Agricultural Survey Methods

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.

Table of Contents:
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

About the Author :
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.

Review :
"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)