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Use of GIS for sampling designs for agricultural surveys


Simulation Study :
In this simulation study village wise data from District Census Hand Book of 1991 for Rohtak district of 492 villages of Haryana has been utilized. The village wise map was digitized using PC-ACR/INFO software and irrigated area of the village has been treated as character under study (Y), whereas, total cultivated area of the village as auxiliary character (X). The whole district has been identified as one zone after testing the spatial correlation coefficients of different order. The over all spatial correlation coefficient for the district was approximately 0.22. The problem is to compare different sampling strategy for estimating population mean = 609.0022 ha. with respect to its accuracy, bias and stability. In this simulation study 100 samples of different sizes has been selected following various sampling procedures and the parameters related to accuracy, bias and stability has been obtained. The results are presented in the following table.

Sample Size Strategy 30 50 100
R.B. R.E. C.V. R.B. R.E. C.V. R.B. R.E. C.V.
STG - I 0.28 - 13.38 0.19 - 10.66 0.12 - 7.72
STG - II 0.74 0.87 14.73 0.73 0.99 10.39 0.15 1.29 6.77
STG - III 0.19 6.26 5.32 0.01 7.00 3.94 0.04 8.16 2.70
STG - IV 3.09 8.38 4.76 1.98 8.60 3.63 0.93 8.79 2.63

STG - I = Simple random sampling without replacement strategy
STG - II = Usual DUST Technique proposed by Arbia with the estimator of SRSWOR
STG - III = Proposed Sampling Strategy with Whi (1)Xhi=1
STG - IV Proposed Sampling Strategy
R.B. = % Relative Bias.
R.E. = Relative Efficiency.
C.V. = Coefficient of variation.

From the above table it can be seen that the performance of the proposed strategy is far better in all respects expect R.B. It can also be observed that performance is improving with increasing sample size.

Multistage sampling design for crop surveys using satellite data
Presently after launching of IRS-IC and IRS-ID the availability of spectral data at different resolutions has been made available. The Wide Field Sensors (WiFS) with spatial resolution of 189.3 meters and LISS-III data of resolution 23 meter are available. There is need to develop methodologies for different surveys using data pertaining to different resolutions as there is wide difference between the data of both the sensors. The data pertaining to LISS-III is very costly as compared to WiFS.

The WiFS data for the large area (say state) under study is acquired and stratum boundaries based on spatial correlation coefficient are made with the help of digital number values of area unit. The district boundaries within the state are digitized and a sample of districts is selected with the help of DUST technique using proportional allocation. The LISS-III data is now processed for only the selected districts. Again in each of the selected districts villages are digitized and a sample of villages is selected using DUST technique. In this way, the villages are selected by using IRS-IC data using stratified multistage sampling design. The land use classes of each selected village can be obtained using ground truth survey. Further, the yield of the crop under study can be obtained by developing suitable models based on crop cutting experiments data. The basic purpose of this stratified sampling design is to get precise estimates of important crop statistics like crop acreage and crop yield and crop yield forecast models from an integrated survey at a relatively much cheaper cost.

Reference
  1. Arabia, G. (1989): Statistical effects of spatial data transformation In: 'Accuracy of spatial data basis'.Eds/ Good Child, M.F. and Gopal, S. Taylor and Frances, London.
  2. Arabia, G. (1993): Use of GIS in spatial statistical surveys. Int. Statist. Rev., 61, 2, pp.339-359.
  3. Arabia, G. & Haining, R.P. (1989): Error propagation through map operations. Working paper. National Centre of Geographic Information and Analysis, UCSB.
  4. Dadhwal, V.K. and Panikar, J.S. (1985). Estimaation of 1983-84 wheat acreage of Marnal (Haryana) using MSS digital data. Scientific Note, Space Application Centere, Ahmedabad.
  5. Dadhwal, V.K., Panikar, J.S. Methavy, T.T. and Jaiswal, S.D. (1987). Wheat acreage estimation of Haryana for 1986-87, using landsat MSS data Scientific Note, Space Application Centre, Ahmedabad.
  6. Dadhwal, V.K. and Sridhar, V.N. (1986). Sampling approach for remote sensing based on crop inventory Scientific Note, Space Application Centre, Ahmedabad.
  7. Goyal, R.C. Singh, R. Chhikara, R.S. (1994). Estimation of crop yield using post stratification based on satellite data J. Ind. Soc. Ag. Statist. 46(2). Pp. 210-222.
  8. Krishnamurthy, Y.V.N. and Ddiga, S. (1976) Remote sensing and GIS for land use planning. Presented at the National Workshop on land use planning organised by Planning Commission and NCAP, New Delhi.
  9. Murthy, C.S. Thiruvongacha Chars, S. Ray P.V. Cd. Jonna, S.(1996). Improved ground sampling and crop yield estimation using satellite data Int. J. Rem Sens. 17(5) 945-956.
  10. Randhir singh, R.C. Goyal and Raj Chhikara (1992): Use of spectral data in crop yield estimation surveys: Int. Jr. of Remote Sensing.
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