Doctoral Dissertations

Date of Award

3-1972

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Agricultural Economics

Major Professor

David W. Brown

Committee Members

Merton B. Badenhop, Charles L. Cleland, Luther H. Keller, Robert S. Dotson, Frank F. Bell

Abstract

India, under its Fourth Five Year Plan, is placing greater emphasis on helping small farmers to increase their productivity and incomes. Small Farmer Development Agencies (SFDA) have been established in selected districts of Mysore and other states to coordinate the activities of relevant government agencies. The concern of this disser-tation was with the program results that might potentially be achieved if the resource constraints, production capabilities, and motivations of the small farmers themselves are taken adequately into account. Possible reasons why small farmers fail to respond to assistance programs were diagnosed. A general approach was suggested for selecting representative farmers, analyzing their potential responses to such help as more credit and irrigation, and assessing the aggregate implications in terms of program benefits and resource needs. This approach,was then illustrated to the extent possible with available farm data from a distributory in the Tungabhadra Irrigation Project in Mysore State. Bearing in mind the likely limitations in time and analytical capacity of district level agencies, a three-stage approach for using information about farmer situations and potential to help determine program content was suggested: Stage 1; The application of budgeting or linear programming to representative farms to ascertain their potential output response and input needs associated with alternative assistance proposals, taking into consideration the individual resource, constraints, technical capacities, and interests that they reflect. Stage II; Aggregation of these results to estimate the areawide benefits to small farmers and costs to the Agency stemming from each program alternative. Stage III; Use of benefit-cost or linear programming analysis to obtain an optimal SFDA program mix, considering the net benefits estimated in Stage II, the resource constraints of the Agency, and its objectives. The Tungabhadra illustration drew on survey data for 40 small farms and related production input-output information that had been synthesized from several sources. In this particular application, irrigated land area and operating capital were found to be the resources that most severely constrained farmers from achieving higher incomes. The linear programming analysis also brought out the fact that it is important to have detailed information about farmers' production conr-straints, such as seasonal distribution of available family and hired labor, if farmer response potentials are to be realistically estimated. When SFDA assistance in the form of production credit and/or more irri-gated land was assumed, the additional credit generally came out as contributing more to incomes than additional irrigated land, especially on the larger representative farms. Labor was not a constraint except under very liberal assumptions about additional capital and irrigated land area. The overall implication for this Tungabhadra situation at least was that if SFDA wants to maximize its net contribution to small farmer incomes, it would not necessarily provide the same "mix" of assistance to every farmer, nor would it rationally expect all farmers to respond with identical changes in crops or practices. This study also explored, using the Tungabhadra data to illustrate where possible, some modifications in the basic procedure that might be considered by agencies like SFDA. These included: (1) use of principal component and factor analysis to divide farms into homogeneous groups for linear programming and aggregation purposes; (2) estimation of out-put changes and input needs, given the reality that farmers do not all take advantage of new opportunities at the same pace; and (3) simple approaches that SFDA workers might consider in the near future to improve the collection and analysis of farm level information for program plan-ning purposes.

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