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Assessing and Visualizing Agricultural Management Practices: A Multivariable Hands-On Approach for Education and Extension

Published online by Cambridge University Press:  20 January 2017

Richard G. Smith ,
Tara Pisani Gareau ,
David A. Mortensen ,
William S. Curran  and
Mary E. Barbercheck
Richard G. Smith*
Affiliation:
Department of Natural Resources and the Environment, University of New Hampshire, Durham, NH 03824
Tara Pisani Gareau
Affiliation:
Biology Department, Boston College, Chestnut Hill, MA 02467
David A. Mortensen
Affiliation:
Department of Crop and Soil Sciences, The Pennsylvania State University, University Park, PA 16802
William S. Curran
Affiliation:
Department of Crop and Soil Sciences, The Pennsylvania State University, University Park, PA 16802
Mary E. Barbercheck
Affiliation:
Department of Entomology, The Pennsylvania State University, University Park, PA 16802
*
Corresponding author's E-mail: richard.smith@unh.edu
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Abstract

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Agroecosystems are inherently complex, and practices aimed at managing one component of the system can have unintended consequences for other components of the system. Management decisions, therefore, can be improved by assessing and understanding the multivariate nature of agricultural systems and the multifunctional character of particular agricultural management practices. The act of simultaneously assessing and evaluating multiple characteristics or functions in agriculture also can be a valuable education and extension activity, because it draws on active and experiential methods of learning and because the process effectively reveals important functions and tradeoffs associated with agroecosystems and their management. Here we introduce a tool (the spider plot) and present a case-study exercise in which we used this tool to evaluate the multiple characteristics and functions of different cover crops within a field day workshop format. We also provide examples of how this approach could be used to assess other management practices or properties of agroecosystems and communicate multivariate concepts within a weed science classroom or extension environment.

Los agro-ecosistemas son intrínsicamente complejos y las prácticas enfocadas al manejo de uno de los componentes del sistema pueden tener consecuencias no intencionadas en otros de los componentes. Por lo tanto, las decisiones de manejo, pueden ser mejoradas mediante la evaluación y comprensión de la naturaleza multi-variable de los sistemas agrícolas y el carácter multifuncional de prácticas específicas de manejo agrícola. El acto de medir y evaluar simultáneamente las múltiples características o funciones en la agricultura puede también ser una valiosa actividad educativa y de extensión, ya que se deriva de métodos activos y experienciales de aprendizaje y debido a que el proceso revela efectivamente funciones importantes, así como también, ventajas y desventajas asociadas con los agro-ecosistemas y sus manejos. Aquí presentamos una herramienta (gráfica de araña) y un ejercicio de estudio de caso en el cual usamos esta herramienta para evaluar las múltiples características y funciones de los diferentes cultivos de cobertura, en el formato de un taller-día de campo. También proporcionamos ejemplos de cómo este enfoque puede usarse para evaluar otras prácticas de manejo o propiedades de agro-ecosistemas y para comunicar conceptos multi-variables en una clase de ciencias de la maleza o en un escenario de extensión.

Keywords

Active learningcover cropsecosystem servicesmultivariatemultifunctionalparticipatoryspider plots
Type
Education/Extension
Information
Weed Technology , Volume 25 , Issue 4 , December 2011 , pp. 680 - 687
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © Weed Science Society of America

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