Risk in the agricultural sector has multiple dimensions or factors and prioritization of these can support decision making. On the other hand, knowing the importance of these risk factors for distinct agricultural activities and how they vary according to geographic zone constitutes relevant information for agricultural development. The objective of this study was to prioritize risk factors that are highly relevant for farmers in Central South Chile. The multicriteria Analytical Hierarchical Process (AHP) methodology was used to define a decision structure with four risk factors or criteria: climate, price and direct cost variability, human factor, and commercialization. In general, results obtained showed that there are no important imbalances in the weightings of different risk factors. Price and cost variability was the most important factor (0.30) whereas climate was the least important (0.20). It also confirmed that there are spatial differences in the weightings obtained for the distinct risk factors which determine distinct risk levels for the respective agricultural activities according to geographic region.
A. Engler, and R. Toledo. 2010. An analysis of factors affecting the adoption of economic and productive data recording methods of Chilean farmers. Cien. Inv. Agr. 37(2): 101-109. Integration of the Chilean domestic economy into international markets has created the need to incorporate more technology, information, and management tools and to generate better entrepreneurial skills at the farm level. These changes require the development of strategic capabilities and farmer changes in attitude. The goal for farmers is to be more prepared for the decision-making process and to have adequate evaluation and control systems to face the complexity of the farm business. The literature suggests that using management tools positively correlates with profits and concludes that management skills are positively related to farmers' well-being. Survey information on 211 farmers from central and southern Chile was used to estimate a probit model where the dependent variable was record-keeping by farmers. The results show that the farmers' educational level, age, membership a Technological Transfer Group, land leasing, and the farmers' own perception of their aversion to risk are statistically significant variables in the model. The model goodness of fit is 0.41, and the model has good predictive power for groups of farmers.
Decisions are strongly influenced by risk and risk preferences of decision makers; however, in Chile there are few studies in the agricultural sector focused on this topic. The present paper analyzes the risk preferences of small producers of raspberries (Rubus idaeus L.) and the production function associated with their production system in the Bío-Bío Region of Chile. Under a mean-variance approach, the estimation procedure uses a flexible utility function to incorporate a variety of risk preference alternatives. Three different estimation procedures were used: Least Squares Estimation, Seemingly Unrelated Regression and Full Information Maximum Likelihood, which revealed the same conclusions. Results showed that small farmers are risk averse (γ = 0.104) and present increasing relative and absolute aversion to risk (θ = 0.099 < 1 and θ < γ, respectively). The hypotheses of risk neutrality (γ = 0) and constant absolute risk aversion (θ = 1) were rejected with 94% and 99% confidence, respectively. The chosen function of production is the Cobb Douglas type, because it presents a better adjustment, and the relevant factors are fertilizer quantity per hectare, the experience of the producer and the planted area. This function presents decreasing returns to scale, then β 2 + β 3 + β 4 is equal to 0.18. The hypothesis of constant returns to scale is rejected with 99% confidence.
El presente boletín contiene cifras actualizadas recientemente sobre producción de astillas en Chile, además de antecedentes generales de exportación del rubro. De esta forma, se logra caracterizar la industria gracias a series estadísticas, generadas mediante la información recopilada en el muestreo a la Industria Forestal Primaria, actividad realizada anualmente por INFOR durante el primer trimestre del año.
El Anuario Forestal 2013 entrega series estadísticas, la mayor parte actualizadas al año 2013, en los siguientes ámbitos: indicadores macroeconómicos nacionales y sectoriales; recursos forestales; producción industrial y consumo de madera; comercio exterior; precios del mercado nacional y de exportación; transporte; disponibilidad de madera y ocupación en la actividad forestal
Este artículo revisa las opciones metodológicas disponibles en herramientas automatizadas en R para la selección de modelos no paramétricos y la realización de proyecciones. Para ello se han utilizado como ejemplo cuatro series mensuales de precios de tableros de madera para el mercado nacional, y ARIMA y métodos de suavizamiento exponencial para el modelamiento de los datos. Los paquetes y funciones disponibles en R son más que suficientes para la selección de modelos y la realización de proyecciones, con un amplio conjunto de opciones para el usuario. Sin embargo, para usar estas herramientas se requiere un nivel de usuario intermedio en series de tiempo y R, a fin de permitir una buena interpretación de los resultados, entender las particularidades de las funciones y crear instrucciones sencillas en el software.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.