Two-stage inference in experimental design using dea : an application to intercropping and evidence from randomization theory

Neste artigo é proposto o uso de medidas de eficiência DEA, com retornos constantes à escala e input unitário, na análise de respostas multidimensionais não negativas de ensaios experimentais. A abordagem proposta concorda com a Análise de Variância (Covariância) clássica para respostas unidimensionais e simplifica a análise estatística para o caso multidimensional. Os melhores tratamentos indicados pela análise otimizam um output combinado, definido por preços sombra, que são as soluções dos problemas lineares de DEA. A abordagem é particularmente útil na análise de experimentos consorciados (plantio simultâneo de mais de uma cultura agrícola). São aqui discutidos dois exemplos. Os resultados são validados via Teoria de Aleatorização, de modo a estudar apropriadamente as questões de correlação e não-normalidade das medidas DEA nas diferentes parcelas experimentais. ________________________________________________________________________________________ ABSTRACTIn this article we propose the use of Data Envelopment Analysis (DEA) measures of efficiency, under constant returns to scale and input equal to unity, in the analysis of multidimensional nonnegative responses in the design of experiments. The approach agrees with the standard Analysis of Variance (Covariance) for univariate responses and simplifies the statistical analysis in the multivariate case. The best treatments provided by the analysis optimize a combined output defined by shadow prices, which are the solutions of the DEA problem. The approach is particularly useful for the analysis of intercropping (crop mixtures) experiments. In this context we discuss two examples. To properly address the issue of correlation and non-normality of DEA measurements in different experimental plots we validate the results via Randomization Theory

Potential improvement of agricultural output for major producers based on dea efficiency measurements

In this article we perform production efficiency analysis for the 40 countries with largest value added by agricultural sector in 2005. Under the assumption of a nonparametric frontier and production observations satisfying a statistical model including both random and inefficiency errors, we estimate an agricultural production function using DEA measures of efficiency with output orientation and variable returns to scale. We found evidence that the set of countries investigated could increase their total value added by agricultural sector for at least 53.9% without increasing input usage with the prevailing technology. This result has a direct impact on issues related to the recent food crisis

Mercado mundial de arroz: concentração, taxas de crescimento, projeções e índice de presença

Resumo O trabalho avaliou o comportamento do mercado mundial de arroz nas dimensões produção, importação e exportação, considerando o período de 1990 a 2019. Foram estimados índices de concentração (razões de concentração dos dois maiores (CR2), dos cinco maiores (CR5) e dos dez maiores (CR10) players, quartéis de concentração e Índice Herfindahl-Hirschmann) e taxas de crescimento, calculadas projeções de médio prazo e proposto um índice de presença dos países nesse mercado. A produção de arroz no mundo apresentou níveis elevados de concentração em 1990 (CR2 58%, CR5 75%, CR10 87%, IHH >1800) e foi se tornando menos concentrada em 2000 e 2010 (IHH entre 1400 e 1550), alcançando em 2019 CR2 de 51%, CR5 de 71%, CR10 de 84% e IHH de 1508. A importação mundial de arroz, que apresentava elevados níveis de concentração em 1990 (CR2 56%, CR5 79%, CR10 94%, IHH 1830), passou por um processo de desconcentração ao longo do tempo, alcançando níveis moderados de concentração em 2019 (CR2 42%, CR5 61%, CR10 80% e IHH 1152). A exportação mundial apresenta elevados níveis de concentração em poucos países (CR2 >70%, CR5 >85%, CR10 >90%, IHH >3000). China, Índia, Indonésia, Tailândia e Estados Unidos apresentam os maiores índices de presença no mercado mundial

Economic efficiency of EMBRAPA’S research centers and the influence of contextual variables

Neste artigo é medida a eficiência econômica dos centros de pesquisa da Empresa Brasileira de Pesquisa Agropecuária (Embrapa). É usado o modelo DEA BCC, cuja medida de eficiência é modelada como função linear das variáveis contextuais capacidade de geração de receita, intensidade de parcerias, melhoria de processos administrativos, racionalização de custos, tamanho e tipo de centro. O modelo é do tipo painel dinâmico, assume correlação intra-temporal estruturada entre os centros de pesquisa e inter-temporal não estruturada. Para desempatar as unidades eficientes é usado um índice heurístico de eficiência que agrega os resultados de eficiência em relação às fronteiras DEA clássica e invertida. O efeito positivo das parcerias na medida de eficiência, invalida as críticas de que o processo de avaliação prejudica a integração e cooperação entre os centros de pesquisa. A melhoria de processos administrativos é a variável mais importante do ponto de vista da significância estatística das variáveis contextuais. _________________________________________________________________________________ ABSTRACTIn this paper we measure, with a DEA BCC model, the economic efficiency of Embrapa’s (Brazilian Agricultural Research Corporation) research centers. We model the DEA economic efficiency as a linear function of the contextual variables revenue generation capacity, partnership intensity, improvement of administrative processes, cost rationalization, size and type of research centers. The model used is of the type dynamic panel and assumes a structured correlation matrix intra times and unstructured inter times. In order to better discriminate the efficient units we used a heuristic efficiency score that aggregates the efficiencies in relation to the original and inverted DEA frontiers. Partnerships positive effect in the efficiency scores does not confirm the thoughts that Embrapa’s performance evaluation process discourages the integration and cooperation of its research centers. From the point of view of statistical significance, the improvement of administrative processes is the most important indicator among the contextual variables

Multidimensional analysis of carrot-lettuce intercroppings under different combinations of population densities

Este trabalho teve como objetivo avaliar o desempenho biológico de sistemas consorciados de cenoura e alface, sob diferentes combinações de densidades populacionais, com uso das análises bivariada de variância e envoltória de dados (DEA). O delineamento experimental usado foi o de blocos ao acaso completos, com cinco repetições, com os tratamentos arranjados em esquema fatorial 4x4. Os tratamentos resultaram da combinação de quatro populações de plantas de cenoura (40, 60, 80 e 100% da população recomendada no cultivo solteiro – PRCS) com quatro populações de plantas de alface (40, 60, 80 e 100% da PRCS). As populações recomendadas para os cultivos solteiros da cenoura e alface foram 500 mil e 250 mil plantas por hectare, respectivamente. Tanto o método bivariado como o método de análise de envoltória de dados são bastante eficazes na discriminação dos melhores sistemas de cultivo consorciados, por meio dos rendimentos das culturas. Os resultados da eficiência produtiva, medidos por modelos DEA, permitem uma análise estatística simples do ensaio consorciado. A robustez do método de análise bivariada de variância assegura a validade dos resultados.The objective of this paper was to evaluate the biological performance of carrot and lettuce intercropping systems under different combinations of population densities, using the bivariate analysis of variance and data envelopment analysis (DEA). The experimental design was the randomized complete blocks, in a 4x4 factorial scheme (carrot densities: 40, 60, 80 and 100% of the recommended sole crop density (RSCD); lettuce densities: 40, 60, 80 and 100% of the RSCD), with five replications. The recommended population density for carrot in sole crop is 500 thousand plants per hectare and for lettuce in sole crop is 250 thousand plants per hectare. Both bivariate method of variance and data envelopment analysis are quite effective in discriminating the best intercropping systems as assessed through component crop yields. The results of the yield efficiency as assessed by DEA models allow a simple statistical analysis of the intercropping experiment. The robustness of the bivariate analysis of variance method assures the validity of the results

Factors affecting the performance of Pantaneiro horses

This study aimed to assess the physical performance of Pantaneiro horses with and without equine infectious anemia (EIA) under functional conditions of cattle management. The horses were subjected to a performance test and split into two groups according to a completely randomized design: animals were chosen from populations testing positive and negative for EIA. Performance was measured as a function of a data envelopment analysis (DEA) model considering four outputs and one unitary input. The output measures were the distance achieved in the performance test, hematocrit as a weighted average over the test duration, respiratory rate as weighted average over the test duration, and the level of lactic acid at the test termination. Weights for the hematocrit and the respiratory rate output variables were determined by means of factor analysis. The performance score was a weighted average of the output variables with the weights defined by the averages of the optimum individual multipliers in the DEA analysis. Contextual variables of interest were age, horse weight, room temperature, and corporal temperature. Only groups and room temperature were statistically significant effects, as indicated by a bootstrap analysis. The performance of group positive for EIA is significantly lower than that of the group negative for EIA and room temperature has a negative effect.Instituto de Genética Veterinari

Factors affecting the performance of Pantaneiro horses

This study aimed to assess the physical performance of Pantaneiro horses with and without equine infectious anemia (EIA) under functional conditions of cattle management. The horses were subjected to a performance test and split into two groups according to a completely randomized design: animals were chosen from populations testing positive and negative for EIA. Performance was measured as a function of a data envelopment analysis (DEA) model considering four outputs and one unitary input. The output measures were the distance achieved in the performance test, hematocrit as a weighted average over the test duration, respiratory rate as weighted average over the test duration, and the level of lactic acid at the test termination. Weights for the hematocrit and the respiratory rate output variables were determined by means of factor analysis. The performance score was a weighted average of the output variables with the weights defined by the averages of the optimum individual multipliers in the DEA analysis. Contextual variables of interest were age, horse weight, room temperature, and corporal temperature. Only groups and room temperature were statistically significant effects, as indicated by a bootstrap analysis. The performance of group positive for EIA is significantly lower than that of the group negative for EIA and room temperature has a negative effect.Instituto de Genética Veterinari