{"product_id":"agricultural-survey-methods-isbn-9780470743713","title":"Agricultural Survey Methods","description":"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 \u003ci\u003eagricultural\u003c\/i\u003e 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. \u003cp\u003eKey features:\u003c\/p\u003e \u003cul type=\"disc\"\u003e \u003cli\u003eBrings together high quality research on agricultural statistics from experts in this field.\u003c\/li\u003e \u003cli\u003eProvides a thorough and much needed overview of developments within agricultural statistics.\u003c\/li\u003e \u003cli\u003eContains summaries for each chapter, providing a valuable reference framework for those new to the field.\u003c\/li\u003e \u003cli\u003eBased upon a selection of key methodological papers presented at the ICAS conference series, updated and expanded to address current issues.\u003c\/li\u003e \u003cli\u003eCovers traditional statistical methodologies including sampling and weighting.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003eList of Contributors xvii\u003c\/p\u003e \u003cp\u003eIntroduction xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 The present state of agricultural statistics in developed countries: situation and challenges 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Current state and political and methodological context 4\u003c\/p\u003e \u003cp\u003e1.2.1 General 4\u003c\/p\u003e \u003cp\u003e1.2.2 Specific agricultural statistics in the UNECE region 6\u003c\/p\u003e \u003cp\u003e1.3 Governance and horizontal issues 15\u003c\/p\u003e \u003cp\u003e1.3.1 The governance of agricultural statistics 15\u003c\/p\u003e \u003cp\u003e1.3.2 Horizontal issues in the methodology of agricultural statistics 16\u003c\/p\u003e \u003cp\u003e1.4 Development in the demand for agricultural statistics 20\u003c\/p\u003e \u003cp\u003e1.5 Conclusions 22\u003c\/p\u003e \u003cp\u003eAcknowledgements 23\u003c\/p\u003e \u003cp\u003eReference 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Census, Frames, Registers and Administrative Data 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Using administrative registers for agricultural statistics 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 27\u003c\/p\u003e \u003cp\u003e2.2 Registers, register systems and methodological issues 28\u003c\/p\u003e \u003cp\u003e2.3 Using registers for agricultural statistics 29\u003c\/p\u003e \u003cp\u003e2.3.1 One source 29\u003c\/p\u003e \u003cp\u003e2.3.2 Use in a farm register system 30\u003c\/p\u003e \u003cp\u003e2.3.3 Use in a system for agricultural statistics linked with the business register 30\u003c\/p\u003e \u003cp\u003e2.4 Creating a farm register: the population 34\u003c\/p\u003e \u003cp\u003e2.5 Creating a farm register: the statistical units 38\u003c\/p\u003e \u003cp\u003e2.6 Creating a farm register: the variables 42\u003c\/p\u003e \u003cp\u003e2.7 Conclusions 44\u003c\/p\u003e \u003cp\u003eReferences 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Alternative sampling frames and administrative data. What is the best data source for agricultural statistics? 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 45\u003c\/p\u003e \u003cp\u003e3.2 Administrative data 46\u003c\/p\u003e \u003cp\u003e3.3 Administrative data versus sample surveys 46\u003c\/p\u003e \u003cp\u003e3.4 Direct tabulation of administrative data 46\u003c\/p\u003e \u003cp\u003e3.4.1 Disadvantages of direct tabulation of administrative data 47\u003c\/p\u003e \u003cp\u003e3.5 Errors in administrative registers 48\u003c\/p\u003e \u003cp\u003e3.5.1 Coverage of administrative registers 48\u003c\/p\u003e \u003cp\u003e3.6 Errors in administrative data 49\u003c\/p\u003e \u003cp\u003e3.6.1 Quality control of the IACS data 49\u003c\/p\u003e \u003cp\u003e3.6.2 An estimate of errors of commission and omission in the IACS data 50\u003c\/p\u003e \u003cp\u003e3.7 Alternatives to direct tabulation 51\u003c\/p\u003e \u003cp\u003e3.7.1 Matching different registers 51\u003c\/p\u003e \u003cp\u003e3.7.2 Integrating surveys and administrative data 52\u003c\/p\u003e \u003cp\u003e3.7.3 Taking advantage of administrative data for censuses 52\u003c\/p\u003e \u003cp\u003e3.7.4 Updating area or point sampling frames with administrative data 53\u003c\/p\u003e \u003cp\u003e3.8 Calibration and small-area estimators 53\u003c\/p\u003e \u003cp\u003e3.9 Combined use of different frames 54\u003c\/p\u003e \u003cp\u003e3.9.1 Estimation of a total 55\u003c\/p\u003e \u003cp\u003e3.9.2 Accuracy of estimates 55\u003c\/p\u003e \u003cp\u003e3.9.3 Complex sample designs 56\u003c\/p\u003e \u003cp\u003e3.10 Area frames 57\u003c\/p\u003e \u003cp\u003e3.10.1 Combining a list and an area frame 57\u003c\/p\u003e \u003cp\u003e3.11 Conclusions 58\u003c\/p\u003e \u003cp\u003eAcknowledgements 59\u003c\/p\u003e \u003cp\u003eReferences 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Statistical aspects of a census 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 63\u003c\/p\u003e \u003cp\u003e4.2 Frame 64\u003c\/p\u003e \u003cp\u003e4.2.1 Coverage 64\u003c\/p\u003e \u003cp\u003e4.2.2 Classification 64\u003c\/p\u003e \u003cp\u003e4.2.3 Duplication 65\u003c\/p\u003e \u003cp\u003e4.3 Sampling 65\u003c\/p\u003e \u003cp\u003e4.4 Non-sampling error 66\u003c\/p\u003e \u003cp\u003e4.4.1 Response error 66\u003c\/p\u003e \u003cp\u003e4.4.2 Non-response 67\u003c\/p\u003e \u003cp\u003e4.5 Post-collection processing 68\u003c\/p\u003e \u003cp\u003e4.6 Weighting 68\u003c\/p\u003e \u003cp\u003e4.7 Modelling 69\u003c\/p\u003e \u003cp\u003e4.8 Disclosure avoidance 69\u003c\/p\u003e \u003cp\u003e4.9 Dissemination 70\u003c\/p\u003e \u003cp\u003e4.10 Conclusions 71\u003c\/p\u003e \u003cp\u003eReferences 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Using administrative data for census coverage 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 73\u003c\/p\u003e \u003cp\u003e5.2 Statistics Canada’s agriculture statistics programme 74\u003c\/p\u003e \u003cp\u003e5.3 1996 Census 75\u003c\/p\u003e \u003cp\u003e5.4 Strategy to add farms to the farm register 75\u003c\/p\u003e \u003cp\u003e5.4.1 Step 1: Match data from E to M 76\u003c\/p\u003e \u003cp\u003e5.4.2 Step 2: Identify potential farm operations among the unmatched records from E 76\u003c\/p\u003e \u003cp\u003e5.4.3 Step 3: Search for the potential farms from E on M 76\u003c\/p\u003e \u003cp\u003e5.4.4 Step 4: Collect information on the potential farms 77\u003c\/p\u003e \u003cp\u003e5.4.5 Step 5: Search for the potential farms with the updated key identifiers 77\u003c\/p\u003e \u003cp\u003e5.5 2001 Census 77\u003c\/p\u003e \u003cp\u003e5.5.1 2001 Farm Coverage Follow-up 77\u003c\/p\u003e \u003cp\u003e5.5.2 2001 Coverage Evaluation Study 77\u003c\/p\u003e \u003cp\u003e5.6 2006 Census 78\u003c\/p\u003e \u003cp\u003e5.6.1 2006 Missing Farms Follow-up 79\u003c\/p\u003e \u003cp\u003e5.6.2 2006 Coverage Evaluation Study 80\u003c\/p\u003e \u003cp\u003e5.7 Towards the 2011 Census 81\u003c\/p\u003e \u003cp\u003e5.8 Conclusions 81\u003c\/p\u003e \u003cp\u003eAcknowledgements 83\u003c\/p\u003e \u003cp\u003eReferences 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Sample Design, Weighting and Estimation 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Area sampling for small-scale economic units 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 87\u003c\/p\u003e \u003cp\u003e6.2 Similarities and differences from household survey design 88\u003c\/p\u003e \u003cp\u003e6.2.1 Probability proportional to size selection of area units 88\u003c\/p\u003e \u003cp\u003e6.2.2 Heterogeneity 90\u003c\/p\u003e \u003cp\u003e6.2.3 Uneven distribution 90\u003c\/p\u003e \u003cp\u003e6.2.4 Integrated versus separate sectoral surveys 90\u003c\/p\u003e \u003cp\u003e6.2.5 Sampling different types of units in an integrated design 91\u003c\/p\u003e \u003cp\u003e6.3 Description of the basic design 91\u003c\/p\u003e \u003cp\u003e6.4 Evaluation criterion: the effect of weights on sampling precision 93\u003c\/p\u003e \u003cp\u003e6.4.1 The effect of ‘random’ weights 93\u003c\/p\u003e \u003cp\u003e6.4.2 Computation of D2 from the frame 94\u003c\/p\u003e \u003cp\u003e6.4.3 Meeting sample size requirements 94\u003c\/p\u003e \u003cp\u003e6.5 Constructing and using ‘strata of concentration’ 95\u003c\/p\u003e \u003cp\u003e6.5.1 Concept and notation 95\u003c\/p\u003e \u003cp\u003e6.5.2 Data by StrCon and sector (aggregated over areas) 95\u003c\/p\u003e \u003cp\u003e6.5.3 Using StrCon for determining the sampling rates: a basic model 97\u003c\/p\u003e \u003cp\u003e6.6 Numerical illustrations and more flexible models 97\u003c\/p\u003e \u003cp\u003e6.6.1 Numerical illustrations 97\u003c\/p\u003e \u003cp\u003e6.6.2 More flexible models: an empirical approach 100\u003c\/p\u003e \u003cp\u003e6.7 Conclusions 104\u003c\/p\u003e \u003cp\u003eAcknowledgements 105\u003c\/p\u003e \u003cp\u003eReferences 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 On the use of auxiliary variables in agricultural survey design 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 107\u003c\/p\u003e \u003cp\u003e7.2 Stratification 109\u003c\/p\u003e \u003cp\u003e7.3 Probability proportional to size sampling 113\u003c\/p\u003e \u003cp\u003e7.4 Balanced sampling 116\u003c\/p\u003e \u003cp\u003e7.5 Calibration weighting 118\u003c\/p\u003e \u003cp\u003e7.6 Combining ex ante and ex post auxiliary information: a simulated approach 124\u003c\/p\u003e \u003cp\u003e7.7 Conclusions 128\u003c\/p\u003e \u003cp\u003eReferences 129\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Estimation with inadequate frames 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 133\u003c\/p\u003e \u003cp\u003e8.2 Estimation procedure 133\u003c\/p\u003e \u003cp\u003e8.2.1 Network sampling 133\u003c\/p\u003e \u003cp\u003e8.2.2 Adaptive sampling 135\u003c\/p\u003e \u003cp\u003eReferences 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Small-area estimation with applications to agriculture 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 139\u003c\/p\u003e \u003cp\u003e9.2 Design issues 140\u003c\/p\u003e \u003cp\u003e9.3 Synthetic and composite estimates 140\u003c\/p\u003e \u003cp\u003e9.3.1 Synthetic estimates 141\u003c\/p\u003e \u003cp\u003e9.3.2 Composite estimates 141\u003c\/p\u003e \u003cp\u003e9.4 Area-level models 142\u003c\/p\u003e \u003cp\u003e9.5 Unit-level models 144\u003c\/p\u003e \u003cp\u003e9.6 Conclusions 146\u003c\/p\u003e \u003cp\u003eReferences 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III GIS and Remote Sensing 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 The European land use and cover area-frame statistical survey 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 151\u003c\/p\u003e \u003cp\u003e10.2 Integrating agricultural and environmental information with LUCAS 154\u003c\/p\u003e \u003cp\u003e10.3 LUCAS 2001–2003: Target region, sample design and results 155\u003c\/p\u003e \u003cp\u003e10.4 The transect survey in LUCAS 2001–2003 156\u003c\/p\u003e \u003cp\u003e10.5 LUCAS 2006: a two-phase sampling plan of unclustered points 158\u003c\/p\u003e \u003cp\u003e10.6 Stratified systematic sampling with a common pattern of replicates 159\u003c\/p\u003e \u003cp\u003e10.7 Ground work and check survey 159\u003c\/p\u003e \u003cp\u003e10.8 Variance estimation and some results in LUCAS 2006 160\u003c\/p\u003e \u003cp\u003e10.9 Relative efficiency of the LUCAS 2006 sampling plan 161\u003c\/p\u003e \u003cp\u003e10.10 Expected accuracy of area estimates with the LUCAS 2006 scheme 163\u003c\/p\u003e \u003cp\u003e10.11 Non-sampling errors in LUCAS 2006 164\u003c\/p\u003e \u003cp\u003e10.11.1 Identification errors 164\u003c\/p\u003e \u003cp\u003e10.11.2 Excluded areas 164\u003c\/p\u003e \u003cp\u003e10.12 Conclusions 165\u003c\/p\u003e \u003cp\u003eAcknowledgements 166\u003c\/p\u003e \u003cp\u003eReferences 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Area frame design for agricultural surveys 169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 169\u003c\/p\u003e \u003cp\u003e11.1.1 Brief history 170\u003c\/p\u003e \u003cp\u003e11.1.2 Advantages of using an area frame 171\u003c\/p\u003e \u003cp\u003e11.1.3 Disadvantages of using an area frame 171\u003c\/p\u003e \u003cp\u003e11.1.4 How the NASS uses an area frame 172\u003c\/p\u003e \u003cp\u003e11.2 Pre-construction analysis 173\u003c\/p\u003e \u003cp\u003e11.3 Land-use stratification 176\u003c\/p\u003e \u003cp\u003e11.4 Sub-stratification 178\u003c\/p\u003e \u003cp\u003e11.5 Replicated sampling 180\u003c\/p\u003e \u003cp\u003e11.6 Sample allocation 183\u003c\/p\u003e \u003cp\u003e11.7 Selection probabilities 185\u003c\/p\u003e \u003cp\u003e11.7.1 Equal probability of selection 186\u003c\/p\u003e \u003cp\u003e11.7.2 Unequal probability of selection 187\u003c\/p\u003e \u003cp\u003e11.8 Sample selection 188\u003c\/p\u003e \u003cp\u003e11.8.1 Equal probability of selection 188\u003c\/p\u003e \u003cp\u003e11.8.2 Unequal probability of selection 188\u003c\/p\u003e \u003cp\u003e11.9 Sample rotation 189\u003c\/p\u003e \u003cp\u003e11.10 Sample estimation 190\u003c\/p\u003e \u003cp\u003e11.11 Conclusions 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Accuracy, objectivity and efficiency of remote sensing for agricultural statistics 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 193\u003c\/p\u003e \u003cp\u003e12.2 Satellites and sensors 194\u003c\/p\u003e \u003cp\u003e12.3 Accuracy, objectivity and cost-efficiency 195\u003c\/p\u003e \u003cp\u003e12.4 Main approaches to using EO for crop area estimation 196\u003c\/p\u003e \u003cp\u003e12.5 Bias and subjectivity in pixel counting 197\u003c\/p\u003e \u003cp\u003e12.6 Simple correction of bias with a confusion matrix 197\u003c\/p\u003e \u003cp\u003e12.7 Calibration and regression estimators 197\u003c\/p\u003e \u003cp\u003e12.8 Examples of crop area estimation with remote sensing in large regions 199\u003c\/p\u003e \u003cp\u003e12.8.1 US Department of Agriculture 199\u003c\/p\u003e \u003cp\u003e12.8.2 Monitoring agriculture with remote sensing 200\u003c\/p\u003e \u003cp\u003e12.8.3 India 200\u003c\/p\u003e \u003cp\u003e12.9 The GEOSS best practices document on EO for crop area estimation 200\u003c\/p\u003e \u003cp\u003e12.10 Sub-pixel analysis 201\u003c\/p\u003e \u003cp\u003e12.11 Accuracy assessment of classified images and land cover maps 201\u003c\/p\u003e \u003cp\u003e12.12 General data and methods for yield estimation 203\u003c\/p\u003e \u003cp\u003e12.13 Forecasting yields 203\u003c\/p\u003e \u003cp\u003e12.14 Satellite images and vegetation indices for yield monitoring 204\u003c\/p\u003e \u003cp\u003e12.15 Examples of crop yield estimation\/forecasting with remote sensing 205\u003c\/p\u003e \u003cp\u003e12.15.1 USDA 205\u003c\/p\u003e \u003cp\u003e12.15.2 Global Information and Early Warning System 206\u003c\/p\u003e \u003cp\u003e12.15.3 Kansas Applied Remote Sensing 207\u003c\/p\u003e \u003cp\u003e12.15.4 MARS crop yield forecasting system 207\u003c\/p\u003e \u003cp\u003eReferences 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Estimation of land cover parameters when some covariates are missing 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 213\u003c\/p\u003e \u003cp\u003e13.2 The AGRIT survey 214\u003c\/p\u003e \u003cp\u003e13.2.1 Sampling strategy 214\u003c\/p\u003e \u003cp\u003e13.2.2 Ground and remote sensing data for land cover estimation in a small area 216\u003c\/p\u003e \u003cp\u003e13.3 Imputation of the missing auxiliary variables 218\u003c\/p\u003e \u003cp\u003e13.3.1 An overview of the missing data problem 218\u003c\/p\u003e \u003cp\u003e13.3.2 Multiple imputation 219\u003c\/p\u003e \u003cp\u003e13.3.3 Multiple imputation for missing data in satellite images 221\u003c\/p\u003e \u003cp\u003e13.4 Analysis of the 2006 AGRIT data 222\u003c\/p\u003e \u003cp\u003e13.5 Conclusions 227\u003c\/p\u003e \u003cp\u003eReferences 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Data Editing and Quality Assurance 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 A generalized edit and analysis system for agricultural data 233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 233\u003c\/p\u003e \u003cp\u003e14.2 System development 236\u003c\/p\u003e \u003cp\u003e14.2.1 Data capture 236\u003c\/p\u003e \u003cp\u003e14.2.2 Edit 237\u003c\/p\u003e \u003cp\u003e14.2.3 Imputation 238\u003c\/p\u003e \u003cp\u003e14.3 Analysis 239\u003c\/p\u003e \u003cp\u003e14.3.1 General description 239\u003c\/p\u003e \u003cp\u003e14.3.2 Micro-analysis 239\u003c\/p\u003e \u003cp\u003e14.3.3 Macro-analysis 240\u003c\/p\u003e \u003cp\u003e14.4 Development status 240\u003c\/p\u003e \u003cp\u003e14.5 Conclusions 241\u003c\/p\u003e \u003cp\u003eReferences 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Statistical data editing for agricultural surveys 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 243\u003c\/p\u003e \u003cp\u003e15.2 Edit rules 245\u003c\/p\u003e \u003cp\u003e15.3 The role of automatic editing in the editing process 246\u003c\/p\u003e \u003cp\u003e15.4 Selective editing 247\u003c\/p\u003e \u003cp\u003e15.4.1 Score functions for totals 248\u003c\/p\u003e \u003cp\u003e15.4.2 Score functions for changes 250\u003c\/p\u003e \u003cp\u003e15.4.3 Combining local scores 251\u003c\/p\u003e \u003cp\u003e15.4.4 Determining a threshold value 252\u003c\/p\u003e \u003cp\u003e15.5 An overview of automatic editing 253\u003c\/p\u003e \u003cp\u003e15.6 Automatic editing of systematic errors 255\u003c\/p\u003e \u003cp\u003e15.7 The Fellegi–Holt paradigm 256\u003c\/p\u003e \u003cp\u003e15.8 Algorithms for automatic localization of random errors 257\u003c\/p\u003e \u003cp\u003e15.8.1 The Fellegi–Holt method 257\u003c\/p\u003e \u003cp\u003e15.8.2 Using standard solvers for integer programming problems 259\u003c\/p\u003e \u003cp\u003e15.8.3 The vertex generation approach 259\u003c\/p\u003e \u003cp\u003e15.8.4 A branch-and-bound algorithm 260\u003c\/p\u003e \u003cp\u003e15.9 Conclusions 263\u003c\/p\u003e \u003cp\u003eReferences 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Quality in agricultural statistics 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 267\u003c\/p\u003e \u003cp\u003e16.2 Changing concepts of quality 268\u003c\/p\u003e \u003cp\u003e16.2.1 The American example 268\u003c\/p\u003e \u003cp\u003e16.2.2 The Swedish example 271\u003c\/p\u003e \u003cp\u003e16.3 Assuring quality 274\u003c\/p\u003e \u003cp\u003e16.3.1 Quality assurance as an agency undertaking 274\u003c\/p\u003e \u003cp\u003e16.3.2 Examples of quality assurance efforts 275\u003c\/p\u003e \u003cp\u003e16.4 Conclusions 276\u003c\/p\u003e \u003cp\u003eReferences 276\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Statistics Canada’s Quality Assurance Framework applied to agricultural statistics 277\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 277\u003c\/p\u003e \u003cp\u003e17.2 Evolution of agriculture industry structure and user needs 278\u003c\/p\u003e \u003cp\u003e17.3 Agriculture statistics: a centralized approach 279\u003c\/p\u003e \u003cp\u003e17.4 Quality Assurance Framework 281\u003c\/p\u003e \u003cp\u003e17.5 Managing quality 283\u003c\/p\u003e \u003cp\u003e17.5.1 Managing relevance 283\u003c\/p\u003e \u003cp\u003e17.5.2 Managing accuracy 286\u003c\/p\u003e \u003cp\u003e17.5.3 Managing timeliness 293\u003c\/p\u003e \u003cp\u003e17.5.4 Managing accessibility 294\u003c\/p\u003e \u003cp\u003e17.5.5 Managing interpretability 296\u003c\/p\u003e \u003cp\u003e17.5.6 Managing coherence 297\u003c\/p\u003e \u003cp\u003e17.6 Quality management assessment 299\u003c\/p\u003e \u003cp\u003e17.7 Conclusions 300\u003c\/p\u003e \u003cp\u003eAcknowledgements 300\u003c\/p\u003e \u003cp\u003eReferences 300\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Data Dissemination and Survey Data Analysis 303\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 The data warehouse: a modern system for managing data 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 305\u003c\/p\u003e \u003cp\u003e18.2 The data situation in the NASS 306\u003c\/p\u003e \u003cp\u003e18.3 What is a data warehouse? 308\u003c\/p\u003e \u003cp\u003e18.4 How does it work? 308\u003c\/p\u003e \u003cp\u003e18.5 What we learned 310\u003c\/p\u003e \u003cp\u003e18.6 What is in store for the future? 312\u003c\/p\u003e \u003cp\u003e18.7 Conclusions 312\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Data access and dissemination: some experiments during the First National Agricultural Census in China 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 313\u003c\/p\u003e \u003cp\u003e19.2 Data access and dissemination 314\u003c\/p\u003e \u003cp\u003e19.3 General characteristics of SDA 316\u003c\/p\u003e \u003cp\u003e19.4 A sample session using SDA 318\u003c\/p\u003e \u003cp\u003e19.5 Conclusions 320\u003c\/p\u003e \u003cp\u003eReferences 322\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Analysis of economic data collected in farm surveys 323\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 323\u003c\/p\u003e \u003cp\u003e20.2 Requirements of sample surveys for economic analysis 325\u003c\/p\u003e \u003cp\u003e20.3 Typical contents of a farm economic survey 326\u003c\/p\u003e \u003cp\u003e20.4 Issues in statistical analysis of farm survey data 327\u003c\/p\u003e \u003cp\u003e20.4.1 Multipurpose sample weighting 327\u003c\/p\u003e \u003cp\u003e20.4.2 Use of sample weights in modelling 328\u003c\/p\u003e \u003cp\u003e20.5 Issues in economic modelling using farm survey data 330\u003c\/p\u003e \u003cp\u003e20.5.1 Data and modelling issues 330\u003c\/p\u003e \u003cp\u003e20.5.2 Economic and econometric specification 331\u003c\/p\u003e \u003cp\u003e20.6 Case studies 332\u003c\/p\u003e \u003cp\u003e20.6.1 ABARE broadacre survey data 332\u003c\/p\u003e \u003cp\u003e20.6.2 Time series model of the growth in fodder use in the Australian cattle industry 333\u003c\/p\u003e \u003cp\u003e20.6.3 Cross-sectional model of land values in central New South Wales 335\u003c\/p\u003e \u003cp\u003eReferences 338\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Measuring household resilience to food insecurity: application to Palestinian households 341\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 341\u003c\/p\u003e \u003cp\u003e21.2 The concept of resilience and its relation to household food security 343\u003c\/p\u003e \u003cp\u003e21.2.1 Resilience 343\u003c\/p\u003e \u003cp\u003e21.2.2 Households as (sub) systems of a broader food system, and household resilience 345\u003c\/p\u003e \u003cp\u003e21.2.3 Vulnerability versus resilience 345\u003c\/p\u003e \u003cp\u003e21.3 From concept to measurement 347\u003c\/p\u003e \u003cp\u003e21.3.1 The resilience framework 347\u003c\/p\u003e \u003cp\u003e21.3.2 Methodological approaches 348\u003c\/p\u003e \u003cp\u003e21.4 Empirical strategy 350\u003c\/p\u003e \u003cp\u003e21.4.1 The Palestinian data set 350\u003c\/p\u003e \u003cp\u003e21.4.2 The estimation procedure 351\u003c\/p\u003e \u003cp\u003e21.5 Testing resilience measurement 359\u003c\/p\u003e \u003cp\u003e21.5.1 Model validation with CART 359\u003c\/p\u003e \u003cp\u003e21.5.2 The role of resilience in measuring vulnerability 363\u003c\/p\u003e \u003cp\u003e21.5.3 Forecasting resilience 364\u003c\/p\u003e \u003cp\u003e21.6 Conclusions 365\u003c\/p\u003e \u003cp\u003eReferences 366\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Spatial prediction of agricultural crop yield 369\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 369\u003c\/p\u003e \u003cp\u003e22.2 The proposed approach 372\u003c\/p\u003e \u003cp\u003e22.2.1 A simulated exercise 374\u003c\/p\u003e \u003cp\u003e22.3 Case study: the province of Foggia 376\u003c\/p\u003e \u003cp\u003e22.3.1 The AGRIT survey 377\u003c\/p\u003e \u003cp\u003e22.3.2 Durum wheat yield forecast 378\u003c\/p\u003e \u003cp\u003e22.4 Conclusions 384\u003c\/p\u003e \u003cp\u003eReferences 385\u003c\/p\u003e \u003cp\u003eAuthor Index 389\u003c\/p\u003e \u003cp\u003eSubject Index 395\u003c\/p\u003e  \"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.\" (\u003ci\u003eSciTech Book News\u003c\/i\u003e, December 2010)\u003cbr\u003e \u003cbr\u003e  \u003cp\u003e\u003cstrong\u003eR. Benedetti\u003c\/strong\u003e, Department of Economics, University of Trento, Italia. \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eM Bee\u003c\/strong\u003e,?Department of Economics, University of Trento, Italia. \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eG Espa\u003c\/strong\u003e, Department of Economics, University of Trento, Italia. \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eF Piersimoni\u003c\/strong\u003e, Italian Central Bureau of Statistics, Italy.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988707852517,"sku":"NP9780470743713","price":186.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470743713.jpg?v=1761781274","url":"https:\/\/k12savings.com\/es\/products\/agricultural-survey-methods-isbn-9780470743713","provider":"K12savings","version":"1.0","type":"link"}