Determining Areas of Growth for Commercial Quandong

1.0 INRODUCTION

The answers to most of the nation’s environmental problems are staring us in the face—they reside in Australia’s own unique native plants, animals, and ecosystems (Archer and Beale 2004), one of which is the desert quandong, biologically called santalum acuminatum (see figure 1). Quandong plant produces a visually appealing yellow-to-red, tart tasting, and dry-textured fruit with slender pale green leaves. Quandong has a wide natural circulation throughout southern Australia from arid desert areas to coastal regions (see Figure 2). Quandong was an important native food source for Native Australians across semi-arid and arid regions in the mainland states, with excess fruit collected and dried for later consumption (Rural resources R&D cooperation, 2001). In the midst of the male members of Central Australia’s Pitjantjatjara people, quandongs were well-thought-out as suitable supernumerary for meat. Quandong was a welcome food source for early white settlers whose name was one of 400 aboriginal words adopted into English from the Wiradjuri languages of south-western New South Wales in 1836 (Hailegebriel, 2007). Nutritionally, the quandong has outstanding anti-oxidant ability, high levels of folate and vitamin E, and is a good source of magnesium, zinc and iron.

Figure 1

Figure 2

Locating sites that match known necessities for a commercial quandong production that would yield both economically and biologically, is one of the first and important steps in trying to build a viable commercial quandong farm. Various researches have attempted this in the past and came out with various suitable location as far as the agricultural produce or industry is concerned. Ajayi (2013) researched on the Site Suitability for Sandalwood production in South Australia. He opined that GIS is a capable device for examining spatial information and establishing a procedure for choice support. Also, Yanjing and Zihan (2011) conducted a suitability research on determining the brown bear habitat in the sanfjallet National Park Sweeden. Three themes were designed in the map generated according to the author, i.e., the human impact emphasis weighted, neutral weighted themes and customized weighted theme. Adapted weighted theme was produced for user discovering denning habitat results with user defined weights. Relating the final maps generated from the human impact emphasis weighted and neutral weighted themes, human impact concentrated in the south area of the National Park.

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The objective of this project is to use criteria modelling techniques to determine the areas that are suitable for the growth and production of commercial Quandong in the area within the mapping extent of South Australia provided. The procedure of criteria modelling is used as a device for data analysis in Geographical Information System (GIS). The most pertinent strategy for criteria assessment is the weighted linear combo (Voogd, 1983). In a weighted straight combo, variables are joined by applying a weight to each one, thereby summing up the outcomes to make a suitability map (Eastman et al. 1995). All in all one of the more principal usage of GIS is the examination and suitability mapping of use of land (Malczewski. 2004). The following criteria were provided in order to achieve the aim of the research:

The average annual rainfall is between 250-500mm,

Soils are well drained including sandy loam or sandy clay,

Terrain slope between 2-20 degrees,

Near to a sealed road.

2.0 METHODS

Multiple-criteria decision analysis (MCDA) assist decision makers in examining impending actions or alternatives based on multiple incommensurable factors or criteria.  This according to Eastman (2009); Figueira et al. (2005) and Malczewski (1999a) is done or achieved by using decision rules to aggregate those criteria to rate or rank the alternatives. Although the decision criteria normally cannot all be taken full advantage of, in selecting an alternative or action, MCDA researchers and practitioners do not view it simply as a quantitative optimization problem that identifies the best probable ‘solutions’. Therefore, in a nut shell, criteria modelling is used to identify sites described by their attributes, which best meet the objectives.

This method have been used by various environmental scientist to locate industrial sites (Aleksandar, Djordje and Ilija. 2014), sandalwool production (Ajayi, 2014) among others, including identifying protected areas.

The study area taken into consideration for this project is the south of Ausralia. Mean annual precipitation for the Flinders Ranges varies from over 500mm in the Mount Remarkable area in the south to less than 200mm on the northern slopes (Schewerdtfeger & Curran 1996). However, precipitation increases both with altitude and latitude. Most downpours are during the winter months, and there is a strong east-west gradient, with those areas to the east.

Therefore, in locating a new commercial quandong orchards, soil, rainfall, minimum temperatures, existing vegetation, water availability, transportation, among others are a variety of criteria that are important. It is of great necessity to consider both the conditions needed to successfully grow commercial quandongs and what it takes to make its production part of the economic growth of the society.

The software package ArcGIS PRO (ESRI) was used in this study to derive our outcomes using the coordinate system Geocentric Datum of Australia (GDA) 1994 and the grid coordinate system based on the Universal Transverse Mercator Projection (MGA) zone 54. Spatial evidence can combined into geographic information system with the aim to model required environmental features and settings to satisfy best state of affairs for optimal development of plants. The process of modelling these real world criteria into decision making application is called the criteria modelling (Lucas, 2012).

This GIS analysis to realize suitability site for commercial quandong production, is a set of rules set by natural resource managers at district, township and various stages of government. The conceptual model can be fashioned by detailed examination of two types of criteria, which are either constraining or preferential criteria in which are empirical and data focused (McVicar, 2010). The constraining criteria are set of limit where commercial quandong can be situated or not. These criteria are set of Boolean values and logical operators in the computer with values 1 or true representing suitable regions or false or 0 showing unfitting locations. Meanwhile, in preferential criterion, the value ranges between values 0 to 1 which set range of suitability. Forinstance, we may want a site that is as close as possible to an existing road for pecuniary reasons, one can indicate this as a preference, rather than Boolean values. The process of getting this values for analysis is called the Fuzzy Logic.

For the purpose of this research, the constraining criteria used include soils that are well drained including sandy loam or sandy clay, a rainfall between 250-500mm, slope terrain between 2-20 degrees and the only preferential criteria to map the area and boundary used include being near to a sealed road. De la Rosa and Diepen, (2003), opined that the measures to control or determine the suitability of land starts by understanding the subject. Consequently, in a way to create the suitability model for a commercial quandong production, a set of measures were embark on to create map the suitability of these area to fit both the constraining and preferential criteria (Appendix A). 

The analysis was conducted in two stages. The first stage involved provision of data layers to be analysed and output into new data layers representing each criterion. In other words, creating and analyzing raster and vector layers that meets the criterion; the average annual precipitation (between 250-500mm), well drained soils (sandy loam or sandy clay), slope terrain (between 2-20 degrees) and nearness to a sealed road. The constraining criteria used for this research are the first three listed while the remaining is the preferential criterion (Table 1). The final layer involved merging these data layers indicating an area most suitable for planting a commercial orchard of native quandongs.

The first process taken before analysis is to examine the data used, the formats and the metadata after which spatial analysis is enabled the criterion are prepared. Analysis started by creating a raster layer from the data set. The annual precipitation was constrained to a min of 250mm and a max value of 500mm utilizing a spatial analyst tool called the raster calculator in order to remove the precipitation value that are not needed.

Secondly, a slope raster layer was produced from the data set and was also constrained to portray 2-20 degree which will show areas of the study site which have either too flat or too steep a slope.

The third aspect of the analysis was setting constraining layer for the soil, the soil vector layer was first converted to a raster layer using the conversion tool spatial analyst to restrain the use of soil layer into one attribute in such a way as to recognize the soil types. The constraining criterion for well-drained soils are areas with surface texture of 60 percentage loam sand, sandy loam and loam which comprises the highest degree of the type of soil needed for quandung production (loam sand, sandy loam “or” loam) and then using the raster calculator to constrain the suitable soil to soil texture B, C or D (B-loam sand, C-sandy loam and D-loam).

In the latter procedure, criteria layers that epitomize range of values between 0 and 1 displaying the degree of suitability that is the preferential criteria of setting up a commercial quandong production orchard close to a sealed road was exhibited by first picking sealed roads using the data base query “select by attribute function” and secondly using the Euclidean distance tool after which the distance were converted from these roads (meters) to a set of preferences whereby 0 means low preference and 1 means high preference. In a nut shell, the equation translates the range of distances from 0-1 with a negative relationship, that is, larger distances from the roads are closer to a weigh of 0.0 while smaller distances from roads are closer to a weight of 1.0.

The final task is combination of all the layers using the raster calculator that is, the criteria modelling and analysis in creating our suitability model to show the level of suitability in any of the mapped area (Appendix A) by combining both sets of criteria (constraining and preferential) together using the raster calculator. This will show the areas that satisfy all our raster combination for the suitability.

Table 1: Constraining criteria and preferential criteria

No

Constraining criteria

Preferential criteria

1

The annual rainfall is between 250-500mm

Near sealed road

2

Soils are well drained including sandy loam or sandy clay

3

Terrain slope between 2-20 degrees

Source: Authors Compilation

Table 2: Biological criteria and Economic criteria

No

Biological criteria

Economic criteria

1

The annual rainfall is between 250-500mm

Near sealed road

2

Soils are well drained including sandy loam or sandy clay

Terrain slope between 2-20 degrees

Source: Authors Compilation

3.0 RESULTS

The results of the evaluation are a combination of criteria for the quandong production; it is the outcome of the criteria used. These outcomes of the result are from the combination of all the layers of the criteria using the raster calculator (ArcGIS pro). The map (Appendix B) shows the entire study area with the result or output of the combination of all analysed criteria and hence, the suitability level of those areas.  These suitability areas were characterized by: well drained soils, a precipitation between 250-500mm, terrain slope between 2-20 degrees and proximity to means of transportation (a sealed road to be precise). The combination of both the preferential and constrained criteria as shown in Appendix B, illustrates a suitability range from 0-1, where possibility of site suitability is stronger at 1 (green) and reduces down to 0 where site is stated at not suitable for commercial quandong. Alongside this is the preferential criteria that is the nearness to a sealed road which serve as a great economic factor in siting a quandong producing orchard. The result as regards the nearness to sealed roads as indicated in the map (Appendix B) are classified as unformed roads, sealed roads, unknown roads and unsealed roads.

The highly suitable locations, down to the less suitable areas are a result of the location fulfilling the criteria used, that is, the preferential criteria and the constraining criteria and the zones which are not suitable have extreme confinement of the criteria utilized.

4.0 DISCUSSION AND SUMMARY

Suitability is a function of product requirements and qualities of the land (Mustafa et al., 2011) therefore, in order to explore the potential area suitable for the quandong production, the analysis have been done.

At the inception of the analysis, two set of criteria map layers where added. The first is a type of data set comprising of grids having data of elevation surfaces in their attribute tables called raster data cells. On the other hand, the data comprise of lines and polygons that can be selected or edited. The raster data layers are the precipitation, slope, and the soil data sets. After the analysis, before combination in the second stage, individual data indicated suitability. For instance, 75.68% of the study area where suitable for planting in terms of annual precipitation suitability between 250mm and 500mm. however, by virtue of been too flat and or too steep, only about 44% approximately have their slope not suitable for the production of commercial quandong. Also, 87.66% of the study area falls within the category of the soil been sandy/loam and well drained, which is a great requirement for the growth of the commercial quandong. This criteria therefore excludes a very minimal area of the study area (12.34%). However, the sealed distance in relation to the study area, where concentrated more in the center and a little percentage at the east part of the study area.

The map generated showing the suitable location for planting a commercial quandong (Appendix B) indicates that areas covered by the green layers (value 1) and at the same instance has sealed roads (deep blue line) across them, are most suitable for siting a commercial quandong. Towards the southwest part of the study area, it will be noted that there are between 1-0.6 suitability values but there are no sealed roads across the areas. This indicates that considering the constraining criteria which makes up the biological criteria for siting a commercial quandong, the southwest part of the study area is suitable for the orchard but production distribution will be difficult and cost of transportation will be high. However, the northern part of the study area are considered to be less suitable due to range of suitability level between 0.4 and 0. Also, this areas have sealed roads far away from them. On the other hand, the southeastern and the northwestern part of the study area have similar suitability cases. This areas have sealed roads close to them but have constraining suitability value at 0. This indicates that the production of quandong in this areas will almost be impossible to yield products let alone have any to transport for economic reasons.

The map created for this suitability (Appendix B) has come about due to the spatial overlay of the criteria given. This project has applied GIS procedures and analysis to help identify areas that are potentially suitable for the production of quandong.

Developing a GIS based thematic database of soil is imperative in yield analysis of suitability for ideal usage of accessible resources (Coleman & Galbraith, 2000).   This multi criteria model supports criteria needed and are very effective to avoid future issues relating to under productivity. However, there are other factors that could have been taken into consideration like the availability of host plant, availability of labour, landuse classification, distance from the town or developed area, nearness to water bodies, forest type, and nearness to markets or processing industries, aspect, and temperature. Also, also the physical assessment of the study area which would have broaden the analysis to make us have an in-depth knowledge about the area, like going for a field work and trying to see what the study area looks like is necessary because fieldwork is an important part of any project or research work. Other limitation however is due to the fact that most studies similar are location based and therefore criteria used might not fit into other sites.

Malczewski (2006) orated that, policies lack a well-defined contrivance for incorporating the decision-maker’s preferences into the GIS methods. This may be understood by incorporating GIS and MCE methods. Integration of the GIS and MCE can help land-use planners and supervisors to improve decision-making techniques (Malczewski, 1999).

5.0 SUMMARY

Suitability model based on GIS for commercial quandong production shows the areas considered to be suitable or less suitable forcommercial quandong production based on the criteria used (constraining and preferential). These areas pointed as suitable for the production of commercial quandong are a result of their fulfilment of both sets of criteria and locations which are not suitable have great limitation of the criteria utilized. This report has yet, discussed the suitable criteria for locating a productive commercial quandong orchard and also the various techniques and procedures used to extract and analyse the conclusion represented in the final map (Appendix B).

The result gotten from this study demonstrates that GIS is set up to be a method that gives greater flexibility and exactness for managing digital spatial data. The absorption of criteria modelling with GIS in this project substantiates that it is a powerful tool to apply for suitability of land or soil for siting any agricultural produce or crop for commercial purposes.

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APPENDIX A: FLOW CHART

SOURCE DATA