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Application of GIS in soil productivity assessment and mapping
Rajith Mukundan
Bharani” Nettayam,
Kachani post Trivandrum
Kerala PIN-695013
INDIA
Phone : 0471-2362199
E – mail : rajithmukundan@rediffmail.com
S. Chellamuthu & R. Sivasamy
Department of Soil Science & Agricultural Chemistry
Tamil Nadu Agricultural University
Coimbatore-641 003
Introduction
Efficient management of natural resources is essential for ensuring food supplies and sustainability in agricultural development. The task of meeting the demands of man without affecting the ecological assets for the future generations is being given top priority by both scientists and planners. Assessing the land qualities specific to a location and identifying the need for conservation practices are essential steps in the process of land use planning for environmentally sustainable agricultural production.
The management and analysis of large volumes of spatial data requires computer based systems called Geographical Information System (GIS) which can be used for solving complex geographical and hydrological problems (Garg, 1991). Geographical information system is defined as a system of computer hardware and software designed to allow users to collect, manage, analyse, and retrieve large volumes of spatially referenced data collected from variety of sources (Aronoff, 1991). The most useful application of GIS in resource investigation is to overlay various thematic maps such as rivers, roads, soils, etc to derive useful results. GIS is a powerful tool for management and analysis of data required for any land developmental activity. The present study has utilized the analytical capabilities of GIS in generation of thematic map on soil productivity. The area under each productivity class for field crops, forage crops and tree crops was also arrived at.
Study area
The study area (Pedappampatti watershed) is located in Udumalpet taluk of Coimbatore district in Tamil Nadu, India. It lies between 100 351 and 100 451 North latitude and 770 051 and 770 151 East longitudes. The total extent is 5816.24 hectares and is covered in the Survey of India toposheet 58 F2.
The geology of the study area comprises of granite and gneiss rocks. The mean annual rainfall is 444 mm and the mean minimum and mean maximum air temperature are 21.5 0C and 32.0 9C, respectively. The soil moisture regime is generally Ustic and the soil temperature regime is "isohyperthermic".
The major crops cultivated in the study area include coconut, banana, maize, sorghum and vegetables. Materials and methods Jeep traverse was made in the study area to collect information on land forms, land use, sample strips and distribution of soils. Detailed soil investigation was carried out in each image interpretation unit of satellite data (IRS-1C LISS III FCC) by examining profile pits. Horizon wise samples were collected from respective pedons of each soil series for detailed analysis of various morphological, physical and chemical properties. The interpretation units having similar soil composition were merged together and made as a single unit. The satellite data of the study area is represented in Fig. 1.
Figure 1. IRS-1C LISS III FCC of the study area
The soil samples collected during survey were air dried, powdered with wooden mallet and sieved through a 2 mm sieve and used for laboratory analysis. For estimation of organic carbon, a portion of the samples were sieved through 60 mesh sieve Based on the morphological, physical and chemical properties, the soils were classified as per USDA soil taxonomy. (Soil Survey Staff, 1998).
Soil productivity assessment
Soil productivity is the capacity of a soil, in its natural environment, for producing a specific plant or sequence of plants under a specific system of management inputs. It emphasizes the capacity of soil to produce crops and is measured in terms of yield of crops.
Soil productivity index
Actual productivity rating
Soil productivity index is a measure of conditions favourable for plant growth and crop production under good environmental conditions based on soil and site parameters that influence yield through mathematical equation. The parametric model developed by Riquier et al (1970) has been employed for assessment of soil productivity wherein nine soil /site factors were used in computing soil productivity index (PI)
P I = H x D x P x T x N/S x O x A x M
Where, H- soil moisture content, D- drainage, P- effective soil depth, T- texture/structure, N- base saturation, S- soluble salts, O-organic matter, A- mineral exchange capacity and M- mineral reserves.
Each factor is rated on a scale from 0-100; the actual percentages are multiplied by each other. The resultant index of productivity is set against a scale placing the soils in one of the five productivity classes.
In the present study, the productivity ratings of soils of Pedappampatti watershed for field crops forage crops and forest tree crops were computed based on the method outlined by Riquier et al (1970). Potential productivity rating
After effecting all the possible improvement factors, the potential productivity rating was worked out and grades were assigned (Riquier et al., 1970).
Coefficient of improvement
The coefficient of improvement was worked out based on the actual productivity and potential productivity ratings as given below:
Preparation of soil map
Visual interpretation of satellite data (IRS-1C LISS III FCC) followed by on screen digitization in Cartalinx software was done to prepare soil map of the study area. The digital map was corrected for topological errors and polygonised to give boundaries for each soil mapping unit. The database for each soil mapping unit was linked to the corresponding polygons using Microsoft Excel package.
Preparation of soil productivity map
The soil productivity ratings of field, forage and tree crops for Pedappampatti watershed were worked out following the procedure of Riquier et al. (1970). The productivity ratings of soil mapping units were calculated and a database was generated in MS-excel. The database was exported to Map Info in dBase IV format, the attribute data table was geocoded to the mapping unit, and soil productivity map was prepared.
Results and discussion
Assessment of soil productivity
Productivity rating for field crops
Effective soil depth was found to be the most limiting factor for crop production though soil moisture was also found to be limiting in soil mapping units 3, 4, 6 and 9. These soils were rated as 'poor' with regard to soil productivity. By improving the soil moisture status the potential productivity can be increased to the status of 'average' for soil mapping units 6 and 9. Even with all the possible management measures, the productivity class of soil mapping units 3 and 4 cannot be increased to a higher level.
The most limiting factor in soil mapping unit 1 was identified as average nutrient content in A horizon and for units 2, 7 and 8 it was soil moisture content. All the three units were grouped under the class 'average'. By giving all possible management measures the soil productivity ratings can be improved to the class 'good' for soil mapping unit 2 though it is not possible in the case of units 7 and 8.
The only soil mapping units to be rated, as 'good' were units 5 and 10 where soil moisture was found to be the most limiting factor. Even after improving the soil moisture status through irrigation the soil productivity class remained the same. The soil productivity rating for field crops is represented in Table 1.
Table 1. Productivity rating of soils of Pedappampatti watershed for field crops
Table 2. Productivity rating of soils of Pedappampatti watershed for forage crops
Table 3. Productivity rating of soils of Pedappampatti watershed for tree crops
Productivity rating for forage crops
For forage crops two productivity classes were identified viz. Average and Good. In the soil mapping units coming under class 'good' (units 2, 5 and 10), soil moisture was found to be the most limiting factor besides texture, average nutrient content in the A horizon and organic matter status. All these limitations were found in soil mapping units under class 'average' besides limitations in effective soil depth.
There are limitations in providing management measures for forage crops when we consider the cost / benefit ratio. Even after improving the soil moisture level, which was found to be the only economical management measure, there was no change in soil productivity class in all the soil mapping units though the productivity ratings increased significantly. The soil productivity rating for forage crops is represented in Table 2.
Productivity ratings for tree crops
Effective soil depth was found to be the most limiting factor for tree crops in most of the soil mapping units except for units 5 and 10 that were considerably deep (>120 cm). Soil moisture content was the most limiting factor for soil mapping unit 5 and texture for soil mapping unit 10. By improving the soil moisture status, the soil productivity rating can be increased to the status of 'good' for these soils.
Soil mapping unit 2 was rated under class 'poor', soil moisture content, drainage and effective soil depth being the limiting factors for crop production. Even with all possible management measures the productivity cannot be increased to a higher category. The remaining soil mapping units were grouped under the class 'extremely poor' having severe limitations in soil moisture content, drainage and effective soil depth. The productivity rating can be increased to the level of 'poor' by improving the soil moisture status in the form of irrigation for soil mapping units 1, 7 and 8. The productivity rating cannot be improved to a higher class for soil mapping units 3, 4, 6 and 9. The soil productivity rating for tree crops is represented in Table 3.
Crop choice for various soil mapping units Based on the potential soil productivity ratings, the soil mapping units identified in the study area can be grouped into four.
Table. 4. Soil productivity status from thematic map
Summary and conclusion
The study thus confirms the capability of GIS to integrate spatial and attribute data and offers a quick and reliable method of appraisal of natural resources. The spatial relationship between different geographically referenced data can be established using a GIS. It also offers the possibility of extrapolating the values to unsampled locations by methods like kriging and surface generation. In the present study this vital tool has been used for digitization of base map, digitization and delineation of soil boundary and also for generation of soil productivity map. The result presented shows the potentialities and constraints of a region with regard to its soil resources and will be a useful tool for any agricultural planning in the study area.
References
Rajith Mukundan
Bharani” Nettayam,
Kachani post Trivandrum
Kerala PIN-695013
INDIA
Phone : 0471-2362199
E – mail : rajithmukundan@rediffmail.com
S. Chellamuthu & R. Sivasamy
Department of Soil Science & Agricultural Chemistry
Tamil Nadu Agricultural University
Coimbatore-641 003
Introduction
Efficient management of natural resources is essential for ensuring food supplies and sustainability in agricultural development. The task of meeting the demands of man without affecting the ecological assets for the future generations is being given top priority by both scientists and planners. Assessing the land qualities specific to a location and identifying the need for conservation practices are essential steps in the process of land use planning for environmentally sustainable agricultural production.
The management and analysis of large volumes of spatial data requires computer based systems called Geographical Information System (GIS) which can be used for solving complex geographical and hydrological problems (Garg, 1991). Geographical information system is defined as a system of computer hardware and software designed to allow users to collect, manage, analyse, and retrieve large volumes of spatially referenced data collected from variety of sources (Aronoff, 1991). The most useful application of GIS in resource investigation is to overlay various thematic maps such as rivers, roads, soils, etc to derive useful results. GIS is a powerful tool for management and analysis of data required for any land developmental activity. The present study has utilized the analytical capabilities of GIS in generation of thematic map on soil productivity. The area under each productivity class for field crops, forage crops and tree crops was also arrived at.
Study area
The study area (Pedappampatti watershed) is located in Udumalpet taluk of Coimbatore district in Tamil Nadu, India. It lies between 100 351 and 100 451 North latitude and 770 051 and 770 151 East longitudes. The total extent is 5816.24 hectares and is covered in the Survey of India toposheet 58 F2.
The geology of the study area comprises of granite and gneiss rocks. The mean annual rainfall is 444 mm and the mean minimum and mean maximum air temperature are 21.5 0C and 32.0 9C, respectively. The soil moisture regime is generally Ustic and the soil temperature regime is "isohyperthermic".
The major crops cultivated in the study area include coconut, banana, maize, sorghum and vegetables. Materials and methods Jeep traverse was made in the study area to collect information on land forms, land use, sample strips and distribution of soils. Detailed soil investigation was carried out in each image interpretation unit of satellite data (IRS-1C LISS III FCC) by examining profile pits. Horizon wise samples were collected from respective pedons of each soil series for detailed analysis of various morphological, physical and chemical properties. The interpretation units having similar soil composition were merged together and made as a single unit. The satellite data of the study area is represented in Fig. 1.
Figure 1. IRS-1C LISS III FCC of the study area
The soil samples collected during survey were air dried, powdered with wooden mallet and sieved through a 2 mm sieve and used for laboratory analysis. For estimation of organic carbon, a portion of the samples were sieved through 60 mesh sieve Based on the morphological, physical and chemical properties, the soils were classified as per USDA soil taxonomy. (Soil Survey Staff, 1998).
Soil productivity assessment
Soil productivity is the capacity of a soil, in its natural environment, for producing a specific plant or sequence of plants under a specific system of management inputs. It emphasizes the capacity of soil to produce crops and is measured in terms of yield of crops.
Soil productivity index
Actual productivity rating
Soil productivity index is a measure of conditions favourable for plant growth and crop production under good environmental conditions based on soil and site parameters that influence yield through mathematical equation. The parametric model developed by Riquier et al (1970) has been employed for assessment of soil productivity wherein nine soil /site factors were used in computing soil productivity index (PI)
Where, H- soil moisture content, D- drainage, P- effective soil depth, T- texture/structure, N- base saturation, S- soluble salts, O-organic matter, A- mineral exchange capacity and M- mineral reserves.
Each factor is rated on a scale from 0-100; the actual percentages are multiplied by each other. The resultant index of productivity is set against a scale placing the soils in one of the five productivity classes.
In the present study, the productivity ratings of soils of Pedappampatti watershed for field crops forage crops and forest tree crops were computed based on the method outlined by Riquier et al (1970). Potential productivity rating
After effecting all the possible improvement factors, the potential productivity rating was worked out and grades were assigned (Riquier et al., 1970).
Coefficient of improvement
The coefficient of improvement was worked out based on the actual productivity and potential productivity ratings as given below:
| Coefficient of Improvement, CI | Potential productivity rating |
| Actual productivity rating |
Preparation of soil map
Visual interpretation of satellite data (IRS-1C LISS III FCC) followed by on screen digitization in Cartalinx software was done to prepare soil map of the study area. The digital map was corrected for topological errors and polygonised to give boundaries for each soil mapping unit. The database for each soil mapping unit was linked to the corresponding polygons using Microsoft Excel package.
Preparation of soil productivity map
The soil productivity ratings of field, forage and tree crops for Pedappampatti watershed were worked out following the procedure of Riquier et al. (1970). The productivity ratings of soil mapping units were calculated and a database was generated in MS-excel. The database was exported to Map Info in dBase IV format, the attribute data table was geocoded to the mapping unit, and soil productivity map was prepared.
Results and discussion
Assessment of soil productivity
Productivity rating for field crops
Effective soil depth was found to be the most limiting factor for crop production though soil moisture was also found to be limiting in soil mapping units 3, 4, 6 and 9. These soils were rated as 'poor' with regard to soil productivity. By improving the soil moisture status the potential productivity can be increased to the status of 'average' for soil mapping units 6 and 9. Even with all the possible management measures, the productivity class of soil mapping units 3 and 4 cannot be increased to a higher level.
The most limiting factor in soil mapping unit 1 was identified as average nutrient content in A horizon and for units 2, 7 and 8 it was soil moisture content. All the three units were grouped under the class 'average'. By giving all possible management measures the soil productivity ratings can be improved to the class 'good' for soil mapping unit 2 though it is not possible in the case of units 7 and 8.
The only soil mapping units to be rated, as 'good' were units 5 and 10 where soil moisture was found to be the most limiting factor. Even after improving the soil moisture status through irrigation the soil productivity class remained the same. The soil productivity rating for field crops is represented in Table 1.
| Soil mapping unit | Actual productivity | Potential productivity (P1) | Co-efficient of improvementCI = P1/P | ||
| Rating | Class | Rating | Class | ||
| 1 2 3 4 5 6 7 8 9 10 |
23.1 32.55 10.7 16.05 36.62 18.31 27.41 27.40 17.12 40.93 |
Average Average Poor Poor Good Poor Average Average Poor Good |
27.99 39.39 12.95 19.43 44.32 22.16 33.16 33.16 20.72 51.18 |
Average Good Poor Poor Good Average Average Average Average Good |
1.21 1.21 1.21 1.21 1.21 1.21 1.21 1.21 1.21 1.25 |
Table 2. Productivity rating of soils of Pedappampatti watershed for forage crops
| Soil mapping unit | Actual productivity | Potential productivity (P1) | Co-efficient of improvementCI = P1/P | ||
| Rating | Class | Rating | Class | ||
| 1 2 3 4 5 6 7 8 9 10 |
29.74 35.30 27.53 29.36 35.32 28.25 29.73 29.73 26.43 38.08 |
Average Good Average Average Good Average Average Average Average Good |
32.65 38.78 30.23 32.25 38.78 31.02 32.65 32.65 29.02 46.08 |
Average Good Average Average Good Average Average Average Average Good |
1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.21 |
Table 3. Productivity rating of soils of Pedappampatti watershed for tree crops
| Soil mapping unit | Actual productivity | Potential productivity (P1) | Co-efficient of improvementCI = P1/P | ||
| Rating | Class | Rating | Class | ||
| 1 2 3 4 5 6 7 8 9 10 |
6.60 7.74 0.67 2.44 26.16 2.32 6.52 6.52 2.16 33.92 |
Extremely poor Poor Extremely poor Extremely poor Average Extremely poor Extremely poor Extremely poor Extremely poor Average |
13.99 16.41 1.43 5.17 55.4 04.92 13.82 13.82 4.60 48.00 |
Poor Poor Extremely poor Extremely poor Good Extremely poor Poor Poor Extremely poor Good |
2.12 2.12 2.12 2.12 2.12 2.12 2.12 2.12 2.12 1.41 |
Productivity rating for forage crops
For forage crops two productivity classes were identified viz. Average and Good. In the soil mapping units coming under class 'good' (units 2, 5 and 10), soil moisture was found to be the most limiting factor besides texture, average nutrient content in the A horizon and organic matter status. All these limitations were found in soil mapping units under class 'average' besides limitations in effective soil depth.
There are limitations in providing management measures for forage crops when we consider the cost / benefit ratio. Even after improving the soil moisture level, which was found to be the only economical management measure, there was no change in soil productivity class in all the soil mapping units though the productivity ratings increased significantly. The soil productivity rating for forage crops is represented in Table 2.
Productivity ratings for tree crops
Effective soil depth was found to be the most limiting factor for tree crops in most of the soil mapping units except for units 5 and 10 that were considerably deep (>120 cm). Soil moisture content was the most limiting factor for soil mapping unit 5 and texture for soil mapping unit 10. By improving the soil moisture status, the soil productivity rating can be increased to the status of 'good' for these soils.
Soil mapping unit 2 was rated under class 'poor', soil moisture content, drainage and effective soil depth being the limiting factors for crop production. Even with all possible management measures the productivity cannot be increased to a higher category. The remaining soil mapping units were grouped under the class 'extremely poor' having severe limitations in soil moisture content, drainage and effective soil depth. The productivity rating can be increased to the level of 'poor' by improving the soil moisture status in the form of irrigation for soil mapping units 1, 7 and 8. The productivity rating cannot be improved to a higher class for soil mapping units 3, 4, 6 and 9. The soil productivity rating for tree crops is represented in Table 3.
Crop choice for various soil mapping units Based on the potential soil productivity ratings, the soil mapping units identified in the study area can be grouped into four.
Group I Forage crops > Field crops > Tree cropsProductivity was maximum for forage crops followed by field crops and tree crops in group I soils. Soil mapping units 1, 3, 4, 6 and 9 qualified to be listed in this group. For group II soils maximum productivity will be obtained for field crops followed by forage crops and tree crops. Soil mapping units 2, 7 and 8 belonged to this group. Tree crops were best suited for soils of soil-mapping unit 5 followed by field crops and forage crops. Soil mapping unit 10 was best suited for field crops followed by tree crops and forage crops. The soil productivity map is presented in Fig. 2.
Group II Field crops > Forage crops > Tree crops
Group III Tree crops > Field crops > Forage crops
Group IV Field crops > Tree crops > Forage crops
| Soil productivity class for | Aerial extent (ha) | Total area (%) | ||
| Field crops | Forage crops | Tree crops | ||
| Average | Average | Extremely poor | 1376.54 | 23.71 |
| Good | Good | Average | 2370.80 | 40.82 |
| Poor | Average | Extremely poor | 2060.10 | 35.47 |
Summary and conclusion
The study thus confirms the capability of GIS to integrate spatial and attribute data and offers a quick and reliable method of appraisal of natural resources. The spatial relationship between different geographically referenced data can be established using a GIS. It also offers the possibility of extrapolating the values to unsampled locations by methods like kriging and surface generation. In the present study this vital tool has been used for digitization of base map, digitization and delineation of soil boundary and also for generation of soil productivity map. The result presented shows the potentialities and constraints of a region with regard to its soil resources and will be a useful tool for any agricultural planning in the study area.
References
- Aronoff, S. 1991 GIS: A management perspective, WDL publications, Ottawa, Canada.
- Garg, P.K. 1991 Development of a catchment scale erosion model for semiarid environment and its implementation through Remote Sensing. Unpublished PhD Thesis. University of Bristol, UK.
- Riquier, J., D. L. Bramo and J. P. Cornet. 1970. A new system of soil appraisal in terms of actual and potential productivity. F. A. O.- AGRL. TESR/70/6, Rome.
- Soil Survey Staff. 1998. Key to Soil Taxonomy, Soil Conservation Service, USDA, Washington, D. C.