Conformity Analysis of Cotton Crop using Remote Sensing and GIS

Conformity Analysis of Cotton Crop using Remote Sensing and GIS

Results & Discussion

1 Weightages of the parameters and categories:
The ranks assigned to the parameters and categories were converted into weightages by using the Rank Sum method as described in the methodology. These weightages were combined by the Simple Additive Weightage method to arrive at the integrated weightage for each polygon. These weights were further thresholded based on the equal interval classification and five categories of suitability derived.

The ranks and weightages of the parameters and their categories are given in tables 1 and 2:

Table 1: Ranks and weightages of the soil

Parameter	Rank	NormalisedWeight	ScaledWeight
Available Water Capacity (AWC)	1	0.4	100
Soil depth	2	0.3	75
Texture	3	0.2	50
Drainage	4	0.1	25

Table 2: Ranks and weightages of the soil characteristics

Parameter	Rank	NormalisedWeight	ScaledWeight
Available Water Capacity (AWC)	1	0.4	100
Soil depth	2	0.3	75
Texture	3	0.2	50
Drainage	4	0.1	25

2 Analysis of satellite data:
The analysis was carried out for one typical districts of Andhra Pradesh, viz., Guntur. The vectorised cotton crop map of Guntur district was presented in fig. 2. The cotton acreage in this district was 134558 hectares.

3 Conformity analysis:
The conformity analysis was carried out in two steps. The first Step comprised of delineating the total arable land into different suitability classes according to the normalized weights derived, explained as above. In the second step, the vectorised cotton layer, as derived from the satellite data was overlaid and the areal extent of cotton crop under different categories were delineated and quantified. The cotton crop under different suitability classes for the study area is presented in Table 3.
Table 3: Area of cotton under different suitability classes of the three districts

Parameter	Category	Rank	NormalisedWeight	ScaledWeight
Soil depth	Very deep Deep Moderately deep Moderately shallow Shallow Very shallow	1 1 2 2 3 4 4	0.4 0.4 0.3 0.3 0.2 0.1 0.1	100 100 75 75 50 25 25
Drainage	Well drained Mod. Well drained Imperfect2 Excessive	1 2 3 3	0.5 0.33 0.167 0.167	100 66.7 33.3 33.3
Texture	Clay Clayey Gravelly clay Loamy Gravelly loam Sandy	1 1 2 3 3 4	0.4 0.4 0.3 0.2 0.2 0.1	100 100 75 50 50 25
Available Water Capacity (AWC)	Very high High Medium Low Very low	4 4 3 2 1	0.4 0.4 0.3 0.2 0.1	100 100 75 50 25

4 Assessment of the conformity analysis:
The assessment of the conformity analysis in terms of the total cotton cropped area vis-à-vis their suitability classes are presented hereunder: The soil suitability map for cotton crop, spatial distribution of cotton crop under different suitability classes, and their overlay are presented in Figs. 2, 3 and 4, respectively. The analysis revealed that in Guntur district, 20.39% of the total cotton crop is under most suitable soil class and 48.34% is under highly suitable class. A substantial portion of 26.97% is under moderately suitable class, 3.26% in least unsuitable and 1.04% in highly unsuitable areas. The average yield of the Guntur district is substantially affected because of a significant proportion of cotton crop under moderately suitable class. Economic levels of agricultural production can be obtained by (a) cultivating cotton in most and high suitable pockets of the district or (b) growing more suitable crops in the areas that are moderately suitable for cotton.

Fig 2: Soil suitability map for cotton crop of Guntur district

Fig.3. Spatial distribution of cotton crop of Guntur district under different suitability

Fig 4: Overlay of spatial distribution of cotton of Guntur district on different cotton suitability Classes.
Summaries and conclusion
The distribution of the cotton crop provides us the choice of the strategies to increase the production through horizontal or vertical approaches. In Guntur district, the proportion of land under moderately suitable regime needs to be diversified for crops other than cotton that suit the pedo-climatic requirements. The study had clearly brought out the spatial distribution of cotton crop as derived from satellite data in conjunction with the soil information as derived from GIS techniques is helpful in crop Management options for intensification or diversification.

Acknowledgements:
The author expresses his gratefulness to Dr. L.Venkataratnam, Group Director, Agriculture & Soils group, NRSA, Hyderabad for showing keen enthusiasm in the study. The author also thankful to Dr. P.K.Garg and Dr. S.K.Ghosh, associate professors, IIT-Roorkee for offering valuable suggestions in the analysis part.

References

CICR 2001 Cotton – March towards New Millenium Eds. CD Mayee, MRK Rao, MS Yadav, 64 pp.
Jack Malczewski 1999 GIS and Multicriteria Decision Analysi. John Wiley & Sons, Inc., New York, 392 pp.
NBSS&LUP 1994, Proceedings of the National Meet on Soil site suitability criteria for different crops 1-20 pp.
Sys, E. Ir., E. van Ranst, J. Debaveyl 1991 Land evaluation Part –I Pronciples in Land Evaluation and Crop Production Calculations. 1-274 pp.
Venkataratnam, L., MV Krishna Rao, T Ravi Sankar, SS Thammappa, S Gopi 1993 Cotton crop acreage estimation and condition assessment method using remote sensing techniques. Proceedings of the National Seminar on Oil seeds Research and Development in India: Status and Strategies, held at Hyderabad, August 2-5, 1993, 430-432.

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