Agriculture Reference
In-Depth Information
Crop yield depends on certain uncontrollable events (weather elements) such as
timing and amount of rainfall, exposure to sunlight, humidity, temperature, wind
speed. Variation of rainfall is an inter-seasonal as well as intra-seasonal problem.
Crop yield also depends on many controllable events such as timing and den-
sity of planting, the timing of harvesting, the timing and amount of maintenance
inputs. Maintenance inputs include water for irrigation and salt leaching, fertilizers,
pesticides, and herbicides.
Multiple linear regression approach is extensively used for modeling situations
where more than one factor influences response variable. The underlying model can
be expressed as
Y
=
a 0 +
a 1 X 1 +
a 2 X 2 +···+ε
(10.39)
where Y is response variable; x 1 , x 2 ,
is error
term. Using this approach, influence of climatic variables on yield of various crops
has been investigated by various researchers. Some researchers use non-parametric
regression method to model the effect of weather elements on crop yield (Chandran
and Prajneshu, 2004 ) .
A time series of crop yield may be divided into three components; the mean
yield, the trend in yield with time, and the residual variation. The mean yield is
determined by the interacting effects of climate, soil, management, technological
and economic factors. The trend is probably mainly due to long-term economic
and/or technological changes. The third component is the variation between years
and it is a prime objective of agricultural meteorologist to understand the role of
weather in this variation.
Uncertainty in weather creates a risky environment for agricultural production.
Crop models that use weather data in simulating crop yields have the potential for
being used to assess the risk of producing a given crop in a particular environment
and assisting in management decisions that anticipate appropriate measures.
...
are explanatory variables; and
ε
10.5.2 Existing Models/Past Efforts
Granger ( 1980 ) investigated the effects of variations in agro-climatically significant
variables on yield of four crops (oats, barley, almonds and walnuts). They obtained
statistical relationships between agro-climatic variables and yield through stepwise
multiple regression techniques and polynomial curve fitting. Quadir et al. ( 2003 )
tried to develop a functional relation of Aman rice yield anomaly with the rainfall.
But to eliminate the trend term, they plotted the temporal yield data correspond-
ing to year (absolute) value; which seems inappropriate (should be relative year
value). They found a quadratic relationship of national Aman rice yield anomaly
with monsoon rainfall of Dhaka. Parthasarathy et al. ( 1992 ) examined the relation-
ship between all-India monsoon rainfall and rainy-season food grain production.
They expressed the rainfall as percentage of mean, and food grain as percentage of
Search WWH ::




Custom Search