Amoloza, Jerry T
Description
This study was carried out to contribute to the understanding
of the factors affecting variations in the yield of Dagge, a native upland
rice variety, in Cale, Tanauan, Batangas, Philippines. The main
hypothesis of the study is that management is an important explanatory
variable on yield.
Viewing the farmer as a "goal-oriented system seeking to
produce a desirable outcome" (Nielson farmer-manager model), he possesses
a biography of past experiences, drives and motivations and...[Show more] capabilities
which produce management behaviour and in turn generate an outcome. With
this in mind, a management survey was done to collect information on the
characteristics of Cale farmers related to their biography, technical
knowledge, drives and motivations, and decision-making. Based on these
characteristics, a pattern analysis technique was utilized to classify
the farmers into homogeneous management groups. Three management groups
were considered and were represented in the production function by dummy
variables, in addition to the usual inputs to production like fertilizer,
labour, etc.
Using the Cobb-Douglas production function, regression models
were formulated to determine the effects of management level on the
general productivity level (intercept) only (Model 1), the factor
elasticity only (Model 2) and both the general productivity level and
factor elasticity as well (Model 3).
Inclusion of the management dummy variables resulted in a
substantial improvement in the fit of the production function. The unadjusted R for the model with only the conventional inputs is 0.35,
while the unadjusted R² for Model 1 is 0.56, for Model 2A is 0.55, for
Model 2B is 0.56, for Model 3A is 0.57 and for Model 3B is 0.59. The
analysis of variance for all the models showed that the contribution
of management to the variations in the yield of Dagge is significant
at the one per cent level. This supported the regression results. Of the models considered, it was argued that the model which
reflects the effect of management through the changes in the factor
elasticity is the most appropriate one to determine and analyze the
effects of management in the production function analysis (Model 2).
The inputs considered with which management is importantly related to
management were fertilizer and labour. It was advanced that the proper
usage of fertilizer, the amount of fertilizer applied, the proper time
of its application and the correct timing of farm operations are a
reflection of the managerial capabilities of the farmer. Model 2A was designed to test whether the management effect
is on fertilizer elasticity only, while Model 2B tests whether the
management effect is on the elasticity of labour only. This model
indicates that management ability of the farmer has an important effect
on the efficiency of fertilizer use, while Model 23 also shows that
labour is an important input on which management may have a direct effect.
Models 2A and 2B were used to examine the farmer's resource
allocation. The results showed that farmers in management group 3 are
better managers of labour and fertilizer inputs than farmers in management
group 2 and management group 1. It appears that management group 3
farmers utilized their resources more efficiently than other farmers.
They also obtained a higher yield, with an average of 2366 kg/ha compared to management group 2 farmers (1861 kg/ha) and management group 1 farmers
(1358 kg/ha).
In general, it appears that management group 3 farmers possess
characteristics which reflect good managerial ability as compared to the
other farmers. The farmers in management group 3 are young and
receptive to change, are more knowledgeable of proper fertilizer and
chemical use than the other farmers and have a different perception of
progress in farming and farming as a way of life from the rest. It
appears from the levels of yield and input use and the regression results
that farmers in management group 3 are better farmer-managers than
farmers in management group 2 and management group 1 farmers.
On the whole, the production function analysis indicated that
a significant percentage of variations in the yield of Dagge can be
explained in terms of differences in resource inputs and productivities.
In addition, it has been demonstrated that management explained a great
proportion of variations in the yield of Dagge. Perhaps a better
understanding of the farmer and his behavioural characteristics would
be a great aid in the formulation of agricultural policies. Hence, it
will be a great help to future researchers and programs on agricultural
productivity to consider differential management abilities in their
studies.
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