Abstract
Meeting the increasing demand for food, a parallel growth in food grain production is
required. This can be achieved by increasing the utilization of inputs and effectively
organizing the management of production. Wheat, being the second most important cereal
crop, its output could also be raised by proper utilization of productive inputs through efficient
management of production factors. The main objective of the present study was to identify and
analyze the possibilities for improving productivity of wheat by increasing the farmers'
productive efficiency. The study employed farm level cross sectional data collected from 293
farm households of three major wheat growing areas of Bangladesh. About 94 percent of the
sample farmers used kanchan variety and a few farmers used an older variety sonalika and a
new variety shatabdi (BARI Gam-21). The variety shatabdi produced highest yield (2653
kg/ha) followed by kanchan (2397 kg/ha) and sonalika (2216 kg/ha). Yield of wheat varied
across locations and among farm categories. The average yield was found to be 2395 kg per
hectare with highest average at Dinajpur (2493 kg/ha) followed by Rajshahi (2477 kg/ha). The
yield at Jamalpur (2189 kg/ha) was less than national average (2210 kg/ha). Among farm
categories, large farmers produced highest yield (2532 kg/ha) followed by medium (2381
kg/ha) and small (2347 kg/ha) farmers. Full package of recommended production technologies
were not adopted by the farmers. Fields recording higher yields were sown timely and
received more manure, fertilizers and irrigation. Other socio-agro-economic factors have also
played roles in the variation in yields. The bio-physical constraints limiting wheat production
were lack of good quality seed, poor seed germination, excessive weed, poor utilization of
irrigation facilities, etc. The estimates of stochastic frontier production function model showed
that nitrogen, sulphur, FYM, and irrigation had a significant positive impact on wheat
production. The mean technical efficiency of wheat growers was 0.84 suggesting that there
existed a technical inefficiency of 16 percent. Likewise the mean allocative efficiency of 0.91
implied that there was an average level of allocative inefficiency of 9 percent. The estimates of
stochastic frontier cost function model showed that FYM price and sowing date dummy were
negatively significant; implying that increased use of FYM and sowing at optimum time
would result in the decrease of production cost. The average economic efficiency was 0.76.
Thus farmers' efficiency could be improved by 24 percent through the improvement of both
technical and allocative efficiency. The coefficients of farmers' education, wheat farming
experience, and training on wheat were negatively significant in the inefficiency effect models
implying that inefficiency decreases with the increase in farmers' education, wheat farming
experience, and training on wheat. The yield gap-I was estimated at 3585 kg/ha resulting
mostly from non-transferable components of technology and environmental factors. Yield
gap-II was 520 kg/ha. This was attributed mainly to technical inefficiency. The biotic and
abiotic factors were combinedly responsible for 26.77 percent yield loss causing a mean yield
gap of 641 kg/ha. The mean yield gap of top 10 percent and all farms were estimated at 559
kg/ha, and 4th quartile and all farms were 456 kg/ha. The study suggests the existence of some
gaps in wheat yield, which may be reduced through policy interventions and adoption of
improved technology.