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American Journal of   
Environment and Climate (AJEC)

Relationship between Climate Variables (Rainfall and Temperature) and Ginger Yield                          
Across the Climate Belts of  Nigeria

Okoye N. N.1*, Nwagbara M. O.2, Weli V. E.3

Volume 2 Issue 3, Year 2023
ISSN: 2832-403X (Online) 

DOI: https://doi.org/10.54536/ajec.v2i3.1846
https://journals.e-palli.com/home/index.php/ajec

Article Information ABSTRACT

Received: September 27, 2023

Accepted: October 30, 2023

Published: November 08 2023

Rainfall and temperature are very important elements and factors of  weather and climate 
needed in the successful production of  crops, including ginger. The relationship of  these 
elements and ginger yield has not been given the due attention in Nigeria. Therefore, this 
study examined the relationship between Climate (Rainfall and Temperature) and Ginger 
Yield across the Climate Belts of  Nigeria. Rainfall and Temperaturedata were obtained for 
the study from Nigerian Meteorological Agency (NiMet), Abuja while ginger yield data were 
collected from the experimental farms of  National Root Crops Research Institute (NRCRI), 
Umudike, Agricultural Development Programme (ADP) and Nigerian Bureau of  Statistics, 
Abuja. These data covered a period of  40 years (1980 -2019) and were analysed using 
simple linear regression, correlation and Analysis of  Variance (ANOVA). Results obtained 
showed thatrainfall and temperature significantly predicted ginger yield across the four 
climate belts at p<0.05:Tropical Monsoon(TM) (F,12.0934)and jointly explained 45.2% of  
variation in ginger yield(r=.672and the R2=.452);Tropical Savanna (TS)(F,17.3452)and jointly 
explained 35.6% of  variation in ginger yield (r = .597 and R2= .356);Warm Semi-Arid (WSA) 
(F,24.9501)and jointly explained 20.9% of  variation in ginger yield (r= .457 and R2= .209); 
and Warm Desert (WD)(F,29.8517) and jointly explained 30.1% of  variation in ginger yield(r 
=.549 and R2= .301).Based on these results, it is concluded here that there is a significant 
relationship between rainfall and temperature and ginger yields over the years and across the 
climate belts. The study recommends among others that planting and harvesting of  ginger 
by farmers should align with the seasons as found in each of  the four climate belts.

Keywords
Climate Variables, Rainfal, 
Temperature, Ginger Yield, 
Climate Belts

1 Agro-met Unit, National Root Crops Research Institute, Umudike, Abia State, Nigeria 
2 Department of  Water Resources and Agrometeorology, Michael Okpara, University of  Agriculture, Umudike, Abia State, Nigeria 
3 Department of  Geography and Environmental management, University of  Port Harcourt, Chuba, Uturu, Abia State, Nigeria
* Corresponding author’s e-mail: nkimoore@yahoo.com

INTRODUCTION
Ginger (Zingiber officinale Rosc.), belonging to the 
Zingiberaceae family originated from Southeast Asia 
(Shuhaimi et al., 2012) and has been cultivated for 
thousands of  years for use as a spice and for herbal 
medicinal purposes (Akram et al., 2011) is a commercially 
important herbaceous perennial plant, usually grown as 
an annual spice. It is extensively cultivated in the tropical 
to temperate climates of  the world for its flavour, and 
pungency, and aromatic and healing characteristics 
associated with its essential oil and oleoresin contents 
(Srinivasan et al., 2018). Ginger can be grown under 
both rainfed and irrigated conditions depending on the 
frequency and distribution of  rainfall (Sharma & Sharma, 
2012).The underground rhizomes are thick, hard and 
much branched, giving rise to primary, secondary and 
tertiary rhizomes. The mature roots of  ginger are fibrous 
and the juice from old ginger roots is extremely potent 
and often used as spices and a quintessential ingredient 
of  Chinese, Korea, Japanese and many South Asian 
cuisines for flavouring dishes (Jakes, 2007). It is also used 
largely as recipes such as ginger bread, cookies, crackers, 
cakes, ginger-ale and ginger beer. The medicinal values 
of  these great ancient spices are widely recognized across 
the continents to contain a number of  unique organic 
phytochemical ingredients that can take care of  some 
human ailments. Recent studies on health related effects 
of  ginger which have also stimulated farmers concern on 

the growth of  the plant have shown the efficacy of  the 
plant in some life challenging ailments such as entero-
toxin induced diarrhoea, diabetic nephropathy, nausea, 
plasma antioxidant, vomiting, high cholesterol, high 
blood pressure and inflammation (Chen et al., 2019; 
Ernest and Pittler, 2008; Kim et al., 2010). The current 
major five exporting countries have been China, Nigeria, 
India, Jamaica, and Brazil Asumugha (2003). Nigeria is 
the fifth largest producer of  ginger in the world (FAO, 
2014) and the largest producer and exporter of  ginger in 
Africa (FAO, 2008). Ginger is one of  ten commodities 
identified by the United States of  America International 
Department (USAID) and Nigerian Export Promotion 
Council (NEPC) in 2002 as having the greatest potential 
for creating increased economic growth, external and 
internal trade, opportunities for employment, and 
increased income and wealth for Nigerians (Sidi et al., 
2014). The cultivation of  ginger in recent years especially 
in the rainforest zone of  Nigeria is on the increase 
(Egbuchua and Enujeke, 2013).
For successful cultivation of  the ginger, a moderate 
rainfall at the sowing time till the rhizomes sprout, fairly 
heavy and well-distributed showers during the growing 
period, and dry weather with a temperature of  280 to 
350C for about a month before harvesting are necessary 
(Zaied & Zouabi, 2016; Garberof  & Jäckering, 2021; 
Singh, et al., 2022). Temperature above 350C and sunlight 
can result in leave scorch particularly in young plants on 



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the other hand, low temperatures induce dormancy while 
high humidity throughout the crop period is necessary 
(Pavlova et al., 2014, Byrnes & Bumb, 2017; Borras, et 
al., 2022, Salnikov et al., 2022; Karatayev et al., 2022). In 
Nigeria, ginger is a crop mostly grown in Tropical Savanna 
climate belt with Kaduna State as the chief  producer 
(Ayodele & Sambo, 2014). Although, in recent times with 
the increase in population growth and demand, ginger 
is now being produced in other climate belts of  Nigeria 
particularly in the Tropical Monsoon (such as Abia, 
Anambra, Cross River, Delta and Imo States), Tropical 
Savanna which includes Kaduna and Nasarawa States, 
Warm Semi-arid (Sokoto and Zamfara) and Warm Desert 
Climate (Bernard 2008, Ayodele & Sambo, 2014). 
In Nigeria, cultivation of  crops is generally rain fed 
and with significant diurnal and seasonal variations in 
temperature thus making rainfall and temperature strong 
factors in crop production in the country. Rainfall does 
not start at the same time across the country and amounts 
also vary from the coast hinterland with the coastal areas 
receiving the highest while the extreme north receives the 
lowest. The success of  ginger cultivation across Nigeria 
could be said to be largely dependent on rainfall and 
temperature, which are not only elements of  weather 
and climate but also factors of  climate. Despite this and 
the estimated magnitude of  nutritional and potential 
economic importance of  ginger farming in Nigeria, the 
impact of  rainfall and temperature on the yields of  ginger 
across the climate belts of  Nigeria has not received the 
deserved attention. 

MATERIALS AND METHODS 
The study area is Nigeria and lies between longitudes 2° 
49’E – 14° 37’E and latitudes 4° 16’N – 13° 52’N (Fig. 
1). It is bounded on the North by the Republic of  Niger, 
East by Cameroon and West by Benin Republic while the 
Southern boundary is Gulf  of  Guinea which is an arm 

of  the Atlantic Ocean (Ofomata, 1975). Nigeria has four 
climate types following Koppen’s Climate Classification. 
These climate types are distinguishable from the southern 
part of  Nigeria to the northern part through north-
central Nigeria. They are Tropical Monsoon climate 
(Am), Tropical Savanna climate (Aw), Warm Semi-Arid 
Climate (Bsh), and Warm Desert Climate (SWh) (but for 
easy identification of  these climate belts in this study, they 
were recoded as TM, TS, WSA and WD respectively).
The Tropical monsoon climate has a very small 
temperature range. The temperature ranges are almost 
constant throughout the year. The southern part of  
Nigeria experiences heavy and abundant rainfall. These 
storms are usually convectional in nature due to the 
regions proximity, to the equatorial belt. The annual 
rainfall received in this region is very high, usually above 
the 2,000mm (78.7 in) rainfall totals giving for tropical 
rainforest climates worldwide. Over 4,000 m (157.5 in) 
of  rainfall is received in the coastal region of  Nigeria 
around the Niger delta area (Mmom, 2003).The Tropical 
Savannah Climate or Tropical Wet and Dry climate, is 
extensive in area and covers most of  Western and north-
central parts of  Nigeria beginning from the Tropical 
Monsoon Climate boundary in Southern Nigeria to 
the Central part of  Nigeria, where it exerts enormous 
influence on the region. This climate exhibits a well-
marked rainy season and a dry season with a single peak 
known as the summer maximum due to its distance 
from the equator. Temperatures are above 18°C (64 
°F) throughout the year (Obasi and Ikubuwaje, 2012). 
Rainfall total in Tropical Savanna Nigeria varies from 
1,100 mm (43.3 in) in the lowlands of  the river Niger 
Benue trough to over 2,000 mm (78.7 in) along the south 
western escarpment of  the Jos Plateau (Okoh et al., 2017).
The Warm Semi-arid Climate or Tropical Dry climate  is 
the predominant climate type in the northern part of  
Nigeria. Annual rainfall totals are lower compared to the 

Figure 1: Map of  Nigeria showing the Study Area (Tropical Monsoon Climate Belt)



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Tropical Monsoon and Tropical Savanna belts of  Nigeria. 
The rainy season in this belt last for only three to four 
months (June–September) while the rest of  the year is 
hot and dry with temperatures climbing as high as 40°C 
(Okoh et al., 2011). Due to their location in the tropics, 
this elevation is high enough to reach the temperate 
climate line in the tropics thereby giving the highlands, 
mountains and the plateau regions standing above this 
height, a cool mountain climate (Oluwole and Ike, 2013).
Nigeria is covered by three types of  vegetation: forests 
(where there is significant tree cover), savannahs 
(insignificant tree cover, with grasses and flowers located 
between trees), and montane land. (The latter is the least 
common, and is mainly found in the mountains near the 
Cameroon border.) Both the forest zone and the savannah 
zone are divided into three parts. Some of  the forest zone’s 
most southerly portion, especially around the Niger River 
and Cross River deltas, is mangrove swamp. North of  
this is fresh water swamp, containing different vegetation 
from the salt water mangrove swamps, and north of  that 
is rain forest (Olajuyigbe & Adaja, 2014).The Savannah 
zone’s three categories are divided into Guinean forest-
savanna mosaic, made up of  plains of  tall grass which 
are interrupted by trees, the most common across the 
country; Sudan savannah, similar but with shorter grasses 
and shorter trees; and Sahel savannah patches of  grass 
and sand found in the Northeast (Mohammed, 2013). 
The data used in this study included the primary and 
secondary data types. The secondary data are the climate 
data covering a period of  40 years from 1980 to 2019 
included mean monthly maximum and minimum 
temperatures; and mean monthly rainfall were obtained 
from the office of  Nigerian Meteorological Agency 
(NiMet), Abuja for this study, and data on ginger in tons 
per hectare were collected from the experimental farms 
of  National Root Crops Research Institute (NRCRI), 
Umudike, Agricultural Development Programme 
(ADP) and Nigerian Bureau of  Statistics, Abuja.. The 
study employed the use of  Statistical Package for Social 
Sciences (SPSS) version 22. Simple linear regression and 
correlation were employed to explain the time series 
variations in meteorological parameters and ginger yield. 
The analysis of  variance (ANOVA) was used because it 
has the capacity to determine the divergence in the mean 
of  data, particularly, when the data sets are up to or 
more than three independent sets (Wahab & Jiao, 2020; 
Kumawat & Yadav, 2021).

RESULTS AND DISCUSSIONS
Table 1 shows the mean monthly distribution of  rainfall 
amounts in the climate belts of  Nigeria. During the study 
period, the month of  January was wetter in the Tropical 
Monsoon (TM) Climate Belt with mean monthly rainfall 
of  33.5mm. The highest mean total rainfall amount of  
348.4mm was recorded in the month of  September 
whereas the lowest mean total rainfall amount of  33.1mm 
was recorded in the month of  December. The month of  
July also recorded high amount of  rainfall of  327.2mm 
followed by June that recorded 291.9mm mean amount of  
rainfall. There is rainfall in all the months of  TM climate 
belt. The Warm Desert (WD) Climate Belt recorded the 
lowest amount of  mean monthly rainfall. Its highest mean 
monthly rainfall of  202.1mm was in the month of  August 
followed by the month of  July which recorded 173.9mm 
amount of  rainfall. The lowest amount of  rainfall of  
6.4mm was recorded in the month of  April. The WD 
climate had total number of  six months of  rainfall which 
starts from the month of  April and ends in the month of  
October. The WD climate recorded the highest number 
of  months without rainfall (January, February, March, 
November and December). The mean monthly rainfall 
amount is higher than that of  the Tropical Savanna (TS) 
Climate Belt which had a mean monthly rainfall for 
January as 9.3mm. In TS climate, the highest mean total 
amount of  265.5mm rainfall was recorded in the month 
of  September as well whereas the lowest of  9.3mm of  
mean rainfall amount was recorded in the month of  
January. The month of  July also recorded high amount 
of  rainfall of  222.9mm, followed by June which recorded 
210.4mm. TS climate had rainfall in all the months. The 
other two climate belts of  Warm Semi-arid (WSA) Climate 
Belt and the Warm Desert Climate are exceptionally dry 
in the month of  January with 0.2mm and 0mm of  mean 
monthly rainfall respectively. The same pattern can be 
seen for February and March. Though, in April, the sign 
of  wetness is witnessed in the Warm Desert Climate and 
a 6.4mm mean monthly rainfall is documented. For WSA 
climate, the highest mean monthly rainfall of  205.7mm 
was observed in the month of  August and 179.4mm in 
the month of  July while the lowest was 0.2mm in the 
month of  January. However, the months of  November 
and December recorded 0.0mm and 0.0mm respectively 
making it have total number of  seven months of  rainfall. 
Table 2 reveals the analysis of  variance for differences in 
the seasonal amounts of  rainfall in the study area. The 

Table 1: Monthly Rainfall (mm) distribution across the climate belts of  Nigeria (1980-2019)
Months Tropical Monsoon

Climate
Tropical Savanna Warm Semi-arid 

Climate
Warm Desert 
Climate

January 33.5 9.3 0.2 0
February 54.8 20.8 0.3 0
March 126.4 55.2 1.8 0.3
April 186 113.7 7.9 6.4
May 248.6 175.2 52.2 30.9
June 291.9 210.4 82.5 75.3



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seasons were the December January and February (DJF), 
March April and May (MAM), June July and August (JJA), 
then September October and November (SON). The 
essence here was to see if  there have been differences in 
the patterns of  rainfall within the seasons of  the year. It is 
also critical to understand that whereas the second season 
(MAM) is critical for the planting, the third season (JJA) 
is majorly for growth and maturity of  root and tuber 
crops in Nigeria, while the fourth (SON) and the first 
(DJF) seasons are critical for the harvest and storage of  
root and tuber crops in Nigeria in the Tropical Monsoon 
Climate. Similarly, the second season (MAM). For the 
Tropical Monsoon, all the seasons appeared to have been 
significantly different at p<0.05. The same can be said 
about the other climate belts of  the country. This explains 
in clear terms that there have been significant changes 

in the seasonal rainfall amounts across the climate belts 
of  the country over the past 40years (1980 – 2019). The 
temperature data presented in Figure 2 shows that in 
the Tropical Monsoon Climate Belt, temperature ranged 
from 25.60C in July to 27.5 in May. The months of  June 
July August, appear cooler due to the effect of  the rainy 
season in this belt at this period of  the year. The Tropical 
Savanna Climate belt observed the highest temperature 
a little differently from the Tropical Monsoon Climates’. 
In this belt the highest temperature was encountered in 
the months of  March and April, with 28.50C and 28.70C 
respectively. The coolest temperature was recorded in 
September with a mean monthly temperature of  26.10C. 
In the Warm Semi-arid Climate, April also recorded 
the highest temperature of  29.50C. The coldest month 
was the month of  January with a temperature of  230C. 

July 327.2 222.9 179.4 173.9
August 282.8 203.8 205.7 202.1
September 348.4 265.5 108.5 102.7
October 277.3 167.4 20.4 13.2
November 103.5 27.3 0 0
December 33.1 11.2 0 0.1
Mean 2313.7 1482.6 659 604.8

Table 2: Seasonal differences in rainfall in the climate belts
Tropical Monsoon
Months Groups Sum of  Squares Df Mean Square F Sig.
DJF Between 902365.12 3 157 7.1363 .006

Within 813565.23 116 22
MAM Between 848565.13 3 144 13.0909 .001

Within 712265.11 116 11
JJA Between 765165.13 3 163 11.6428 .003

Within 622365.15 116 14
SON Between 635565.21 3 164 13.6666 .000

Within 479765.11 116 12
Tropical Savanna 
DJF Between 772639.33 3 451 6.6323 .040

Within 684730.31 116 68
MAM Between 580822.56 3 131 7.7058 .031

Within 474914.16 116 17
JJA Between 259005.85 3 168 16.800 .000

Within 164097.54 116 10
SON Between 147189.23 3 289 15.2105 .000

Within 44280.22 116 19
Warm Semi-arid Climate
DJF Between 880364.11 3 201 10.5789 .001

Within 704455.45 116 19
MAM Between 557547.15 3 189 12.6000 .000

Within 245638.24 116 15
JJA Between 644729.99 3 204 9.2727 .001

Within 192178.99 116 22



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The reason for the low temperature in January is the 
closeness of  the region to the Sahara Desert and the cold 
tropical continental air mass which is more boisterous 
at that time of  the year thereby affecting temperature 
of  the area. This same condition applies to the Warm 
Desert Climate, which also had its coldest mean monthly 
temperature in the month of  January (22.60C). Table 3 

reveals the ANOVA computations for differences in the 
seasonal mean temperature in the study area. For the 
Tropical Monsoon, all the seasons appeared to have been 
significantly different at p<0.05. In the Tropical Savanna 
belt all the other seasons of  the year were significantly 
different at p<0.05, but DJF was not significant at p>0.05. 
This could be possibly caused by the closeness of  the 

SON Between 843564.12 3 228 17.5384 .000
Within 563455.11 116 13

Warm Desert Climate
DJF Between 868557.39 3 296 12.3333 .000

Within 622659.45 116 24
MAM Between 730354.01 3 189 14.5384 .000

Within 631255.7 116 13
JJA Between 606760.77 3 263 16.4375 .000

Within 550862.47 116 16
SON Between 499464.16 3 223 10.6190 .001

Within 239065.85 116 21
DJF=December, January, February; MAM=May, June, July; JJA=June, July, August; SON=September, October, November

Figure 2: Mean monthly temperature distribution across the climate belts of  Nigeria

Table 3: Seasonal differences in temperature across the climate belts in Nigeria 
Tropical Monsoon
Months Groups Sum of  Squares Df Mean Square F Sig.
DJF Between 900365.22 3 123 10.2500 .000

Within 854565.2 116 12
MAM Between 808765.17 3 181 12.0667 .000

Within 762965.15 116 15
JJA Between 717165.13 3 103 5.7222 .012

Within 671365.10 116 18
SON Between 625565.08 3 134 6.3810 .010

Within 579765.05 116 21
Tropical Savanna 
DJF Between 672639.08 3 89 1.4127 1.04

Within 586730.77 116 63
MAM Between 500822.47 3 112 5.8947 .034

Within 414914.16 116 19
JJA Between 329005.85 3 193 4.2889 .041

Within 243097.54 116 45



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region to the region where the tropical continental air 
mass prevails at the time of  the year. In the Warm Semi-
Arid Climate, it was MAM that was not significant at 
p>0.05. The other seasons of  the year were significant 
at p<0.05. Finally, the Warm Desert Climate had all 
seasons of  the year statistically significantly different at 
p<0.05 except DJF season that was not significant at 

p>0.05. Figure 3a reveals the time series plot for rainfall 
in the Tropical Monsoon Climate belt. The plot yielded a 
model with an R2 0.6 and Y=1.85x + 2269.5. The model 
revealed a quasi-decadal pattern with no much noticed 
anomaly or deviation. Also, Figure 3b showed that there 
are some inherent anomalies in the rainfall distribution. 
Particularly, 1986 was exceptionaly dry and represented 

SON Between 157189.23 3 123 7.2352 .020
Within 71280.926 116 17

Warm Semi-arid Climate
DJF Between 980364.23 3 108 9.8182 .000

Within 744455.67 116 11
MAM Between 508547.11 3 192 2.4304 .231

Within 272638.55 116 79
JJA Between 636729.99 3 134 9.5714 .003

Within 199178.56 116 14
SON 830564.006 3 117 7.8000 .005

624455.698 116 15
Warm Desert Climate
DJF Between 718557.39 3 161 1.6598 .067

Within 662659.08 116 97
MAM Between 830354.01 3 110 5.0000 .001

Within 774455.7 116 22
JJA Between 606760.77 3 143 3.7632 .051

Within 550862.47 116 38
SON Between 494964.16 3 119 4.9583 0.041

Within 439065.85 116 24
DJF=December, January, February; MAM=May, June, July; JJA=June, July, August; SON=September, October, November.

Figure 3: Rainfall and temperature time series and anomaly plots for the Tropical Monsoon Climate Belt. 



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the year with the driest rainfall with anormaly value of  
-350mm. In Figure 3c the temperature timeseries plot is 
presented. In the figure the temperature ranges between 
260C to 30.50C. The temperature appeared to be on the 
increase contineously, although with some anomalies. 
There are two deviations within the continum, as seen 
in the year 1995 and 2004. In Figure 3d showed the 
anormalies in the temperature data, where 1995 and 2002 
were very cool with the temperature anomalies of  -1.50C 
for each of  them. 
Figure 4a reveals the time series plot for rainfall in the 
Tropical Savanna Climate belt. The plot yielded a model 
with an R2=0.046 and Y=58.15X. The model revealed a 

quasi-decadal pattern with no much anomaly or outliers. 
On the other hand, Figure 4b showed that there are 
some inherent anomalies in the rainfall distribution. 
Particularly, 1996 was abnormaly dry and represented 
the year with the driest rainfall with anormaly value of  
-45mm. In Figure 4c the temperature timeseries plot is 
presented. In the figure the temperature ranges between 
260C to 29.50C. The temperature appeared to be on the 
increase contineously, although with some anomalies. 
There are two deviations within the continum, as seen 
in the year 2016 and 2018. In Figure 4d showed the 
anormalies in the temperature data, where 2013 and 2015 
were very cool with the temperature anomalies of  -0.130C 

Figure 4: Rainfall and Temperature time series and anomaly plots for the Tropical Savanna Climate Belt.

and 0.150C respectively. Figure 5a reveals the time series 
plot for rainfall in the Warm Semi-arid Climate belt. The 
plot yielded a model with an R2 0.7 and Y=35.05X. The 
model revealed a quasi-decadal pattern with two peaks 
while Figure 5b shows that there are some inherent 
anomalies in the rainfall distribution. Particularly, 1995 
was abnormaly dry and represented the year with the 
driest rainfall with anormaly value of  -48mm. In Figure 
5c, the temperature timeseries plot is presented. In the 
figure the temperature ranges between 260C to 280C. The 
temperature appeared to be on the increase contineously, 
although with some anomalies. There is one peak 
within the continum, as seen in the year 2010. In figure 
5d,it is shown that the anormalies in the temperature 
data, where 2010 was very cool with the temperature 
anomaly of  - 0.150C. Figure 6a reveals the time series 
plot for rainfall in the Warm Desert Climate belt. The 
plot yielded a model with an R2 = 0.55 and Y=1.17x 
+ 692.73. The model revealed a quasi-decadal pattern 
with two peaks. However, Figure 6b showed that there 

are some inherent anomalies in the rainfall distribution. 
Particularly, 2000 was abnormaly dry and represented 
the year with the driest rainfall with anormaly value of  
-212mm. In Figure 6c, the temperature timeseries plot is 
presented. In figure 6c, the temperature ranges between 
26.50C to 31.50C. The temperature appeared to be on the 
increase continuously, although with some anomalies and 
a decade long consistent temperature distribution. There 
is three peaks within the continum, as seen in the years 
1988 (30.50C) 1997 (30.70C) and 2017 (310C). Figure 6d 
shows the anormalies in the temperature data, where 
1997 was very hot with the temperature anomaly of  
2.50C. Most of  the agricultural practices in the country 
are climate dependent. It is therefore possible to trace 
the yields of  crops to the climate type of  the place. It is 
also very common to use the amount and buoyancy of  
rainfall and temperature to measure these relationships. 
This is because these elements of  weather and climate 
are pervasive and have controlling effects on other 
components of  the environment. Table 4indicates the 



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Figure 5: Rainfall and Temperature time series and anomaly plots for the Warm Semi-Arid Climate Belt.

Figure 6: RRainfall and temperature time series and anomaly plots for the Warm Desert Climate Belt.

relationship between climate (rainfall and temperature) 
and the yields of  ginger over the years. Here, the 
dependent variable (ginger) is regressed against rainfall 
and temperature (predictors). In the Tropical Monsoon 
Climate Belt, the rainfall and temperature significantly 
predicted ginger yield (F=12.0934; p<0.05) which 
indicates that temperature and rainfall play significant 
role in the yield of  ginger in the Tropical Monsoon 
Climate Belt. The model revealed a positive relationship 

(r=0.672) and the R2 was .452. This means that rainfall 
and temperature jointly explained 45.2% of  variation in 
ginger yield in the Tropical Monsoon Belt of  Nigeria. 
In the Tropical Savanna Climate Belt, the rainfall 
and temperature significantly predicted ginger yield 
(F=17.3452; p<0.05) which indicates that temperature 
and rainfall play significant role in the yield of  ginger in 
the Tropical Savanna Climate Belt. The model revealed a 
positive relationship (r=.597) and the R2 was .356. This 



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means that rainfall and temperature jointly explained 
35.6% of  variation in ginger yield in the Tropical Savanna 
Belt of  Nigeria. In the Warm Semi-Arid Climate Belt, 
the rainfall and temperature significantly predicted 
ginger yield (F=24.9501; p<0.05) which indicates that 
temperature and rainfall play significant role in the yield 

of  ginger in the Warm Semi-Arid Climate Belt (b1= R; 
.173 P<0.05, b2= T.343 P<0.05). The model revealed a 
positive relationship (r=.457) and the R2 was .209. This 
means that rainfall and temperature jointly explained 
20.9% of  the variation in ginger yield in the Warm Semi-
arid environment of  Nigeria. 

Table 4: The relationship between ginger yields and climate (rainfall and temperature) in the climate belts of  Nigeria
Climate zone Regression weights N Beta R R2 F P-value
TM Rainfall, Temp→Ginger 40 .332 .672 .452 12.0934 0.004

.423
TS Rainfall, Temp→Ginger 40 .354 .597 .356 17.3452 0.003

.341
WSA Rainfall, Temp→Ginger 40 .173 .457 .209 24.9501 0.040

.343
WD Rainfall, Temp→Ginger 40 .317 .549 .301 29.8517 0.002

.281
N:B: TM=Tropical Monsoon; TS=Tropical Savanna; WSA= Warm Semi-Arid Climate; WD=Warm Desert Climate.

In the Warm Desert Climate environment, rainfall 
and temperature significantly predicted ginger yield 
(F=29.8517; p<0.05) which implies that temperature and 
rainfall play significant role in the yield of  ginger in the 
Warm Desert Climate environment. The model revealed 
a positive relationship (r=.549) and the R2 was .301. This 
means that rainfall and temperature jointly explained 
30.1% of  variation in ginger yield in the Warm Desert 
Climate Belt of  Nigeria.

Discussion of  Findings
Climate, especially rainfall and temperature, is generally 
perceived to affect the growth, development and yield 
of  crops in Nigeria (Agele & Bolarinwa, 2018). Annual, 
seasonal and monthly rainfall and temperature values 
have been seen from the results of  the study that they 
vary with climate belt thus affecting the yields of  ginger 
across the climate belts of  Nigeria. The relatively low 
amounts of  rainfall of  WSA and WD Climate Belts as 
against TM and TS threatened the yield of  ginger in the 
belts which call for irrigation if  maximum yield is to be 
attained (Agele & Bolarinwa, 2018). This is corroborated 
by Zaied & Zouab, (2016), Garbero, & Jäckering (2021) 
and Singh, et al. (2022) who stated that successful 
production of  ginger can be attained where a moderate 
rainfall at the sowing time till the rhizomes sprout, fairly 
heavy and well-distributed showers during the growing 
period, and dry weather with a temperature of  280 to 
350C for about a month before harvesting are available. 
On the other hand, flooding and water-logging resulting 
from heavy rainfall and water stress such as drought 
resulting inadequate rainfall reduce the yields of  root 
and tuber crops including ginger (Ayanlade, et al., 2010). 
Generally, the identified climate events that affect ginger 
farming are extreme rainfall (TM & TS), Flooding (TM, 
TS & WSA) and drought conditions (TS, WSA & WD). 
This is in line with the work of  (Nwachukwu et al., 2012) 

who affirms that rainfall and temperature significantly 
affect agricultural production.
The assessment of  the mean monthly temperature for 
the various climatic belts showed that there has been a 
difference in the monthly temperatures which ranged 
from 0.50C to 2.10C. In the TM Climate Belt, the months 
of  October and April were hottest, while December was 
coolest. The coldness experienced in December in this 
belt was caused by the prevailing harmattan wind, which 
blows over the TM climate belt at this time of  the year. 
The changes in the temperature hotness or coldness were 
traceable largely to the controls of  the climate of  Nigeria. 
And these include; but not limited to air masses (Tropical 
Maritime and Tropical Continental), elevation, rotation 
and revolution. On the anthropogenic side, urbanization, 
transportation, gas flaring, deforestation and the burning 
of  bushes could be held accountable (Onwuka, 2012), as 
climate change becomes a topic of  concern around the 
world (Hamisi & Abdillah, 2022: Amosah et al., 2023). 
In the other belts that is, the TS, WSA and WD, the 
months of  April, May and November were very critical 
as they stood out as the very hottest and erratic months 
of  the year, when it came to temperature. This finding 
corroborated the finding of  (Ayodele & Sambo, 2014). 
The temperature ranged from 25.6 0C in July to 27.5 0C 
in May for the TM. The months of  June July August, 
were cooler because of  the rainy season in this belt at 
this period of  the year. The Tropical Savanna Climate belt 
observed the highest temperature a little differently from 
the Tropical Monsoon Climates belt in which temperature 
ranged from 26.10C in December to 28.70Cin April. In 
the Warm Semi-Arid Climate Belt, April also recorded the 
highest temperature of  29.5 0C. The coldest month was 
the month of  January with a temperature of  230C. The 
adducible reason for this low temperature in January is the 
closeness of  the region to the Sahara Desert and the cold 
tropical continental air mass which is more boisterous at 



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Am. J. Environ. Clim. 2(3) 91-100, 2023

that time of  the, year thereby affecting temperature of  
the area. This same condition applies to the Warm Desert 
Climate Belt, which also had its coldest mean monthly 
temperature in the month of  January (22.60C). This 
finding is in line with (Onyeka, 2014), who argued that 
the Sahara Desert and the Atlantic Ocean play significant 
roles in the climatic outcomes of  Nigeria. Whereas, the 
Atlantic Ocean provides a cooling effect to the south, 
the Sahara Desert play both cooling and heating effect 
to the north section of  the country (Onwuka, 2012).In 
general, the temperatures across the climate belts are 
relatively good for the successful production of  ginger 
as the temperatures are not above 350C to leave scorch 
particularly in young plants and also not low to the 
extent of  inducing dormancy.The works of  Byrnes & 
Bumb(2017); Borras, et al. (2022) and Salnikov et al.(2022) 
corroborate this assertion.

CONCLUSION
There is a significant relationship between rainfall and 
temperature and ginger yield from 1980 to 2019. Also, 
rainfall is more variable than temperature over the period 
studied and across the climate belts. Ginger is purely a 
product of  the rainfall and temperature as observed, other 
climate variables and factors notwithstanding. This study 
recommended that climate smart agriculture should be 
encouraged. Planting and harvesting of  ginger by farmers 
should align with the seasons as found in each of  the 
four climate belts. Also, improvement in the agricultural 
practice and technology for farming is greatly needed. If  
Nigeria continues to rely on rainfall for ginger production 
without involving irrigation when the rains are gone will 
make the country not maximize the yield of  the crop, and 
by extension reducing the economic benefits from ginger. 
Therefore, dams should be built by governments at all 
levels (Federal, State and Local Government Area) so as 
to encourage irrigation.

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