Land use planning in conjunction with nature:- A case study in Amethi

Land use planning in conjunction with nature:- A case study in Amethi

A. Ghosal, S. Ghosh
C/- O. R. Unit, CSIR Complex NPL campus
Pusa New Delhi-12 India

Abstract
Since time immemorial, agricultural practices have kept pace with nature in spatial context. Natural factors like calinity/alkalinity (indicated PH). Texture-that refers to the permeability of soil and irritability which refers to slope, depth of water table etc. have been quantified at village level-the unit of decision-making. Proper weightage has been given to each factor through statistical analysis. Finally, a composite rank has been arrived at indicating the suitability of the land for agricultural/other practices. The model pertains to assessment and quantification of natural support for different land use pattern under different agroclimatic constraints.

Introduction
The crux of land use planning for a region is to make the best use of its natural ingredients by adopting strategies, which make it productive either through agricultural or industrial development. According, the first step is to set p a knowledge base regarding various agro-economic characteristics. e.g. water availability, rainfall, soil characteristics (salinity/alkalinity indicated by Ph values in different portions of the region, texture of soil) depth of water table, population distribution, etc. The second step is to make an assessment of the agricultural (or industrial) potential on the basis of specific norms, and of the actual state of development attained at present. The third and final step is to compare potentials of various subareas of the region with their states of development in respect of agricultural or industrial development, and make recommendations for further growth in the future.

This paper briefly reports an in-depth study made in the Amethi block of Sultanpur district under the aegis of the Natural Resource Data Management System of the Department of Science and Technology, govt. of India. This block has 85 villages; hence spatial characteristics at the village level have been identified through a computerized knowledge base. The concerned area is mainly agricultural and does not have any minerals for industrial exploitation. Accordingly the study has been focused on the assessment of agricultural potential along with evaluation of the present state of development. A simple statistical method has been suggested for assessing agricultural potential in the form of an index; likewise a measure for present state of development has also been made. The models which form the basis of the study have been explained in subsequent sections.

Assreement of potential.
Agricultural potential of each of 85 villages in the Amethi Block has been assessed with the help of three factors, viz. salinity, soil texture and irrigability. The salinity criterion has been given three ranks: 1 for PH value less than 8.5, 2 for PH value ranging between 8.5 and 9.5 and 3 for PH value exceeding 9.5 Soil texture has been given three ranks-1 for loam texture, 2 for clayey loam and 3 for clay texture. Ranks 1 to 4 for irrigability have been decided on the basis of multiple criteria through a system of heuristics which can be applied for future construction of expert systems. Ranking has been done such that a low rank suggests better quality and a high rank suggests worse quality. For a specific village, if the same weightage be given to three criteria as above, the total of ranks, or composite rank (C.R.) will be 3 for the best potential and 10 for the worst potential.

Irritability ranking is bases on the information of slope, erosion resistance and depth of water table.

Logically, the composite rank as a measure of agricultural potential should also include the criterion of water availability, either through the data on rainfall, river discharge or availability of ground water. Detailed information on ground water availability was not available till the paper has been prepared; however a study is being done to collect this information. There is only one rain gauge station in the region under review, so it has been assumed that the entire block received almost the same amount of rainfall through years. Accordingly, the composite rank developed for each village gives a measure for relative potential within the block rather than on a global basis.

While refining the composite rank, a method of weightage of three criteria by factor analysis (a well-know statistical technique, see Kendall and Stuart (Ref. 4) has been developed. The weightage may be additive or multiplicative.

Classification of 85 villages according to potential has been presented in Figure 4.

Assrssment Of State Of Development
The state of agricultural development of each village has been measured through seven indicators given below:-

Gross irrigated area as percentage of gross sown area;
Ratio of Iron ploughs to wooden plough;
Number of powertillers per thousand hectares;
Number of 4-wheeled tractors per thousand hectares;
Number of carts per thousand hectares of gross cropped area;
Number of sugarcane crushers per thousand hectares of area under sugarcane;
Proportion of net sown area to gross sown area.

TEXT MISSING PAGE P-16-3 TO P-17-3

It may however be remarked that there is scope for refining the study. An appropriate statistical model for Yi may be as follows.

Where A is a constant relating to the jth index, B is a parameter, relating to the 1th village and is a random variable with zero mean and unit variance. A study on this model will be communicated later.

The higher values of Yi suggest better state of development, while lower values indicate less development (see fig. 5).

Policy prescription:
On the basis of analysis done in sections 2 and 3, a two-way classification of villages was done by taking (a) agricultural potential and (b) state of development into view. The agricultural potential was categorized into three groups.

CR varying from 4.0 from to 5.5:
CR varying from 5.51 to 7.0;
CR varying form 7.01 to 10.0

The state of agricultural development was divided into two groups:

Y above 15.77;
Y less than 15.77.

The Table 1 below shows the code numbers of the villages lying in each of the six cells in the two-way classification. It is obvious that the cell (A-I) identifies the villages with best potential and best state of development, while the cell B-III identifies the village, with least potential and worst stage of development.

A rational strategy for future development would be to help agricultural growth and development in areas which have better potential and attainment (in other words, in cells A-I; A-II in Table 1), while the regions poor in potential and attainment (Like cells B-III). Should be provided resources for industrial development (see Refs. 1,2).

TABLE is missing

Concluding remarks:
The paper gives a methodology and an actual analysis to measure potential and attainment with the help of available statistical data, and subsequently to provide prescription for future planning on the basis of a two-way classification as explained in section 4. There is scope to refine the study through a more sophisticated statistical model and also by using fuzzy sets in interpreting Table 1. The future direction will be to constructs expert systems to help a decision maker (see Ref. 3).

Acknowledgements:
The authors are grateful to the Department of Science and Technology (specially Dr. R.K. Midha, Director, Natural Resources Data Management Systems) for sponsoring and providing encouragement to the studies, to the UP Government, District authority at Sultanpur without whose help the study could not be done. They also thank the President of the Society of Management Science and Applied Cybernetics (SOMAC) for his keen interest in the study and to Mr. Vandana Tulsyan for assistance in the work.

References:

1.	Bhat L.S.	:	Strategy for Integrated Area Development, Case Study Of North Kanara,New Delhi, 1987
2.	Bhat L.S. Kundu A. Das B.N. Sharma A.N Bhat D.R. Sastry C.S Mahapatra R.C	:	Micro Level Planning: A Case Study of Karnal area, Haryana, India, New Delhi 1976
3.	Ghosal A.	:	Pragmatic Cybernetic (ed.), South Asia, New Delhi (1986)
4.	Kendall, M.G. and Stuart A	:	More Advanced Statistics (vol. I-III) Griffin, Londan (1963)