Browsing by Author "Silva, Emiliana"
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- Animal Grazing System EfficiencyPublication . Silva, Emiliana; Santos, Carlos; Mendes, Armando B.This chapter proposes to estimate the technical efficiency in agricultural grazing systemsgrazing systems (dairy, beef and mixed) in Azores, in the year 2002. This research used 184 agricultural farms of FADN – Farm Accountancy Data Network. DEA, a non-parametric methodology, was used to estimate efficiency by means of DEAP software. The results have shown that the average technical efficiency in the dairy grazing system was 63.2% (CRS) and was higher (71.4%) in VRS, and the scale efficiency was about 89.2%. In beef grazing system, the average technical efficiency (CRS) was 69.4%; VRS and the scale efficiency were 82.9 and 84.2%, espectively. In the mixed grazing system, the average technical efficiency (CRS) was 89%, the VRS was higher (99.24%) and the scale efficiency was 89.8%. The mixed system is the most efficient, and about half (46.7%) of the farms were efficient. In the dairy grazing system and in the beef systems, only 9.8 and 11.1% were efficient farms. The efficiency is generally higher in mixed systems than in dairy and beef systems.
- An Approach to Variable Aggregation in Efficiency AnalysisPublication . Noncheva, Veska; Mendes, Armando B.; Silva, EmilianaIn the nonparametric framework of Data Envelopment Analysis the statistical properties of its estimators have been investigated and only asymptotic results are available. For DEA estimators results of practical use have been proved only for the case of one input and one output. However, in the real world problems the production process is usually well described by many variables. In this paper a machine learning approach to variable aggregation based on Canonical Correlation Analysis is presented. This approach is applied for efficiency estimation of all the farms in Terceira Island of the Azorean archipelago.
- Azorean agriculture efficiency by PARPublication . Noncheva, Veska; Mendes, Armando B.; Silva, EmilianaThe producers always aspire at increasing the efficiency of their production process. However, they do not always succeed in optimizing their production. In the last years, the interest on Data Envelopment Analysis (DEA) as a powerful tool for measuring efficiency has increased. This is due to the large amount of data sets collected to better understand the phenomena under study, and, at the same time, to the need of timely and inexpensive information. The “Productivity Analysis with R” (PAR) framework establishes a user-friendly data envelopment analysis environment with special emphasis on variable selection and aggregation, and summarization and interpretation of the results. The starting point is the following R packages: DEA (Diaz-Martinez and Fernandez-Menendez, 2008) and FEAR (Wilson, 2007). The DEA package performs some models of Data Envelopment Analysis presented in (Cooper et al., 2007). FEAR is a software package for computing nonparametric efficiency estimates and testing hypotheses in frontier models. FEAR implements the bootstrap methods described in (Simar and Wilson, 2000). PAR is a software framework using a portfolio of models for efficiency estimation and providing also results explanation functionality. PAR framework has been developed to distinguish between efficient and inefficient observations and to explicitly advise the producers about possibilities for production optimization. PER framework offers several R functions for a reasonable interpretation of the data analysis results and text presentation of the obtained information. The output of an efficiency study with PAR software is self- explanatory. We are applying PAR framework to estimate the efficiency of the agricultural system in Azores (Mendes et al., 2009). All Azorean farms will be clustered into homogeneous groups according to their efficiency measurements to define clusters of “good” practices and cluster of “less good” practices. This makes PAR appropriate to support public policies in agriculture sector in Azores.
- Azorean Agriculture Efficiency by PARPublication . Mendes, Armando B.; Noncheva, Veska; Silva, EmilianaThe producers always aspire at increasing the efficiency of their production process. However, they do not always succeed in optimising their production. In the last years, the interest on Data Envelopment Analysis (DEA) as a powerful tool for measuring efficiency has increased. This is due to the large amount of data sets collected to better understand the phenomena under study and, at the same time, to the need of timely and inexpensive information. The "Productivity Analysis with R" (PAR) framework establishes a user-friendly data envelopment analysis environment with special emphasis on variable selection, aggregation, summarisation and interpretation of the results. The starting point is the following R packages: DEA (Diaz-Martinez and Fernandez-Menendez 2008) and FEAR (Wilson 2008). The DEA package performs some models of data envelopment analysis presented in Cooper et al. (2007). FEAR is a software package for computing nonparametric efficiency estimates and testing hypotheses in frontier models. FEAR implements the bootstrap methods described in Simar and Wilson (2000). PAR is a software framework using a portfolio of models for efficiency estimation and also providing results explanation functionality. PAR framework has been developed to distinguish between efficient and inefficient observations and to explicitly advise the producers about possibilities for production optimisation. PAR framework offers several R functions for a reasonable interpretation of the data analysis results and text presentation of the obtained information. The output of an efficiency study with PAR software is self-explanatory. We are applying PAR framework to estimate the efficiency of the agricultural system in Azores (Mendes et al. 2009). All Azorean farms will be clustered into homogeneous groups according to their efficiency measurements to define clusters of "good" practices and cluster of "less good" practices. This makes PAR appropriate to support public policies in agriculture sector in Azores.
- An azorean dairy farms typologyPublication . Silva, Emiliana; Barbel, JulioThe objective of this paper was to define types of Azorean farms from a panel data of 174 farms of The European database of Farm Accountancy Data Network of the Azores, Portugal. This study used cluster analysis, the Ward method. The results, allowed the identification of three types of grazing systems of dairy farms as follows: 1) extensive grazing systems (less than smaller 1.4 cows per hectare); 2) moderate intensive grazing system (1.4 to 2.4 cows per hectare); and 3) intensive grazing system (more than 2.4 cows per hectare).
- A biodiversidadePublication . Gabriel, Rosalina; Borges, Paulo A. V.; Silva, Emiliana"[…]. O estudo da diversidade biológica – ou biodiversidade – e da ecologia está intimamente relacionado com a promoção da conservação da natureza. O termo "biodiversidade" nasceu no forum "BioDiversity" em Washington, em 1986, e foi pela primeira vez publicado em 1988 pela National Academy Press (Wilson, 1988 a, b). Rapidamente, o conceito tornou-se popular e segundo Wilson (1992) pode ser definido como: "... a variedade de organismos considerados a todos os níveis, desde a genética às espécies e aos ecossistemas1" (ver igualmente Reaka-Kudla et al., 1996). Esta definição incorpora três escalas de diversidade (genes, espécies, ecossistemas) que são actualmente consideradas como interdependentes, embora continuem a ser estudadas empiricamente em separado. Uma visão diferente do que consiste a biodiversidade foi recentemente apresentada por Hubbell (2001) que considera que este termo é sinónimo de "... riqueza em espécies e abundância relativa de espécies no espaço e no tempo2". Por outro lado, o conceito de diversidade funcional implica que as comunidades mais diversas não sejam necessariamente aquelas que possuem mais espécies, mas sim as que possuem maior número de grupos funcionais (isto é, guildes3) (Hector, 1998; Gaston e Spicer, 2004). Por exemplo, embora não sejam particularmente ricas em espécies, as florestas naturais dos Açores funcionam de forma eficaz, já que possuem o número de espécies suficiente nos vários grupos funcionais realizando funções únicas. […]."
- Canonical correlation analysis and DEA for azorean agriculture efficiencyPublication . Mendes, Armando B.; Noncheva, Veska; Silva, EmilianaIn this paper we will document the application of canonical correlation analysis to variable aggregation using the correlations of the original variables with the canonical variates. A case study, about farms in Terceira Island, with a small data set is presented. In this data set of 30 farms we intend to use 17 input variables and 2 output variables to measure DEA efficiency. Without any data reduction procedure several problems known as “curse of dimensionality” are expected. With the data reduction procedures suggested it was possible to conclude quite acceptable and domain consistent conclusions.
- Canonical Correlation Analysis in Variable Aggregation in DEA.Publication . Mendes, Armando B.; Noncheva, Veska; Silva, EmilianaNeste trabalho documenta-se a aplicação de análise de correlações canónicas à agregação de variáveis em DEA, usando as correlações entre as variáveis originais e os componentes canónicos extraídos. É apresentado um caso de estudo que utiliza um pequeno conjunto de dados sobre explorações agrícolas na ilha terceira. Neste conjunto de 30 explorações agrícolas pretende-se usar 17 variáveis de input e 2 de output para avaliar a eficiência usando DEA. Sem qualquer redução de dados, vários problemas conhecidos como "praga da dimensionalidade" seriam esperados. Com os procedimentos sugeridos foi possível obter resultados razoáveis e de acordo com o conhecimento de domínio actual.
- Dairy farming systems' adaptation to climate changePublication . Silva, Emiliana; Mendes, Armando B.; Rosa, Henrique José DuarteThe measure of climate change in dairy farms can be achieved by using the emissions of methane by the ruminants converted in CO2 equivalent (CO2-eq). In order to know the impact of future quotes of methane in the Azorean dairy milk farms, a decision model is built to the Azorean intensive grazing system of dairy farms. Some scenarios of methane levels reductions from 10 to 75% are considered and their impact is evaluated upon dairy farms income, level of CO2-eq emissions and intensity level of grazing system. The results have shown that any reduction of the methane level always implies a consequent decrease in income. If the CO2-eq has to be limited than there is the need to find alternative income activities for farmers in order to preserve economic sustainability.
- Decision support for enhanced productivity with R software: an Azorean farms case study.Publication . Mendes, Armando B.; Noncheva, Veska; Silva, EmilianaAzores is a Portuguese insular territory where the main economic activity is dairy and meat farming. Dairy policy depends on Common Agricultural Policy of the European Union and is limited by quotas. On top of that the transformation sector had implemented a program for penalising the worst quality agricultural raw materials. The current historical context is particularly complex as some major changes are likely to occur. This is the case for the increase prices of some food products in international markets and, locally, the end of milk quota system. The multiplying effect of agriculture in both a small economy and the Azorean society, makes of major interest this kind of work not only to protect the income of farmers, but also to keep the society in equilibrium on employment matters and reduce immigration cycles. In this context, decision makers need information and knowledge for deciding the best policies in promoting quality and best practices. So, in this project we apply benchmarketing methodologies to estimate the efficiency of the agricultural system in Azores. We also propose to identify the inefficiency units and delineate action plans for correcting production or organizational identified problems. The data analysis will be possible using non parametric methods like data envelopment analysis – DEA. We develop a new data-driven methodology, called PAR (Productivity Analysis with R), which combines DEA with a statistical technique need for analysing a reduced number of farms. All Terceira (the second biggest island) farms are analyze according to their efficiency measurements to define groups of “good” practices and groups of “less good” practices. This makes the system appropriate to support public policies in agriculture sector in Azores. The decision makers we intend to support are of two different levels: farmers or services responsible for agriculture improvement and political decision makers. These two types of decision makers need information that is very specific and concrete in the first case and much more aggregated and general in the second case. The data analysis methods we are using can support the needs of both decision makers’ types, but the software interface must be specific designed. PAR project is designed to provide a bridge from mathematical models to productivity study using R statistical software. Several DEA models are described in literature. Some of them are implemented as functions in statistical software R which are being used for PAR system. Some works in restricted data sets were already done for the dairy sector in Azores using different approaches, by the authors. We use this data and results to validate and correct the software system we are developing. R statistical software is not very user friendly. Much programing is needed to make the output of the PAR computer program self explanatory and easily understandable.
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