RIESGO EN LA AGRICULTURA: EL CASO DE LA PAPA (Solanum tuberosum) EN AYACUCHO Y LIMA

La producción agrícola es muy riesgosa, especialmente en el caso de la papa, y el riesgo es diferenciado entre la Costa y la Sierra. En la Costa, Lima muestra altos rendimientos por hectárea mientras que, en la Sierra, Ayacucho muestra bajos rendimientos. Con el objetivo de comparar los niveles de riesgo en los productores de papa blanca en las regiones de Ayacucho y Lima, y la incidencia de las variables probabilísticas (precio en chacra, rendimiento por hectárea, costos de fertilizantes, semillas, pesticidas) en el margen bruto de beneficio económico por hectárea, se efectúa una simulación estocástica de Montecarlo, con el software @Risk. Se sometió a prueba la hipótesis de que Ayacucho presentaría mayores niveles de riesgo que Lima. Se encontró que Lima tiene 65,5% de escenarios de margen bruto positivo y Ayacucho sólo 1,6%. Las principales fuentes de riesgo en la producción de papa blanca, son el precio en chacra y los rendimientos por hectárea. La variación del margen bruto por hectárea por incrementos en el precio en chacra de un 10% es de 6,6% para Ayacucho y de 9,1% para Lima. En el caso de los rendimientos un incremento de 10% en rendimiento eleva el margen en 4,6% en Ayacucho, mientras en Lima, sólo 3,3%. Se concluye que debe incrementarse los rendimientos en Ayacucho para mejorar los beneficios.

Using Bayesian Networks to Support Managing Technological Risk on Software Projects

Risk management is essential in software project management. It includes activities such as identifying, measuring and monitoring risks. The literature presents different approaches to support software risk management. In particular, the researchers popularly used Bayesian Networks because they can be learned from data or elicited from domain experts. Even though the literature presents many Bayesian networks (BN) for software risk management, none focus on technological risk factors. Given this, this paper presents a BN for managing risks of software projects and the results of a static validation performed through a focus group with eight practitioners. As a result, the practitioners agreed that our proposed to manage technological risks of software projects using BN is valuable and easy to use. Given the successful results, we concluded that the proposed solution is promising.

Development of Rule-Based Software Risk Assessment and Management Method with Fuzzy Inference System

There is an enormous budget and financial plan in software development projects, and it is required that they take a huge investment to carry on. When looked at, the costs depend on the global substantial information about software development: in 1985, $150 billion; in 2010, $2 trillion; in 2015, $5 trillion; and in 2020, over $7 trillion. Additionally, on the first new days of 2021, a day-by-day Apple Store’s quantity has been approximately $500 million. In spite of the expenditures and the margins that are dramatically expanding and increasing each year, the phase of software development accomplishment is not high enough. In light of the “CHAOS” report arranged in 2015, just 17% of the software projects were finished in an opportune way, in the allotted financial plan, and as per the necessities. However, 53% of the software projects were finished in the long run or potentially over a spending plan as well as without satisfying the prerequisites precisely. In addition, software development projects were not completed and were dropped out as well in the ratio of 30%. Also, the “CHAOS” report published in 2020 has figured out that only 33% of the software projects were completed successfully all over the world. In order to cope with these unsuccessful and failure results, an effective method for software risk assessment and management has to be specified, designated, and applied. In this way, before causing trouble that has the power of preventing successful accomplishment of software development projects, software risks are able to be noticed and distinguished on time. In this study, a new and original rule set, which could be used and carried out effectively in software risk assessment and management, has been designed and developed based on the implementation of fuzzy approached technique integrated with machine learning algorithm—Adaptive Neuro-Fuzzy Inference System (ANFIS). By this approach and technique, machines (computers) are able to create several software risk rules not to be seen, not to be recognized, and not to be told by human beings. In addition, this fuzzy inference approach aims to decrease risks in the software development process in order to increase the success rate of the software projects. Also, the experimental results of this approach show that rule-based software risk assessment and management method has a valid and accurate model with a high accuracy rate and low average testing error.

Análise econômica de uma unidade de produção de alevinos na Região da Transamazônica, Sudoeste Paraense

O objetivo deste estudo foi analisar economicamente uma unidade de produção de alevinos na região da Transamazônica, estado do Pará. O empreendimento localiza-se no município de Altamira e apresenta 1,17 hectare de lâmina d’água, dividido em oito viveiros escavados utilizados na manutenção de matrizes e alevinagem de tambaqui Colossoma macropomum e piauçu Leporinus macrocephalus. Adotou-se a metodologia do custo operacional e indicadores de eficiência econômica, além de uma análise de risco com auxílio do software @RISK. O custo de implantação foi estimado em R$ 297.800,00, o custo operacional efetivo R$ 341.131,00, o custo operacional total por milheiro de alevinos R$ 121,78, a receita bruta anual em R$ 558.600,00 e o lucro operacional mensal de R$ 15.718,28. As análises de rentabilidade e de risco do negócio atestaram valor presente líquido de R$ 1.063.062,95, taxa interna de retorno de 72%, relação benefício custo de R$ 1,55 e um período de retorno do capital de 1,3 ano, com probabilidades de ocorrência de 51,1%, 50,3%, 51,4% e 84,2%, respectivamente. Concluiu-se que a produção de alevinos de tambaqui e piauçu para atendimento da demanda de piscicultores do Sudoeste paraense, representa um investimento rentável na região da Transmazônica.

Resource Optimization-Based Software Risk Reduction Model for Large-Scale Application Development

Software risks are a common phenomenon in the software development lifecycle, and risks emerge into larger problems if they are not dealt with on time. Software risk management is a strategy that focuses on the identification, management, and mitigation of the risk factors in the software development lifecycle. The management itself depends on the nature, size, and skill of the project under consideration. This paper proposes a model that deals with identifying and dealing with the risk factors by introducing different observatory and participatory project factors. It is assumed that most of the risk factors can be dealt with by doing effective business processing that in response deals with the orientation of risks and elimination or reduction of those risk factors that emerge over time. The model proposes different combinations of resource allocation that can help us conclude a software project with an extended amount of acceptability. This paper presents a Risk Reduction Model, which effectively handles the application development risks. The model can synchronize its working with medium to large-scale software projects. The reduction in software failures positively affects the software development environment, and the software failures shall reduce consequently.