J. Nig. Soc. Phys. Sci. 4 (2022) 883

Journal of the
Nigerian Society

of Physical
Sciences

Suitability Analysis for Yam Production in Nigeria using
Satellite and Observation Data

Tertsea Igbawuaa,∗, Martha Hembafan Gbangera, Fanan Ujohb

aDepartment of Physics, Federal University of Agriculture, Makurdi, Nigeria
bCenter for sustainability and Resilient Infrastructure and Communities (SaRIC), School of the Built Environment and Architecture, London South Bank

University, London, UK

Abstract

Identification of suitable areas for yam production is critical for ensuring yield in yam production in Nigeria. The study is aimed at determining
suitable lands for yam production in Nigeria. Climate, soil, and environmental parameters that have a high contribution to yam production were
used in developing a yam production suitability map using the Analytical Hierarchy Process (AHP). The AHP was used in deriving weights
through a pairwise comparison technique. According to the findings, highly suitable (HS), suitable (S), marginally suitable (MS), and not suitable
(NS) regions accounted for 11.79, 82.68, 4.05, and 1.47% of the study area, respectively. The Normalized Difference Vegetation Index (NDVI), a
measure of vegetation vigor, was higher in HS, followed by S regions, and then MS regions. Similarly, climate variables in HS regions were more
favorable for plant growth, followed by S regions and MS regions. The correlation between precipitation and temperature is high and significant
only in the HS class, despite the fact that NDVI and climate variables are significantly connected in all the suitability classes. The output map,
thus determined, provides information on highly suitable, suitable or marginally suitable lands that are of practical importance to agriculturists.

DOI:10.46481/jnsps.2022.883

Keywords: Analytical hierarchy process, Yam production, Nigeria, NDVI, Yield gap, Multi-criterion decision making

Article History :
Received: 22 June 2022
Received in revised form: 10 October 2022
Accepted for publication: 15 October 2022
Published: 06 November 2022

c© 2022 The Author(s). Published by the Nigerian Society of Physical Sciences under the terms of the Creative Commons Attribution 4.0 International license

(https://creativecommons.org/licenses/by/4.0). Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Communicated by: O. J. Oluwadare

1. Introduction

The United Nations World Food Programme’s (UNWFP)
live Hunger Map identified about 860 million individuals with
insufficient food consumption and a total of 16 countries with
very high levels of hunger. Nigeria accounts for 52.9 million (or
6.2%) of the global population with insufficient food consump-
tion [1]. Laganda [2] asserts that of the causes of world hunger,
climate change is the one that can be most accurately predicted

∗Corresponding author tel. no: +234 8036952266
Email address: tertsea.igbawua@uam.edu.ng (Tertsea Igbawua)

using scientific tools. It is therefore imperative that scientific
knowledge is employed in resolving the global food crisis, be-
ginning with national and/or regional studies, especially for the
food crops that thrive in specific regions of the world, such as
yam. The emergence of agricultural methods came out as a
result of man’s quest for food in order to survive, and the agri-
cultural methods have occasionally continued to advance [3].

Yam is a tropical tuber crop that is grown primarily for do-
mestic consumption and commercial use in Africa. Generally,
there are several species of yam in the genus Dioscorea, but
only six are proven to be economically important staple species.

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Igbawua et al. / J. Nig. Soc. Phys. Sci. 4 (2022) 883 2

According to Blench [4], these include Dioscorea rotundata,
Dioscorea bulbifera, Dioscorea dumetorum, Dioscorea alata,
Dioscorea esculenta, Dioscorea cayennensis, also known as
white guinea, aerial, trifoliate, purple, Chinese, and yellow yam,
respectively. Awoniyi & Omonona [5], Ike & Inoni [6], con-
firmed that the most prevalent and economically significant species
in Nigeria are Dioscorea rotundata and Dioscorea alata.

In 2012 and 2019, Nigeria produced approximately 65.00
[7] and 67.34% [8] of the world’s yams, which were cultivated
on 2.90 [7] and 6.24 million hectares [8], respectively, and were
worth billions of dollars. In addition to its value as a staple
crop, in Nigeria, yams are a major source of income for most
individuals and have tremendous cultural importance because
they are employed in numerous religious and cultural rituals.
Every year, festivals are held in various cultures to mark the
harvest season of yams. Given the current financial crisis Nige-
ria is facing, upscaling yam production for large scale export
can become a major export earner, which will in turn ease pres-
sure on the heavily import-dependent economy. Despite being
the world’s top producer of yams, there are many factors that
contribute to the food crisis in Nigeria. They include: changing
climatic variables; shrinkage in size of farm lands due to pop-
ulation increase; poor access to fertilizers and improved vari-
eties; conservative approach to agricultural extension programs
and services; obstructive market forces; poor terrain and soil
quality; farmers’ limited financial resources; disease and pests;
and improper timing of planting crops [9–14].

Crop production is influenced by meteorological variables,
hence it is important to understand these variables and how they
interact in order to determine how they together affect agricul-
tural productivity [15]. Currently, yam is one of the most ex-
pensive food items in Nigeria, yet it is also intensely sought af-
ter. In addition to improving foreign earnings from exports, the
improved production and availability of yams in Nigeria would
significantly improve access to food for many Nigerians, as the
nation strives towards attaining the Sustainable Development
Goal (SDG-2) which aims to eradicate hunger, establish food
security and enhanced nutrition, and advance sustainable agri-
culture. These SDGs can be used as tools for implementing
measures that can be used for minimizing the effects of climate
change that pose a serious risk to the entire ecosystem [16].

Several studies on agricultural land suitability for produc-
tion of different crops across Nigeria have been reported by
different researchers. For instance, Ikusemoran and Hajjatu
[17] studied site suitability for yam, rice and cotton produc-
tion in Adamawa state, Nigeria. In another study, Kunda and
Jajere [18], worked on suitability analysis for agricultural lands
in Nasarawa state, Nigeria. Ahmed and Jeb [19], researched
on suitability analysis for cultivation of sorghum in Kano state,
Nigeria. Zemba et al. [20] reported on suitability analysis for
decision-making in cassava cultivation in Adamawa state, Nige-
ria. Ujoh et al. [14], worked on suitability analysis for the pro-
duction of rice in Benue state, Nigeria. Ajala et al. [21] studied
soil suitability analysis for cassava production in Kwara state,
Nigeria. In Rivers state, Ondo, Katsina and Oya states, Peter
and Aumwerri [22], Tenkpa and Balogun [23], Abdullahi et al.
[24] and Ayaode [25] evaluated agricultural lands for the pro-

duction of Citrus, Cocoa, Millet and Rice respectively using
GIS, AHP or a combination of both. Generally, factors affect-
ing land suitability do not have an identical level of significance.
Hence, numerous factors must be taken into account in order to
determine if a particular area of land is suitable for agricultural
production or not. The weights (influence) of these criteria and
the scores of the sub-criteria were calculated using a variety of
ways [26].

The objective of the current study was to ascertain the agri-
cultural areas suitable for yam production using AHP and the
GIS technique. The AHP technique is one of the multi-criteria
decision-making methods that are frequently employed in agri-
cultural land use suitability analyses [26]. Yam production is
irregular across Nigeria. An understanding of the areas suit-
able for yam production would therefore become a vital start-
ing point for addressing the insufficiency of yam production in
Nigeria. Previous similar studies conducted (on yam specifi-
cally) in Nigeria [4-5,27] either focused on some sections of
the country rather than a national spatial scope, and/or did not
adopt the use of geo-spatial tools, thereby suggesting a critical
knowledge gap that is most likely a major contributory factor
in the relatively low yam production levels across Nigeria. This
study therefore closes the identified knowledge gap by adopting
geospatial tools in analyzing key weather and soil parameters
across all sections of Nigeria.

2. Materials and Methods

2.1. Study Area

Nigeria is located between 4◦ and 14◦ latitude and 3◦ and
15◦ longitude (Figure 1). The Swamp Forest, Tropical Rain-
forest, Guinea Savannah, Sudan Savannah, and Sahel Savannah
are among the vegetation types found on the 923,769 square
kilometers of land [28]. The dry and wet seasons are the two
basic seasons in Nigeria. A dusty air flux from the Sahara desert
known as Harmattan characterizes the dry season, while an air
flux from the South Atlantic Ocean characterizes the rainy sea-
son [29]. Projected increases in surface temperature, variable
precipitation, land and vegetation degradation, rises in sea level
and flooding, drought and desertification, increased frequency
of severe weather events, changes in fresh water resources, and
the loss of biodiversity are all signs that Nigeria’s climate sys-
tem is changing [30-31]. Nigeria has a tropical climate in gen-
eral, but there are many regions within the country that have
different climate conditions. Mangrove forests, swamps, and
hot, humid temperatures can be found along the southern ocean
coast, particularly along the Niger Delta, resulting in extremely
wet conditions. The Niger and Benue river valleys meet in the
inland area of Nigeria, which is the country’s largest and most
expansive area, with a diverse flora.

2.2. Data and Methods

2.2.1. Data
The data records used in this research work include: cli-

mate, soil, and environmental datasets. The climate data com-
prised of precipitation and temperature variables was obtained

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Igbawua et al. / J. Nig. Soc. Phys. Sci. 4 (2022) 883 3

Figure 1. Study Area

from the Climate Research Unit (CRU), and relative humidity
from the European Center for Medium Range Weather Forecast
(ECMWF) version 5, Reanalysis data (ERA-5). The soil data
set is comprised of pH, cation exchange capacity (CEC), and
soil organic carbon (SOC). Land use and land cover data were
obtained from the European Space Agency (ESA). The slope
data was retrieved from the Shuttle Radar Topography Mission
(SRTM) Digital Elevation Model (DEM). All the various data
sources used in this work are shown in Table 1.

2.2.2. Analytical Hierarchy Process (AHP)
The AHP method was used to develop a yam suitability

map for large scale yam production. The AHP method, doc-
umented by Saaty [32], is a Multi-Criteria Decision-making
(MCD) technique for resolving complex issues [33-34]. Fig-
ure 2 illustrates the procedure used in modeling the suitability
of yam production in Nigeria. The steps are (1) identification
and assessment of criteria and sub-criteria classes; (2) determi-
nation of a Geography Information System (GIS) database with
the criteria and sub-criteria, with each criterion’s relevance be-
ing weighted and obtained using the MCD-AHP technique; (3)
determination of the final suitability map for yam production
from the weighted overlay process in ArcGIS; and (4) evalua-
tion of vegetation and climate trends in the various suitability
classes.

Following the preparation of the thematic layers (steps 1 to
2), the AHP process was used to assign weights to all of the cri-
teria considered in this study. The criteria used in the work are
important for yam growth and development. They include tem-
perature, precipitation, relative humidity (Rh), pH, slope, soil
organic carbon (SOC), land cover, and cation exchange capac-
ity (CEC) presented in raster format.

Prior to creating the pairwise matrix, reclassification was

Figure 2. Research Flowchart. CEC refers to Cations exchange Capacity and
pH refers to potency of hydrogen

used to standardize the layers of each sub-criterion. A codifica-
tion of yam suitability options was then built using thresholds
found in related literature, as illustrated in Table 1. The sub
criteria were reclassified as ”HS”, ”S”, ”MS”, and ”NS”, rep-
resenting Highly Suitable, Suitable, Moderately Suitable, and
Not Suitable regions respectively.

The weighting of these influencing factors is based on fac-
tors that favor yam growth and expert opinion. A parameter
with a high weight indicates a layer that will have a significant
impact on yam production and vice versa. The relative impor-
tance of the values used for each parameter was assigned based
on Saaty’s scale of preference (1–9). Additionally, the weights
were assigned by reviewing earlier research and field experi-
ence. Table 2 provides the Saaty’s relative relevance rating [32].
A pairwise wise comparison matrix will be formed which will
show the thematic layers’ allocated weight and rank. To esti-
mate the weights, the eigenvector method is used to determine
the largest eigenvalue [35]. The pairwise comparison matrix
(PCM) B of m criteria constructed based on Saaty’s scale of
importance (Table 2), which is of the order (m × m), is the basis

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Igbawua et al. / J. Nig. Soc. Phys. Sci. 4 (2022) 883 4

Table 1. Parameters, data sources and their suitability classification
Parameter Data source Data Range

S1 S2 S3 NS
Temp.
(oC)

http://data.ceda.ac.uk/badc/cru/data/cru ts/cru ts 4.00/data/ 20-30 30-34 34-35 <20 >35

Prec.
(mm/yr)

http://data.ceda.ac.uk/badc/cru/data/cru ts/cru ts 4.00/data/ 1000-2000 2000-3500 >3500

pH http://dx.doi.org/10.3334/ORNLDAAC/1247 5-7.4 7.4-7.8 5.1-5.6 <5.1
>7.8

Rel. Hu-
midity (%)

http://cds.climate.copernicus.eu High Mid Low

CEC
(cmol/kg)

http://dx.doi.org/10.3334/ORNLDAAC/1247 17-23 10-16 5-10 <5

Land
Cover
(ESA
sentinel)

http://www.esa-landcover-cci.org/ Croplands,
Tree Cover

Mosaic
Natural
Vegetation

Grasslands,
Sparse
vegetation,
shrubs,
herbs

Urban,
Built,
water

Slope (%) http://srtm.csi.cgiar.org/ 0-4 4-8 8-20 >20
SOC
(g/kg)

http://dx.doi.org/10.3334/ORNLDAAC/1247 >50 30-50 0-30

for the factors influencing the weights [36].

B =


b11 b12 . . . . . . . . . . b1m

b21 b22 . . . . . . . . . . b2m

bm1 bm2 . . . . . . . . . . bmm

 = (bi j)m×m (1)
Similarly, B =

[
bi j

]
, i, j = 1, 2, 3, . . . . . . .m;

In general, the matrix B has the properties of reciprocality and
consistency, expressed as

bi j = 1/bi j(i , j)

And

bi j = bik/b jk for any i, j and k (2)

The weight of all the parameters was obtained using the pair-
wise comparison matrix and the maximum characteristic root
was determined as

B∗v∗ = λmaxv
∗ (3)

Where, matrix B∗ represents the estimate of B [35], v∗ the pri-
ority vector and λmax the Eigen value for B.
The Consistency Ratio (CR) is a criterion for judging a decision
made at random [37]; it is given by

CR = CI/RI (4)

Where, CI is the consistency index while RI is the random
consistency index which depends on the order n of the matrix
[32]. CI is given by

CI = λmax − 1/m − 1 (5)

Table 2. Saaty’s Scale of Importance
Variable Preferences
1 Equal Importance
3 Moderate Importance
5 Strong Importance
7 Very Strong Importance
9 Extreme Importance
2, 4, 6, 8 Intermediate Values between adjacent values

2.2.3. Validation of Results
The gridded Advanced Very High Resolution Radiometer

(AVHRR) Third Generation Normalized Difference Vegetation
Index (NDVI3g) of the Global Inventory Modeling and Map-
ping Studies (GIMMS) with a temporal resolution of 15 days
was utilized for validation of the derived suitability classes [38,
39]. The NDVI spans from +1.0 to -1.0 and in locations with
barren rock, sand, or snow (< 0.1), values are often relatively
low. Senescent crops and sparse vegetation like shrubs and
grasslands (approx. 0.2 to 0.5) might contribute to moderate
vegetation. High NDVI values (approx. 0.6 to 0.9) are cor-
related with dense vegetation, such as that seen in temperate
and tropical forests, or with crops in their prime developmental
stage [40]. NDVI is given [41] by

N DV I =
BN IR − BRED
BN IR + BRED

(6)

Where BN IR and BRED represents the Near-Infrared and Red
bands respectively.

2.2.4. Pearson Correlation Coefficient (r)
Pearson correlation analysis was employed to ascertain the

link between vegetation and climate parameters in the various
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Igbawua et al. / J. Nig. Soc. Phys. Sci. 4 (2022) 883 5

suitability classes.

r =

∑m
k=1

(
y j − y

)
(k j − k)√∑m

k=1 (y j − y)
2
.
∑m

k=1 (k j − k)
2

(7)

Where y and k are the variables and m refers to the length of the
variables.

3. Results and Discussion

3.1. Spatial Distribution of variables

Figure 3 depicts the studied maps of (a) mean temperature
(2m) from 1985 to 2015, with values ranging from 20.3 to 31.1◦

C, (b) Annual mean precipitation, ranging from 286.7 to 2055.4
mm/yr, (c) relative humidity, ranging from 40.7 to 91.2 %, (d)
CEC from 0.0 to 85.0 cmol/kg, (e) SOC from 0.0 to 4.2, (f) pH
from 4.3 to 8.5, (g) various land cover classes, and (h) slope (%
rise) from 0.0 to 45.0. High temperature values are most preva-
lent in the northern part of the study area. Relative humidity and
precipitation, on the other hand, are more widely distributed in
the southern part of the research area. The spatial maps of CEC,
SOC and pH revealed variable distributions across the study
area. The slope map displayed in Figure 3 indicates the highest
values around the Mambilla and Jos plateaus, while low values
are scattered around the Niger-Benue trough and Niger Delta
area. In Figure 4, the examined parameters (Figure 3) are used
independently for the evaluation of suitable areas for yam pro-
duction. Each parameter yields a varying degree of suitability
for yam cultivation in Nigeria based on the suitability groups or
classes: Highly Suitable (HS), Suitable (S), Moderately Suit-
able (MS) and Not Suitable (NS).

The yam is a tropical root tuber and vine that is intolerant of
cold. Temperatures between 25 and 30◦C are required for yam
tuber growth, while temperatures below 20◦C stifle growth [42,
43]. High temperatures exceeding 35 ◦C, according to Adifon
et al. [44], are detrimental to yam growth and development and
may cause fluctuations in moisture content, which may have an
influence on agricultural activities if the plants have adaptation
problems [15]. The majority of yam varieties require an average
of 7 to 9 months of growing time before harvesting. To fully
exploit the yam’s production potential, it requires over 1,500
mm [42, 43] of annual precipitation distributed evenly over the
vegetation period. In contrast, high precipitation amounts in ex-
cess of 3500 mm are not too good for yam cultivation and may
affect its production. As a result, a long rainy season during
the growing season has a positive impact on yam yield, espe-
cially if precipitation is well distributed during the wet season
with adequate humidity. As a key weather factor that affects the
amount of moisture in the atmosphere, humidity has a consider-
able impact on agricultural output [15]. Seasons and regions in
the northern section of the country with abrupt heavy precipita-
tion amounts and distributed within 2 to 3 months may not be
favorable for yam production. After the few months of heavy
precipitation, the remaining months may be too dry for the yam,
except in areas where irrigation measures are put in place. Ar-
guably, the plant can withstand longer periods of drought, but

this reduces the yield significantly. In terms of storage, low hu-
midity and dry conditions are necessary for long-term storage
to avoid damage to the yields.

Yams also require red loam soils with a pH range of 5.0 to
7.0 [45]. The sandy clay loams are the best soils for growing
yams. In Nigeria, yams can be grown in almost any soil type
if the ridges are raised properly and stuffed with organic mat-
ter. The organic material in the soil enhances the soil structure,
works as a steady source of manure, and promotes high yam
tuber production. Other critical requirements for yam growth
include sufficient drainage, adequate aeration, crumbly soil, ad-
equate moisture supply, and sticks or tiny trees to serve as stacks
for the leaves to creep and develop properly without interfer-
ence.

The gradient (slope) of the research area is very important
in determining the pattern of land use, which has a direct im-
pact on groundwater flow in any area [45]. A research work
by Poesen [46] reported that as the slope angle increased, so
did the rate of infiltration [47]. The slope of an area influences
surface water infiltration. In a given region, groundwater infil-
tration at the surface may not occur at the same gradient. Areas
with gentle slopes experience the highest infiltration and mini-
mal amount of surface water runoff, whereas regions with steep
slopes may experience high runoff and minimal infiltration of
surface water [34].

3.2. Determination of Pairwise Matrix and integration of the-
matic layers

Using equations 1 to 5 and Table 2, a pairwise compari-
son of all the thematic layers (rasters) was performed in order
to create a PCM. The sub-classes (sub-criteria) of each layer
rank were assigned scores from 1 to 9 in relation to their in-
fluence on yam production. The pairwise matrix is given in
Table 3. The results show that temperature had the highest in-
fluence, followed by precipitation and land cover, with weights
of 30.9, 24.7, and 15.9, respectively. Each thematic layer and
their associated attribute classes were assigned weights and rat-
ings before being integrated (overlayed) in ArcGIS software us-
ing the spatial analyst tool box. The final overlayed map is
given in Figure 5. During the pairwise comparison matrix via
the MCD analysis, the consistency index was obtained as 7.2%,
which indicates a fair judgment. The highly suitable (HS) areas
are mainly scattered in the south and north-central areas, while
the marginally suitable (MS) areas are mostly located in the far
north. Arguably, the whole country is suitable (S) for yam pro-
duction, with most of the pixels scattered across the study area.
A few areas that are not suitable (NS) for yam production com-
prise bare land (open fields), water, and urban areas. Figure 6
shows the percentage composition of landmass for each suit-
ability class. The result indicates that HS, S, MS, and NS areas
account for 11.79, 82.68, 4.05, and 1.47% of the study area,
respectively. HS areas are primarily found in the north-central
and southern regions, while S areas are scattered all over the
study area from north to south. Meanwhile, MS areas are ma-
jorly observed in the country’s extreme north. NS areas are
comprised of wetlands, bare areas, open spaces, and urban cen-
ters.

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Igbawua et al. / J. Nig. Soc. Phys. Sci. 4 (2022) 883 6

Table 3. PCM of all the variables relevant to yam crop suitability in Nigeria
TEMP PRE LCD CEC SOC pH R.HUM SLP WEIGHT

TEMP 1.000 2.000 3.000 5.000 4.000 6.000 5.000 7.000 30.9
PRE 0.500 1.000 3.000 4.000 4.000 5.000 5.000 7.000 24.7
LCD 0.333 0.333 1.000 3.000 3.000 5.000 4.000 6.000 15.9
CEC 0.200 0.250 0.333 1.000 3.000 4.000 3.000 3.000 10.0
SOC 0.250 0.250 0.333 0.333 1.000 4.000 3.000 5.000 8.1
pH 0.167 0.200 0.200 0.250 0.250 1.000 3.000 3.000 4.5
R.HUM 0.200 0.200 0.250 0.333 0.333 0.333 1.000 1.000 3.2
SLP 0.143 0.143 0.167 0.333 0.200 0.333 1.000 1.000 2.7

Figure 3. Maps of (a) mean temperature (oC), (b) annual mean precipitation
(mm), (c) relative humidity (%) from 1985-2015. (d) Cations Exchange Ca-
pacity (cmol/ kg), (e) Soil Organic Carbon (%) (f) Land cover, (g) slope (%).
CL is for cropland, MNV mosaic natural vegetation, SHRB shrubs, SPV sparse
vegetation, BL Bare land, HBC herbaceous, TC tree cover, GL grassland, UBN
Urban and WT water

3.3. Relationship between NDVI and Climate parameters in
various suitability classes

According to Nwankwo and Ukhurebor [3], monitoring cli-
mate variability is crucial because of the functions it plays in the

Figure 4. Raster map showing suitability criteria for yam cultivation Nigeria,
constraint (a) mean temperature, (b) mean precipitation, (c) relative humidity
(d) Cations Exchange Capacity, (e) Soil Organic Carbon, (f) Land cover, (g)
slope

agricultural system. In this work, the interaction between NDVI
and climate variables was determined in the various suitability
classes in Nigeria. Even though there are many vegetation in-
dices used in studying vegetation activity, NDVI is one of the
most extensively used. The NDVI index measures the green-

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Igbawua et al. / J. Nig. Soc. Phys. Sci. 4 (2022) 883 7

Figure 5. Map of land suitability analysis for yam field cultivation based on
multi-criterion decision method

Figure 6. Pie chart showing percentage composition of the suitability classes

ness of vegetation over a given area. It can be used as a proxy
for vegetation productivity [41, 48] and phenology [40]. The
NDVI across the seasons in HS, S, and MS suitability classes
was extracted and presented in Figure 7. The results show that
NDVI is above average (>0.5) from Apr to Dec for the HS class,
and from Jun to Nov for the S class. In contrast, for the MS
class, NDVI is below average (<0.5) for all the months. Simi-
larly, precipitation is > 100 mm/month between April and Oc-
tober for the HS class, between May and September for the S
class, and between June and July for the MS class. Tempera-
tures are higher during the start of the season (Apr-May) and
lower in Jul-Aug for all classes. NDVI and precipitation are
bimodal in HS suitability (Figure 7). In contrast, NDVI and
precipitation are unimodal in S and MS. Also, temperatures are
bimodal in all the suitability classes, with peaks in the MAM
and SON seasons. Meanwhile, NDVI for the HS class has peaks
in MAM and SON while a single peak exists in JJA for the S
and MS classes. Overall, the NDVI and precipitation range in
the HS class is higher, followed by the S class, and then the MS
class. In contrast, the temperature range in HS is lower, fol-
lowed by S class, and then MS class. This shows that there is
abundant precipitation in the HS class and temperature remains
the limiting climatic variable. In contrast, temperature (precip-
itation) is normally high (low) in the MS class. Meanwhile, the

S class has moderate precipitation and temperatures, which are
just sufficient for yam production. Our method shows that there
was consistency in the MCD analysis during the assignment of
weights. Given that temperature and rainfall are directly related
to how biologically plants grow, there is a strong association be-
tween agricultural productivity and these weather factors [15].

Figure 8, 9, and 10 show the scatterplot matrix of NDVI,
precipitation, and temperature for the various suitability classes.
The results show that in the HS class (Figure 8), NDVI is posi-
tively and significantly correlated with precipitation, r = 0.552
(p < 0.01), and NDVI is negatively and significantly corre-
lated with temperature, r = -0.254 (p < 0.01). Meanwhile,
precipitation correlates negatively and insignificantly with tem-
perature in the HS class, r = -0.429 (p < 0.01). In S class
(Figure 9), NDVI was positively and significantly correlated
with precipitation, r = 0.702 (p < 0.01); NDVI was negatively
and significantly correlated with temperature, r = -0.161 (p <
0.01). Meanwhile, precipitation correlates negatively and in-
significantly with temperature in the S class, r = -0.048 (p =
0.325). Also, in the MS class (Figure 10), NDVI is positively
and significantly correlated with precipitation, r = 0.685 (p < 0.01),
and NDVI is negatively and significantly correlated with tem-
perature, r = -0.129 (p < 0.01). Meanwhile, precipitation cor-
relates positively and insignificantly with temperature in the
HS class, r = 0.068 (p = 0.165). The NDVI trends in the
HS, S, and MS classes are 0.000228, 0.000164, and 0.000037
NDVI/month, respectively. For precipitation, the trends in the
HS, S, and MS classes are -0.002459, 0.015347, and 0.0355287
mm/month, respectively. In the HS, S, and MS classes, the tem-
perature trends are 0.000944, 0.001344, and 0.001761 ◦C/month,
respectively. Specifically, NDVI increases with an increase in
precipitation at different lags (1 to 2 months) for all the suit-
ability classes. In contrast, NDVI decreases with an increase
in temperature for all the suitability classes. The best agree-
ment between NDVI and temperature is at the start (MAM) and
end (SON) of the season. The work shows that Nigeria’s cli-
mate system is primarily influenced by precipitation, followed
by temperature, which is in agreement with a report by Igbawua
et al. [49].

3.4. Yam Production and Yield Gap in Nigeria
As earlier mentioned, Awoniyi & Omonona [5], Ike & Inoni

[6] noted that Dioscorea rotundata (white yam) and Dioscorea
alata (water yam) are the major yam species cultivated in Nige-
ria. Over the years, Benue, Taraba, Nassarawa, Enugu, Kaduna,
Cross River, Ondo, Ekiti, Kogi, and Niger [50]. From Fig-
ure 5 and 6, the total area of land in HS, S, and MS avail-
able for yam production is 107,059.4474, 746,718.8272, and
33,074.9333 km2 respectively, which represents 10,705,944.74,
74,671,882.72 and 3,307,493.33 hectares respectively. Accord-
ing to Verter and Bečvářová [7], the yield rate was between
20–50 tons/hectare in years with bumper harvests, while about
10 tons/hectare was in years with poor harvests.

To get the yield gap, we adopted the average yield in years
of poor harvest (to account for worst case scenarios) to compute
the expected yield in HS, S, and MS. The expected yield for
MS, S, and HS using the yield rate of 10 tons/hectare indicates

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Igbawua et al. / J. Nig. Soc. Phys. Sci. 4 (2022) 883 8

Figure 7. Seasonal NDVI for HS, S and MS suitability classes

Figure 8. Scatter plot matrix for NDVI and climate parameters for HS region

Figure 9. Scatter plot matrix for NDVI and climate parameters for S region

107.06, 746.72, and 33.07 million tons per hectare in the study
area. The total area comprising HS, S, and MS is about 886.85

Figure 10. Scatter plot matrix for NDVI and climate parameters for MS region

million tons. The expected yield is far above the actual yield
of 38.00 [7] and 50.05 [8] million tons for 2012 and 2019, re-
spectively. The yield gap between the actual and expected yield
is more than 800 million tons. In general, the expected yield is
not always achieved due to a variety of factors such as declining
soil fertility, poor planting methods, high labor costs, increase
in pest [51], crop rotation, climate change, floods, droughts, and
persistent displacement of farmers from their settlements. This
is in addition to other factors such as increasing population,
which is driving land fragmentation, and declining soil produc-
tivity as a result of shorter fallow periods and prolonged chem-
ical application (synthetic fertilizer and pesticides/herbicides).
Also, the expected yield can never be achieved because most of
the fertile lands are yet to be ploughed and they are covered by
other land cover types.

4. Conclusion

Yam, arguably one of the main food items in homes across
Nigeria, is not cultivated all year round. It is heavily depen-
dent and reliant on rain during the wet season and is, there-
fore, a mono-seasonal crop. A suitability analysis for yam pro-
duction in Nigeria was done to determine suitable regions for
yam cultivation, labeled as highly suitable (HS), suitable (S),
marginally suitable (MS) and not suitable (NS). The approach
uses both subjective and objective techniques to assign weights
to variables through the GIS and AHP methods. The variables
were later overlayed into a single suitability map in ArcGis
10.5. Given the high potential of yam production in Nigeria,
it is essential to determine the suitable regions for yam produc-
tion over the study area to improve food security and maximize
foreign exchange.

Our findings show that HS, S, MS, and MS were 11.79,
82.68, 4.05, and 1.47 % of the study area. Further analysis
showed that vegetation vigor (activity) in the HS regions was
higher, followed by S regions, and then MS regions. Simi-
larly, climate variables in HS regions were more favorable for
plant growth, followed by S regions and MS regions. This has

8



Igbawua et al. / J. Nig. Soc. Phys. Sci. 4 (2022) 883 9

been confirmed by Pearson correlation analysis between vege-
tation (NDVI) and climate parameters in the various suitability
classes. Our findings also show that there is a high yield gap
in yam production over the study area. The yam crop is not the
only crop that is produced on the land that is suitable for its pro-
duction. Thus, yam production is not maximized over the study
area. Other factors include crude agricultural practices, mul-
tiple cropping, floods, climate change, drought, urbanization,
and population increase.

Conclusively, this approach is very useful in land suitabil-
ity assessment. The methods and materials are very cheap and
readily available. The results from this work can serve as a
guide for future yield projections in yam production by policy-
makers and agriculturists. Understanding the lands best suited
for yam planting would reduce wastage of resources increase
productivity, which will help improve food security. The opti-
mization of yam yield in Nigeria will go a long way in improv-
ing local consumption and foreign exchange.

Acknowledgment

The authors wish to appreciate the editor and the reviewers
for their valuable comments that improved the quality of this
study.

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