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Agricultural Survey Methods

Roberto Benedetti (Editor), Federica Piersimoni (Editor), Marco Bee (Editor), Giuseppe Espa (Editor)
ISBN: 978-0-470-66546-6
434 pages
March 2010
Agricultural Survey Methods (0470665467) cover image


Due to the widespread use of surveys in agricultural resources estimation there is a broad and recognizable interest in methods and techniques to collect and process agricultural data. This book brings together the knowledge of academics and experts to increase the dissemination of the latest developments in agricultural statistics. Conducting a census, setting up frames and registers and using administrative data for statistical purposes are covered and issues arising from sample design and estimation, use of remote sensing, management of data quality and dissemination and analysis of survey data are explored.

Key features:

  • Brings together high quality research on agricultural statistics from experts in this field.
  • Provides a thorough and much needed overview of developments within agricultural statistics.
  • Contains summaries for each chapter, providing a valuable reference framework for those new to the field.
  • Based upon a selection of key methodological papers presented at the ICAS conference series, updated and expanded to address current issues.
  • Covers traditional statistical methodologies including sampling and weighting.

This book provides a much needed guide to conducting surveys of land use and to the latest developments in agricultural statistics. Statisticians interested in agricultural statistics, agricultural statisticians in national statistics offices and statisticians and researchers using survey methodology will benefit from this book.

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Table of Contents

List of Contributors.


1 The present state of agricultural statistics in developed countries: situation and challenges.

1.1 Introduction.

1.2 Current state and political and methodological context.

1.3 Governance and horizontal issues.

1.4 Development in the demand for agricultural statistics.

1.5 Conclusions.



Part I Census, Frames, Registers and Administrative Data.

2 Using administrative registers for agricultural statistics.

2.1 Introduction.

2.2 Registers, register systems and methodological issues.

2.3 Using registers for agricultural statistics.

2.4 Creating a farm register: the population.

2.5 Creating a farm register: the statistical units.

2.6 Creating a farm register: the variables.

2.7 Conclusions.


3 Alternative sampling frames and administrative data. What is the best data source for agricultural statistics?

3.1 Introduction.

3.2 Administrative data.

3.3 Administrative data versus sample surveys.

3.4 Direct tabulation of administrative data.

3.5 Errors in administrative registers.

3.6 Errors in administrative data.

3.7 Alternatives to direct tabulation.

3.8 Calibration and small-area estimators.

3.9 Combined use of different frames.

3.10 Area frames.

3.11 Conclusions.



4 Statistical aspects of a census.

4.1 Introduction.

4.2 Frame.

4.3 Sampling.

4.4 Non-sampling error.

4.5 Post-collection processing.

4.6 Weighting.

4.7 Modelling.

4.8 Disclosure avoidance.

4.9 Dissemination.

4.10 Conclusions.


5 Using administrative data for census coverage.

5.1 Introduction.

5.2 Statistics Canada’s agriculture statistics programme.

5.3 1996 Census.

5.4 Strategy to add farms to the farm register.

5.5 2001 Census.

5.6 2006 Census.

5.7 Towards the 2011 Census.

5.8 Conclusions.



Part II Sample Design, Weighting and Estimation.

6 Area sampling for small-scale economic units.

6.1 Introduction.

6.2 Similarities and differences from household survey design.

6.3 Description of the basic design.

6.4 Evaluation criterion: the effect of weights on sampling precision.

6.5 Constructing and using ‘strata of concentration’.

6.6 Numerical illustrations and more flexible models.

6.7 Conclusions.



7 On the use of auxiliary variables in agricultural survey design.

7.1 Introduction.

7.2 Stratification.

7.3 Probability proportional to size sampling.

7.4 Balanced sampling.

7.5 Calibration weighting.

7.6 Combining ex ante and ex post auxiliary information: a simulated approach.

7.7 Conclusions.


8 Estimation with inadequate frames.

8.1 Introduction.

8.2 Estimation procedure.


9 Small-area estimation with applications to agriculture.

9.1 Introduction.

9.2 Design issues.

9.3 Synthetic and composite estimates.

9.4 Area-level models.

9.5 Unit-level models.

9.6 Conclusions.


Part III GIS and Remote Sensing.

10 The European land use and cover area-frame statistical survey.

10.1 Introduction.

10.2 Integrating agricultural and environmental information with LUCAS.

10.3 LUCAS 2001–2003: Target region, sample design and results.

10.4 The transect survey in LUCAS 2001–2003.

10.5 LUCAS 2006: a two-phase sampling plan of unclustered points.

10.6 Stratified systematic sampling with a common pattern of replicates.

10.7 Ground work and check survey.

10.8 Variance estimation and some results in LUCAS 2006.

10.9 Relative efficiency of the LUCAS 2006 sampling plan.

10.10 Expected accuracy of area estimates with the LUCAS 2006 scheme.

10.11 Non-sampling errors in LUCAS 2006.

10.12 Conclusions.



11 Area frame design for agricultural surveys.

11.1 Introduction.

11.2 Pre-construction analysis.

11.3 Land-use stratification.

11.4 Sub-stratification.

11.5 Replicated sampling.

11.6 Sample allocation.

11.7 Selection probabilities.

11.8 Sample selection.

11.9 Sample rotation.

11.10 Sample estimation.

11.11 Conclusions.

12 Accuracy, objectivity and efficiency of remote sensing for agricultural statistics.

12.1 Introduction.

12.2 Satellites and sensors.

12.3 Accuracy, objectivity and cost-efficiency.

12.4 Main approaches to using EO for crop area estimation.

12.5 Bias and subjectivity in pixel counting.

12.6 Simple correction of bias with a confusion matrix.

12.7 Calibration and regression estimators.

12.8 Examples of crop area estimation with remote sensing in large regions.

12.9 The GEOSS best practices document on EO for crop area estimation.

12.10 Sub-pixel analysis.

12.11 Accuracy assessment of classified images and land cover maps.

12.12 General data and methods for yield estimation.

12.13 Forecasting yields.

12.14 Satellite images and vegetation indices for yield monitoring.

12.15 Examples of crop yield estimation/forecasting with remote sensing.


13 Estimation of land cover parameters when some covariates are missing.

13.1 Introduction.

13.2 The AGRIT survey.

13.3 Imputation of the missing auxiliary variables.

13.4 Analysis of the 2006 AGRIT data.

13.5 Conclusions.


Part IV Data Editing and Quality Assurance.

14 A generalized edit and analysis system for agricultural data.

14.1 Introduction.

14.2 System development.

14.3 Analysis.

14.4 Development status.

14.5 Conclusions.


15 Statistical data editing for agricultural surveys.

15.1 Introduction.

15.2 Edit rules.

15.3 The role of automatic editing in the editing process.

15.4 Selective editing.

15.5 An overview of automatic editing.

15.6 Automatic editing of systematic errors.

15.7 The Fellegi–Holt paradigm.

15.8 Algorithms for automatic localization of random errors.

15.9 Conclusions.


16 Quality in agricultural statistics.

16.1 Introduction.

16.2 Changing concepts of quality.

16.3 Assuring quality.

16.4 Conclusions.


17 Statistics Canada’s Quality Assurance Framework applied to agricultural statistics.

17.1 Introduction.

17.2 Evolution of agriculture industry structure and user needs.

17.3 Agriculture statistics: a centralized approach.

17.4 Quality Assurance Framework.

17.5 Managing quality.

17.6 Quality management assessment.

17.7 Conclusions.



Part V Data Dissemination and Survey Data Analysis.

18 The data warehouse: a modern system for managing data.

18.1 Introduction.

18.2 The data situation in the NASS.

18.3 What is a data warehouse?

18.4 How does it work?

18.5 What we learned.

18.6 What is in store for the future?

18.7 Conclusions.

19 Data access and dissemination: some experiments during the First National Agricultural Census in China.

19.1 Introduction.

19.2 Data access and dissemination.

19.3 General characteristics of SDA.

19.4 A sample session using SDA.

19.5 Conclusions.


20 Analysis of economic data collected in farm surveys.

20.1 Introduction.

20.2 Requirements of sample surveys for economic analysis.

20.3 Typical contents of a farm economic survey.

20.4 Issues in statistical analysis of farm survey data.

20.5 Issues in economic modelling using farm survey data.

20.6 Case studies.


21 Measuring household resilience to food insecurity: application to Palestinian households.

21.1 Introduction.

21.2 The concept of resilience and its relation to household food security.

21.3 From concept to measurement.

21.4 Empirical strategy.

21.5 Testing resilience measurement.

21.6 Conclusions.


22 Spatial prediction of agricultural crop yield.

22.1 Introduction.

22.2 The proposed approach.

22.3 Case study: the province of Foggia.

22.4 Conclusions.


Author Index.

Subject Index.

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"All over the world, agricultural surveys are conducted to gather a large amount of information on the classic crops, yields, livestock, and other agricultural resources. The survey and analysis methods have tended to be locally devised to meet local or national conditions, cultures, and goals, but over the past few years, efforts have been made to establish methods that would allow comparison and evaluation across national and cultural boundaries. A summary of that effort is provided here in 22 methodology papers selected from presentations at the International Conference on Agricultural Statistics in 1998, 2001, 2004, and 2007. They address issues in census, frames, registers, and administrative data; sample design, weighting, and estimation; geographical information systems and remote sensing; data editing and quality assurance; and data dissemination and survey data analysis. Mathematicians and economists looking toward agriculture, agricultural scientists looking at statistics, and researchers and policy-making looking at the intersection could all find the volume to be a valuable reference." (SciTech Book News, December 2010)

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