A Physical Evaluation of Land Suitability for Rice : A Methodological Study using GIS*
Results and discussion
The suitability map resulting from the spatial overlay of factors in the Lower Namphong Watershed is presented in figure 2. The area of suitability evolution is shown in table 3.
Figure 2. Land Suitability for Rice in the Lower Namphong Watershed, Northeast Thailand
Table 3 The suitability area for rice in the lowest Namphong Watershed, Northeast Thailand
| Suitability class |
Area (km2) |
% |
| Highly suitable |
208.30 |
6.97 |
| Moderately suitable |
868.26 |
29.03 |
| Marginally suitable |
1265.47 |
42.32 |
| Unsuitable |
530.27 |
17.73 |
| (Water body) |
36.63 |
1.23 |
| (Village) |
81.48 |
2.72 |
| Total |
2990.41 |
100 |
The study provides an approach to identify parametric values in modeling the land suitability for rice. The theme layers to be input in the modeling are assigned the rating value as attribute data. Overall insight into the factors affecting the suitability of land can be provided spatially and quantitatively. The result indicated that the highly suitable land cover an area of about 208.3 km
2 and is restricted to the irrigated areas with high NAI. Some 17.73 percent of the watershed is unsuitable area for rice which corresponds to the sloping land. It has become increasingly apparent that computer based GIS and remote sensing data can provide the means to model land suitability effectively.
To assess the reliability of the methodology developed, the suitability classes were checked against the rice yield. The rice yields in he study area, were on average 4171.87, 2968.75 and 2078.12 kg/ha for the unit of class generated S
1, S
2 and S
3 respectively. For moiré accurate results, average rice yields should be periodically collected, possibly 4-5 continues years. This will need further investigation to establish the resultant in relation to rice yield.
In conclusion, with analysis of spatial modeling it is possible to assess the land suitability with higher accuracy. In addition the modeling provided an approach to the improvement of rice yield by enhancing the component of modeling input.
Reference
-
Bandibas D. J. 1995. An Automated Land Evaluation System Using Artificial Neural Network Based Expert's Knowledge. GIS and Remote Sensed Data. Asia-Pacific Remote Sensing Journal Vol. 8
-
FAO 1983. Guidelines : Land Evaluation for Rainfed Agriculture Soils Bulletin No. 52 Rome: 237
-
Land Development Department 1990 Land Evaluation for Economic crops. Min of Agriculture and cooperatives.
-
Land Development Department 1996 Land Evaluation for Economic crops Min. of Agriculture and cooperatives.
-
Land Development Department 1972. Detailed-Reconnaissance Soil Map (1:100, 000)
Khon Kaen. Min of Agriculture and cooperatives..
-
Land Development Department 1973a. Detailed-Reconnaissance Soil Map (1:100,000) Khon Kaen. Min of Agriculture and cooperatives.
-
Land Development Department of 1973b. Detailed-Reconnaissance Soil Map (1:100,000) Maha Sarakham. Min of Agriculture and cooperatives.
-
Department of Mineral Resources. 1981. Geological Map of Thailand (1:250,000) Min. of Industry.
-
Department of Meteorology. 1991. Rainfall Statistics. Min. of Transportation.
-
Department of Irrigation. 1989. Irrigated area in Nongwai, Namphong.
-
Royal Thai Survey Department 1980. Topographic Map 1:50,000 Mapsheet No. 5541I, No. 55421-IV, No. 5543II, III No. 5641IIIV, No. 5642III, IV.
-
Radcliffe D. J. and Rochette L. Maize in Angonia. An analysis of factors of production. Field Report 30, FAO/UNDP MOZ/75/011, Maputo.
-
Sys. C., Ranst. V and Debaveye. J. 1991. Land Evaluation Part I, Part II Agricultural publication No. 7, ITC Ghent.
-
Sys. C., Ranst. V, Debaveye J. and Beernaert. F. 1993. Land Evaluation Part III, crop requirements. Agr publication No. 7, ITC Ghent.
