate of growth of the economy, the most important indicator of how a national economy is faring. The growth rates of each sector, such as agriculture, manufacturing, etc., are also important measures.

There are three ways of calculating GDP: the production approach, which aggregates the production or `value added' of all goods and services produced in all sectors of the economy; the income approach, which aggregates the incomes generated in the process of producing the goods and services; and the expenditure approach, which aggregates the expenditure of the users of all goods and services (ABS 1994a). Under the production approach, the value added for agriculture


9

is the value of all agricultural production less the value of inputs used (fertilizer, pesticides, etc.). The current price value of production can be calculated by taking the production of each agricultural commodity and applying a suitable price, such as the 'farm-gate' price. (Inputs need to be deducted because these are part of the production of the manufacturing sector.)

Constant price estimates for the agricultural sector can be calculated in one of several ways: calculate values using prices in the base year rather than the current year; revalue the current price estimates using a price index; or use quantity indicators to estimate the constant price change.

Various agricultural statistics are needed to compile national accounts, including: value and quantity of all crop and livestock commodities produced; prices of agricultural commodities; value of farm inputs; household expenditure; capital expenditure; farm income; wages; exports; and imports.

11

3. Issues in Defining Agricultural Statistics Needs

In Chapter 2, we looked at who uses agricultural statistics and how they use the statistics. In this chapter, we examine the issues that need to be considered in defining exactly what statistics required to meet user needs, especially:

• what data items are required; • how often the statistics are needed; • at what geographic level the statistics are required; • whether the statistics are required by certain dates, and if so, why; • what concepts and definitions should be used for the statistics; • what statistical output is needed by users; • how the data should be classified; and • how reliable do the statistics need to be?

3.1 Agricultural data items

To some, agricultural statistics means measuring how much crop is produced and how many livestock there are. Such information is usually the centre-piece of the agricultural statistics system, but other data are also important.

One way to view agricultural statistics is in terms of stocks, flows and outcomes (SIAP 1990, pp. 26-27). The agricultural statistics system must firstly measure the stocks of resources available for agriculture, including the number of farms, farm population, amount of agricultural and, rainfall, number of livestock, etc. Secondly, it must measure the flows resulting from those stocks, including crop and livestock production, inputs, farm labor, consumption, exports and imports. Finally, the statistical system must measure the outcomes in relation to the national goals and priorities. For example, if food security is a priority, indicators of nutritional status, health status, mortality, people in food deficit, etc. will be needed to evaluate the success of development programs.

To help understand the scope of agricultural data requirements, the FAO has developed a broad classification of agricultural data items (FAO 1986a). The classification identifies all major agricultural data needs, grouped under 15 headings (Table 3.1).

In addition to agriculture-related data, other more general statistics are also needed for agricultural planning and policy making, and for the evaluation of agricultural development .projects. These include data on:

• GDP/national accounts: to assess the performance of the agricultural sector in comparison with other sectors of the economy.

• Population, demography: to assess agricultural output in relation to food needs and to identify the target beneficiaries of development projects.

• Nutrition: to help in assessing food needs and planning the development of specific crops. • Health, mortality: to assess the health impacts of agricultural development. • Consumer prices: to monitor food supply. • Population censuses: to measure the social and economic status of the rural population,

including education, income, housing, access to services, and employment in agricultural industries and occupations.

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Table 3.1 Classification of agricultural data items

FAO Code Heading Description of data items 01 02 03 04 05 06 07-08 09 10 11 12 13 14 15 16-17

Identification General characteristics of farm holdings Characteristics of farm households Farm labor Land and water resources Crop cultivation Livestock Farm machine} and equipment Farm buildings and structures Ancillary activities on farm holdings Agricultural credit, marketing and stocks of agricultural commodities Agricultural prices Post-harvest crop losses Household income and expenditure Other agricultural data

Identification: number and geographical distribution of farmholdings. Type of holding (household. co-operative, etc.); use of hiredmanager: economic activities carried out (agriculture and other). Household size: demographic, anthropometric and othercharacteristics of persons in farm households. Labor force characteristics of persons in farm households (whethereconomically active, occupation, work on the holding); paid farmlabor: labor costs. Farm area: land use; land tenure: land fragmentation (number ofparcels per holding. size of parcels, etc.); irrigation; shiftingcultivation; soil types; land degradation. Area of each temporary crop planted and harvested; production and yield of each temporary crop: characteristics of permanent crops crop damage and crop loss: use of fertilizers, different seed types and pesticides; cropping patterns. Type of livestock production system; numbers and characteristics(age, sex. purpose, etc.) of each type of livestock; production of each livestock product (meat, milk, hides/skins, wool, eggs); use ofinputs. Number, type and ownership of farm machinery used for agricultural production. Number, use and other characteristics of farm buildings andstructures; construction costs. Forestry resources (existence of forest trees, area and production, etc.); fisheries resources (existence of fisheries, type, etc.). Use of credit and source; marketing activities. Prices received by farmers for agricultural produce; prices paid byfarmers for agricultural inputs; export and import prices. Amount of crop lost after harvest. Income and expenditure of farm households. Data such as physical resources, imports and exports of agriculturalcommodities, and agro-meteorology.

Source: FAO 1986a.

3.2 Statistical concepts and definitions It is one thing to decide that statistics are needed on, say, number of farm holdings or farm

labor; it is another to specify exactly what is meant by these terms and how they should be measured. In this section, we look at concepts and definitions for the measurement of agricultural data. This is one of the most difficult areas in statistical development work. Even apparently very simple concepts, such as crop area or number of livestock, are often very difficult to define precisely.

3.2 .1 Defining the agricultural sector

One of the first things to be considered in designing an agricultural statistics system is how the agricultural sector should be defined. The normal crop and livestock activities of small subsistence farmers are clearly part of the agricultural sector, but what about activities such as gathering of wild fruit, food processing, and providing agricultural services? Also, how does one:

Issues in Defining Agricultural Statistic Needs

13

handle non-agricultural activities carried out by farmers or agricultural activities carried out by non-agricultural enterprises?

One way of defining the agricultural sector is to use the International Standard Industrial Classification of All Economic Activities (ISIC) (UN 1990). ISIC classifies each economic unit (farm, business, etc.) into certain groupings according to the predominant type of economic activity carried out by that unit. Units are classified into major divisions, each of which is further subdivided into divisions, groups and classes. Agriculture, Hunting and Forestry is one major division; Manufacturing is another.

ISIC provides the basis for defining industries or sectors. The agricultural production sector can be defined as three components of the Agriculture, Hunting and Forestry Major Division, namely: (i) Growing of crops, market gardening, horticulture (Group 011); (ii) Farming of animals (Group 012); and (iii) Growing of crops combined with farming of animals (Group 013). The agricultural sector defined in this way includes only those units predominantly engaged in agricultural production activities. These agricultural units may also engage in non-agricultural activities, while some non-agricultural units may engage in agricultural production activities. Units engaged primarily in processing of crop and livestock products and hunting of animals are excluded.

The main advantage of this approach is that it provides a common basis for all economic statistics. This is especially important in the compilation of national accounts, as it ensures that each sector of the economy is clearly defined without gaps or overlaps. Thus, for example, units predominantly engaged in food processing will be included as part of the manufacturing sector, not the agricultural sector.

Another way to define the agricultural sector is on the basis of production of all agricultural commodities by all economic units (SIAP 1990, p. 19). This provides complete coverage of agricultural activities and is often used in agricultural censuses and surveys. Agricultural censuses usually cover all households with agricultural land or livestock, not just households predominantly engaged in agriculture. Ideally, crop production statistics also cover all production in the country.

3.2.2 Defining the statistical unit

One of the most important issues in planning a statistical data collection is to decide from whom the data will be collected. In most countries, much of the agricultural production activity is undertaken by households. Household surveys are therefore one of the most common types of agricultural data collection. There are various household-related concepts, such as `families' and 'households'. The most common unit used in population and related statistics is the household, defined as a group of related or unrelated persons living in the same house and making common arrangements for the provision of food (UN 1980). This definition is not always easy to apply: a person may work in town but return to his/her village each weekend; two households may live in the same dwelling; or a person may have two wives and families living in separate houses. The use of the concepts of 'defacto' (the location of a person at a certain time) and 'dejure' (the place ~k here the person usually lives) also need to be considered.

Many agricultural surveys are based on the collection of data from some type of farm unit. The concept of a farm may appear to be simple; it is anything but! Issues such as what constitutes agricultural land, the treatment of ownership and tenancy of land, and delineating separate farm units within complex family relationships need to be considered. Usually, the concept of .agricultural holding' is used to define a farm unit in agricultural statistics (see Box 3.1).

Agricultural surveys sometimes involve the collection of data from businesses. The business unit needs to be clearly identified. Two ways to define a business unit are: the activities of the business at a particular location (the `establishment'); and all activities of a business under single ownership (the `enterprise') (UN 1990, pp. 19-27).

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Box 3. 1 Defining a farm. In its Program for the World Census of Agriculture, 2000 (FAO 1995, p. 25), FAO defines an

agricultural holding as: 'an economic unit of agricultural production under single management comprising all livestock kept and all land used wholly or partly for agricultural production purposes, without regard to title, legal form, or size.'

In applying this definition, one must first clarify what is meant by land used for agricultural purposes. Should it include only land formally classified as agricultural land? Should small vegetable plots in the village area be included? Should urban households be considered as agricultural holdings if they have small vegetable plots? Perhaps a minimum size criterion should be applied to exclude anyone with less than, say. 0.02 Ha of cultivated land.

Then there is the question of whether agricultural holdings should be identified by ownership or operation. For agricultural statistics it is usually better to measure land operators than land owners, as suggested by the word 'used' in FAO's definition. Sometimes, the same land is operated by two households; a land owner may grow rice on land during the wet season, but rents out the land to someone else during the dry season. How should this be treated without double counting?

It is sometimes hard to distinguish between a person renting land and one working as an employee for a land owner. Land can be rented under various arrangements: a fixed amount of money or produce, a fixed share of the produce, exchange of labor or other services, etc. If B operates A's land in return for, say, 30% of the crop, B may be identified as the agricultural holding. However, this arrangement is little different from A paying B 70% of the crop in return for the labor B provided to produce the crop (in which case, A is the agricultural holding). The key issue is who is making the farm operational decisions.

Determining relationships between family members is another problem in defining agricultural holdings. Land is commonly owned by different members of the same family due to past land distribution schemes or legal limits on the amount of land that any individual can own. If a husband and wife both own land but operate it as a single unit, this obviously represents one agricultural holding unit. But what if the land is in the children's names? As a son grows up and has his own family, he may operate his land independently from his parents, even though he may continue to live in the same house as his parents. Some people may operate their own land and in addition have land that they operate in partnership with someone else. Should this be considered as two agricultural holdings, that is, one for the individual and one for the partnership?

Then there are agricultural holdings with livestock but no land. What about someone with just a few chickens? This is common in many countries, even in urban areas. A size criterion could be applied but this can be difficult to specify. For example, a minimum criterion of two cattle/buffaloes, five pigs/sheep, or 20 poultry could be applied, but what if someone has one cow, four pigs and 15 chickens? There is also the problem of whether to count animals according to whether they are owned or kept. The FAO definition uses the word 'kept'. Even this is not clear-cut as there can be complicated arrangements for looking after livestock. For example, B may look after A's cows and, in exchange, gets to keep the cow dung. Alternatively, B may take A's yaks to winter pastures, in exchange for slaughtering a certain number of animals. The question of who is making operational decisions is once again the key to defining the agricultural holding. (In the case of the yaks, is A or B the operator?)

The definition of an agricultural holding also assumes that the concept of a household is clearly understood (see main text). In practice, the agricultural holding is usually the same unit as the household. Since agricultural production operations are inter-related with the household management.

3.2.3 Defining data items

Let us now look at the problems in defining some of the more important agricultural data items. Crop area can be measured by the area planted or the area harvested (FAO 1982b, p.

9). The area planted may be important to measure seed requirements and the potential productivity of the land, whereas the area harvested reflects the actual productivity. The reference period needs to be clearly specified; the statistics can refer to the calendar year, financial year, or agricultural year.


15

How should crops planted in December and harvested in March be considered in reporting data for a calendar year?

There are also various concepts of crop yield and production (FAO 1982b, pp. 11-12). The biological yield refers to the production which could be achieved if there were no harvest or post -harvest losses. The harvested yield refers to the actual crop harvested taking into account harvest losses, but ignoring losses in post-harvest operations such as cleaning, threshing, and winnowing and drying the economic yield is the yield after post-harvest operations.

Income is a very difficult concept, even without considering the reporting problems usually inherent in the collection of such sensitive data. In many countries, the cash economy is not importent in the agricultural sector, with most agricultural produce being used for home consumption. How should this be treated in measuring income? Barter arrangements are also common. Farm laborers often receive remuneration in non-cash form, such as agricultural produce, or perform work under some sort of labor exchange arrangement. Food or housing may often be provided even where cash payments are made (UN 1977). There is also the question of whether the imputed rental value of a person's own property should be included as income.

Employment and unemployment are two other very difficult concepts, especially for the agricultural sector. Broadly, a person is employed if he or she is working. How should `work' be defined? The concept of `economic work' is usually applied (this excludes household work), but this may not be suitable in a farm situation where the dividing line between household work and work on the farm is blurred. Unemployment is even more difficult to measure because it is just an abstract concept. Some concrete criteria are needed to determine whether a person is unemployed. The generally used international definition of unemployment stipulates that a person of working (often 15 years of age and over) is unemployed if he/she satisfies three criteria: (i) not working; (ii) available for work; and (iii) actively looking for work (ABS 1986).

Other employment-related concepts can sometimes provide a better picture of labor force conditions than employment and unemployment. Concepts such as full- and part-time work and under-employment can be useful. In rural areas of developing countries, unemployment is often not of primary interest since everyone in farm households does some farm work, especially at planting and harvesting time. Under-employment may be of more concern because of the seasonality of agricultural work and the lack of alternative employment opportunities.

One of the keys to defining data items is defining the reference period, taking into account conceptual, operational and seasonal factors. Livestock numbers can only be meaningfully collected in respect of a single point of time. For household expenditure data, a reference period of even one year may be needed because of irregular or seasonal expenditure patterns. For employment and unemployment data, a one week reference period is often used.

International standards in data concepts and definitions have been developed for different areas of statistics. These are intended to help countries in planning statistical data collections and to promote comparability in statistics between countries. FAO has provided various standards and guidelines for agricultural statistics. The United Nations Statistical Office has provided guidelines for population, income and other household statistics; ILO has done the same for employment, unemployment and other labor-related statistics. The international standards are meant to be guidelines only, with each country taking into consideration local conditions and user needs.

3.3 Frequency and geographical level of data

Two of the most important issues in defining statistical needs are how often the data should be produced and at what geographic level the data are needed. These issues have important implications for how a statistical system is designed and how much it will cost. A statistical system to provide monthly data on livestock slaughtering in each district would need to be designed

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differently from one which only provided annual data on livestock slaughtering at the national level. The former system would also cost more.

Some statistical information is needed annually; some may only be needed from time to time, whereas other data may be needed as frequently as daily or even hourly. Crop cultivation is based on an annual cycle and therefore much of the basic crop statistics, such as crop area and production, is needed annually. In South and Southeast Asia, where there are distinct wet and dry seasons, the crop statistics usually need to show cropping activities separately for both seasons. Some other crop-related statistics, such as the use of fertilizers, are often also required annually to monitor changes in farm practices and improvements in productivity.

Data on livestock numbers and production are usually also needed annually to measure the performance of the livestock sector. However, it is not always necessary to undertake annual data collections, because current livestock statistics can often be estimated using past data on the age sex structure of livestock herds (which are often available from an agricultural census) and information on fertility, mortality and slaughtering rates. In-depth livestock production surveys may need to be conducted from time to time.

Many farm characteristics such as farm size, farm labor, income and land tenure, do not change very much from year to year and data need not be provided every year. Analyzing year to year changes in average farm size, for example, would be unlikely to show any significant features, whereas an analysis of changes over a five or ten year period could highlight important structural changes.

Prices of agricultural commodities change daily (or even hourly) and data need to be available frequently if they are to be useful for making market-related decisions. The consumer price index is used to monitor general price trends and the cost of living and is usually produced monthly or quarterly.

The geographical issue is important because the data collection methodology may depend directly on it. If land use data are needed for each village, a census or land use mapping exercise would be needed. If, on the other hand, data are required at the national and provincial levels only, then a sample survey of farmers could be suitable. In a sample survey, the geographical level of data required directly affects the sample size. A sample of about 3,000 rice farmers might be required to provide rice production statistics for each of the, say, 15 provinces in a country; if data were needed for each of the, say, 100 districts, the sample size might need to be increased to about 20,000.

For agricultural planning and policy making, most interest centers on the national and, to a lesser extent, state or provincial statistics. For crop production statistics, for example, the national data are fundamental in assessing food supplies and monitoring the contribution of agriculture to the economy. State/provincial level data are used by the national government and the state/regional administrations to assess agricultural conditions in different parts of the country and to help plan development activities. Data at lower geographical levels (districts, villages, etc.) are useful for project planning and for lower level administration.

In considering the geographical level of data required, one needs to be aware of the uses of the data and the decisions that depend on the data. The national and state/provincial governments are usually the key players in agricultural planning and policy making. Lower level administrative units usually have only limited decision making powers and little funding. The statistical priorities should be determined accordingly.

3.4 Types of statistical presentations required

In this section, we examine another important issue in defining data needs namely, the types of statistical presentations required.


17

Where countries have relied on reporting systems for their agricultural statistics (see Chapter 4), the range of statistical information provided has usually been quite limited. For statistics on livestock herds, for example, the only data often available are the numbers of each type of livestock. There is a lot more to be gleaned from the statistics, without collecting additional data.

To illustrate, let us consider the Livestock Pilot Survey, 1995, Lao PDR (Lao MAF 1995b). This was a household survey undertaken in selected provinces to provide data on livestock herds. Households reported the numbers of each type of livestock owned. The primary output showed the numbers of each type of livestock in each province (Table 3.2).

Table 3.2 Livestock numbers ('000) by type for selected provinces, Lao PDR, 1995.

Type of livestock Vientiane Oudomxay Cjiampasack

Municipality

Cattle 59.2 29.6 129.2 Buffaloes 64.3 37.4 110.5 Pigs 17.3 101.8 69.8 Chickens 766.2 480.4 711.6

Source: Lao MAF 1995b. What else can we learn about the livestock from the information collected? As well as the -number

of cattle, buffaloes, etc., it is also useful to look at the ownership of those animals. information on the numbers of households owning each type of livestock provides insight into the livestock activities of farmers in different areas (Table 3.3). The table highlights the importance of pigs to farmers in Oudomxay, compared with the other provinces. Table 3.3 Number of households owning each livestock type ('000) for selected provinces, Lao

PDR, 1995.

Average herd sizes are another interesting aspect of the data (Table 3.4). Although a higher

percentage of farmers in Oudomxay own pigs, pig herd sizes are only slightly higher than in other provinces.

Table 3.4 Average livestock herd size by type for selected provinces, Lao PDR, 1995 Type of livestock Vientiane Oudomxay Champasack Municipality Cattle 5.1 3.1 4.7 Buffaloes 3.7 2.8 2.6 Pigs 3.1 4.0 2.0 Chickens 27.0 16.2 15.0

Source: Lao MAF 1995b.

Farm area

No. of cattle/buffaloes No land < 1.00 ha 100-199 ha 2.00-299 ha ≥ 3.00 ha Total 1 or 2 head 4.4 13.0 12.3 5.1 4.7 39.5 3 or 4 head 22 7.6 10.9 8.4 3.9 33.2 5 to 9 head 1.6 5.9 11.7 4.5 4.6 28.3 10 or more head 0.6 0.7 2.6 3.2 4.3 11.3 Total 8.7 27.2 37.4 21.2 17.6 112.2


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As well as knowing how many households own livestock, we may also be interested in how many households have different herd sizes. A frequency distribution, showing households according to how many animals they own, highlights the patterns of livestock ownership (Table 3.5).

Table 3. 5 Number and percent of cattle owners by number of cattle owned - selected provinces,

Lao PDR, 1995

Cross-tabulations are useful for examining relationships between variables. For example, the distribution of livestock owners according to the number of livestock owned and farm area enables us to compare the livestock distribution for small and large farms (Table 3.6). Table 3.6 Number of livestock owners ('000) by number of animals and farm area, four province total*,

Lao PDR, 1995

* The four provinces are: Vientiane Municipality, Oudomxay, Champasack and Borikhamxay.

Other types of presentations are also used in agricultural statistics. Price indices provide a useful way of showing overall trends in prices. The concentration index measures the equity of land distribution. The Gini coefficient measures the equity of income distribution.

3.5 Classifications

The term classification describes the grouping of data into categories. The geographical dimension is one example of a classification. The industrial classification, ISIC, discussed in Section 3.2, is another. Other classifications are also used in the collection and presentation of agricultural statistics and these needs to be articulated in defining data needs.

3.5.1 Classification of crops

To present crop statistics, a way of classifying crops by crop type is needed. Crops are normally described as either temporary or permanent. Temporary crops are those which need to be newly planted for further production after the harvest; permanent crops do not need to be newly planted each year. One way to classify each crop under these two headings is by its botanical name. For statistical purposes, a better way is to categorize crops by their end-use. Thus, sugar

Vientiane Municipality Champasack

No. of cattle '000 Percent

Oudomxay'000 Percent

'000 Percent I or 2 head 3.7 32 4.9 51 12.3 45 3 or 4 head 3.2 27 2.8 29 5.5 20 5 to 9 head 3.3 28 1.8 19 6.6 24 10 or more head 1.5 13 0.1 1 3.0 11 Total 11.6 100 9.6 100 27.3 100


Farm area

No. of cattle/buffaloes No land < 1.00 ha 100-199 ha 2.00-299 ha ≥ 3.00 ha Total 1 or 2 head 4.4 13.0 12.3 5.1 4.7 39.5 3 or 4 head 22 7.6 10.9 8.4 3.9 33.2 5 to 9 head 1.6 5.9 11.7 4.5 4.6 28.3 10 or more head 0.6 0.7 2.6 3.2 4.3 11.3 Total 8.7 27.2 37.4 21.2 17.6 112.2



19

cane (botanical name Saccharum officinarum) is assigned to one of three groups according to it’s or the production of sugar, fodder for animals, or thatching.

In its Programmed for the World Census of Agriculture, the FAO recommends the following main groupings for temporary crops: cereals used for grain; tuber, root and bulk crops; leguminous plants used for grain; industrial crops; vegetables for human consumption; special horticultural crops; fodder crops; crops grown for seed; and other. Permanent crops are assigned to two main groupps: fruit and nut trees; and industrial crops. Further levels of sub-division are given so that, for example, industrial crops (temporary) are classified according to: sugar; oilseed crops; spices; fibre crops: and other. These are further subdivided; oilseed crops, for example, are classified according roundnut, soybean, etc. (FAO 1995, pp. 73-79).

3.5.2 Land use classification

In presenting statistics on agricultural land use, one needs to consider how such concepts as productive land, agricultural land and arable land will be defined. The FAO has provided recommendations in its guidelines for agricultural censuses (FAO 1986b, 1995). The land use classification used for the National Sample Census of Agriculture, Nepal, 1991/92 was adapted from the FAO guidelines (Table 3.7).

Table 3 7 Land use by agricultural holdings, Nepal, 1991/92

Source: Nepal CBS 1994a The presentation in Table 3.7 clearly defines the different categories of land. Thus, under this

classification, arable land refers to land under temporary crops, temporary meadows and 'allow land, but excludes permanent crops. Agricultural land covers arable land, permanent crops and permanent pastures. Woodland and forest is considered non-agricultural land.

3.5.3 Other classifications

Other classifications used in the presentation of agricultural statistics include: • Farm size: typical ranges are: less than 0.50 ha; 0.50 - 0.99 ha; etc. One of the important uses

of the farm size classification is to identify what may be called small, medium and large farms.

• Age of population: age data are often presented in five year age ranges: 0 to 4, 5 to 9, 10 to 14, etc.

Land use Area ('000 ha) Percent Agricultural land 2,392.9 92.1

Arable land 2.323.4 89.5 Land under temporary crops 2,284.6 88.0 Other arable land (meadows, fallow) 38.8 L5

Land under permanent crops 29.4 1.1 Permanent pasture 36.9 1.4 Ponds 3.3 0.1

Non-agricultural land 204.5 7.9

Woodland and forest 108.8 4.2 Other land 95.7 3.7

Total area of holdings 2,597.4 100.0

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• Income: income ranges need to be suitable for identifying particular categories of households, such as those below the `poverty line'.

3.6 Reliability of data

One of the most important issues in defining statistical needs is specifying how accurate the data should be. This is especially important in sample surveys, where the reliability requirements directly affect decisions about the sample size. It is sometimes difficult for users to accept any errors in statistics. There are errors (sampling and non-sampling errors) present in all data collections; including censuses (see Section 5.1). The reliability needed depends on how the data are to be used. A survey to measure the health risks of a particular drug would need to provide very accurate data, because errors in the data could lead to incorrect decisions being made about the use of the drug which could result in (i) human suffering or (ii) banning of a useful drug. Box 3. 2 Rice production statistics, Cambodia: what accuracy is needed?

In Box 2.1, we saw how statistics can be used to assess the rice needs and availability in a country. A lot

of different types of data were used in the analysis each is subject to error. What implications does this have for the results of the analysis?

In Cambodia, the production of rice paddy in 1995 was estimated as 3.3 million tons (WFP 1996). This was the best crop in many years and resulted in a surplus of milled rice of 140,000 tons. The rice production statistics were obtained from reports provided by commune officials and from crop cutting surveys. These data were far from perfect.

What if the production figure was wrong? Suppose the correct figure was 3.2 million tons. This is 3% lower, which would translate into about 50,000 tons less milled rice available for food and a much smaller surplus of about 90,000 tons. This may seem serious, but it needs to be put into perspective given the accuracy of other data used in estimating the rice surplus.

To determine the rice available for food in the country, the paddy rice production is adjusted to take account of:

• Post-harvest crop losses. Often, data are not available and it is necessary to make assumptions based on studies in other countries. The percentage of grain lost during post-harvest processing is variously estimated as between 10 and 15%.

• Grain used for seed. Data are often unavailable and assumptions are made, using the experiences of other countries.

• Milling losses. A conversion factor of 62% is often assumed. • Rice exports and imports. External trade statistics are weak in many countries and usually fail to

reflect the (often very significant) unofficial trade. • Food stocks. The quantity of food stocks in private hands is usually difficult to estimate.

To determine the amount of rice needed to feed the population, data on population and per capita rice consumption are needed. Accurate population estimates are not always available; in Cambodia, there has been no recent population census undertaken, and there are a number of different population estimates. For per capita rice consumption, reliable data are often not available. There are large differences in food consumption patterns between countries and it is not always appropriate to apply data from other countries.

The rice balance calculations are very sensitive to the assumptions about food consumption. An increase of only 5 kg in the per capita annual rice consumption in Cambodia, from say 151 to 156 kg per year, would mean an increase of about 50,000 tons in the consumption of rice. This would have the same effect on the rice surplus/deficit as a 3% understatement in the paddy production.

Although it is important for a country to have reliable rice production statistics, it is not worth putting excessive resources into getting 'perfect' production statistics, if the other data used in the food analysis are of very poor quality.


21

More commonly, some errors in the statistics can be tolerated. Consider data on fertilizer usage in Lao PDR obtained from farm surveys undertaken in 1994 and 1995 (Lao MAF 1995a; 1996). In the 1994 survey, an estimated 29% of rice growers in Champasack Province and 14% in Borikhamxay Province used chemical fertilizers. The conclusion users would seek to draw from these figures is that: `fertilizer is more widely used by farmers in Champasack than in Borikhamxay'. This conclusion would still be valid even if the estimates were subject to quite high errors. The situation may be different if the main focus was on comparisons between the two surveys. The percentage of rice growers using chemical fertilizers in Champasack increased from 19% in 1994 to 34% in 1995. The conclusion users would like to make is that: `the use of fertilizer in Champasack increased between 1994 and 1995'. Any errors in the estimates would make it difficult to make such conclusions. The issue of `level' and `movement' estimates is discussed further in Section 3.7.

Often, in statistical presentations, the user is not so much interested in each individual figure in the presentation, as in the overall picture provided by the presentation. Consider the farm size distribution table from the farm survey undertaken in Lao PDR in 1994 (Table 3.8).

Table 3.8 Distribution of farms by farm size - Oudomxay and Champasack

Provinces, Lao PDR, 1994. No. ('000) Percent No. ('000) Percent< 0.50 ha 3.6 12 2.2 4 Q50 - 0.99 ha 11.9 38 9.4 17 1.00 - 1.49 ha 9.1 29 13.9 25 1.50-1.99ha 3.8 12 9.8 18 2.00 - 2.99 ha 2.2 7 12.0 22 ? 3.00 ha 0.4 1 7.8 14 Total 31.0 100 55.1 100 Source: Lao MAh

The main interest is not in each figure in the table, but in the overall shape of the farm size

distribution and the differences between the two provinces. The significant feature of the table is that Oudomxay tends to have smaller farms than Champasack, not that, for example, there are 9.400 farms in Champasack with between 0.50 and 0.99 ha of land. It doesn't matter if each individual figure is subject to even a large error, provided this does not affect the shape of the distribution.

3.7 Level and movement estimates Where statistics are to be provided on a regular basis such as monthly, quarterly or annually it is

important to be clear about what information is required by users and how it will be used. If one looks at maize production in Nepal in 1992/93 (Table 3.9), attention could focus on either of two things: (i) maize production in 1992/93 was 1,290,500 tons, or (ii) maize production increased by 85,800 tons (or 7.1%) between 1991/92 and 1992/93. The first figure is a`level' estimate; the second is a`movement' estimate. The level estimate is important, inter alia, to calculate the food availability; the movement estimate is used to measure changes over time.

Table 3. 9 Area, production and yield of maize, Nepal, 1990/91 to 1992/93

1990/91 1991/92 1992/93Area harvested ('000 ha) 7~7.7 754.1 775.2Production ('000 tons) 1,231.0 1,204.7 1,290.5Yield (tons/ha) 1.62 1.60 1.66Source: Nepal MOA 1993.

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The emphasis to be given to level and movement estimates should be stipulated in defining data needs because it affects the design of the data collection. This applies particularly to sample surveys, where it is much more difficult to accurately measure movements than levels. Just because one has a reliable level estimate, it does not follow that the corresponding movement estimate will also be reliable. If maize production in Nepal in 1992/93 was over-estimated by only 50,000 tons, the percentage increase in maize production between 1991/92 and 1992/93 would fall from 7.1 to 3.0%. If movement estimates are important, sample surveys can be designed to provide greater reliability for those estimates. This is achieved by keeping the same sample units from one period to the next.

3.8 Timing of data needs

Another important factor in defining data needs is specifying when the statistics are needed. Governments often have commitments which impose a timetable on the statistics. Some examples are: a regular donors meeting, during which the crop statistics are used to assess the food situation in the country; the government's annual budget, which includes presentations of current and forecast statistics; and the preparation of national accounts or other national statistical summaries.

Sometimes, the timetable for data is difficult to achieve. The first crop statistics are often required before the end of the year, prior to the end of the crop harvest. To meet such requirements, a systematic programmed for release of statistics is needed, with data released as 'forecasts', `preliminary estimates' and `final estimates'. For the typical agricultural season in South or South East Asia, forecasts could be provided in September, based on crop plantings. Further information could be released in December or January in the form of preliminary estimates, based on harvest information available at that time. Final estimates could be released in March or April once the final crop surveys have been completed.

Censuses and surveys take time to conduct and process and these needs to be borne in mind when planning the timetable for release of statistics. Where reporting systems have been widely used for collecting statistics, users have become used to getting data quickly, sometimes even before the event! To conduct a farm survey, time is needed to interview farmers and take crop measurements, return questionnaires to head office, process and check data, and prepare the results. A hastily conceived and implemented survey will result in poor quality, statistics. For a survey of several thousand farmers, to release the results a few months after the data collection is a satisfactory achievement. Examples of recent farm surveys carried out in the Asian region, and their release dates, are:

• Pilot Livestock Sample Survey, Mongolia: sample size 1,006 households, data collected June 1994, results issued November 1994 (Mongolian SSO 1994).

• Livestock Pilot Survey. Lao PDR: sample size 1,714 households, data collected May1995, results issued September 1995 (Lao MAF 1995b).

• Rice Crop Survey, Lao PDR: sample size 3,266 households, data collected October 1995to January 1996, results issued March 1996 (Lao MAF 1996).

• Rice Crop Pilot Survey, Cambodia: sample size 1,888 households, data collected October1995 to January 1996, results issued February 1996 (Cambodian MAFF 1996).

An agricultural census takes even longer. For the National Sample Census of Agriculture, Nepal, 1991/92, data were collected from 122,270 farmers during the first half of 1992; the first results were released in mid 1993, with full results available early in 1994 (Nepal CBS 1993). This was considered an outstanding achievement, due in part to the support received from the UNDP and FAO.

The Australian Bureau of Statistics releases data from its monthly labor force survey of 30,000 households about a month after the data collection (ABS 1996c). This is achieved


23

well-managed field systems and advanced computer and communication facilities. For the 1996 Australian Population Census, which was undertaken in August 1996 and involved the collection of data from 6 million households and 18 million populations, the first results will be issued in July 1997 with the final results to be available by March 1998 (ABS 1996a).

It is best to have a fixed timetable for the release of regular statistics as this helps foster the perception that the statistics are objective and not subject to political intervention. A fixed timetable can also help businesses, especially where financial markets are sensitive to the statistics.

3.9 Specifying output requirements for a census or survey

When an agricultural census or survey is to be conducted, the data needs must be specified in detail at the very beginning of the planning phase. The various issues discussed in this chapter must be given consideration. Output table formats should be prepared, showing what data are to be tabulated and how the information will be presented. The table formats should be shown exactly as the data will appear in the final statistical report (FAO 1996a, pp. 69-77). An example of output specifications for a survey of rice production in Cambodia is shown in Figure 3.1.

What do these output specifications tell us about the types of issues raised in this chapter? The table formats show:

• the data items to be collected (rice area harvested by variety, use of fertilizer and pesticide, sex of household head);

• the statistical presentations required (area harvested, counts of rice growers, cross tabulations); • the geographic level required (some data are required for each province, other data for the

country as a whole): • the reference period (calendar year 1995); and • the classification required for the rice area distribution.

Output specifications help in the planning and design of the census or survey, especially in regard to the questionnaire design, the data processing system and the publication programmed. They also help in the sample design. The specifications in Figure 3.1 indicate that a sampling approach would be feasible, with a sample of perhaps 200 rice growers in each province. (The ample size would need to be larger if district data were needed, if Tables B, C and D were required at the provincial level, or if more detailed area ranges were required in Table B.)

Specifying output requirements in advance is crucial to ensuring that user needs are met without this, the user may find, after a survey has been conducted, that the questionnaire does not provide the information needed. For example, to produce Table D in Figure 3.1, it would be necessary for farmers to report on the use of fertilizer for each of the two rice varieties. If the output tables were not specified beforehand, the questionnaire might be designed to ask only for the use of fertilizer for the farm as a whole; this would make it impossible to produce 'Table D. conversely, not pre-specifying output requirements could result in the questionnaire including redundant questions. In Figure 3.I, fertilizer data are required to be collected for each rice variety (for Table D), but pesticide use is not (Table C).

In specifying data needs for an agricultural census or survey, users should be aware that might not always be operationally feasible to collect the statistics exactly as specified. Often, other related measures (or `proxies') are used. For example, household income might be deemed to be too expensive or difficult to collect because of the predominance of home consumption, barter arrangements or non-cash income. Other data reflecting a household's wealth or spending power such as household expenditure, size or type of structure of the household's dwelling, ownership of housesehold appliances or farm equipment, and occupation could be collected. Such data might even already be available from another source.

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In planning an agricultural census or survey, users generally want full coverage of all agricultural activities. This is sometimes not feasible because of cost or operational problems. Sometimes, major cities or remote areas are omitted and very small operators are excluded. Often, it comes down to a question of whether it is worth spending, say, 30% of the budget for a census to cover the last 5% of the agricultural land (and, even then, perhaps not cover it well). Omitting 5 or even 10% of agriculture from a census or survey does not diminish its usefulness, especially for analyzing trends Figure 3.1 Example output table specifications

Table A Rice area harvested ('000 ha) by variety and province, Cambodia, 1995.

Province Local Variety Improved Total

Phnom Penh Kandal Kampong Cham Svay Rieng

" “

Cambodia

x.xxx.x x,xxx.x x,xxx.x x,xxx.x x.xxx.x x,xxx.x

x.xxx.x

x,xxx.x x.xxx.x x.xxx.x x,xxx.x x.xxx.x x.xxx.x

x,xxx.x

x,xxx.x x.xxx.x x.xxx.x x.xxx.x x,xxx.x x,xxx.x

x.xxx.x

Table B Percent of rice growers by rice area harvested and sex of household head, Cambodia, 1995.

Rice area harvested Male Sex of household head

Female Total Less than 0.50 ha 0.50 - 0.99 ha 1.00 - 1.49 ha 1.50 - 1.99 ha 2.00 ha and over Total

Average area harvested (ha)

xx.x xx.x xx.x

xx.x xx.x 100.0

xx.x

xx.x xx.x xx.x xx.x xx.x 100.0

xx.x

xx.x xx.x xx.x xx.x xx.x 100.0

xx.x

Table C Number of rice growers ('000) by use of fertilizers and pesticides, Cambodia, 1995.

Use of fertilizer Used Use of pesticides

Did not use Total Used fertilizer x.xxx.x Did not use fertilizer x,xxx.x

Total x,xxx.x

x,xxx.x x,xxx.x

x,xxx.x

x.xxx.x x,xxx.x

x,xxx.x

Table D Number of rice growers ('000) by use of fertilizers and rice variety, Cambodia, 1995.

Use of fertilizer Local Variety Improved Total

Used fertilizer x,xxx.x x.xxx.x x,xxx.x Did not use fertilizer x,xxx.x x,xxx.x x,xxx.x

Total x,xxx.x x,xxxx x,xxx.x


25

Box 3. 3 Output tables and questionnaire design

Output tables and questionnaires sometimes cause confusion. They are two different things: the output tables set out the final results of a census or survey, whereas the questionnaire is simply the vehicle used to collect the information required to produce those output tables.

The format of a survey questionnaire typically does not resemble the survey output tables. If data on unemployment are required, the word 'unemployed' will not be used in the questionnaire. Instead, a series of questions on work status, job search and availability for work will be asked. The unemployment figures, shown in the output tables will be calculated based on the answers to those questions. A well-designed questionnaire can also seem rather incomprehensible, because it is structured so that enumerators only ask relevant questions. For unemployment, for example, it is not necessary to ask questions about job search for people who are working (e.g... ABS 1991).

A common weakness in surreys is that survey questionnaires are poorly designed and fail to adequately reflect the output needs. A typical example in agricultural surveys is asking farmers to report detailed information on their cropping system for each parcel of land (Figure 3.2).

Figure 3. 2 A poorly designed questionnaire. Record all of the crops grown by this farmer during 199 5 on each parcel of land.

Parcel I Parcel 2 Parcel 3 Parcel 4 Parcel 5 Parcel 6

Area (ha) Crop I

Description ............. ........... ............ ............ ............ ............ Variety ............. ........... ............ ............ ............ ............

Crop 2 Description ............. ........... ............ ............ ............ ............ Variety ............. ........... ............ ............ ............ ............



Crop 5

Description ............. ........... ............ ............ ............ ............ Variety ............. ........... ............ ............ ............ ............

This information is very interesting to analyze for an individual farmer, but is very difficult to summarize for statistical purposes. The user is probably interested in data such as the percentage of farmers double cropping, the area of double crops/mixed crops, and cropping intensity. These measures cannot be derived from the questionnaire as it stands. (The questionnaire is also not suitable for data processing).

Descriptive or qualitative information collected in a questionnaire is also often not useful for providing statistical output. Asking farmers to describe the problems they face serves no statistical purpose unless ~answers can be classified into various problem categories, such as: need for credit, water shortages, etc.

One point often overlooked in designing questionnaires is that one additional question on a questionnaire does not mean one additional piece of information or one additional output table for the user. Each additional question multiplies the amount of information potentially available. If a question on sex of household head is 'added to a survey questionnaire, all output data can be classified by male and female. There is a strong interest in the gender issue in agriculture, and adding this one question to agricultural surveys can provide much important information on gender differences.

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3.10 Framework for planning of the agricultural statistics system In this chapter, we have discussed the various issues to be considered in defining statistical' needs

and how all these are brought together to specify output requirements for an agricultural: census or survey. To complete our discussion on data needs, we will now look at the overall' agricultural statistics requirements, and how to plan the programmed of data collection activities to meet those needs.

In many countries, the current agricultural statistics system simply consists of basic crop area and production statistics, provided annually. Agricultural planners and policy makers, faced with a policy or planning problem, find that they need much more data than this. The response is usually to run a survey. Because of the immediacy of the problem, the survey is usually designed and conducted very quickly and the data quality will often be poor. (One often finds that policy" analysts spend most of their time on data collection work, rather than on solving policy problems.)

What is needed is an integrated agricultural statistics programmed to provide planners and policy makers with data covering the whole spectrum of information needs. This should include not only annual crop production statistics, but also other regular and irregular data required, such as cost of production, income and expenditure, employment, prices, etc. The statistical programmed should also provide for an agricultural census to be conducted from time to time (usually every ten years). One cannot always anticipate future policy problems, but a well-planned agricultural statistics system can go a long way towards meeting data needs as they arise.

One approach to planning the agricultural statistics system is to use 'information modeling'. Information modeling would provide a framework for describing the decision making processes for agriculture, the factors relevant to the decision making, the information needed about those factors, and the inter-relationships between the various information (Simsion 1994). The information model can be presented as a diagram showing these processes and relationships. Definitions and classifications for data items can also be provided.

To help plan the agricultural statistics system, it is useful to list all the agriculture-related data items (as in Table 3.1); identifying who needs the data, what geographic level is required, how often the data are required, when the data are needed, and the priority attached to the data. A short summary of the needs for crop statistics in a typical rice based agricultural country of the Asian region is shown in Table 3.10. Note that this is only one component of the agricultural statistics system, specifically Data Type 06 in Table 3.1. A similar plan is needed for the other 14 categories of data.

With such a plan, the government is able to assess how the data requirements can be met and what types of data collections are needed. By examining the needs for crop statistics in Table 3.10, for example, we can see that the following data collections would be needed:

• an annual sample survey or reporting system to provide national and provincial data on rice plantings;

• annual sample surveys or reporting systems to provide national and provincial data on the production of rice and other major crops;

• an annual survey of fertilizer suppliers to provide national and provincial data on the quantity of fertilizer used;

• a triennial sample survey to provide more detailed crop cultivation data, such as use of inputs and cropping patterns, at the national and provincial levels;

• a triennial data collection to provide national and provincial data on the production of secondary crops and permanent crops; and

• a decennial agricultural census to provide detailed crop data, down to the district level.


27

Table 3.10 Example of outline needs for crops statistic in a typical agricultural statistics system

Description of data Frequency Geographic level Priority 1. Area of wet season rice planted by variety

(required by October Annual National

Province High Medium

2. Wet and dry season rice crop: area planted, area damage, area destroyed, area harvested, production, yield

Annual National Province

High High

3. Other main temporary crops: area harvested, production for each crop

Annual National Province

High High

4. Number or farmers growing each main temporary crop, cropping pattern

Triennial Decennial

National Province National Province District Village

High High High High High Low

5. Crop area for secondary crops Triennial Decennial

National National Province District Village

High High High High Low

6. Amount of fertilizer used by type Annual National Province

High High

7. Numbers of farms using fertilizers, pesticides, improved seeds

Triennial Decennial

National Province National Province District Village

High High High High High Low

8. Production of permanent crops Annual Triennial

National Province National Province

Medium Low Medium High

9. Characteristic of permanent crop: area, number of trees, productive, and non productive trees

Decennial National Province District Village

High High High Low

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Sources of Data for Agricultural Statistics-Reporting System

29

4. Sources of Data for Agricultural Statistics - Reporting Systems

The usual textbook approach to agricultural statistics is to focus on the use of agricultural censuses and surveys as the primary means of collecting data. Censuses and surveys are very expensive to conduct and countries often find it difficult to commit sufficient funds or personnel for such data collection operations. A cheaper option is needed. Many countries use the 'reporting system' method, in which the statistics are compiled based on reports provided by field workers, such as village officials, district agricultural officers, agricultural extension workers, or certain 'key informants'. Such reporting systems are commonly used for providing current crop and livestock statistic.

Reporting systems for agricultural statistics find ready application in socialist countries, where the state exercises some degree of control over the agricultural sector. In a collectivized -system of agriculture, most data can be obtained directly from the state enterprises concerned. Where there is private ownership of land, the land taxation system often provides a source of data. This is often administered by the village head that is required to maintain records of the area of land owned by each farmer. Sometimes, farmers are taxed according to land type, land use or crops grown, and this can further help in the reporting of crop statistics. Cadastral information is also commonly available. Depending on the circumstances, local officials sometimes may also have information on crop production (through state procurement of agricultural produce) or input use (which may be distributed through a government agency). Where such administrative information exists, the reporting method often provides quite reliable statistics.

However, reporting systems often go beyond the reporting of available data. For crop production and yield, in particular, local officials generally have little factual information on which :~ base their reporting and tend to rely on their own assessments. Such data are usually not very reliable.

Reporting system - case study, Cambodia

Cambodia uses a reporting system for its regular crop and livestock statistics. The approach is similar to that used in many other countries of the Asian region, including Lao PDR, Viet Nam, Myanmar and Nepal.

There are five levels of administration in Cambodia: national, province, district, commune and village. The basis of the statistical reporting system is the village and commune administrations. In each village, there is a village head whose responsibilities include reporting of statistics. A similar administrative structure is also in place in each commune. Village heads. Prepare estimates of the area and production of rice and other major crops and these estimates are submitted to the commune administration. Commune officials calculate commune totals, which are the submitted to the district agricultural office. District totals are then calculated and submitted to the provincial agricultural office. Finally, provincial totals are submitted to the Ministry of agriculture, Forestry and Fisheries in Phnom Penh. The final statistics are issued by the Ministry in Phnom Penh.

Reporting system - case study, Mongolia

Reporting systems are usually very effective in collectivized systems of agriculture, such as existed in Mongolia up until the late 1980s. Each livestock co-operative and state farm reported

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statistics to the aimak (province). Aimaks prepared provincial totals which were submitted to the State Statistical Office and the Ministry of Food and Agriculture in Ulaan Baatar.

The reporting method for agricultural statistics has some advantages over other data collection methods.

First, it is cheap and easy. The preparation of statistical reports is usually part of the responsibilities of the reporting officials. Usually, they are not required to do any data collection, work as such -just submit a report.

Second, statistics can be produced quickly. Reports can be prepared immediately after or (even before!) the event and can be quickly forwarded on by telephone, radio, fax or through computer modems. Because data are summarized at each administrative level, no central processing of data is required. In Mongolia, data are transmitted to aimaks by telephone or radio and each aimak transmits aimak summaries to Ulaan Baatar via a computer link. The statistics are often available within a few days.

Third, the reporting system can be seen as a good way of tapping into the stock of knowledge of local officials and field workers.

Lastly, it is usually possible to produce more detailed data than are available from other sources. For example, data on minor crops or estimates of year-to-year change often cannot be provided from sample surveys because of high sampling errors.

Statisticians tend to spurn the reporting system approach because it does not conform to, sound statistical theory and practice. The method is seen as having a number of weaknesses.

First, the data reported may not be accurate. How can staff in a district agricultural office accurately estimate the rice production and average yield in the district? Where transportation difficult during the monsoon season as often happens in countries of South and Southeast Asia and funding for field work is scarce, field staff may have little direct contact with farmers throughout most of the main rice growing season.

Second, it is difficult to validate the data quality where only aggregate information provided. In Cambodia, the Ministry of Agriculture, Forestry and Fisheries in Phnom Penh only receives provincial totals from its agricultural reporting system. Were there villages which did not report? If so, were they omitted or estimated by commune or district officials? Are there, transcription or arithmetic errors? Are there reporting errors or discrepancies in the way different villages reported the data? None of these can be verified.

Third, much potentially valuable statistical information is lost in the aggregation process. Cambodia, crop data below the provincial level are not available in the Ministry of Agriculture. Forestry and Fisheries in Phnom Penh; if district data are needed, they need to go to each, provincial agricultural office. Also, the data are limited to what is given in the reports; there is no way of obtaining additional data without a new reporting operation. The area of rice harvested mad be available from the reported statistics, but what if one wants to analyze the distribution of farmers according to the area of rice harvested? One of the advantages of agricultural censuses and surveys is that they collect data at the farmer level, and additional data, such as the rice area distribution, can be generated from the census/survey data file.

Fourth, it is difficult to establish and ensure adherence to sound statistical concepts and definitions for the reporting of data. Where reported data come from administrative record (village registers, land tax records, etc.), data will be defined according to the requirements of the administrative system. The statistical requirements may be quite different (see Box 4.1).


31

Box 4. 1 Do administrative sources provide good area statistics? In most agricultural reporting systems, reporting officials are able to report land-related data using

administrative sources such as cadastral information and land tax assessment records. Ideally, this would mean that data are reported factually. This is not always the case. Record keeping in the villages is often -.adequate (sometimes the land records just consist of information written on loose pieces of paper); cadastral records are often not kept up-to-date: unauthorized farming goes on without the knowledge of (or overlooked by) village officials; or the land tax may be assessed using estimates of land area (often made by the farmer), not actual land measurements.

Even if the land records are accurate. The data provided from those records may not be what is needed for statistical purposes. There are several reasons for this: • Land is commonly registered under the names of different members of the household. Statistically,

one is normally more interested in measuring an agricultural holding unit, which is difficult to identify from land records. This will not affect the reporting of total crop area, but it is difficult to obtain data such as average farm size and the farm size distribution.

• Land records will show who owns the land. Statistically, it is usually better to define farm units according to who operates the land.

• The village administration is responsible for the land located within the village. People living in a village may have land in other villages. For agricultural statistics purposes, it would be better to identify who lives in the village and collect data in respect of all the land they operate.

• Land taxation often only applies to land classified as agricultural land. Some crops may be grown on non-agricultural land, especially in small plots around the village. It is often important for the reported statistics to include this land.

• The accuracy of the data may vary because of taxation priorities. If wetland is taxed more heavily than dry land, more effort might go into ensuring that wetland is measured accurately.

• Data on 'parcels' are sometimes needed to analyze land fragmentation. The statistical concept of a parcel - that is 'a piece of land entirely surrounded by other land, water, road, forest, and etc. not forming part of the holding' (FAO 1995, p. 35) - is different from a parcel used in cadastral work.

Fifth, data provided in reporting systems are often subject to arbitrary adjustment as reports are transmitted up through the various administrative levels. Agricultural field staff may feel their performance will be judged by the reported statistics. Output targets set by governments as well as political factors can also influence the reported data. Crop yields may be overstated (to meet targets) or understated (to help get food assistance). Crop area may be overstated (to show the success of agricultural development program) or understated (to show a decline in shifting cultivation).

Box 4. 2 Reliability of rice production statistics in Viet Nam.

In January 1996, the General Statistical Office of Viet Nam announced that the 1995 rice production statistics had been seriously overstated (Bangkok Post, 23 January 1996). Earlier, it had been reported that the 1995 production was 30.5 million tons, an increase of 16% compared with the 1994 figure of 26.2 million tons. The 1995 figure was revised down to 27.5 million tons.

The statistics were obtained from assessments made by officials in villages, districts and provinces. Investigations revealed that overstatement in the production figures was widespread in many provinces. The main problems identified were inaccuracies in estimates made by officials and a tendency for crop yield surveys to be biased towards high yielding farms. It was also acknowledged that local officials tended to overstate production to receive some credit for the success of the crop.

Sixth, it is impossible to measure the accuracy of the data obtained from reporting systems. One of the benefits of sample surveys is that the sampling errors of the statistics can be measured. This is discussed further in Chapter 6.

In many countries, the economic reforms introduced over the last few years have led to a breakdown of existing statistical reporting systems. We look at the experiences of two countries: Mongolia and Cambodia.

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Effect of market reforms - case study, Mongolia

By 1994, there were 167,260 `herdsman households' in Mongolia, replacing the 255 livestock co-operatives that had existed before 1990. Several hundred business units were also created to replace the state farms. The government retained a shareholding in some of the-businesses. In 1994, 91% of livestock were privately owned, compared with only 32% in 1910 (Mongolian SSO 1995).

Certain administrative functions of the co-operatives were assumed by 'somon' (district) administrations. Statistical staff, which had been responsible for reporting statistics in the cooperatives, was required to continue their statistical reporting, but their sources of data had disappeared. Initially, private operators retained some links with the somon administration business enterprises were often managed by former collective or state farm officials - and this helped to maintain the flow of data. However, as the role of the private sector in agricultural production and marketing widened and people became more aware of their independence for government control, somon officials were forced to rely more and more on their own subject, .estimates for much of the data.

In 1994, a pilot sample survey of herdsmen was conducted by the State Statistical Office two aimaks, Sukhbaatar and Hovd, to evaluate the use of sample survey methods for the collecti, of livestock statistics (Mongolian SSO 1994). The FAO provided assistance for this work. The survey has not been further developed and the agricultural statistics - now somewhat reduced volume - continue to be based on somon reports.

Effect of market reforms - case study, Cambodia

The economic reforms initiated during the late 1980s and accelerated since the United Nations sponsored elections in 1993, did not change the structure of ownership of agricultural production, as in Mongolia. However, the dismantling of socialist controls meant that village and commune officials were no longer as closely involved in farm operations. There was also weakening of the village and commune administration. These officials had worked in an honorary capacity only, and with the change in the structure and role of government, statistical reporting tended to be neglected. Most villages and communes ceased the reporting altogether and the statistics were largely based on estimates supplied by district level officials, usually with litlle knowledge of conditions in the field.

In 1995, pilot sample surveys of crops and livestock were conducted, with FAO assistance in a number of provinces to evaluate the use of sampling methods (Cambodian MAFF 1995, 1996). The surveys have not been further developed.

Efforts to improve agricultural statistics systems, especially in former socialist countries have mainly focused on agricultural censuses and surveys. However, it must be recognized that reporting systems, despite their weaknesses, will continue to be an important component of the agricultural statistics system in many countries often in conjunction with censuses and surveys. Reporting systems are particularly suited to data for early warning information systems Section 7.2), where the emphasis is on timeliness, even if it is at the expense of reliability.

What can be done to improve the quality of data in reporting systems? A number measures can be taken.

Well designed and tested reporting forms should be used. Commonly, reporting officers are just supplied with a list of data items to be reported. This leaves it up to the reporting officer to decide what data gathering is to be carried out, what definitions are to be used in the data reporting and in what format the statistics are to be reported. Data are often not reported consistently making it difficult to interpret and summarize, and to assess its reliability. Reporting forms should be developed in much the same way as a questionnaire for an interview survey. Care must be taken


33

provide clear descriptions of each item to be reported and extensive field testing of reporting ms should be carried out.

Reporting officers should be given clear instructions on how they are to obtain the required information. Some reporting systems may require the reporting officer to undertake some data collection activity or to meet with certain people. For example, to report crop losses, an extension worker may be required to meet with each village head to assess crop conditions in the village. Reporting forms should be used to record the results of all the information gathering activity; for example, recording the comments of village heads for the crop loss reporting. This would assist ad staff in the reporting work and also help in assessing data quality. Reporting officers should also be provided with an instruction manual, describing what data collection or other information gathering activity is to be undertaken, how to fill out reporting inns, what definitions are to be used, and when information is to be reported. Reporting officers should be intensively trained in the reporting procedures. Measures should also be implemented to monitor and control the reporting operation.

Box 4. 3 An improved reporting system for crop statistics in Cambodia

In January 1996, the FAO and the World Food Program undertook a joint mission to assess the food supply

situation in Cambodia. In planning the mission, they recognized that it would be difficult to make accurate food assessment without good rice production statistics. The existing statistics were not considered reliable because of weaknesses in the crop reporting system. To develop an improved statistical methodology, through sample surveys for example would take time; the best alternative in the time available ~ the mission was to improve the reporting system (WFP 1996).

A commune reporting form was developed to collect data on the area of rice planted, damaged and harvested, as well as the population and number of farmers. (Yields were collected via crop cutting surveys.) -he form was tested in several communes. Some problems were identified, in particular the reporting of two any maturing rice crops grown on the same land during the season created some confusion. The reporting form was consequently strengthened where necessary. An instruction manual for completing the reporting form was also prepared.

Each district agricultural office nominated one staff member to co-ordinate the data collection. District co-coordinators were brought to the provincial agricultural office for training. Each district co-coordinator convened a meeting of commune chiefs to explain how the reporting was to be done and to distribute the -.porting forms. Commune chiefs returned the completed reporting forms a few days later. The information supplied came from commune records.

All commune reporting forms were returned to Phnom Penh. A computer system was prepared to process the forms. Computer edit checks were applied to identify and correct errors such as inconsistencies in the reported area data, missing or incorrectly recorded data, and unusual data. Summary tables were prepared at the national, provincial, district and commune levels.

A small evaluation survey was conducted, based on a random sample of communes. Information on crop conditions in each village of the sample communes was collected from the village chief. This information was used to verify the data reported by the commune.

The area planted figures were generally a little lower than from the existing reporting system. -Estimates of crop loss were higher. One of the important outputs was the commune data base, which is proving to be invaluable to international organizations and planning agencies.

Where possible, the original reporting forms should be transmitted through the various

administrative levels, rather than just summary data. In a village crop reporting system, the village reporting forms should be provided, not the commune, district or provincial summaries. This would help to eliminate errors and enhance the range of data available; commune and district data could be provided as well as provincial and national figures. Having the `raw' data available would t also reduce the likelihood of data being `tampered' with. It would also enable suitable editing to be carried out to identify and correct errors.

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Finally, mechanisms should be developed to evaluate the reported data. In a village level

reporting system, data could be evaluated through a program of interviews with village heads or a small sample survey of farmers

35

5. Sources of Data for Agricultural Statistics - Agricultural Censuses

In Chapter 4, we discussed the collection of agricultural statistics through reports provided by agricultural field workers or local administrative officials. We saw that this approach has the advantage of providing data cheaply and quickly, but that there are often serious data quality problems because field workers are unable to accurately report the data. It would be better to take the guesswork out of the statistics and collect the data directly from the primary source, that is, the farmer.

It is often said that reporting methods are better than the collection of data from farmers because farmers may be unable or unwilling to provide the required information. It is argued that extension workers and village officials have technical expertise in agriculture and are familiar with local agricultural conditions, and are therefore well-placed to accurately report the statistics. Let us first challenge this supposition.

The problems in collecting data from farmers are certainly real. However, farmers know .heir crop production better than anyone else. This is, after all, their livelihood. If farmers are unable to answer the question: `how many kilograms of rice did you produce in 1995?’ this does not mean that the farm survey approach is not feasible; it just means that the question is being asked in the wrong way. It may be necessary to collect production data through a series of questions or to ask farmers to report in more familiar units. Careful design and testing of questionnaires and field procedures, combined with well-trained and motivated data collection staff, are the keys to successful data collection in farm surveys. Reporting problems in farm surveys are further discussed in Section 7. 1.

The direct collection of data from farmers can be undertaken through a census or a sample survey. A census is the collection of data from all units (in this case all farmers), whereas a sample survey collects data from only some units. In this chapter, we look at the census approach to the collection of agricultural statistics. Most people are familiar with population censuses. A population census is the collection of population-related data from all people in the country. Most countries undertake population censuses every five or ten years, to provide data such as: age, sex, marital status, other demographic characteristics, educational qualifications, employment, housing characteristics, and sometimes income. Population census data are provided down to low level administrative units, such as districts or villages.

An agricultural census is similar to a population census except that it involves the enumeration of all farmers in the country. (Some agricultural censuses are undertaken on a sample oasis - see Section 5.1). In most countries, it is not possible to collect annual crop and livestock statistics through a census; usually there are too many farmers to do this. Sample surveys are commonly used for these statistics. One exception in the Asia - Pacific region is Australia, where an annual census of about 150,000 farmers is carried out by mail (ABS 1994b). Such a large data collection operation would not be possible in most other countries.

Many countries undertake an agricultural census every ten years to provide detailed data on the structure of agriculture. Data are collected from each farmer (or, more correctly, each agricultural holding - see Section 3.2) on aspects that change only slowly over time, such as land use, land tenure, farm size, use of farm machinery and inputs, farm labor, etc. Data are provided Town to low level administrative units.

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Other types of censuses may also be undertaken to collect agricultural statistics, including censuses of abattoirs, markets, rice mills and food processing establishments.

5.1 Comparison of censuses and sample surveys

Before discussing agricultural censuses in detail, let us examine why censuses are needed. A sample survey has a number of advantages over a census: it is much cheaper and easier to undertake, data can be collected and processed much more quickly, and more timely results can be produced. On the other hand, estimates from sample surveys are subject to sampling errors that is the errors arising because the estimates are based on a sample of units rather than all units.

Does the existence of sampling errors mean that a census provides more accurate data than a sample survey? Not necessarily. Both censuses and sample surveys are subject to non-sampling errors. Non-sampling errors are all errors in statistical data, other than sampling errors. These include: errors in the concepts, definitions, questionnaires and field procedures; errors in listing and identifying statistical units; misreporting of data; data collection errors made by enumerators: non-response; and data processing errors. Non-sampling errors often cannot be recognized (how does one know if a survey respondent is giving factual answers?); are difficult to control (apart from thorough training and supervision of field and processing staff); and are usually impossible to quantify (unlike sampling errors). The smaller the data collection operation, the easier it is to control non-sampling errors, thus, non-sampling errors are usually much smaller in sample surveys than in censuses. Sample surveys often provide more reliable data than censuses, despite the sampling errors. In fact, sample surveys are often used to correct under enumeration and reporting errors in censuses.

Why then are censuses needed? There are several reasons (FAO 1996a, pp. 37-39): • In a census, data can be provided for small administrative units, such as districts or

villages. Such estimates from a sample survey would be subject to very high sampling errors unless the sample size is very large.

• A census enables detailed cross-tabulations to be produced. In a population census, one can analyze the employment status of people according to their age, sex, marital status and birthplace. In an agricultural census, the relationship between land and livestock can be examined by analyzing the number of farms classified by farm size and numbers of each type of livestock. Such data from a sample survey would have high sampling errors.

• A census can be used to analyze rare events, such as the number of persons aged 90 and above (from a population census) or rarely grown crops (from an agricultural census). Once again, sample estimates would be subject to very high sampling errors.

• Census data can be used to make estimates in intern census years. Using population and demographic information from a population census, one can make population projections for future years. In an agricultural census, data on the area of each crop planted can provide a base for the estimation of crop production in future years. Data on livestock numbers can be used in the same way.

• A census can be used to establish a 'master sampling frame'. For an agricultural census, the master sampling frame is a list of all agricultural holdings or a list of administrative units (such as villages) with data on number of farms, farm area, etc. for each unit. Such a frame can be used for undertaking agricultural sample surveys.

The apparently contradictory term `sample census' has now entered the statistician’s vocabulary. (The term `census based on sample enumeration' is only slightly less incorrect (FAO1995, p. 21).) The need for costly censuses is increasingly being questioned around the world and many population and agricultural censuses are now undertaken on a sample basis. To qualify as a census, the sample size should be large enough to meet the main census objectives, such as fine

Sources of Data for Agricultural Statistic-Agricultural Census

37

level geographic data, cross-tabulations, and the master sampling frame. The National Sample Census of Agriculture, Nepal, 1991/92 was undertaken using a sample of about 122,000 agricultural holdings, sufficient to provide data for each of the 75 districts (Nepal CBS 1994b, pp. 18-20). The Agricultural Census, Myanmar, 1993 was also undertaken on a sample basis with an example of about 250,000 holdings, sufficient to provide data for each of the 277 townships.

5.2 World Census of Agriculture Program

Governments have undertaken agricultural censuses in one form or another for centuries. Ancient governments needed food to feed their populations and money to fund their military ventures. Early agricultural censuses were used to measure agricultural production and assess taxation.

Modern agricultural censuses are directed more towards statistical than administrative purposes. During the late 1920s, the International Institute of Agriculture organized an international program of agricultural censuses to provide global estimates of crop production (FAO 1986b, pp. 1-2, 1992a, pp. 5-8). The plan was to undertake censuses in the northern hemisphere in 1929 and in the southern hemisphere in 1930. It was intended that such censuses would be undertaken every ten years. A similar census program was planned for 1940, but could not be fully implemented because of the Second World War.

After its creation in 1946, the FAO assumed responsibility for what came to be known as the World Census of Agriculture Program. There were census programs in 1950, 1960, 1970, 1980 and 1990. Planning is under way for the Year 2000 Program. Census programs since 1950 have not sought to have each country undertake censuses at the same time; the Year 2000 program, for example, is intended to cover censuses to be undertaken some time between 1996 and 2005.

The 1930 and 1940 censuses were only partially successful because of the large volume of data collected and the associated costs and operational problems. The census programs since 1950 have had much more modest objectives. Emphasis has shifted away from measuring agricultural production to providing information on structural aspects of agriculture. This provided for a more manageable data collection operation and helped to encourage greater participation from countries in the census program. The data content has been modified over the years to reflect current issues and priorities. For the 2000 Program, data on soil characteristics and soil degradation have been added because of current concerns about the environment and sustainable development (FAO 1995, p. 63). The following data are proposed for collection in the 2000 Program:

• 01. Identification: agricultural holding and holder identification. (The holder is the person who has management control over the agricultural holding.)

• 02. General characteristics of farm holdings: legal status of the holding; whether the holding produces for sale or home consumption; whether the holding has a hired manager; manager's remuneration arrangements; whether the holding is engaged in other economic activities and the type of activity (e.g., forestry, manufacturing).

• 03. Characteristics of farm households: household size; age, sex, marital status and education attainment of household members.

• 04. Farm labor: occupation and labor force status of household members; work done on the holding by household members and whether it is permanent or occasional work; whether permanent and occasional farm labor is used on the holding; number of permanent farm laborers by sex.

• 05. Land and water resources: (for each parcel of land) area; land tenure; land use; area irrigated; area affected by salty soil or high water table; existence of drainage facilities;

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use of shifting cultivation and year cleared for shifting cultivation; soil characteristics: soil degradation; area of land rented out.

• 06. Crop cultivation: type and area of each temporary crop grown; type, area and number of trees of productive and non-productive age in plantations, and number of scattered trees, for each permanent crop; whether the holding uses inorganic fertilizer and amount used; whether the holding uses pesticides and frequency of applications; whether the holding uses high yield variety seeds for each crop.

• 07-08. Livestock: type of livestock system (ranching, nomadic, etc.); animal numbers by ranching, type, age, sex and purpose; poultry numbers by type.

• 09. Farm machinery and equipment: whether specified types of machinery are used on the holding and source (e.g., owned by holding).

• 10. Farm buildings and structures: whether non-residential buildings are on the holding tenure of each building; area used for different purposes.

• 11. Ancillary activities on farm holdings: whether forest trees are on the holding; total, area of forest trees, area reforested, age of trees; whether forest products are harvested and value of sales; whether fisheries activities are carried out on the holding; whether there is aquaculture installation, type of installation, value of sales.

The FAO provides guidelines to assist countries in carrying out agricultural censuses. Apart from proposals on the data to be collected, the guidelines also contain recommendations or concepts and definitions, methodology, and output. The guidelines for the 2000 Census Program were issued in 1995 (FAO 1995). The FAO has also provided technical assistance to help countries in planning and conducting agricultural censuses.

The FAO reports that 103 countries participated in the 1980 round of censuses (FAG 1992a, pp. 12-15). There was even greater participation in the 1990 round of censuses, including many countries in the Asia - Pacific region. Some countries, such as Myanmar, participated for the first time; others, such as Nepal, have now undertaken several censuses.

5.3 Case study - National Sample Census of Agriculture, Nepal, 1991/92 5.3.1

5.3.1 Background

Nepal has participated in the last four World Census of Agriculture Programs of 1960, 1970, 1980 and 1990. The censuses were undertaken by Nepal's national statistical office, the Central Bureau of Statistics (Nepal CBS 1994b). The FAO and UNDP helped in the planning and implementation of the censuses.

Countries such as Nepal recognize the importance of agricultural censuses. The census data have provided valuable insight into the rapid changes that have taken place in the structure o: agriculture in Nepal since 1960. Agricultural censuses are technically complex and require a large commitment of both personnel and financial resources.

There are three levels of administration in Nepal. The national government is based in the country's capital, Kathmandu. The country is divided into 75 districts, each headed by a district governor. Offices of all the main government departments and ministries are located in each district serving between 20,000 and 60,000 households. Districts are divided into ward, (approximately 20,000 in total), each containing between 50 and 100 households and headed by a ward chief. The ward chief has limited local administrative responsibilities.

Districts are grouped into five development regions and three ecological belts. The development regions: Eastern, Central, Western, Mid-western and Far Western, provide convenient groupings for development planning purposes. The ecological belts are: Mountain, Hill


39

and Terai. The government requires data from the agricultural census for each district, development region, ecological belt and for the country as a whole.

A good understanding of the administrative structure of the country and the functions of the different levels of government is important for planning an agricultural census. This helps in understanding data needs and priorities and in developing a suitable census methodology. The emphasis on district data in Nepal meant that it was feasible to use a sampling approach for the census. If ward level data had been required, a complete enumeration census would have been needed.

The National Sample Census of Agriculture, Nepal, 1991/92 covered the whole of Nepal, including urban areas. Only the household sector was included: agricultural activities undertaken by government organizations, businesses, etc. were excluded.

Urban areas are important to agriculture in many countries. Urban households commonly have small agricultural plots which can provide an important source of vegetables. Often, urban households also own a few farm animals, especially chickens. In agricultural censuses in some other countries, certain remote areas are omitted because they are not important for agricultural purposes or because transportation is difficult. In most countries, agricultural activity is mainly carried out by households, and it is sometimes expedient to omit other types of agriculture.

5.3.2 Statistical design

The statistical unit used was the agricultural holding, defined using FAO guidelines (see Box 3.1), with minimum size criteria applied. An agricultural holding was any household with:

• area under crops greater than or equal to one matomuri (0.0 1272 ha) in Hill or Mountain districts, or greater than or equal to eight dhur (0.01355 ha) in the Terai; or

• two or more head of cattle or buffaloes, or the equivalent of other livestock types (one cattle/buffalo was considered equivalent to 2.5 sheep/goats or 10 poultry).

FAO guidelines call for all agricultural units to be included in agricultural censuses, regardless of their size. This is appropriate where small operators make a significant contribution to agricultural production. However, very small units are often omitted for cost or operational reasons.

All four agricultural censuses have been undertaken on a sample basis, using a similar methodology. A two-stage sample design was used with a sample of enumeration areas selected at the first stage and a sample of agricultural holdings selected in each sample enumeration area. Enumeration areas were defined as wards or groups of wards, usually containing between 30 and 100 agricultural holdings. The total sample size was 122,270 agricultural holdings, representing 4.47% of all holdings. Between 1,250 and 2,000 holdings were sampled in each district. The sampling aspects of the census are discussed more fully in Section 6.3.

A population census was undertaken before the agricultural census and included a number of questions on agriculture and livestock. This information was used to help in the formation of enumeration areas and in the sample selection.

Sample agricultural censuses are now becoming the norm. It would be very difficult for Nepal to carry out a full enumeration of all 2.7 million agricultural holdings in the country. According to the CBS, the sample size and design were adequate to provide district data of `sufficient reliability to be useful for most purposes' (Nepal CBS 1994b, p. 8). Higher level data were even more reliable. It was not possible to provide reliable data at the ward level.

The two-stage sampling approach is used in many agricultural censuses and surveys around the world. A number of similar sample designs can be seen in the sample survey case studies in. Section 6.3.The co-ordination of the population and agricultural censuses was a feature of the agricultural census planning and design.

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5.3.3 Data collected

The data content of the census was based on FAO guidelines for the 1990 World Census of Agriculture Program (FAO 1986b). This was very similar to the 2000 Program described in Section 5.2. The data collected were:

• 01. Identification: holding and holder identification. • 02. General characteristics of farm holdings: age and sex of holder; type of other work done

by holder; holder's main occupation; whether hired manager is used. • 03. Characteristics of farm households: age and sex of household members. • 04. Farm labor (for each household member) whether economically active employment and

unemployment, reasons for not looking for a job, whether work is done on holding, duration of work on holding; (for hired labor) number of permanent workers by sex, whether occasional workers are employed.

• 05. Land and water resources: (for each parcel) area, wet and dry land, whether irrigate source of irrigation, land tenure, land use; (for holding) number of parcels, area, whether land rented out, whether holding is irrigated during the year, whether there are any drainage facilities.

• 06. Crop cultivation: (for each parcel) crop stand, type of annual crop and area harvested type of permanent crop, compact area and number of productive and non-productive trees, number of scattered trees; use of fertilizers for major crops, type and quantity fertilizer used; use of pesticides; use of irrigation; use of improved seeds.

• 07-08. Livestock: animal numbers by type according to age and sex; poultry numbers. • 09. Farm machinery: use of items of machinery and equipment; number and source of items. • 10. Farm buildings and structures: whether non-residential buildings used for agricultural

purposes; tenure and type of these buildings. • 11. Ancillary activities on farm holdings: existence of forest trees and fisheries on the holding;

number of forest trees; type and area of fishing installation. • 12. Agricultural credit: whether agricultural loan received and the loan source.

The census questionnaires and procedures were pilot tested in 1991. Instruction manual were prepared for field staff. Not all data items proposed by the FAO were collected in the census. Some items of special interest to Nepal were added, including labour force characteristics household members and the age/sex breakdown of livestock.

The need for careful planning and design of statistical collections has been highlighted throughout this working paper. The development of an agricultural census takes a minimum of 18 months. The design and testing of questionnaires and field procedures is a very important component of the census development work. Instruction manuals are essential for the training of enumerators and for use in the data collection.

5.3.4 Organization of field operations

The CBS appointed a District Census Officer and several field supervisors in each district to oversee the census field operations. Some 1,100 enumerators were appointed to undertake the census data collection. Most of the field staff was seconded from various government offices to work on the census. A three-week training course for District Census Officers and supervisors was held in Kathmandu. This was followed by two-week training courses for enumerators in each district.

The census data collection was carried out in two phases: the Hill and Terai areas between January and March 1992, and the Mountain areas between April and June 1992. This split was necessary because of the climate (winter was not a suitable enumeration time in the mountains and shortages of data collection staff.


41

Census enumerators first visited each selected enumeration area to prepare a list of all agricultural holdings. Supervisors selected the sample holdings from these lists. Enumerators then returned to collect the data by interview with each selected holding.

The weakest link in most censuses and surveys is the data collection. Every effort can be made to ensure the highest technical standards are applied to the design of questionnaires and field procedures, sample design and selection, and data processing, but the quality of the data collected from farmers is in the hands of the enumerators. Collecting statistics is not glamorous work and there is usually much variability in the competence and motivation of field staff. The important elements in a successful data collection are: intensive training of field staff, good field instruction manuals, and a well-designed field system. and sound management and supervision of the data collection. Overall, the data collection for the census in Nepal was satisfactory, but some data collection problems were acknowledged.

5.3.5 Computer processing system development

The census processing was undertaken by the CBS in Kathmandu using microcomputers. The CBS developed computer systems for data entry, editing and tabulation for the census, using the software package CLIPPER 5.01.

An agricultural census is of no value if the final product is just a stack of completed questionnaires. The data must be entered into the computer, edited, and aggregated to provide output tables. This task is often overlooked in the census planning. Processing an agricultural census of this size was a major undertaking for the CBS.

Planning the census processing is not just a question of finding staff and computer facilities for the processing work. The computer systems themselves first need to be developed and this requires staff with skills in computer system development. The system development work needs to be done before the census enumeration so that processing can begin as soon as questionnaires are returned from the field.

Up until recent years, a mainframe computer would have been needed to process a data collection the size of the agricultural census in Nepal. The availability of cheap and powerful microcomputers in recent years has made it possible for microcomputers to be used for even very large data collections. Data processing work has become much more manageable with easy-to-use data base and statistical analysis software available for use on microcomputers.

The computers used for the census processing in Nepal were 386 models, with 80 MB hard disk and 4 MB RAM - quite primitive and slow by today's standards. The limited capacity of the computers placed constraints on the design of the computer system. For example, district data files were between 2 and 4 MB in size and had to be stored on diskette in compressed form. It was not possible to store higher level data files. Just a few years on, a 200 MHz Pentium with 1 GB hard disk would process the census with ease.

5.3.6 Data entry

The data entry work was contracted out to nine private data processing firms. The CBS monitored the quality of the data entry by checking questionnaires on a sample basis. It was decided to contract out the data entry work because the CBS did not have sufficient staff and computers for the task. Most statistical offices have an in-house data entry capability but it may not be able to handle a large once-off operation such as a census. The problem is usually not lack of data entry staff (data entry work is very simple and temporary staff with the necessary elementary keyboard skills can usually be recruited and quickly trained for this work), but the lack of computers. Some countries have purchased a large number of computers for processing their agricultural censuses. This can be a costly option unless there is a continuing need for the

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computers once the census is complete. In Myanmar, the Settlement and Land Record, Department processed the 1993 Agricultural Census in-house using about 30 computers purchased for this purpose. The computers were earmarked for other uses after the census.

Contracting out data entry work can lead to quality control problems; sample checks, a, used in Nepal, are needed. Confidentiality can be another problem. Statistical laws may preclude outsiders from accessing census questionnaires; in any case, measures are needed to ensure that the census questionnaires are properly handled and that the integrity of the statistical office is safeguarded.

The data entry work was completed within six months, which was a very satisfaction outcome.

5.3.7 Edit checking

Once data entry had been completed, a series of over 100 computer checks were applied to verify the data on each questionnaire. An interactive system was used. The computer displayed or, the screen each apparent error, which was checked and amended on the spot by CBS staff.

The editing was carried out over a 15 month period. It was time-consuming work but was essential to ensure the data were of a high quality. The editing was designed to identify several types of errors: missing data (e.g., land was reported but no parcels of land were shown); data inconsistencies (e.g., area of different land use types did not add to the total farm area); and unusual figures (e.g., age of household head was less than 15). Both reporting and data entry error, were detected. (It is easy to make the mistake of entering `1000' as `100' or `10000', which can have a catastrophic effect on the census results.)

The use of interactive editing was an interesting approach, which proved to be very successful. However, it may not always be suitable as it requires substantial staff trained to asses’ errors and amend data. An alternative approach is to generate a printed list of errors, which are then checked and amended by editing staff.

5.3.8 Output tables

The computer system was designed to produce a set of 22 output tables for each required geographic level, that is, one set for each district, development region and ecological belt, as well as one for the whole country. The tables present data on each of the topics covered by the census. Each table is classified by size of holding (Table 5.1).

The size of holding groupings were: holdings with no land, under 0.1 ha, 0.1 ha and under 0.2 ha, 0.2 ha and under 0.5 ha, 0.5 ha and under 1.0 ha, 1.0 ha and under 2.0 ha, 2.0 ha and under 3.0 ha, 3.0 ha and under 4.0 ha, 4.0 ha and under 5.0 ha, 5.0 ha and under 10.0 ha, and 10.0 ha and over.

An important aspect of the output of agricultural censuses and surveys is data on number of holdings. Thus, as well as data on the area of each crop grown, the census also provides data on the number of holdings growing each crop. (Such information is generally not available from most agricultural reporting systems.)


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Table 5. 1 List of output tables for National Sample Census of Agriculture, Nepal, 1991/92.

No. Description Table I Number of holdings; area of wet and dry land; number of land parcels; average number of parcels per

holding; distribution of holdings by number of parcels. Table 2 Number of holdings and area for each land tenure type. Table 3 Number of holdings and area for each land use type. Table 4 Number of holdings and area for each source of irrigation; number of holdings renting out land: number

of holdings with drainage facilities. Table 5 Number of holdings and area for each main temporary crop group (cereals. legumes, tubers and bulbs,

cash crops, oil seeds, spices, vegetables). Table 6 Number of holdings and area according to pure stand and mixed crops. Table 7 Number of holdings and area for each temporary crop (i.e., early paddy, main paddy, upland paddy,

wheat, etc.). Table 8 Permanent crops: number of holdings and area for productive and non-productive plantation trees;

number of plantation and scattered trees. Table 9 Number of holdings using improved seeds, insecticides, irrigation, organic and inorganic fertilizers; area

treated with inorganic fertilizer and quantity used. Table 10 Number of holdings and number of animals for each livestock and poultry type classified by age and sex. Table 1 1 Agricultural implement: number of holdings and number of implements used for each type of

implement. Table 12 Non-agricultural buildings: number of holdings and buildings for each building type. Table 13 Number of holdings with forest trees; number of forest trees; distribution of holdings classified by number of forest

trees; number of holdings with fisheries by type of fisheries; area of fish ponds. Table 14 Number of holdings receiving agricultural credit according to source. Table 15 Number of holdings classified by age and sex of holder. Table 16 Farm population classified by age and sex. Table 17 Number of holders according to whether holder is head of household; number of holdings with and without a hired

manager; number of holders doing work off the holding. Table 18 Number of holdings classified by number of economically active household members; number of male and female

economically active household members. Table 19 Number of male and female household members classified by duration of work. Table 20 Number of male and female household members classified by duration of work on the holding. Table 21 Economically inactive household members classified by reason for not being active. Table 22 Number of holdings employing permanent and occasional outside workers; number of male and female permanent

outside workers.

Source: Nepal CBS 1993

In a census of this nature, the range of output tables is virtually unlimited, and judgment is necessary on what are the most important tables to serve user needs. The use of size of holding as a classification variable for all the tables highlights the farm size dimension of the various census characteristics and facilitates comparisons between small and large farms. The size of holding groupings and other classifications in the tables conform with FAO recommendations (FAO 1986b). This ensures comparability with agricultural census data from other countries. Other classification variables could also have be used in the tables. For example, the same set of 22 tables could have been produced using a classification by household size, rather than size of holding. This would help in examining the problems faced by small and large households. Other possible classification variables include:

• number of cattle (to analyze the relationship between land and livestock); • number of permanent workers (to study hired labor and its effect on farm activities); • land tenure (to uncover differences in agricultural practices between those owning and renting

land); • holder's age (to identify problems faced by young farmers and to determine whether they are more likely to

adopt improved farm practices); and • holder's sex (to highlight problems faced by women in agriculture).

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5.3.9 Census reports

The CBS issued 85 reports presenting the main census results: one national level report, five development region reports; three ecological belt reports; 75 district reports; and one report summarizing data for development regions and ecological belts. Each report contained the output tables with explanatory text. The reports were produced by computer in 'camera-read, form and were progressively issued between March and December 1993.

The use of microcomputers for processing censuses and surveys has made it easier to prepare statistical publications. Computer systems can be designed to print output tables in fin.: publication format or, as done in Nepal, combine the output tables with text to print a report read for publication. The release of all 85 census reports within 18 months of the completion of the census data collection was a very satisfactory outcome, and could not have been achieved without the reports being generated by computer.

The release of such an extensive set of census reports helped to promote wide use of the census results. However, printing costs were very high. There is now increasing emphasis given t other means of dissemination, especially in electronic form (see Box 2.2).

The release of publications district by district as the processing was completed ensured that users were provided with data as early as possible. A more systematic data release program often preferred. A preliminary report, containing some summary national information (often hand tabulated), is commonly issued first (sometimes only a few months after the census). This can be followed later by the detailed national report and then the lower level reports.

5.3.10 Census analysis

As well as the 85 main census reports, the CBS also issued three additional reports: • The main highlights of the census were issued in January 1994. The report contained some

graphical presentations, commentary on the main findings, and summary tables. • A detailed analysis of the census results was provided in a second analytical report issue in June

1994. • A technical report, containing a description of the census methodology, a presentation of sampling

errors, an assessment of the sample design, and an evaluation of data quality, was issued in February 1994.

The analysis was a feature of the agricultural census. The detailed output tables presented in the main census reports provided the information required by `serious' users, but other user might easily be overwhelmed by the large volume of data given. The analytical reports helped to pave the way to a better understanding of the data and its policy implications, and to stimulate interest in the census results.

The census analysis undertaken represents just the `tip of the iceberg'. Agricultural censuses provide a wealth of data. The analytical reports aim to encourage further exploration the issues raised. Specific topics, such as women in agriculture, small farms, etc., could be studied further. It would also be useful to prepare `highlights' publications or hand-outs for each development region, ecological belt and district.

The final technical report is an extremely valuable document. It helped in interpreting census results and understanding the limitations of the data. The analysis of sampling errors, which included a presentation of various sample design parameters, such as standard error, relative standard error, coefficient of variation, design effect and measure of homogeneity, will be invaluable in the planning and design of future agricultural censuses and surveys.

The publication tables were also available to users on diskette. To meet the need - additional tables in the future, data files were converted for use by the statistical analysis package SPSS. Staff of the CBS was trained to use the SPSS package.


45

There was some demand for the census data to be made available at the agricultural holding level. The CBS was not willing to release such data because of confidentiality concerns. These concerns are valid (see Box 5.1).

An agricultural census is costly and it is important that the results are fully utilized. Because censuses are carried out only every ten years and the data cover aspects that do not change very quickly over time, the census results are of interest to users for many years. We have already noted the wide range of output that can be produced from a census - much more than presented in the census reports. A user interested in studying potato production, for example, might want the full set of 22 tables just for those holdings growing potatoes. In planning for a census, consideration needs to be given to how such needs will be met.

The computer system for tabulating the census results in Nepal involved special purpose computer programs. Additional programs would be needed for any new output tables required. There are advantages in using a generalized statistical processing package for the census tabulation, as it is much easier to produce additional tables. Options available include SPSS, SAS and IMPS.

User seminars are often a good way to publicize the availability and usefulness of census data. Box 5. 1 Unit record data and confidentiality.

In agricultural censuses and surveys, data are collected from individual farmers for use in calculating

census/survey aggregates. Such as national totals and averages. Data for individual farmers are of no statistical interest in their own right, but are needed for some types of statistical analysis, such as analyzing relationships between data items using regression analysis. The release of such unit data is normally restricted because of the need to protect the confidentiality of the data provided by farmers. Sometimes, this is required by law. Deleting names does not guarantee that individuals cannot be identified. Suppose we wanted to find out the agricultural census information for a Mr. Savanh and he is known to be 45 years old and living in Khong village. If the census data file shows that there is only one farmer matching those characteristics, then that farmer is obviously Mr. Savanh and all his census information can be read from the data file.

There are several ways to overcome this problem. One way is to remove fine level geographic data to -make it harder to find a match. Another way is to replace reported data with ranges, such as five year age ranges, instead of individual ages. A third way is to randomly amend reported data so that matching of individuals is impossible, but the aggregate statistics remain correct.

5.4 Use of agricultural censuses for policy analysis and research

Data from agricultural censuses can be useful for policy analysis and research studies on a wide range of issues, including:

• the role of women in agriculture; • characteristics of a particular crop (e.g., cassava); • problems faced by small farms; • characteristics of a particular livestock production system (e.g., pigs); • the structure of agriculture in a particular district; • the inter-relationship between crop and livestock production; and • sources of farm labor.

To illustrate the use of agricultural census data for such policy analysis and research studies, we examine three typical policy issues: an evaluation of the role of women in agriculture; a study of the problems faced by small farms; and an analysis of cassava production.

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Case study - evaluation of the role of women in agriculture

The need to promote greater participation of women in agricultural development is a priority concern to most governments. Suppose the government commissions a study to report or the current status of women in agriculture and recommend suitable policies to address the problems women face. How could agricultural census information help in this study?

Agricultural censuses collect three types of data of relevance to the gender issue (FAO 1993, pp. 10-11):

• sex of holder, which provides information on the characteristics of holdings operated by women, the problems they face, and how they differ from holdings operated by men;

• sex of household members, which provides information on women in farm household especially their participation in farm and other work activities; and

• number of female agricultural workers, which provides information on the opportunities for women to do paid agricultural work.

To understand how these three types of data could be used in the study, let us examine some of the questions to be addressed in the study and the particular census data that would hell: - answer those questions (Table 5.2).

Note that an alternative view is that women's present participation in agriculture is largely unrecognized and this is the problem women face. The data relevant to this issue could be obtained by comparing the differences in the responses of males and females to questions on work activities.

Table 5. 2 Evaluation of the role of women in agriculture - issues highlighted by agricultural census.

Issue Census output tables l. Are farms operated by women smaller than Average farm size for male and female holders; number of

2.

those operated by men?

Do women farmers use better agricultural

holdings classified by sex of holder and size of holding (e.g., less than 0.50 ha. 0.50-0.99 ha, etc.). Number of holdings classified by sex of holder and whether

3.

practices than men (in regard to use of farmmachinery, fertilizers, pesticides, improved seeds)?

Do women farmers cultivate land more

farm machinery is used; similar tables for improved seeds, fertilizers and pesticides.

Average cultivation intensity for male and female holders;

4.

intensively than male farmers?

Do women tend to grow different crops than men?

number of holdings classified by sex of holder and cultivation intensity (e.g., under 1.00, L00-1.49, etc.).

Percentage of male and female holders growing each crop.

5. What farm labor problems do women farmers Number of holdings classified by sex of holder and whether

6.

have?

To what extent do women in farm households

outside labor is employed; average number of economically active household members for male and female holders. Number of household members classified by sex and whether

7.

participate in agricultural activities?

What opportunities are there for women to

they are economically active; number of household members classified by sex and whether they work on the holding; number of household members classified by sex and hours worked. Number of male and female paid permanent agricultural

work as paid farm labor? workers.


47

In many countries, population pressures and shortages of land have led to fragmentation of 3aricultural land and a preponderance of small farms. In these circumstances, the viability of small farms and the need for programs to assist small farms are often issues of concern to the government. Let us consider how census data could be used for a study to assess the viability of small farms and recommend policies to help them.

Census output tables showing various data classified by size of holding enable the characteristics of small and large holdings to be compared. Some of the issues the census would help to address and the output tables relevant to those issues are shown in Table 5.3.

Table 5. 3 Study of the problems of small farms - issues highlighted by agricultural census. Issue Census output tables 1. How have farm sizes changed over time? Number of holdings, area of holdings, average size of

holding, number of land parcels for present and past censuses

2. How many small farms are there and where are they? Number of holdings classified by size of holding (e.g., less than 0.50 ha, 0.50-0.99 ha, etc.) and geographic area (e.g., district).

3. How equitable is the land distribution? Percentage of holdings in each size o holding group and percentage of farm area in those groups; also the concentration index.

4. Are small farms cultivated more intensively than large farm?

Average cultivation intensity for each size holding group; number of holdings classified by size holding and cultivation intensity (e.g., under 1.00, 1.00-1.49, etc).

5. Are agricultural practice on large farms (in regard to use farm machinery, fertilizers, pesticides, improved seeds) better than on small farms?

Number of household is classified by size of holding and whether farm machinery is used; similar tables for improved seeds, fertilizer and pesticides.

6. How many farm household do not have sufficient land to support their family?

Number of holdings classified by size of holding and household size.

7. To what extent do households on small farms rely on outside work to supplement farm income?

Number of holdings classified by size of holding and whether holder works off the holding; number of household members classified by size of holding and months of work on the holding

8. What role does livestock play in supplementing the farm income of households with small farm?

Number of holdings classified by size of holding and whether holdings has each type of livestock.

Case study – analysis of cassava production

Often, government seek to encourage farmers to grow a particular crop, such as cassava, because growing conditions or export markets are favorable or because of the need to reduce dependency on imports. To help formulate a plan for increasing cassava production, the government would first need to assess the existing status of cassava production in the country. How would the agricultural census help in this assessment?

The data for this analysis would come from tabulations of cassava growers. Instead of the published census tables (which refer to all agricultural holdings), we would need the same just for cassava growers. In this way cassava growers can be analyzed for the whole range of census topics, such as the use of machinery and inputs, cropping patterns, relationship with livestock, farm labor, etc.

Some of the important issues of interest and the census output tables relevant to those issues are shown in table 5.4.

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Table 5.4 Analysis of cassava production-issues highlighted by agricultural census.

5.5 Case study - analysis of small farms, Nepal

In Section 5.4, we looked at the use of agricultural census data for policy analysis and research. We considered three typical policy issues and identified the main census output table that would help address those issues. We will now look at an actual example. The issue examined is the problem of small farms in Nepal. The data source is the National Sample Census of Agriculture, Nepal, 1991/92 (Nepal CBS 1994a, 1994c).

Throughout this discussion, an agricultural holding with less than 0.5 ha of land is called a `small farm', and a holding with 2.0 ha of land or more is called a `large farm'. A selection of 17output tables of interest to the small farm issue is given in the appendix. The main findings and conclusions from these tables are:

Trends in farm size and land fragmentation • The number of farmers in Nepal has been growing at a faster rate than the area of agricultural

land; between 1961/62 and 1991/92, the number of agricultural holding, increased by 78% while the area of agricultural land increased by only 54% (Table A1).

• As a consequence, the average farm size has decreased significantly over the 30 year period from 1.11 ha to 0.96 ha (Table A 1).

• Agricultural land has become more fragmented over the years; between 1981/82 and 1991/92, the number of land parcels increased from 9.5 million to 10.8 million while the average parcel size declined from 0.26 ha to 0.24 ha (Table A1).

• Although land has become more fragmented, farmers now tend to have fewer parcels of land because of the smaller farm sizes; the average number of parcels per holding declined from 4.4 to 4.0 between 1981/82 and 1991/92 (Table A1).

Issue Census output tables 1. How many cassava growers are there an where are they? Number of cassava growers classified by geographic area

(e.g., district). 2. How much cassava is grown in different areas of the

countries? Area of cassava classified by geographical area.

3 How many cassava growers are large producers of cassava?

Numbers of cassava growers classified by cassava area (e.g., less than 0.50 ha, 0.50-0.99 ha, etc.).

4. To what extent is cassava production mechanized? Numbers of cassava growers classified by whether farm machinery is used.

5. What sort of cropping system is used by cassava growers?

Number of cassava growers classified by whether each specific crop is also grown; number of cassava growers classified by cassava area and area of other crops.

6. What are the labor requirements for cassava production? Number of cassava growers classified by cassava area (e.g., less than 0.50 ha, 0.50-0.99 ha, etc.) and months of work on holdings by household members (e.g., less than 6 months, 6-8 months, 9-12 months)

7. How are the labor requirements for cassava production different from rice production?

Average labor input per hectare for cassava growers and rice growers.

8. How is cassava production integrated with livestock activities?

Number of cassava growers classified by cassava area (e.g., less than 0.50 ha, 0.99 ha, etc.) and number of cattle; similar table other livestock types.


49

Farm sizes around the country • Farm sizes are larger in the south of the country; in 1991/92, the average size of holding in the

southern Terai belt was 1.26 ha, compared with 0.77 ha in the Hill belt and 0.68 ha in the Mountain belt (Table A2).

• Farm sizes are smaller in the west of the country; the average holding size in 1991/92 was 0.78 ha in Far Western Development Region, compared with 1.25 ha in Eastern Development Region (Table A2).

Land distribution • Small farms are predominant in Nepal; 43% of land holdings were less than 0.5 ha in size in

1991/92, whereas only 11% were more than 2.0 ha in size (Table A3). • The land distribution has become more equitable over recent years; in 1991/92, the smallest

50% of holdings operated 16% of the agricultural land, compared with only 6% in 1981/82 (Table A3; see also Nepal CBS 1994c, p. 13).

• The land distribution is least equitable in the Terai belt; in 1991/92, the concentration index was 0.54 in the Terai belt, compared with 0.43 in the Hill belt and 0.45 in the Mountain belt (Table A2).

• Most small farms are fully owned; in 1991/92, 85% of small holdings were fully owned (Table A4).

Land use - small and large farms • Large farms have a disproportionate share of wetland areas; in 1991/92, only 44% of the land on

small holdings was wetland, compared with 71% for large holdings (Table AS). • Large farms also have a disproportionate share of the irrigated land; 64% of large

holdings had irrigated land in 1991/92, compared with 41% of small holdings (Table A6).

Crop cultivation by small farms • Wheat is becoming an increasingly important crop for small farms; only 22% of small holdings

planted wheat in 1981/82 but this increased to 52% in 1991/92 (Table A7). • Small and large farms differ in the types of crops grown; in 1991/92, rice made up 44%of the

crop area on large holdings but only 26% on small holdings, maize (25%) andmillet (12%) being more important crops for small farms (Table A8).

• Small farms are cropped more intensively than large farms; in 1991/92, cropping intensity was 1.88 for small holdings, compared with 1.66 for large holdings (Table A9).

• Scattered fruit trees are common on small farms; in 1991/92, 40% of small holdings had some fruit trees (Table A 10).

• Small farms make less use of chemical fertilizers and improved seeds than large farms; for small holdings, only 42% of wheat growers used chemical fertilizers in 1991/92 and 24% used improved seeds, compared with 80% using chemical fertilizers and 43% using improved seeds for large holdings (Table A 11).

Livestock on small farms • Livestock is an important activity for small farms; small holdings have an average of 2.9 cattle

and 7.4 chickens (Table A 13).

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Household characteristics of small farms • Small farms are commonly operated by young people; 31% of small holders are less than 35

years of age, compared with 15% of large holders (Table A 15). • The average household size for small farms is 5.1 (Table A 15).

Farm size and labor force activities • The smaller the farm, the more need there is for the holder to do off-farm work; in 1991/92,

30% of holders of small holdings worked off the holding, compared with only 11% for large holdings (Table A 16).

• Women on small farms engage in more work activities than those on large farms small holdings, 68% of women aged 10 years and above participated in labor force activities in 1991/92, compared with 57% for large holdings (Table A 17).

Other issues • Small farms have less access to advanced technology; 32% of large holdings used an iron plough

in 1991//92, compared with only 5% of small holdings (Table A 12). • Small farms rely to a great extent on money lenders, relatives, etc. for credit; only 6% of small

holdings had credit from a lending institution in 1991/92, compared with 24% of large holdings (Table A 14).

Such analysis helps the government to identify alternative policy actions for

considered to address the problems highlighted. Some options suggested by the analysis of small farm Nepal are shown in Table 5.5.

Table 5. 5 Analysis of small farms in Nepal - problems identified by agricultural census and possible policy measures.

Problem identified by census analysis Possible action to address the problem 1. Farm sizes are becoming too small; land is too

fragmented.

Make more land available for agriculture, providealternative employment opportunities in rural areas: research ways to improve farm productivity; help

2. Farm sizes are smallest in the west of the country and in the Hill and Mountain belts.

Focus measures in no. I on areas with small I farm sizes.

3. Young people face problems in getting enough land.

Develop programs as in no. I targeting young people

4. Small farms have too little wetland and irrigated land.

Establish small-scale irrigation schemes designed to small farms; help farmers with small land holdings to purchase better land.

5. Small farms use insufficient fertilizers and improved seeds

Train farmers in the use of better cropping practices; assist small farms to purchase inputs; implement other policies to increase the supply of inputs (such as abolishing price controls and import restrictions).

6. Small farms have less access to advanced technology

Establish co-operative systems for farm machinery.

7. Farmers with small land holdings have difficultygetting agricultural credit from institutional sources.

Provide funds to agricultural banks, earmarked for use by farmers with small land holdings

8. Farmers with small land holdings rely on outside work to support their families.

Establish industries in rural areas to increase off-farm employment opportunities; provide educational opportunities to improve work prospects.

Data from agricultural censuses also help in costing alternative policy actions. For example, option

to help small farms might be to provide an exemption on land tax for farmers with less


51

than 0.2 ha of land. The number of such farmers in Nepal is estimated as 436,800 (Table A3), and the cost of the proposal can be worked out accordingly.

5.6 Other uses of agricultural censuses

Baseline information for agricultural development projects Agricultural censuses are designed to provide detailed structural information on agriculture for

small geographic areas, which makes them an ideal source of baseline data for agricultural 3evelopment projects. Often, a project area will not correspond to a standard geographic unit, such as a district, but instead cover selected villages or communes. In an agricultural census, each agricultural holding is coded down to the lowest identifiable geographic unit (often the village), and it is therefore possible to produce census data for any defined groupings of that unit (subject to, sampling errors, if applicable). Thus, census data can be provided for any defined project area.

Census data can also be provided for any required target group of holdings. Thus, for a project designed to promote cassava production, data specific to cassava growers can be provided, .or example, the land tenure of cassava growers, use of inputs by cassava growers, etc. (Table 5.4). If the project was to focus on cassava grown in association with wheat, then census data specific to -~hose holdings growing both cassava and wheat can be provided.

Estimation of current crop and livestock statistics Generally, it is not possible to collect current crop and livestock statistics through annual

agricultural censuses, and sample surveys or reporting systems are employed. Both these methods have limitations: estimates from sample surveys may have high sampling errors, especially data for minor crops and for low level administrative units; and data from reporting systems are often of doubtful reliability because of reporting problems. Agricultural censuses are often used to improve =.e statistics from these sources.

One approach to annual crop statistics is to use census estimates of the area of each crop at, say, the district level as a base for the statistics. An annual reporting system can be established to assess the changes in the area of each crop in each district, which can be applied to the census figures and aggregated across districts to obtain the current crop area estimates. A sample survey can be carried out every few years to `correct' the estimates formed in this way. Production and yield estimates can be obtained using crop cutting surveys.

Agricultural censuses can be helpful in compiling current statistics on permanent crops. Often, little current information on permanent crops is available, and the collection of such data is difficult because many fruit trees are not in plantations. (In Nepal, nearly half of all agricultural holdings had some permanent crops in 1991/92, but well over one half of all orange, apple, and other fruit trees were scattered around the holding (Nepal CBS 1994c)). Census data on the plantation area of crops and the number of scattered trees, together with detail on trees of productive and non-productive age, can be used to project future tree numbers and productivity, as basis for estimating current production.

Census data on livestock numbers can also be useful for estimating current livestock statistics. Data on the age/sex composition of livestock herds, used in conjunction with information on fertility, death and slaughtering rates obtained from other sources, enables one to project livestock numbers and estimate milk and meat production.

Analysis using census data files In Sections 5.4 and 5.5, we saw how much valuable research and policy analysis can be

undertaken using census output tables. Researchers and analysts can do much more than this with

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52

the census data. An agricultural census collects a wide range of structural and operational data for each farmer, which provides a unique source of information for analyzing relationships between different characteristics.

For example, using correlation or regression analysis, one can study the relationship between cropping intensity and variables such as family size, age of farmer, education of farmer and employment status. By identifying such relationships, a researcher can gain a better understanding of the reasons farmers make decisions and their likely response to particular policy actions. If there is a significant relationship between family size and cropping intensity, for example, this could reflect the importance farmers attach to meeting the family's food needs or alternatively, the effect of having additional labor available for farm work.

To do this type of statistical analysis, users need to have access to the census data files that is the computer files containing the agricultural holding level data. This raises the question of confidentiality (see Box 5.1).

Private sector Agricultural censuses provide a valuable source of data for the private sector. Unlike most official

statistics, agricultural censuses provide fine level geographic data and other detailed desegregations, which can be very useful for assessing market opportunities in different localities.

Using agricultural census data, a supplier of potato harvesters, for example, can determine, how many potato growers there are in each district, what area is planted to potatoes, and ho many growers use particular types of equipment. This can help in identifying where the greatest market opportunities exist and where to site retail outlets. Input suppliers can do the same the agricultural census might show that fertilizer use in a certain district is low, suggesting strongmarket potential in that area. Food processing companies can use the census data to help inassessing raw material supplies and in finding suitable locations for their plants. A fruit processing company, for example, would make use of information on the number of fruit growers in each district and the number of productive and non-productive fruit trees. A company planning labor-intensive industry in a district can use the census data to assess the availability of labor - farm households and the pool of existing skills. Data from agricultural censuses are also useful for assessing the demand from farmers for non-agricultural goods, such as fuel and building materials: (Panse 1966).


53

53

6. Sources of Data for Agricultural Statistics - Sample Surveys Sample surveys in one form or another have been undertaken for centuries (Szulc 1965, pp. 568-

570). However, it was not until early in this century that statisticians started to give serious attention to the statistical principles underlying the sampling method and the application of sampling to official statistics. Initially, there were concerns about the validity and accuracy of data from sample surveys, concerns that are sometimes still heard in countries where sample surveys are still quite new. Studies carried out during the 1930s confirmed that the sampling method does work and this led to a general acceptance of sampling by the statistical community. The sampling method was quickly adopted by statistical offices during the 1940s and 1950s. The approach was very attractive because it provided statistics more cheaply and quickly, enabling the range of statistics to be expanded. During these two decades, the basic theory of sampling was documented and various sampling techniques were developed to handle different types of applications. Today, statistical offices around the world rely on sample surveys for all types of statistics. A typical sample survey program for a national statistical office includes: surveys of farmers carried out at various times throughout the year to provide current crop and livestock statistics; irregular in-depth surveys of farmers to provide special data, such as cost of production; monthly or quarterly household surveys to collect statistics on population and labor force; irregular household surveys to provide statistics on health, income and expenditure, nutrition, etc.; and surveys of businesses to provide statistics on employment, wages and business activity.

6.1 Random and non-random sampling

One of the early debates on sample surveys concerned how samples should be selected, in particular, the relative merits of `random' and `non-random' sampling. Random sampling involves the selection of a sample according to certain probability rules. The simplest type of random sample is one selected by `lottery', that is, each unit in the population being studied has the same probability of selection in the sample. In practice, sampling schemes are usually more complex than this, with units having different probabilities of selection. In a farm survey, for example, large farms may be sampled more heavily than small farms. The basic requirement of random sampling is that all units have a known (but not necessarily equal) non-zero probability of selection in the sample. The term `random' in its general use suggests some sort of haphazard or indiscriminate procedure; in statistics, it implies a clearly defined probability procedure. The two meanings are quite different. There are various non-random sampling methods. `Quota' sampling is one of the most common. For a farm survey, for example, the sample would be selected by choosing a predetermined number of farms of certain types, according to criteria such as farm size or cropping system, so that the characteristics of the sample farms match those of all farms. Other non-random sampling methods include: `volunteer' samples (inviting people to write or telephone to participate in the survey); `convenience' samples (selecting sample units which are readily available or most convenient); and `judgment' samples (using judgment to select typical sample units for the survey) (Stephan and McCarthy 1958).

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Early statisticians favored non-random sampling (especially quota sampling) over random sampling because it seemed to guarantee a better representation of the population being measured. However, three major weaknesses emerged.

Firstly, controlling certain characteristics in the sample does not guarantee a representative sample. A sample of farms with fixed sample numbers in each district and farm size group will not be representative for other characteristics such as crops grown and growing conditions. Crop cutting surveys based on the selection of a fixed number of high and low yielding farms may not provide a good geographic coverage. Random sampling ensures a representative sample by giving all units a chance of selection in the sample

The second criticism of non-random sampling methods is the subjective element in the sample selection process. In farm surveys, there will be a tendency to select farms that are `friendly', close to town, or easy to reach by vehicle or public transport. This can lead to major biases. Farmers close to town will usually be different from their counterparts elsewhere because they have better access to markets and other town facilities. The advantage of random sampling that the sample selection is wholly determined by the sampling method; there is no subjectivity involved.

The third, and perhaps most important, weakness in non-random sampling is that it impossible to measure the accuracy of the sample estimates. In random sampling, certain conclusions can be made about the reliability of the sample estimates. This is further discussed in Section 6.5.

Random sampling is now generally accepted as being superior to non-random sampling and has been the basis for all the sampling theory and methods developed since the 1940s.

Common examples of poorly conceived surveys are: `street corner surveys' (people walking past a particular location will not be representative of the whole population); surveys based on a random sample of people with telephones (low income people tend not to have a telephone and therefore will be under-represented in the sample); and volunteer surveys (this ma. attract more assertive people or be liable to manipulation).

6.2 Sampling methods used in agricultural surveys To select the sample for any sample survey, one needs to create a `sampling frame'.

The sampling frame is the list of all the units covered by the survey. For a farm survey, it is often no: possible to create a list of all agricultural holdings in the country. A technique known as `multistage sampling' is often employed to overcome this problem. In multi-stage sampling, the sample is selected in stages. Thus, a sample of villages can be selected first and then a sample of agricultural holdings selected within each of these villages. In this two-stage sampling method. sampling frames are needed for both stages of sampling: a list of all villages in the country to select the sample of villages, and a list of agricultural holdings in the selected villages to select the sample of holdings.

Multi-stage sampling is widely used for major farm surveys around the world. Its chief advantage is that it is easier to create lists of holdings just in the selected villages, rather than for the whole country. Data collection is also cheaper because the sample holdings are clustered in the selected villages, rather than being spread across the whole country. However, sampling errors tend to be higher.

Other sampling techniques commonly used in agricultural sample surveys include: • Simple random sampling. This is lottery method ,referred to earlier. • Stratified sampling. In stratified sampling, the sampling frame is divided into `strata'

(singular `stratum') and the sample is selected independently within each.

Sources of Data for Agricultural Statistics-Sample Surveys

55

small farm sizes. The main advantage of stratified sampling is that it ensures a more representative sample - the farm survey would contain the right number of large and small holdings. It also enables the sampling fractions to be varied; for example, large holdings could be sampled more heavily than small holdings.

• Probability Proportional to Size Sampling (PPS). Instead of units having the same chance of selection in the sample, as in simple random sampling, each unit can be assigned a probability of selection based on a certain size measure. This approach is often used in the selection of villages, where the probability of selection of a village is determined by the number of holdings or farm area in the village (as obtained from a population or agricultural census).

• Systematic sampling. In systematic sampling, the list of units is ordered in some way and the sample is selected by running a `skip' down the list. For a sample of villages, the village list can be ordered geographically. The main advantage of systematic sampling is that it ensures a more representative sample; for example, good geographic spread in a sample of villages. Systematic sampling can be used with either simple random sampling (called `systematic random sampling') or with PPS sampling.

So far, we have considered surveys carried out by collecting data from a sample of agricultural holdings. These are known as `list sample' designs. There is another approach. In `area sample' designs, the sampling unit is a physical piece of land, called a `segment'. In an area sample survey, a sample of segments is selected and data are collected in respect of each of these segments. Area sample designs are especially suited to collecting land-related data, such as crop area (FAO 1996b).

For area sample designs, a sampling frame of segments needs to be created, covering the whole survey area. Aerial photographs, satellite images and maps can be used to create the frame. There are various ways of forming segments. One way is to define contiguous area units with well defined boundaries. Another way is to define square segments based on map co-ordinates.

Any of the usual sampling methods can be used for area sample designs. Segments are often stratified according to land use or their agricultural importance, the more important agricultural areas being sampled more heavily. PPS sampling is sometimes used for the selection of segments, with the size measure being the area of the segment. Multi-stage sampling is also commonly used, with a sample of villages or other similar area selected at the first stage and a sample of segments selected in each sample village.

One of the advantages of area sampling is that land area data for the sample segments may be calculated from the aerial photographs or maps. This avoids the need for estimation by the farmer (which may not be accurate) or field measurement by the enumerator (which is time consuming). Area sample designs can also be used to collect other farm level data, in addition to land-related information. This is done by associating each sample segment with the agricultural holding operating the land in the segment.

Area sample designs often provide more reliable data than list sample designs. However, the area sample approach is not always feasible because of the unavailability or high cost of obtaining maps and aerial photographs, or because the required technical expertise is not available. List sampling continues to be the most widely used method for agricultural surveys. As well as list and area samples, there are also other types of agricultural sample surveys. Of particular importance are crop cutting surveys which involve the sampling of small plots of land to estimate crop yield.

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6.3 Agricultural sample surveys - case studies

In this section, we consider seven examples of agricultural surveys undertaken in countries of the Asia - Pacific region. They illustrate different approaches to basically the same type of farm survey, reflecting differences in the survey objectives and data requirements, the information available for the sample design and selection, and the circumstances within the country concerned. In one case (Australia), the sample was selected directly from a register of all farmers in the country. In other cases, no such register existed. The agricultural censuses in Nepal and Fiji illustrate the use of sampling methodology for censuses, the former using a multi-stage list sample design and the latter an area sample design. The surveys in Bhutan, Lao PDR and Cambodia employed various forms of multi-stage list sampling. The Survey of Area and Production of Ma, Crops in Nepal used a form of area sampling, in which area data were obtained from cadastral records.

Annual Agricultural Finance Survey, Australia This survey is carried out annually by the Australian Bureau of Statistics to collect far

finance data (ABS 1996d). The sample is selected from a register of businesses. The reregister contains details of all businesses in Australia, together with their structure and industry. The 1994/95 survey covered all businesses with agriculture as the principal activity and with agricultural operations valued at A $ 22,500 or more. The statistical unit was the `farm business representing the legal entity owning the farm unit. This concept is similar to the agricultural holding unit used in farm surveys in other countries.

The sample was selected using stratified random sampling, with the states as the strata. There were some multi-state farm businesses which were selected separately. The sample size was 2,600. Data were collected on turnover, operating surplus, profit, rate of return, operating cost capital expenditure, debt and assets. Both agricultural and non-agricultural activities were covered. The survey was carried out by personal interview.

National Sample Census of Agriculture, Nepal, 1991/92 A detailed description of this census was given in Section 5.3. The census was undertaken by the

Central Bureau of Statistics (Nepal CBS 1994b). The statistical unit was the agricultural holding, defined as a household with more than about 0.01 ha of land under crops or more than a given number of livestock (two cattle/buffaloes, five sheep/goats or 20 poultry). Two-stage list sampling was used. A sample of enumeration areas was selected at the first stage and then, in each of these areas, a sample of agricultural holdings was selected for inclusion in the census Enumeration areas were defined as wards or groups of wards.

The total sample was 122,270 agricultural holdings. The number of enumeration areas selected in each district was 50, 60, 70 or 80, depending on the importance of the district from an agricultural viewpoint (as measured by the total area under the eight major crops). Between 20 and 30 sample holdings were selected in each enumeration area, making a total of between 1,250 and 2,000 holdings in each district. In the district of Manang, there were only 902 agricultural holding, in all, and these were completely enumerated. Taking a sample of at least 1,250 holdings in each district ensured that reliable data were obtained for each district The larger sample in the more important agricultural districts meant that data were even more reliable in those areas.

One feature of the agricultural census design was the co-ordination of activities with the 1991 Population Census. Information collected in the population census was used to estimate the number of agricultural holdings and farm area in each ward. This was used to help form enumeration areas of suitable size for the agricultural census. It was also used in the sample


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selection; the sample of enumeration areas was selected using PPS sampling (with number of agricultural holdings as the size measure) and systematic sampling (enumeration areas were ordered by farm area). The population census was not used to directly identify agricultural holding units for the agricultural census because of the time between the two censuses (about one year) and the use of more in-depth questions in the agricultural census to more accurately measure agricultural activity.

To select the sample of agricultural holdings, a list of holdings in each sample enumeration area was prepared. Holdings were stratified into four types based on how much land and how many livestock they had. Systematic sampling was used to select the sample across strata. Data were collected by interview for each sample agricultural holding.

Agricultural Census, Fyi, 1991 An area sample design was used for the Agricultural Census, Fiji, 1991 (FAO 1992b). This approach was used because of problems in compiling accurate lists of farmers in previous agricultural censuses. Using land use maps, the country was first divided into nine strata based on type of agriculture. For example, Stratum 10 was described as: `areas cultivated between 70 and 100% by annual and/or permanent crops'. Segments were formed by examining topographic maps and aerial photographs. The segments were between 0.5 and 3.0 square kilometers in area. A sample of segments was selected within each stratum using systematic random sampling, based on a geographic ordering. The area of each sample segment was calculated from aerial photographs using pluviometers. In the field, enumerators collected the land-related characteristics from each of the sample segments. The households operating land in the sample segments were identified and interviewed to collect other household data.

Agronomic Survey, Bhutan, 1988 and 1989 This survey was undertaken over two years, covering the western half of the country in 1988

and the eastern half in 1989 (Bhutan CSO 1988, 1990a, 1990b). The survey was carried out by the Central Statistics Office, in conjunction with the Department of Agriculture, to provide basic crop data for each dzongkhag (district) and for the whole country.

The sampling unit was the agricultural holding, defined as a household operating any agricultural land or keeping any livestock. Two-stage sampling was used, with enumeration areas as the first stage unit and agricultural holdings as the second stage unit. Enumeration areas were defined as the area covered by a chupen (a local official) and consisted of a village or a group of villages.

The sample size was 5,198 holdings. The sample size in each dzongkhag varied from 206 to 584, depending on the number of holdings in the dzongkhag (which varied from less than 1,000 to over 12,000). This sample allocation was a compromise between providing good dzongkhag estimates (for which equal sample sizes in each dzongkhag would be optimum) and good national estimates (for which equal sample fractions in each dzongkhag would be optimum). This reflected the need for both dzongkhag and national level data.

The only data available for the sample design were estimates of the number of households (not agricultural holdings) in each enumeration area, available from dzongkhag records. This was used as the size measure for a PPS sample selection of enumeration areas. Enumeration areas within each dzongkhag were stratified by `gewog' (the level of administration between the dzongkhag and the village). The sample of agricultural holdings in each enumeration area was selected using systematic random sampling.

One of the features of the sample design was the consideration given to transportation problems. A village could be as much as several days walk from the nearest road. Where an

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enumeration area could be reached in less than a day, four holdings were sampled, and otherwise, eight holdings were taken. This is a good way of ensuring value for money in survey enumeration work. If it takes a long time to travel to a village, it is cost-effective to enumerate a large number, farmers in the village; if traveling time is small, it is better to spread the sample across as many villages as possible.

Staple Food Crop Pilot Survey, Lao PDR, 1994 The survey was carried out by the Ministry of Agriculture and Forestry to provide statistics, on

the 1994 wet season rice crop for each of four provinces (Lao MAF 1995a). The sampling unit was a `household with agricultural land', that is, a household with land used

for growing temporary or permanent crops. Two-stage sampling was used, with village sampled at the first stage and households with agricultural land sampled at the second stage.

The sample size was 1,494 households. The number of villages selected for the survey each province was between 58 and 60. An average of six households with agricultural land were selected in each sample village, making up a sample of between 350 and 410 households in each, province. The design reflected the emphasis on provincial level data - it was not useful to provide four-province totals - and provided data of similar reliability in each province.

A data collection operation was undertaken prior to the survey to obtain information to help in the sample design and selection. A list of villages was compiled using field materials prepare: for the 1995 Population Census. District Agricultural Offices supplied estimates of the number households with agricultural land and the farm area in each village. The sample of villages was selected using systematic PPS sampling, based on farm area as the size measure with village ordered by district. The sample of households in each enumeration area was selected using systematic random sampling.

This survey illustrates one way of dealing with the lack of agricultural information for the sample design and selection; namely, mount a data collection operation to get sample design information. In similar surveys in Bhutan (see above) and Cambodia (see below), a different approach was used, namely, select the sample using whatever limited information is already available. Pre-survey data collection operations of the type carried out for the Lao survey can - costly. Although the use of such supplementary information can provide a more efficient sample design, the same gains can be achieved, usually at lower cost, by taking a bigger sample size.

Rice Crop Pilot Survey, Cambodia, 1995 The survey was carried out by the Ministry of Agriculture, Forestry and Fisheries to provide

statistics on the 1995 wet season rice crop for each of four provinces (Cambodian MA1996). The sampling unit was a `household with crop land', where crop land was defined as land outside the village area used for growing temporary crops. Two-stage sampling was used, with villages as the first stage unit and households with crop land as the second stage unit. The sample size was 1,888 households. The number of villages selected for the survey each province was between 56 and 60. Eight households were sampled in each selected village. As for the Lao survey, the design reflected the emphasis on providing data of the same reliability each province. The survey is an example of a sample design with virtually no prior information. The only thing available was a list of villages and estimates of the rice area grown in each province. There was no population or household information available, nor was there any information on farm area or farm households. The sample of villages was selected using systematic random sampling, based on a geographic ordering. The sample of households in each enumeration area was also selected using systematic random sampling.


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The survey involved three related data collection activities. In the first phase, enumerators collected data on crop area and production through interviews with all sample households. In the second phase, enumerators, using measuring tapes and compasses, measured the actual area of the land reported for a 10% sub-sample of households. These data were used to adjust the area data reported by farmers during the interview phase of the survey. The third phase involved crop cutting for a 15% sub-sample of households.

Survey of Area and Production of Major Crops, Nepal This survey was developed by the Ministry of Agriculture during the 1970s to provide ongoing

statistics on the area of each crop grown (FAO 1982a). A form of area sampling was used. The cultivated land in the country was divided into strata based on soil fertility, agricultural practices, etc. Each stratum was sub-divided into segments and a 15% sample of segments was selected for the survey. Each segment comprised a number of parcels, the area of each parcel being known from cadastral records. Enumerators visited selected segments once in each of the three seasons and recorded the crops grown on each parcel. The area of each crop was then estimated based on the known area of each parcel.

The survey covered all areas in the country that had been cadastral surveyed; a reporting system of data collection was used in other areas. Problems were experienced with the survey because the cadastral records were not kept up-to-date as new land was cleared and parcel boundaries changed.

6.4 Crop cutting surveys

It has been reported that crop cutting surveys were used in Russia to estimate crop yields from as early as the 17th century (Szulc 1965, p. 569). The crop cutting technique as we know it today was developed in the 1940s and 1950s and has been widely used around the world since (FAO 1982b, pp. 68-88). In crop cutting surveys, a sample of plots is selected and, in each sample plot, the crop is harvested and weighed. The method is most suited to crops planted in a uniform manner, such as grain crops. It is more difficult where crops are harvested over a period of time, such as root crops. The plot size depends on the particular crop; for example, a square 2 x 2 meter plot is often used for grain crops. (A variation of the crop cutting method is sampling fruit trees to estimate yield based on counting or photographing fruit on each sample tree).

A crop cutting survey is like any other sample survey, except that the sampling unit is not a household or a farmer, but a plot of land. Various sampling methods can be used. One approach is multi-stage sampling: first, select a random sample of farms (as in any other farm survey); next, for each farmer, list all fields containing the particular crop and select a random sample of fields from this list; and finally, for each sample field, select a random sample of plots. There are various ways to do the plot selection: using a grid, taking random co-ordinates on the sides of the field, or even throwing an object randomly into the field.

It is often very difficult to apply strict random sampling procedures to crop cutting surveys. One problem is ensuring that enumerators are in the field at the exact time a selected field is ready for harvesting. Often, a less rigid sampling procedure is used. Sometimes, enumerators are sent to randomly selected villages with instructions to choose fields for crop cutting according to certain criteria; for example, a certain number of fields in `below average', `average' and `above average' condition. Alternatively, a district agricultural office might just be given a quota of crop cuts to do and it decides where the crop cutting will be done. These approaches have the same weaknesses as any non-random sample: does the sample represent all areas, do yield estimates reflect the effects of crop damage, does the sample represent crops harvested at different times, etc.?

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Throughout the years, crop cutting has been the favored method of estimating production and yield because it has been seen as providing 'perfect' data. The alternative of using data reported by farmers certainly has its problems (Section 7.1). However, crop cutting surveys .have not always been successful. There are a number of reasons for this.

Firstly, crop cutting surveys are time-consuming and expensive. To crop cut a small sample plot of rice may take several hours. The enumerator must; walk to the field (which could be some distance from the farmer's house); select and mark the plot; harvest the grain; return with the grain: to the farmer's house: and then thresh, winnow and clean the grain. The grain must then be dried and weighed, which could involve a return trip to the farm if the grain is not taken away by enumerator. Enumerators must be supplied with cutting implements, measuring tapes, storage bags, balances, etc. It is often difficult for countries to find the staff and funds for this work.

Secondly, there are the logistical problems of getting enumerators to the field at the exact time of the harvest. The enumerator may be required to make a first visit to the village to select the fields for crop cutting and then return at the time of the harvest. Each sample field md harvested at a different time and therefore several visits to the village may be needed.

Thirdly, it is difficult to accurately mark plots in the field and decide whether plants on the boundary lay inside or outside the plot. There is sometimes a tendency to include to many plants inside the plot, leading to overestimation of crop yields. The effect can be significant sizes are small.

Fourthly, despite the best laid sampling plans, there is a natural tendency for `typical’ 'average' plots to be selected, in preference to randomly selected plots that the enumerator considers not to be `representative'. Damaged crops, in particular, will usually be avoided.

Fifthly, external factors can influence the results of crop cutting surveys. Farmers may believe that it is in their interests for their yield to be seen to be low (because of taxation implications) or high (to be recognized as successful). Enumerators can also be under pressure, the crop cutting survey to provide certain results. These factors can undermine even the best designed statistical system.

Lastly, the crop cutting method only works if the enumerator cuts the crop at the same and uses the same harvesting method as the farmer does. In crop cutting surveys, only small amounts of grain are handled under well-controlled conditions. This may result in smaller, than experienced by the farmer and lead to overstatement in the yield estimates. Sometime enumerator may have to do the crop cutting before the grain is ready for harvest, which ma" in an understatement of the yield.

Case study - Cambodia Crop cutting surveys for Cambodia's staple food crop, rice, have been undertaken for many

years. Each District Agricultural Office is instructed to undertake some crop cutting during season, using 5 x 2 meters rectangular plots. The results are reported to the Ministry of Agriculture Forestry and Fisheries in Phnom Penh for use in the preparation of the national rice production statistics. It is generally acknowledged that the crop cutting data have not been reliable because poor geographic coverage and the lack of control over the number of crop cuts taken and the field procedures used. Funding and transport have often not been available for this work.

An improved crop cutting procedure was tested in the 1995 Rice Crop Pilot Survey, undertaken by the Ministry of Agriculture, Forestry and Fisheries with FAO assistance, ir provinces (Cambodian MAFF 1996). An outline of the survey methodology is given in Section 6.3. A random sample of farmers was selected for the survey and enumerators visited each selected farmer during October 1995 to collect details of the rice planted during the 1995 wet season 15% sub-sample of farmers was selected for inclusion in the crop cutting survey. For each sample farmer, one rice field was randomly selected from the list of rice fields identified during


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main survey. A sketch of each field was prepared, showing the length of the sides. Enumerators arranged with each farmer to return for the crop cutting at the time of the harvest.

On returning for the crop cutting, the enumerator selected two plots of size 2 x 2 meters in each sample field. To determine the location of the first plot, two random numbers were selected: (i) a number between one and one half of the perimeters of the field (R); and (ii) a number between one and one eighth of the perimeter of the field (S). Starting from any point on the perimeter of the field, the enumerator walked `R' paces clockwise around the perimeter, and then `S' paces into the field. This point defined one corner of the sample plot. The procedure was repeated if the selected point was outside the field, too close to the edge of the field to mark the plot, or in a part of the field in which the crop had been destroyed. Plots in which the crop was damaged, but not completely destroyed, were retained. The second sample plot was located by selecting two more random numbers and repeating the above process, this time walking anti-clockwise around the field. (It is very difficult to apply a strict probability procedure to select a plot in an irregularly shaped field; the method used was only an approximate random procedure, designed to be simple for enumerators to implement.)

The boundaries of each plot were marked in the field with two-meter long bamboo poles. A 2.828 meters pole was used to mark the diagonal (to ensure the plot was square). Bamboo poles were used in preference to cord to make it easier to separate tillers inside and outside the plot.

The farmer was encouraged to participate in the crop cutting to help ensure that the crop cutting procedures used were the same as for the normal harvest. The harvested grain was threshed, cleaned, winnowed and weighed. Enumerators paid the farmers for the grain and took it back to the Provincial Agricultural Office for drying and final weighing. A moisture tester was used to monitor the moisture content of the grain during the drying.

The yield estimates from the crop cutting survey were much higher than had been shown in the official statistics in earlier years (Table 6.1). The statistics before 1995 may have understated the yields, but it was felt that the crop cutting estimates may have been a little too high. The official figure in 1995 was higher than in earlier years because of the good crop harvest in that year and because the results of the crop cutting survey were taken into account. One interesting finding was that the crop cutting estimates were much higher than the farmer's forecasts provided during the main survey in October, one or two months before the harvest. This may reflect reporting problems in the survey or the apprehension of farmers in the face of uncertain weather conditions. Table 6. 1 Comparison of estimates of wet season rice yield (tons/ha) - selected provinces, Cambodia.

Estimate Kandal Prey Veng Svay Rieng Takeo Farmers' forecast (1995) 1.97 1.21 0.99 1.33 Crop cutting survey (1995) 2.42 2.06 1.27 1.98 Official estimate (1995) 2.15 1.60 1.20 1.65 Official estimate (1994) 1.60 1.10 1.14 1.40 Source: Cambodian MAFF 1996

Overall, the survey was a success. Key factors in this were: the use of well-defined field

producers, the preparation of a detailed enumerator's instruction manual, and extensive enumerator training. The return of crop cutting reports and crop samples to Phnom Penh are also helped to ensure the survey was carried out according to instructions.

However, transportation was a problem. Enumerators needed to make three visits to each village: the first visit in October, plus separate visits to crop cut the two sample fields. Farmers in Cambodia plant different rice varieties at various times throughout the season resulting in rice being harvested any time between September and January, which makes it difficult to organize crop cutting. Some fields had already been harvested prior to the survey; these were substituted

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The World Food Program also undertook a crop cutting study in the same year in those provinces not covered by the above pilot survey (WFP 1996). Because of time and funding constraints, it was not possible to employ the same sampling approach as the pilot survey. Instead, a random sample of communes was selected and enumerators were required to choose an`average' rice field for crop cutting in each sample commune. Two plots were selected for crop cutting in each field using the same methodology as the pilot survey. The method of selection of fields was not as good as in the pilot survey, but at least it provided a good geographic spread , the sample. Once again, the yield figures estimated from the study were higher than the official figures.

6.5 Sampling errors All estimates obtained from sample surveys are subject to sampling errors. These are error arising

because the estimates are based on a sample of units rather than all units. The sampling error of a survey estimate depends on two main factors: the sample size and the variability of the data item being measured. All other things being equal, the larger the sample size, the smaller the sampling error will be. The variability factor needs some explanation.

Consider the two data items `farm area' and `number of chickens', to be estimated from an agricultural survey in Nepal. Most farmers in Nepal have between 0.5 and 1.5 hectares of land and anywhere up to several hundred chickens. This means that there is less variability in farm area than in number of chickens. The larger the variability, the larger the sampling error will be. Therefore the survey estimate of total farm area would have a lower sampling error than the estimate of total number of chickens.

It is possible to make an estimate of the sampling error on a survey estimate. The sarnple size is known and the variability (the `population variance') can be measured from the sample data. Two measures of sampling error are commonly used: the standard error and the relatif standard error. To understand the interpretation of these measures, let us examine the National Sample Census of Agriculture, Nepal, 1991/92 (Table 6.2).

Table 6. 2 Sampling errors on selected items, agricultural census, Nepal, 1991/92.

The maize area harvested was estimated as 768,730 ha. The standard error on that estimate was

7,970 ha. This means that: • there is a 67% chance that the actual maize area was between 760,760 and 776.700 (768,730 ±

7,970); or • there is a 95% chance that the actual maize area was between 752,790 and 784,670 - (768,730 f

15,940). Is the estimate of maize area harvested more reliable than the estimate of farm population? The

standard error is certainly less - 7,970 ha for maize area, compared with 36,950 for farm - population - but this does not provide a meaningful comparison because the magnitudes of the two estimates are quite different. An error of 36,950 on a farm population figure of over 16 million., may be more acceptable than an error of 7,970 on the much smaller maize area figure 768,730 ha. To compare the reliability of different estimates, it is better to look at the standarderrors in relative terms. The relative standard error, that is the standard error expressed apercentage of the estimate, provides such a measure. On this basis, the farm population estitmate

Data item Estimate Standard error Relative standard Farm population 16,258,22 36.950 0.2% Maize area harvested (ha) 768,730 7,970 1.0% Source: Nepal CBS 1994b.


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(relative standard error 0.2%) is more reliable than the maize area estimate (relative standard error 1.0%).

Box 6. 1 Costing of policy options: implications of sampling errors

Suppose the government is considering paying each potato grower US $ 50 as part of a program to promote potato production. To estimate the cost of the proposal,. the government needs to know how many potato growers there are in the country. A sample survey could be undertaken to provide this information.

Suppose the survey shows that there are an estimated 30,000 potato growers in the country, indicating that the proposal can be expected to cost US $ 1.5 million. This is not an exact figure because the estimate of number of potato growers is subject to sampling error. If the standard error on the estimate of number of potato growers is 2.000, this would mean that there is a 95% chance that the actual number of potato growers is in the range 26,000 to 34.000, or, equivalently, that the cost of the proposed scheme will be between US $ 1.3 million and US $ 1.7 million. Also, there is a 2.5% chance that the number of potato growers is more than 34,000. If the government budgeted US $ 1.5 million for the project, there would be a 2.5% chance of overspending by more than US $ 200.000. There is also a 17% chance of overspending by more than US $ 100,000. (The same can also be said about the chance of under spending).

This type of analysis can be very useful in designing a sample survey. If the government considered the risks of a budget blow-out of the magnitude indicated not acceptable, a larger sample size would be necessary.

Box 6. 2 A brief lesson on sample design.

The design of sample surveys in large statistical offices is undertaken by specialist sampling statisticians. Sampling theory is a complex subject, which is beyond the scope of this working paper. However, there are some simple sample design tools which can be helpful to both the data user and the sampling practitioner. We look at one very simple mathematical formula which can be a valuable aid in quickly assessing the likely reliability of sample estimates and the sample size needs.

In the following, we consider the sampling error on a survey estimate of the total number (or percentage) of units with a certain characteristic, for example, the number of farms which grow potatoes, obtained from a farm survey. The relative standard error of this estimate can be approximated by:

s = 100/V np where: n = total sample size for the survey: and p = approximate proportion of units having the characteristic

Thus, if it can be assumed, from prior knowledge of potato production, that about 40% of farmers grow potatoes,

and then a sample of 1.000 farmers would provide an estimate of number of potato growers with a relative standard error of about 5% (100N400). On the other hand, if only about I in 40 farmers grow cucumbers, the relative standard error of the estimate of number of cucumber growers from the same sample would be about 20% (100√25). Thus, a sample of 1,000 farmers would give quite a good estimate of potato growers, but a rather poor estimate of cucumber growers. If it was important for the survey to provide good data for cucumbers, a bigger sample would be needed.

This should only be used as a guide since it assumes simple random sampling is used. For stratified or PPS sampling, sampling errors would usually be lower; for multi-stage sampling, sampling errors would be higher.

To assess the sampling errors for other types of survey estimates, such as crop area, production, yield. number of livestock, etc., one needs information about the population variance. This is often available from other surveys, for example, as provided for the National Sample Census of Agriculture, Nepal, 1991/92 ,Nepal CBS 1994b, pp. 52-57).

It is difficult to be definitive about what is an acceptable sampling error as it depends on the importance and use of the data. Generally, a relative standard error of less than 2% denotes a highly reliable estimate. (Both estimates in Table 6.2 should be considered very reliable.) Relative

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standard errors up to 10% are usually considered satisfactory. Estimates with relative standard errors greater than 20% are often not sufficiently reliable for most uses.

Mention should also be made of the related issue of spurious accuracy in the presentation of sample survey results. One often sees survey results showing that the area of wheat planted was asay, 14,572.47 ha. If the relative standard error was, say, 10%, the estimate could be in error r. some thousands of hectares. To show the figure to the nearest 100 square metres gives misleading impression of the figure's accuracy, as well as making it more difficult for users work with the data. The estimate would be better shown as 14,600 ha.

6.6 Popular misconceptions about sample surveys Sample surveys are still quite new in many countries and there is often scepticism about validity

of sample survey data. A number of questions are raised.

Aren't censuses the only way to get accurate data? It is sometimes argued that governments need accurate data for their important policy and

planning work and therefore cannot accept sample based statistics because they are subject sampling errors.

Censuses do not give `perfect' data. All statistics whether from a sample survey, a census or any other source are subject to non-sampling errors. Non-sampling errors are usually smaller sample surveys than in censuses. Data from sample surveys can sometimes be more reliable than data from censuses.

It is usually neither possible nor necessary for governments or any other user to have statistics which are completely error-free.

Sample surveys are OK for small research studies, but are they suitable for official statistics?

Sample surveys are widely used for official statistics in agriculture and other fields. Statistical offices face increasing funding pressures and sample surveys provide a cost-effective alternative to censuses and other data collection methods. Sample surveys often provide the option for collecting statistics, especially in countries in transition to a market economy, where existing statistical systems have broken down and insufficient funds exist to undertaken censuses.

Shouldn't a sample be selected to mirror the population? Random sampling provides the best way to ensure a representative sample. Non random sampling

methods have several fundamental weaknesses, as highlighted in Section 6.1.

How is it possible to make estimates for the country as a whole based on a sample? Some people find it difficult to accept the notion that one can estimate the total production

for the whole country based on data collected from a small sample of farmers within only some villages.

Consider the following example. Suppose there are 10,000 rice farmers in a province and a simple random sample of 500 is selected. The total rice production of these 500 sample farmer is say, 1,250 tons. To estimate the total production for all 10,000 farmers, common sense tells us that the sample total should be grossed up by a weighting factor of 10,000/500 = 20, giving an estimate of 25,000 tons. This may seem too simplistic, but it is one of the merits of random sampling that, although it has its foundation in complex probability theory, its application is usually very simple.


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A well-designed and implemented survey of 1,000 farmers is more useful than a poorly designed and hastily implemented survey of 10,000.

In more complex sample designs, the weighting factors vary for different sample units according to the probabilities of selection. In stratified sampling with, for example, a 20% sample of large farms and a 4% sample of small farms, the weighting factors would be 5 for large farms and 25 for small farms. This stands to reason; if small farms are undersampled compared with large farms, they should be given a greater weight in the calculation of totals.

How is it possible to measure the accuracy of sample estimates? The ability to measure sampling errors is a notion that many find difficult to accept. If the

area under maize in Nepal is unknown and a survey is undertaken to estimate that figure, how can one determine how accurate that sample estimate is? Obviously, it is not possible to measure the actual error on a sample estimate. However, as we have seen in Section 6.5, it is possible to determine 'confidence intervals' for a survey estimate. In the National Sample Census of Agriculture, Nepal, 1991/92, for example, one can infer that there is a 95% chance that the actual maize area in Nepal was between 752,790 and 784,670 ha (Table 6.2).

What is the minimum sample size necessary for survey estimates to be valid? It is sometimes suggested that there is a certain fixed sample size or sampling fraction,

for example, a 5% sample or 1,000 sample units, needed to get valid results for any survey. Deciding on the sample size is much more complex than that. As noted in Section 6.5, the sampling error on a survey estimate depends on the sample size and the population variance. The population variance differs according to the data being collected. Thus, the sample size needed for a survey of maize production would be different from that needed for a survey of fertilizer usage. Also, a maize production survey in Nepal would need a different sample size from the equivalent survey in Australia. The geographic level at which data are required is also an important factor in determining the sample size; a survey to provide potato production estimates for each district would need a larger sample than one designed to provide only national data.

Usually, surveys are designed to meet a number of different data requirements. A crop survey, for example, might be used to provide data on the area under each crop, as well as the use of fertilizers and other inputs. The sample size would need to be determined so that each of the data needs are met.

Will sample errors be halved if the sample size is doubled? If the sample size is doubled, the standard errors only decrease by about 40% (1/-~2). This is an important relationship in assessing the benefits of increasing the sample and in seeking to meet increased needs. If the objectives of a survey are expanded so that district, rather than provincial, estimates are required, the sample size would need to be increased many times over.

Will increasing the sample size help overcome weaknesses in sampling methodology?

A bad survey will yield bad data no matter how large the sample. If the sample of farms contains too many large units because of sample selection weaknesses, taking additional sample -arms would not make the sample more representative or the survey results more accurate. If Farmers under-report their crop production because of weaknesses in the field system, any additional sample farmers would report data with a similar bias and the accuracy of the survey results would not show any improvement.

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How is it possible to select a valid sample for a crop survey without having prior information on crops?

In Section 6.3, we saw the variety of approaches used for designing crop surveys. Sometimes, a lot of information was available to help in the sample design; in Australia, the names and addresses of each farmer in the country were known, and in the sample census of Nepal. Number of agricultural holdings and farm area in each village were known. Such information can help make the sample design more efficient, but it is not essential. In Cambodia, no information at all was available for the survey design, apart from a list of all villages in the country.

In random sampling, shouldn't all units have the same chance of selection? It has already been noted that random sampling requires that all units have a known probability

of selection in the sample, not necessarily the same probability. It is usually better to vary the probabilities of selection. Large farms are often sampled more heavily than small f.17since they contribute more to agricultural production.

If statistics are required for each province, should the same sampling fraction be taken in each province?

On the surface, there is some logic in the view that, if a farm survey is required to provide crop estimates for each province, then the same sampling fraction should be taken in each province. This is incorrect. In fact, the same sample size (approximately) should be taken in province, regardless of the number of farms in the province.

Surveys often provide data at various geographic levels. Most farm surveys provide de data at both the national level and at the next administrative level below that (state or province). We have just seen that, for provincial estimates, it is best to take the same sample size in each province. To get the most reliable national estimates, however, it is best to take the same sampling fraction in each province. A compromise sample allocation can be used to meet the needs for both national and provincial data. The design of the Agronomic Survey in Bhutan, described in Section 6.3, is one example of how this can be done.

Is it possible to produce statistics for each district if the sample was designed to provide only provincial data?

A survey may be designed specifically to provide provincial data, but this does not preclude it being used to provide data for other geographic units. A well-designed questionnaire will include full geographic details for each sample farm, commonly down to the village level. This information is recorded as geographic codes and entered into the computer along with other survey data. With this information, one can define any geographic unit (a district. a group of districts, a group of villages, etc.) and produce survey estimates for that grouping. The only limitation is sampling error; the smaller the geographic unit, the larger the sampling error will be. In most sample surveys, only limited geographical desegregation of data is possible.

6.7 Problems in introducing sample surveys in countries in transition In Chapter 2 and 4, we looked at the information needs of centrally planned on market economies

and the effect of market reforms on agricultural reporting systems. It was noted that sample surveys are needed to replace the existing reporting systems. These have been difficult to implement in many countries

One reason is the cost. Under centralized planning conditions, the task of collecting statistics simply involved field staff preparing a statistical report and transmitting it to the next


67

level of administration. It is much more expensive to collect data in sample surveys. Funds need to be found to field test questionnaires, train enumerators, pay enumerators for data collection work, and transport enumerators to the field. There are also additional costs in the survey processing, such as purchase of computers and paying data processing operators. In the difficult conditions that have usually accompanied economic reforms, governments have often been unable to find sufficient funds for such statistical activities.

In a tightly controlled socialist society, statistical reporting is usually well-entrenched as part of the responsibilities of state enterprises and local officials. In a newly liberalized economy, private businesses and households are often hesitant about co-operating in the government's data collection operations because of past experience with government intervention in their affairs. People also enjoy exercising a newly won freedom which they sometimes feel removes them from statistical reporting obligations, especially where adequate statistical laws are not in place.

Even if households and private businesses are willing to provide statistical information, it is often difficult for them to do so because their record keeping is not as good as the former state enterprises or village administrations. A livestock co-operative in Mongolia would keep daily records of milk output, but a herdsman would have no reason to do this. Without such records, the herdsman would have difficulty reporting milk production for the last month, or even the last week.

In a centrally planned system, designing a data collection is usually very simple: design a reporting format and send it out to field offices or state enterprises to complete. To undertake a household sample survey requires much more statistical design work. Alternative data collection methodologies may need to be evaluated; daily milk production, for example, might need to be collected through diaries kept by farmers, rather than by interview. Work needs to go into the design and testing of questionnaires and training of data collection staff. Promotional activities to encourage public support for the survey may also be necessary. All this takes time and money.

Countries also face staffing and organizational problems. During the period of transition, the pay of government officials often deteriorates, leading to poor staff morale and low productivity. There tends to be a high turnover of trained staff, attracted to better paid positions in the private sector. As the size of government contracts, fewer statistical staff is available at a time when more demands are being placed on the statistical service. Often, new statistical skills need to be acquired, especially since sample surveys are quite new to many countries. In Mongolia, a strong statistical organization continued to exist in the post-socialist period - albeit with fewer staff - but staff had had little previous experience with sample surveys in the former collectivized system of agriculture. In Cambodia, the skill base of the country had been decimated by years of political turmoil and warfare which seriously hindered efforts to improve the statistical system. The FAO has assisted several countries in the Asian region to upgrade statistical skills to overcome such problems.

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69

7. Additional Topics

7.1 Reporting problems in agricultural censuses and surveys

In Chapter 5, it was noted that farmers may find it difficult, or be unwilling, to report accurate data in agricultural censuses and surveys, but that these problems can generally be overcome through the careful design of questionnaires and data collection procedures, and the use of well-trained and motivated data collection staff. In this section, we examine this further.

Some common problems in reporting by farmers in agricultural censuses and surveys are: • Farm size. The farmer may not have any land records documents available because of

weaknesses in the land registration system. There can also be confusion about definitional issues, especially regarding land rented in and out, land bought and sold, land in the village area, and community land. The farmer may also not be familiar with standard area measures, such as hectares.

• Crop area. As well as the problems of reporting farm size described above, there can also be uncertainty about how to report double and mixed cropping, and whether to report planted or harvested area.

• Crop yield and production. This can be difficult for farmers to report because records are usually not kept, especially where the crop is for home consumption or is harvested over a period of time (such as fruit trees). Farmers are commonly not familiar with standard production measures, such as kilograms.

• Livestock numbers. There can be uncertainty over definitional issues, such as the reference period for reporting animals and the concepts of `owning' and `keeping' livestock.

• Livestock production. It is usually difficult for farmers to report milk production for, say, the last month because of recall problems and the lack of records. Unfamiliarity with standard measures can also be a problem. There may also be definitional issues, such as whether meat production should be recorded as the live weight, killed weight or dressed weight.

• Income. Definitional issues, in particular the treatment of cash and non-cash income, produce for home consumption and barter arrangements, and the lack of records cause most problems.

• Cost of production, nutrition and employment. These data are very difficult for farmers to report because of the detail required (e.g., all farm costs for a twelve month period), the complex conceptual issues (e.g., how to define work on the farm), and the lack of records.

How can good design of questionnaires and field procedures help overcome these problems? Firstly, it must be understood that designing a questionnaire for a farm survey in Cambodia, for example, involves more than preparing a few questions from the comfort of one's office in Phnom Penh (or, for that matter, New York or Rome). It is no good asking a farmer: `how many kilograms of rice did you produce in 1995?', if the farmer doesn't understand what is meant by production or is unfamiliar with the kilogram measure.

To design a questionnaire, one first needs to define the concepts to be measured and then develop clear and unambiguous questions to provide the required information. The questionnaire must be extensively tested to make sure it works: does the farmer understand the questions; does the enumerator understand the questions; does it provide the information required; how accurate is the information reported; is there a better way to ask the questions; and would another data

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collection approach be better? Enumerators must also be well-trained. If the questionnaire i~ required in different languages, care is needed in the translation to ensure that it reflects the required statistical concepts. (It is often difficult to find a suitable word for `agricultural holding’, When a survey questionnaire fails, it is usually not because the data cannot be collected, but because the questionnaire is poorly designed and inadequately tested. The statistician cannot take refuge in the explanation that farmers were not able to answer the questions in a survey; it is the statistician's job to make sure the questionnaire works. Of course, where bias can be anticipated, it should be controlled. For instance, it is sometimes important to know the sex of the respondent since male and female household members may have different perceptions of their relative farm inputs.

Different approaches used to collect crop area and production data are discussed below.

Collecting area data in agricultural censuses and surveys There are various ways to collect area data in agricultural censuses and surveys. Often, land

records information is available in the district or village administration and this can be used to verify or correct area data reported by farmers (FAO 1982b, pp. 43-67). Aerial photographs or satellite images can be used in the same way, especially in area sample designs. In some countries, the village head is often present when interviewing farmers for a census or survey and is able to confirm area information at the time of the interview. In using administrative information, in reporting for farm surveys, care is needed to ensure that the area information obtained is consistent with the area concept used in the survey (see Box 4.1).

If the farmer does not know the area of his/her land or is unable to report in standard area measurements, an indirect approach is often used. Farmers are sometimes asked to report the amount of seed used for the crop planting, which is then converted to area using conversion factors appropriate to the crop. The problem with this is that seed rates depend on the crop variety, location, quality of seed, etc. Seed rates may also change over time with the adoption of new cropping practices. An up-to-date study of seed rates is needed. The seed rate for rice in Lao PDR varies from about 50 to 80 kg/ha, depending on the province and variety (Lao MAF 1996, p. 18) In neighboring Cambodia, the seed rates appear to be much higher: between 70 and 100 kg/ha (Cambodian MAFF 1996, p. 13). The Cambodian seed rates may be higher because of poor quality seed or the practice of planting excess seed due to uncertainty about the weather. The latter could distort area measures based on seed rates. Farm surveys undertaken in Bhutan have used the langdo as a unit of area (Bhutan CSO 1988, 1990a, 1990b). A langdo is the area of land that two bullocks can plough in one day. Care is required with this measure as it depends on factors such as soil conditions, condition of bullocks, etc.

Another way of collecting area data is for the enumerator to actually measure the land reported by the farmer (FAO 1982b, pp. 54-67). The enumerator first sketches the field in the form of a polygon, with the sides approximating the boundaries of the field. He/she then measures the length of the sides (with a measuring tape) and the angles (with a compass). The slope can also be measured, if necessary. A programmable calculator is used to calculate the area. (It can also determine if there has been an error with the measurements.) This can be done quite quickly - a typical field can be measured in less than half an hour - but it does add significantly to the time required for the data collection. Sometimes, area measurement is used for a sub-sample of farms. with the results used to adjust the area data reported by farmers.

Collecting crop production data in agricultural censuses and surveys There has been much discussion amongst statisticians about the relative merits of farmer

interviews and crop cutting as a means of collecting crop production statistics (SIAP 1990, pp. 69-70)

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Farmers are often unable to report production data in standard weight units, but they are usually able to report in some form of local unit. Generally, this is the container used to gather the crop after harvest or to store the grain; typically a basket or a bag. This can work well if there is a standard basket or bag size throughout the country or even in certain regions of the country. However, this is not always the case. It may be necessary to question the farmer on the size of the container or even to sight the container. Farmers may even report production based on the number of pairs of baskets (because harvested grain may be carried from the field in a pair of baskets at either end of a bamboo pole carried across the fartner's shoulders).

There are often reporting problems related to the concept of production. The statistical concepts of harvested and economic yield are sometimes difficult to apply. Subsistence rice farmers often think of production as how much grain (usually unmilled) goes into store for food for the family. This takes into account post-harvest losses, but excludes grain used for seed, animal feed, sale, loan repayment or share-cropping obligation. To collect production data from farmers, it may be necessary to ask a series of questions on the various uses of the grain harvested.

Sometimes, farmers are able to accurately report data but are reluctant to do so because of concerns about taxes, crop procurement, or other government administrative actions. Often, the organization collecting the statistics is the same as the one collecting the tax or performing other government administrative functions, which does not help achieve good statistical reporting.

Special efforts are needed to gain support for statistical work from farmers. Countries with good statistical services have legal guarantees on the confidentiality of statistical information and, importantly, have earned the confidence of the public by strictly respecting the confidentiality principle. Public awareness campaigns can be a useful way to encourage public support for statistical work, especially for major statistical collections such as censuses. The support of the local administration is also important in statistical work, as is the effort of enumerators to establish good relations with farmers.

7.2 Early warning information systems

The central element of the agricultural statistics system in most countries is the provision of data on crop area and production. These statistics are used for medium and long-term food policy analysis, as well as assessing the performance of the agricultural sector and its contribution to the national economy. These data need to be very accurate and the data collection systems must be of the highest statistical standards. Sample surveys are often used.

The collection and publication of these statistics takes time and the statistics will not usually be available until some months after the end of the crop harvest. For the main wet season rice crop in South and Southeast Asia, harvested between October and December, final crop statistics may not become available until February or March. The government cannot wait this long for information. It needs to monitor conditions throughout the season and take action, as necessary, to deal with any imminent crop failure or food shortages. An early warning information system is needed.

There are various types of early warning information. Agro-meteorological information can help in forecasting crop production and assessing the likelihood of crop shortfalls in different parts of the country. Regular price data can be used to monitor the supply of agricultural commodities. Information on the condition of crops can help in forecasting crop production and in identifying problems, such as a pest attack, shortages of inputs, etc. The early warning information system should be designed for the specific conditions of the country concerned, and focus on providing information needed by the government for short-term planning and decision making. Examples of such decisions are: releasing reserve food stocks to help overcome food shortages, requesting international assistance to help in dealing with natural disasters or other emergencies, importing

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ertilizer to overcome shortages, providing seed to help in replanting a crop destroyed by floods. and transporting food from food surplus to food deficit areas (FAO 1990).

The main requirement of an early warning information system is for the information to be available quickly. It is no use getting information about a natural disaster a month after the event. When the response is required immediately. Accuracy and statistical rigor often need to be sacrificed in the interests of getting information to the government quickly. A crop report provided over the telephone by each district will be more useful than a well-designed sample survey, which take months to process. Early warning information does not even need to be quantitative. A descriptive report from each district on the status of crop planting can, by itself, be valuable in alerting the government to likely problems later in the season. However, reporting of quantitative information. does help in summarizing and interpreting the data; the status of crop planting, for example, can be reported as the percentage of the crop planted to date.

The difference between early warning information and agricultural statistics as such should be emphasized. For medium and long-term planning, the government needs objective, reliable quantitative agricultural statistics. For short term planning, it needs timely information in whatever form available to alert it to current problems. The two are part of the overall agricultural information requirements but serve quite different purposes.

One important element of early warning systems is the forecasting of crop production Often, countries provide such forecast estimates at different times throughout the crop season. The forecasts are updated as more information about the crop becomes available with the approach of the harvest. The first forecast could be made before planting, based on the farmer's intentions for the season. This could be updated as information on plantings become available and as reports on the condition of the crop are received throughout the season.

There are various crop forecasting techniques. Often, the forecasts are crude estimate based on existing crop area information, taking into account the weather, field assessments of crop conditions, and the past yields. At the other end of the scale, crop forecasting can be done using complex statistical models. Such models describe the relationship between crop production and key variables affecting it, such as crop area planted rainfall, etc.

Sometimes, well-designed sample surveys are used to provide data to help with the forecasting. In some countries, a survey of planting intentions is carried out at the beginning of the season and this is followed up with another survey of crop plantings once the planting completed.

7.3 The rounding problem

One of the most misunderstood problems in statistical presentations concerns the rounding of data. Let us consider a typical agricultural survey output table, showing crop area classified by sseason and by variety for two provinces (Table 7.1).

Table 7. 1 Crop area (ha) by season/variety and province.

Example table only

Everything is in order with this table. The only thing is that it might be easier to read the table if the figures were rounded to the nearest 100 (Table 7.2)

Crop season/variety Province A

Province B

TotalWet season 251.474 344,287 595,761Dry season 112.670 91,309 203.979

Total 364.144 435,596 799,740

Local variety 247946 351,149 599,095Improved variety 116,198 84,447 200.645

Total 364.144 435.596 799.740

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Table 7. 2 Crop area ('000 ha) by season/variety and province (rounded).

Crop season/variety Province A Province B Total Wet season 251.5 344.3 595.8 Dry season 112.7 91.3 204.0

Total 364.1 435.6 799.7

Local variety 247.9 351.1 599.1 Improved variety 116.2 84.4 200.6

Total 364.1 435.6 799.7 Example table only - each figure rounded to the nearest 100.

The table now no longer adds up. Let us try to correct this by amending the column and row

totals (Table 7.3). Table 7. 3 Crop area ('000 ha) by season/variety and province (rounded and corrected).

Crop season/variety Province A Province B Total Wet season 251.5 344.3 595.8 Dry season 112.7 91.3 204.0

Total 364.2 435.6 799.8

Local variety 247.9 351.1 599.0 Improved variety 116.2 84.4 200.6

Total 364.1 435.5 799.6 Example table only - component figures rounded to the nearest 100. row and column totals based on sums of rounded figures.

The table now adds up but there is now a much worse problem: the two sets of figures for total crop area are different. Other ways of amending the table, e.g., by adjusting component items instead of totals, will prove equally unsuccessful. The inconsistencies in the data may not be confined to just this table; by amending the total estimate for local variety, the figure may now be inconsistent with data shown in other tables. It is statistically impossible to force rounded figures to add up to totals and, at the same time, maintain consistency in the data. What can be done? It would be unacceptable to present conflicting estimates for the same data item. The only thing to do is to accept the rounded figures as they stand (Table 7.2); hence, the explanation often seen in statistical reports to the effect that there may be minor differences between sums of component items and totals because of rounding.

The same problem can arise in sample surveys even where there is no apparent rounding. This occurs where a non-integer weighting factor is used in the survey estimation. (For a simple random sample of 659 farms out of a total of 4,958, the weighting factor would be calculated as 4,958/659 = 7.52352.) Any survey estimate - even one shown to the last digit - is then a rounded figure. (If 411 out of the 659 sample farmers were males, the number of male farmers would be estimated as 411 x 7.52352 = 3,092.16672, which would be rounded to 3,092.) Rounding the weighting factor would introduce too much error in the survey estimate. (If the weight was rounded to 8, the number of male farmers would be estimated as 3,288.)

Any rounding of data in the processing of agricultural censuses and surveys needs to be handled with great care.

7.4 Statistical organization There are various models for organizing a country's statistical services. Some countries have a

centralized statistical system, in which a strong central statistical office is created with responsibility for undertaking most major national statistical activities. Other countries have chosen a decentralized approach, with each line ministry collecting statistics in its respective area

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of responsibility, for example, agricultural statistics by the Ministry of Agriculture, industrial statistics by the Ministry of Industry, etc. Many countries have elements of both centralized and decentralized systems.

The centralized statistical system has a number of advantages (Szulc 1965, pp. 31-37): • The separation of statistical work from policy and programmed work helps promote a more

independent and objective statistical service. • The quality of information reported by farmers and others in statistical collections i~ usually

better when statistical work is separated from tax assessment and other government administrative functions.

• Centralizing statistical work provides for the more effective use of the available (and often scarce) specialist statistical skills and data processing resources.

• The legal basis for the collection of statistics and protection of confidentiality of statistical data can be more clearly defined for a central statistical office.

• A centralized statistical system makes it easier to set statistical priorities and to provide aco-ordinated and integrated programme of statistical work in the various fields of statistics, with a minimum of duplication, and consistency in statistical standards.

One example of a highly centralized statistical system is Australia. The Australian Bureau of Statistics (ABS) is responsible for the collection and publication of major statistics in all economic and social fields, including agriculture, industry, labor, prices and national accounts. It also conducts quinquennial population censuses. About 100 staff work on agricultural statistics around the country, carrying out annual censuses of crop and livestock commodities, annual surveys of agricultural finance, and other regular and irregular agricultural surveys. The ABS prepares annual statistics on: land use, crop area and production, planting intentions, number of trees and production of permanent crops, area of pastures, irrigation and use of inputs, livestock numbers, livestock production, livestock forecasts, value of production of crops and livestock, and farm finances. Statistics on foreign trade and prices are also provided. The ABS has an extensive agricultural statistics publication program. It also issues data on diskette, CD-ROM, and through various on-line services. Other agencies carry out some in-depth agricultural surveys in co-operation with the ABS. The ABS operates under an Act of Parliament which outlines its responsibilities in the collection and publication of statistics, the co-ordination of statistical activities of other organizations, and the protection of confidentiality. The Act also stipulates the statistical obligations of the public (ABS 1995a, 1995b).

Most countries of the Asia - Pacific region have adopted a more decentralized approach. Bangladesh and Indonesia have highly centralized statistical systems, Nepal and India are partly centralized, and the Philippines is highly decentralized (ESCAP 1988, pp. 175-176). In some countries, the statistical system is still evolving.

One of the main reasons for countries adopting a decentralized approach to statistics is that they find it difficult to fund a fully functioning central statistical office. Line ministries are often the only source of statistics anyway, especially the reports and assessments of field staff. Often. line ministries are better placed than the statistical office to conduct censuses or surveys because they have field staff available to be used as a data collection resource.

Even in a decentralized statistical system, a national statistical office is needed to coordinate the statistical activities of different organizations. Statistical co-ordination is important to ensure that data collection work is not duplicated, and that statistics are collected on a sound and consistent basis through the application of standards in definitions, classifications and methodology (ESCAP 1988, pp. 178-180). Statistical co-ordination is especially important for national accounts statistics which bring together data from each sector of the economy. These statistics will only be meaningful if consistent statistical concepts and definitions are applied in the collection of statistics across all sectors.

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The author's view is that, in principle, a centralized statistical system is better. It is not coincidental that two countries with highly centralized statistical systems: Canada and Australia, are considered to have the best statistical services in the world (The Economist, 11 September 1993).

77

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Appendix

National Sample Census of Agriculture, Nepal, 1991192: Output Tables for Analysis of Farm Size

Table A 1 Area and fragmentation of holdings. Nepal, 1961162 to 1991192.

1961162 1971172 1981182 1991192 No. of holdings ('000) 1.540.0 1.721.2 2.194.0 2.736.1 No. of land holdings ('000) 1.518.0 1.707.3 2.185.7 2,703.9 Area of holdings ('000 ha) 1.685.4 1.654.0 2,4637 2.597.4 Average holding size (ha) 1.1 I 0.97 1 . I 3 0.96 Number of parcels ('000) 10.318.2 12,2825 9.516.4 10.806.2 Average parcelsholding 6.8 7 2 4 4 4 0 Average parcel size (ha) 0.16 0.13 0 26 0.24 Concentration index' 0.64 0.63 0.65 0.52 Source: Nepal CBS 1994% 1994~. * The concentration index is a measure of the land distribution - the higher

the index. the less equitable is the land distribution

Table A 2 Area of holdings - development regions and ecological belts, 1991192.

Number of Area of Average Concentration holdings holdings hold. size index' ('000) ('000 ha) (ha)

Development Region Eatern 636.4 783.2 1.25 0.52 Central 855.3 719.7 0.85 0 51 Western 608.8 566.4 0.94 0.49 Mid-western 371.5 324.7 0.88 0.5 1 Far Western 264.1 203.3 0.78 0.54

Ecological Belt Mountain 260.7 176.8 0.68 0 45 Hill 1.3577 1.046.2 0.77 0.43 Terai 1.1176 1.374.3 1.26 0.54

Nepal 2.736.1 2.597.4 0.96 0.52 * See footnote to Table A I.

Table A 3 Distribution of land holdings by size of holding. Nepal, 1991192.

Holdings Area of holdings Size of holding Number Percent Area Percent

('000) ('000 ha) < 0.10 ha 173.0 6.4 9.6 0.4 0.10 - 0.19 ha 263.8 9.8 38.0 1.5 0.20 - 0.49 ha 729.3 27.0 244.8 9.4 0.50 - 0.99 ha 71 1.7 26.3 499.5 19.2 l .OO - 1.99 ha 529.5 19.6 716.5 27.6 2.00 - 2.99 ha 168.4 6.2 400.2 15.4 3.00 - 3.99 ha 59 6 2.2 202.4 7.8 4.00 - 4 99 ha 28.6 1.1 125.7 4.8 5.00 - 9.99 ha 32.0 1.2 209.3 8.1 2 10.00 ha 8.2 0.3 151.3 5.8 Total land holdings 2.703.9 100.0 2.597.4 100.0

Appendix

Table A 4 Percent of land holdings by type of tenure and size of holding, Nepal, 1991192.

One tenurc form blore than Total Total Size of holding Owned Rented Other Total one tenure form holdings ('000) < 0.10 ha 87.4 4.8 1.7 93.9 6 1 100.0 173.0 0.10-0.19ha 84.1 2.4 1 .1 87.5 12.5 100 0 263.8 0.20 - 0.49 ha 84.5 1.5 0.8 86.8 13.2 100.0 729.3 0.50 - 0.99 ha 83.5 1 .0 0.6 85.1 14.9 100 0 711.7 I .OO - 1.99 ha 79.9 I .6 0.3 81.8 18 2 100.0 529.5 2.00 - 2 99 ha 77 0 2.4 0 1 79.5 20.5 100.0 168.4 3.00 - 3.99 ha 77.4 1.3 78.7 21 3 100.0 59.6 4.00 - 4.99 ha 80.4 0.9 81.3 18.7 1000 28.6 5.00 - 9.99 ha 83.9 0.5 84 4 15 6 100.0 32.0 2 10.00 ha 90.5 0.2 90 7 9.3 100.0 8.2 Total 82.8 1.7 0.6 85.2 14.8 100.0 2.703.9

Table A 5 Area of land holdings by type of land and size of holding, Nepal, 1991192.

Area ('000 ha) Percent Size of holding Wetland Dryland Total wetland < 0.50 ha 129.8 162.6 292.3 44.4 0.50 - 1.99 ha 672.1 543.9 1.216 1 55.3 2 2.00 ha 769.6 319.4 1.089.0 70.7 Total 1.5715 1.025.9 2.597 4 60.5

Table A 6 Land holdings with irrigated land by size of holding, Nepal, 1991192.

Holdings Percent of Size of holding with irrigation holdings

('000) <O IOha 40.2 23.2 010-0.19ha 97 1 36.8 0.20 - 0.49 ha 341.8 46.9 0.50 - 0.99 ha 392.2 55.1 I .OO - 1.99 ha 317.5 60.0 2.00 - 2 99 ha 103.7 61.6 3.00 - 3.99 ha 381 6.10 4.00 - 4.99 ha 19 3 67.5 5.00 - 9.99 ha 21.7 67.8 t 10.00 ha 6.0 73.1 Holdings with irrigation 1.377.5 50.9

National Sample Census of Agriculture, Nepal

Table A 7 Number of holdings with temporary crops ('000) by crop type and size of holding, Nepal, 1981182 and 1991192.

Crop Size of holding

< 0 50 ha 0.50 - 1 99 ha 2 2.00 ha Total 1981182 1991192 1981182 1991192 1981182 1991192 1981182 1991192

Cereal grains Rice Wheat Ma~ze Millet Barleq

Legumes Tubers Cash crops Oilseeds Splces Vegetables

Holding with crops 1.079 9 1.164 4 728.3 1.240 9 349 2 296 7 2.157.4 2.701 9 Total land holdings 1.099.7 1.166 0 734 5 1.241 1 351.6 296 8 2.185.7 2.703.9

Table .A 8 Distribution of temporary crop area by crop h p e and size of holding, Nepal, 1991192.

Slze of holding < 0.50 ha 0.50 - 1.99 ha 2 2 00 ha Total

Crop Av. area Percent Av. area Percent Av. area Percent Av. area Percent (ha) (ha) (ha) (ha)

Cereal grains 0.35 83 5 131 8 2 2 4 11 76 3 I .20 80.0 Rice 0 11 26.0 0.52 32.9 2 37 44.0 0 55 36.5 Wheat 0.08 185 0.25 1 5 9 0 77 14 2 0 23 15.6 Maize 0.10 2 4 8 0 3 5 2 2 2 0 70 13.0 0.28 18.9 Millet 0 0 5 11.6 0.14 9 1 0.22 4.0 0.1 I 7.4 Barley 0 01 1 9 0.02 I 4 0.03 0.6 0 02 I I

Legumes 0 0 2 5.9 0.12 7.3 0.56 10.5 0 13 8.4 Tubers 0 0 1 2.6 0.03 2.1 0 09 1 6 0 03 1.9 Cash crops - 0.5 0.02 1 0 0 14 2 5 0.02 1.5 Oilseeds 0 0 2 5 1 0 0 9 5 9 0 40 7.4 0.10 6.4 Spices - 0 6 0 0 1 0 5 0.05 I .O 0.01 0.7 Vegetables 0 01 1 9 0.01 0 9 0 04 0 7 0.01 I .O Total temp. crops 0 4 2 1000 1.59 100.0 5.39 100 0 1 5 0 100.0 Av holding size 0 25 0.98 3 67 0.96

Table A 9 Cropping intensity by size of holding. Nepal, 1991192.

Size of holding < 0 50 ha 0 50 - 1 99 ha 2 2 00 ha Total

lrahle land ('000 ha) 260.8 1.1014 9612 2.323.4 1 and under temporary crops ('000 ha) 258.8 1.085.8 939.9 2.284.6 Tfnipcrar) crops grown ('000 ha) 491 .O 1.972.6 1.599 4 4.063 0 i r pp~n: ~ntens~t!' 1 88 1 79 1 66 1.75

I--,: .r,>ppln: ~ntensity is the area of temporary crops grown on arable land dirided by .- . ..,. z r . 3 ~ l t arable land.

Appendix

Table A 10 Sumber of holdingsnirh permanent crops ('UUU) by crop h p e and size of holding, Sepal. 1991192.

Crop Size of holding < 0.50 ha 0.50 - 1.99 ha > 2.00 ha Total

Citrus fruit Orange 104.8 150.3 22.3 277.5 Lemon 59.5 104.9 25 6 190.0 Lime 50.9 93.0 20.3 164.2 Sweet orange 10.1 12.0 2.2 24.4 Other citrus fruit 42.8 57.3 13.9 113.9

Other fruit Mango 103.1 238.7 117 6 459.5 Banana 183.3 287.4 64.2 534.9 Guava 79.9 131.9 34.6 246.5 Jackfruit 25.2 63 4 33.5 122.1 Pineapple 5.4 15.4 4.9 25.7 Lychee 7.7 20.4 1 4 9 43.0 Pear 23.1 43.8 8 8 75.7 Apple 2 1 . 1 28.4 4 9 54.4 Plum 73.3 99.0 179 190.2 Papaya 63.2 90.1 21.0 174.3 Pomegranate 7.8 1 1.0 4.0 22 9 Other fru~t 45.3 73.1 31.1 149.5

Holdings with perm. crops 464.5 667.0 182.9 1.314.5 Total land holdings 1.166.0 1.241.1 2968 2.703.9

Tahle A 11 Rice, wheat and maize growers: use of selected inputs by size of holding, Nepal, 1991192.

Size of holding Holdings Area of Improved Pesticides Chemical fenilizers with crop crop seeds

('000) ('000) (9'0 growers) (46 erowers) (%6 growers) (9.6 crop area) Rice

< 0.50 ha 743.2 127.9 19.5 9.1 45.1 47.0 0.50 - 1.99 ha 1.020.5 649.2 24 4 13.6 48.8 45.1 2 2.00 ha 273.8 704.2 34.7 23.0 59.1 46.9

Total 2,037.5 1.481.2 24.0 13.2 48.8 46 1 Wheat

< 0.50 ha 608.2 90.7 24.1 3.9 42.1 72.4 0.50 - 1.99 ha 804.5 314 4 32.3 5.6 548 46.4 2 2.00 ha 223.1 227.9 43.0 9.0 79.5 59.1

Total 1.635.8 633.1 30.7 5.4 52.4 50.0 Maize

< 0.50 ha 804.9 121.9 9.4 1.9 21.5 3.0 0.50 - 1.99 ha 901.3 438.2 13.0 3.1 23.7 9.5 2 2.00 ha 166.3 208.7 17.6 5.4 21.7 I 4 8

Total 1.872.6 768.7 1 1.9 2.8 22.6 18.3

"l'ational Sample Census o f .4griculture, Nepal

Table A 12 Percent ofland holdings using agricultural equipment by type of equipment and size of holding, Sepal. 1991192.

Equipment h pe Size of holdinp . . . .

< 0. 50 ha 0.5 0- 1 99 ha 2 2.00 ha Total lron plough 4.8 13.2 32.3 11.7 Shallow tube-uell Deep tube-uell Tractor Thresher Pumping set Animal dramn cart Sprayer No. of land hold~nes

Table A 13 Number of holdings with livestock and livestock numbers by main livestock type and size of holding. Vepal, 1991192.

Livestock h pe Size of holding No land < 0.50 ha 0.50 - 1.9 9 ha 2 2 00 ha Total

Holdings ('000) with: Cattle 21 6 719.3 1.050.6 275.6 2.067.1 Chaunri 4 1 4.1 0.7 9 0 Buffaloes 9.3 4311 676.8 190.6 1.307 8 Goats 16.0 495 9 699.3 171.6 1.382.8 Sheep 0.6 30.7 46.1 15.3 92.7 Pigs 3 4 92.5 134.1 37.5 267.5 Chickens 12.6 563.5 683.7 140.7 1,400.4 Ducks I .5 25.6 40.1 25.4 92 6 Pigeons I . I 42.4 1 15.0 57.3 215.8

Total holdings 3 2 1 1.166.0 1.24 1.1 296.8 2,7361 No. of livestock ('000)

Cattle 66.3 2.062.9 3.8 18.0 1.412.0 7.359.3 Chaunri 1.3 24.6 27.0 5 7 58.6 Buffaloes 18 7 829.4 1.660.8 607.5 3,l 16.3 Goats 65.0 1.677.8 2.950.3 822.4 5,5 15.5 Sheep 5 6 172.3 310.6 114.4 602.8 Pigs 13.3 154 8 241.2 86.5 495.8 Chickens 498.9 4.178.2 5.766.7 1.889.4 12.333.1 Ducks 10.3 64.9 107.6 97.5 280.3 Pieeons 10.2 211.5 601.0 597.2 1.419.9

Table A 14 Number and percent of holdings with agricultural credit by source of credit and size of holding, Nepal, 1991192.

Size of holding Institutional source Non-instit. source All holdings Holdings Percent Holdings Percent Holdings Percent

('000) (.000) ('000) No land 1.3 4.0 5.2 16.2 32.1 100.0 <0.10 ha 0.10 -0 .19 ha 0.20 - 0.49 ha 0.50 - 0.99 ha l .OO - 1.99 ha 2.00 - 2.99 ha 3.00 - 3.99 ha 4.00 - 4.99 ha 5.00 - 9.99 ha > 10.00 ha Total

Appendix

Table A 15 Percent of holders' by age and size of holding, Sepal, 1991/92.

Age of holder Size of holding No land < 0.50 ha 0.50 - 1 .99 ha > 2.00 ha Total

< 25 5.3 6 4 4.4 2.9 5.1 25-34 24.8 24.5 18.2 12.6 20.3 3 5 4 4 33.2 29.7 28.0 23.6 28.3 45-54 20.3 2 1.5 25.3 28 5 24.0 55-64 12.2 12.1 16.0 21.3 14.9 2 65 4.1 5.9 8.0 I1 1 7.3

Total 100.0 100.0 100.0 100.0 100.0 Ave. household sire 5.4 5. I 6.2 8.3 5.9 * The holder is the person with management control over the holding

Table A 16 Holders' by work status and size of holding, Nepal, 1991192.

Holder's work status Size of holding < 0.50 ha 0.50 - 1.99 ha > Z O O ha Total

Number of holders ('000) , ,

On14 work on holding Also do other work off the holding:

Main work in agriculture Main work not in agriculture

Total Total holders

Percent of holders Only work on holding Also do other work off the holding:

Main work in agriculture Main work not in agriculture

Total Total See footnote to Table Al 5.

Table .A 17 Farm population aged 10 years and above hy lahour force status, sex and size of holding, Nepal, 1991192.

Labour force status Size of holdine during 1991192 < 0 50 ha 0.50 - 1.99 ha t 2.00 ha Total Males

Worked ('000) Did not work ('000)

Total males ('000) Participation rate (%)'

Females Worked ('000) Did not work ('000)

Total females ('000) Participation rate (%)'

Persons Worked ('000) Did not work ('000)

Total persons ("000) Participation rate (%)' 73.3 70.9 61.3 70.8

* The participation rate is the number of workers as a percentage of the total.

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