Annual forecasting of the Australian macadamia crop - integrating tree census data with statistical climate-adjustment models

To facilitate marketing and export, the Australian macadamia industry requires accurate crop forecasts. Each year, two levels of crop predictions are produced for this industry. The first is an overall longer-term forecast based on tree census data of growers in the Australian Macadamia Society (AMS). This data set currently accounts for around 70% of total production, and is supplemented by our best estimates of non-AMS orchards. Given these total tree numbers, average yields per tree are needed to complete the long-term forecasts. Yields from regional variety trials were initially used, but were found to be consistently higher than the average yields that growers were obtaining. Hence, a statistical model was developed using growers' historical yields, also taken from the AMS database. This model accounted for the effects of tree age, variety, year, region and tree spacing, and explained 65% of the total variation in the yield per tree data. The second level of crop prediction is an annual climate adjustment of these overall long-term estimates, taking into account the expected effects on production of the previous year's climate. This adjustment is based on relative historical yields, measured as the percentage deviance between expected and actual production. The dominant climatic variables are observed temperature, evaporation, solar radiation and modelled water stress. Initially, a number of alternate statistical models showed good agreement within the historical data, with jack-knife cross-validation R2 values of 96% or better. However, forecasts varied quite widely between these alternate models. Exploratory multivariate analyses and nearest-neighbour methods were used to investigate these differences. For 2001-2003, the overall forecasts were in the right direction (when compared with the long-term expected values), but were over-estimates. In 2004 the forecast was well under the observed production, and in 2005 the revised models produced a forecast within 5.1% of the actual production. Over the first five years of forecasting, the absolute deviance for the climate-adjustment models averaged 10.1%, just outside the targeted objective of 10%.

Report on the collection of salmon creel census data from Rivers Ribble and Hodder

The River Ribble bailiff team completed a total of 377 salmonid creel censuses by interviewing anglers fishing on the banks of the Rivers Ribble and Hodder during 1994. The study was undertaken for a number of reasons: (i) , To determine which areas of the river were fished by anglers. (ii) To ascertain which fishing methods were used in these areas. (iii) To identify the success of each fishing method within each area of the River Ribble system. In a limited 1993 sampling programme, 55 censuses were carried out. Results from these indicated that fishing with worm as bait was the most common method (39% of anglers interviewed) and also resulted in the greatest number of salmon being caught (61% of all salmon). The 1994 census data were collected from all areas of the Ribble system during the months of June to October. The data presented here are for anglers fishing for salmon only or for those fishing for salmon and sea trout at the same time. All of the fish caught were salmon.

Contributions towards smart cities : exploring block level census data for the characterization of change in Lisbon

The interest in using information to improve the quality of living in large urban areas and its governance efficiency has been around for decades. Nevertheless, the improvements in Information and Communications Technology has sparked a new dynamic in academic research, usually under the umbrella term of Smart Cities. This concept of Smart City can probably be translated, in a simplified version, into cities that are lived, managed and developed in an information-saturated environment. While it makes perfect sense and we can easily foresee the benefits of such a concept, presently there are still several significant challenges that need to be tackled before we can materialize this vision. In this work we aim at providing a small contribution in this direction, which maximizes the relevancy of the available information resources. One of the most detailed and geographically relevant information resource available, for the study of cities, is the census, more specifically the data available at block level (Subsecção Estatística). In this work, we use Self-Organizing Maps (SOM) and the variant Geo-SOM to explore the block level data from the Portuguese census of Lisbon city, for the years of 2001 and 2011. We focus on gauging change, proposing ways that allow the comparison of the two time periods, which have two different underlying geographical bases. We proceed with the analysis of the data using different SOM variants, aiming at producing a two-fold portrait: one, of the evolution of Lisbon during the first decade of the XXI century, another, of how the census dataset and SOM’s can be used to produce an informational framework for the study of cities.

January 2011: 2010 Census Data

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Small-area census data services by microcomputer applications of the REDATAM system in Latin America and the Caribbean

Analiza las facilidades que ofrece el sistema REDATAM (recuperación de datos de áreas pequeñas por computador) para los censos de 1990 en América Latina y el Caribe.

Geographically disaggregated census data for planning in developing countries

Includes bibliography

The urban poor in Brazil in 1996: a new poverty profile using PPV, PNAD and census data

Includes bibliography

Mexico 1970: occupational stratification, income and region: an analysis of census data

Includes bibliography

The use of census data for national development planning Focus on the 2010 population and housing census

Includes bibliography.