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Experiment Background
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Horticulture is a priority sector in agriculture by virtue of its immense potential in
improving the socio-economic conditions of the farmers. It is the sector giving maximum
returns in agriculture. Gujarat is leading the charts in the production and productivity of some
of the major fruits and vegetables. The inflow of new technologies in agriculture sector
coupled with export facilities for horticulture produce has led to huge opportunities of growth
for horticulture industry.
In Gujarat, horticulture sector contributes about 29 per cent to the GDP and 37
per cent of export of the agricultural commodities which plays an important role in ensuring
food security, income generation and uplifting the socio-economic status of farmers. The
total area under horticultural crops had increased from 11.03 lakh ha (2005-06) to 18.30 lakh
ha (2019-20) with production rising from 116.74 lakh MT (2005-06) to 237.83 lakh MT
(2019-20), giving it the fourth position in the country which indicates a significant growth of
the sunrise sector in the state. Banana, mango, citrus and sapota are the major fruit crops
grown in Gujarat. At present, Gujarat has a share of 13.36 per cent in banana production
and is ranked second in terms of banana productivity in India. Moreover, with regard to
seven districts of South Gujarat, the districts of Bharuch the highest area(12286 ha),
production (896878MT) and productivity (73.00 MT/ha) under banana crop.(Directorate of
Horticulture, Gujarat)
Moreover, the Government has allocated a significant proportion of its resources to
agricultural research in the state. TFP encompasses the impact of technical change as well
as change in the level of inputs. Although, there have been several attempts to capture pay
off to agricultural research at aggregate level, such attempts at agro-climatic zone level or
regional level particularly for individual horticultural crops like banana have barely been
made. Thus, an attempt will be made to capture the total factor productivity of banana crop
as there is a need to understand whether research and development activities in banana
crop have contributed to the crop‘s output in the region. |
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Research Methodology
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Location of Study: South Gujarat Region
Data Analysis:
The present study is based on secondary data. The major fruit crop of South Gujarat
i.e. banana will be selected for the study. For this, time series data pertaining to the cost of
cultivation of banana crop for a thirty six years period from 1986-87 to 2021-22 will be
collected from Department of Agricultural Economics, N.M. College of Agriculture, Navsari
Agricultural University, Navsari. Further, the study divided in to two periods i.e. pre and post
NHM to capture the implication of NHM. This data is collected and compiled under the State
Government Scheme for ―Farm Cost Studies of Important Crops in Gujarat State‖. The data
for rural literacy, NPK consumption, cropping intensity, Road/ Rail density, electricity, net
irrigated area will be collected from Directorate of Economics and Statistics, Government of
Gujarat.
The TFP will be estimated taking into account one output and seven input variables.
Output index includes only the main product, whereas input index comprises, seed (kg/ha),
manures (tonne/ha), fertilizers (kg/ha), human labour (man days/ha), bullock labour
(pair days/ha), irrigation (Rs/ha) and insecticide/ pesticide (Rs/ha). Data on input quantities |
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and their value are available for all the input variables expect irrigation and
insecticides/pesticides for which indirect method will be used to compute the quantity. To
remove the impact of inflation, cost of cultivation data for inputs and output will be deflated at
2011-12 constant prices, with the help of wholesale price indexes (WPI) for farm inputs and
wholesale price index (WPI) of banana crop, respectively. The labour prices were deflated
with the help of consumer price index (CPI) for agricultural labourers of Gujarat.
To find out the factors affecting TFP index, the time series data on independent
variables will be collected from various sources i.e., Statistical Abstracts of Gujarat, Socio
Economic Review of Gujarat State, Directorate of Economics and Statistics, Gandhinagar,
Gujarat and Agricultural Statistics at a Glance, GoI, Ministry of Agriculture and Farmers
Welfare, Directorate of Economics and Statistics, New Delhi. In case of variables not
available for South Gujarat, data of Gujarat state will be used as a proxy.
Analytical Framework:
(i) Estimation of Technological Changes:
The technological change will be estimated using Total Factor Productivity (TFP)
concept.TFP attempts to measure the amount of increase in total output, which is not
accounted for by increase in total inputs. The TFP approach is considered an apt tool to
examine and understand the growth in agricultural productivity and to separate out the effect
of inputs and other factors like technology, infrastructure and farmers knowledge on
productivity growth. The change in TFP signifies the technological change.
The TFP index is computed as the ratio of an index of output to an index of
aggregate inputs. Growth of TFP index is thus the growth of output index less the growth of
input index. The trend of TFP index indicates whether production growth is taking place in a
cost effective and sustainable manner or not.
Tornqvist Theil discrete approximation to the Divisia index is a very useful method for
TFP indices computation. The use of TFP indices gained prominence since Diewert (1976,
1978) proved that Tornqvist Theil discrete approximation to the Divisia index was consistent
in aggregation and superlative to linear homogeneous trans logarithmic production function.
Calculation of Tornqvist-Theil indices of output and input:
Total output index (TOI) = TOIt/ TOIt-1 =Π (Qjt/Qjt-1) (Rjt+ Rjt-1)1/2
Total input index (TII) = TIIt/ TIIt-1 =Π (Xit/ Xit-1) (Sit+ Sit-1)1/2
Where Rjt is the share of ‗j‘th output in total revenue from fennel cultivation, Qjt is output of the
‗j‘th commodity in tth year, Sit is the share of ‗i‘th input in total cost, Xit is quantity of ‗i‘th input in
‗t‘th year, with chain linking, an index is calculated for two successive periods t and t-1, over
the whole period t0 to T (sample from time t=0 to t=T) and the separate indexes are then
multiplied together.
TOI(t) = TOI(1). TOI(2)……… TOI(t-1)
TII(t) = TII(1). TII(2)……… TII(t-1)
TFP INDEX : TFPt = (TOIt / TIIt)
The above equations provide the indices of total output, total input and TFP indices
for the period‗t‘. |
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(ii)Sources of Total Factor Productivity Growth:
The TFP is influenced by a number of factors such as research, extension, human capital,
infrastructural facilities, rainfall and irrigation etc. The changes in the factors that produce
growth in TFP have vital importance to estimate how much each of these sources
contributes to the growth of TFP. To examine the determinants of TFP, multiple regression
technique will be used.
In order to identify the source of growth of TFP, following eleven explanatory
variables will be used i.e.
RES_STOK (research stock per ha of crop area);
EXT_STOK (extension stock per ha of crop area);
LIT_R (rural literacy rate, %);
NPRATIO (ratio of N to P2O5 nutrients used);
CI (cropping intensity, %);
ROAD (road density, km per 100 sq km);
RAIL (rail density, km per 100 sq km);
ELECT_AG (electricity consumption per ha of crop area);
IRR_NI (net irrigated area to net sown area, %)
RAIN (average annual rainfall in state, mm)
TREND (Year)
For the second objective IRR and return to investment will be calculated. (Ref: Kumar
et al. 2003) |
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