Students use U.S. Geological Survey (USGS) real-time, real-world seismic data from around the planet to identify where earthquakes occur and look for trends in earthquake activity. They explore where and why earthquakes occur, learning about faults and how they influence earthquakes. Looking at the interactive maps and the data, students use Microsoft Excel to conduct detailed analysis of the most-recent 25 earthquakes; they calculate mean, median, mode of the data set, as well as identify the minimum and maximum magnitudes. Students compare their predictions with the physical data, and look for trends to and patterns in the data. A worksheet serves as a student guide for the activity.

Many disciplines (geotechnique, hydraulics, geology, environment, pharmaceutics…) analyze the size distribution of poly-disperse solids. This document explains the principles of a modal decomposition method (MDM) to fit any grain size distribution (GSD) and decompose it into subpopulations of grains or modes. The examples here are in the field of geotechnique. Several researchers have proposed methods to fit GSD data with a theoretical curve. These previous methods were shown to have a coefficient of determination, R2, typically in the 0.5–0.9 range. The proposed MDM has a R2 that usually exceeds 0.999, a marked advantage. For field investigations, soil samples taken in boreholes are remoulded mixtures of thin layers. These samples have lost information on stratification but the MDM can recover this information, as shown in published case studies. The MDM improves the prediction of hydraulic conductivity, K, in stratified soils, which is central for groundwater and pollution studies. Single layers in stratified soils are found to be unimodal, with a single population of grains. Multimodal soils are either homogeneous (till, crushed stone) or stratified (sandy aquifers). Sand samples may have up to four sub-populations or modes in their GSDs. Free Excel spreadsheets are provided to any person who wants to analyze or decompose a GSD into its subpopulations of grains. The free Excel files are made available through Scholars Portal Dataverse. An Excel file with a few GSD examples to be treated is also provided. De nombreuses disciplines (géotechnique, hydraulique, géologie, environnement, pharmacie...) analysent la granulométrie de solides variés. Ce document explique les principes de la méthode de décomposition modale (MDM) pour décrire n'importe quelle distribution de la taille des grains (GSD) et la décomposer en sous-populations de grains, ou modes. Les exemples présentés ici sont du domaine de la géotechnique. Plusieurs chercheurs ont proposé des méthodes permettant d'ajuster les données granulométriques à une courbe théorique. Ces méthodes antérieures, c’est démontré, ont un coefficient de détermination, R2, généralement compris entre 0,5 et 0,9. La MDM proposée a un R2 qui dépasse généralement 0,999, ce qui constitue un avantage significatif. Pour les études in-situ, les échantillons de sol prélevés dans les trous de forage sont des mélanges remaniés de couches minces. Ces échantillons ont perdu des informations sur la stratification, mais la MDM peut récupérer ces informations, comme l’ont montré des études de cas publiées. La MDM améliore la prédiction de la conductivité hydraulique, K, dans les sols stratifiés, ce qui est essentiel pour les études sur les eaux souterraines et la pollution. Les couches simples d’un sol stratifié s'avèrent être unimodales, avec une seule population de grains. Les sols multimodaux sont soit homogènes (till, pierre concassée), soit stratifiés (aquifères sableux). Les échantillons de sable peuvent avoir jusqu'à quatre sous-populations ou modes dans leurs GSD. Des feuilles de calcul Excel gratuites sont fournies à toute personne qui souhaite analyser ou décomposer une GSD en ses sous-populations de grains. Les fichiers Excel gratuits sont disponibles sur Scholars Portal Dataverse. Un fichier Excel contenant quelques exemples de données de GSD à traiter est également fourni.

Abstract

The 2003 Agriculture Sample Census was designed to meet the data needs of a wide range of users down to district level including policy makers at local, regional and national levels, rural development agencies, funding institutions, researchers, NGOs, farmer organisations, etc. As a result the dataset is both more numerous in its sample and detailed in its scope compared to previous censuses and surveys. To date this is the most detailed Agricultural Census carried out in Africa.

The census was carried out in order to: · Identify structural changes if any, in the size of farm household holdings, crop and livestock production, farm input and implement use. It also seeks to determine if there are any improvements in rural infrastructure and in the level of agriculture household living conditions; · Provide benchmark data on productivity, production and agricultural practices in relation to policies and interventions promoted by the Ministry of Agriculture and Food Security and other stake holders. · Establish baseline data for the measurement of the impact of high level objectives of the Agriculture Sector Development Programme (ASDP), National Strategy for Growth and Reduction of Poverty (NSGRP) and other rural development programs and projects. · Obtain benchmark data that will be used to address specific issues such as: food security, rural poverty, gender, agro-processing, marketing, service delivery, etc.

Geographic coverage

Tanzania Mainland and Zanzibar

Analysis unit

• Households
• Individuals

Universe

Large scale, small scale and community farms.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The Mainland sample consisted of 3,221 villages. These villages were drawn from the National Master Sample (NMS) developed by the National Bureau of Statistics (NBS) to serve as a national framework for the conduct of household based surveys in the country. The National Master Sample was developed from the 2002 Population and Housing Census. The total Mainland sample was 48,315 agricultural households. In Zanzibar a total of 317 enumeration areas (EAs) were selected and 4,755 agriculture households were covered. Nationwide, all regions and districts were sampled with the exception of three urban districts (two from Mainland and one from Zanzibar).

In both Mainland and Zanzibar, a stratified two stage sample was used. The number of villages/EAs selected for the first stage was based on a probability proportional to the number of villages in each district. In the second stage, 15 households were selected from a list of farming households in each selected Village/EA, using systematic random sampling, with the village chairpersons assisting to locate the selected households.

Mode of data collection

Face-to-face [f2f]

Research instrument

The census covered agriculture in detail as well as many other aspects of rural development and was conducted using three different questionnaires: • Small scale questionnaire • Community level questionnaire • Large scale farm questionnaire

The small scale farm questionnaire was the main census instrument and it includes questions related to crop and livestock production and practices; population demographics; access to services, resources and infrastructure; and issues on poverty, gender and subsistence versus profit making production unit.

The community level questionnaire was designed to collect village level data such as access and use of common resources, community tree plantation and seasonal farm gate prices.

The large scale farm questionnaire was administered to large farms either privately or corporately managed.

Questionnaire Design The questionnaires were designed following user meetings to ensure that the questions asked were in line with users data needs. Several features were incorporated into the design of the questionnaires to increase the accuracy of the data: • Where feasible all variables were extensively coded to reduce post enumeration coding error. • The definitions for each section were printed on the opposite page so that the enumerator could easily refer to the instructions whilst interviewing the farmer. • The responses to all questions were placed in boxes printed on the questionnaire, with one box per character. This feature made it possible to use scanning and Intelligent Character Recognition (ICR) technologies for data entry. • Skip patterns were used to reduce unnecessary and incorrect coding of sections which do not apply to the respondent. • Each section was clearly numbered, which facilitated the use of skip patterns and provided a reference for data type coding for the programming of CSPro, SPSS and the dissemination applications.

Cleaning operations

Data processing consisted of the following processes: · Data entry · Data structure formatting · Batch validation · Tabulation

Data Entry Scanning and ICR data capture technology for the small holder questionnaire were used on the Mainland. This not only increased the speed of data entry, it also increased the accuracy due to the reduction of keystroke errors. Interactive validation routines were incorporated into the ICR software to track errors during the verification process. The scanning operation was so successful that it is highly recommended for adoption in future censuses/surveys. In Zanzibar all data was entered manually using CSPro.

Prior to scanning, all questionnaires underwent a manual cleaning exercise. This involved checking that the questionnaire had a full set of pages, correct identification and good handwriting. A score was given to each questionnaire based on the legibility and the completeness of enumeration. This score will be used to assess the quality of enumeration and supervision in order to select the best field staff for future censuses/surveys.

CSPro was used for data entry of all Large Scale Farm and community based questionnaires due to the relatively small number of questionnaires. It was also used to enter data from the 2,880 small holder questionnaires that were rejected by the ICR extraction application.

Data Structure Formatting A program was developed in visual basic to automatically alter the structure of the output from the scanning/extraction process in order to harmonise it with the manually entered data. The program automatically checked and changed the number of digits for each variable, the record type code, the number of questionnaires in the village, the consistency of the Village ID Code and saved the data of one village in a file named after the village code.

Batch Validation A batch validation program was developed in order to identify inconsistencies within a questionnaire. This is in addition to the interactive validation during the ICR extraction process. The procedures varied from simple range checking within each variable to the more complex checking between variables. It took six months to screen, edit and validate the data from the smallholder questionnaires. After the long process of data cleaning, tabulations were prepared based on a pre-designed tabulation plan.

Tabulations Statistical Package for Social Sciences (SPSS) was used to produce the Census tabulations and Microsoft Excel was used to organize the tables and compute additional indicators. Excel was also used to produce charts while ArcView and Freehand were used for the maps.

Analysis and Report Preparation The analysis in this report focuses on regional comparisons, time series and national production estimates. Microsoft Excel was used to produce charts; ArcView and Freehand were used for maps, whereas Microsoft Word was used to compile the report.

Data Quality A great deal of emphasis was placed on data quality throughout the whole exercise from planning, questionnaire design, training, supervision, data entry, validation and cleaning/editing. As a result of this, it is believed that the census is highly accurate and representative of what was experienced at field level during the Census year. With very few exceptions, the variables in the questionnaire are within the norms for Tanzania and they follow expected time series trends when compared to historical data. Standard Errors and Coefficients of Variation for the main variables are presented in the Technical Report (Volume I).

Sampling error estimates

The Sampling Error found on page (21) up to page (22) in the Technical Report for Agriculture Sample Census Survey 2002-2003

Menée depuis 1997, l'Enquête sur les dépenses des ménages a été effectuée chaque année dans les dix provinces. Les données pour les territoires sont disponibles pour 1998, 1999 et tous les deux ans par la suite. À compter de l'année de référence 2004, l'Enquête sur les réparations et les rénovations effectuées par les propriétaires-occupants fait partie de l'Enquête sur les dépenses des ménages. L'objet principal de l'enquête est d'obtenir des renseignements détaillés sur les dépenses des ménages pendant l'année de référence (l'année civile précédente). On recueille également des renseignements sur les caractéristiques des logements ainsi que sur l'équipement ménagers. Les renseignements sur les habitudes de dépenses, les caractéristiques du logement et l'équipement ménager des ménages canadiens pendant l'année 2007 ont été obtenus en demandant aux résidants dans les 10 provinces et trois territoires de se rappeler les dépenses engagées au cours de l'année civile précédente (pour les habitudes de dépenses) ou l'information à la date de l'interview (pour les caractéristiques du logement et l'équipement ménager).

L'Enquête sur les dépenses des ménages de 2003 a été menée de janvier à mars 2004. Les renseignements sur les habitudes de dépenses, les caractéristiques du logement et l’équipement ménager des ménages canadiens pendant l’année 2003 ont été obtenus en demandant aux gens dans les dix provinces et les trois territoires de se rappeler les dépenses engagées au cours de l’année civile précédente (pour les habitudes de dépenses) ou l’information au 31 décembre (pour les caractéristiques du logement et l’équipement ménager).

L'Enquête sur les dépenses des ménages de 2000 a été menée de janvier à mars 2001. Les renseignements sur les habitudes de dépenses, les caractéristiques du logement et l’équipement ménager des ménages canadiens pendant l’année 2000 ont été obtenus en demandant aux résidants dans les dix provinces et les trois territoires de se rappeler les dépenses engagées au cours de l’année civile précédente (pour les habitudes de dépenses) ou l’information au 31 décembre (pour les caractéristiques du logement et l’équipement ménager).