How Do Climate and Nonclimatic Variables Influence the Production of Agricultural Staple Crops in Vulnerable Rural Communities in the Bawku Municipality of Northern Ghana?

Rashida Ayumah; Felix Asante; Lawrence Guodaar; Gabriel Eshun

doi:10.1155/2020/6484019

How Do Climate and Nonclimatic Variables Influence the Production of Agricultural Staple Crops in Vulnerable Rural Communities in the Bawku Municipality of Northern Ghana?

Ayumah, Rashida;Asante, Felix;Guodaar, Lawrence;Eshun, Gabriel 2020-05-20 00:00:00 Hindawi Advances in Agriculture Volume 2020, Article ID 6484019, 13 pages https://doi.org/10.1155/2020/6484019 Research Article How Do Climate and Nonclimatic Variables Influence the Production of Agricultural Staple Crops in Vulnerable Rural Communities in the Bawku Municipality of Northern Ghana? 1 1 2 1 Rashida Ayumah, Felix Asante , Lawrence Guodaar, and Gabriel Eshun Kwame Nkrumah University of Science and Technology, Geography and Rural Development, College of Humanities and Social Sciences, Faculty of Social Sciences, PMB, University Post Oﬃce, Kumasi, Ghana University of Adelaide, Geography, Environment and Population, Faculty of Arts, School of Social Sciences, Napier, Adelaide, SA 5005, Australia Correspondence should be addressed to Felix Asante; couzon_species@yahoo.com Received 20 September 2019; Accepted 22 April 2020; Published 20 May 2020 Academic Editor: Ga´bor Kocsy Copyright © 2020 Rashida Ayumah et al. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We examined the inﬂuence of climate (temperature and rainfall) and nonclimatic variables (soil fertility using soil pH and organic matter) on the production of agricultural staple crops (maize [Zea mays L.], millet [Pennisetum glaucum L.], and rice [Oryza sativa L.]) in vulnerable communities in the Bawku Municipality of northern Ghana. Using ﬁve selected farming communities as study sites, multiple datasets were obtained from primary and secondary sources. Participatory approaches together with questionnaires were used as data collection tools to quantify and qualify climate (temperature and rainfall) and nonclimatic variables (soil fertility using soil pH and organic matter) and crop production. )e Mann–Kendall trend test results indicate a signiﬁcant variation in annual rainfall for the 15-year period (1999 to 2013) with a relatively stable mean temperature variation in the Municipality. )e results of the multiple regression indicate that climatic and nonclimatic factors, particularly rainfall, soil pH, and organic matter have a signiﬁcant positive eﬀect on maize, millet, and rice when other factors are held constant. We conclude that to ease the burden of climate on production, better irrigation facilities be provided for the Municipality and weather forecasting information on the pending growing season be made available to farmers to enable them take informed decision. Also, policy on climate adaptation should take into account the interaction of external drivers of climate and nonclimatic variables to better build farmers’ resilience for food security at the local level. Low precipitation considerably aﬀects agricultural sys- 1.Introduction tems of many SSA countries [12]. Many staple crops have )e dynamic oscillation of the Earth’s climate is a reality. )e suﬀered drastic declines which consequently aﬀected the livelihood of smallholder farmers in many parts of SSA variation in the climate system across spatial scales is what is termed as climate variability (henceforth CV) [1]. Under including Ghana [13]. )e big question is how susceptible global warming, it is projected that climate variables in- food crop production might be to the interaction of climate cluding temperature and rainfall will vary with dispropor- and nonclimatic variables? Scholarship on how speciﬁc tional eﬀects on agriculture [2–4]. )e complexity of CV crops are adversely inﬂuenced by CV is abound in many remains a cardinal challenge for sustainable agriculture and parts of the world. For instance, Osbourne and Wheeler [9] food security [4–11]. Regionally, Sub-Saharan Africa illu- indicate that, globally, between the years 1960 and 2009, rice minates high level of vulnerability to food insecurity due to yields declined considerably due to low rainfall. Scientiﬁc the considerable impacts CV has on agricultural systems evidence suggests that, in many agricultural landscapes, crop which remain the “oxygen” that supports the economies. yields are expected to fall between 10% and 20% because of 2 Advances in Agriculture Extensive research on CV risks on crops in Ghana has increased CV risks [14]. In a related analysis, the IPCC estimated that 34 million to over 600 million are likely to indicated the threats CV poses to agricultural crops [23, 24, 28]. However, little information is available in the suﬀer from starvation by 2080 [15]. )us, it is increasingly relevant to ascertain the dynamics of how climate variables literature on how climate and nonclimatic predictors, par- interact to inﬂuence crop production in order to develop ticularly temperature, rainfall, soil pH, and organic matter strategic policies that will be sustainable for food security interact to dynamically inﬂuence the production of agri- [16]. Apart from rainfall, high temperature is another cli- cultural staple crops in vulnerable rural communities in mate determinant of many agricultural crops [4]. It is es- northern Ghana. )e challenge is, therefore, to investigate timated that for each 1 C rise in average temperature, the and ﬁll this knowledge gap for a good policy link aimed at income of farmers will potentially reduce by about 10% [5]. eﬀective adaptative capacity building to mitigate the inﬂu- )is may likely be intensiﬁed in many )ird World countries ence of climatic and nonclimatic risks on staple crop pro- duction. Speciﬁcally, this paper aims to quantify and qualify due to their limited ability to adapt. Consequently, the economic hardship of farmers could intensify as a result of how climate and nonclimatic variables inﬂuence the pro- duction of agricultural staple crops with emphasis on maize the impacts on livelihoods [17]. )e myriad of impacts according to the IPCC [1] would be experienced diﬀerently (Zea mays L.), millet (Pennisetum glaucum L.), and rice across the continent due to other socioeconomic challenges. (Oryza sativa L.) cultivated in many vulnerable rural Nonclimatic factors are also recognized as important communities in the Bawku Municipality of northern Ghana. factors that inﬂuence crop yield [18, 19]. Technological and management factors such as irrigation, crop varieties, 2. Materials and Methods and fertilizer application are suggested as important nonclimatic factors that can directly or indirectly inﬂu- 2.1. Study Site. )e Bawku Municipality is located in the ence crop production, particularly maize [20]. Soil fertility extreme north-eastern part of Ghana and is one of the two is argued as an important nonclimatic driver that can Municipalities in the Upper East Region of Ghana (Figure 1). 1 1 ° ° considerably aﬀect crop productivity, especially grains It lies between latitudes 10 40 and 11 11 north of the 1 1 ° ° [19]. However, the consideration for nonclimatic variables Equator and longitudes 0 18 west and 0 6 east of the and their concomitant risks on agricultural systems, Greenwich Meridian. )e Bawku municipality has a total land particularly crop production is largely given little atten- size of about 247.23720 km . )e municipality is bounded to tion [21]. It is suggested that the interface of climate and the north by Burkina Faso, to the south by the Garu-Tempane nonclimatic factors are disproportional in terms of their District, to the west by Binduri District, and to the east by impacts on crop production [22]. )us, exploring how Pusiga District [29]. )e Municipality is located within the these variables inﬂuence diﬀerent staple crops is impor- Sudan Savanna zone with variable rainfall pattern and tant, particularly in SSA and Ghana where many people unfavourable soil characteristics to support agriculture. depend on agricultural crop production for their )e research covered ﬁve communities in the Bawku livelihood. Municipality. )ese are Mognori, Kuka, Gosezi, Zabugu, Ghana has not been exempted from the phenomenon of and Gentiga communities. )e choice on the study sites was CV. )is is because of the level of exposure and sensitivity of informed by the vulnerability of the Municipality to CV and agricultural systems to CV in the country. Rainfall patterns staple insecurity. have shifted, coupled with protracted drought spells and increased temperature conditions [23]. )ese manifestations are considerably conspicuous in the northern sector with 2.2. Data Collection. We employed both qualitative and more expected future variations [6]. )is situation will quantitative data in the study for a comprehensive under- signiﬁcantly constrain agricultural systems in many parts of standing of the issues. )e two datasets were obtained from Ghana, particularly the northern sector [8, 24]. Since the primary and secondary sources using questionnaires, focus agricultural sector employs a large proportion of the people group discussions (FGDs), and structured interview pro- of Ghana, any uncertainties due to CV could, therefore, tocols. Questionnaires were administered in a face-to-face cause great devastation to the economy of the country. )e manner to food crop farmers. Focus Group Discussions were inadequate rainfall in 1982–1983 is a testament to how carried out in each community to generate conversations drought destroyed most crops and negatively aﬀected more that uncover individual opinions regarding the eﬀects of CV than 12 million people in the country [25]. Furthermore, in on food crop. Structured interviews were employed to obtain 2007, northern Ghana was wrecked by marauding ﬂood additional information from oﬃcials of the Ministry of Food waters that aﬀected three hundred and seventeen thousand and Agriculture (MoFA), Savanna Agriculture Research (317,000) people with the Central Gonja District being Institute (SARI), for their direct contact with farmers in the described as an environmental calamity; because, about Municipality and the Ghana Meteorological Agency (GMA). twenty-six thousand eight hundred and twenty-two (26,822) )e purpose was to explain and verify the ﬁndings from the acres of farmlands were destroyed [26]. Crop production questionnaires. )e study relied on documented records of data for staples such as maize (Zea mays L.), millet (Pen- monthly observations of climate variables compiled by the nisetum glaucum L.), and rice (Oryza sativa L.) in Northern GMA for a period of 15 years. )e use of a 15-year data was Ghana have been declining since the year 2010/2011 due to considered as adequate because consistent collation of data low rainfall [27]. over periods of 15 years or more according to Hochman et al. Advances in Agriculture 3 2 0 2 Miles District capital District boundary Towns Study area Road network Figure 1: Map of Bawku Municipality showing the study communities (source: Bawku Municipal Assembly (2015)). [30] provides a good basis for crop yield analysis in relation where e � deviation of sampling, N � population size and to CV. )e climate variables used for the study were tem- n � sample size. perature and rainfall [1, 5]. Production data on key food )e model has a conﬁdence level of 95% and a 5% error crops; maize (Zea mays L.), millet (Pennisetum glaucum L.), margin [31]. )e sample frame of the study consisted of a and rice (Oryza sativa L.) in the study area from 1999 to 2013 master list of household farmers of the ﬁve communities were also obtained from MoFA in Bawku for analysis. In obtained from the Planning Department of the Bawku addition, data on soil fertility in the Bawku Municipality Municipal Assembly. A total of 214 household farmers were over the period under investigation were obtained from the randomly sampled for the survey across the study com- Soil Research Institute (SRI) in Kumasi. To assess which soil munities (Table 1). fertility variables are important in explaining the variations on crop yield, a correlation analysis was performed on staple 2.3. Data Analysis. )e quantitative data obtained from the crops and soil fertility variables (soil pH, organic matter, farmers were processed and analysed using descriptive nitrogen, and phosphorus). Organic matter was found to be statistics. Frequency tables, cross-tabulations, bar graphs, correlated with maize and millet while soil pH was correlated and pie charts were used as tools for representing the survey with rice. Soil pH was therefore used as a proxy for soil data. Also, time series analysis was used to examine the trend fertility in rice production and organic matter as a proxy for in annual maximum and minimum temperatures as well as soil fertility in millet and maize production. rainfall over the 15-year period (1999 to 2013) with the help )e study participants were sampled simple randomly of MAKESENS Excel Software. )e magnitude of the trends and purposively. )e random sample of the survey partic- of the climate variables was tested by the Mann–Kendall ipants were based on the model proposed by Kasiulevicius ˇ trend test. )e trend was quantiﬁed using Sen’s slope et al. [31]: formula: x − x n � , (1) j k Q � , i � 1, 2, 3, . . . , N, j> k, (2) 1 + N(e) j − k 4 Advances in Agriculture Table 1: Sample size of selected communities. )e qualitative data recorded from the focus group discussions and structured interviews were analysed the- Household Sample size for Community matically based on diﬀerent responses and used as vital population communities elements of written text to better understand how farmers Mognori 61 51 perceived the eﬀect of climate variability on food crop Zabugu 66 55 production. Gozesi 52 47 Kuka 35 31 Gentiga 33 30 3. Results and Discussion Total 247 142 3.1. Rainfall Variation and Trend. Rainfall amount and Source: GSS, 2010. timing inﬂuence the yield of crops. Low rainfall amounts can be detrimental to crop yield, particularly if dry periods occur during the critical stages of development of the crop [8]. )e where x and x values are time j and k, respectively. )e j k total annual amount of rainfall and the trend over the 15- median of these N values of Q is Sen’s estimator of the slope. year period in the Bawku Municipality are displayed in Also, the variability of rainfall, temperature, and food crop Figure 2. )e mean annual total rainfall from 1999–2013 is production was analysed using the coeﬃcient of variation. 901.9 mm. )is was determined by dividing the standard deviation by )e total annual rainfall for the period ranged from a the mean. low of 217.9 mm in 2008 to a high of almost 1376 mm in A multiple regression model (Ordinary Least Square 2007. Generally, the Mann–Kendall trend test of the procedure) was used to analyse the inﬂuence of climatic rainfall dataset from 1999 to 2013 shows that at 5% sig- (temperature and rainfall) and nonclimatic (soil pH and niﬁcance level, the trend is not statistically signiﬁcant. organic matter) factors on three major food crops, maize Sen’s estimate (−26.567) indicates a decreasing trend (Zea mays L.), millet (Pennisetum glaucum L.), and rice (Figure 2). )is is congruent to the ﬁndings of Asante and (Oryza sativa L.), over the 15-year period (1999 to 2013) by Amuakwa-Mensah [36] whose study of climate change holding all other confounding factors constant through the and variability in Ghana reported decreasing trends of help of the Predictive Analytic Software (PASW) version 21. total rainfall amounts in northern Ghana. )e variability )e multiple regression model was used due to the fact that it in the year to year rainfall particularly towards the de- has been widely used in analyzing the eﬀects/impacts of creasing trend is a cautioning sign to the farming com- climate variability/change on food crop production [7, 32]. munities in the Municipality as this may adversely aﬀect To ensure the robustness and validity of the estimates of the their livelihoods. multiple regression model, the underpinning assumption of From Figure 3, the years 2005, 2008, 2010, and 2011 had a normal distributions, homoscedasticity, and serially un- negative deviation signifying meteorological drought (pe- correlated errors were tested using Jacque–Bera, riod of below average precipitation) periods with the worst Breusch–Pagan–Godfrey test, and Breusch–Godfrey Serial drought occurring in 2008. Global record of rainfall indi- Correlation LM Test (Antonakis and Deitz, 2011 cited in cates a substantially high rainfall in 2005 and 2010 that Ballance) [33]. )e level of signiﬁcance for the study was set resulted in severe ﬂoods in many parts of the world [37]. )e at 0.05. )e linear regression model used is similar to the positive deviation in 1999 and 2007 according to Paeth et al. model used by Onoja and Ajie [34] to analyse how food [38] is attributable to the El Niño-Southern Oscillation crops respond to climate variability and microeconomic (ENSO) eﬀects which caused much rainfall in Sub Sahara policies reform in Nigeria. )e model is presented as Africa. )is implies that severe ﬂoods might have occurred Y � βo + β X + β X + β X + · · · + β X + µi, (3) 1 i 2 i 3 i k in 1999 and 2007 in the Bawku Municipality. Report by the Daily Graphic online [39] that ﬂoods occurred in the Upper where Y is the dependent variable; X is the independent West, Upper East, and the Northern Regions of Ghana variable while µ is the stochastic error term, and βo is the conﬁrms possibility of such earlier events. )is may have intercept of the model. Natural logarithms of the variables aﬀected agricultural activities in the Municipality. )e es- were taken to strengthen out exponential growth pattern and timated annual anomaly of rainfall (Figure 3) indicates that reduce the potential heterogeneity of variance of error terms; the total amount of annual rainfall varied substantially from that is, to stabilize variance [35]. year to year. )is conﬁrms the ﬁndings of Amikuzuno and Donkoh [23] that the prevailing evidence in the rainfall LnRice � βo + β ln RAi + β ln TEi + β ln SPHi + µ , i 1 2 3 i pattern observed in the semiarid regions of Africa is highly LnMillet � βo + β ln RAi + β ln TEi + β ln ORGi + µ , i 1 2 3 i variable. LnMaize � βo + β ln RAi + β ln TEi + β ln ORGi + µ , With regard to farmers’ observations, all the respondents i 1 2 3 i claim to have observed rainfall variation in the 15 years (4) spanning 1999 to 2013. Approximately, 37% of farmers were where RA � annual rainfall in millimeters in the Bawku of the view that the amount of rainfall has reduced. )irty- Municipality; TE � mean annual temperature in C in the ﬁve of the respondents representing 16.4% reported that the Bawku Municipality; SPH � soil pH; ORG � organic matter; length of the rainy season had reduced. Interestingly, 28.5% µ � stochastic error term; and βo � intercept of the model. and 18.2% noticed an irregularity in the amount of rainfall Advances in Agriculture 5 1500.00 Q = –26.567 1000.00 500.00 0.00 1998 2000 2002 2004 2006 2008 2010 2012 2014 Data Sen’s estimate Figure 2: Total annual rainfall trend in the Bawku Municipality in the past 15 years. 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 –500 –1000 Figure 3: Annual rainfall deviations in the Bawku Municipality (1999–2013). and a reduction in how long the rainy season lasts re- However, the Mann–Kendall trend statistics for mean an- spectively (Table 2). nual minimum temperature at 5% signiﬁcance level was not Similarly, key informants from SARI, MoFA, and GMA statistically signiﬁcant. had also noted some pronounced variations in the rainfall )e mean minimum temperature for 1999 (22.7 C) was pattern. According to the key informants, the rains either found to be the same as the mean value (22.7 C) for the 15- come earlier or later than expected. )is was supported by a year period, showing no change for 1999. However, the years male farmer in Gentiga who pointed out in a focus group 2002 to 2006 and 2010 illustrate a signiﬁcant increase in the discussion that minimum temperature with a positive deviation of between ° ° 0.2 C and 0.9 C above the baseline average (Figure 5). )e (i) “When we were young, our wells and rivers were full in rest of the years recorded negative deviations with the the rainy season which enabled us to get water in the highest decrease of 1.53 C observed in 2012. )is signiﬁcant dry season, drawing water from wells was not diﬃcult interannual variation in minimum temperature may have but nowadays our wells and rivers have little water aﬀected crop production in the Municipality. )is is because even in the rainy season... ;e rains do not fall as they a decrease in minimum temperatures aﬀects night time plant used to. My biggest worry is its unpredictable nature” respiration rate and possibly reduces crop yield [40]; (FGD, 2014) meanwhile, when plants are exposed to higher minimum Generally, it was observed that respondents were much temperatures, it decreases their ability to grow and also perturbed by the abnormality of the rainfall pattern in the reduces crop yield [41]. Municipality which sometimes made it diﬃcult to accurately predict when to start planting. )e results presented show farmers observations are in line with the historical rainfall 3.2.2. Mean Annual Maximum Temperature. )e mean annual maximum temperature varied between 33.6 C and data for the 15-year period. 35.8 C for the 15-year period. )e total mean maximum temperature from 1999–2013 was 35.05 C. )e trend sta- 3.2. Minimum and Maximum Temperature Variation and tistics of the Mann–Kendall test at 5% signiﬁcance level for Trend the mean annual maximum temperature is not statistically signiﬁcant. However, the observed trend of Sen’s estimate 3.2.1. Mean Annual Minimum Temperature. Figure 4 (−0.017) of the Mann–Kendall test for the mean annual presents the mean annual variation/trend in minimum maximum temperature over the 15-year period portrays a temperature in the Bawku Municipality. )e mean annual decreasing trend (Figure 6). minimum temperature from 1999 to 2013 ﬂuctuated be- Notwithstanding the above information, the mean ° ° ° tween 21.2 C and 23.4 C with a mean value of 22.7 C. Sen’s maximum temperatures from 1999 to 2013 show distinctive estimate (−0.027) of the Mann–Kendall test indicates a interannual variation (Figure 6). In general, the maximum signiﬁcant decreasing trend in the mean annual minimum temperature decreased below the mean for the years 2000, temperature (Figure 4) for the 15-year period (1999 to 2013). 2004, 2007, 2008, 2009, 2012, and 2013, indicating that these Rainfall amount (mm) 6 Advances in Agriculture Table 2: Respondents’ observation of the manifestations of rainfall variation. Reduction in the Irregularities in the Reduction in the Irregularities in the length of the rainy length of the rainy Total amount of rainfall amount of rainfall season season Freq % Freq % Freq % Freq % Freq % 79 36.9 35 16.4 61 28.5 39 18.2 142 100 Freq � frequency. 24.00 Q = –0.027 23.00 22.00 21.00 1998 2000 2002 2004 2006 2008 2010 2012 2014 Data Sen’s estimate Figure 4: Mean annual trend in minimum temperature in the Bawku Municipality. 0.5 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 –0.5 –1 –1.5 –2 Figure 5: Mean annual deviation in minimum temperature ( C) in the Bawku Municipality. 36.00 35.50 35.00 34.50 Q = –0.017 34.00 33.50 33.00 1998 2000 2002 2004 2006 2008 2010 2012 2014 Data Sen’s estimate Figure 6: Mean annual trend in maximum temperature in the Bawku Municipality. years were relatively cooler. )e highest decrease occurred in other hand, the year 2005 was marked as the warmest year ° ° 2004 with a decrease of 1.6 C below the baseline average. with a positive deviation of 0.8 C, which was greater than the )is conﬁrms the report by Asante and Amuakwa-Mensah global record of 0.62 C. In addition, the mean annual de- [36] that very cold winds were experienced in 2004. On the viation (Figure 7) shows more warm years than cold years Maximum temperature (°C) Minimum temperature (°C) Advances in Agriculture 7 0.5 –0.5 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 –1 –1.5 –2 Figure 7: Mean annual deviation in maximum temperature ( C) in the Bawku Municipality. for the 15-year period. )is has the tendency to cause 76.1% ﬂuctuations in crop output in the Municipality. )ough, there are signiﬁcant discrepancies in the interannual min- 10.3% imum and maximum temperature in the Municipality, the coeﬃcient of variation (0.0155) for the mean annual tem- Increase in Sometimes high and temperature other times low perature shows slight variation. )is conﬁrms the ﬁndings of Amikuzuno and Donkoh [23] that Northern Ghana has not Figure 8: Respondents’ opinion on the manifestations of tem- signiﬁcantly experienced notable temperature variation. perature variation. Superimposing the annual mean maximum temperature on the annual rainfall deviation for the 15-year period shows ﬁve years, there was a signiﬁcant variation in the output of that eﬀective rainfall for food crop production in 2005, 2010, the food crops. and 2011 was very low. )is is because total annual rainfall )e low level of production in 2013, according to the for these years was below the baseline average while the Municipal Director of MoFA, was as a result of the carving temperature was high. Low annual rainfall coupled with high out of a new district (Binduri District) from the Munici- temperature on one hand results in poor organic matter pality. He explained that the splitting of the Bawku Mu- content, making soil suitability for cropping one of the nicipality has reduced the production level of food crop since major problems in crop production [42]. )is is likely to most food crop farmers are now part of the Binduri District. have had negative consequences on food crop production. However, the year 2010 marked the worst season for maize On the other hand, the high rainfall coupled with low and rice farmers when output levels fell tremendously. )is maximum temperature in 2007 and 2012 suggests that ef- is most likely due to the high maximum temperature and a fective rainfall for these years were high. )is may have low amount of rainfall recorded in 2010 [8, 44, 45]. Not- beneﬁted some crops. High temperature and high rainfall withstanding, the output of millet increased in 2010. Given were recorded in 1999 and 2003. According to Shakoor et al. that millet is drought-tolerant and grown as a famine crop [43], high temperature and high rainfall are beneﬁcial for all [8, 45], most farmers may have shifted to the cultivation of tropical crops but will produce negative eﬀects if these millet when rainfall was expected to be below average in climatic variables are increased too much in the future. 2010. )is reﬂects the assertion by a key informant from With respect to respondents’ observations, the results SARI that millet production has almost replaced maize in the indicate that most farmers (86.4%) had observed tempera- Municipality due to the observed short and erratic rainfall ture variation in the last 15 years. Majority of the farmers pattern. )is may have contributed to the increased millet (76.1%) who observed temperature variation believed that production in 2010. )e variation in maize (CV � 0.4598), temperature has been increasing for the past 15 years millet (CV � 0.5653), and rice (CV � 0.3860) for the 15-year (Figure 8), whereas 10.3% observed irregularities in period could have implications for food security and live- temperature. lihoods of farmers. )ough most farmers asserted that temperatures have increased, the observed mean annual temperature for the Municipality showed a decreasing trend. Farmers’ obser- 3.4. Eﬀect of Climate Variability on Food Crop Production. vations are therefore at variance with the observed trend. We assessed and quantiﬁed the eﬀects of climate variability Perhaps, the daily and monthly temperatures observed by on food crop production using a multiple regression model. the farmers were signiﬁcant enough for them to notice some )e multiple regression results of the residual diagnostic test increase in temperature. It is clear from the results that of assumptions are presented in Table 3. farmers are aware of the temperature variation of the )e residual diagnosis for the test for heteroscedasticity Municipality through their own experiences of the past. for rice, maize, and millet using the Breusch–Pagan–Godfrey test did not show any heteroscedasticity at 5% signiﬁcance level; thus, the variance of the error term is constant. 3.3. Variation in Crop Yield (1999–2013). Figure 9 shows variation in the output ﬁgures of the three major food crops )e result of the serial correlation in determining the autocorrelation shows that there is no correlation among the grown in the Municipality from 1999 to 2013. )e ﬁrst ﬁve years of the study period depict high production for the residuals in the regression model for maize (Zea mays L.), millet (Pennisetum glaucum L.), and rice (Oryza sativa L.). major food crops as compared to the second ﬁve years of the study period. Even though production was high for the ﬁrst )e test for normality using Jarque–Bera statistics also Percentage 8 Advances in Agriculture 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Maize Millet Rice Figure 9: Major food crop production in the Bawku Municipality. Table 3: Diagnostic test statistics for food crop production. Rice Maize Millet Diagnosis Test-statistics Probability Test-statistics Probability Test-statistics Probability Heteroscedasticity 3.31184 0.3460 0.4810 0.2140 5.8669 0.2109 Autocorrelation 0.61641 0.4324 0.0869 0.7682 2.0130 0.1560 Normality 3. 3790 0.1846 0.9865 0.6106 2.8627 0.2389 indicates that the residuals are normally distributed. )e Table 4: Regression statistics for rice production. results of the diagnostic test for rice, maize, and millet Standard T- production, therefore, satisﬁed the assumptions of the Variable Coeﬃcient Probability error statistics multiple regression model. )is implies that there are no Constant −7.619448 21.26389 21.26389 0.7269 problems that will signiﬁcantly aﬀect the regression results LnaverageTemp 1.077336 1.077336 0.170053 0.8681 for rice, maize, and millet production. LnRainfall 0.323560 0.232461 1.391894 0.1915 Lnsoil-Ph 6.644933 2.564687 2.590934 0.0251 R squared � 0.5838, adjusted R squared � 0.4704, and F-statistics � 5.1452 3.4.1. Eﬀect of Climate Variability on Rice (Oryza sativa L.) (p< 0.0182). Regression is signiﬁcant at the 0.05 level. Production. )e results of the log-linear regression model used for estimating the inﬂuence of climatic (mean annual temperature and annual rainfall) and nonclimatic (soil pH) Table 5: Summary statistics of climate variables and major food variables on rice production is presented in Table 4. crop production. )e coeﬃcient of determination (R � 0.5838) of the results shows that approximately 58.4% of the variation in Standard Coeﬃcient of Variables Mean the log of rice is explained by the log of average temperature, deviation variation annual rainfall, and soil fertility. )e remaining percentage Rainfall 901.92 301.50 0.33429 (41.6%) could be attributed to other factors such as seed Temperature 28.89 0.45 0.01545 Maize 24880.38 11440.62 0.4598 varieties, method of cultivation, etc. )e insigniﬁcant levels Millet 22172.45 12533.54 0.5653 of temperature and rainfall could be attributed to the low Rice 20979.07 8097.14 0.3860 variation in average temperature (Table 5) during the study period (1999–2013) and use of irrigation for rice production in the Sudan Savanna Zone [24, 46]. However, the result area with low soil pH could have adverse eﬀects on the indicates that soil pH is statistically signiﬁcant at 5% output of rice. From the regression results, it could be (p< 0.0251) and positively inﬂuences rice production. )is conﬁrmed that soil pH rather than rainfall and temperature may be due to the desirable soil pH (5.5 to 6.5) for rice was an inﬂuential factor determining the variation in rice production in the Bawku Municipality. )e result, therefore, yield in the Municipality from 1999 to 2013. implies that a 1% increase in soil pH potentially leads to a 6.6% increase in rice yield holding other variables constant. )e estimation, therefore, shows that rice production in the 3.4.2. Eﬀect of Climate Variability on Maize (Zea mays L.) Municipality for the 15-year period was largely dependent Production. Table 6 presents the results of the multiple on soil pH. )is result is similar to the ﬁndings of Azman regression used for estimating the inﬂuence of annual et al. [47] who observed in their study that relative rice yield rainfall, average temperature, and soil organic matter on is aﬀected by soil pH. )is suggests that growing rice in an maize yield. From the results, the F-statistic is statistically Production metric tones Advances in Agriculture 9 Table 6: Regression statistics for maize production. Table 7: Regression statistics for millet production. Standard T- Standard T- Variable Coeﬃcient Probability Variable Coeﬃcient Probability error statistics error statistics Constant 27.67440 28.20512 0.981184 0.3476 Constant −5.155400 31.74246 −0.162413 0.8739 LnaverageTemp −3.913351 8.010983 −0.488498 0.6348 LnaverageTemp 8.268311 9.015681 0.917103 0.3788 LnRainfall 0.668053 0.271932 2.456692 0.0319 LnRainfall −0.043149 0.306036 −0.140992 0.8904 LnOrganicmatter 1.872949 0.777664 2.408429 0.0347 LnOrganicmatter 2.595624 0.875195 2.965767 0.0128∗ R squared � 0.6091, adjusted R squared � 0.5025, and F-statistics � 5.7141 R squared � 0.5060, adjusted R squared � 0.3712, and F-statistics � 3.7552 (p< 0.0131). Regression is signiﬁcant at the 0.05 level. (p< 0.0444). ∗Regression is signiﬁcant at the 0.05 level. Nigeria and Northern Ghana appear to be insensitive to low signiﬁcant at the 5% level (p< 0.0131). )is means that the rainfall, respectively. )us, a low level of rainfall does not explanatory variables speciﬁed in the maize production necessarily reduce millet production. )e estimation, model are jointly signiﬁcant. therefore, shows that in the Municipality, millet production )e results show that about 60.9% (R � 0.6091) of the is highly dependent on organic matter and not rainfall and variation in the log of maize is explained by the log of temperature which were found to be statistically insigniﬁ- average temperature, annual rainfall, and organic matter. cant in explaining the variation in millet output during the )e remaining percentage (39.1%) could be attributed to 15-year period. other factors such as seed varieties, method of cultivation, etc. )e annual rainfall (p < 0.0319) and organic matter (p< 0.347) at 5% signiﬁcance level are statistically signiﬁcant 3.5. Perceived Eﬀects of Climate Variability on Food Crop and positively contribute to maize production. )e result, Production therefore, implies that a 1% increase in soil organic matter 3.5.1. Eﬀects of Rainfall Variability on Food Crop Production results in a 1.9% increase in maize yield holding other variables constant. )is means that maize production in the as Perceived by the Farmers. To triangulate the secondary data results with primary data, farmers’ experiences were Municipality would be partly inﬂuenced by nonclimatic also obtained (Figure 10). )e results show that all the variables. )e result also shows that every 1% increase in rainfall potentially leads to a 0.7% increase in maize pro- farmers perceived rainfall variability to adversely aﬀect the production of their main staple crops. A 57 years old male duction holding other variables constant. )is also means that as rainfall increases, maize yield also increases. )ese respondent explained that validate the study objective on the inﬂuence of observed (i) “When I started farming, the rains usually came in the climatic trends on food crop production. However, the fourth and ﬁfth months of the year (April/May) which average temperature is not statistically signiﬁcant at 5% was the time for land preparation (tilling) and signiﬁcance level. )us, the estimation shows that maize sometimes for planting. ;is normally supported the production in the Municipality is principally dependent on cultivation of several crops. But these days, the rains rainfall and organic matter. come and go at any time. Sometimes the rains don’t come until the eighth or ninth month of the year making it diﬃcult to plant millet, sorghum, maize, rice 3.4.3. Eﬀect of Climate Variability on Millet (Pennisetum and other crops. In fact, it is not easy to predict when glaucum L.) Production. Table 7 shows the results of the log- the rains will come and stop. I am particularly worried linear regression model used for estimating the inﬂuence of about what will happen to my farming business in the climatic (temperature and rainfall) and nonclimatic (organic future because that is what I depend on” (FGD, 2014). matter) on millet production. )e F-statistics is statistically signiﬁcant at 5% signiﬁ- Statistically, all the respondents (100%) indicated that the cance level (p< 0.0444). )is implies that the explanatory manifestations of rainfall variability have aﬀected the variables speciﬁed in the millet production model, namely, growing season in the past 15 years. Also, nearly all the annual rainfall, average temperature, and organic matter are respondents (93%) were of the view that the variable nature jointly signiﬁcant. From Table 7, about 50.6% (R � 0.5060) of rainfall in the Municipality has reduced the length of the growing season while the remaining respondents (7%) of the variation in the log of millet is explained by the log of average temperature, annual rainfall, and organic matter. observed irregularities in how long the growing season lasts. All the respondents (7%) who observed irregularities in the )e remaining percentage (49.4%) could be attributed to other factors such as seed varieties, method of cultivation, length of the growing season reported that crop yield had etc. )e results further reveal that organic matter is statis- moderately reduced. More than 76% and 16% of those who tically signiﬁcant with a p-value of 0.0128 (Table 7). )e reported of a reduction in the length of the growing season implication is that, holding other variables constant, a 1% also reported that crop yields have severely and moderately increase in organic matter potentially leads to a 2.6% in- reduced, respectively. )e responses diﬀered among the crease in millet yield. )e result corroborates the ﬁndings of study communities. For instance, all the respondents in Gozesi and Gentiga pointed out that the shortened length of Tunde et al. [45] and Amikuzuno and Donkoh [23] who noted that millet production in the Kwara State in Northern the growing season has severely reduced crop yields, whereas 10 Advances in Agriculture 22% 16.4% 14% 20 13% 10.7% 11.2% 3.7% 1.9% Mognori Zabugu Gozesi Kuka Gentiga Severely reduced crop yield Figure 10: Respondents view on the eﬀect of growing season on crop yield. 23.4% 22.9% 15.9% 12.6% 11.7% Mognori Zabugu Gozesi Kuka Gentiga Figure 11: Respondents view on the eﬀect of temperature variability on crop production. ° ° respondents in Mognori (10.7%), Zabugu (1.9%), and Kuka 30 C–34 C for maize production would reduce maize yield (3.7%) reported that crop yields have reduced moderately signiﬁcantly [48–51]. (Figure 10). In support of the above ﬁndings, interviews with oﬃcials Although most respondents attributed the reduction in of MoFA and SARI revealed that high temperatures during crop yield to a shorter growing season, discussants of focus the main cropping season and dry season gardening lead to groups from the selected communities acknowledged factors stunted growth and low yields of crops like maize, sorghum, such as pests and diseases, wrong use of fertilizer, outmoded onions, groundnut, and other vegetables. )e oﬃcial from farm practices, inadequate funds, and intertribal conﬂicts as SARI explained that high temperatures during the growing some of the major factors aﬀecting crop production. )e season negatively aﬀects the respiration and transpiration of above nonclimatic factors coupled with low rainfall amounts plants and in turn aﬀects plant development and yield. )is and frequent droughts have the propensity to worsen food assertion is also buttressed by McCarl [52] and Fosu-Mensah insecurity among the most vulnerable households in the [8] whose ﬁndings revealed that high temperatures inﬂuence Municipality [8, 45]. From the results, respondents’ ob- the respiration needs and raises water demand for plant servations of the eﬀect of rainfall variability on food crop growth. Consequently, these factors will aﬀect crop devel- production are in line with the regression results for maize opment and reduce crop yield [53]. production but inconsistent with rice and millet production. Probing further to ascertain other possible factors re- )e inconsistency may be due to the inﬂuence of non- sponsible for low yield or crop failure, discussants of focus climatic factors on food crop production as outlined by the groups from the selected communities outlined poor farm farmers. Drawing on the results of the regression analysis practices, poor seeds, and misapplication of fertilizers as the and respondents’ observations, it can be argued that rainfall key factors that may aﬀect crop production. Despite the variability for the 15-year period had a signiﬁcant inﬂuence adverse eﬀects of temperature variability on food crop on the variation of food crop production in the Municipality. production revealed by the survey, discussants of a focus group at Zabugu explained that millet thrives well under high temperatures with little amount of water. As a result, 3.5.2. Perceived Eﬀects of Temperature Variability on Food more millet is being produced in recent times. )is ﬁnding Crop Production. Also, we assessed farmers’ perceived ef- supports previous studies on how millet and other drought- fects of temperature variability on food crop production. )e tolerant crops are well adapted to high temperatures results show that majority of the respondents (86.4%) were [8, 46, 54]. of the view that temperature variability negatively aﬀects Similarly, some discussants at Gentiga and Gozesi also crop production especially maize. Out of the 86.4% re- argued that increased temperature during the harvest time spondents who claimed that temperature variability had helps reduce postharvest losses in cereals, particularly maize, badly aﬀected crop production, 23.4%, 22.9%, 15.9%, 11.7%, and rice. )e results revealed by the respondents on the and 12.6% were from Mognori, Zabugu, Gozesi, Kuka, and eﬀects of temperature variability on food crop production Gentiga respectively (Figure 11). )ese farmers asserted that supports the idea that climatic variables may have both there was a reduction in maize yield. Several studies have negative and positive implications on food crop production. demonstrated similar results. Previous studies have observed In general, the respondents in the Municipality seem to that temperature increase above the requirement of between have noticed that temperature variability negatively aﬀects Percentage Percentage Advances in Agriculture 11 maize production. However, the regression results indicated be accessed from the Municipal Directorate of the Ministry otherwise. )e discrepancy between farmers’ observations of Food and Agriculture, Bawku. and the regression results may be due to the use of the mean annual temperature that mostly conceals daily extremes. In Conflicts of Interest addition, the disparity may also be due to the inﬂuence of nonclimatic factors on food crop production as outlined by )ere are no conﬂicts of interest for this paper. the farmers. References 4.Conclusion and Policy Implications [1] Intergovernmental Panel on Climate Change, “Climate )is paper analysed how climate (rainfall and temperature) change 2007,” in ;e Physical Science Basis. Contribution of and nonclimatic variables (soil fertility using soil pH and Working Group I to the Fourth Assessment Report of the In- organic matter) inﬂuence the production of agricultural tergovernmental Panel on Climate Change, S. Solomon, staple crops of maize (Zea mays L.), millet (Pennisetum D. Qin, M. Manning et al., Eds., pp. 83–996, Cambridge glaucum L.), and rice (Oryza sativa L.) in vulnerable rural University Press, Cambridge, UK, 2007. communities in the Bawku Municipality of northern [2] S. T. Kandji, L. Verchot, and J. Mackensen, Climate Change and Variability in the Sahel Region: Impacts and Adaptation Ghana. )e study revealed a decreasing trend with sig- Strategies in the Agricultural Sector, World Agroforestry niﬁcant variation in the rainfall pattern while mean tem- Centre (ICRAF), Nairobi, Kenya, 2006. perature showed a marginal decreasing trend with less [3] L. Scott, Climate Variability and Climate Change: Implications variation over the 15-year period. )e multiple regression for Chronic Poverty, Overseas Development Institute, London, model revealed that rainfall and organic matter had a UK, 2008. signiﬁcant positive inﬂuence on maize production. )us, [4] Intergovernmental Panel on Climate Change, “Summary for maize production in the Municipality is mainly dependent policy makers,” in ;e Assessment of Impacts, Adaptation and on rainfall and organic matter. Similar to previous reports, Vulnerability. Contribution of Working Groups II to the Fifth mean temperature, and rainfall had no signiﬁcant inﬂuence Assessment Report of the Intergovernmental Panel on Climate on millet and rice production in the Municipality. Change, C. B. Field, V. R. Barros, D. J. Dokken et al., Eds., Nonetheless, organic matter and soil pH had a signiﬁcant pp. 1–32, Cambridge University Press, Cambridge, UK, 2014. 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Publisher: Hindawi Publishing Corporation
Copyright: Copyright © 2020 Rashida Ayumah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ISSN: 2356-654X
eISSN: 2314-7539
DOI: 10.1155/2020/6484019
Publisher site: See Article on Publisher Site

Abstract

Hindawi Advances in Agriculture Volume 2020, Article ID 6484019, 13 pages https://doi.org/10.1155/2020/6484019 Research Article How Do Climate and Nonclimatic Variables Influence the Production of Agricultural Staple Crops in Vulnerable Rural Communities in the Bawku Municipality of Northern Ghana? 1 1 2 1 Rashida Ayumah, Felix Asante , Lawrence Guodaar, and Gabriel Eshun Kwame Nkrumah University of Science and Technology, Geography and Rural Development, College of Humanities and Social Sciences, Faculty of Social Sciences, PMB, University Post Oﬃce, Kumasi, Ghana University of Adelaide, Geography, Environment and Population, Faculty of Arts, School of Social Sciences, Napier, Adelaide, SA 5005, Australia Correspondence should be addressed to Felix Asante; couzon_species@yahoo.com Received 20 September 2019; Accepted 22 April 2020; Published 20 May 2020 Academic Editor: Ga´bor Kocsy Copyright © 2020 Rashida Ayumah et al. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We examined the inﬂuence of climate (temperature and rainfall) and nonclimatic variables (soil fertility using soil pH and organic matter) on the production of agricultural staple crops (maize [Zea mays L.], millet [Pennisetum glaucum L.], and rice [Oryza sativa L.]) in vulnerable communities in the Bawku Municipality of northern Ghana. Using ﬁve selected farming communities as study sites, multiple datasets were obtained from primary and secondary sources. Participatory approaches together with questionnaires were used as data collection tools to quantify and qualify climate (temperature and rainfall) and nonclimatic variables (soil fertility using soil pH and organic matter) and crop production. )e Mann–Kendall trend test results indicate a signiﬁcant variation in annual rainfall for the 15-year period (1999 to 2013) with a relatively stable mean temperature variation in the Municipality. )e results of the multiple regression indicate that climatic and nonclimatic factors, particularly rainfall, soil pH, and organic matter have a signiﬁcant positive eﬀect on maize, millet, and rice when other factors are held constant. We conclude that to ease the burden of climate on production, better irrigation facilities be provided for the Municipality and weather forecasting information on the pending growing season be made available to farmers to enable them take informed decision. Also, policy on climate adaptation should take into account the interaction of external drivers of climate and nonclimatic variables to better build farmers’ resilience for food security at the local level. Low precipitation considerably aﬀects agricultural sys- 1.Introduction tems of many SSA countries [12]. Many staple crops have )e dynamic oscillation of the Earth’s climate is a reality. )e suﬀered drastic declines which consequently aﬀected the livelihood of smallholder farmers in many parts of SSA variation in the climate system across spatial scales is what is termed as climate variability (henceforth CV) [1]. Under including Ghana [13]. )e big question is how susceptible global warming, it is projected that climate variables in- food crop production might be to the interaction of climate cluding temperature and rainfall will vary with dispropor- and nonclimatic variables? Scholarship on how speciﬁc tional eﬀects on agriculture [2–4]. )e complexity of CV crops are adversely inﬂuenced by CV is abound in many remains a cardinal challenge for sustainable agriculture and parts of the world. For instance, Osbourne and Wheeler [9] food security [4–11]. Regionally, Sub-Saharan Africa illu- indicate that, globally, between the years 1960 and 2009, rice minates high level of vulnerability to food insecurity due to yields declined considerably due to low rainfall. Scientiﬁc the considerable impacts CV has on agricultural systems evidence suggests that, in many agricultural landscapes, crop which remain the “oxygen” that supports the economies. yields are expected to fall between 10% and 20% because of 2 Advances in Agriculture Extensive research on CV risks on crops in Ghana has increased CV risks [14]. In a related analysis, the IPCC estimated that 34 million to over 600 million are likely to indicated the threats CV poses to agricultural crops [23, 24, 28]. However, little information is available in the suﬀer from starvation by 2080 [15]. )us, it is increasingly relevant to ascertain the dynamics of how climate variables literature on how climate and nonclimatic predictors, par- interact to inﬂuence crop production in order to develop ticularly temperature, rainfall, soil pH, and organic matter strategic policies that will be sustainable for food security interact to dynamically inﬂuence the production of agri- [16]. Apart from rainfall, high temperature is another cli- cultural staple crops in vulnerable rural communities in mate determinant of many agricultural crops [4]. It is es- northern Ghana. )e challenge is, therefore, to investigate timated that for each 1 C rise in average temperature, the and ﬁll this knowledge gap for a good policy link aimed at income of farmers will potentially reduce by about 10% [5]. eﬀective adaptative capacity building to mitigate the inﬂu- )is may likely be intensiﬁed in many )ird World countries ence of climatic and nonclimatic risks on staple crop pro- duction. Speciﬁcally, this paper aims to quantify and qualify due to their limited ability to adapt. Consequently, the economic hardship of farmers could intensify as a result of how climate and nonclimatic variables inﬂuence the pro- duction of agricultural staple crops with emphasis on maize the impacts on livelihoods [17]. )e myriad of impacts according to the IPCC [1] would be experienced diﬀerently (Zea mays L.), millet (Pennisetum glaucum L.), and rice across the continent due to other socioeconomic challenges. (Oryza sativa L.) cultivated in many vulnerable rural Nonclimatic factors are also recognized as important communities in the Bawku Municipality of northern Ghana. factors that inﬂuence crop yield [18, 19]. Technological and management factors such as irrigation, crop varieties, 2. Materials and Methods and fertilizer application are suggested as important nonclimatic factors that can directly or indirectly inﬂu- 2.1. Study Site. )e Bawku Municipality is located in the ence crop production, particularly maize [20]. Soil fertility extreme north-eastern part of Ghana and is one of the two is argued as an important nonclimatic driver that can Municipalities in the Upper East Region of Ghana (Figure 1). 1 1 ° ° considerably aﬀect crop productivity, especially grains It lies between latitudes 10 40 and 11 11 north of the 1 1 ° ° [19]. However, the consideration for nonclimatic variables Equator and longitudes 0 18 west and 0 6 east of the and their concomitant risks on agricultural systems, Greenwich Meridian. )e Bawku municipality has a total land particularly crop production is largely given little atten- size of about 247.23720 km . )e municipality is bounded to tion [21]. It is suggested that the interface of climate and the north by Burkina Faso, to the south by the Garu-Tempane nonclimatic factors are disproportional in terms of their District, to the west by Binduri District, and to the east by impacts on crop production [22]. )us, exploring how Pusiga District [29]. )e Municipality is located within the these variables inﬂuence diﬀerent staple crops is impor- Sudan Savanna zone with variable rainfall pattern and tant, particularly in SSA and Ghana where many people unfavourable soil characteristics to support agriculture. depend on agricultural crop production for their )e research covered ﬁve communities in the Bawku livelihood. Municipality. )ese are Mognori, Kuka, Gosezi, Zabugu, Ghana has not been exempted from the phenomenon of and Gentiga communities. )e choice on the study sites was CV. )is is because of the level of exposure and sensitivity of informed by the vulnerability of the Municipality to CV and agricultural systems to CV in the country. Rainfall patterns staple insecurity. have shifted, coupled with protracted drought spells and increased temperature conditions [23]. )ese manifestations are considerably conspicuous in the northern sector with 2.2. Data Collection. We employed both qualitative and more expected future variations [6]. )is situation will quantitative data in the study for a comprehensive under- signiﬁcantly constrain agricultural systems in many parts of standing of the issues. )e two datasets were obtained from Ghana, particularly the northern sector [8, 24]. Since the primary and secondary sources using questionnaires, focus agricultural sector employs a large proportion of the people group discussions (FGDs), and structured interview pro- of Ghana, any uncertainties due to CV could, therefore, tocols. Questionnaires were administered in a face-to-face cause great devastation to the economy of the country. )e manner to food crop farmers. Focus Group Discussions were inadequate rainfall in 1982–1983 is a testament to how carried out in each community to generate conversations drought destroyed most crops and negatively aﬀected more that uncover individual opinions regarding the eﬀects of CV than 12 million people in the country [25]. Furthermore, in on food crop. Structured interviews were employed to obtain 2007, northern Ghana was wrecked by marauding ﬂood additional information from oﬃcials of the Ministry of Food waters that aﬀected three hundred and seventeen thousand and Agriculture (MoFA), Savanna Agriculture Research (317,000) people with the Central Gonja District being Institute (SARI), for their direct contact with farmers in the described as an environmental calamity; because, about Municipality and the Ghana Meteorological Agency (GMA). twenty-six thousand eight hundred and twenty-two (26,822) )e purpose was to explain and verify the ﬁndings from the acres of farmlands were destroyed [26]. Crop production questionnaires. )e study relied on documented records of data for staples such as maize (Zea mays L.), millet (Pen- monthly observations of climate variables compiled by the nisetum glaucum L.), and rice (Oryza sativa L.) in Northern GMA for a period of 15 years. )e use of a 15-year data was Ghana have been declining since the year 2010/2011 due to considered as adequate because consistent collation of data low rainfall [27]. over periods of 15 years or more according to Hochman et al. Advances in Agriculture 3 2 0 2 Miles District capital District boundary Towns Study area Road network Figure 1: Map of Bawku Municipality showing the study communities (source: Bawku Municipal Assembly (2015)). [30] provides a good basis for crop yield analysis in relation where e � deviation of sampling, N � population size and to CV. )e climate variables used for the study were tem- n � sample size. perature and rainfall [1, 5]. Production data on key food )e model has a conﬁdence level of 95% and a 5% error crops; maize (Zea mays L.), millet (Pennisetum glaucum L.), margin [31]. )e sample frame of the study consisted of a and rice (Oryza sativa L.) in the study area from 1999 to 2013 master list of household farmers of the ﬁve communities were also obtained from MoFA in Bawku for analysis. In obtained from the Planning Department of the Bawku addition, data on soil fertility in the Bawku Municipality Municipal Assembly. A total of 214 household farmers were over the period under investigation were obtained from the randomly sampled for the survey across the study com- Soil Research Institute (SRI) in Kumasi. To assess which soil munities (Table 1). fertility variables are important in explaining the variations on crop yield, a correlation analysis was performed on staple 2.3. Data Analysis. )e quantitative data obtained from the crops and soil fertility variables (soil pH, organic matter, farmers were processed and analysed using descriptive nitrogen, and phosphorus). Organic matter was found to be statistics. Frequency tables, cross-tabulations, bar graphs, correlated with maize and millet while soil pH was correlated and pie charts were used as tools for representing the survey with rice. Soil pH was therefore used as a proxy for soil data. Also, time series analysis was used to examine the trend fertility in rice production and organic matter as a proxy for in annual maximum and minimum temperatures as well as soil fertility in millet and maize production. rainfall over the 15-year period (1999 to 2013) with the help )e study participants were sampled simple randomly of MAKESENS Excel Software. )e magnitude of the trends and purposively. )e random sample of the survey partic- of the climate variables was tested by the Mann–Kendall ipants were based on the model proposed by Kasiulevicius ˇ trend test. )e trend was quantiﬁed using Sen’s slope et al. [31]: formula: x − x n � , (1) j k Q � , i � 1, 2, 3, . . . , N, j> k, (2) 1 + N(e) j − k 4 Advances in Agriculture Table 1: Sample size of selected communities. )e qualitative data recorded from the focus group discussions and structured interviews were analysed the- Household Sample size for Community matically based on diﬀerent responses and used as vital population communities elements of written text to better understand how farmers Mognori 61 51 perceived the eﬀect of climate variability on food crop Zabugu 66 55 production. Gozesi 52 47 Kuka 35 31 Gentiga 33 30 3. Results and Discussion Total 247 142 3.1. Rainfall Variation and Trend. Rainfall amount and Source: GSS, 2010. timing inﬂuence the yield of crops. Low rainfall amounts can be detrimental to crop yield, particularly if dry periods occur during the critical stages of development of the crop [8]. )e where x and x values are time j and k, respectively. )e j k total annual amount of rainfall and the trend over the 15- median of these N values of Q is Sen’s estimator of the slope. year period in the Bawku Municipality are displayed in Also, the variability of rainfall, temperature, and food crop Figure 2. )e mean annual total rainfall from 1999–2013 is production was analysed using the coeﬃcient of variation. 901.9 mm. )is was determined by dividing the standard deviation by )e total annual rainfall for the period ranged from a the mean. low of 217.9 mm in 2008 to a high of almost 1376 mm in A multiple regression model (Ordinary Least Square 2007. Generally, the Mann–Kendall trend test of the procedure) was used to analyse the inﬂuence of climatic rainfall dataset from 1999 to 2013 shows that at 5% sig- (temperature and rainfall) and nonclimatic (soil pH and niﬁcance level, the trend is not statistically signiﬁcant. organic matter) factors on three major food crops, maize Sen’s estimate (−26.567) indicates a decreasing trend (Zea mays L.), millet (Pennisetum glaucum L.), and rice (Figure 2). )is is congruent to the ﬁndings of Asante and (Oryza sativa L.), over the 15-year period (1999 to 2013) by Amuakwa-Mensah [36] whose study of climate change holding all other confounding factors constant through the and variability in Ghana reported decreasing trends of help of the Predictive Analytic Software (PASW) version 21. total rainfall amounts in northern Ghana. )e variability )e multiple regression model was used due to the fact that it in the year to year rainfall particularly towards the de- has been widely used in analyzing the eﬀects/impacts of creasing trend is a cautioning sign to the farming com- climate variability/change on food crop production [7, 32]. munities in the Municipality as this may adversely aﬀect To ensure the robustness and validity of the estimates of the their livelihoods. multiple regression model, the underpinning assumption of From Figure 3, the years 2005, 2008, 2010, and 2011 had a normal distributions, homoscedasticity, and serially un- negative deviation signifying meteorological drought (pe- correlated errors were tested using Jacque–Bera, riod of below average precipitation) periods with the worst Breusch–Pagan–Godfrey test, and Breusch–Godfrey Serial drought occurring in 2008. Global record of rainfall indi- Correlation LM Test (Antonakis and Deitz, 2011 cited in cates a substantially high rainfall in 2005 and 2010 that Ballance) [33]. )e level of signiﬁcance for the study was set resulted in severe ﬂoods in many parts of the world [37]. )e at 0.05. )e linear regression model used is similar to the positive deviation in 1999 and 2007 according to Paeth et al. model used by Onoja and Ajie [34] to analyse how food [38] is attributable to the El Niño-Southern Oscillation crops respond to climate variability and microeconomic (ENSO) eﬀects which caused much rainfall in Sub Sahara policies reform in Nigeria. )e model is presented as Africa. )is implies that severe ﬂoods might have occurred Y � βo + β X + β X + β X + · · · + β X + µi, (3) 1 i 2 i 3 i k in 1999 and 2007 in the Bawku Municipality. Report by the Daily Graphic online [39] that ﬂoods occurred in the Upper where Y is the dependent variable; X is the independent West, Upper East, and the Northern Regions of Ghana variable while µ is the stochastic error term, and βo is the conﬁrms possibility of such earlier events. )is may have intercept of the model. Natural logarithms of the variables aﬀected agricultural activities in the Municipality. )e es- were taken to strengthen out exponential growth pattern and timated annual anomaly of rainfall (Figure 3) indicates that reduce the potential heterogeneity of variance of error terms; the total amount of annual rainfall varied substantially from that is, to stabilize variance [35]. year to year. )is conﬁrms the ﬁndings of Amikuzuno and Donkoh [23] that the prevailing evidence in the rainfall LnRice � βo + β ln RAi + β ln TEi + β ln SPHi + µ , i 1 2 3 i pattern observed in the semiarid regions of Africa is highly LnMillet � βo + β ln RAi + β ln TEi + β ln ORGi + µ , i 1 2 3 i variable. LnMaize � βo + β ln RAi + β ln TEi + β ln ORGi + µ , With regard to farmers’ observations, all the respondents i 1 2 3 i claim to have observed rainfall variation in the 15 years (4) spanning 1999 to 2013. Approximately, 37% of farmers were where RA � annual rainfall in millimeters in the Bawku of the view that the amount of rainfall has reduced. )irty- Municipality; TE � mean annual temperature in C in the ﬁve of the respondents representing 16.4% reported that the Bawku Municipality; SPH � soil pH; ORG � organic matter; length of the rainy season had reduced. Interestingly, 28.5% µ � stochastic error term; and βo � intercept of the model. and 18.2% noticed an irregularity in the amount of rainfall Advances in Agriculture 5 1500.00 Q = –26.567 1000.00 500.00 0.00 1998 2000 2002 2004 2006 2008 2010 2012 2014 Data Sen’s estimate Figure 2: Total annual rainfall trend in the Bawku Municipality in the past 15 years. 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 –500 –1000 Figure 3: Annual rainfall deviations in the Bawku Municipality (1999–2013). and a reduction in how long the rainy season lasts re- However, the Mann–Kendall trend statistics for mean an- spectively (Table 2). nual minimum temperature at 5% signiﬁcance level was not Similarly, key informants from SARI, MoFA, and GMA statistically signiﬁcant. had also noted some pronounced variations in the rainfall )e mean minimum temperature for 1999 (22.7 C) was pattern. According to the key informants, the rains either found to be the same as the mean value (22.7 C) for the 15- come earlier or later than expected. )is was supported by a year period, showing no change for 1999. However, the years male farmer in Gentiga who pointed out in a focus group 2002 to 2006 and 2010 illustrate a signiﬁcant increase in the discussion that minimum temperature with a positive deviation of between ° ° 0.2 C and 0.9 C above the baseline average (Figure 5). )e (i) “When we were young, our wells and rivers were full in rest of the years recorded negative deviations with the the rainy season which enabled us to get water in the highest decrease of 1.53 C observed in 2012. )is signiﬁcant dry season, drawing water from wells was not diﬃcult interannual variation in minimum temperature may have but nowadays our wells and rivers have little water aﬀected crop production in the Municipality. )is is because even in the rainy season... ;e rains do not fall as they a decrease in minimum temperatures aﬀects night time plant used to. My biggest worry is its unpredictable nature” respiration rate and possibly reduces crop yield [40]; (FGD, 2014) meanwhile, when plants are exposed to higher minimum Generally, it was observed that respondents were much temperatures, it decreases their ability to grow and also perturbed by the abnormality of the rainfall pattern in the reduces crop yield [41]. Municipality which sometimes made it diﬃcult to accurately predict when to start planting. )e results presented show farmers observations are in line with the historical rainfall 3.2.2. Mean Annual Maximum Temperature. )e mean annual maximum temperature varied between 33.6 C and data for the 15-year period. 35.8 C for the 15-year period. )e total mean maximum temperature from 1999–2013 was 35.05 C. )e trend sta- 3.2. Minimum and Maximum Temperature Variation and tistics of the Mann–Kendall test at 5% signiﬁcance level for Trend the mean annual maximum temperature is not statistically signiﬁcant. However, the observed trend of Sen’s estimate 3.2.1. Mean Annual Minimum Temperature. Figure 4 (−0.017) of the Mann–Kendall test for the mean annual presents the mean annual variation/trend in minimum maximum temperature over the 15-year period portrays a temperature in the Bawku Municipality. )e mean annual decreasing trend (Figure 6). minimum temperature from 1999 to 2013 ﬂuctuated be- Notwithstanding the above information, the mean ° ° ° tween 21.2 C and 23.4 C with a mean value of 22.7 C. Sen’s maximum temperatures from 1999 to 2013 show distinctive estimate (−0.027) of the Mann–Kendall test indicates a interannual variation (Figure 6). In general, the maximum signiﬁcant decreasing trend in the mean annual minimum temperature decreased below the mean for the years 2000, temperature (Figure 4) for the 15-year period (1999 to 2013). 2004, 2007, 2008, 2009, 2012, and 2013, indicating that these Rainfall amount (mm) 6 Advances in Agriculture Table 2: Respondents’ observation of the manifestations of rainfall variation. Reduction in the Irregularities in the Reduction in the Irregularities in the length of the rainy length of the rainy Total amount of rainfall amount of rainfall season season Freq % Freq % Freq % Freq % Freq % 79 36.9 35 16.4 61 28.5 39 18.2 142 100 Freq � frequency. 24.00 Q = –0.027 23.00 22.00 21.00 1998 2000 2002 2004 2006 2008 2010 2012 2014 Data Sen’s estimate Figure 4: Mean annual trend in minimum temperature in the Bawku Municipality. 0.5 1999 2000 2001 2002 2002 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 –0.5 –1 –1.5 –2 Figure 5: Mean annual deviation in minimum temperature ( C) in the Bawku Municipality. 36.00 35.50 35.00 34.50 Q = –0.017 34.00 33.50 33.00 1998 2000 2002 2004 2006 2008 2010 2012 2014 Data Sen’s estimate Figure 6: Mean annual trend in maximum temperature in the Bawku Municipality. years were relatively cooler. )e highest decrease occurred in other hand, the year 2005 was marked as the warmest year ° ° 2004 with a decrease of 1.6 C below the baseline average. with a positive deviation of 0.8 C, which was greater than the )is conﬁrms the report by Asante and Amuakwa-Mensah global record of 0.62 C. In addition, the mean annual de- [36] that very cold winds were experienced in 2004. On the viation (Figure 7) shows more warm years than cold years Maximum temperature (°C) Minimum temperature (°C) Advances in Agriculture 7 0.5 –0.5 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 –1 –1.5 –2 Figure 7: Mean annual deviation in maximum temperature ( C) in the Bawku Municipality. for the 15-year period. )is has the tendency to cause 76.1% ﬂuctuations in crop output in the Municipality. )ough, there are signiﬁcant discrepancies in the interannual min- 10.3% imum and maximum temperature in the Municipality, the coeﬃcient of variation (0.0155) for the mean annual tem- Increase in Sometimes high and temperature other times low perature shows slight variation. )is conﬁrms the ﬁndings of Amikuzuno and Donkoh [23] that Northern Ghana has not Figure 8: Respondents’ opinion on the manifestations of tem- signiﬁcantly experienced notable temperature variation. perature variation. Superimposing the annual mean maximum temperature on the annual rainfall deviation for the 15-year period shows ﬁve years, there was a signiﬁcant variation in the output of that eﬀective rainfall for food crop production in 2005, 2010, the food crops. and 2011 was very low. )is is because total annual rainfall )e low level of production in 2013, according to the for these years was below the baseline average while the Municipal Director of MoFA, was as a result of the carving temperature was high. Low annual rainfall coupled with high out of a new district (Binduri District) from the Munici- temperature on one hand results in poor organic matter pality. He explained that the splitting of the Bawku Mu- content, making soil suitability for cropping one of the nicipality has reduced the production level of food crop since major problems in crop production [42]. )is is likely to most food crop farmers are now part of the Binduri District. have had negative consequences on food crop production. However, the year 2010 marked the worst season for maize On the other hand, the high rainfall coupled with low and rice farmers when output levels fell tremendously. )is maximum temperature in 2007 and 2012 suggests that ef- is most likely due to the high maximum temperature and a fective rainfall for these years were high. )is may have low amount of rainfall recorded in 2010 [8, 44, 45]. Not- beneﬁted some crops. High temperature and high rainfall withstanding, the output of millet increased in 2010. Given were recorded in 1999 and 2003. According to Shakoor et al. that millet is drought-tolerant and grown as a famine crop [43], high temperature and high rainfall are beneﬁcial for all [8, 45], most farmers may have shifted to the cultivation of tropical crops but will produce negative eﬀects if these millet when rainfall was expected to be below average in climatic variables are increased too much in the future. 2010. )is reﬂects the assertion by a key informant from With respect to respondents’ observations, the results SARI that millet production has almost replaced maize in the indicate that most farmers (86.4%) had observed tempera- Municipality due to the observed short and erratic rainfall ture variation in the last 15 years. Majority of the farmers pattern. )is may have contributed to the increased millet (76.1%) who observed temperature variation believed that production in 2010. )e variation in maize (CV � 0.4598), temperature has been increasing for the past 15 years millet (CV � 0.5653), and rice (CV � 0.3860) for the 15-year (Figure 8), whereas 10.3% observed irregularities in period could have implications for food security and live- temperature. lihoods of farmers. )ough most farmers asserted that temperatures have increased, the observed mean annual temperature for the Municipality showed a decreasing trend. Farmers’ obser- 3.4. Eﬀect of Climate Variability on Food Crop Production. vations are therefore at variance with the observed trend. We assessed and quantiﬁed the eﬀects of climate variability Perhaps, the daily and monthly temperatures observed by on food crop production using a multiple regression model. the farmers were signiﬁcant enough for them to notice some )e multiple regression results of the residual diagnostic test increase in temperature. It is clear from the results that of assumptions are presented in Table 3. farmers are aware of the temperature variation of the )e residual diagnosis for the test for heteroscedasticity Municipality through their own experiences of the past. for rice, maize, and millet using the Breusch–Pagan–Godfrey test did not show any heteroscedasticity at 5% signiﬁcance level; thus, the variance of the error term is constant. 3.3. Variation in Crop Yield (1999–2013). Figure 9 shows variation in the output ﬁgures of the three major food crops )e result of the serial correlation in determining the autocorrelation shows that there is no correlation among the grown in the Municipality from 1999 to 2013. )e ﬁrst ﬁve years of the study period depict high production for the residuals in the regression model for maize (Zea mays L.), millet (Pennisetum glaucum L.), and rice (Oryza sativa L.). major food crops as compared to the second ﬁve years of the study period. Even though production was high for the ﬁrst )e test for normality using Jarque–Bera statistics also Percentage 8 Advances in Agriculture 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Maize Millet Rice Figure 9: Major food crop production in the Bawku Municipality. Table 3: Diagnostic test statistics for food crop production. Rice Maize Millet Diagnosis Test-statistics Probability Test-statistics Probability Test-statistics Probability Heteroscedasticity 3.31184 0.3460 0.4810 0.2140 5.8669 0.2109 Autocorrelation 0.61641 0.4324 0.0869 0.7682 2.0130 0.1560 Normality 3. 3790 0.1846 0.9865 0.6106 2.8627 0.2389 indicates that the residuals are normally distributed. )e Table 4: Regression statistics for rice production. results of the diagnostic test for rice, maize, and millet Standard T- production, therefore, satisﬁed the assumptions of the Variable Coeﬃcient Probability error statistics multiple regression model. )is implies that there are no Constant −7.619448 21.26389 21.26389 0.7269 problems that will signiﬁcantly aﬀect the regression results LnaverageTemp 1.077336 1.077336 0.170053 0.8681 for rice, maize, and millet production. LnRainfall 0.323560 0.232461 1.391894 0.1915 Lnsoil-Ph 6.644933 2.564687 2.590934 0.0251 R squared � 0.5838, adjusted R squared � 0.4704, and F-statistics � 5.1452 3.4.1. Eﬀect of Climate Variability on Rice (Oryza sativa L.) (p< 0.0182). Regression is signiﬁcant at the 0.05 level. Production. )e results of the log-linear regression model used for estimating the inﬂuence of climatic (mean annual temperature and annual rainfall) and nonclimatic (soil pH) Table 5: Summary statistics of climate variables and major food variables on rice production is presented in Table 4. crop production. )e coeﬃcient of determination (R � 0.5838) of the results shows that approximately 58.4% of the variation in Standard Coeﬃcient of Variables Mean the log of rice is explained by the log of average temperature, deviation variation annual rainfall, and soil fertility. )e remaining percentage Rainfall 901.92 301.50 0.33429 (41.6%) could be attributed to other factors such as seed Temperature 28.89 0.45 0.01545 Maize 24880.38 11440.62 0.4598 varieties, method of cultivation, etc. )e insigniﬁcant levels Millet 22172.45 12533.54 0.5653 of temperature and rainfall could be attributed to the low Rice 20979.07 8097.14 0.3860 variation in average temperature (Table 5) during the study period (1999–2013) and use of irrigation for rice production in the Sudan Savanna Zone [24, 46]. However, the result area with low soil pH could have adverse eﬀects on the indicates that soil pH is statistically signiﬁcant at 5% output of rice. From the regression results, it could be (p< 0.0251) and positively inﬂuences rice production. )is conﬁrmed that soil pH rather than rainfall and temperature may be due to the desirable soil pH (5.5 to 6.5) for rice was an inﬂuential factor determining the variation in rice production in the Bawku Municipality. )e result, therefore, yield in the Municipality from 1999 to 2013. implies that a 1% increase in soil pH potentially leads to a 6.6% increase in rice yield holding other variables constant. )e estimation, therefore, shows that rice production in the 3.4.2. Eﬀect of Climate Variability on Maize (Zea mays L.) Municipality for the 15-year period was largely dependent Production. Table 6 presents the results of the multiple on soil pH. )is result is similar to the ﬁndings of Azman regression used for estimating the inﬂuence of annual et al. [47] who observed in their study that relative rice yield rainfall, average temperature, and soil organic matter on is aﬀected by soil pH. )is suggests that growing rice in an maize yield. From the results, the F-statistic is statistically Production metric tones Advances in Agriculture 9 Table 6: Regression statistics for maize production. Table 7: Regression statistics for millet production. Standard T- Standard T- Variable Coeﬃcient Probability Variable Coeﬃcient Probability error statistics error statistics Constant 27.67440 28.20512 0.981184 0.3476 Constant −5.155400 31.74246 −0.162413 0.8739 LnaverageTemp −3.913351 8.010983 −0.488498 0.6348 LnaverageTemp 8.268311 9.015681 0.917103 0.3788 LnRainfall 0.668053 0.271932 2.456692 0.0319 LnRainfall −0.043149 0.306036 −0.140992 0.8904 LnOrganicmatter 1.872949 0.777664 2.408429 0.0347 LnOrganicmatter 2.595624 0.875195 2.965767 0.0128∗ R squared � 0.6091, adjusted R squared � 0.5025, and F-statistics � 5.7141 R squared � 0.5060, adjusted R squared � 0.3712, and F-statistics � 3.7552 (p< 0.0131). Regression is signiﬁcant at the 0.05 level. (p< 0.0444). ∗Regression is signiﬁcant at the 0.05 level. Nigeria and Northern Ghana appear to be insensitive to low signiﬁcant at the 5% level (p< 0.0131). )is means that the rainfall, respectively. )us, a low level of rainfall does not explanatory variables speciﬁed in the maize production necessarily reduce millet production. )e estimation, model are jointly signiﬁcant. therefore, shows that in the Municipality, millet production )e results show that about 60.9% (R � 0.6091) of the is highly dependent on organic matter and not rainfall and variation in the log of maize is explained by the log of temperature which were found to be statistically insigniﬁ- average temperature, annual rainfall, and organic matter. cant in explaining the variation in millet output during the )e remaining percentage (39.1%) could be attributed to 15-year period. other factors such as seed varieties, method of cultivation, etc. )e annual rainfall (p < 0.0319) and organic matter (p< 0.347) at 5% signiﬁcance level are statistically signiﬁcant 3.5. Perceived Eﬀects of Climate Variability on Food Crop and positively contribute to maize production. )e result, Production therefore, implies that a 1% increase in soil organic matter 3.5.1. Eﬀects of Rainfall Variability on Food Crop Production results in a 1.9% increase in maize yield holding other variables constant. )is means that maize production in the as Perceived by the Farmers. To triangulate the secondary data results with primary data, farmers’ experiences were Municipality would be partly inﬂuenced by nonclimatic also obtained (Figure 10). )e results show that all the variables. )e result also shows that every 1% increase in rainfall potentially leads to a 0.7% increase in maize pro- farmers perceived rainfall variability to adversely aﬀect the production of their main staple crops. A 57 years old male duction holding other variables constant. )is also means that as rainfall increases, maize yield also increases. )ese respondent explained that validate the study objective on the inﬂuence of observed (i) “When I started farming, the rains usually came in the climatic trends on food crop production. However, the fourth and ﬁfth months of the year (April/May) which average temperature is not statistically signiﬁcant at 5% was the time for land preparation (tilling) and signiﬁcance level. )us, the estimation shows that maize sometimes for planting. ;is normally supported the production in the Municipality is principally dependent on cultivation of several crops. But these days, the rains rainfall and organic matter. come and go at any time. Sometimes the rains don’t come until the eighth or ninth month of the year making it diﬃcult to plant millet, sorghum, maize, rice 3.4.3. Eﬀect of Climate Variability on Millet (Pennisetum and other crops. In fact, it is not easy to predict when glaucum L.) Production. Table 7 shows the results of the log- the rains will come and stop. I am particularly worried linear regression model used for estimating the inﬂuence of about what will happen to my farming business in the climatic (temperature and rainfall) and nonclimatic (organic future because that is what I depend on” (FGD, 2014). matter) on millet production. )e F-statistics is statistically signiﬁcant at 5% signiﬁ- Statistically, all the respondents (100%) indicated that the cance level (p< 0.0444). )is implies that the explanatory manifestations of rainfall variability have aﬀected the variables speciﬁed in the millet production model, namely, growing season in the past 15 years. Also, nearly all the annual rainfall, average temperature, and organic matter are respondents (93%) were of the view that the variable nature jointly signiﬁcant. From Table 7, about 50.6% (R � 0.5060) of rainfall in the Municipality has reduced the length of the growing season while the remaining respondents (7%) of the variation in the log of millet is explained by the log of average temperature, annual rainfall, and organic matter. observed irregularities in how long the growing season lasts. All the respondents (7%) who observed irregularities in the )e remaining percentage (49.4%) could be attributed to other factors such as seed varieties, method of cultivation, length of the growing season reported that crop yield had etc. )e results further reveal that organic matter is statis- moderately reduced. More than 76% and 16% of those who tically signiﬁcant with a p-value of 0.0128 (Table 7). )e reported of a reduction in the length of the growing season implication is that, holding other variables constant, a 1% also reported that crop yields have severely and moderately increase in organic matter potentially leads to a 2.6% in- reduced, respectively. )e responses diﬀered among the crease in millet yield. )e result corroborates the ﬁndings of study communities. For instance, all the respondents in Gozesi and Gentiga pointed out that the shortened length of Tunde et al. [45] and Amikuzuno and Donkoh [23] who noted that millet production in the Kwara State in Northern the growing season has severely reduced crop yields, whereas 10 Advances in Agriculture 22% 16.4% 14% 20 13% 10.7% 11.2% 3.7% 1.9% Mognori Zabugu Gozesi Kuka Gentiga Severely reduced crop yield Figure 10: Respondents view on the eﬀect of growing season on crop yield. 23.4% 22.9% 15.9% 12.6% 11.7% Mognori Zabugu Gozesi Kuka Gentiga Figure 11: Respondents view on the eﬀect of temperature variability on crop production. ° ° respondents in Mognori (10.7%), Zabugu (1.9%), and Kuka 30 C–34 C for maize production would reduce maize yield (3.7%) reported that crop yields have reduced moderately signiﬁcantly [48–51]. (Figure 10). In support of the above ﬁndings, interviews with oﬃcials Although most respondents attributed the reduction in of MoFA and SARI revealed that high temperatures during crop yield to a shorter growing season, discussants of focus the main cropping season and dry season gardening lead to groups from the selected communities acknowledged factors stunted growth and low yields of crops like maize, sorghum, such as pests and diseases, wrong use of fertilizer, outmoded onions, groundnut, and other vegetables. )e oﬃcial from farm practices, inadequate funds, and intertribal conﬂicts as SARI explained that high temperatures during the growing some of the major factors aﬀecting crop production. )e season negatively aﬀects the respiration and transpiration of above nonclimatic factors coupled with low rainfall amounts plants and in turn aﬀects plant development and yield. )is and frequent droughts have the propensity to worsen food assertion is also buttressed by McCarl [52] and Fosu-Mensah insecurity among the most vulnerable households in the [8] whose ﬁndings revealed that high temperatures inﬂuence Municipality [8, 45]. From the results, respondents’ ob- the respiration needs and raises water demand for plant servations of the eﬀect of rainfall variability on food crop growth. Consequently, these factors will aﬀect crop devel- production are in line with the regression results for maize opment and reduce crop yield [53]. production but inconsistent with rice and millet production. Probing further to ascertain other possible factors re- )e inconsistency may be due to the inﬂuence of non- sponsible for low yield or crop failure, discussants of focus climatic factors on food crop production as outlined by the groups from the selected communities outlined poor farm farmers. Drawing on the results of the regression analysis practices, poor seeds, and misapplication of fertilizers as the and respondents’ observations, it can be argued that rainfall key factors that may aﬀect crop production. Despite the variability for the 15-year period had a signiﬁcant inﬂuence adverse eﬀects of temperature variability on food crop on the variation of food crop production in the Municipality. production revealed by the survey, discussants of a focus group at Zabugu explained that millet thrives well under high temperatures with little amount of water. As a result, 3.5.2. Perceived Eﬀects of Temperature Variability on Food more millet is being produced in recent times. )is ﬁnding Crop Production. Also, we assessed farmers’ perceived ef- supports previous studies on how millet and other drought- fects of temperature variability on food crop production. )e tolerant crops are well adapted to high temperatures results show that majority of the respondents (86.4%) were [8, 46, 54]. of the view that temperature variability negatively aﬀects Similarly, some discussants at Gentiga and Gozesi also crop production especially maize. Out of the 86.4% re- argued that increased temperature during the harvest time spondents who claimed that temperature variability had helps reduce postharvest losses in cereals, particularly maize, badly aﬀected crop production, 23.4%, 22.9%, 15.9%, 11.7%, and rice. )e results revealed by the respondents on the and 12.6% were from Mognori, Zabugu, Gozesi, Kuka, and eﬀects of temperature variability on food crop production Gentiga respectively (Figure 11). )ese farmers asserted that supports the idea that climatic variables may have both there was a reduction in maize yield. Several studies have negative and positive implications on food crop production. demonstrated similar results. Previous studies have observed In general, the respondents in the Municipality seem to that temperature increase above the requirement of between have noticed that temperature variability negatively aﬀects Percentage Percentage Advances in Agriculture 11 maize production. However, the regression results indicated be accessed from the Municipal Directorate of the Ministry otherwise. )e discrepancy between farmers’ observations of Food and Agriculture, Bawku. and the regression results may be due to the use of the mean annual temperature that mostly conceals daily extremes. In Conflicts of Interest addition, the disparity may also be due to the inﬂuence of nonclimatic factors on food crop production as outlined by )ere are no conﬂicts of interest for this paper. the farmers. References 4.Conclusion and Policy Implications [1] Intergovernmental Panel on Climate Change, “Climate )is paper analysed how climate (rainfall and temperature) change 2007,” in ;e Physical Science Basis. Contribution of and nonclimatic variables (soil fertility using soil pH and Working Group I to the Fourth Assessment Report of the In- organic matter) inﬂuence the production of agricultural tergovernmental Panel on Climate Change, S. 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How Do Climate and Nonclimatic Variables Influence the Production of Agricultural Staple Crops in Vulnerable Rural Communities in the Bawku Municipality of Northern Ghana?

How Do Climate and Nonclimatic Variables Influence the Production of Agricultural Staple Crops in Vulnerable Rural Communities in the Bawku Municipality of Northern Ghana?

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How Do Climate and Nonclimatic Variables Influence the Production of Agricultural Staple Crops in Vulnerable Rural Communities in the Bawku Municipality of Northern Ghana?

How Do Climate and Nonclimatic Variables Influence the Production of Agricultural Staple Crops in Vulnerable Rural Communities in the Bawku Municipality of Northern Ghana?

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