Farm diversification is the product of both internal and external factors. The internal factors are the crop production factors in which farmers choice of crop depends on different aspects like economy, agro-meteorology and farm management. The external factors are the situational factors that had indirect influence in the farm diversification such as government policies, extension supports, socio personal and mental aspects. The perceived response of farmers towards these dimensions are obtained through paired comparison method as portrayed below. Further, the sub factors that qualify each aspects against which responses are obtained through paired comparison to ascertain the important one. The responses were analyzed through multi-criteria decision analysis otherwise known as Analytical Hierarchy Process (AHP).
Hierarchy construction
The hierarchy is defined by breaking down the overarching objective of farm diversification into basic elements such as agro-meteorology dimension (C1), farm management dimension (C2), economic dimension (C3), policy dimension (C4) and extension dimension (C5). The review of literature and authors’ critical judgments has led to the suggestion of the hierarchical model by grouping the dimensions into two major factors such as internal and external factors. The sub factors that constituted each dimension which was identified during pre-survey viz. suitable for diverse soil type (C11), suited for winter season (C12), short duration (C13), suitable for dry spell (C14), less no. of irrigation (C21), less investment (C22), suitable for infertile land (C23), nearby market vicinity (C24), high returns (C31), less labour requirement (C32), less input cost (C33), availability of infrastructural facilities (C41), access to institutional credit (C42), Employment opportunity (C43), availability of schemes (C44), availability of adequate training (C45), availability of procurement centre (C51), attractive MSP (C52), guidance from extension staff (C53), timely supply of seed material (C54) and adequate marketing support (C55) are placed in the following (Fig 1).
Pairwise comparison matrix
The contribution of one component over another must be assessed using a psychological scale that progresses along the psychological continuum, with the components ordered using the psychophysical approach. A total of 240 diversified farmers took part in this research, which aimed to compare the relative importance of influencing factors related to diversification. For the AHP analysis, the extended average of values was used.
Saaty (1988) suggested range of 1-9 was used to achieve pairwise comparisons of major elements. There are two equally relevant things. One object has a significant advantage over another.
The weightage scores assigned by the various diversified farmers were pooled together and a pairwise average score was calculated. The pairwise score was represented as a matrix. Table 1 revealed the AHP scale values.
The pairwise comparison matrix shows the importance of the factors influencing diversification towards seasonal commercial crops. From Table 1, it reviewed that economic dimension are equally to moderately importance than agro- meteorological dimension so, the mean value is 1.25, at the same time the opposite of reciprocal value is 1/1.25 = 0.800, following that policy dimension, extension dimension and farm management dimension are moderately to strongly important than agro- meteorological dimension so, the mean value is 3.2, 3.4 and 3.3 and the reciprocal value is 0.435, 0.455 and 0.455 respectively. At the same time policy dimension, extension dimension and farm management dimension are moderately important preferred than economic dimension, the mean value is 2.3, 2.2 and 2.2 and the opposite reciprocal value is 0.308, 0.294 and 0.303 respectively. Similarly, the pairwise comparison matrix method was completed for seasonal commercial crops (Table 1).
Normalizing the matrix
The average score of pairwise items in the normalised matrix was used to determine the overall value of one variable over another. Each element in the matrix was divided by its column total to create a normalised pairwise matrix as shown in the (Table 2).
Obtaining the corresponding rating by averaging the values in each row
The weighted matrix was generated by dividing the sum of the normalised column of the matrix by the number of parameters used to create it. Furthermore, this average score indicates the percentage contribution of each element to the overall target.
The consistency ratio: calculation and checking
The pairwise matrix scale can be assigned by diversified farmers in cauvery delta zone without regard for the relative value of each variable. If this is the case, one’s initial score will not accurately represent fact. A consistency check is required to assess the score’s validity and reliability. To ensure that the original preference ratings remained consistent, the consistency calculation is done.
The consistency ratio is calculated in three steps:
Calculation of consistency measure, consistency vector and 𝜆
For actual rows with the average column, the matrix multiplication function =MMULT() is used to calculate the consistency test. The weights vector is multiplied by the pairwise matrix to determine the consistency measure.
The consistency vector is determined by dividing the consistency measure by the weight of the average criterion.
l was determined by averaging the consistency vector’s value as shown in Table 3.
From the Table 3, it showed that economic dimension in seasonal commercial crops was the most important criteria which imply highest weightage score with 0.328. Following that agro-meteorological dimension, extension dimension, policy dimension, farm management dimension got the weightage score of 0.288, 0.175, 0.128, 0.081 respectively.
Calculation of consistency index (CI)
AHP’s accuracy review is a must-have method. AHP allows for valuation inconsistencies, but they should not be more than 10%. The following formula was used to measure consistency index:
l max = Averaging the value of the consistency vector.
n = Number of criteria.
CI for seasonal commercial crops = 5.491 - 5/4 = 0.123
Calculation of consistency ratio (CR)
consistency ratio was obtained by using the formula below:
CI = Consistency index value.
RI= Table value.
Random Index (RI)
The RI was obtained from the random inconsistency indices given by
Saaty (1988), which is furnished below.
Consistency ratio for seasonal commercial crops CR= 0.123/1.12=0.110 which shows that the ratio is lesser than 10% as the set of judgements made by farmers are reliable. Similarly, the weights are calculated for sub factors are shown in the Table 4. The results of the analysis are presented in Table 4, denoting the important factors responsible for farm diversification as perceived by the farmers.
Farmers are getting low returns in the cultivation of paddy but in the case of seasonal commercial crops they are getting high returns. According to the farmers perception there is steady decline in yield, income from cultivation of paddy is very made the farmers to shift.
In the study area, the net income earned by the farmers from one acre of cotton and one acre of maize was around Rs. 27000 and Rs. 15000 respectively. As far as the labour and input cost requirements are concerned, less labor and less inputs are sufficient for growing seasonal commercial crops. Moreover, farmers can easily manage these with the available family labour except at the time of sowing and harvesting.
Farmers felt that both maize and cotton are suitable for growing in alluvial and clay soil that prevalent in Cauvery Delta Zone. Seasonal commercial crops can withstand dry summer spell and cold winter seasons.
Moreover, Cotton crop fetches reasonable procurement price Rs. 50- 60 / kg. The minimum support price of Rs. 5255/ quintal which trigger the farmers to diversify to this crop.
