Near Infra-red (NIR) reflectance spectroscopy is an instrumental technique to analyse the chemical composition (eg. nitrogen content) of organic materials. As an approach it is rapid, accurate and hence cost effective. Composition is currently determined by calibration equations which relate traditional wet chemical measurements to NIR spectral measurements for the same sample. This thesis examines statistical methods of estimating composition from the NIR spectra and suggests new methods. The relative merits of each of the methods is described. Variation within the spectra is also affected by physical attributes like particle size. Several transformations are examined for their ability to reduce non-chemical differences. These include some transformations previously found to be useful and also some new approaches. Methods of calibration are then investigated. Of the 'standard* methods, stepwise multiple linear regression, principal component regression, latent root regression and partial least squares are discussed. In addition, some new methods are considered. Firstly, three new calibration models are derived which, like some of the 'standard' methods, use information from the whole spectra. Next is a slightly different approach, whereby only the information from informative areas of the spectra, called 'windows', are used for calibration. Finally, hierarchical models for combining information from different sample sets in a flexible way are considered and adapted for NIR data types.

Thesis (M.S.)--University of Wisconsin--Madison, 1964. / eContent provider-neutral record in process. Description based on print version record. Bibliography: l. 228-237.

The LIFT Program was introduced by the Canadian Wheat Board and the Federal Cabinet in March of 1970. The program was designed to reduce the number of acres seeded to wheat in the spring of 1970 and thereby reduce the size of wheat stocks in Canada. The objectives of this study of the LIFT Program were; 1. to determine which factors were important in predicting the extent of participation by individual farmers in the LIFT Program; 2. to determine how effective the LIFT Program was in reducing wheat acreage. Data for the study was collected by means of interviewing a random sample of farm operators in the grain growing areas of Alberta and Saskatchewan. Multiple linear regression was used to determine the factors which were most important in predicting the extent of participation in the LIFT Program. The importance of qualitative variables was analysed by chi-square and analysis of variance techniques. The following eight variables were found to be important in predicting the extent of participation in the LIFT Program. 1. number of bushels of wheat on hand per cultivated acre 2. age of operator 3. knowledge of the LIFT Program 4, acres of wheat in 1969 5. grade completed in school by operator's wife 6. "attitude towards change" score 7. attitude towards the LIFT Program, and 8. percentage of summer fallow in the normal crop rotation. These eight variables explained 61.94% of the variation in the extent of participation in the LIFT Program. It was concluded that knowledge of the LIFT Program, attitude towards the LIFT Program, dissemination of information about the program by group methods and by government sources all had an important influence on the extent of participation. It was further concluded that the LIFT Program either directly or indirectly influenced about two thirds of the wheat acreage reduction that occurred in 1970. / Land and Food Systems, Faculty of / Graduate

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The objective of this research is to find, if and how, real and financial technological advancements influence interactions in the agricultural commodities market. Statistical testing was conducted to identify potential correlation and any positive linear relationships in the price data between agricultural commodities and their complementary agricultural chemical producers. Testing if higher trade volumes in this industry result from higher technology usage were initiated by identifying shared linear growth between general index volumes and agricultural commodities, alongside their chemical complement stock volumes. The scientific development of chemical engineering mirrors financial technologies influencing trader behavior as a significant contributor to agricultural chemical developments. Multiple tests conducted used a variety of dependent variables as included in those specified combinations that may suggest a significant influence on potential volatility and correlation amongst these sectors. The general result of this research showed evidence of a shared relationship among price and volume for ag-tech related stocks with respect to a generalized index (S&P500). The shared relationship between sample price averages generated from the S&P500 and the S&P500 Fertilizers & Agricultural Chemicals Sub-Industry Index (S5FERT) price suggests the growing involvement of traders in the agricultural commodities market and related speculative effects. Heteroscedasticity was indicated among the S&P500 sample volume averages and “Big Four” ag-tech stocks, supporting that more traders are entering markets because of technological advancements relative to trade platform accessibility. Lastly, agricultural chemical production and agricultural commodity returns demonstrate a common positive trend, suggesting that the agricultural commodity market and trade are gaining moment as a result of technological advancements.

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The subject of the thesis "Analysis of the profitability of a selected agricultural entity - ZD Strmilov" is the elaboration of a real analysis of the profitability of a functioning small agricultural enterprise. The diploma thesis is divided into two parts, the first of which is a theoretical part, which focuses on the general characteristics of a small business and its economic use,with an explanation of its establishment. The second, practical part, provides a realistic analysis of the profitability of an existing small farm with the production of agricultural commodities. In conclusion, the work is completed with supplements, which can contribute in the future to increasing the profitability of a small agricultural enterprise and its further development.

[pt] O agronegócio possui grande importância para a economia brasileira, representando uma parcela significativa do PIB e das exportações totais do país. Assim como em outros processos produtivos inseridos em ambiente de incerteza, a atividade agropecuária necessita de instrumentos que minimizem o risco, principalmente, o risco de preço e auxiliem no processo de tomada de decisão dos agentes participantes do agronegócio. Neste contexto, os mercados futuros constituem-se como alternativas financeiras no gerenciamento de riscos através das operações de hedge. Porém, a eficiência destas operações depende da aplicação de metodologias adequadas que conduzam ao conhecimento mais preciso sobre os preços futuros. Deste modo, o objetivo principal deste trabalho é avaliar a aplicação dos modelos de difusão de saltos, tão bem sucedidos para a estrutura a termo da taxa de juros, para o caso de commodities; focando na realização de previsões. A análise empírica será realizada a partir da série histórica de preços da commodity agrícola café arábica negociada na BM&F. A metodologia empregada é fundamentada no artigo de Sanjiv Das (1998). Nesta tese será estimada uma classe de modelos estocásticos descritos pela literatura, tais como o processo de reversão à média, o movimento geométrico browniano, bem como suas variantes com jumps. Efeitos ARCH e GARCH também serão considerados na modelagem. O processo de estimação será desenvolvido tanto por métodos tradicionais quanto por Algoritmos Genéticos. Diante do problema exposto e da escassez de estudos modernos direcionados a abordagem das commodities agrícolas no país, o tema proposto justifica- se como motivação para pesquisa científica e tecnológica. / [en] The agribusiness has great importance for the Brazilian economy, representing a significative share of GDP and total exports of the country. Like other production processes inserted in an environment of uncertainty, the agricultural activity needs instruments that minimize the risk, especially, the risk of price and assist in decision-making process of agents participating in agribusiness. In this context, the future markets constitute themselves as financial alternatives in risk management through the hedge operations. However, the efficiency of these operations depends on the application of appropriate methodologies that lead to more precise knowledge about future prices. Thus, the main objective of this work is to evaluate the application of jumps diffusion models, so successful for the structure of interest rate, in the case of commodities; focusing on the achievement of forecast. The empirical analysis will be carried out from the historical range of arabic coffee agricultural commodity´s prices traded on BM&F. The methodology used is based on the paper from Sanjiv Das (1998). In this thesis it will be estimated a class of stochastic models described in the literature, such as the mean-reversion process, the geometric Brownian motion and its variants with jumps. ARCH and GARCH effects will also be considered in the modeling. The process of estimation will be developed by traditional methods as well as by Genetic Algorithms. Facing the problem and the shortage of modern studies directed to the approach of the agricultural commodities in the country, the topic proposed is a motivation for scientific and technological research.