IMPACT OF DRIP IRRIGATION ON FARMING SYSTEM: EVIDENCE FROM SOUTHERN INDIA - Management, Performance, and Applications of Micro Irrigation Systems - page 28

TABLE 2 (Continued)

Crops Drip villages Control villages

Before After Before After

Ragi 4.19 0.0 . .

Sorghum 14.78 2.5 20.36 19.77

Sugarcane . . 11.85 11.17

Turmeric 0.0 7.1 2.47

Vegetables including tomato 30.73 21.52 27.74 27.74

A signifi cant shift towards crops such as coconut, grapes was commonly observed
in the drip villages (Table 2). The main reasons were scarcity human labor and water.
For this reason, a reduction in area under vegetables was also observed. Thus, the
micro irrigation could be promoted in the regions with high scarcity of water and
labor. As a cropping pattern decides the adoption and suitability of drip irrigation,
widespread adoption of micro irrigation could be promoted in the regions where shift
towards crops like coconut and banana is common.

2.4.3 TRANSITION PROBABILITY AND STEADY STATE PROBABILITY OF
CHANGES IN CROPPING PATTERN
Significant changes in the cropping pattern were observed in the study area. As the
changes in cropping pattern favor the adoption of drip irrigation technologies, we were
also interested in studying the type of transition that has taken place in the cropping
pattern. For this, employing Markov chain analysis, the transition and steady state
probabilities were computed and have been presented in Table 3. Markov analysis is
a way of analyzing the current movement of variable in an effort to predict its future
movement. In the transition probability matrix, the rows identify the current state of
the cropping pattern being studied and the columns identify the alternatives to which
the cropping pattern could move. Here, the row probabilities are associated with crops
retention and move to other crops (i.e., shift to other crops), while the column prob-
abilities are associated with crops retention and move towards the crop (i.e., shift
towards the crops, gain to the particular crop). The transition probability presented in
the Table 3 depicts the cropping pattern changes over time.
The diagonal elements represent probability of retaining the same crop in future.
For instance, the probability of retaining banana crop was worked out to be 57%.
Similarly, for coconut the probability of retention was 75%. The analysis shows that
the probability of shifting of the area under maize to banana was 18%, to coconut was
18%, to tomato was 15%, to grapes was 13% and to other crops was 4%. The probabil-
ity of retention of maize crop was 29%. Similarly, the vegetables have shown retention
probability of only 24%. The probability of shifting area of vegetables to banana was
12%, to coconut was 20%, and to grapes was 12%. What will happen in the future if
this pattern of changes in the cropping pattern occurs? If this kind of transition contin-
ues, around 32% of the cropped area will assume area under coconut and grapes will