EconPapers    
Economics at your fingertips  
 

Applying Spatial Analysis to Create Modern Rich Pictures for Grassland Health Analysis

Fabiellen C. Pereira, Carol M. S. Smith, Thomas M. R. Maxwell, Stuart M. Charters, Chris M. Logan, Mitchell Donovan, Sadeepa Jayathunga and Pablo Gregorini
Additional contact information
Fabiellen C. Pereira: Department of Agricultural Science, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7674, New Zealand
Carol M. S. Smith: Centre of Excellence Designing Future Productive Landscapes, Lincoln University, Lincoln 7674, New Zealand
Thomas M. R. Maxwell: Department of Agricultural Science, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7674, New Zealand
Stuart M. Charters: Centre of Excellence Designing Future Productive Landscapes, Lincoln University, Lincoln 7674, New Zealand
Chris M. Logan: Department of Agricultural Science, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7674, New Zealand
Mitchell Donovan: AgResearch Limited Invermay Agricultural Centre, Puddle Alley, Private Bag 50014, Mosgiel 9053, New Zealand
Sadeepa Jayathunga: Scion, 49 Sala Street, Private Bag 3020, Rotorua 3046, New Zealand
Pablo Gregorini: Department of Agricultural Science, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7674, New Zealand

Sustainability, 2021, vol. 13, issue 20, 1-23

Abstract: Grasslands are complex and heterogeneous ecosystems, and their health can be defined by the cumulative ability of their components to evolve, adapt, and maintain their integrity in the presence of stress/disturbance and provide ecosystem services. Herein, a design approach is used to generate alternative and multifunctional pastoral livestock production systems that enhance grassland health. As a way of understanding the complexity of grasslands and initiating the design process using systems thinking, rich pictures emerge as a useful method. As rich pictures are subjective views, geographic information systems (GIS) could be applied to improve the veracity of their outcomes, as both techniques are forms of an analytical process. This paper reports the application of GIS to a case study of a high-country farm to generate and combine different thematic maps to create a modern rich picture. The rich picture is a combination of remote sensing data (altitude, slope, aspects, and the Normalized Difference Vegetation Index (NDVI)), and on-the-ground data (plant species distribution and diversity and soil chemical, biological, and physical parameters). Layers were combined using a multi-criteria evaluation (MCE) based on the analytical hierarchy process (AHP) to create a final rich picture. The results highlight dissimilarities in perceptions of what underpins ‘grassland health’ between researchers in different fields and with different perspectives. The use of GIS produced a modern rich picture that enhanced the understanding of grassland health and allowed for the identification of gaps, values, and possibilities for future research work.

Keywords: design; pastoralism; systems thinking; geographic information systems; health (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/20/11535/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/20/11535/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:20:p:11535-:d:659844

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11535-:d:659844