Agricultural Survey Methods (eBook, PDF)

Redaktion: Benedetti, Roberto; Espa, Giuseppe; Bee, Marco; Piersimoni, Federica

• Format: PDF

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 434
• Erscheinungstermin: 3. März 2010
• Englisch
• ISBN-13: 9780470665466
• Artikelnr.: 38201222

Autorenporträt

R. Benedetti, Department of Economics, University of Trento, Italia. M Bee,?Department of Economics, University of Trento, Italia. G Espa, Department of Economics, University of Trento, Italia. F Piersimoni, Italian Central Bureau of Statistics, Italy.

Inhaltsangabe

List of Contributors xvii
Introduction xxi
1 The present state of agricultural statistics in developed countries:
situation and challenges 1
1.1 Introduction 1
1.2 Current state and political and methodological context 4
1.2.1 General 4
1.2.2 Specific agricultural statistics in the UNECE region 6
1.3 Governance and horizontal issues 15
1.3.1 The governance of agricultural statistics 15
1.3.2 Horizontal issues in the methodology of agricultural statistics 16
1.4 Development in the demand for agricultural statistics 20
1.5 Conclusions 22
Acknowledgements 23
Reference 24
Part I Census, Frames, Registers and Administrative Data 25
2 Using administrative registers for agricultural statistics 27
2.1 Introduction 27
2.2 Registers, register systems and methodological issues 28
2.3 Using registers for agricultural statistics 29
2.3.1 One source 29
2.3.2 Use in a farm register system 30
2.3.3 Use in a system for agricultural statistics linked with the business
register 30
2.4 Creating a farm register: the population 34
2.5 Creating a farm register: the statistical units 38
2.6 Creating a farm register: the variables 42
2.7 Conclusions 44
References 44
3 Alternative sampling frames and administrative data. What is the best
data source for agricultural statistics? 45
3.1 Introduction 45
3.2 Administrative data 46
3.3 Administrative data versus sample surveys 46
3.4 Direct tabulation of administrative data 46
3.4.1 Disadvantages of direct tabulation of administrative data 47
3.5 Errors in administrative registers 48
3.5.1 Coverage of administrative registers 48
3.6 Errors in administrative data 49
3.6.1 Quality control of the IACS data 49
3.6.2 An estimate of errors of commission and omission in the IACS data 50
3.7 Alternatives to direct tabulation 51
3.7.1 Matching different registers 51
3.7.2 Integrating surveys and administrative data 52
3.7.3 Taking advantage of administrative data for censuses 52
3.7.4 Updating area or point sampling frames with administrative data 53
3.8 Calibration and small-area estimators 53
3.9 Combined use of different frames 54
3.9.1 Estimation of a total 55
3.9.2 Accuracy of estimates 55
3.9.3 Complex sample designs 56
3.10 Area frames 57
3.10.1 Combining a list and an area frame 57
3.11 Conclusions 58
Acknowledgements 59
References 60
4 Statistical aspects of a census 63
4.1 Introduction 63
4.2 Frame 64
4.2.1 Coverage 64
4.2.2 Classification 64
4.2.3 Duplication 65
4.3 Sampling 65
4.4 Non-sampling error 66
4.4.1 Response error 66
4.4.2 Non-response 67
4.5 Post-collection processing 68
4.6 Weighting 68
4.7 Modelling 69
4.8 Disclosure avoidance 69
4.9 Dissemination 70
4.10 Conclusions 71
References 71
5 Using administrative data for census coverage 73
5.1 Introduction 73
5.2 Statistics Canada's agriculture statistics programme 74
5.3 1996 Census 75
5.4 Strategy to add farms to the farm register 75
5.4.1 Step 1: Match data from E to M 76
5.4.2 Step 2: Identify potential farm operations among the unmatched
records from E 76
5.4.3 Step 3: Search for the potential farms from E on M 76
5.4.4 Step 4: Collect information on the potential farms 77
5.4.5 Step 5: Search for the potential farms with the updated key
identifiers 77
5.5 2001 Census 77
5.5.1 2001 Farm Coverage Follow-up 77
5.5.2 2001 Coverage Evaluation Study 77
5.6 2006 Census 78
5.6.1 2006 Missing Farms Follow-up 79
5.6.2 2006 Coverage Evaluation Study 80
5.7 Towards the 2011 Census 81
5.8 Conclusions 81
Acknowledgements 83
References 83
Part II Sample Design, Weighting and Estimation 85
6 Area sampling for small-scale economic units 87
6.1 Introduction 87
6.2 Similarities and differences from household survey design 88
6.2.1 Probability proportional to size selection of area units 88
6.2.2 Heterogeneity 90
6.2.3 Uneven distribution 90
6.2.4 Integrated versus separate sectoral surveys 90
6.2.5 Sampling different types of units in an integrated design 91
6.3 Description of the basic design 91
6.4 Evaluation criterion: the effect of weights on sampling precision 93
6.4.1 The effect of 'random' weights 93
6.4.2 Computation of D2 from the frame 94
6.4.3 Meeting sample size requirements 94
6.5 Constructing and using 'strata of concentration' 95
6.5.1 Concept and notation 95
6.5.2 Data by StrCon and sector (aggregated over areas) 95
6.5.3 Using StrCon for determining the sampling rates: a basic model 97
6.6 Numerical illustrations and more flexible models 97
6.6.1 Numerical illustrations 97
6.6.2 More flexible models: an empirical approach 100
6.7 Conclusions 104
Acknowledgements 105
References 105
7 On the use of auxiliary variables in agricultural survey design 107
7.1 Introduction 107
7.2 Stratification 109
7.3 Probability proportional to size sampling 113
7.4 Balanced sampling 116
7.5 Calibration weighting 118
7.6 Combining ex ante and ex post auxiliary information: a simulated
approach 124
7.7 Conclusions 128
References 129
8 Estimation with inadequate frames 133
8.1 Introduction 133
8.2 Estimation procedure 133
8.2.1 Network sampling 133
8.2.2 Adaptive sampling 135
References 138
9 Small-area estimation with applications to agriculture 139
9.1 Introduction 139
9.2 Design issues 140
9.3 Synthetic and composite estimates 140
9.3.1 Synthetic estimates 141
9.3.2 Composite estimates 141
9.4 Area-level models 142
9.5 Unit-level models 144
9.6 Conclusions 146
References 147
Part III GIS and Remote Sensing 149
10 The European land use and cover area-frame statistical survey 151
10.1 Introduction 151
10.2 Integrating agricultural and environmental information with LUCAS 154
10.3 LUCAS 2001-2003: Target region, sample design and results 155
10.4 The transect survey in LUCAS 2001-2003 156
10.5 LUCAS 2006: a two-phase sampling plan of unclustered points 158
10.6 Stratified systematic sampling with a common pattern of replicates 159
10.7 Ground work and check survey 159
10.8 Variance estimation and some results in LUCAS 2006 160
10.9 Relative efficiency of the LUCAS 2006 sampling plan 161
10.10 Expected accuracy of area estimates with the LUCAS 2006 scheme 163
10.11 Non-sampling errors in LUCAS 2006 164
10.11.1 Identification errors 164
10.11.2 Excluded areas 164
10.12 Conclusions 165
Acknowledgements 166
References 166
11 Area frame design for agricultural surveys 169
11.1 Introduction 169
11.1.1 Brief history 170
11.1.2 Advantages of using an area frame 171
11.1.3 Disadvantages of using an area frame 171
11.1.4 How the NASS uses an area frame 172
11.2 Pre-construction analysis 173
11.3 Land-use stratification 176
11.4 Sub-stratification 178
11.5 Replicated sampling 180
11.6 Sample allocation 183
11.7 Selection probabilities 185
11.7.1 Equal probability of selection 186
11.7.2 Unequal probability of selection 187
11.8 Sample selection 188
11.8.1 Equal probability of selection 188
11.8.2 Unequal probability of selection 188
11.9 Sample rotation 189
11.10 Sample estimation 190
11.11 Conclusions 192
12 Accuracy, objectivity and efficiency of remote sensing for agricultural
statistics 193
12.1 Introduction 193
12.2 Satellites and sensors 194
12.3 Accuracy, objectivity and cost-efficiency 195
12.4 Main approaches to using EO for crop area estimation 196
12.5 Bias and subjectivity in pixel counting 197
12.6 Simple correction of bias with a confusion matrix 197
12.7 Calibration and regression estimators 197
12.8 Examples of crop area estimation with remote sensing in large regions
199
12.8.1 US Department of Agriculture 199
12.8.2 Monitoring agriculture with remote sensing 200
12.8.3 India 200
12.9 The GEOSS best practices document on EO for crop area estimation 200
12.10 Sub-pixel analysis 201
12.11 Accuracy assessment of classified images and land cover maps 201
12.12 General data and methods for yield estimation 203
12.13 Forecasting yields 203
12.14 Satellite images and vegetation indices for yield monitoring 204
12.15 Examples of crop yield estimation/forecasting with remote sensing 205
12.15.1 USDA 205
12.15.2 Global Information and Early Warning System 206
12.15.3 Kansas Applied Remote Sensing 207
12.15.4 MARS crop yield forecasting system 207
References 207
13 Estimation of land cover parameters when some covariates are missing 213
13.1 Introduction 213
13.2 The AGRIT survey 214
13.2.1 Sampling strategy 214
13.2.2 Ground and remote sensing data for land cover estimation in a small
area 216
13.3 Imputation of the missing auxiliary variables 218
13.3.1 An overview of the missing data problem 218
13.3.2 Multiple imputation 219
13.3.3 Multiple imputation for missing data in satellite images 221
13.4 Analysis of the 2006 AGRIT data 222
13.5 Conclusions 227
References 229
Part IV Data Editing and Quality Assurance 231
14 A generalized edit and analysis system for agricultural data 233
14.1 Introduction 233
14.2 System development 236
14.2.1 Data capture 236
14.2.2 Edit 237
14.2.3 Imputation 238
14.3 Analysis 239
14.3.1 General description 239
14.3.2 Micro-analysis 239
14.3.3 Macro-analysis 240
14.4 Development status 240
14.5 Conclusions 241
References 242
15 Statistical data editing for agricultural surveys 243
15.1 Introduction 243
15.2 Edit rules 245
15.3 The role of automatic editing in the editing process 246
15.4 Selective editing 247
15.4.1 Score functions for totals 248
15.4.2 Score functions for changes 250
15.4.3 Combining local scores 251
15.4.4 Determining a threshold value 252
15.5 An overview of automatic editing 253
15.6 Automatic editing of systematic errors 255
15.7 The Fellegi-Holt paradigm 256
15.8 Algorithms for automatic localization of random errors 257
15.8.1 The Fellegi-Holt method 257
15.8.2 Using standard solvers for integer programming problems 259
15.8.3 The vertex generation approach 259
15.8.4 A branch-and-bound algorithm 260
15.9 Conclusions 263
References 264
16 Quality in agricultural statistics 267
16.1 Introduction 267
16.2 Changing concepts of quality 268
16.2.1 The American example 268
16.2.2 The Swedish example 271
16.3 Assuring quality 274
16.3.1 Quality assurance as an agency undertaking 274
16.3.2 Examples of quality assurance efforts 275
16.4 Conclusions 276
References 276
17 Statistics Canada's Quality Assurance Framework applied to agricultural
statistics 277
17.1 Introduction 277
17.2 Evolution of agriculture industry structure and user needs 278
17.3 Agriculture statistics: a centralized approach 279
17.4 Quality Assurance Framework 281
17.5 Managing quality 283
17.5.1 Managing relevance 283
17.5.2 Managing accuracy 286
17.5.3 Managing timeliness 293
17.5.4 Managing accessibility 294
17.5.5 Managing interpretability 296
17.5.6 Managing coherence 297
17.6 Quality management assessment 299
17.7 Conclusions 300
Acknowledgements 300
References 300
Part V Data Dissemination and Survey Data Analysis 303
18 The data warehouse: a modern system for managing data 305
18.1 Introduction 305
18.2 The data situation in the NASS 306
18.3 What is a data warehouse? 308
18.4 How does it work? 308
18.5 What we learned 310
18.6 What is in store for the future? 312
18.7 Conclusions 312
19 Data access and dissemination: some experiments during the First
National Agricultural Census in China 313
19.1 Introduction 313
19.2 Data access and dissemination 314
19.3 General characteristics of SDA 316
19.4 A sample session using SDA 318
19.5 Conclusions 320
References 322
20 Analysis of economic data collected in farm surveys 323
20.1 Introduction 323
20.2 Requirements of sample surveys for economic analysis 325
20.3 Typical contents of a farm economic survey 326
20.4 Issues in statistical analysis of farm survey data 327
20.4.1 Multipurpose sample weighting 327
20.4.2 Use of sample weights in modelling 328
20.5 Issues in economic modelling using farm survey data 330
20.5.1 Data and modelling issues 330
20.5.2 Economic and econometric specification 331
20.6 Case studies 332
20.6.1 ABARE broadacre survey data 332
20.6.2 Time series model of the growth in fodder use in the Australian
cattle industry 333
20.6.3 Cross-sectional model of land values in central New South Wales 335
References 338
21 Measuring household resilience to food insecurity: application to
Palestinian households 341
21.1 Introduction 341
21.2 The concept of resilience and its relation to household food security
343
21.2.1 Resilience 343
21.2.2 Households as (sub) systems of a broader food system, and household
resilience 345
21.2.3 Vulnerability versus resilience 345
21.3 From concept to measurement 347
21.3.1 The resilience framework 347
21.3.2 Methodological approaches 348
21.4 Empirical strategy 350
21.4.1 The Palestinian data set 350
21.4.2 The estimation procedure 351
21.5 Testing resilience measurement 359
21.5.1 Model validation with CART 359
21.5.2 The role of resilience in measuring vulnerability 363
21.5.3 Forecasting resilience 364
21.6 Conclusions 365
References 366
22 Spatial prediction of agricultural crop yield 369
22.1 Introduction 369
22.2 The proposed approach 372
22.2.1 A simulated exercise 374
22.3 Case study: the province of Foggia 376
22.3.1 The AGRIT survey 377
22.3.2 Durum wheat yield forecast 378
22.4 Conclusions 384
References 385
Author Index 389
Subject Index 395

Rezensionen

"All over the world, agricultural surveys are conducted to gather alarge amount of information on the classic crops, yields,livestock, and other agricultural resources. The survey andanalysis methods have tended to be locally devised to meet local ornational conditions, cultures, and goals, but over the past fewyears, efforts have been made to establish methods that would allowcomparison and evaluation across national and cultural boundaries.A summary of that effort is provided here in 22 methodology papersselected from presentations at the International Conference onAgricultural Statistics in 1998, 2001, 2004, and 2007. They addressissues in census, frames, registers, and administrative data;sample design, weighting, and estimation; geographical informationsystems and remote sensing; data editing and quality assurance; anddata dissemination and survey data analysis. Mathematicians andeconomists looking toward agriculture, agricultural scientistslooking at statistics, and researchers and policy-making looking atthe intersection could all find the volume to be a valuablereference." (SciTech Book News, December 2010)