Giuseppe Espa

Agricultural survey methods

List of Contributors. Introduction. 1 The present state of agricultural statistics in developed countries: situation and challenges. 1.1 Introduction. 1.2 Current state and political and methodological context. 1.3 Governance and horizontal issues. 1.4 Development in the demand for agricultural statistics. 1.5 Conclusions. Acknowledgements. Reference. Part I Census, Frames, Registers and Administrative Data. 2 Using administrative registers for agricultural statistics. 2.1 Introduction. 2.2 Registers, register systems and methodological issues. 2.3 Using registers for agricultural statistics. 2.4 Creating a farm register: the population. 2.5 Creating a farm register: the statistical units. 2.6 Creating a farm register: the variables. 2.7 Conclusions. References. 3 Alternative sampling frames and administrative data. What is the best data source for agricultural statistics? 3.1 Introduction. 3.2 Administrative data. 3.3 Administrative data versus sample surveys. 3.4 Direct tabulation of administrative data. 3.5 Errors in administrative registers. 3.6 Errors in administrative data. 3.7 Alternatives to direct tabulation. 3.8 Calibration and small-area estimators. 3.9 Combined use of different frames. 3.10 Area frames. 3.11 Conclusions. Acknowledgements. References. 4 Statistical aspects of a census. 4.1 Introduction. 4.2 Frame. 4.3 Sampling. 4.4 Non-sampling error. 4.5 Post-collection processing. 4.6 Weighting. 4.7 Modelling. 4.8 Disclosure avoidance. 4.9 Dissemination. 4.10 Conclusions. References. 5 Using administrative data for census coverage. 5.1 Introduction. 5.2 Statistics Canada s agriculture statistics programme. 5.3 1996 Census. 5.4 Strategy to add farms to the farm register. 5.5 2001 Census. 5.6 2006 Census. 5.7 Towards the 2011 Census. 5.8 Conclusions. Acknowledgements. References. Part II Sample Design, Weighting and Estimation. 6 Area sampling for small-scale economic units. 6.1 Introduction. 6.2 Similarities and differences from household survey design. 6.3 Description of the basic design. 6.4 Evaluation criterion: the effect of weights on sampling precision. 6.5 Constructing and using strata of concentration . 6.6 Numerical illustrations and more flexible models. 6.7 Conclusions. Acknowledgements. References. 7 On the use of auxiliary variables in agricultural survey design. 7.1 Introduction. 7.2 Stratification. 7.3 Probability proportional to size sampling. 7.4 Balanced sampling. 7.5 Calibration weighting. 7.6 Combining ex ante and ex post auxiliary information: a simulated approach. 7.7 Conclusions. References. 8 Estimation with inadequate frames. 8.1 Introduction. 8.2 Estimation procedure. References. 9 Small-area estimation with applications to agriculture. 9.1 Introduction. 9.2 Design issues. 9.3 Synthetic and composite estimates. 9.4 Area-level models. 9.5 Unit-level models. 9.6 Conclusions. References. Part III GIS and Remote Sensing. 10 The European land use and cover area-frame statistical survey. 10.1 Introduction. 10.2 Integrating agricultural and environmental information with LUCAS. 10.3 LUCAS 2001 2003: Target region, sample design and results. 10.4 The transect survey in LUCAS 2001 2003. 10.5 LUCAS 2006: a two-phase sampling plan of unclustered points. 10.6 Stratified systematic sampling with a common pattern of replicates. 10.7 Ground work and check survey. 10.8 Variance estimation and some results in LUCAS 2006. 10.9 Relative efficiency of the LUCAS 2006 sampling plan. 10.10 Expected accuracy of area estimates with the LUCAS 2006 scheme. 10.11 Non-sampling errors in LUCAS 2006. 10.12 Conclusions. Acknowledgements. References. 11 Area frame design for agricultural surveys. 11.1 Introduction. 11.2 Pre-construction analysis. 11.3 Land-use stratification. 11.4 Sub-stratification. 11.5 Replicated sampling. 11.6 Sample allocation. 11.7 Selection probabilities. 11.8 Sample selection. 11.9 Sample rotation. 11.10 Sample estimation. 11.11 Conclusions. 12 Accuracy, objectivity and efficiency of remote sensing for agricultural statistics. 12.1 Introduction. 12.2 Satellites and sensors. 12.3 Accuracy, objectivity and cost-efficiency. 12.4 Main approaches to using EO for crop area estimation. 12.5 Bias and subjectivity in pixel counting. 12.6 Simple correction of bias with a confusion matrix. 12.7 Calibration and regression estimators. 12.8 Examples of crop area estimation with remote sensing in large regions. 12.9 The GEOSS best practices document on EO for crop area estimation. 12.10 Sub-pixel analysis. 12.11 Accuracy assessment of classified images and land cover maps. 12.12 General data and methods for yield estimation. 12.13 Forecasting yields. 12.14 Satellite images and vegetation indices for yield monitoring. 12.15 Examples of crop yield estimation/forecasting with remote sensing. References. 13 Estimation of land cover parameters when some covariates are missing. 13.1 Introduction. 13.2 The AGRIT survey. 13.3 Imputation of the missing auxiliary variables. 13.4 Analysis of the 2006 AGRIT data. 13.5 Conclusions. References. Part IV Data Editing and Quality Assurance. 14 A generalized edit and analysis system for agricultural data. 14.1 Introduction. 14.2 System development. 14.3 Analysis. 14.4 Development status. 14.5 Conclusions. References. 15 Statistical data editing for agricultural surveys. 15.1 Introduction. 15.2 Edit rules. 15.3 The role of automatic editing in the editing process. 15.4 Selective editing. 15.5 An overview of automatic editing. 15.6 Automatic editing of systematic errors. 15.7 The Fellegi Holt paradigm. 15.8 Algorithms for automatic localization of random errors. 15.9 Conclusions. References. 16 Quality in agricultural statistics. 16.1 Introduction. 16.2 Changing concepts of quality. 16.3 Assuring quality. 16.4 Conclusions. References. 17 Statistics Canada s Quality Assurance Framework applied to agricultural statistics. 17.1 Introduction. 17.2 Evolution of agriculture industry structure and user needs. 17.3 Agriculture statistics: a centralized approach. 17.4 Quality Assurance Framework. 17.5 Managing quality. 17.6 Quality management assessment. 17.7 Conclusions. Acknowledgements. References. Part V Data Dissemination and Survey Data Analysis. 18 The data warehouse: a modern system for managing data. 18.1 Introduction. 18.2 The data situation in the NASS. 18.3 What is a data warehouse? 18.4 How does it work? 18.5 What we learned. 18.6 What is in store for the future? 18.7 Conclusions. 19 Data access and dissemination: some experiments during the First National Agricultural Census in China. 19.1 Introduction. 19.2 Data access and dissemination. 19.3 General characteristics of SDA. 19.4 A sample session using SDA. 19.5 Conclusions. References. 20 Analysis of economic data collected in farm surveys. 20.1 Introduction. 20.2 Requirements of sample surveys for economic analysis. 20.3 Typical contents of a farm economic survey. 20.4 Issues in statistical analysis of farm survey data. 20.5 Issues in economic modelling using farm survey data. 20.6 Case studies. References. 21 Measuring household resilience to food insecurity: application to Palestinian households. 21.1 Introduction. 21.2 The concept of resilience and its relation to household food security. 21.3 From concept to measurement. 21.4 Empirical strategy. 21.5 Testing resilience measurement. 21.6 Conclusions. References. 22 Spatial prediction of agricultural crop yield. 22.1 Introduction. 22.2 The proposed approach. 22.3 Case study: the province of Foggia. 22.4 Conclusions. References. Author Index. Subject Index.

Contextual classification in image analysis: an assessment of accuracy of ICM

This paper considers the performances of the ICM image classification technique contrasted with the maximum likelihood ordinary discriminant analysis (ML). The latter technique is the most widely used in an applied context by space agencies and remote sensing units. The two methods are compared in terms of the global accuracy produced and in terms of the spatial continuity properties of classification errors. ICM outperforms ML in most experimental cases in terms of the global accuracy produced. However, in some instances, it has a more marked tendency to produce classification errors that are short-distance correlated.

Weighting Ripley’s K-Function to Account for the Firm Dimension in the Analysis of Spatial Concentration:

The spatial concentration of firms has long been a central issue in economics under both the theoretical and the applied point of view mainly due to the important policy implications. A popular approach to its measurement, which does not suffer from the problem of the arbitrariness of the regional boundaries, makes use of micro data and looks at the firms as if they were dimensionless points distributed in the economic space. However, in practical circumstances the points (firms) observed in the economic space are far from being dimensionless and are conversely characterized by different dimension in terms of the number of employees, the product, the capital, and so on. In the literature, the works that originally introduce such an approach disregard the aspect of the different firm dimension and ignore the fact that a high degree of spatial concentration may result from the case of both many small points clustering in definite portions of space and only few large points clustering together (e.g., few large firms). We refer to this phenomenon as clustering of firms and clustering of economic activities. The present article aims at tackling this problem by adapting the popular K-function to account for the point dimension using the framework of marked point process theory.

A Tree-Based Approach to Forming Strata in Multipurpose Business Surveys

The design of a stratified sample from a finite population deals with two main issues: the definition of a rule to partition the population, and the allocation of sampling units in the selected strata. This article examines a tree-based strategy which plans to solve jointly these issues when the survey is multipurpose and multivariate information, quantitative or qualitative, is available. Strata are formed through a scissorial algorithm that selects finer and finer partitions by minimizing, at each step, the sample allocation required to achieve the precision levels set for each surveyed variable. In this way, large numbers of constraints can be satisfied without drastically increasing the sample size, and also without discarding variables selected for stratification or diminishing the number of their class intervals. Furthermore, the algorithm tends to not define empty or almost empty strata, so avoiding the need for ex post strata aggregations. The procedure was applied to redesign the Italian Farm Structure Survey. The results indicate that the gain in efficiency held using our strategy is nontrivial. For a given sample size, this procedure achieves the required precision by exploiting a number of strata which is usually a very small fraction of the number of strata available when combining all possible classes from any of the covariates.

Conditional versus unconditional industrial agglomeration: disentangling spatial dependence and spatial heterogeneity in the analysis of ICT firms’ distribution in Milan

A series of recent papers have introduced some explorative methods based on Ripley’s K-function (Ripley in J R Stat Soc B 39(2):172–212, 1977) analyzing the micro-geographical patterns of firms. Often the spatial heterogeneity of an area is handled by referring to a case–control design, in which spatial clusters occur as over-concentrations of firms belonging to a specific industry as opposed to the distribution of firms in the whole economy. Therefore, positive, or negative, spatial dependence between firms occurs when a specific sector of industry is seen to present a more aggregated pattern (or more dispersed) than is common in the economy as a whole. This approach has led to the development of relative measures of spatial concentration which, as a consequence, are not straightforwardly comparable across different economies. In this article, we explore a parametric approach based on the inhomogeneous K-function (Baddeley et al. in Statistica Nederlandica 54(3):329–350, 2000) that makes it possible to obtain an absolute measure of the industrial agglomeration that is also able to capture spatial heterogeneity. We provide an empirical application of the approach taken with regard to the spatial distribution of high-tech industries in Milan (Italy) in 2001.

Study of archaeological areas by means of advanced software technology and statistical methods

Abstract The aim of this work is to show how the most advanced technology together with spatial analysis can be usefully employed to investigate historical and archaeological phenomena. In this note some preliminary results are shown. Two geographical information systems (GIS) were structured in an integrated way. The first GIS is a vector-like system while the other is a raster-like one. Moreover, some applications regarding the environmental reconstruction of a part of the investigated area are proposed. Then the identification and the modeling of archaeological site maps by means of point pattern analysis are proposed. Finally, an auto-logistic model to predict archaeological site is presented. This topic is currently under investigation.

EEG findings in cooled asphyxiated newborns and correlation with site and severity of brain damage

OBJECTIVE EEG and MRI are useful tools to evaluate the severity of brain damage and to provide prognostic indications in asphyxiated neonates. Aim of our study is to analyze the relationship between serial neonatal EEGs and severity and sites of brain lesions on MRI in neonates undergoing hypothermia, following a hypoxic-ischemic injury. PATIENTS AND METHODS Forty-eight term newborns underwent hypothermia. Serial videoEEG recordings were taken at 6, 24, 48 and 72 h and during 2nd week of life. Brain MRI was performed at the end of 2nd postnatal week and correlated with EEG. RESULTS EEGs improved during the first days. At the first recording 25 infants showed a severe or very low amplitude EEG pattern while at the 2nd week only 7 showed such patterns. As regards MRI, 21 infants showed a predominant Basal Ganglia and Thalami damage, 4 infants showed a predominant focal Thalami lesion and 23 showed normal imaging or just mild White Matter abnormalities. Severity of EEG pattern was associated with the odds of having MRI lesions at Basal Ganglia, Thalami, White Matter, Internal Capsule, but not at Cortex. Infants who showed only mild EEG abnormalities in the first 2 days had no Basal Ganglia and Thalami MRI lesion. The persistence of a discontinuous EEG at the 2nd week recording is always associated with Basal Ganglia and Thalami damage. CONCLUSION The severity of EEG background is associated with severity and site of MRI lesion pattern in neonates treated with hypothermia because of hypoxic-ischemic encephalopathy.

Firms’ Bankruptcy and Turnover in a Macroeconomy

The so-called “rational expectations revolution” that has completely reshaped economic theory and general equilibrium theory in the last two decades has, incidentally, brought earlier ideas on the crucial importance of agents’ knowledge, information and beliefs to the forefront forcing modem followers of those ideas to reconsider them far more deeply, systematically and rigorously (Arrow (1986), Hahn (1977, 1981)). It soon turned out that when agents act upon beliefs and engage in out-of-equilibrium learning, heterogeneity (of beliefs) and self-referentiality (of market outcomes)1 may determine large sets of multiple equilibria, and of dynamic paths of the economy, which collapse onto the unique rational-expectations (RE) competitive general equilibrium only under a number of restrictive conditions2.

Approximate maximum likelihood estimation of the autologistic model

Approximate Maximum Likelihood Estimation (AMLE) is a simple and general method recently proposed for approximating MLEs without evaluating the likelihood function. The only requirement is the ability to simulate the model to be estimated. Thus, the method is quite appealing for spatial models because it does not require evaluation of the normalizing constant, which is often computationally intractable. An AMLE-based algorithm for parameter estimation of the autologistic model is proposed. The impact of the numerical choice of the input parameters of the algorithm is studied by means of extensive simulation experiments, and the outcomes are compared to existing approaches. AMLE is much more precise, in terms of Mean-Square-Error, with respect to Maximum pseudo-likelihood, and comparable to ML-type methods. Although the computing time is non-negligible, the implementation is straightforward and the convergence conditions are weak in most practically relevant cases.

R&D, firm size and incremental product innovation

This article addresses an issue that is debated in the economics of innovation literature, namely the existence of increasing returns to R&D expenditures and firm size, in product innovation. It explores further how the firms structural characteristics and contextual factors affect the sustained introduction of new components over a relatively long time period. Taking advantage of an original and unique database comprising information on new product announcements by leading semiconductor producers, we show that: (i) decreasing returns to size and R&D expenditures characterize the innovation production function of the sampled firms; (ii) producers operating a larger product portfolio exhibit a higher propensity to introduce new products than their specialized competitors; (iii) aging has positive bearings on the firms ability to innovate.