The effectiveness of drought risk management strategies in western Iran

International Journal of Disaster Risk Reduction 40 (2019) 101159

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The effectiveness of drought risk management strategies in western Iran Hadi Almasi, Jafar Tavakkoli

T

∗

Department of Geography, Faculty of Literature & Humanities, Razi University, Kermanshah, University Street, Taq Bostan Blvd, Iran, 6714414941

ARTICLE INFO

ABSTRACT

Keywords: Farmers Drought Drought perception Drought risk management Adaptation West of Iran

In recent decades, many areas of the world have been affected by drought and suffered enormous losses. Although crisis management is necessary, it seems inadequate. Hereupon, risk management and a proactive approach are being emphasized in dealing with this phenomenon. Kermanshah Province in western Iran is also exposed to frequent droughts due to its location in semi-arid regions and therefore adoption of Drought Risk Management Strategies (DRMS) is inevitable. This study is conducted to identify farmers’ DRMS and their determinants in four villages of the mentioned province. Strategies were prioritized using mean and standard deviation. The difference between villages in terms of DRMS was determined by ANOVA and Scheffe post hoc tests. The determinants of DRMS adoption were studied utilizing linear regression. The results suggested that farmers prefer inexpensive DRMS to capital intensive ones. In the case of adopting the latter strategies, the number of livestock units, second job, and irrigated land area was the most effective factors respectively. In contrast, the income dependency on agriculture, second job, and educational level were the most effective variables for acceptance of inexpensive strategies, respectively. The second job in both groups of strategies and the educational level in inexpensive strategies had a negative impact on the adoption of strategies. The findings may have a considerable contribution to the appropriate policy-making of DRMS in the world, especially in developing countries. These countries, in addition to mitigating structural, organizational, technological, and financial barriers to advanced DRMS, can develop low-cost DRMS for small farms and poor farmers.

1. Introduction The severity and frequency of climatic droughts and, consequently, hydrological, agricultural and economic droughts are largely influenced by the type of human interaction with the environment. The paradigm shift from adaptation to nature to domination over it has led to numerous environmental impacts, with climate change and global warming being its major consequences [1]. In recent decades, drought has affected vast areas of the world more extensively and frequently [2–4]. The 1942 drought in Bengal, India was the main cause of a widespread famine which claimed the lives of 1.5 million people [5]. In Kenya, wheat yields declined by 45% in 2009 compared to 2010 [2]. Australia's wheat production fell by 46% in 2006 (ibid). In 2012, a devastating drought in Yunnan province of China hit more than 6.3 million people and halted the access of 2.4 million people to drinking water. Also, agricultural industries lost approximately $317 million (ibid). In the United States, eighteen droughts between 1980 and 2013 cost more than $253 billion [6]. Concerning its nature, drought is typically characterized by slow onset and slow recovery. Since drought is usually not associated with rapid widespread infrastructure damage or loss of life, it does not draw ∗

the same attention as other natural disasters do [6,7]. Hence, in dealing with drought there is a need for some changes: turning the passivity to readiness, avoiding impermanent and occasional measures, active risk management in a continuous process (word bank, 2002; [5]. Unlike drought crisis management, risk management enhances self-reliance and reduces the level of dependency on governmental support and other aids [8]. However, few measures have been taken to understand and reduce drought risks historically. To address this problem, it is essential to detect high-risk areas as well as necessary measures for the reduction of the risks before droughts [7]. Villagers and farmers are particularly more vulnerable to drought. The enhancement of farmers' awareness of risk management, application of social capital, the participation of all stakeholders, and the establishment of social organizations and networks are beneficial in this regard [9]. In view of several droughts in the last decade and inefficiency of crisis management methods, the identification and promotion of DRMS and factors affecting farmers’ adaptation have a significant impact on the reduction of damage and vulnerability of rural communities. Literature review on drought risk management in rural communities implies that farmers usually adopt a variety of strategies to reduce the

Corresponding author. E-mail addresses: [email protected] (H. Almasi), [email protected], [email protected] (J. Tavakkoli).

https://doi.org/10.1016/j.ijdrr.2019.101159 Received 30 October 2018; Received in revised form 12 April 2019; Accepted 17 April 2019 Available online 25 April 2019 2212-4209/ © 2019 Elsevier Ltd. All rights reserved.

International Journal of Disaster Risk Reduction 40 (2019) 101159

H. Almasi and J. Tavakkoli

risk: rainwater harvesting, improvement of soil quality for increasing water holding capacity, adoption of deficit-irrigation methods, and application of modern irrigation techniques have been employed and recommended for water management in many cases [10–14]. Farmers have implemented strategies such as land consolidation, cropping pattern change, planting date change, and substitution of drought-tolerant species as farm management practices [10,14–18]. Villagers have significantly adopted the development strategies for livestock and crop insurance, livelihood diversification, and off-farm activities in order to the reduce drought losses [14,18–21]. For livestock management, strategies such as changes in livestock type and flock composition, fodder storage for livestock, and conversion of low-yield farmlands to pastures have been adopted by villagers [10,18,22]. First and foremost, these strategies and other measures require localization in accordance with regional and local conditions. In droughts, governments or non-governmental organizations offer different strategies to farmers (such as using modern irrigation methods and non-agricultural jobs), which appear inadequate and sometimes less feasible [23–25]. In many cases, farmers do not welcome and accept the strategies for reasons such as lack of capital and infrastructure, as well as technological and cultural constraints. Villagers mostly rely on their own experiences and methods in this regard, which are usually coordinated with the ecological, social, and economic conditions of their living environment [8,26]. Therefore, the farmers’ awareness of factors affecting the selection and adoption of these strategies is of particular importance in addition to detection of DRMS. The literature review of the determinants of DRMS for farmers reveals the impact of several factors. Some studies have suggested that farmers' age is effective in risk management. Deressa, Hassan, Ringler, Alemu & Yesuf [27] as well as Karami [28] demonstrated that older farmers have better adaptation to drought due to their experience. Contrary to this finding, Hisali, Birungi & Buyinza [29] found that older people are more vulnerable to drought because of their disagreement with employment of labor due to their concern about the reduction of savings. Similarly, Sadeghloo & Sojasi Qeidari [26] found that drought management strategies are less adopted by older farmers. The findings of Hassan & Nhemachena [30] do not show a clear-cut effect for the gender factor on drought risk management; except that male-headed households are more likely than their female-headed counterparts to adapt by switching from mono-cropping to irrigation, multiple cropping, and mixed systems. The findings of Deressa et al. [27] indicated that male-headed households are more adaptable to drought and more likely to take action on soil conservation, changing crop varieties, and tree planting. Similarly, Deressa, Hassan, & Ringler [31] noted that large household sizes positively affect the adaptation to drought when the head of household is male. Given the impact of the level of education on adoption of DRMS, the results of Ochieng, Kirimi, & Makau [32]; Deressa et al. [27]; Deressa et al. [31]; Kohansal, Ghorbani and Rafee [56]; Darjani [24]; and Karami [28] revealed that the probability of adaptation to drought increases in line with educational level of the head of household. On the other hand, Magombeyi & Taigbenu [11] argued that there is no consensus on the impact of education, although it is expected that high educational attainment can be effective in farmers’ acceptance of adaptation strategies and new technologies. Especially in peasant farming, large family sizes increase the probability of adoption of DRMS, as more family members equal to more workforces to perform tasks in peak seasons [31,33]. Further, farming experience has a positive effect on the acceptance of DRMS which significantly increases the probability of selecting different varieties of crop and changing the planting date and fertilizer type [33]. Habiba et al. [16] argued that land ownership in comparison with tenancy increases the probability of farmers’ adaptation to drought, which confirms the positive impact of ownership incentives in this regard. In contrast, Abid et al. [33] noted that tenant farmers are more probable to change the crop type, planting date, and fertilizer compared

to landowners. The more adaptation of tenants is due to their higher awareness of farm income, as they have to pay for rentals. In addition, the size of agricultural land is also effective in the adoption and implementation of DRMS [28,30,34]. The study by Bryan, Ringler, Okoba, Roncoli, Silvestri & Herrero [35] and Ochieng et al. [32] revealed that households possessing larger agricultural lands had a better performance in terms of tree planting, soil conservation, and water resources management. Also, Abid et al. [33] concluded that the cultivated land area of households had a positive and significant effect on changes in the type and variety of crops. Deressa et al. [31] found that livestock farming has a positive effect on the better adaptation of farmers to drought. Also, Bryan et al. [35] showed that farmers simultaneously involved in agriculture and livestock farming are probably more prepared to change their crop varieties. The investigations by Sadeghloo & Sojasi Qeidari [26] and Hassan & Nhemachena [30] indicated that farmers benefiting from advanced technologies had a higher level of resilience to drought and better acceptance of climate change adaptation strategies. Several studies suggested the importance of household income and capital in better drought risk management. Since high income provides more financial capital for rural households, these two factors have often a positive impact on the adoption of DRMS. This is particularly evident for capital-intensive strategies such as land consolidation, modern irrigation systems, and crop insurance [27,28,30,33,34,36–38]. Numerous studies also suggested that farmer's better and more access to loans and credit resources increases the possibility of adoption of DRMS. This mostly applies to expensive strategies with most researchers emphasizing that provision of credit facilities and inexpensive loans is a way of encouraging farmers to undertake essential and costly risk management strategies [27,29–32,37–39]. Furthermore, provision of extension courses on climate change and DRMS has increased the possibility of acceptance and implementation of the proposed solutions by farmers, compared to those with no access to extension training [27,29–33,35,37]. Madison [40] believed that adaptation to drought is a two-step process: first, awareness of the fact that the climate is changing followed by the response to these changes by adapting to them. Some studies demonstrated that farmers' perception of drought affects better acceptance of DRMS [16,30]. Nevertheless, there are other studies which rejected the existence of such a relationship and believed that farmers are more affected by the last climatic event in many cases or that their perception of drought may be overshadowed by their wishes, beliefs, and personality traits [35,37]. In this regard, Bahta, Jordaan & Muyambo [41] did not consider farmers' perceptions solely limited to drought and showed that state aid, social networks, gender discrimination, psychological stress, theft, and insecurity affect farmers’ adaptability to drought. As with most Middle Eastern countries, Iran is located in the arid belt of the planet and has been dealing with water scarcity and drought for a long time. Kermanshah province, as the study area, has also a semi-arid and steppe climate. The province includes 711000 ha of dry farming lands and 235000 ha of irrigated agricultural lands (RWCKP: [42]. The calculation of Standardized Precipitation Index (SPI) between 2008 and 2013 indicated the most severe drought was observed during 2008–2009 crop year in Kermanshah province [43]; Kermanshah Meteorological Office, personal communication, February 2013). Hereupon, the counties of the province were divided into four groups according to SPI and the counties including Ravansar, Kangavar, Islamabad-e-Gharb, and Sarpol-e-Zahab were selected considering the appropriate spatial distribution. Eventually, the villages of Khoramabad-e-Sofla, Soleymanabad, Mohammad-Alikhani, and Jalalvand-eOlya were selected as final analysis units. Empirical evidence and preliminary field information indicated that the villages have encountered problems such as crop yield reduction, water scarcity, decreased irrigated lands, and declined livestock farming due to the 2

International Journal of Disaster Risk Reduction 40 (2019) 101159

H. Almasi and J. Tavakkoli

Fig. 1. Research conceptual model.

area of 24640 km2 [44]. It's relative and biological population densities are 79 and 266 people per km2, respectively. Also, 30.3% of the population lives in rural areas of the province (SCI [45]: (Fig. 2). A major part of this province is formed by the karstic landforms which play an important role in supplying and feeding aquifers, such that it has more than 550 karst springs [46]. The average rainfall trends of study regions (1995–2014) indicate that there is no significant difference in the long-run rainfall pattern of the investigated counties. Therefore, the differences between studied areas are largely influenced by climatic conditions (Fig. 3). Kermanshah province can be divided into three climatic zones based on temperature, precipitation, and topography: 1) Cold zone which is observed in the highlands of the province. Its major climatic characteristic is mild to hot summers and cold to freezing winters. In this zone, the mean summer

droughts in recent years. Therefore, this research seeks to answer two pivotal questions: (1) which DRMS are chosen by farmers of selected villages to deal with drought? (2) Which determinants affect the DRMS adopted by farmers? Based on the literature review and considering field detections, access to data and executive limitations, some factors are identified and presented in the conceptual model of the research (Fig. 1). 2. Study area and research methodology 2.1. Study area The rural regions in Kermanshah province (Iran) are investigated in this research as the geographic study area. Kermanshah Province has an

Fig. 2. Location of investigated villages in Kermanshah province, Iran. 3

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H. Almasi and J. Tavakkoli

Fig. 3. Average rainfall trends of study regions 1995–2014 [47].

and winter temperatures are 26.6 °C and 4.3 °C, respectively. The average precipitation is 538 mm, mostly in the form of snow. 2) Tropic zone including lowlands located in the west of the province. In this zone, the mean summer and winter temperatures are 32.5 °C and 11 °C, respectively, and the average rainfall is 385 mm. 3) The temperate zone where the mean summer and winter temperatures are 26.1 °C and 1.4 °C, respectively and the average precipitation equals 441 mm [48]. Cultivated lands of Kermanshah province have an area of 946000 ha, with 75% for dry farming and 25% for irrigated agriculture. In this province, the dominant cropping pattern is traditional and its major agricultural products include wheat, barley, pea, and corn. Corn is very vulnerable to drought due to high water demands (OAJK: [49]. Recent droughts have caused widespread damage in the province. For example, the agriculture sector of the province suffered damage by 6065 billion IRR only in 2008 [49].

counties of the province between 2008 and 2013. Because of more severity of drought in 2008–2009, this crop year was selected as the base year for the selection of study area. Afterward, the counties of the province were divided into four groups according to the severity of drought. In each group, one county was selected according to the geographic distribution (particularly, avoidance of selecting adjacent counties), natural characteristics (by excluding mountainous counties with land limitation and dispersion or high slopes that may reduce the possibility of implementation of some DRMS), and agricultural situation (especially dominance of agricultural economy). In this way, Ravansar, Kangavar, Islamabad-e-Gharb, and Sarpol-e-Zahab counties were selected. In the next step, a village was randomly selected from each county using the code assigned to each village by SCI and a random number table which included: Khoramabad-e-Sofla, Soleymanabad, Mohammad-Alikhani, and Jalalvand-e-Olya. The statistical population consisted of 359 heads of households2 in the mentioned villages (Table 1). The sample size was calculated 186 using the Cochran formula, which was increased to 200 to eliminate possible errors. The value was distributed in the studied villages by proportional allocation. Also, the sample members were selected randomly in each village. At first, the list of village's households was taken from rural municipalities. After encoding the households, sample households were selected via a random number table and were directly questioned by door knocking. We utilized a questionnaire for the head of household as the

2.2. Research methodology Considering the nature of the subject, this study is an applied research and relies on descriptive-analytical methodology. Also, based on documentary studies and interviews, 10 major DRMS were identified and evaluated through the survey. Further, 15 independent variables were investigated and tested versus the dependent variable (DRMS) in terms of individual, economic, and perceptive aspects of drought. The standardized precipitation index (SPI) was used to select the statistical population.1 In the first step, the SPI was calculated for all

(footnote continued) is the standard deviation of long-term precipitation data at a given station [54].

i

1 McKee et al. proposed the standardized precipitation index (SPI) to quantify precipitation and monitor drought conditions on 3-, 6-, 12-, 24- and 48-month scales [55]. The index is a powerful, flexible, and yet easy tool for calculation. It only requires precipitation as the input parameter [52]. This index is also useful for analyzing wet and dry periods as well as comparing the regions with different climates. The SPI is calculated according to the following equation, where P is the annual precipitation, P is the mean long-term precipitation, and

SPI =

Pi

P i

2 Since DRMS in agriculture mainly adopted by the head of the household, the statistical population of the survey was the rural householders. In the surveyed sample, all the heads of households were male persons at the age of 19–73.

4

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Table 1 Selected villages and sample size according to SPI. SPI

Counties

Sample county

Village

Population (household)

Sample size

near normal

Kermanshah, Sarpol-e-Zahab, Songhor, Gilan-e-Gharb, Paveh, Dalahoo, Sahneh, Harsin, Javanroud Islamabad-e-Gharb Kangavar Ravansar, Ghasr-e-Shirin –

Sarpol-e-Zahab

Jalalvand-e-Olya

84

57

Islamabad-e-Gharb Kangavar Ravansar –

Mohamad-Alikhani Soleymanabad Khoramabad-e-Sofla –

76 96 103 359

54 42 47 200

moderately dry severely dry extremely dry Total

Table 2 Farmers’ individual characteristics according to villages. Variables

Villages Khoram abad Sofla (n = 57)

Soleiman abad (n = 54)

Mohammad alikhani (n = 42)

Jalalvand olya (n = 47)

Groups comparison

Mean Min Max Mean Min Max mode percent

43.30 21 73 3.93 1 7 Diploma 28.1

42.65 19 73 3.91 1 6 Diploma 35.2

44.45 22 71 4.07 1 7 Diploma 31

43.98 23 71 3.98 1 7 Diploma 27.7

F = 0.137 Sig = 0.938

second job

mode percent

no 63.2

no 68.5

no 78.6

no 78.7

Farming experience

Mean Min Max

18.09 3 43

17.74 4 40

21.31 5 40

20.02 4 46

respondent age Family size Educational level

research tool which its validity was approved by experts panel and reliability confirmed via Cronbach's alpha test (α = 0.72). Descriptive statistics were used to describe the data while Kruskal-Wallis test, Pearson and Spearman correlation coefficients, ANOVA, Scheffe post hoc test, and linear regression were employed to analyze the data.

F = 0.108 Sig = 0.955 Kruskal Wallis Chi-square: 2.124 Sig: 0.547 Kruskal Wallis Chi-square: 4.394 Sig: 0.222 F = 1.149 Sig = 0.330

test indicated no significant difference between the villages. Also, 63.5% of the respondents owned 1–4 ha and 0.5% of them possessed 5–8 ha of irrigated lands. The average of this value for the studied villages was 1.13 ha. The results of F-test showed a significant difference between the villages studied in this regard; as Khoramabad-e-Sofla village is located next to the Qaresou River, where greater irrigated agricultural lands are available. It was also observed that 43% of investigated households had 26-50 and 3% of them possessed 51–100 livestock units.3 The average number of farmers’ livestock units was 18.5 units. The F-test indicated a significant difference between the studied villages. Annual income can play an important role in adopting DRMS. The average annual income of respondents was 80 million IRR in the studied villages. The F-test demonstrated that there is a significant difference among the studied villages in terms of income at a 95% confidence level. Further, about half of respondents earned 25–50% of their income from agriculture and for the rest of their income, they relied on off-farm activities. Regarding access to loans, about 40% of the farmers reported that they have moderate access to credit and loans (Table 3).

3. Findings 3.1. Individual characteristics of respondents The maximum frequency (45%) of respondents' age distribution was related to the age group younger than 39 years and the minimum of this value (4.5%) was related to the age group of 70 and above. The average age of rural householders was 43.5. The most frequent number of farmers’ family size was 4–7 people with 69.5% and the lowest was 1–3 people with 30.5%. The average value for family size was 4. Concerning the literacy rate, 30.5% of the respondents had a diploma and 4.5% of them had a bachelor's degree. Amongst the surveyed farmers, only 28.5% had second jobs, mostly employed as construction workers (14%). For farming experience, the highest frequency (29%) was observed in the 26 years and above class, while the lowest value (15%) was related to farmers with less than 5-year experience. The results of the F-test and Kruskal-Wallis test revealed no significant difference between the villages in terms of individual characteristics (Table 2).

3.3. Farmers’ knowledge and perception of drought All householders were asked to provide a brief definition of drought. 76% of them defined drought as a decrease in precipitation and another 24% described it as less rainfall and rangeland drying. Also, 34.5% of farmers reported 5 perceived droughts during the last 10 years, while 1.5% of them remembered only 1 drought during the same period.

3.2. Economic characteristics of respondents The investigation into the status of agricultural land ownership indicated that smallholding system is prevailing in all four villages. A total of 40.5% of the farmers owned lands with an area of between 5 and 8 ha and only 3.5% of them possessed lands with an area of 13 ha and above. The average land area of the farmers was about 6 ha. The F-

3 The Animal Science Research Institute (ASRI) of Iran defined the livestock unit as a sheep weighing 45–50 kg or other types of livestock such as goat, native cattle, crossbred cattle, purebred cattle, and camel weighing 0.8, 5, 7.5, 12.5, and 12.5 kg, respectively [53].

5

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Table 3 Farmers’ economic characteristics according to villages. Variables

Freehold land area (ha) Irrigated land area (ha) Number of livestock unit Average annual income (Million Rial) Income dependency on agriculture Access to the loan

Villages Khoramabad Sofla (n = 57)

Soleiman abad (n = 54)

Mohammad alikhani (n = 42)

Jalalvand olya (n = 47)

Groups comparison

Mean Min Max Mean Min Max Mean Min Max Mean Min Max mode percent

6.26 0 19 1.58 0 6 11.16 0 49 85.8 30 200 51–75% 33.3

6.44 2 14 1.19 0 4 22.33 0 54 77.6 30 150 25–50% 66.7

5.86 2 22 0.93 0 4 26.1 0 65 91.4 20 200 25–50% 50

5.77 2 12 0.68 0 2 16.19 0 44 69.6 30 150 25–50% 55.3

F = 0.682 Sig = 0.564

mode percent

3 49.1

3 46.3

3 47.6

2 40.4

F = 6.873 Sig = 0.000 F = 1.083 Sig = 0.357 F = 3.048 Sig = 0.030 Kruskal Wallis Chi-square: 30.541 Sig: 0.000 Kruskal Wallis Chi-square: 39.045 Sig: 0.000

Table 4 Farmers’ perception of drought according to villages. Variables

Villages

number of perceived drought (last 10 years) mode percent prior awareness of drought (months) mode percent Serious attention to the drought warning mode percent Farmers' perception of drought severity

Khoram abad Sofla (n = 57)

Soleiman abad (n = 54)

Mohammad ali khani (n = 42)

Jalalvand olya (n = 47)

Groups comparison

5 times 42.1 3 months 86 medium 70.2

4 times 35.2 3 months 85.2 medium 53.7

5 times 45.2 3 months 78.6 medium 61.9

4 times 36.2 3 months 85.1 medium 66

severely dry 51.9

severely dry 52.4

severely dry 74.5

F = 2.123 Sig = 0.099 F = 2.814 Sig = 0.040 Kruskal Wallis Chi-square: 1.397 Sig: 0.706 Kruskal Wallis Chi-square: 11.462 Sig: 0.009

mode severely dry percent 52.6

Notably, the F-test represented no significant difference between the studied villages despite their geographical and climatic differences (Table 4). In addition, 84% of the farmers were aware of the last drought 3 months before the event, while it seemed insufficient to manage drought risks. In terms of awareness about drought, 38% of the farmers relied on personal experience, 47.5% were informed by experienced farmers, and only 14.5% became aware of the probability of drought by the Department of Agriculture. It was also found that none of the farmers had received extension training on drought risk management or adaptation to drought. In addition, 63% of the farmers were moderately concerned about the drought warning. Many farmers believed that if seriousness about drought means practical actions for adaptation, their options are very limited in this regard (Table 4). The farmers’ perceptions of drought severity (FPDS) were also measured over five crop years. Accordingly, 57.5% of farmers assessed the drought as severely dry. The Kruskal-Wallis test showed a significant difference between the studied villages. In addition, the drought severity was assessed in the villages in crop years 2008–2009 and 2011–2012 for comparing the drought severity perceived by farmers and the drought severity in terms of SPI (Fig. 4). As revealed in Fig. 4, the drought severity perceived by farmers was greater than the severity shown by SPI in each year. Interestingly, Khoramabad-e-Sofla village in Ravansar experienced extremely dry and severely dry conditions according to SPI. However, farmers expressed a lower drought severity in the village for the same period. One of the main reasons for this difference is the location of the village along the

Qaresou River and the majority of irrigated agricultural lands. On the other hand, the result of the Spearman correlation coefficient revealed a negative and significant relationship between non-agricultural jobs and the perception of drought severity (Correlation: 0.233; Sig: 0.001). Also, a similar correlation test indicated that there is a positive and significant relationship between the number of respondents’ livestock units and their perception of drought severity (Correlation: 0.260; Sig: 0.000). Since livestock farming is mainly dependent on pastures in the region and pastures suffer more damage than cultivated lands during droughts, this relationship seems justifiable. 3.4. Farmers’ DRMS Cultivation of drought-resistant seeds, alteration to plowing and land reclamation, and crop date change were the first to third priorities among the risk management strategies with mean ranks of 4.36, 4.35 and 4.34, respectively. On the contrary, livestock and crop insurance, off-farm jobs, and modern irrigation techniques were the last priorities with mean ranks of 3, 2.89, and 1.52, respectively. The Kruskal-Wallis test was applied to evaluate the differences between the villages in terms of adoption of each drought risk management strategy. The results suggested that there is a significant difference between the strategies adopted by farmers of the villages, except for “livestock and crop insurance” and “land consolidation” (Table 5). The question of the next step is: which villages have significant differences in the adoption of DRMS and what are the effective factors 6

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Fig. 4. Comparison of FPDS with SPI.

of each drought risk management strategy, it is confirmed that DRMS can be evaluated in three distinct groups and their effective factors may be different: 1) general DRMS which is a result of all statements measured in this regard; 2) costly or capital-intensive strategies, including modern irrigation techniques, level of land consolidation, livestock and crop insurance, off-farm jobs and fodder storage not affordable by poor farmers; 3) inexpensive or less costly strategies including use of drought-resistant seeds, irrigation at night, drought-tolerant livestock, changing planting date of crops, alteration of plowing and land preparing methods, which more farmers may accept and adopt them. The effect of independent variables on dependent variables (DRMS) is addressed based on the mentioned classification. In the first step, the variables without significant relationships dropped of the study utilizing Pearson and Spearman correlation coefficients. Afterward, the determinants of DRMS adopted by farmers were identified using a linear regression model. The results of the regression analysis of these three groups are as follows:

Table 5 Prioritization of DRMS adopted by farmers. strategy

Using modern irrigation methods Use of drought-resistant seeds Irrigation at night livestock and crop insurance Level of land consolidation Non-agricultural jobs Drought resistant livestock Change planting date of crops Changing plowing and land preparation Storage of livestock forage

Mean

SD (σ)

rank

Kruskal Wallis test Chi-square

sig

1.52 4.36 3.05 3 3.17 2.89 3.57 4.34 4.35

0.743 0.662 1.060 1.480 0.771 1.388 1.437 0.661 0.660

10 1 7 8 6 9 5 3 2

71.513 34.992 23.975 2.975 2.655 8.576 22.416 74.47 75.448

0.000∗∗ 0.000∗∗ 0.000∗∗ 0.398 0.448 0.035∗ 0.000∗∗ 0.000∗∗ 0.000∗∗

3.75

1.479

4

11.431

0.010∗

in this regard? To answer this question, the total score of drought risk management items was first calculated. Since one of the prerequisites of parametric tests is the normal distribution of feature in a given population, Kolmogorov-Smirnov test (KeS test) was used in this regard. The results of the test demonstrated that the score of drought risk management is greater than 0.05 and is not significant (Z = 1.018; Sig = 0.251). Therefore, the variable has a normal distribution and parametric statistics can be used for its analysis. For examining the significance of differences in drought risk management among the studied villages, the analysis of variance (ANOVA) suggested a significant difference between the villages at a 99% confidence level (F = 7.075; Sig = 0.000). The Scheffe post hoc test was used to determine which villages have a significant difference in drought risk management. The results of the test indicated that there is an actual and significant difference between Soleymanabad and the other villages, while other villages are not significantly different from each other. This might be due to the fact that livestock farming more relies on pastures in Soleymanabad than in the other villages (Fig. 5 and Table 6).

1) First group (DRMS): the adjusted coefficient of determination is 0.371 in this group. In other words, the independent variables explain 37.1% of the variance of DRMS adopted by the farmers surveyed in this study. The investigation into significance level and standardized beta coefficient indicate that variables such as irrigated land area, average annual income, number of livestock units, farmers' family size, and freehold land area are significant and rank first to fifth priorities in the explanation of DRMS, respectively (Table 7). The negative beta for the variable of freehold land area can be because of the fact that most farmers had second occupations in this group and were less sensitive to yield per area due to their vast lands. The number of respondents' livestock units was also noticeable in two aspects. Firstly, those who had more livestock units had more income. Secondly, the authors’ previous studies on Western Iran suggest that villagers have a great tendency towards urbanization so that many of them practically live in cities and cultivate annual plants like wheat in villages. This group, called absent farmers, does not pay attention to DRMS as much as resident farmers do in villages (See Ref. [50].

3.5. Determinants of farmers DRMS According to Table 5 and Fig. 5, which compare the villages in terms 7

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Fig. 5. DRMS adopted by farmers according to villages. Table 6 Scheffe post hoc test to determine the difference between villages in terms of DRMS. village

village

Mean difference

sig

Khoramabadsofla Soleimanabad

Soleiman abad Khoramabad sofla Mohamadali khani Jalalvand olya Soleiman abad Soleiman abad

2.862a −2.862a −3.680a −3.179a 3.680a 3.179a

0.011 0.011 0.001 0.006 0.001 0.006

Mohamadalikhani Jalalvandolya a

Table 8 Regression analysis of capital-intensive DRMS for farmers (enter method). Predictive variables

B

SE

Beta

T

Sig

Constant value Second job Freehold land area (ha) Irrigated land area (ha) Average annual income (Million Rial) Access to the loan Number of livestock unit

14.524 −1.796 −0.035 0.703 0.131 0.130 0.049

0.843 0.396 0.064 0.165 0.051 0.151 0.010

– −0.307 −0.041 0.291 0.186 0.052 0.336

17.236 −4.539 −0.551 4.255 2.568 0.861 4.665

0.000 0.000 0.583 0.000 0.011 0.390 0.000

Note: R = 0.575 R2 = 0.331 ADJ.R2 = 0.310.

The mean difference is significant at the 0.05 level.

2) Second group (capital-intensive DRMS): the correlation test indicated that only six independent variables had a significant relationship with the dependent variable in this group introduced into the regression equation. The result suggests that the adjusted coefficient of determination is 0.310 for this group. In other words, the independent variables explained 31% of the variance of costly DRMS for the farmers surveyed in this study. An investigation into the significance level and standardized beta coefficients demonstrated that the variables such as the number of livestock units, second job, irrigated land area, and average annual income were

significant and ranking first to fourth priorities for the explanation of costly DRMS for the farmers, respectively. As observed, the second job has a negative impact on the adoption and implementation of the strategies in this group. In other words, those who have off-farm jobs and income along with farming often pay less attention to the strategies (Table 8). 3) Third group (inexpensive DRMS): in this group, 12 independent variables had a significant relationship with the dependent variable. According to the regression result, the adjusted coefficient of determination was 0.370 for this group. So it can be said that the

Table 7 Regression analysis of DRMS for farmers (enter method). Predictive variables

B

SE

Beta

T

Sig

Constant value respondent age Family size Educational level Farming experience Freehold land area (ha) Irrigated land area (ha) Average annual income (Million Rial) Income dependency on agriculture Number of livestock units number of perceived drought (last 10 years) Farmers' perception of drought severity

28.349 −0.082 0.728 −0.484 −0.025 −0.278 1.336 0.334 1.043 0.068 −0.160 0.820

3.719 0.064 0.300 0.331 0.072 0.111 0.285 0.093 0.765 0.019 0.388 0.608

– −0.259 0.234 −0.172 −0.058 −0.183 0.316 0.272 0.095 0.266 −0.035 0.110

7.623 −1.282 2.425 −1.464 −0.344 −2.515 4.694 3.610 1.363 3.668 −0.412 1.348

0.000 0.202 0.016 0.145 0.731 0.013 0.000 0.000 0.175 0.000 0.680 0.179

Note: R = 0.637 R2 = 0.406 ADJ.R2 = 0.371.

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H. Almasi and J. Tavakkoli

Table 9 Regression analysis of inexpensive DRMS for farmers (enter method). Predictive variables

B

SE

Beta

T

Sig

Constant value respondent age Family size Educational level Farming experience Second job Irrigated land area (ha) Average annual income (Million Rial) Income dependency on agriculture Number of livestock unit number of perceived drought (last 10 years) Serious attention to the drought warning Farmers' perception of drought severity

15.156 −0.055 0.319 −0.439 0.008 −1.610 0.231 0.106 2.678 0.037 0.005 −0.718 0.851

2.545 0.041 0.193 0.209 0.046 0.583 0.176 0.055 0.647 0.012 0.247 0.299 0.388

– −0.276 0.161 −0.245 0.028 −0.246 0.086 0.136 0.384 0.225 0.002 −0.140 0.179

5.955 −1.359 1.652 −2.097 0.164 −2.762 1.313 1.942 4.140 3.110 0.019 −2.402 2.193

0.000 0.176 0.100 0.037 0.870 0.006 0.191 0.054 0.000 0.002 0.985 0.017 0.030

Note: R = 0.639 R2 = 0.408 ADJ.R2 = 0.370.

for their drought risk management. In contrast, crop and livestock insurance, off-farm activities, and modern irrigation techniques were placed in the latest priorities, respectively. The determinants of DRMS adopted by farmers vary from inexpensive to costly strategies. In the latter case, the variables of the number of livestock units, second job, irrigated land area, and average annual income ranked as first to fourth priorities, respectively. On the other hand, for inexpensive strategies, variables such as dependency on agricultural income, second job, level of education, number of livestock units, farmers’ perception of drought severity, serious attention to drought warnings, and average annual income were prioritized as the first to seventh levels in the explanation of DRMS for farmers. It was also observed that the respondents with off-farm jobs and incomes along with agriculture were less concerned about the strategies. Given the conditions of the region and considerable migration of the youth from the villages, a higher level of education has a negative effect on the acceptance of strategies. In both groups of strategies, the positive effect of the number of livestock units was firstly due to higher income followed by the possibility of more investment by farmers. Nevertheless, more importantly, given the high tendency to urbanization in western villages of Iran, it is a good indicator for identification of absent farmers who do not care about DRMS as much as farmers who live in rural areas do. Comparing the results with the literature of the same area (Iran) indicates that unlike Karami [28]; farmers’ age does not have a significant effect on the adoption of DRMS. Also, in contrast with Kohansal et al. (2009), Darjani [24]; and Karami [28]; with the increase in the level of education of the head of household, the acceptance of low-cost DRMS has decreased. Contrary to Karami [28] and Rezaeemoghadam et al., [34]; freehold land area (ha) negatively affected DRMS adoption. However, the irrigated land area (ha) had a positive impact on the adoption of expensive DRMS. Approving [28,34,36]; the average annual income positively affected DRMS adoption. Also, in contrast to Zamani et al. [39]; access to loans does not have a significant effect on DRMS adoption by farmers. To sum up, it can be maintained that the farmers welcome inexpensive risk management strategies more due to socio-economic conditions, despite their good perception of drought and its severity. Hence, policy-makers in developing countries need to pursue promoting advanced and capital intensive risk management strategies via reducing its structural, institutional, technological, and financial barriers. Further, they have to develop low-cost and local strategies which are more appropriate and affordable for small farms and poor farmers.

independent variables explained 37% of the variance of inexpensive DRMS for the surveyed farmers. Also, investigation of significance level and standardized beta coefficient indicated that the variables such as income dependency on agriculture, second job, educational level, number of livestock units, farmers’ perception of drought severity, serious attention to drought warning, and average annual income are significant ranking the first to seventh priorities for explanation of inexpensive DRMS for the farmers, respectively. Herein, the second job and level of education had a negative impact on the adoption of the strategies. Given the conditions of the region, this situation can be attributed to migration of the youth from the villages especially those with higher educational attainments (Table 9). 4. Discussion and conclusion Climate change has caused frequent droughts in the world particularly in arid regions [33]. The environmental, economic, and social consequences of this phenomenon are dramatic both globally and locally. In most developing countries, drought crisis management is poor and inefficient [2,17] due to financial and structural weaknesses. In addition, although risk management is an essential strategy for improving the resilience of vulnerable communities against this phenomenon, it has not been generalized systematically and acceptably for villagers. Farmers sometimes encounter new risks which are imposed on them by structural, financial, and technological restrictions, through the adoption of many risk management strategies proposed by governments and non-governmental organizations. Therefore, identification of farmers’ risk management strategies and their effective factors can considerably contribute to reasonable and realistic planning in this regard. In this research, identification and analysis of DRMS for farmers were studied in four villages in Kermanshah province, west of Iran. The results indicated that about 60% of the farmers perceived the drought severity as severely dry. The severity perceived by the farmers was often overestimated compared to the actual drought calculated according to SPI. Since agricultural and hydrological droughts usually occur after meteorological droughts and their socioeconomic impacts emerge with delays [51], the difference between the farmers' perception and the SPI index may be related to this delay. It was also found that respondents with off-farm jobs perceived the drought severity at a lower level. Conversely, with increases in livestock units of respondents, their perception of drought became more severe. This is mainly due to the drying of pastures in communities relying on the herding system. The farmers employ different strategies including cultivation of drought-resistant seeds, alteration to plowing and land preparing methods, and changing planting date in the order of priority

Declarations of interest None. 9

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Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijdrr.2019.101159. Classification of drought conditions in terms of SPI SPI values

Condition

2.0+ 1.5 to 1.99 1.0 to 1.49 -.99 to .99 −1.0 to −1.49 −1.5 to −1.99 −2 and less

extremely wet very wet moderately wet near normal moderately dry severely dry extremely dry

[52].

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