Using GIS for Grape Suitability Analysis
By Ranendu Ghosh, Space Applications Center, Indian Space Research Organization
Analysis of climate, soil, water, and current land use is required before suitable crops can be suggested for an area. A recent study by the Space Applications Center of the Indian Space Research Organization (ISRO) in Ahmedabad, India, used GIS to identify areas that would be suitable for growing grapes.
The study was carried out in the Kachchh district of the Gujarat state. In this state, located on the northwestern coast of India, the main season for growing crops is the kharif, or monsoon season, that spans July to October. During years when monsoons are mild, productivity can be very low.
Following a devastating earthquake in 2001, various digital databases were created in ArcInfo as part of the Kachchh Development Project (KDP) carried out by the Space Applications Center. These databases generated imagery from Indian Remote Sensing satellites (IRS 1C/1D) that was merged at 1:25,000 scale. Current land use, soil, and aspect data was used for suitability analysis. The aspect data was created using Global 30 Arc Second Elevation Data (GTOPO30) digital elevation model (DEM) data.
The grape suitability classifications used were based on the Food and Agriculture Organization of the United Nations land suitability classifications shown in Figure 1. The soil properties GIS layers included information on depth, texture, internal drainage, acidity (pH), electrical conductivity (EC), percentage of organic carbon (OC), and the percentage of calcium carbonate (CaCO3). For the purposes of calculation, values of 0, 1, 2, and 3 were assigned for the suitability classes—NS, S3, S2, and S1, respectively. The analysis was performed in ArcInfo and Microsoft Excel.
| Classification |
Description |
| S1 |
Most suitable |
| S2 |
Favorably suitable |
| S3 |
Marginally suitable |
| NS |
Not suitable |
Figure 1: Suitability classifications
For the soil properties, such as texture, EC, and percentage of CaCO3, depth-weighted recalculations were done for several depth ranges at or below 25 cm by using weighting factors for different profile sections. Soil profiles were subdivided into equal sections. Each section's weighting factor is shown in Figure 2. For properties, such as pH and percentage of OC, average values were taken from 25 cm soil.
| Depth (cm) |
No. of sections |
Weighting factor |
| 125�150 |
6 |
2, 1.5, 1, 0.75, 0.5, 0.25 |
| 100�125 |
5 |
1.75, 1.5, 1, 0.5, 0.25 |
| 75�100 |
4 |
1.75, 1.25, 0.75, 0.25 |
| 50�75 |
3 |
1.5, 1, 0.5 |
| 25�50 |
2 |
1.25, 0.75 |
| 25 |
1 |
1 |
Figure 2: Weighting Factors
From soil layers, various derived layers, such as pH, depth, texture, drainage, EC, OC, and CaCO3, were created. Ranks were assigned to the values for these nine layers as shown in Figure 3. After being ranked, these layers were converted to grids and multiplied. Land use rankings were used to isolate agriculture, wasteland, grazing land, and prosopis land uses and assign these uses a value of 1. All other land uses were assigned a value of 0. The resultant grid, after multiplication, was reclassified into S1, S2, S3, and NS as follows from final grid values and reconverted to a feature file again.
Properties |
Ranks |
3 |
2 |
1 |
0 |
Texture |
Clay loam/ Sandy loam/ Loam |
Sandy clay loam |
Loamy sand/
Sandy clay |
Clay/Sand |
Maximum Depth (cm) |
>100 |
>75 and <100 |
>40 and <75 |
<40 |
Drainage (HS-Code) |
2 (Moderately low) |
3 (Moderately high) |
1 (Low) |
4 (High) |
pH |
>6.5 and <7.5 |
>7.5 and <8 |
>8 and <9 |
Other values |
EC |
<2 |
>2 and <4 |
>4 and <8 |
>8 |
%OC |
>0.5 |
>0.2 and <0.5 |
>0.05 and <0.2 |
<0.05 |
% CaCO 3 |
<2 |
>2 and <7.5 |
>7.5 and <15 |
>15 |
Aspect |
5 (South) |
4/6 (SE/SW) |
na |
1/2/3/7/8 |
Land use |
na |
na |
Agriculture/ Wasteland/ Grazing land/ Prosopis |
Other |
Figure 3: Rank assignment as related to soil characteristics
The study demonstrates an approach for crop suitability analysis in a GIS environment. The approach can be replicated for any other crop provided the required database is available in digital form.
For more information, contact
Ranendu Ghosh
FLPG/RESIPA
Space Applications Center
Ahmedabad 380 015
Gujarat, India
E-mail: ranendu@sac.isro.org
Acknowledgment
The author thanks the project coordinator for the Kachchh Development Project for allowing him to use the data for analysis in this study.
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