Agricultural Survey Methods
, by Benedetti, Roberto; Piersimoni, Federica; Bee, Marco; Espa, Giuseppe- ISBN: 9780470743713 | 0470743719
- Cover: Hardcover
- Copyright: 5/24/2010
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.
The Present state of agricultural statistics in developed countries, situation and challenges | |
Introduction | |
Current State and Political and Methodological Context | |
Governance and Horizontal Issues | |
Development in Demands for Agricultural Statistics and Challenges | |
Conclusions | |
Census, frames, registers and administrative data | |
Using Administrative Registers for Agricultural Statistics | |
Introduction | |
Registers, Register Systems and Methodological Issues | |
Using Registers for Agricultural Statistics | |
Creating a Farm Register-the Population | |
Creating a Farm Register-the Statistical Units | |
Creating a Farm Register-the Variables | |
Conclusions | |
Bibliography | |
Alternative Sampling Frames and Administrative Data. Which is the Best Data Source for Agricultural Statistics? | |
Introduction | |
Administrative Data | |
Administrative Data versus Sample Surveys | |
Direct Tabulation of Administrative Data | |
Errors in administrative registers | |
Errors in Administrative Data | |
Alternatives to Direct Tabulation | |
Calibration and Small Area Estimators | |
Combined Use of Different Frames | |
Area Frames | |
Conclusions | |
Bibliography | |
Statistical Aspects of a Census | |
Introduction | |
Frame | |
Sampling | |
Non-sampling Error | |
Post Collection Processing | |
Weighting | |
Modeling | |
Disclosure Avoidance | |
Dissemination | |
Conclusions | |
Bibliography | |
Using Administrative Data for Census Coverage | |
Introduction | |
Statistics Canada's Agriculture Statistics Program | |
1996 Census | |
Strategy to Add Farms to the Farm Register | |
2001 Census | |
2006 Census | |
Towards the 2011 Census | |
Conclusions | |
Bibliography | |
Sample design, weighting, and estimation | |
Area Sampling for Small-Scale Economic Units | |
Introduction | |
Similarities and Differences from Household Survey Design | |
Description of the Basic Design | |
Evaluation Criterion: the Effect of Weights on Sampling Precision | |
Constructing and Using 'Strata of Concentration' (StrCon) | |
Numerical Illustrations and More Flexible Models | |
Conclusions | |
Bibliography | |
On the Use of Auxiliary Variables in Agricultural Surveys Design | |
Introduction | |
Stratification | |
Probability Proportional to Size Sampling | |
Balanced Sampling | |
Calibration weighting | |
Combining ex ante and ex post auxiliary information: a simulated approach | |
Conclusions | |
Bibliography | |
Estimation with Inadequate Frames | |
Introduction | |
Estimation Procedure | |
Bibliography | |
Small Area Estimation with Applications to Agriculture | |
Introduction | |
Design Issues | |
Synthetic and Composite Estimates | |
Area Level Models | |
Unit Level Models | |
Conclusions | |
Bibliography | |
GIS and remote sensing | |
The European Land Use and Cover Area-Frame Statistical Survey (LUCAS) | |
Introduction | |
Integrating Agricultural and Environmental Information with LUCAS | |
LUCAS 2001-2003: Target Region, Sample Design and Results | |
The Transect Survey in LUCAS 2001-2003 | |
LUCAS 2006: A Two-phase Sampling Plan of Unclustered Points | |
Stratified Systematic Sampling with a Common Pattern of Replicates | |
GroundWork and Check Survey | |
Variance Estimation and Some Results in LUCAS 2006 | |
Relative Efficiency of the LUCAS 2006 Sampling Plan | |
Expected Accuracy of Area Estimates with the LUCAS 2006 Scheme | |
Non-sampling Errors in LUCAS 2006 | |
Conclusions | |
Bibliography | |
Area Frame Design for Agricultural Surveys | |
Introduction | |
Pre-Construction Analysis | |
Land-Use Stratification | |
Sub-Stratification | |
Replicated Sampling | |
Sample Allocation | |
Selection Probabilities | |
Sample Selection | |
Sample Rotation | |
Sample Estimation | |
Conclusions | |
Bibliography | |
Accuracy, Objectivity and Efficiency of Remote Sensing for Agricultural | |
Statistics | |
Introduction | |
Satellites and Sensors | |
Accuracy, objectivity and cost-efficiency | |
Main approaches to use EO for crop area estimation | |
Bias and subjectivity in pixel counting | |
Simple correction of bias with a confusion matrix | |
Calibration and regression estimators | |
Some examples of Crop area estimation with remote sensing in large regions | |
The GEOSS best practices document on EO for crop area estimation | |
Sub-pixel analysis | |
Accuracy assessment of classified images and land cover maps | |
General data and methods for yield estimation | |
Forecasting yields | |
Satellite images and vegetation indices for yield monitoring | |
Some examples of Crop yield estimation / forecasting with remote sensing | |
Bibliography | |
Estimation of Land Cover ParametersWhen Some Covariates Are Missing | |
Introduction | |
The AGRIT Survey | |
Imputation of the Missing Auxiliary Variables | |
Analysis of the 2006 AGRIT data | |
Conclusions | |
Bibliography | |
Data editing and quality assurance | |
A Generalized Edit and Analysis System for Agricultural Data | |
Introduction | |
The System Development | |
Analysis | |
Development Status | |
Conclusions | |
Bibliography | |
Statistical Data Editing for Agricultural Surveys | |
Introduction | |
Edit Rules | |
The Role of Automatic Editing in the Editing Process | |
Selective Editing | |
An Overview of Automatic Editing | |
Automatic Editing of Systematic Errors | |
The Fellegi-Holt Paradigm | |
Algorithms for Automatic Error Localization of Random Errors | |
Conclusions | |
Bibliography | |
Quality in Agricultural Statistics | |
Introduction | |
Changing Concepts of Quality | |
Assuring Quality | |
Conclusions | |
Bibliography | |
Statistics Canada's Quality Assurance Framework Applied to Agricultural | |
Statistics | |
Introduction | |
Evolution of Agriculture Industry Structure and User Needs | |
Agriculture Statistics - A Centralized Approach | |
Quality Assurance Framework | |
Managing Quality | |
Quality Management Assessment | |
Conclusions | |
Bibliography | |
Data dissemination and survey data analysis | |
The DataWarehouse: A Modern System forManaging Data | |
Introduction | |
The Data Situation in NASS | |
What is a Data Warehouse? | |
How Does it Work? | |
What We Learned | |
What is in Store for the Future? | |
Conclusions | |
Data Access and Dissemination: Some Experiments During the First | |
Agricultural Census in China | |
Introduction | |
Data Access and Dissemination | |
SDA General Characteristics | |
A Sample Session Using SDA | |
Conclusions | |
Bibliography | |
Analysis of Economic Data Collected in Farm Surveys | |
Introduction | |
Requirements of Sample Surveys for Economic Analysis | |
Typical Contents of a Farm Economic Survey | |
Issues in Statistical Analysis of Farm Survey Data | |
Issues in EconomicModelling Using Farm Survey Data | |
Case Studies | |
Bibliography | |
Measuring Household Resilience To Food Insecurity: Application To Palestinian Households | |
Introduction | |
The Concept of Resilience and its Relation to Household Food Security | |
From Concept to Measurement | |
Empirical Strategy | |
Testing Resilience Measurement | |
Conclusions | |
Bibliography | |
Spatial Prediction of Agricultural Crop Yield | |
Introduction | |
The Proposed Approach | |
Case Study: the Province of Foggia | |
Conclusions | |
Bibliography | |
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