Building Synergism Through Resilience Measuring ...

Integrated Natural Resource Management: The Way Forward

Vinod Kumar Tripathi Amitava Rakshit V.K. Chandola R.M.Singh A.K. Nema Saswat Kumar Kar

NEW DELHI PUBLISHERS New Delhi, Kolkata

Integrated Natural Resource Management: The Way Forward by Vinod Kumar Tripathi et al. Published by New Delhi Publishers, New Delhi.

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Chapter

14 Building Synergism Through Resilience Measuring and Predicting Instrument: Ground Zero Results from National Calamity Devastated Province of India

S. P. Lal, K . S. Kadian and C. K . Rai Abstract Manuscript covers the only national calamity in the history of India, its ramification, farmers’ resilience towards their profession and explanatory variables determining the resilience after18th August, 2008 Kosi flood. 160 respondents who were affected by the calamity but still pursuing agriculture were selected. Respondents were later categorized as 141 non-resilient and 19 resilient respondents based on the 19-item scale value of resilience in relation to farmers’ profession Scale (RFP-Scale). Binary logistic model was used to identify the factors influencing the ‘resiliency level’, consequently determining the ‘disenchantment’ among the farmers. The analysis revealed that Nagelkerke R square value of 61.40% and the Wald statistics were significant at 1% level for the explanatory variables viz., cropping intensity and community engagement, while variables like non-farm activity, external assistance and agrarian fortitude were significant at 5% level; agricultural innovativeness was significant at 10% level, whereas closeness of agri-support system was not found to be significant; while, education was negatively significant at 1 percent level. On the basis of result a conceptual model was suggested for measuring and building resilience among the distressed farmers that will be based on inter and intra discipline confluence. Since, the World Bank project is running in full swing in the study area and so factors determining resilient farmers can be vital input. Finally, as frequent flood is exacerbating farmers’ agricultural livelihood, so problem must be resolved through bilateral arrangements between Indian and Nepalese officials. The suggested conceptual model will help in infusing synergism among scientist, industry and farmers. Keywords: Adaptation, Binary logistic, Disenchantment, Kosi, Quit, Resilience

“I have always tried to turn every disaster into an opportunity (John D Rockefeller)”. India agriculture is facing double whammy in the form of farmers’ disenchantment towards agriculture and their suicide. The living condition of majority of farmers in

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India is pathetic to such an extent that a large number of farmers in India want to leave agriculture as their profession. In fact, India’s primary sector i.e. agriculture is getting setback in the form of farmers’ antipathy due to number of reasons viz., decrease in per capita land holding, non-availability of input, dearth of irrigation source, low productivity, lack of fair and remunerative price etc. The same fact can also be supported through the empirical evidence that as per the 59th Round of National Sample Survey Office (NSSO 2003), 40 percent of the farmers expressed their opinion to leave farming. Almost a decade later another survey by Centre for Studies of Developing Societies survey (CSDS Lokniti 2014) in India highlighted that 61 percent of farmers would prefer other employment but not agriculture, if they find an employment opportunity in the city, while 60 percent of the farmers wanted their offspring to migrate to and settle in a city. As one of the recent findings predict that up to 2050 about 60 percent of the India’s population may shift to city, primarily in search of employment opportunities (PTI 2016). Another double setback India faces is that 2,035 farmers are losing ‘main cultivator’ status every single day from the last 20 years (Census of India 2011) and incongruously 550 jobs have been disappearing every single day in the last four years (Prahar 2016) and thus in the phase of jobless growth in India, depeasantized farmers have nothing to do but sit idle. The whole episode can be summed up by the fact that employment grew by 2 percent per annum in contrary the growth in labour force was 2.5 percent, aftermath is overall increase in unemployment (Labour Bureau 2016). Apart from this, the Asia-Pacific region accounted for about 91 percent of the world’s total deaths and around 49 percent of the total economic damage due to the impact of natural hazards (UN ESCAP 2008) and these calamities severely affect the agrarian community, which comprises 49 percent of India’s populace (Census of India 2011). In continuity, the Kosi flood was the most disastrous floods in the history of Bihar province in 2008, which was declared as ‘national calamity’ and so far, it is the sole calamity in the history of India to be officially declared so (Lal et al. 2014b, 2015). It would be noteworthy to mention here that Bihar is an impoverished and densely populated state in eastern India having per capita GSDP (Gross State Domestic Product) INR 25,023 in 2011-12, lowest among all the states in India with PCI (Per Capita Income) Score of 1 in a reversed rescaled index (Rajan 2013). Data from National Crime Records Bureau reveals another scary picture i.e. there was 628 and 2683 agrarian strife in India from 2014 to 2015 respectively i.e. rise of 327% but in Bihar rise was from 243 to 1156, deciphering a quantum jump of 376% (NCRB 2014, 2015). Later years (2009, 2010 and 2013) in the same region were seen as dry years and were declared as drought hit areas in Bihar, India (Lal et al. 2015a). Over 90 percent of the flood affected inhabitants was dependent on agricultural livelihoods and were badly affected by the devastating flood (Government of Bihar report to World Bank 2010). Repercussion of flood was: 273,000 acres of arable land had been leaved fallow due to sand-casting with long-term dent for the agriculture, environment, and livelihoods. In nutshell, the

Building Synergism Through Resilience Measuring and Predicting Instrument: …

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destruction was of such a magnitude that it took roughly 2 years for Government of Bihar to finalize economic-need assessment report for submission to World Bank. Finally, World Bank permitted, Bihar Kosi Flood Recovery Project worth US$ 259.00 million on September 9, 2010 with Project ID: P122096, having closing date on June 30, 2018 (World Bank 2010, Lal et al. 2016). No follow-up study is usually conducted to note consequences of a natural calamity in the profession of the farmers. So, a Likert type scale was developed to measure resilience in relation to farmers’ profession (RFP-Scale) in order to know the farmers disenchantment and how they are coping with the difficult situation (Lal et al. 2015b). It would be worth mentioning that presently, resilience has been studied in a number of disciplines: predominantly in medical, psychosocial and environmental sciences but its use is scarce in agricultural science. In this backdrop, the present investigation was done with the following three research objectives: i) To measure resiliency level of the respondents in relation to their profession i.e. agriculture ii) To figure out predictive variables determining the resiliency level of the farmers iii) To suggest strategies to prevent farmers disenchantment towards agriculture

Materials and Methods Study sites and sampling techniques The present investigation was undertaken in the purposively selected ‘2008 flood’ affected region of Bihar State in which five districts of north Bihar viz., (Bihar is divided into 3 agro-climatic zone and incidentally all the flood affected district falls under Zone–II) Supaul, Madhepura, Saharsa, Araria and Purnia were severely affected by the flood. Out of these, the first three districts were the worst hit with over 95 percent of the damage. So, the 1st two districts were purposively selected viz., Supaul and Madhepura because the extents of damage in these two districts were much higher than the remaining one. A multistage sampling technique was applied for selection of blocks, villages and respondents. So, two affected blocks from each district were selected randomly. Thus, from Supaul district, Basantpur and Chattapur blocks; and from Madhepura district, Kumarkhand and Murliganj blocks were selected. Consequently, a total of 4 affected blocks were selected for the study purpose. From each selected block 2 villages were selected randomly viz., Sitapur and Gidarmari village from Basantpur block, Matiyari and Tangri village from Chattapur block, Gadhiya and Raghunia village from Kumarkhand block and Kolhaypatti and Pratapnagar village from Murliganj block. An inclusive list of respondents were prepared who had faced the wrath of calamity but still pursuing agriculture as an occupation in all the eight selected villages. From each village 20 respondents were randomly selected comprising 160 respondents

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as the final sample for the study. Thus, present investigation was carried out on 160 respondents in 8 villages following the exploratory research design. The fundamental instrument for this study was Likert-type scale having 19 statements (9 positive and 10 negative) of ‘resilient in relation to farmers’ profession’ Scale (RFP-Scale) through which the primary data were garnered (Lal et al. 2015b). The data were solicited by personal interview method from all the respondents in their vernacular, keeping in view the objectives of the study. So, in its 1st attempt RFPScale was applied to measure resilience and factors determining it among selected farmers affected by ‘national calamity’.

Statistical analysis All 4 broad statistics were used viz., descriptive, probability, inferential and applied statistics (econometric method) to analyze the quantitative data for drawing the meaningful conclusion. As RFP-Scale, that was developed for the research followed the method of Likert’s summated ratings, so it automatically cascaded under the domain of ordinal measurement, where mean value can’t be calculated to know the proclivity of the respondents for a particular item. So, non-parametric Pearson Chi-square test was exercised to figure out whether the respondents were in accord or disaccord with the RFP-Scale statements. The respondents were categorized into five groups namely ‘In need, Fragile, Vulnerable, Coping and Resilient after getting the answer of the farmers in 3 point continuum scale by using cumulative square root of frequency (CSRF) method and these nomenclature was opted from Mind Chi (2014). Besides, a range of socio-economic-demographic variables were also collected that could act as explanatory variables for the ‘resiliency level’ of the respondents including 22 variables in totality viz., non-farm activity, employment generation, use of traditional knowledge, cropping intensity, access to information, ICT tools, award, education, extension contact, debt, experience, agricultural innovativeness, saving habit, alternative skill, number of enterprises in which household members are engaged, migration, availability of credit facilities, women employment in a household, closeness of agri-support system, agrarian fortitude, community engagement and external assistance. To identify the variables responsible for resilience a number of logit models were devised and then tested. Although authors have presented the logit versions, probit forms were also tried thoroughly. However, as there was minute variation between them, only the logit is reported here comparing resilient and non- resilient farmers to identify significant variables for discrete dependent variables taking binary value of either 0 or 1. The dependent variable takes the value 1 with a probability to be resilient (p), or the value 0 with probability to be non-resilient (1 - p). In this research, farmers were grouped as resilient and non- resilient based on their scale value, a score of 1 was given to resilient and value 0 was given to non- resilient. The expression of the model is given below vis-à-vis most successful resilience binary logistic model was:


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Depending upon the explanatory variables included, logit model was postulated as Ln = βo+β1 CIi+β2 CASPi+β3 EDUi+β4 AFi+β5 AIi+β6 CEi+β7 NFAi+β8 EAi +ei Where, CIi

=

Cropping intensity of the ith farmer

CASSi =

Closeness of agri-support system of the ith farmer

EDUi =

Education of the ith farmer in number of years

AFi

=

Agrarian fortitude of the ith farmer

AIi

=

Agricultural Innovativeness of the ith farmer

CEi

=

Community engagement of the ith farmer

NFAi =

Non-farm activity of the ith farmer

EAi

External assistance of the ith farmer

=

In order to bring simplicity and avoid complexity all expected sign of the variables in the final model were taken positive (+). The following two research hypotheses were framed for the present research: Null Hypothesis (H0): There is no difference in resiliency level of the respondents in relation to their profession and there is no statistical significant relationship among factors affecting the resiliency level.

Results and Discussion A critical look on Table 1 shows that the majority of the farmers were less resilient towards their profession. They strongly responded in favour, only for the few positive statement of the scale like: a) I enjoy working together with other farmers, while mutually sharing the knowledge; b) If market systems are disrupted due to natural calamity, I will be less vulnerable to food insecurity than other consumers i.e., moderate number of the farmers agreed with the positive and disagreed with the negative statements of the scale. Lion’s share of the respondents varied with the negative statements viz., ‘I simply give my land due to government rules like: Special economic zone (SEZ)/ any other pressure, even if, I am not willing.’ and ‘I think varieties suitable to climate change will not help me to counter natural vagaries’. In response to the statement, “I am thinking to quit agriculture due to various problems associated with this occupation” 26.88 percent of the respondent agreed with the statement, this result was in contrary with the findings of NSSO which revealed that 40 percent of the farmers wish to quit farming (NSSO 2003) and CSDS Lokniti (2014) which explored that 61 percent farmers want to renounce farming. The overall resilience of the farmers was moderate towards profession. These findings have clearly reflected the need of building resilience of the farmers in relation to their profession. Hence, the resilience building programme should be extended to all villages of the country for rebuilding the faith of farmers toward their profession.


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Table 1 Resilience in relation to Farmers’ Profession Scale (RFP-Scale) values S.No.

Statements

A

UD

DA

c2

1

Other farmers take advice from me regarding farming activities.

22 (13.75)

10 (06.25)

128 (80.00)

158.15**

2(-)

I simply give my land due to government rules like: SEZ/any other pressure, even if, I am not willing.

5 (3.13)

13 (8.12)

142 (88.75)

221.71**

3

If I don’t get subsidies, I report the issue to concerned authority/ file an RTI.

12 (7.50)

31 (19.38)

117 (73.12)

117.39**

4(-)

I think varieties suitable to climate change will not help me to counter natural vagaries.

6 (3.75)

12 (7.50)

142 (88.75)

221.45**

5

I enjoy working together with other farmers, while mutually sharing the knowledge.

140 (87.50)

4 (2.50)

16 (10)

212.60**

6(-)

Instead of long-term and sustainable benefits I prefer short term immediate benefits.

76 (47.50)

16 (10.00)

68 (42.50)

39.80**

7

I try to pay back the ‘agricultural loan’ within the prescribed time.

50 (31.24)

43 (26.88)

67 (41.88)

5.71

8(-)

I rarely tried to expand my farm/livestock number in a profitable way.

117 (73.12)

19 (11.88)

24 (15.00)

114.24**

9

2 I have pre-planned measures to protect my important resources (livestock, other (1.24) accessories), if flood warning/natural calamity is alarmed again.

7 (4.38)

151 (94.38)

268.51**

10(-)

I don’t take care for market information (provided by Govt. or private) to sell my farm produces.

75 (46.88)

31 (19.38)

54 (33.74)

18.61**

11

If market systems are disrupted due to natural calamity, I will be less vulnerable to food insecurity than other consumers.

83 (51.88)

8 (5.00)

69 (43.13)

59.64**

12(-)

Farm insurance isn’t an important measure for 13 occupational security. (8.12)

4 (2.50)

143 (89.38)

226.89**

13

If I don’t get remunerative price in the local market, I try to sell my commodity in other nearby profitable markets.

21 (13.13)

13 (8.12)

126 (78.75)

149.11**

14(-)

Following more than one livelihood option will give me extra burden.

19 (11.88)

7 (4.38)

134 (83.74)

184.36**

15

I introduced many desirable changes in my farming/Livelihood pattern after the calamity.

13 (8.12)

5 (3.13)

142 (88.75)

221.71**

16(-)

I am easily influenced by others’ profession and starts thinking that my own occupation is inferior to it.

89 (55.63)

13 (8.12)

58 (36.25)

54.76**

17

I use pesticides/chemical fertilizers as the last resort, because I know it is harmful for my health, soil & environment.

23 (14.38)

14 (8.75)

123 (76.87)

137.26**


159

18(-)

I may have to sell my farm land, livestock etc. if natural calamity hampers my crop production in the coming years.

29 (18.13)

45 (28.12)

86 (53.75)

32.41**

19(-)

I am thinking to quit agriculture due to various problems associated with this occupation.

43 (26.88)

27 (16.88)

90 (56.24)

40.21**

A: Agree, UD: Undecided, DA: Disagree. (-) symbolizes negative statements. Equal number of positive and negative statements in the final scale was taken to reduce the effects of social desirability and positive response bias. *Significant at 5 percent level; **Significant at 1 percent; (df =3-1= 2). Table values of chi-sq at 2 df was 5.99 and 9.21 at 5 and 1 percent level of significance respectively.

Categorization of farmers using RFP-Scale Data analyzed in the Table 2 shows that the respondents were categorized into five strata having scale scores for 19 statements for a respondent range from 19-57. This methodology would give us real time analysis of our respondents, who belonged to one of these 5 categories. In that, majority of them were in the category of ‘in need’ i.e. 33.75 percent followed by fragile, vulnerable, coping and resilient—23.75 percent, 17.50 percent, 13.12 percent and 11.88 percent respectively. Table 2: Distribution of respondents on the basis of Resilience in relation to Farmers’ Profession Scale value (RFP-Scale value) Strata In need (19-29) Fragile (30-37) Vulnerable (38-44) Coping (45-51) Resilient (52-57)

Respondents (n=160) Frequency

Percentage

54 38 28 21 19

33.75 23.75 17.50 13.12 11.88

df

c2-Value

4

25.813**

The minimum score was 19 and maximum was 57 i.e. range from 19 to 57. *Significant at 5 percent level; **Significant at 1 percent; (df = 5-1= 4). Table values of chi-sq at 4 df was 9.49 and 13.28 at 5 and 1 percent level of significance respectively.

Open ended question to know the farmers’ mindset Apart from RFP-Scale, an open ended question was asked from the respondents i.e. what can be the possibilities if a person gets trauma/shock/tribulation? Table 3 reflected that the first thing that came in the mind of most of the responses was PTSD (96.25%) and merely 03.75 per cent first response was PTG. From this it can be concluded that concept of ‘post traumatic growth’ is vanishing from the mind or it has gone to subconscious part of the brain. So, time has come to teach them about the concept and benefits of PTG.

160


Table 3: Distribution of respondents on the basis of their reaction towards trauma Variable

Respondents (n=160) Frequency

Percentage

PTSD (Post traumatic stress disorder) PTG (Post traumatic growth)

154 006

96.25 03.75

Explanatory variables influencing the resilient farmers 160 respondents from ‘national calamity’ affected province of India was randomly selected, who were later classified as 141 non-resilient and 19 resilient respondents (Table 2) based on the scale value of resilience in relation to farmers’ profession Scale (RFP-Scale) developed by the Lal et al. (2015). Initially 22 predictive variables were taken but the final model was fitted with 8 factors. 8 predictive/explanatory variables that are selected for binary logistic regression model has been discussed based upon the model output. The analysis asserted that 90.0 percent was the overall percentage correctness of the total prediction for the ‘resiliency level’ for both non-resilient and resilient respondents (Table 4). Additionally, percentage correctness of the model was 100.0 percent and 15.8 percent for non-resilient and resilient farmers, respectively. Percentage correctness for resilient farmers was less because only 19 farmers could transcend the barrier of resiliency level in the RFP-Scale. This empirical fact clearly depicted that the variables predicted the model fairly as Nagelkerke R square statistics indicated overall modest fit for the model was 0.614 or 61.40%. Binary logistic analysis revealed the Wald statistics were significant at 1 per cent level for the explanatory variables viz., Cropping intensity and community engagement, while variables like non-farm activity, external assistance and agrarian fortitude were significant at 5 percent level; agricultural innovativeness was significant at 10 percent level, whereas in contrary to a priori expectation, Closeness with agri-support system wasn’t found to have a significant influence even at 10 percent level; while, education was negatively significant at 1 percent level. The result of the explanatory variables that significantly and non- significantly influenced the resiliency level is presented below: Cropping intensity (CI): It was operationally defined as the percentage of the gross cropped area to the net sown area for a particular respondent. Post-calamity CI was considerably reduced due to sand-casting, undulation, soil erosion and water-logging of fertile land. But, even then those farmers who had relatively elevated lands, at that place land were less affected by calamity and so they had been able to take more crops per year than their counterparts. CI was found to be statistically significant at P