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Drought occurrences and its implications on the households in Yobe state, Nigeria

Drought occurrences and its implications on the households in Yobe state, Nigeria The study assesses the extent of droughts and its implications on the households in the study area. This is to highlight the need to integrate drought adaptation options into the government development plans. Strategies for drought adaptation options in the study area have often been made without experimental foundations placed on the extent of drought and its implications on the households. To achieve this, the study employed Normalized Rainfall Index (NRI) to determine the extent of droughts and its implications on the households, which has much to offer in terms of policy decisions. The study also utilized questionnaire administrated to 400 households to determine the annual income from different occupations that yielded more income to the people in the study area using one-way analysis of variance (ANOVA). The NRI shows that the study area was characterized by mild to severe drought events. The first (1986–1995) and third (2006–2017) decades experienced high incidences of droughts, while the second decade (1996–2005), witnessed the least incidences of droughts. The result of the economic activities of the households reveals that 65% of the total household respondents were involved in farming, while 35% were involved in non-farming activities as their major source of livelihood. The analysis of variance on the economic activities that generated more income to the households in Yobe State shows that farming activities provided more opportunities for income generation. Consequently, agriculture in the study area is currently being constrained by the frequent occurrence of droughts. Thus, there is a need for integrated development schemes aimed at livelihood diversification and increasing the adaptive capacity of households to drought in the study area. Keywords: Drought, Desertification, Vulnerability, Normalized rainfall index Background National Aeronautics and Space Administration (NASA) Drought is one of the most globally recognized hazards shows that about 900,000 km2 of former savanna grass- that damage an environment. It occurs when there is land in the region of Africa has been severely decertified significant rainfall deficit that causes hydrological imbal- between the early 1960s and 1986 due to persistent ances and affects the land productive systems. Drought drought occurrences (O'Connor 1995). Moreover, Bates practically occurs in all climatic regions with both high et al. (2008) state that one-third of African population and low mean rainfalls (Um et al. 2017). It can result in lives in drought-prone areas. The drought has become a damage to agricultural production as well as to the nat- recurrent event in many parts of Africa, after the ural environment and human society (Gidey et al. 2018). drought of the early 1970s that devastated the Sahel re- Drought is considered to be a natural disaster that dif- gion. Dai et al. (2004) have shown that there is about fers from other natural disasters because it has a gradual 40% decline in annual rainfall total in West Africa, from creeping feature (Ayoade 1988; Yue et al. 2018). Liu et the year 1968–1990 as compared with the 30 years be- al., (2018) opine that drought develops slowly with pro- tween 1931 and 1960. Thus, frequent drought occur- longed effects that gradually increases in severity and rences are threatening the human existence in African tends to persist over a long period of time even after it savanna regions and consequently making the house- has stopped. Analysis of the orbital photographs from holds highly vulnerable to drought. In Northern Nigeria, there are several records of drought occurrences which resulted in famines, such as Correspondence: zejude12@gmail.com Farming System Research Programme, National Cereals Research Institute, the droughts of 1903 and 1911–1914 (Shiru et al. 2018). Badeggi P.M.B 8, Bida, Niger State, Nigeria © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Eze Geoenvironmental Disasters (2018) 5:18 Page 2 of 10 Other droughts occurred in 1919, 1924, 1935 (Tarhule States of Gombe to the southwest, Bauchi to the west, and Woo 1997), 1951–1954, 1972–1973, 1984–1985 Jigawa to the northwest, Borno to the east, and to the (Mortimore 1989), 2007 and 2011 (Abaje et al. 2013). North by the Niger Republic. The study area has a popu- Consequently, the situation is being exacerbated by the in- lation of 2,532,395, according to 2006 census, and a land creasing human population, overgrazing, over-cultivation, mass of 47,153 km . The density is thus 29.92 persons and high poverty rate (Eze 2017). The predominant eco- per square kilometre. nomic activity in the region is farming. Moreover, the re- The climate regime of the study area is characterized gion is known for its grain products such as maize, millet, by single long dry season followed by a shorter wet sea- sorghum, and wheat (Eze 2017). Agricultural production son. In the Sahel, mean annual rainfall is less than in Nigeria particularly in Northern Nigeria is mainly 500 mm and may fall to 250 mm (Ayoade 1988). In the rain-fed and naturally prone to vagaries of rainfall variabil- Sudan savanna, it is 800 mm to 1000 mm. The number ity (Tiamiyu et al. 2015). During the years of abundant of wet months is about 4 months in the northern guinea rainfall, households in the region experience bumper har- savanna zone to less than 2 months in the Sahel savanna vest from both crop and livestock productions. Con- zone (Folland et al., 1986; Anyadike, 1993). Mean annual versely, during the years with rainfall deficit, the temperature is 26oC increasing towards the Sahel zone. households experience crop failure, poor crop yield, lead- Potential evapo-transpiration exceeds rainfall except for ing to famine, hunger, and death, including livestock (Eze the few months. Vegetation of the study area is divided et al. 2018). Khalil (1974) shows that about 300, 000 live- into the Wooded Tropical savanna, Mixed combratac- stock died during the drought that occurred in the year eous woodland and wooded savannah. The Wooded 1972 to 1973. The number of livestock that died during tropical savanna also known as Sahel savanna is found in this period represents about 13% of the total livestock the northern part of the state, where rainfall is less than population in the north-eastern part of Nigeria (Khalil 500 mm per annum. The vegetation consists of thorn 1974). Moreover, in the year 1987, towards the end of the bushes and small trees which grow under dry conditions. growing season, huge crop losses to drought were re- Mixed combrataceous woodland covers the central part corded in many parts of northern Nigeria, particularly, in of the study area. It has more rainfall than the wooded the Sahel region (Mortimore 1989). tropical savanna and it consists of scrub vegetation inter- Studies on the impact of drought have been undertaken spersed with tall trees (Nwaka, 1991). The wooded sa- in various parts of the world (Oladipo 1993; Obaje et al., vanna covers most of the southern part of the state, with 2013; Wu et al., 2015a, 2015b;Olagunju 2015; Hosseiniza- a higher grass growth. It is also known as northern deh et al. 2015;Umet al. 2017;LIU et al. 2018;Yue etal. guinea savanna. The main trees are the isoberlina spe- 2018). Majority of the studies concentrated on the impact cies. Administratively, there are seventeen Local Govern- of drought on agriculture, health, water resources, causes ment Areas (LGAs) in Yobe State. The semi-arid zone in and effects on the land, mitigation and the extent of the study comprises of the Sudan and the Sahel regions, drought. Little or no work has been done in the study area which by its nature and characteristics are susceptible to to determine quantitatively drought occurrences and its im- drought and desertification. These zones are most vul- plications on the households using Normalized Rainfall nerable to drought and desertification because the pre- Index (NRI), which has much to offer in terms of policy de- dominant economic activity of the people in the cisions. Oladipo (1993) argues that the degree of impact of communities is farming (Musa and Shaib 2010). a particular drought on the environment and the extent to which it may affect the households depends on the length Study design of the period separating the drought event from the previ- The study adopted the survey design. The study popula- ous one. Moreover, Abaje et al. (2013) state that there is a tion consists of 443,375 households in Yobe State (Na- need for appropriate techniques to be used to determine tional Population Commission 2006). Out of this drought occurrence so as to reduce its impact on the envir- number, 400 households were proportionally selected onment. Against this background, the research seek to de- and interviewed based on the relative population of each termine quantitatively the drought occurrences and its of the 17 LGAs using Yamane (1967) sample size selec- implications on the households, in order to provide the tion method (Table 1). basis for the design and mainstreaming of drought adapta- The data utilized for this study were obtained from tion into the development process in the study area. primary and secondary sources. The primary data were obtained through an administered questionnaire and Materials and methods in-depth interviews. Purposive sampling technique was Study area employed to select the key informants and communities Yobe State is on latitudes 10°30’N to 13°25’N and longi- studied while the simple random sampling technique tudes 9°35′E and 12°30′E. It is bounded by the Nigerian was employed to select the households. Ten key Eze Geoenvironmental Disasters (2018) 5:18 Page 3 of 10 Table 1 Sample Size of the Household Respondents in the (Gaussian) distribution. If the absolute value of Z or Z 1 2 Study Area is greater than 1.96, a significant deviation from the nor- Local Govt Number of Households Sample Size of Respondents mal curve is indicated at the 95% confidence level. Area per LGAs per LGA Normalized Rainfall Index (NRI), as defined by Türkes Bade 26,193 24 (1996) was used to show periods of different drought in- Busari 21,435 19 tensities in the study area. This index uses annual rain- fall totals and the standard deviation to indicate the Damaturu 17,212 15 shortage of rainfall in any given year. The Index for a Fika 24,622 23 given station was computed for each year from 1986 to Fune 52,489 52 2017 (Table 4). The Annual rainfall amount (R ) tsy Geidam 31,243 27 expressed in millimeters, Long term mean (L ) and the tm Gujba 22,430 22 Standard deviation (S ) as recorded in Table 3 has been td Gulani 20,396 18 used in calculating the NRI. The classification was based on Türkes (1996) modified version of classification of Jakusko 43,585 39 normalized rainfall index. Karasuwa 20,752 18 NRI =(R − L )/S (3) tsy tm td Machina 12,583 11 Where R = the rainfall total for the station during a tsy Nangere 15,807 17 year (or season). Nguru 29,200 26 L = the long term mean (of the period specified for tm Potiskum 36,341 35 the station) and. S = Standard deviation of the annual (or seasonal) Tarmuwa 16,394 13 td rainfall total for that station. Yunusari 27,478 22 The Table 2 below is the Türkes (1996) modified Yusufari 25,215 19 version of the normalized rainfall index classification. Total 443,375 400 In this very study, the modified classification of NRI Source: National Population Commission was adopted. Data on the annual income from the different occupa- informants were selected for in-depth interviews in tion of the respondents were collected to determine the each LGA giving a total of 170 respondents. The sec- occupation that yielded more income to the people in ondary data utilized were climatic data on rainfall, the study area using one-way analysis of variance from 1986 to 2017. (ANOVA). Hypotheses (H and H ) therefore, were for- o 1 mulated and tested as shown below. Hypotheses for the income generated from various Data analysis occupation The data collected were analysed using descriptive statis- H : There is no significant difference in income gener- tics (standard deviation, the coefficient of variation, ated from the various occupation of the study population skewness and kurtosis), percentages and Analysis of in the study area. Variance (ANOVA). Normality of the annual rainfall H : There is significant difference in income generated series for Nguru, Potiskum, and Maiduguri was tested from the various occupation of the study population in using the standardized coefficients of Skewness (Z1) and the study area. Kurtosis (Z2) statistics as defined by Brazel and Balling (1986). The standardized coefficient of Skewness (Z1) was calculated as: 3=2 Table 2 Modified Classes of NRI Values N < N 3=N 2=N 1=3 Z ¼df Σ ðxi−xÞ g= z Xi−xÞ g e=ð6=NÞ (1) Index Character of Rainfall Vulnerability level i−1 i−1 1.31 or more Very wet Highly vulnerable However, the standardized coefficient of Kurtosis (Z2) 0.86 to 1.30 Moderately wet Vulnerable was determined as 0.51 to 0.85 Mildly wet Slightly vulnerable N N 4=N 2=N 1=2 Z ¼df Σ ðxi−xÞ g=f z ðXi−xÞÞ g e−3=ð24=NÞ (2) 0.50 to −0.50 Near normal Not vulnerable i−1 i−1 −0.51 to − 0.85 Mild drought Slightly vulnerable Where x is the long term mean of x values and N is −0.86 to −1.30 Moderate drought Vulnerable the number of years in the sample. These statistics were −1.31 or less Severe drought Highly vulnerable used to test the null hypothesis that the individual tem- poral samples came from a population with a normal Source: Adapted from Turkes (1996) Eze Geoenvironmental Disasters (2018) 5:18 Page 4 of 10 Table 3 Summarized rainfall statistics for the study area (Maiduguri, Nguru and Potiskum stations) Stations Number of Years Total amount of Rainfall Long term Mean Std deviation Variance Skewness Kurtosis Maiduguri 31 1554.60 50.15 11.47 131.64 0.63 0.16 Nguru 31 1082.90 34.93 6.34 40.19 0.52 −0.87 Potiskum 31 1700.20 54.85 8.30 68.81 −1.01 1.15 Source: Computed by the author Results the Meanwhile the Coefficient of Variation (CV) was Tests for normality of rainfall in the study area considerable (Table 3). The results of the coefficient of Skewness and Kurtosis both show that rainfall series had no significant devi- Normalized rainfall index (NRI) ation from the normal curve (1.96) at 95% confidence NRI = (Rtsy – Ltm)/ Std for Potiskum station for 1986 = level. Therefore, the rainfall sequence was normally dis- 811.4–720/100.2 = 0.91. tributed. The Standard Deviation (Std) decreased below Table 4 Computation of Normalized Rainfall Index (NRI) for the study area (Maiduguri, Nguru and Potiskum Stations) Years NRI for Potiskum Character of rainfall NRI for Nguru Character of rainfall NRI for Maiduguri Character of rainfall 1986 0.91 Moderately wet −0.41 Near normal −0.49 Near normal 1987 −1.36 Severe drought −2.11 Severe drought −1.92 Severe drought 1988 0.38 Near normal 0.14 Near normal 0.45 Near normal 1989 2.05 Very wet −2.27 Severe drought 0.36 Near normal 1990 − 0.37 Near normal 0.15 Near normal −2.81 Severe drought 1991 −1.17 Moderate drought − 1.72 Severe drought − 1.77 Severe drought 1992 − 1.18 Moderate drought 0.54 Mildly wet −0.2 Near normal 1993 −1.45 Severe drought −2.21 Severe drought −1.63 Severe drought 1994 0.68 Mildly wet 2.78 Very wet −2.26 Severe drought 1995 −1.15 Moderate drought −0.77 Mild drought 0.90 Moderately wet 1996 0.26 Near normal 0.99 Moderately wet 0.39 Near normal 1997 0.29 Near normal 1.58 Very wet −0.90 Moderate drought 1998 1.02 Moderately wet 0.72 Mildly wet 0.98 Moderately wet 1999 0.24 Near normal 0.39 Near normal 2.08 Very wet 2000 −1.65 Severe drought −1.6 Severe drought 0.86 Moderately wet 2001 0.55 Mildly wet −0.45 Near normal 1.39 Very wet 2002 −0.29 Near normal 0.58 Mildly wet −2.64 Severe drought 2003 0.22 Near normal 1.01 Moderately wet 0.59 Mildly wet 2004 −2.16 Severe drought −2.84 Severe drought 0.42 Near normal 2005 0.47 Near normal 0.56 Mildly wet 3.18 Very wet 2006 −0.91 Moderate drought −0.14 Near normal −0.75 Mild drought 2007 −0.48 Near normal 0.83 Mildly wet 1.42 Very wet 2008 1.02 Moderately wet −1.94 Severe drought −0.44 Near normal 2009 −0.54 Mild drought −1.02 Moderate drought −0.25 Near normal 2010 −1.53 Severe drought 0.88 Moderately wet −3.38 Severe drought 2011 −0.53 Mild drought −0.98 Moderate drought 0.35 Near normal 2012 1.02 Moderately wet 0.87 Moderately wet 1.10 Moderately wet 2013 −0.13 Near normal −0.63 Mild drought −1.47 Severe drought 2014 0.90 Moderately wet 0.61 Mildly wet −0.19 Near normal 2015 −0.33 Near normal −1.21 Moderate drought −0.57 Mild drought 2016 0.57 Mildly wet 0.69 Mildly wet 0.53 Mildly wet 2017 −1.27 Moderate drought −0.97 Moderate drought −1.19 Moderate drought Source: Computed by the Author using Microsoft Excel Eze Geoenvironmental Disasters (2018) 5:18 Page 5 of 10 Fig. 1 Normalized rainfall index for Maiduguri station (Data source: Nigerian Meteorological Agency Abuja) The NRI calculations for the rest of the stations per parts of the study area (Potiskum station), severe year followed the same procedure. The results are shown droughts occurred in 1987, 1993, 2000 and 2010, with in Table 4. moderate droughts that occurred in 1991, 1992, 1995, 2006 and 2017, while mild droughts occurred in 2009 The incidence of droughts in the study area and 2011(Fig. 3). The results on the Normalized Rainfall Index analysis in the study area are also presented graphically in Figs. (1– Economic activities of the households in the study area 3) for Maiduguri, Nguru and Potiskum stations. The re- The survey on the economic activities of the households sults show that the study area experiences mild to severe reveals that the study population is involved in five drought events. Severe drought conditions occurred in major economic activities that influence their livelihood the northeastern and central parts of the study area security (Table 5). The result shows that 65% of the total (Maiduguri station) in 1987, 1990, 1991, 1993, 1994, household respondents were involved in farming as their 2002, 2010 and 2013 with a moderate drought that oc- major source of livelihood. However, 35% of the total curred in 1997 and 2017, including mild droughts of population was involved in non-farming activities such 2006 and 2015 (Fig. 1). In the Northwestern part of the as trading (15%), civil service (11%), artisan (6%) and study area (Nguru station), severe droughts occurred in fishing (3%). Table 5 also shows the percentage varia- 1987, 1989, 1991, 1993, 2000, 2004 and 2008 with mod- tions in household respondents involved in different erate droughts in 2009, 2011, 2015 and 2017 and also economic activities in the seventeen LGAs in the study mild droughts in 1995 and 2013 (Fig. 2). In the southern area. The result shows that Bade, Damaturu, Nguru, Eze Geoenvironmental Disasters (2018) 5:18 Page 6 of 10 Fig. 2 Normalized rainfall index for Nguru station (Data source: Nigerian Meteorological Agency Abuja) Potiskum, Fika, Gujba and Gulani have 56%, 77%, 81%, is greater than p-value at 0.05 level of significance, Ho 71%, 44%, 46%, 49% of the total household respondents was rejected and H1 was accepted. Thus, there is a sta- that were engaged in non-farming activities respectively. tistically significant difference in the income of the re- Moreover, Karasuwa, Jakusko, Machina, Yunusari, Yusu- spondents from different occupations in the study area. fari, Busari, Fune, Nangere, Geidam and Tarmuwa have This implies that occupation with higher mean income 28%, 26%, 27%, 27%, 21%, 27%, 28%, 30%, 30% and 28% level provides more opportunities to the people for in- of the total household respondents that were engaged in come generation. non-farming activities respectively. Discussion Income status of the respondents in relation to their The impact of droughts on the environment and human economic activities comfort in the study area The result on the mean annual income of the respon- The incidences of drought in the study area indicate dents generated from five different occupations was that, out of 32 years under study, only 12 years were free farming (N121, 450), Trading (N58, 741), Civil service from drought. The first (1986–1995) and third (2006– (N33, 000), Fishing (N28, 064) and Artisan (N28, 737) 2017) decades experienced very high incidences of (Table 6). However, the analysis of variance to determine droughts, particularly mild to severe droughts, thus which occupation that yielded more income to the making the decades the driest period. However, the sec- people of Yobe State produced an F-ratio of 13.77 and ond decade (1996–2005), witnessed the least incidences p-value of .00001 (Table 7). Since the calculated F-ratio of droughts, thus making the period relatively wet Eze Geoenvironmental Disasters (2018) 5:18 Page 7 of 10 Fig. 3 Normalized rainfall index for Potiskum station (Data source: Nigerian Meteorological Agency Abuja) compared to other decades. Some of the droughts that opportunities for income generation. Apart from trading occurred during the period of study were localized, while with few manufactured goods, most of the industries in other incidences of droughts affected the entire study the study area use the raw materials sourced locally es- area with serious negative consequences on the vital life pecially from farm produce. Therefore, the livelihood of support systems (rivers, wetlands and rangelands) that the households living in the study area predominantly provide means of livelihood to the households. depends on agriculture. Consequently, poor regions of However, the researcher observed that the respondents the world, such as Africa, which depends on agriculture, were predominantly involved in 5 major economic activ- have been described as one of the most vulnerable re- ities. The greater percentage of the total respondents gions to the impacts of climatic and environmental was engaged in farming activities (65%) than changes, particularly drought, desertification and flood non-farming activities (35%) for the purpose of generat- (Reid and Vogel 2008, Tschakert 2007). Thus, in-depth ing income which was used to enhance their livelihood. interviews with the key informants show that water scar- The study observed that most respondents from other city due to the occurrence of droughts affects the agri- employment such as trading, civil service, artisan and cultural outputs in the study area. There were food fishing activities were also involved in farming. More- shortages resulting from an abnormal reduction in crop over, the analysis of variance on the economic activities yield due to droughts. Irrigation projects which would that generated more income to the households in the have served to mitigate these problems were also af- study area shows that farming activities provided more fected by water shortages as most of the dams dried up Eze Geoenvironmental Disasters (2018) 5:18 Page 8 of 10 Table 5 Major economic activities of the respondents in the during droughts, thus, exacerbates the impact of droughts study area on the environment and human comfort. Abaje et al. LGA’s Farming Trading Civil Service Artisan Fishing (2013) state that during drought periods, the land is under increased stress from both human beings and livestock, Bade 11 (44%) 5 (22%) 2 (8%) 4 (16%) 2 (10%) through unsustainable agricultural practices, leading to in- Busari 14 (73%) 2 (12%) 1 (4%) 1 (5%) 1 (6%) creasing desertification (Oyekale 2009; Eze et al. 2018). Damaturu 4 (23%) 3 (20%) 6 (40%) 2 (17%) 0 (0%) Mortimore et al. (2009) have shown that overgrazing be- Fika 13 (56%) 6 (26%) 2 (10%) 2 (8%) 0 (0%) comes destructive during drought, when large areas that Fune 37 (72%) 4 (8%) 3 (6%) 5 (10%) 2 (4%) would normally have been available for grazing dry up, an- Geidam 19 (70%) 3 (12%) 1 (5%) 1 (4%) 2 (9%) imals are forced to feed on any available edible vegetation they could find. This may be harsh enough to cause severe Gujba 12 (54%) 3 (15%) 3 (13%) 2 (7%) 2 (11%) damage to the environment, particularly in the study area. Gulani 9 (51%) 5 (23%) 2 (12%) 1 (8%) 1 (6%) Nyong et al. (2003) argue that once the unsafe balance of Jakusko 29 (74%) 4 (10%) 3 (8%) 2 (5%) 1 (3%) the plant communities adapted to the characteristically Karasuwa 13 (72%) 3 (15%) 1 (7%) 1 (6%) 0 (0%) variable climate is upset by persistent drought, complete Machina 8 (73%) 1 (10%) 1 (9%) 1 (8%) 0 (0%) ecological recovery may be impossible, even when the Nangere 12 (70%) 3 (18%) 2 (7%) 1 (5%) 0 (0%) rains return leading to severe desertification. The increasing drought occurrences in the study area, Nguru 5 (19%) 9 (35%) 6 (23%) 4 (15%) 2 (8%) therefore, have great implications for the household liveli- Potiskum 10 (29%) 15 (43%) 6 (17%) 4 (11%) 0 (0%) hoods. The occurrence of mild drought results in a reduc- Tarmuwa 9 (72%) 1 (9%) 2 (12%) 1 (5%) 0 (2%) tion in crop yield and cattle weight loss, whereas the Yunusari 16 (73%) 2 (9%) 2 (8%) 1 (4%) 1 (5%) occurrence of severe drought results in total crop loss, in Yusufari 15 (79%) 2 (10%) 1 (5%) 0 (6%) 0 (0%) increased mortality rates of livestock (Abaje et al. 2013; Total 260 59 44 24 13 Wu et al. 2015a, 2015b). The reduction in crop yield and livestock loss affects revenue generation and household % 651511 6 3 income. Consequently, about 65% of the total respondents Source: Fieldwork 2017 depends primarily on farming, thus increasing drought oc- currences affects household revenue generation and food Table 6 Annual Income of the Respondents from different availability in the study area. Eze et al. (2018) argue that Occupations households whose livelihood depends on farming suffer LGA’s Farming Trading Civil service Fishing Artisan losses during drought because the crop yield and livestock Bade 111,650 56,000 34,000 29,564 37,135 production are reduced and also, the weight and strength of cattle for the purpose of draught power is drastically re- Busari 53,770 27,250 25,000 30,560 27,000 duced. Moreover, the frequent incidences of droughts re- Damaturu 85,670 167,450 51,000 46,553 sulted to the lowering of water table and sustenance of Fika 149,450 45,325 30,000 26,972 few rivers. Thus, developmental projects that depend on Fune 119,630 32,745 26,000 21,126 water from rivers and groundwater sources suffer a great Geidam 79,820 27,000 27,000 32,675 23,000 setback during and after drought. The lowering of water Gujba 193,240 69,000 36,000 24,530 31,704 table has a negative effect on the construction of wells and boreholes. This is because the depth of the water table in- Gulani 188,467 57,000 32,000 28,225 30,567 creases, and may not be reached depending on a place, Jakusko 75,550 26,125 26,000 17,500 thus reducing water availability for the household uses, Karasuwa 69,450 36,000 28,000 19,350 particularly those that depend on surface and groundwater Machina 82,650 31,000 31,000 21,235 sources. Therefore, dependency on agriculture increases Nangere 128,725 34,000 34,000 31,075 the vulnerability of the households to drought. Moreover, Nguru 162,770 139,765 48,000 39,435 46,600 persistent and substantial reduction in the provision of ecosystem services as a result of the incidence of droughts Potiskum 356,540 156,000 49,000 42,750 possesses great threats to agricultural productivity. Tarmuwa 94,950 42,000 33,000 22,840 Yunusari 57,670 27,815 27,000 21,645 23,450 Recommendations Yusufari 54,650 24,115 24,000 17,875 19,670 To address the impact of drought on households, food Mean 121,450 58,741 33,000 28,064 28,737 systems have to become more efficient and resilient (Eze STD 75,290 47,572 8551 6724 9465 et al. 2018). Moreover, climate-smart practices aim to Source: Fieldwork 2017 improve food security, help communities adapt to Eze Geoenvironmental Disasters (2018) 5:18 Page 9 of 10 Table 7 Analysis of variance of income of respondents from different occupations in Yobe state Source of Variation Sum of Squares Degree of Freedom (DF) Mean Sum of Squares F-ratio P-Value Between Groups 100,736,213,073 4 25,184,053,268 13.77243 .00001 Within Groups 129,829,514,017 71 1,828,584,704 Total 230,565,727,090 73 Significant at 0.05 confidence level (Source: Fieldwork 2017) drought and contribute to drought mitigation by adapt- diversification and increasing the adaptive capacity of ing to appropriate practices, developing enabling policies households to drought in the study area. Although the and mobilizing needed finances. Therefore, we recom- results of this study indicate the specific features of a mend the short and long-term measures to protect the State, future research should focus on a national level, households against the disaster and problems of drought which is highly aggregated. More capacity and work is through the following: needed particularly at the national level to assess the ex- tent of drought and its impact on the households. (i) Establishment of irrigation system: Irrigation system Finally, the study has successfully used Normalized (Tube-well) will help households that are Rainfall Index and Analysis of variance to determine the dependent on farming to cultivate and harvest extent of drought occurrences and its implications on crops during drought or shortfall in rainfall amount. the households, which has much to offer in terms of pol- This can be done by providing tube-well for every icy decisions. registered farmer in the state, by the government. Acknowledgements (ii) Use of improved crop varieties: Making early I am sincerely indebted to my lecturers in the Department of Geography, maturing and drought resistant crops available and University of Nigeria Nsukka, for exposing me to a wider range of knowledge, provided me with relevant materials and their helpful comments affordable will enable farmers to cultivate and during my PhD programme. harvest crops within a short period, while drought- resistant crops should be able to survive and grow Funding with little water available in the soil. The new im- This research was not supported by any government or non-governmental organization. It was self-sponsored and supported by family members. proved crop varieties enhance the reduction of drought impacts on farmers. Availability of data and materials (iii)Livelihood diversification: Government should The datasets used and analysed during the current study are available from create economic activities that will generate non- the corresponding author. Thus, it can be made available on reasonable request. farm employment to reduce the impact of drought on the households. These could be achieved Author’s contributions through finance and technical assistance such as The whole research work (design of the study, data collection, analysis, loans and capacity building. When finance and interpretation and writing the manuscript) were carried out by the author alone. The author read and approved the final manuscript. technical assistance are given to the households, it could motivate them to venture into small and Competing interests medium scale businesses such as skill acquisition, The author declares that he has no competing interests. (such as production of local briquettes, tailoring, cake making, trades and others, which on its own Publisher’sNote will generate employment Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Conclusion Received: 30 June 2018 Accepted: 9 October 2018 In conclusion, there are high incidences of drought in the study area. The frequent occurrence of droughts has References impacted negatively on the environment and human Abaje, I.B., O.F. Ati, E.O. Iguisi, and G.G. Jidauna. 2013. Droughts in the Sudano- comfort. However, the predominant households in the Sahelian Ecological Zone of Nigeria: Implications for Agriculture and Water study area are highly dependent on agriculture. Conse- Resources Development. Global Journal of Human Social Science 13 (2): 12–23. Anyadike, R.N.C. 1993. 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Eze Geoenvironmental Disasters (2018) 5:18 Page 10 of 10 ch/pdf/technical-papers/climate-change-water-en.pdf - 28/doc13.pdf Tiamiyu, S.A., J.N. Eze, T.M. Yusuf, A.T. Maji, and S.O. Bakare. 2015. Rainfall (accessed: June, 2018). variability and its effect on yield of Rice in Nigeria. International Letters of Brazel, S.W., and R.C. Balling. 1986. Temporal analysis of long-term atmospheric Natural Sciences 49: 63–68. moisture levels in Phoenix, Arizona. Journal of Climate and Applied Tschakert, P. 2007. Views from the vulnerable: Understanding climatic and other Meteorology 25: 112–117. stressors in the Sahel. Global Environmental Change 17: 381–396. Türkes, M. 1996. Meteorological drought in Turkey: A historical perspective, 1930. Dai, A., P.J. Lamb, K.E. Trenberth, P. Hulme, D. Jones, and P. Xie. 2004. The recent 93. Drought Network News 8 (3): 17–21. Sahel drought is real. International Journal of Climatology 24: 1323–1331. Um, M., Y. Kim, D. Park, and J. Kim. 2017. 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Mortimore, M.J. 1989. Adapting to drought: Farmers. In Famines and desertification in West Africa. Cambridge: Cambridge University Press. Musa, H.D., and B. Shaib. 2010. Integrated remote sensing approach to desertification monitoring in the crop-rangeland area of Yobe State, Nigeria. Journal of Sustainable Development in Africa. 12 (5): 236–250. National Population Commission. 2006. Data for National Planning and Development. Population by class-size of households. Abuja: National Population Commission. http://population.gov.ng/wp-content/themes/ expo18/documents/Pr%20Vol%209%20Size%20of%20Household.zip. Nwaka, G.I. 1991. Pedogenesis and Soil Resources. In Gadzama, N.M. et al. (eds). Arid Zone. Hydrology and water Resources. University of Maiduguri press. 235–262. Nyong, A., A. Adepetu, V. Ihemegbulem, and D. Dabi. 2003. Vulnerability of rural households to drought in northern Nigeria. Assessment of impacts and adaptation to climate change (AIACC) Notes 2 (2): 6–7. O'Connor, T.G. 1995. Transformation of a savanna grassland by drought and grazing. African Journal of Range and Forage Science. 12 (2): 53–60. Oladipo, E.O. 1993. Some aspects of the spatial characteristics of drought in northern Nigeria. Natural Hazards. 8: 171–188. Olagunju, T.E. 2015. Drought, desertification and the Nigerian environment: A review. Journal of Ecology and the Natural Environment 7 (7): 196–209. Oyekale, A.S. 2009. Climatic variability and its impacts on agricultural income and households’ welfare in southern and northern Nigeria. Electronic Journal of Environmental, Agricultural and Food Chemistry. 8 (7): 443–465. Reid, P., and C. Vogel. 2008. Living and responding to multiple stressors in South Africa: Glimpses from Kwazulu-Natal. Global Environmental Change 16: 195–206. Shiru, M.S., S. Shahid, N. Alias, and E.S. Chung. 2018. Trend analysis of droughts during crop growing seasons of Nigeria. Sustainability 10 (871): 1–13. https:// doi.org/10.3390/su10030871. Tarhule, A., and M.-K. Woo. 1997. Towards an interpretation of historical droughts in northern Nigeria. Climatic Change 37: 601–616. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geoenvironmental Disasters Springer Journals

Drought occurrences and its implications on the households in Yobe state, Nigeria

Geoenvironmental Disasters , Volume 5 (1): 10 – Dec 1, 2018

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Abstract

The study assesses the extent of droughts and its implications on the households in the study area. This is to highlight the need to integrate drought adaptation options into the government development plans. Strategies for drought adaptation options in the study area have often been made without experimental foundations placed on the extent of drought and its implications on the households. To achieve this, the study employed Normalized Rainfall Index (NRI) to determine the extent of droughts and its implications on the households, which has much to offer in terms of policy decisions. The study also utilized questionnaire administrated to 400 households to determine the annual income from different occupations that yielded more income to the people in the study area using one-way analysis of variance (ANOVA). The NRI shows that the study area was characterized by mild to severe drought events. The first (1986–1995) and third (2006–2017) decades experienced high incidences of droughts, while the second decade (1996–2005), witnessed the least incidences of droughts. The result of the economic activities of the households reveals that 65% of the total household respondents were involved in farming, while 35% were involved in non-farming activities as their major source of livelihood. The analysis of variance on the economic activities that generated more income to the households in Yobe State shows that farming activities provided more opportunities for income generation. Consequently, agriculture in the study area is currently being constrained by the frequent occurrence of droughts. Thus, there is a need for integrated development schemes aimed at livelihood diversification and increasing the adaptive capacity of households to drought in the study area. Keywords: Drought, Desertification, Vulnerability, Normalized rainfall index Background National Aeronautics and Space Administration (NASA) Drought is one of the most globally recognized hazards shows that about 900,000 km2 of former savanna grass- that damage an environment. It occurs when there is land in the region of Africa has been severely decertified significant rainfall deficit that causes hydrological imbal- between the early 1960s and 1986 due to persistent ances and affects the land productive systems. Drought drought occurrences (O'Connor 1995). Moreover, Bates practically occurs in all climatic regions with both high et al. (2008) state that one-third of African population and low mean rainfalls (Um et al. 2017). It can result in lives in drought-prone areas. The drought has become a damage to agricultural production as well as to the nat- recurrent event in many parts of Africa, after the ural environment and human society (Gidey et al. 2018). drought of the early 1970s that devastated the Sahel re- Drought is considered to be a natural disaster that dif- gion. Dai et al. (2004) have shown that there is about fers from other natural disasters because it has a gradual 40% decline in annual rainfall total in West Africa, from creeping feature (Ayoade 1988; Yue et al. 2018). Liu et the year 1968–1990 as compared with the 30 years be- al., (2018) opine that drought develops slowly with pro- tween 1931 and 1960. Thus, frequent drought occur- longed effects that gradually increases in severity and rences are threatening the human existence in African tends to persist over a long period of time even after it savanna regions and consequently making the house- has stopped. Analysis of the orbital photographs from holds highly vulnerable to drought. In Northern Nigeria, there are several records of drought occurrences which resulted in famines, such as Correspondence: zejude12@gmail.com Farming System Research Programme, National Cereals Research Institute, the droughts of 1903 and 1911–1914 (Shiru et al. 2018). Badeggi P.M.B 8, Bida, Niger State, Nigeria © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Eze Geoenvironmental Disasters (2018) 5:18 Page 2 of 10 Other droughts occurred in 1919, 1924, 1935 (Tarhule States of Gombe to the southwest, Bauchi to the west, and Woo 1997), 1951–1954, 1972–1973, 1984–1985 Jigawa to the northwest, Borno to the east, and to the (Mortimore 1989), 2007 and 2011 (Abaje et al. 2013). North by the Niger Republic. The study area has a popu- Consequently, the situation is being exacerbated by the in- lation of 2,532,395, according to 2006 census, and a land creasing human population, overgrazing, over-cultivation, mass of 47,153 km . The density is thus 29.92 persons and high poverty rate (Eze 2017). The predominant eco- per square kilometre. nomic activity in the region is farming. Moreover, the re- The climate regime of the study area is characterized gion is known for its grain products such as maize, millet, by single long dry season followed by a shorter wet sea- sorghum, and wheat (Eze 2017). Agricultural production son. In the Sahel, mean annual rainfall is less than in Nigeria particularly in Northern Nigeria is mainly 500 mm and may fall to 250 mm (Ayoade 1988). In the rain-fed and naturally prone to vagaries of rainfall variabil- Sudan savanna, it is 800 mm to 1000 mm. The number ity (Tiamiyu et al. 2015). During the years of abundant of wet months is about 4 months in the northern guinea rainfall, households in the region experience bumper har- savanna zone to less than 2 months in the Sahel savanna vest from both crop and livestock productions. Con- zone (Folland et al., 1986; Anyadike, 1993). Mean annual versely, during the years with rainfall deficit, the temperature is 26oC increasing towards the Sahel zone. households experience crop failure, poor crop yield, lead- Potential evapo-transpiration exceeds rainfall except for ing to famine, hunger, and death, including livestock (Eze the few months. Vegetation of the study area is divided et al. 2018). Khalil (1974) shows that about 300, 000 live- into the Wooded Tropical savanna, Mixed combratac- stock died during the drought that occurred in the year eous woodland and wooded savannah. The Wooded 1972 to 1973. The number of livestock that died during tropical savanna also known as Sahel savanna is found in this period represents about 13% of the total livestock the northern part of the state, where rainfall is less than population in the north-eastern part of Nigeria (Khalil 500 mm per annum. The vegetation consists of thorn 1974). Moreover, in the year 1987, towards the end of the bushes and small trees which grow under dry conditions. growing season, huge crop losses to drought were re- Mixed combrataceous woodland covers the central part corded in many parts of northern Nigeria, particularly, in of the study area. It has more rainfall than the wooded the Sahel region (Mortimore 1989). tropical savanna and it consists of scrub vegetation inter- Studies on the impact of drought have been undertaken spersed with tall trees (Nwaka, 1991). The wooded sa- in various parts of the world (Oladipo 1993; Obaje et al., vanna covers most of the southern part of the state, with 2013; Wu et al., 2015a, 2015b;Olagunju 2015; Hosseiniza- a higher grass growth. It is also known as northern deh et al. 2015;Umet al. 2017;LIU et al. 2018;Yue etal. guinea savanna. The main trees are the isoberlina spe- 2018). Majority of the studies concentrated on the impact cies. Administratively, there are seventeen Local Govern- of drought on agriculture, health, water resources, causes ment Areas (LGAs) in Yobe State. The semi-arid zone in and effects on the land, mitigation and the extent of the study comprises of the Sudan and the Sahel regions, drought. Little or no work has been done in the study area which by its nature and characteristics are susceptible to to determine quantitatively drought occurrences and its im- drought and desertification. These zones are most vul- plications on the households using Normalized Rainfall nerable to drought and desertification because the pre- Index (NRI), which has much to offer in terms of policy de- dominant economic activity of the people in the cisions. Oladipo (1993) argues that the degree of impact of communities is farming (Musa and Shaib 2010). a particular drought on the environment and the extent to which it may affect the households depends on the length Study design of the period separating the drought event from the previ- The study adopted the survey design. The study popula- ous one. Moreover, Abaje et al. (2013) state that there is a tion consists of 443,375 households in Yobe State (Na- need for appropriate techniques to be used to determine tional Population Commission 2006). Out of this drought occurrence so as to reduce its impact on the envir- number, 400 households were proportionally selected onment. Against this background, the research seek to de- and interviewed based on the relative population of each termine quantitatively the drought occurrences and its of the 17 LGAs using Yamane (1967) sample size selec- implications on the households, in order to provide the tion method (Table 1). basis for the design and mainstreaming of drought adapta- The data utilized for this study were obtained from tion into the development process in the study area. primary and secondary sources. The primary data were obtained through an administered questionnaire and Materials and methods in-depth interviews. Purposive sampling technique was Study area employed to select the key informants and communities Yobe State is on latitudes 10°30’N to 13°25’N and longi- studied while the simple random sampling technique tudes 9°35′E and 12°30′E. It is bounded by the Nigerian was employed to select the households. Ten key Eze Geoenvironmental Disasters (2018) 5:18 Page 3 of 10 Table 1 Sample Size of the Household Respondents in the (Gaussian) distribution. If the absolute value of Z or Z 1 2 Study Area is greater than 1.96, a significant deviation from the nor- Local Govt Number of Households Sample Size of Respondents mal curve is indicated at the 95% confidence level. Area per LGAs per LGA Normalized Rainfall Index (NRI), as defined by Türkes Bade 26,193 24 (1996) was used to show periods of different drought in- Busari 21,435 19 tensities in the study area. This index uses annual rain- fall totals and the standard deviation to indicate the Damaturu 17,212 15 shortage of rainfall in any given year. The Index for a Fika 24,622 23 given station was computed for each year from 1986 to Fune 52,489 52 2017 (Table 4). The Annual rainfall amount (R ) tsy Geidam 31,243 27 expressed in millimeters, Long term mean (L ) and the tm Gujba 22,430 22 Standard deviation (S ) as recorded in Table 3 has been td Gulani 20,396 18 used in calculating the NRI. The classification was based on Türkes (1996) modified version of classification of Jakusko 43,585 39 normalized rainfall index. Karasuwa 20,752 18 NRI =(R − L )/S (3) tsy tm td Machina 12,583 11 Where R = the rainfall total for the station during a tsy Nangere 15,807 17 year (or season). Nguru 29,200 26 L = the long term mean (of the period specified for tm Potiskum 36,341 35 the station) and. S = Standard deviation of the annual (or seasonal) Tarmuwa 16,394 13 td rainfall total for that station. Yunusari 27,478 22 The Table 2 below is the Türkes (1996) modified Yusufari 25,215 19 version of the normalized rainfall index classification. Total 443,375 400 In this very study, the modified classification of NRI Source: National Population Commission was adopted. Data on the annual income from the different occupa- informants were selected for in-depth interviews in tion of the respondents were collected to determine the each LGA giving a total of 170 respondents. The sec- occupation that yielded more income to the people in ondary data utilized were climatic data on rainfall, the study area using one-way analysis of variance from 1986 to 2017. (ANOVA). Hypotheses (H and H ) therefore, were for- o 1 mulated and tested as shown below. Hypotheses for the income generated from various Data analysis occupation The data collected were analysed using descriptive statis- H : There is no significant difference in income gener- tics (standard deviation, the coefficient of variation, ated from the various occupation of the study population skewness and kurtosis), percentages and Analysis of in the study area. Variance (ANOVA). Normality of the annual rainfall H : There is significant difference in income generated series for Nguru, Potiskum, and Maiduguri was tested from the various occupation of the study population in using the standardized coefficients of Skewness (Z1) and the study area. Kurtosis (Z2) statistics as defined by Brazel and Balling (1986). The standardized coefficient of Skewness (Z1) was calculated as: 3=2 Table 2 Modified Classes of NRI Values N < N 3=N 2=N 1=3 Z ¼df Σ ðxi−xÞ g= z Xi−xÞ g e=ð6=NÞ (1) Index Character of Rainfall Vulnerability level i−1 i−1 1.31 or more Very wet Highly vulnerable However, the standardized coefficient of Kurtosis (Z2) 0.86 to 1.30 Moderately wet Vulnerable was determined as 0.51 to 0.85 Mildly wet Slightly vulnerable N N 4=N 2=N 1=2 Z ¼df Σ ðxi−xÞ g=f z ðXi−xÞÞ g e−3=ð24=NÞ (2) 0.50 to −0.50 Near normal Not vulnerable i−1 i−1 −0.51 to − 0.85 Mild drought Slightly vulnerable Where x is the long term mean of x values and N is −0.86 to −1.30 Moderate drought Vulnerable the number of years in the sample. These statistics were −1.31 or less Severe drought Highly vulnerable used to test the null hypothesis that the individual tem- poral samples came from a population with a normal Source: Adapted from Turkes (1996) Eze Geoenvironmental Disasters (2018) 5:18 Page 4 of 10 Table 3 Summarized rainfall statistics for the study area (Maiduguri, Nguru and Potiskum stations) Stations Number of Years Total amount of Rainfall Long term Mean Std deviation Variance Skewness Kurtosis Maiduguri 31 1554.60 50.15 11.47 131.64 0.63 0.16 Nguru 31 1082.90 34.93 6.34 40.19 0.52 −0.87 Potiskum 31 1700.20 54.85 8.30 68.81 −1.01 1.15 Source: Computed by the author Results the Meanwhile the Coefficient of Variation (CV) was Tests for normality of rainfall in the study area considerable (Table 3). The results of the coefficient of Skewness and Kurtosis both show that rainfall series had no significant devi- Normalized rainfall index (NRI) ation from the normal curve (1.96) at 95% confidence NRI = (Rtsy – Ltm)/ Std for Potiskum station for 1986 = level. Therefore, the rainfall sequence was normally dis- 811.4–720/100.2 = 0.91. tributed. The Standard Deviation (Std) decreased below Table 4 Computation of Normalized Rainfall Index (NRI) for the study area (Maiduguri, Nguru and Potiskum Stations) Years NRI for Potiskum Character of rainfall NRI for Nguru Character of rainfall NRI for Maiduguri Character of rainfall 1986 0.91 Moderately wet −0.41 Near normal −0.49 Near normal 1987 −1.36 Severe drought −2.11 Severe drought −1.92 Severe drought 1988 0.38 Near normal 0.14 Near normal 0.45 Near normal 1989 2.05 Very wet −2.27 Severe drought 0.36 Near normal 1990 − 0.37 Near normal 0.15 Near normal −2.81 Severe drought 1991 −1.17 Moderate drought − 1.72 Severe drought − 1.77 Severe drought 1992 − 1.18 Moderate drought 0.54 Mildly wet −0.2 Near normal 1993 −1.45 Severe drought −2.21 Severe drought −1.63 Severe drought 1994 0.68 Mildly wet 2.78 Very wet −2.26 Severe drought 1995 −1.15 Moderate drought −0.77 Mild drought 0.90 Moderately wet 1996 0.26 Near normal 0.99 Moderately wet 0.39 Near normal 1997 0.29 Near normal 1.58 Very wet −0.90 Moderate drought 1998 1.02 Moderately wet 0.72 Mildly wet 0.98 Moderately wet 1999 0.24 Near normal 0.39 Near normal 2.08 Very wet 2000 −1.65 Severe drought −1.6 Severe drought 0.86 Moderately wet 2001 0.55 Mildly wet −0.45 Near normal 1.39 Very wet 2002 −0.29 Near normal 0.58 Mildly wet −2.64 Severe drought 2003 0.22 Near normal 1.01 Moderately wet 0.59 Mildly wet 2004 −2.16 Severe drought −2.84 Severe drought 0.42 Near normal 2005 0.47 Near normal 0.56 Mildly wet 3.18 Very wet 2006 −0.91 Moderate drought −0.14 Near normal −0.75 Mild drought 2007 −0.48 Near normal 0.83 Mildly wet 1.42 Very wet 2008 1.02 Moderately wet −1.94 Severe drought −0.44 Near normal 2009 −0.54 Mild drought −1.02 Moderate drought −0.25 Near normal 2010 −1.53 Severe drought 0.88 Moderately wet −3.38 Severe drought 2011 −0.53 Mild drought −0.98 Moderate drought 0.35 Near normal 2012 1.02 Moderately wet 0.87 Moderately wet 1.10 Moderately wet 2013 −0.13 Near normal −0.63 Mild drought −1.47 Severe drought 2014 0.90 Moderately wet 0.61 Mildly wet −0.19 Near normal 2015 −0.33 Near normal −1.21 Moderate drought −0.57 Mild drought 2016 0.57 Mildly wet 0.69 Mildly wet 0.53 Mildly wet 2017 −1.27 Moderate drought −0.97 Moderate drought −1.19 Moderate drought Source: Computed by the Author using Microsoft Excel Eze Geoenvironmental Disasters (2018) 5:18 Page 5 of 10 Fig. 1 Normalized rainfall index for Maiduguri station (Data source: Nigerian Meteorological Agency Abuja) The NRI calculations for the rest of the stations per parts of the study area (Potiskum station), severe year followed the same procedure. The results are shown droughts occurred in 1987, 1993, 2000 and 2010, with in Table 4. moderate droughts that occurred in 1991, 1992, 1995, 2006 and 2017, while mild droughts occurred in 2009 The incidence of droughts in the study area and 2011(Fig. 3). The results on the Normalized Rainfall Index analysis in the study area are also presented graphically in Figs. (1– Economic activities of the households in the study area 3) for Maiduguri, Nguru and Potiskum stations. The re- The survey on the economic activities of the households sults show that the study area experiences mild to severe reveals that the study population is involved in five drought events. Severe drought conditions occurred in major economic activities that influence their livelihood the northeastern and central parts of the study area security (Table 5). The result shows that 65% of the total (Maiduguri station) in 1987, 1990, 1991, 1993, 1994, household respondents were involved in farming as their 2002, 2010 and 2013 with a moderate drought that oc- major source of livelihood. However, 35% of the total curred in 1997 and 2017, including mild droughts of population was involved in non-farming activities such 2006 and 2015 (Fig. 1). In the Northwestern part of the as trading (15%), civil service (11%), artisan (6%) and study area (Nguru station), severe droughts occurred in fishing (3%). Table 5 also shows the percentage varia- 1987, 1989, 1991, 1993, 2000, 2004 and 2008 with mod- tions in household respondents involved in different erate droughts in 2009, 2011, 2015 and 2017 and also economic activities in the seventeen LGAs in the study mild droughts in 1995 and 2013 (Fig. 2). In the southern area. The result shows that Bade, Damaturu, Nguru, Eze Geoenvironmental Disasters (2018) 5:18 Page 6 of 10 Fig. 2 Normalized rainfall index for Nguru station (Data source: Nigerian Meteorological Agency Abuja) Potiskum, Fika, Gujba and Gulani have 56%, 77%, 81%, is greater than p-value at 0.05 level of significance, Ho 71%, 44%, 46%, 49% of the total household respondents was rejected and H1 was accepted. Thus, there is a sta- that were engaged in non-farming activities respectively. tistically significant difference in the income of the re- Moreover, Karasuwa, Jakusko, Machina, Yunusari, Yusu- spondents from different occupations in the study area. fari, Busari, Fune, Nangere, Geidam and Tarmuwa have This implies that occupation with higher mean income 28%, 26%, 27%, 27%, 21%, 27%, 28%, 30%, 30% and 28% level provides more opportunities to the people for in- of the total household respondents that were engaged in come generation. non-farming activities respectively. Discussion Income status of the respondents in relation to their The impact of droughts on the environment and human economic activities comfort in the study area The result on the mean annual income of the respon- The incidences of drought in the study area indicate dents generated from five different occupations was that, out of 32 years under study, only 12 years were free farming (N121, 450), Trading (N58, 741), Civil service from drought. The first (1986–1995) and third (2006– (N33, 000), Fishing (N28, 064) and Artisan (N28, 737) 2017) decades experienced very high incidences of (Table 6). However, the analysis of variance to determine droughts, particularly mild to severe droughts, thus which occupation that yielded more income to the making the decades the driest period. However, the sec- people of Yobe State produced an F-ratio of 13.77 and ond decade (1996–2005), witnessed the least incidences p-value of .00001 (Table 7). Since the calculated F-ratio of droughts, thus making the period relatively wet Eze Geoenvironmental Disasters (2018) 5:18 Page 7 of 10 Fig. 3 Normalized rainfall index for Potiskum station (Data source: Nigerian Meteorological Agency Abuja) compared to other decades. Some of the droughts that opportunities for income generation. Apart from trading occurred during the period of study were localized, while with few manufactured goods, most of the industries in other incidences of droughts affected the entire study the study area use the raw materials sourced locally es- area with serious negative consequences on the vital life pecially from farm produce. Therefore, the livelihood of support systems (rivers, wetlands and rangelands) that the households living in the study area predominantly provide means of livelihood to the households. depends on agriculture. Consequently, poor regions of However, the researcher observed that the respondents the world, such as Africa, which depends on agriculture, were predominantly involved in 5 major economic activ- have been described as one of the most vulnerable re- ities. The greater percentage of the total respondents gions to the impacts of climatic and environmental was engaged in farming activities (65%) than changes, particularly drought, desertification and flood non-farming activities (35%) for the purpose of generat- (Reid and Vogel 2008, Tschakert 2007). Thus, in-depth ing income which was used to enhance their livelihood. interviews with the key informants show that water scar- The study observed that most respondents from other city due to the occurrence of droughts affects the agri- employment such as trading, civil service, artisan and cultural outputs in the study area. There were food fishing activities were also involved in farming. More- shortages resulting from an abnormal reduction in crop over, the analysis of variance on the economic activities yield due to droughts. Irrigation projects which would that generated more income to the households in the have served to mitigate these problems were also af- study area shows that farming activities provided more fected by water shortages as most of the dams dried up Eze Geoenvironmental Disasters (2018) 5:18 Page 8 of 10 Table 5 Major economic activities of the respondents in the during droughts, thus, exacerbates the impact of droughts study area on the environment and human comfort. Abaje et al. LGA’s Farming Trading Civil Service Artisan Fishing (2013) state that during drought periods, the land is under increased stress from both human beings and livestock, Bade 11 (44%) 5 (22%) 2 (8%) 4 (16%) 2 (10%) through unsustainable agricultural practices, leading to in- Busari 14 (73%) 2 (12%) 1 (4%) 1 (5%) 1 (6%) creasing desertification (Oyekale 2009; Eze et al. 2018). Damaturu 4 (23%) 3 (20%) 6 (40%) 2 (17%) 0 (0%) Mortimore et al. (2009) have shown that overgrazing be- Fika 13 (56%) 6 (26%) 2 (10%) 2 (8%) 0 (0%) comes destructive during drought, when large areas that Fune 37 (72%) 4 (8%) 3 (6%) 5 (10%) 2 (4%) would normally have been available for grazing dry up, an- Geidam 19 (70%) 3 (12%) 1 (5%) 1 (4%) 2 (9%) imals are forced to feed on any available edible vegetation they could find. This may be harsh enough to cause severe Gujba 12 (54%) 3 (15%) 3 (13%) 2 (7%) 2 (11%) damage to the environment, particularly in the study area. Gulani 9 (51%) 5 (23%) 2 (12%) 1 (8%) 1 (6%) Nyong et al. (2003) argue that once the unsafe balance of Jakusko 29 (74%) 4 (10%) 3 (8%) 2 (5%) 1 (3%) the plant communities adapted to the characteristically Karasuwa 13 (72%) 3 (15%) 1 (7%) 1 (6%) 0 (0%) variable climate is upset by persistent drought, complete Machina 8 (73%) 1 (10%) 1 (9%) 1 (8%) 0 (0%) ecological recovery may be impossible, even when the Nangere 12 (70%) 3 (18%) 2 (7%) 1 (5%) 0 (0%) rains return leading to severe desertification. The increasing drought occurrences in the study area, Nguru 5 (19%) 9 (35%) 6 (23%) 4 (15%) 2 (8%) therefore, have great implications for the household liveli- Potiskum 10 (29%) 15 (43%) 6 (17%) 4 (11%) 0 (0%) hoods. The occurrence of mild drought results in a reduc- Tarmuwa 9 (72%) 1 (9%) 2 (12%) 1 (5%) 0 (2%) tion in crop yield and cattle weight loss, whereas the Yunusari 16 (73%) 2 (9%) 2 (8%) 1 (4%) 1 (5%) occurrence of severe drought results in total crop loss, in Yusufari 15 (79%) 2 (10%) 1 (5%) 0 (6%) 0 (0%) increased mortality rates of livestock (Abaje et al. 2013; Total 260 59 44 24 13 Wu et al. 2015a, 2015b). The reduction in crop yield and livestock loss affects revenue generation and household % 651511 6 3 income. Consequently, about 65% of the total respondents Source: Fieldwork 2017 depends primarily on farming, thus increasing drought oc- currences affects household revenue generation and food Table 6 Annual Income of the Respondents from different availability in the study area. Eze et al. (2018) argue that Occupations households whose livelihood depends on farming suffer LGA’s Farming Trading Civil service Fishing Artisan losses during drought because the crop yield and livestock Bade 111,650 56,000 34,000 29,564 37,135 production are reduced and also, the weight and strength of cattle for the purpose of draught power is drastically re- Busari 53,770 27,250 25,000 30,560 27,000 duced. Moreover, the frequent incidences of droughts re- Damaturu 85,670 167,450 51,000 46,553 sulted to the lowering of water table and sustenance of Fika 149,450 45,325 30,000 26,972 few rivers. Thus, developmental projects that depend on Fune 119,630 32,745 26,000 21,126 water from rivers and groundwater sources suffer a great Geidam 79,820 27,000 27,000 32,675 23,000 setback during and after drought. The lowering of water Gujba 193,240 69,000 36,000 24,530 31,704 table has a negative effect on the construction of wells and boreholes. This is because the depth of the water table in- Gulani 188,467 57,000 32,000 28,225 30,567 creases, and may not be reached depending on a place, Jakusko 75,550 26,125 26,000 17,500 thus reducing water availability for the household uses, Karasuwa 69,450 36,000 28,000 19,350 particularly those that depend on surface and groundwater Machina 82,650 31,000 31,000 21,235 sources. Therefore, dependency on agriculture increases Nangere 128,725 34,000 34,000 31,075 the vulnerability of the households to drought. Moreover, Nguru 162,770 139,765 48,000 39,435 46,600 persistent and substantial reduction in the provision of ecosystem services as a result of the incidence of droughts Potiskum 356,540 156,000 49,000 42,750 possesses great threats to agricultural productivity. Tarmuwa 94,950 42,000 33,000 22,840 Yunusari 57,670 27,815 27,000 21,645 23,450 Recommendations Yusufari 54,650 24,115 24,000 17,875 19,670 To address the impact of drought on households, food Mean 121,450 58,741 33,000 28,064 28,737 systems have to become more efficient and resilient (Eze STD 75,290 47,572 8551 6724 9465 et al. 2018). Moreover, climate-smart practices aim to Source: Fieldwork 2017 improve food security, help communities adapt to Eze Geoenvironmental Disasters (2018) 5:18 Page 9 of 10 Table 7 Analysis of variance of income of respondents from different occupations in Yobe state Source of Variation Sum of Squares Degree of Freedom (DF) Mean Sum of Squares F-ratio P-Value Between Groups 100,736,213,073 4 25,184,053,268 13.77243 .00001 Within Groups 129,829,514,017 71 1,828,584,704 Total 230,565,727,090 73 Significant at 0.05 confidence level (Source: Fieldwork 2017) drought and contribute to drought mitigation by adapt- diversification and increasing the adaptive capacity of ing to appropriate practices, developing enabling policies households to drought in the study area. Although the and mobilizing needed finances. Therefore, we recom- results of this study indicate the specific features of a mend the short and long-term measures to protect the State, future research should focus on a national level, households against the disaster and problems of drought which is highly aggregated. More capacity and work is through the following: needed particularly at the national level to assess the ex- tent of drought and its impact on the households. (i) Establishment of irrigation system: Irrigation system Finally, the study has successfully used Normalized (Tube-well) will help households that are Rainfall Index and Analysis of variance to determine the dependent on farming to cultivate and harvest extent of drought occurrences and its implications on crops during drought or shortfall in rainfall amount. the households, which has much to offer in terms of pol- This can be done by providing tube-well for every icy decisions. registered farmer in the state, by the government. Acknowledgements (ii) Use of improved crop varieties: Making early I am sincerely indebted to my lecturers in the Department of Geography, maturing and drought resistant crops available and University of Nigeria Nsukka, for exposing me to a wider range of knowledge, provided me with relevant materials and their helpful comments affordable will enable farmers to cultivate and during my PhD programme. harvest crops within a short period, while drought- resistant crops should be able to survive and grow Funding with little water available in the soil. The new im- This research was not supported by any government or non-governmental organization. It was self-sponsored and supported by family members. proved crop varieties enhance the reduction of drought impacts on farmers. Availability of data and materials (iii)Livelihood diversification: Government should The datasets used and analysed during the current study are available from create economic activities that will generate non- the corresponding author. Thus, it can be made available on reasonable request. farm employment to reduce the impact of drought on the households. These could be achieved Author’s contributions through finance and technical assistance such as The whole research work (design of the study, data collection, analysis, loans and capacity building. When finance and interpretation and writing the manuscript) were carried out by the author alone. The author read and approved the final manuscript. technical assistance are given to the households, it could motivate them to venture into small and Competing interests medium scale businesses such as skill acquisition, The author declares that he has no competing interests. 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Journal

Geoenvironmental DisastersSpringer Journals

Published: Dec 1, 2018

Keywords: Environment, general; Earth Sciences, general; Geography, general; Geoecology/Natural Processes; Natural Hazards; Environmental Science and Engineering

References