Title Details

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.

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