Dietary diversity, nutritional status, and agricultural commercialization: evidence from adult men of rural farm households

Dietary diversity, nutritional status, and agricultural commercialization:
evidence from adult men of rural farm households
Olutosin Ademola Otekunrin a,⁎, Idris Akanbi Ayinde a
, Rahman Akintayo Sanusi a
, Oluseye Olusegun Onabanjo b
a
Department of Agricultural Economics and Farm Management, Federal University of Agriculture, Abeokuta (FUNAAB), Nigeria
b
Department of Nutrition and Dietetics, Federal University of Agriculture, Abeokuta (FUNAAB), Nigeria
A B S T R A C T
A R T I C L E I N F O
Keywords:
Farm households
Malnutrition
Dietary diversity
Adult men
Nigeria
Body mass index (BMI)
Crop Commercialization Index (CCI)
Access to healthy and affordable diet hinged on the realization of Sustainable Development Goal 2, higher productivity
and, economic prosperity while it is difficult for a poorly nourished people to achieve optimum production of goods
and services. This study assessed whether dietary diversity (DD) and nutritional status of adult men are associated
with crop commercialization index (CCI) levels of agricultural households in two states of Southwestern Nigeria.
This research utilized 352 farm households, comprising 277 adult male members. The individual version of dietary di-
versity score (DDS) of 9 food groups was used to calculate adult men’s DDS over a 24-h recall. Anthropometric data was
obtained using body mass index (BMI) while CCI levels was estimated for each agricultural household. Logistic regres-
sion and ordered logit models were used to examine the determinants of adult men’s dietary diversity and nutritional
status respectively. Hundred percent of adult men consumed starchy staples, with 11.2% consuming egg, 5.8% milk
and milk products and 0.4% consuming organ meat over 24-h recall. Adult men of CCI 2 and CCI 4 agricultural house-
holds recorded overweight prevalence of >20% in Ogun state while the association between DDS and CCI was statis-
tically insignificant suggesting that being a member of any of the CCI households may not guarantee the consumption
of healthy diets among adult men. From Logit regression analysis, it is more likely for adult men with higher farm size
to attain the minimum DDS of 4 food groups than those with smaller size of farmland (OR = 4.78; 95% CI: 1.94, 11.76;
p = 0.001). The age, farm experience, and cassava marketing experience were positively related to the likelihood of
obtaining the minimum DDS. For adult men to achieve a healthy diet, their diet pattern must incorporate a more di-
versified intake of food from different food groups capable of improving their nutritional status. This study emphasized
the need for relevant stakeholders to provide adequate nutrition knowledge intervention programmes capable of im-
proving the diets and nutrition of adult men and other members of farm households.
1. Introduction
Adulthood is regarded as a peculiar state of being an adult that spans
through physical and mental maturity. The world population continues to
increase while the proportion of adult population also continues to go up
steadily. According to Countrymeters (based on the latest United Nations
data), population of the world stood at 8,013,686,811 as of 31 August
2022 where male population was 4,043,347,465 (50.5%) and the female
population stood at 3,970,339,788 (49.5%) [1]. However, Nigeria popula-
tion as of 31 August 2022 stood at 218, 365,381 with male population of
110,575,350 (50.6%) and female population of 107,790,058 (49.4%)
while 120,034,037 persons (55.9%) are in the age-group of 15-64 years
old [2]. As health, food and nutrition security of children and adolescents
of any population are critical [3,4] those of adults (>20 years old) are
equally of paramount importance especially as they form the critical mass
contributing to the economic development of the country.
In the recent report of FAO and other partners, the prevalence of hunger
(undernourishment) rose from 8.0% in 2019 to 9.8% in 2021 with esti-
mated value between 702 and 828 million people of the world affected
by hunger in 2021 with the impact aggravated by the emergence of
COVID-19 pandemic in December 2019 [5]. Africa has the highest numbers
of population (20.2%) suffering from undernourishment in the world when
compared to about 25 percent found in other regions of the world (Asia,
Latin America & Caribbean, Oceania, and Northern America and Europe)
in 2021 [5].
Further, in Nigeria, the prevalence of hunger is not different as the coun-
try ranked 106th
from 136 countries captured in the 2022 Global Hunger
Index (GHI) with GHI score of 27.3, falling in the ‘serious’ category of
GHI severity scale. The proportion of undernourished in the Nigerian pop-
ulation rose from 8.9% in 2000 to 12.7% in 2022, while just behind Papua
New Guinea (103rd
), Comoros (104th
) and, Rwanda (105th
) in 2022 GHI
ranking [6]. However, 2.3 billion people in the world (about 30%
Dialogues in Health 2 (2023) 100121
⁎ Corresponding author.
E-mail address: otekunrin.olutosina@pg.funaab.edu.ng (O.A. Otekunrin).
http://dx.doi.org/10.1016/j.dialog.2023.100121
Received 16 December 2022; Received in revised form 22 January 2023; Accepted 3 March 2023
Available online 11 March 2023
2772-6533/© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.
0/).
Contents lists available at ScienceDirect
Dialogues in Health
journal homepage: www.elsevier.com/locate/dialog

population) were reported to be facing moderate-severe food insecurity in
2021 while according to gender, higher burden of moderate-severe food in-
security was experienced by women (31.9%) when compared to that of
men (27.6%) in 2021 [5]. According to 2022 Global Food Security Index
(GFSI) scores [7] based on regional average index scores (see Fig. 1), sub-
Saharan Africa (SSA) had an average of 47 point, and remained the only re-
gion that performed below the global average (62.2) while the North
America led the index with 78.6. However, Nigeria ranked 107th
out of
113 countries captured in the 2022 GFSI ranking and was 25th
out of the
28 SSA countries, with final GFSI score of 42 (42/100). According to the re-
port, Nigeria had the lowest score (25) in affordability category “affordabil-
ity of food” is one of the four pillars used in estimating the index) which is
the lowest (113th
) among all countries captured in the 2022 GFSI index
scores. This was occasioned by unprecedented rise in the food costs, ex-
treme climate conditions-flooding, terrorism, and inadequate or non-
existent food safety-net programmes especially among the most vulnerable
population of the country – rural women and young children [7].
Access to healthy and affordable diet hinged on the realization of Sus-
tainable Development Goal 2, higher productivity and, economic prosper-
ity in the world while it is difficult for a poorly nourished people to
achieve optimum production of goods and services. However, the recent
upsurge in food prices has led to reduction in the affordability of nutritious
foods globally as occasioned by the adverse effect of COVID-19 pandemic
[5,8–11]. Globally, diets are nowhere near being healthy and sustainable
and, have not received substantial improvement in last the 10 years [12].
Recent report found out that no regions of the world met the recommenda-
tions for healthy and sustainable diets [12].
However, developing countries have smallest consumption of fruits and
vegetables coupled with highest burden of underweight, while on the flip
side, developed countries have the highest consumption of processed
foods (red meat and dairy) with increased health and environmental effects
and also the highest prevalence of overweight and obesity [8,11,12].
Further, recent estimates revealed that 149.2 million under-five chil-
dren are stunted, 45.4 million are wasted, and 38.9 million are overweight.
However, 2.2 billion adults are overweight or obese (women, 40.8%; men,
40.4%) while 451.8 million are underweight (women, 9.1%; men, 8.1%).
Among adults, unhealthy diets resulted into more than 281 million years
of life lost (YLLs) [12]. In most sub-Saharan African countries, studies
have reported that larger population of the undernourished people are
members of smallholder farm households whose means of livelihood are
largely dependent on agriculture [4,13].
Agricultural commercialization has been identified as one of the viable
drivers of household welfare outcomes (such as increased income, food and
nutrition security) of rural dwellers especially in sub-Saharan Africa (SSA)
[14–17]. However, previous studies have shown that farm households’ in-
volvement in agricultural output markets pave way for better financing in
agricultural production process, resulting in increased productivity and
may also lead to improvement in farm households’ food and nutrition
security, provided a part of the income is devoted for the purchase of diver-
sified and nutritious food [17–19].
Previous studies in Africa have focused on the dietary diversity (DD) of
women especially those of child bearing age category [11,20–21] and
others, from the perspective of under-five children of agricultural and
non-agricultural households [3,22–23]. Further, some studies investigated
DD of some other age groups in the households such as adolescents
[3,24,25] while studies regarding the nutritional status (NS) of members
of household have also been given some level of priority in literature
[26–28].
Previous works that centred on the DD of adult male members espe-
cially the agricultural households are scarce. Among few studies on dietary
diversity of adult male members was carried out in Tanzania [29] (Ochieng
et al. 2017) where adult male members’ DD was qualitatively assessed.
Other studies outside Africa include [30], which investigated diet quality
of Mexican men in relation to their socioeconomic status while [31] exam-
ined factors influencing nutrition outcomes among adult male employees in
Nepal. However, this study focused on the influence of commercialization
levels on dietary diversity and nutritional status of adult men of agricultural
households.
2. Methods and materials
2.1. Study Area
This research was implemented in Ogun and Oyo states of Nigeria.
However, Nigeria is located in West Africa with the land mass of 923,768
square kilometre, latitude 10° 00ˡ N and 8° and 00ˡ E [32]. The capital of
Ogun state is Abeokuta while the capital of Oyo state is Ibadan as indicated
in Fig. 2 a & b.
2.2. Sampling procedure and data collection
This study employed multi-stage sampling procedure. In the first stage,
two cassava producing states were randomly selected from South-West
Nigeria. Then, 5 Local Government areas (LGAs) were selected from Oyo
state and 3 LGAs from Ogun state making 8 LGAs in the two states. Thirdly,
24 communities was selected from the 8 LGAs while in the fourth stage, six-
teen cassava farming households was selected from 24 communities giving
a total of 384 farm households. The data were collected through semi-
structured questionnaire which contained details of the following; socio-
economic characteristics, farm income, non-farm income, expenditure on
food, transport cost, access to health and environment factors. However,
only data from 352 farm households were found usable after data cleaning,
having 277 adult male members. Further, anthropometric details such as
adult men’s height and weight were measured and recorded accordingly.
Also, over a 24-h recall, total food groups eaten by the adult men were re-
corded and used as a means of calculating the each DDS of adult men of
Fig. 1. Authors’ graph using GFSI 2022 (Economist Impact, 2022).
O.A. Otekunrin et al. Dialogues in Health 2 (2023) 100121
2

the households. The anthropometric measurements were used in determin-
ing the nutrition outcome. The Body mass index (BMI) was calculated as the
body weight in kilogrammes divided by the square of the body height in
metres (kg/m2
) of each adult men of cassava farm households. The BMI is
widely used to classify the nutritional status of adults [5]. Adult men are
classiﬁed (WHO standard) as underweight if BMI is below 18.5kg/m2
, nor-
mal or healthy weight if BMI is 18.5kg/m2
–24.9kg/m2
, overweight if BMI
is 25–29.9kg/m2
, and obese if BMI ≥30.0kg/m2
[26,35,36].
2.3. Ethical consideration
Informed consent was obtained from all individual that participated in
this study. The AEFM Review Board of FUNAAB approved this study. Fur-
thermore, the Ethics Review Committee of Oyo State Ministry of Health
also approved the study with Ref No. AD13/479/4420A
.
2.4. Statistical analysis
2.4.1. Estimating cassava commercialization levels
In this study, the levels of cassava commercialization was determined
using a household crop commercialization index (CCI), [17] Wakaba
et al. 2022. The CCI was computed as the proportion of the total value of
harvested cassava that was sold as indicated in equation (1);
CCIi ¼
Gross value of crop salehhi,yearj
Gross value of crop productionhhi,yearj
100 (1)
Different quantities of cassava roots were sold across different market-
ing channels which include; farm gate, close market centres; processors
and other farmers in the neighbourhood. The prevailing cassava market
prices was used at the time of data collection. The CCI scores ranged from
zero (CCI = 0; subsistence) to hundred (CCI = 100; full commercializa-
tion). Using this approach, cassava farm households were grouped into
four on the basis of their cassava commercialization levels - CCI 1-4
[11,37].
2.5. Assessing adult men’s dietary diversity score (DDS)
In this study, IDDS questionnaire (for the adult male members) was used
in determining dietary diversity (DD) of farm households. This approach re-
veals how diverse the diets of the members in a particular period of time
which could be over the period of 30-day, 7-day, or 24-hr. In this study,
24-hr recall period was used. However, nine food groups was adapted for
Fig. 2. a: Map of Ogun State [33]. b: Map of Oyo State [34].
3

20 year-old men. However, a minimum of 4 food group out of the 9 was
used as recommended benchmark by World Health Organization (WHO)
[3,37–40]. The IDDS incorporated both foods prepared at home and the
away-from-home foods. The 9 food groups captured in IDDS include:
(1) Starchy Staples, (2) Dark green leafy vegetables, (3) Other vitamin A
rich fruits and, (4) Other fruits and vegetables (5) Organ meat (offals),
(6) Meat and fish, (7) Eggs, (8) Legumes, nuts and seeds, (9) Milk and
milk products [3,38–41]. The dietary diversity score (DDS) of adult male
members (20 years old) was determined by summing the food groups
eaten in the previous day to the survey. However, a score of 1 was given
to every food group giving a total of 9. The percent of adult men having a
minimum of 4 food groups was estimated.
2.6. Anthropometric assessment of adult men
In this study, a height metre (stadiometre) was used to measure the
height of adult men of farm households while the weight was measured
using a bath room weighing scale. However, the reading was done to the
nearest 0.1kg in order to obtain accurate measurements. The BMI was cal-
culated as expressed above (sampling procedure and data collection sec-
tion) and compared with four WHO BMI categories. In this case, the adult
men are categorized as underweight, normal or healthy weight, over-
weight, and obese [26,35–37,42].
2.7. Modelling the determinants of adult men’s dietary diversity and nutritional
status
The drivers of DD among adult men were analysed using logit regres-
sion model [37]. The dependent variable (DD) is binary and designated
as 1 if an adult man has ≥4 food groups, and zero otherwise, given as a
function of a vector of explanatory variables assumed to affect dietary di-
versity of adult men. The explanatory variables used in the model; age of
adult men, household head gender, household size, farm size, farm experi-
ence, marital status, year of education, farm income, road condition, cas-
sava marketing experience, toilet access among others. Following [43],
the logit regression is expressed as indicated in equation (2):
Logit p
ð Þ ¼ ln
p
1−p

¼ β0 þ βiXi þ Ui ð2Þ
Here, p denotes the likelihood of having dietary diversity of having
≥4 FGs, the β0
is are the parameter estimates of the independent variables,
the X0
is represent the regressors and U0
is are the random error terms [43].
The determinants of adult men’s nutritional status were analyzed
employing multivariate ordered logit model. In this study, the BMI (depen-
dent variable) is used to classify the nutritional status of adult men. The four
BMI categories are: (i) underweight (ii) healthy weight (iii) overweight and
(iv) obesity as expressed in [35] and [36]. However, it has been observed
that the use of ordered logit or ordered probit models is basically a matter
of choice as both are employed in estimating ordinal survey data [44–45].
Further, it is observed that logit coefficients are always expressed in log-
odds unit and will not be appropriate giving it ordinary least-square (OLS)
interpretation but rather as marginal effects (ME) (to estimate changes in
the likelihood of BMI results in respect of explanatory variables [46–48].
In this model, the observed ordinal variable is given as Z and it is a function
of another variable z* not estimated. The z* has threshold points as given in
[44] and [45] and expressed in equation (3):
z
i ¼ x0
i β þ εi ð3Þ
z∗
i , the hidden variable of the BMI categories of adult men i,x′
i is a vector of
explanatory variables describing agricultural household i, β is a vector of
parameters to be estimated and εi is a random error term following a stan-
dard normal distribution. However, given WHO recommendation, BMI is
categorised into 4 outcomes (1) underweight (2) normal or healthy weight
(3) overweight and (4) obesity. Similarly, [49] employed ordered probit
model using 2013 National Demographic and Health Survey (NDHS) data
and categorizing anthropometric indices of under-5 into four (well
nourished, stunted, wasted, and underweight).
Choice rule:
zi ¼
1 if z
i ≤μ1 underweight
ð Þ
2 if μ1 ≤z
i ≤μ2 healthy weight
ð Þ
3 if μ2 ≤z
i ≤μ3 overweight
ð Þ
4 if z
i μ3 obesity
ð Þ
0
B
B
B
@
ð4Þ
3. Results
3.1. Description of cassava farm households and adult men-related factors
The findings in Table 1 showed the representation of cassava farm fam-
ilies and adult men-related factors. The findings revealed that the average
age of adult male members was 50.6 years old, household size was 6 per-
sons and, household head (HH) year of schooling was 7.1 years. Mean-
while, the average farm size of households was 1.5 hectares while farm
income and non-farm income was N133, 949 (415 US $) and N81, 206
(251 US $) per annum respectively. The mean monthly food expenditure
and cassava production for the households was N22, 643 (70 US $) and
11, 353kg respectively. Also, the average cassava farming experience,
adult men’s DDS and BMI were 11.7 years, 5.1 and 23.1 Kg/m2
respec-
tively. The mean crop commercialization index (CCI) levels for the two
states was 0.61.
The findings on the food groups eaten by adult men of the farm families
in a day prior to the survey are presented in Table 2. The DDS findings
showed that 100 percent of adult men in the farm households consumed
carbohydrate-dense foods (i.e. mostly food prepared from starch: “fufu”,
“ogi”, pounded yam, “eba” etc). Meanwhile, 62% of them ate legumes,
nuts and seeds (Ogun, 69.2%; Oyo, 4.8%) while about 67% consumed
dark green leafy vegetables in the two states (Ogun, 56%; Oyo, 72.6%). Sur-
prisingly, only one person (0.4%) of adult men consumed organ meat, 5.8%
of milk/milk products while 12% ate eggs in study areas within 24-h re-
call. Meanwhile, 10 percent of the adult men failed to attain the accepted 4
food groups in the two states. The DDS average score of 5.13 (Ogun) and
5.06 (Oyo) was obtained among adult men. This findings indicated that
most of the men attained above 4 groups recommended even though
most were of low quality diets especially starchy foods.
Table 1
Representation of farm household and men-associated factors.
Variables Representation Mean ± SD
Age Adult male member’s age (years) 50.6 ± 12.4
Household size Number of Persons in the farm family 6.4 ± 2.8
HH education Year of education 7.1 ± 4.5
Farm Size farmland size of farm family (ha) 1.5 ± 1.0
FarmInc Household farm income (naira) N133,949 ± N118,499
Non-farmInc Household non-farm income (naira) N81,206 ± N54,334
FarmExp farming experience (year) 16.5 ± 11.0
Cass marketing
Exp
Cassava marketing experience (years) 11.7 ± 9.2
Food Expenditure Food expenditure of farm family
(naira)
N22,643 ± N10,605
DistMarket Distance to market (Km) 8.8 ± 4.1
Transport cost Transport cost on monthly basis N3,535 ± N1,383
CropShare Crop Commercialization index level 0.61 ± 0.29
Cassava Prdn Cassava production (Kg) 11,353 ± 8,680
Cassava sold Cassava root sold (naira) N8,098.45 ± N7,380
DDS Dietary diversity score (0-9) 5.1 ± 0.94
Men’s ht Adult men’s height (Metre) 1.67 ± 0.07
Men’s wt Adult men’s weight (Kg) 64.32 ± 8.71
BMI Body mass index of adult men
(Kg/m2
)
23.1 ± 3.1
Field Survey Data, 2020 Note: SD = Standard Deviation; 1 US $ = N323.
4

3.2. Assessing cassava commercialization levels
The extent of commercialization revealed the commercialization levels
of the individual households which is categorised into four (CCI 1- CCI 4).
The CCI levels were obtained via CCI of every farm household as expressed
above (equation 1). The findings indicated that 6.6% (in Ogun) and 14.0%
(in Oyo) of farm households did not take part in the sales of cassava roots in
any market outlets (zero commercialization – CCI 1). Notably, 9.9% (in
Ogun) and 18.8% (in Oyo) of farm households was categorized as low
level commercialization (CCI 2). Meanwhile, 28.6% and 29.6% of small-
holder farm households was categorised as medium-high level commercial-
ization (CCI 3) while 54.9% and 37.6% fell into very-high level
commercialization in Ogun and Oyo states respectively. The maximum
CCI attained in the two states was 95.1%
3.3. Exploring DDS of adult men of farm households and CCI levels
The findings of the DDS of adult men of cassava farm households and
their corresponding commercialization levels (CCI 1 -4) are presented in
Table 3. It revealed that only 6.3% of the adult male members belonging
to zero commercialization households (CCI 1) failed to meet the minimum
4 FGs in the both states. Meanwhile, 100% of men in Ogun state and 85.7%
of them in Oyo state (belonging to low commercialization level) consumed
≥ 4 food groups in 24-h. However, only 12% (in Ogun) and 10% (in Oyo)
of adult men of CCI 4 households failed to meet the minimum FGs. In gen-
eral, this finding revealed that greater percent (90.3%) of adult men in all
the four commercialization households met the minimum 4 food groups
in the study areas. Furthermore, the relationship between adult men DDS
and CCI as shown by the scatter plot (Fig. 3), the presence of positive asso-
ciation between the two variables with Correlation Coefficient (r) = 0.07.
The relationship between adult men’s DDS and CCI levels was not statisti-
cally significant.
3.4. Adult men’s nutritional status
The findings on the nutritional status of adult men are presented in
Table 4. About 68% and 77% of adult male members were found to be hav-
ing normal or healthy weight in Ogun and Oyo states respectively. Twenty-
two percent of men in Ogun state were overweight with 3.3% obesity while
it is reduced in Oyo state with about 17% overweight and 5% obesity
among adult men. The prevalence of underweight was less than 5% in
Oyo state while Ogun state recorded 6.6% underweight prevalence
among adult men. The BMI mean score in the two states stood at
23.2Kg/m2
(Ogun, 23.4 Kg/m2
; Oyo, 23.0 Kg/m2
) indicating that majority
of the adult male members of cassava farm household fall within the normal
or healthy weight range.
3.5. Exploring adult men’s malnutrition across CCI levels
The findings of the nutritional status of adult men are shown in Table 5.
The findings revealed about 83% (in Ogun) and 73% (in Oyo) of the adult
men of zero commercialization households (CCI 1) had normal or healthy
weight. Meanwhile, adult men of CCI 1 households recorded about 17%
and 23% prevalence of overweight in Oyo and Ogun states respectively.
Table 2
Food groups of adult men of farm households.
S/N Food Groups Ogun
(n=91)
Oyo
(n=186)
Pooled
(n=277)
n (%) n (%) n (%)
1 Starchy staples 91 (100) 186 (100) 277 (100)
2 Dark green leafy vegetables 51 (56.0) 135 (72.6) 186 (67.1)
3 Other vitamin A rich fruits and
vegetables
88 (96.7) 180 (96.8) 268 (96.8)
4 Other fruits and vegetables 80 (87.9) 154 (82.8) 234 (84.5)
5 Organ meat 1 (1.1) 0 (0) 1 (0.4)
6 Meat and fish 71 (78.0) 145 (78.0) 216 (78.0)
7 Eggs 12 (13.2) 19 (10.2) 31 (11.2)
8 Legumes, nuts and seeds 63 (69.2) 109 (54.8) 172 (62.1)
9 Milk and milk products 2 (2.2) 14 (7.5) 16 (5.8)
Food groups cut-off
4 Food groups 10 (11.0) 17 (9.1) 27 (9.7)
≥4 Food groups 81 (89.0) 169 (90.9) 250 (90.3)
Dietary Diversity Mean
score ± SD
5.13 ± 0.99 5.06 ± 0.98 5.09 ± 0.97
Field Survey Data, 2020 Note: SD = Standard Deviation; n = number.
Table 3
Exploring adult men’s DDS across CCI levels.
CCI Levels Adult men’s DDS State
Ogun (n=91) Oyo (n=186)
n (%) n (%)
Zero level (CCI 1) Food groups 4 0 (0.0) 2 (7.7)
Food groups ≥4 6 (100) 24 (92.3)
Total 6 (100) 26 (100)
Low level (CCI 2) Food groups 4 0 (0.0) 5 (14.5)
Food groups ≥4 9 (100) 30 (85.7)
Total 9 (100) 35 (100)
Medium-high Level (CCI 3) Food groups 4 4 (15.4) 3 (5.5)
Food groups ≥4 22 (84.6) 52 (94.5)
Total 26 (100) 55 (100)
Very-high Level (CCI 4) Food groups 4 6 (12.0) 7 (10.0)
Food groups ≥4 44 (88.0) 63 (90.0)
Total 50 (100) 70 (100)
Total Food groups 4 10 (11.0) 17 (9.1)
Food groups ≥4 81 (89.0) 169 (90.9)
Total 91 (100) 186 (100)
Pearson Chi-Square (χ2
), p-value 0.829; 0.842 1.835; 0.601
Fig. 3. Association between adult men’s DDS and household CCI.
5

Zero percent obesity prevalence was recorded among zero commercializa-
tion households in the two states while we found 22% prevalence of over-
weight among men of CCI 1 households. Further, the findings in Table 5
showed a rather higher prevalence of overweight (33.3%) among men of
low commercialization households in Ogun state while about 11% over-
weight prevalence was found in Oyo state. However, less than 5 percent
(3.7%) prevalence of underweight and about 73% prevalence of healthy
weight was found among men of CCI 2 households. Likewise in CCI 3
households, 19.2% (Ogun) and 14.5% (Oyo) overweight prevalence was re-
corded while 7.7% (Ogun) and 1.8% (Oyo) underweight prevalence was
found in the two states. Another higher prevalence of overweight (22%)
was recorded among men of CCI 4 households in Ogun state while obesity
stood at 6.0%.
The findings also indicated that men of CCI 2 CCI 4 recorded over-
weight prevalence of more than 20% in Ogun state during the period of
this study. Meanwhile, in Oyo state, more than 70% healthy weight was re-
corded in all the four commercialization levels in Ogun state. However,
prevalence of obesity among men was less than 5% in all the four commer-
cialization households in the two states. Furthermore, the association be-
tween adult men BMI and CCI as shown in the scatter plot (Fig. 4)
revealed also the presence of an unsound positive association between the
two variables with (r) = 0.003. However, the relationship between
adult men’s BMI and CCI levels (Table 5) was found not to be statistically
significant.
3.6. Drivers of adult men’s dietary diversity and nutritional status
The logistic regression results of the determinants of adult men’s dietary
diversity (first case) and malnutrition status (underweight, normal weight,
overweight and, obesity) of farm households respectively are presented in
Table 6 7. Table 6 indicated factors influencing DD of adult men of cas-
sava farm families. It revealed that explanatory variables such as age of
adult men (p 0.05), farm size (p 0.001), and farm experience (p
0.05) and, cassava marketing experience (p 0.05) were all positively
related to the probability of obtaining minimum DDS.
Also, in the Logit regression analysis, the odds of reaching the 4 food
groups (minimum DDS) is five times higher for farm households with larger
farm size than for those with smaller size of farmland (OR = 4.78; 95% CI:
1.94, 11.76; p = 0.001). Additionally, the odds of reaching the required
DDS is 0.88 time smaller for farm households with higher cassava farming
experience than for those with lower farming experience (OR = 0.88; 95%
CI: 0.79, 0.98; p = 0.02). The odds of attaining minimum DDS for adult
men is same for households with more experience in cassava marketing
or sales of cassava roots and for those with lower cassava marketing expe-
rience (OR = 1.12; 95% CI: 1.01, 1.24; p = 0.039). Considering the com-
mercialization level (crop sold ratio) variable, the variable was not
statistically significant (p = 0.934) but was found to be positively associ-
ated with the DD of the adult men.
However, Table 7 showed the factors influencing the adult men’s nutri-
tional status in the study areas. The adult men nutritional categories were
ordered while the OLM for the drivers of the nutrition classes were signifi-
cant (P 0.01). The threshold value reflecting the nutrition classes, μ1, μ2
and, μ3 indicated the ranking [37,49]. The Table 7 revealed the findings
of the OLM and the ME of all explanatory variables on the likelihood of
adult men’s malnutrition classes. Household head gender, household size,
distant to nearest market, and source of food were among the explanatory
variables that significantly impacted adult men’s nutritional status.
Additionally, as farm household size increases the probability of adult
male member being underweight and having healthy weight reduces but
the probability of being overweight increases. Conversely, as the farm
household produces their own food, the likelihood of adult male member
of the household having healthy weight increases by 9.7% while the likeli-
hood of being overweight or obese reduces 10.2% and 2.1% respectively.
However, as the distance from farm to market increases, the likelihood of
adult male member being underweight/normal weight reduces but the
probability being overweight or obese go up. Similarly, the results in
Table 7 indicated that a unit increase in access to extension service reduces
the likelihood of being underweight by 1.84% but in contrary, make
probability of being overweight go up by 8.4%.
Notably, the results revealed that cassava commercialization variable
(crop sold ratio) was insignificant and negatively associated with the nutri-
tional status of the adult men.
4. Discussion
The results of the DDS of adult men indicated that 100 percent of men of
farm households consumed starchy staples within 24-h recall period
(Fig. 3). The consumption of carbohydrate-dense food is usually common
among rural farm households in African setting [3,8,48]. The result was
Table 5
Exploring men’s nutritional status across CCI levels.
CCI Levels Men’s Nutritional status State
Ogun (n=91) Oyo (n=186)
n (%) n (%)
Zero Level (CCI 1) Underweight 0 (0.0) 1 (3.8)
Normal weight 5 (83.3) 19 (73.1)
Overweight 1 (16.7) 6 (23.1)
Obesity 0 (0.0) 0 (0.0)
Total 6 (100) 26 (100)
Low Level (CCI 2) Underweight 1 (11.1) 2 (5.7)
Normal weight 5 (55.6) 27 (77.1)
Overweight 3 (33.3) 4 (11.4)
Obesity 0 (0.0) 2 (5.7)
Total 9 (100) 35 (100)
Medium-high Level (CCI 3) Underweight 2 (7.7) 1 (1.8)
Normal weight 19 (73.1) 43 (78.2)
Overweight 5 (19.2) 8 (14.5)
Obesity 0 (0.0) 3 (5.5)
Total 26 (100) 55 (100)
Very-High Level (CCI 4) Underweight 3 (6.0) 2 (2.9)
Normal weight 33 (66.0) 54 (77.1)
Overweight 11 (22.0) 13 (18.6)
Obesity 3 (6.0) 1 (1.4)
Total 50 (100) 70 (100)
Total Underweight 6 (6.6) 6 (3.2)
Normal weight 62 (68.1) 143 (76.9)
Overweight 20 (22.0) 31 (16.7)
Obesity 3 (3.3) 6 (3.2)
Total 91 (100) 186 (100)
Pearson Chi-Square (χ2
); p-value 4.442; 0.880 5.708; 0.769
Table 4
Nutritional status of adult men of farm households.
Body Mass Index (BMI) Nutritional status n (%)
(n=91) (Ogun State)
18.50 Underweight 6 (6.6)
18.50-24.99 Healthy weight 62 (68.1)
≥25.00 Overweight 20 (22.0)
≥30.00
Mean score ± SD 23.4 ± 3.3
Obesity 3 (3.3)
BMI (n=186) (Oyo State)
18.50-24.99 Healthy weight 143 (76.9)
≥25.00 Overweight 31 (16.7)
≥30.00 Obesity 6 (3.2)
BMI (n=277) (Pooled)
18.50-24.99 Healthy weight 205 (74.0)
≥25.00 Overweight 51 (18.4)
≥30.00 Obesity 9 (3.2)
Field Survey Data, 2020. Note: SD = Standard Deviation.
6

corroborated by similar studies from South Africa and Ethiopia where they
observed that all farm households consumed carbohydrate-related foods
[50–53]. Further, a similar study among women of farm households in
Nigeria reported same 100% consumption of starchy staples [11]. Mean-
while, the consumption of eggs (11.2%), milk and milk products (5.8%),
and organ meat (0.4%) was very low among adult men in the two states
of the study areas. This indicated that most men of farm households con-
sume carbohydrate-dense foods such as cereals, roots and tubers which
are grossly micronutrients inadequate. More men consumed food rich
in vegetables and fruits in the two states (dark green leafy vegetables;
Ogun = 56%, Oyo = 73%; Fruits/other vegetables; Ogun = 96.7%,
Oyo = 96.8%). Previous studies have reported that consumption of
energy-dense foods are positively associated with overweight in adults
especially in poor households [10,54–59].
Examining the dietary diversity of adult men vis-à-vis cassava commer-
cialization levels indicated that only 10% of them did not reach the mini-
mum 4 food groups in 24-h recall in the two states. This indicated that in
all the four commercialization levels, reaching the minimum food groups
may not be an issue but the quality of the food groups consumed may
pose a serious concern. Further, adult men of farm households in the
study areas consumed large chunk of energy-dense food (quantity) while
Fig. 4. Association between adult men’s BMI and household CCI.
Table 6
Logistic regression results: Correlates of adult men’s dietary diversity.
Variables OR RSE 95% CI p-value
Age 0.70** 0.123 0.4919 0.9847 0.041
Age square (years) 1.00** 0.002 1.0003 1.0075 0.032
+HH Gender 0.16 0.357 0.0018 13.5805 0.416
Household Size (Number) 1.04 0.126 0.8151 1.3131 0.780
Farm Size (Ha) 4.78*** 2.196 1.9418 11.7601 0.001
Farm experience (years) 0.88** 0.048 0.7923 0.9808 0.021
+Marital status 2.34 2.219 0.3639 15.0222 0.371
Education (years) 1.15 0.124 0.9270 1.4185 0.207
Farm Income (Naira) 1.00 0.001 1.0000 1.0000 0.201
Nonfarm Income (Naira) 1.00 0.001 1.0000 1.0000 0.451
Distance to market (Km) 0.88 0.092 0.7123 1.0769 0.208
Food Expenditure (Naira) 1.00 0.001 1.0000 1.0001 0.613
+Road condition 0.31 0.230 0.0700 1.3401 0.116
Cassava marketing exp (years) 1.12** 0.059 1.0056 1.2389 0.039
+Toilet 0.99 1.089 0.1138 8.5698 0.991
+Healthcare 0.25 0.268 0.0287 2.0889 0.198
+Nutrition Training 0.70 1.085 0.0342 14.4670 0.820
Crop sold ratio 1.11 1.3867 0.0959 12.8468 0.934
Constant 157903.3 736237 16.9666 1.47e+09 0.010
Note: (+) is dummy variable from 0 to 1, ***significant at 1 % level, **Significant
at 5% level, *Significant at 10%. Number of observation = 277. Note: OR means
Odd ratio; RSE = Robust Standard Error; CI = Confidence Interval. Wald chi2
(18) = 60.67 Prob chi2
= 0.0000 Log pseudo likelihood = -35.289925 Pseudo
R2
= 0.2857
Table 7
Ordered logit regression results: Determinants of men’s nutritional status.
Underweight Healthy
Wt
Overweight Obesity
Variable Coefficients dy/dx dy/dx dy/dx dy/dx
Age −0.0232 0.0008 0.0029 −0.0031 −0.0006
(0.0146) (0.0006) (0.0018) (0.0019) (0.0004)
+HH Gender 1.4204** −0.0279*** −0.2771* 0.2320** 0.0729
(0.6280) (0.0100) (0.1486) (0.1066) (0.0536)
Household Size 0.1162* −0.0041* −0.0144* 0.0155* 0.0030
(0.0605) (0.0023) (0.0077) (0.0080) (0.0019)
Farm Size 0.1194 −0.0042 −0.0148 0.0159 0.0031
(0.2107) (0.0074) (0.0262) (0.0280) (0.0055)
Education
(years)
−0.0418 0.0015 0.0052 −0.0056 −0.0011
(0.0305) (0.0011) (0.0038) (0.0041) (0.0009)
Farm Income −1.63e-06 5.67e-08 2.02e-07 −2.16e-07 −4.19e-08
(2.60e-06) (0.0000) (0.0000) (0.0000) (0.0000)
Nonfarm
Income
1.67e-06 −5.82e-08 −2.07e-07 2.22e-07 4.30e-08
(3.70e-06) (0.0000) (0.0000) (0.0000) (0.0000)
Dist to market 0.0699* −0.0024* −0.0087* 0.0093* 0.0018*
(0.0357) (0.0015) (0.0045) (0.0048) (0.0011)
+Source of
food
−0.7596** 0.0261 0.0973** −0.1027** −0.0207**
(0.3031) (0.0115) (0.0420) (0.0423) (0.0103)
+extension
service
0.5914* −0.0184* −0.0832 0.0840* 0.0175
(0.3356) (0.0102) (0.0535) (0.0501) (0.0132)
+healthcare 0.2365 −0.0083 −0.0290 0.0175 0.0060
(0.2920) (0.0104) (0.0358) (0.0385) (0.0076)
Crop sold ratio −0.0344 0.0012 0.0043 −0.0046 −0.0009
(0.5357) (0.0187) (0.0664) (0.0713) (0.0138)
/cut1 −3.4201
(0.8888)
/cut2 1.2622
(0.8565)
/cut3 3.4691
(0.9038)
Note: (+) is dummy variable from 0 to 1, ***significant at 1 % level, **Significant
at 5% level, *Significant at 10%. dy/dx is the Marginal Effect (ME), Figures in paren-
theses are robust standard errors. Note: Wt = Weight.
Number of observation = 277, Log Pseudo likelihood = -205.12462, Wald chi2
(12) = 27.03,
Probability chi2
= 0.0077, Pseudo R2
= 0.0526
7

jettisoning the quality aspect of healthy diets which may predispose adult
men of farm families to multiple burdens of malnutrition such as insuffi-
cient micronutrient consumption, overweight and obesity [10,55–59].
In addition, Fig. 3 revealed the relationship between adult men’s dietary
diversity score and crop commercialization index of cassava farm house-
holds indicated a weak positive association (r = 0.07) between the vari-
ables. It showed that as the CCI rises, adult men’s DDS may continue to
increase. It is observed that about 90% of adult men consumed more than
4 food groups out of 9 (with mean DDS = 5.09) within 2-h recall period.
However, the major concern about the DDS of adult men was the unaccept-
ably low consumption of nutrient adequate diets as highlighted above. This
predisposes adult men to hidden hunger, overweight and obesity. Other
studies corroborated our findings by reporting that the mere meeting the
minimum 4 food groups may not guarantee consumption of healthy (nutri-
tious) diet [60,61].
In another vein, the association between adult men’s DDS and CCI
(Table 3) was statistically insignificant revealing that being a member of
any of the CCI households does not guarantee the consumption of healthy
diets among adult men. The findings revealed some high levels of overweight
among adult men. Although, about 68% of men in Ogun state had healthy or
normal weight while those in Oyo had about 70%. About 22% (Ogun) and
16.7% (Oyo) were found to be overweight. However, less than 4% prevalence
of obesity was recorded among adult men in the two states. The overweight
prevalence in the two states was closer to that of adult men’s national crude
estimate of overweight which was 25.2% but was lower to that of obesity
(12.9%) [62]. Another similar study in Nepal found the prevalence of over-
weight among adult male workers to be 22.1% while that of obesity was
5.2% [31]. This result is contrary to the findings in North-West Nigeria (Ka-
tsina state), where the prevalence of overweight among adult men in the
rural setting was 41.9% which was higher than the overweight prevalence
among adult men of farm households in Ogun state (22%) [63].
Having 20% overweight prevalence among adult men in Ogun state
calls for concerns as studies have shown that overweight and obesity are
firmly associated with cardio-metabolic disorders such as high blood pres-
sure, high blood glucose, coronary heart disease, cancers among others
[62,64]. Further, Fig. 3 indicated the association between adult men’s
BMI and CCI indicated an unsound positive association (r = 0.003) be-
tween the two variables. This indicated that as CCI goes up, adult men’s
BMI may rise. Meanwhile, this result is premised on the fact that men’s nu-
tritional status (through BMI) vis-à-vis their farm households’ CCI levels
which indicated that all the four CCI levels had not less than 70% of adult
men with normal weight. The highest prevalence of overweight (23.1%)
was recorded in CCI 1 (zero level) in Oyo state while prevalence of obesity
was zero percent in the CCI 1 farm households of Ogun and Oyo states.
Other three CCI levels (CCI 2-4) recorded 20% overweight prevalence
in the two states. In addition, underweight and obesity among adult men
of cassava farm households in all four CCI levels in both Ogun and Oyo
states was 5%. However, the association between adult men’s nutritional
status and CCI (Table 5) in the two states were statistically insignificant,
showing that CCI levels (CCI 1-4) may not determine adult men’s nutri-
tional status in the study areas.
In another vein, the results of the determinants of dietary diversity of
adult men is presented in Table 6. The results showed that age was signifi-
cantly correlated with adult men meeting the recommended DDS. It indi-
cated that as the age of men of farm household increases, the likelihood
of reaching the minimum DDS (4 food groups) go up. This may be con-
nected to the fact that as the male members become older, they tend to
have more knowledge about healthy eating pattern that may improve
their DDS. This result is similar to [53] but was contrary to other studies
that reported that age of adult household head negatively influenced DD
[65,66]. Further, men belonging to farm households with increased farm
size are more likely to achieve the recommended DDS in the study areas.
This is in line with a priori expectation in such a way that as farmland in-
creases in size, the production level go up, resulting in increased productiv-
ity and income which may lead to farm households producing/ or
purchasing nutritious foods and consuming a more diverse diets. This result
is supported by [29] who posited that farm households with larger farm-
land recorded higher household DDS in Tanzania. However, the findings
in this study emphasized that increase in household’s farming experience
reduces the likelihood of adult male members meeting the
recommended DDS.
Furthermore, the crop sold ratio (cassava commercialization variable)
was found not to be statistically significant, implying that CCI levels of
farm households may influence dietary diversity of adult men’s farm house-
holds in the study areas. Other similar studies also found commercialization
variable not influencing dietary diversity of under-five children [3], adoles-
cents [4] but contrary to [11] that reported that dietary diversity of 49
years old women of smallholder farm households in Southwestern Nigeria
was influenced by CCI levels.
Moreover, the ordered logit regression results of factors influencing adult
men’s nutritional status (underweight, healthy weight, overweight, and obe-
sity) as indicated in Table 7 showed that a unit increase in male-headed
household increases the likelihood of adult men of being overweight while
it reduces the likelihood of being underweight. This implies that male house-
hold heads influences the prevalence of overweight among adult male mem-
bers. This result was supported by [29] who posited that most adult male
members usually augment their diets with consumption of food away-
from-home and this may result into becoming overweight or obese espe-
cially with the consumption of energy-dense (high calorie) foods.
Furthermore, household size is another variable influencing the nutri-
tional status of adult men. As the size of the household increases by one per-
son, the likelihood of adult men being underweight and having healthy
weight reduces but surprisingly increases the probability of becoming over-
weight. As posited by [29] about the possibility of adult men
complementing their diets by eating away-from-home, increase in house-
hold size may not significantly affect their eating pattern such as becoming
underweight. This eating lifestyle of adult men may further make them be-
come overweight even as the household size go up.
However, the ability of the farm families to produce their own food in-
creases the likelihood of adult men having normal weight and reduces the
probability of becoming overweight or obese. When farm households pro-
duce their own foods such as cereals, vitamin-rich fruits and vegetables,
and other protein-rich foods while making substantial quantities available
for home consumption will definitely improve the dietary quality of the
members and having good nutritional status (healthy weight). However,
if the eating pattern of the adult male members was towards a balanced
diet (consuming nutritious and high-quality foods) will lead to a reduction
of adult male members being overweight and obese. Previous study from
Zambia supported the own-food production in order to improve the nutri-
tional status of members [67].
In addition, one kilometre increase in the distance from farm to the
nearest market reduces the likelihood of adult male members of the house-
holds being underweight or having normal weight but contrarily increases
the probability of being overweight or obese. This results suggest that farm
households that are far from the near market may be left with no option
than to sell their cassava produce on the farm (at farm gate price) which
removes the stress of traveling to far distant town or market (no transporta-
tion/transaction cost) to sell their cassava root [68]. If these households fall
in the category of very high commercialization households (CCI 4), the in-
creased cassava production and marketing may lead to higher income level
that part of it may be used in purchasing highly nutritious foods that may
improve their nutritional status of farm households.
Furthermore, a unit increase in the farm household’s access to extension
service, reduces the likelihood of adult male member becoming under-
weight by 1.8% but increases the probability of being overweight by
8.4%. This implies that when cassava farm households have access to exten-
sion services rendered by the extension officers especially concerning agri-
cultural innovations and diet-related information may positively improve
the food consumption patterns of the households leading to better nutri-
tional status of the adult men. Consequently, the results also indicated
that a unit rise in access to extension service may equally make the preva-
lence of overweight among adult men go up. This is contrary to a priori
8

expectation but may be due to the fact that food consumption of adult men
are usually augmented by their away-from-home food consumption pattern
[29]. This food consumption pattern may predispose them to becoming
overweight regardless the influence of extension services rendered.
Also, the crop sold ratio (cassava commercialization variable) was not sta-
tistically significant, indicating that CCI levels of farm households was not
one of the influencing factors determining the nutritional status of adult
men. This finding was contrary to other studies that reported that CCI levels
of farm families was one of the influencing factors determining the malnutri-
tion status of under-5 [4] and adolescents in Southwestern Nigeria [28].
4.1. The limitations of the study
Previous studies on the investigation of dietary diversity and nutritional
status of adult men of smallholder farm families in Nigeria are quite scare.
According to our literature search, this is the first study exploring the DD
and nutritional status of adult men of smallholder cassava commercializing
households. One of the major limitations of this study is the cross-sectional
nature of the study which made it inappropriate to generalize the findings
of this study for all cassava farm households on a national level. Also, this
study employed 24-h recall for investigating DDS of adult men of farm
households while the results may be different if we adopted 7-day recall.
This study centred on adult men of cassava farm households and the find-
ings may be different from other age-groups (under-5, adolescents, and
women) of the same farm families.
5. Conclusion
This study revealed that commercialization levels (CCI 1-4 i.e. from zero
commercialization to very-high commercialization households) may not
determine the dietary diversity and nutritional status of adult men in the
two states. The study concluded that reaching the minimum DDS (for indi-
vidual category i.e. IDDS) of four (4) food groups out of nine (9) by most of
adult men of farm households was not enough to guarantee high-quality
diets capable of improving their nutritional status. This was premised on
the fact that majority of the food groups consumed by adult men were
carbohydrate-dense (high calories) foods with little or no animal/plant
sourced proteins, and micro nutrient-rich diets. However, for adult men
to achieve a healthy diet, their diet pattern must incorporate a more diverse
intake of food from different food groups capable of improving their nutri-
tional status. This study emphasized the critical need for adult men of farm
households to consciously check what is predisposing them to becoming
overweight as highlighted earlier in this study. There is a need for relevant
stakeholders to provide adequate nutrition knowledge intervention
programmes capable of improving the diets and nutritional status of adult
men of farm households in the study areas.
Funding
No funding was received for this study.
Declaration of Competing Interest
All authors declare that they have no conflict of interest.
Acknowledgement
We are grateful to the members of cassava farm households in Ogun and
Oyo states for participating in this study.
References
[1] Countrymeters. World Population. (Accessed 31 August 2022). https://countrymeters.
info/en/World; 2022.
[2] Countrymeters. Population of Nigeria. (Accessed 31 August 2022). https://
countrymeters.info/en/Nigeria; 2022.
[3] Otekunrin OA, Otekunrin OA, Ayinde IA, Sanusi RA, Onabanjo OO, Ariyo O. Dietary di-
versity, environment and health-related factors of under-five children: evidence from
cassava commercialization households in rural South-West Nigeria. Env Sci Poll Res.
2022;29(13):19432–46. https://doi.org/10.1007/s11356-021-17221-y.
[4] Otekunrin OA, Otekunrin OA. Exploring dietary diversity, nutritional status of adoles-
cents among farm households in Nigeria: do higher commercialization levels translate
to better nutrition? Nut Food Sc. 2022. https://doi.org/10.1108/NFS-03-2022-0104.
[5] FAO, IFAD, UNICEF, WFP,WHO. The State of Food Security and Nutrition in the World
2021. Repurposing food and agricultural policies to make healthy diets more affordable.
Rome: FAO; 2022. https://doi.org/10.4060/cc0639en.
[6] von Grebmer K, Bernstein J, Resnick D, Wiemers M, Reiner L, Bachmeier M, et al. Global
Hunger Index: Food Systems Transformation and Local Governance. Bonn:
Welthungerhilfe; and Dublin: Concern Worldwide; 2022. (Accessed on 10 December
2022). https://www.globalhungerindex.org/.
[7] Economist Impact. Global Food Security Index 2022; 2022. (Accessed 17 October
2022). https://impact.economist.com/sustainability/project/food-security-index/.
[8] Garnett T. Plating up solutions. Science. 2016;353(6305):1202–4. https://doi.org/10.
1126/science.aah4765.
[9] Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S, et al. Murray
CJL (2019) Food in the Anthropocene: The EAT–lancet commission on healthy diets
from sustainable food systems. Lancet. 2019;393:447–92. https://doi.org/10.1016/
S0140-6736(18)31788-4.
[10] Otekunrin OA, Otekunrin OA. Healthy and sustainable diets: implications for achieving
SDG2. In: Leal Filho W, Azul AM, Brandli L, Özuyar PG, Wall T, editors. Zero Hunger.
Encyclopedia of the UN Sustainable Development Goals. Cham: Springer; 2021.
https://doi.org/10.1007/978-3-319-69626-3_123-1.
[11] Otekunrin OA, Otekunrin OA. Dietary diversity choices of women: evidence from cas-
sava farming households in Nigeria. Arch Curr Res Int. 2021;21(4):11–22. https://doi.
org/10.9734/ACRI/2021/v21i430240.
[12] Global Nutrition Report. Stronger commitments for greater action. UK, Development
Initiatives: Bristol; 2022. https://globalnutritionreport.org/reports/2022-global-
nutrition-report/.
[13] International Food Policy Research Institute (IFPRI). Global Food Policy Report
Washington. DC: International Food Policy Research Institute; 2017.
[14] Poole N (2017) Smallholder Agriculture and Market Participation. FAO and
Practical Action Publishing. Accessed 10 November 2022. chrome-extension://
efaidnbmnnnibpcajpcglclefindmkaj/https://www.fao.org/3/i7841e/i7841e.pdf.
[15] Poulton C. What is Agricultural Commercialization, why is it Important, and how do we
measure it? APRA Work. Paper. Futur Agric Consort. 2017;6, (Accessed 12 October
2022). https://www.future-agricultures.org/publications/working-papers-document/
what-is-agricultural-commercialisation-why-is-it-important-and-how-do-we-measure-
it/.
[16] Otekunrin OA, Momoh S, Ayinde IA. Smallholders’ Farmers’ Market Participation:
Concepts and Methodological Approaches from Sub-Saharan Africa. Cur Agric Res.
2019;7(2):139–57. https://doi.org/10.12944/CARJ.7.2.02.
[17] Wakaba D, Ateka J, Mbeche R, Oyugi L. Determinants of Irish potato (Solanum tuberosum)
commercialization and market participation by farmers in Nyandarua County, Kenya.
J Agric Food Res. 2022;10:1–10. https://doi.org/10.1016/j.jafr.2022.100382.
[18] Abdullah Rabbi F, Ahamad R, Ali S, Chandio AA, Ahmed W, Iiyas A, et al. Determinants
of commercialization and its impact on the welfare of smallholder rice farmers by using
Heckman’s two-stage approach. J Saudi Soc Agric Sci. 2019;18(2):224–33. https://doi.
org/10.1016/j.jssas.2017.06.001.
[19] Raj KGC, Hall RP. The commercialization of smallholder farming— a case study from
the rural western middle hills of Nepal. Agriculture. 2020;10(5):1–16. https://doi.
org/10.3390/agriculture10050143.
[20] Zerfu TA, Umeta M, Baye K. Dietary diversity during pregnancy is associated with re-
duced risk of maternal anemia, preterm delivery, and low birth weight in a prospective
cohort study in rural Ethiopia. Am J Clin Nutr. 2016;103(6):1482–8. https://doi.org/
10.3945/ajcn.115.116798.
[21] Bellows AL, Canavan CR, Blakstad MM, Mosha D, Noor RA, Webb P, et al. The Relation-
ship between Dietary Diversity among Women of Reproductive Age and Agricultural
Diversity in Rural Tanzania. Food and Nutr Bull. 2019;41(1):1–11. https://doi.org/10.
1177/0379572119892405.
[22] Aemro M, Mesele M, Birhanu Z. Dietary diversity and meal frequency practices among
infant and young children aged 6–23 months in Ethiopia: a secondary analysis of
Ethiopian demographic and health survey. J Nutr Metab. 2011;2013:1–8. https://doi.
org/10.1155/2013/782931.
[23] Custodio E, Herrador Z, Nkunzimana T, Węziak-Białowolska D, Perez-Hoyos A,
Kayitakire F. Children’s dietary diversity and related factors in Rwanda and Burundi:
a multilevel analysis using 2010 Demographic and Health Surveys. PLoS ONE. 2019;
14(10):1–17. https://doi.org/10.1371/journal.pone.0223237.
[24] Nithya DJ, Bhavani RV. Dietary diversity and its relationship with nutritional status
among adolescents and adults in rural India. J Bio Sci. 2018;50(3):397–413. https://
doi.org/10.1017/S0021932017000463.
[25] Keats E, Rappaport A, Shah S, Oh S, Jain R, Bhutta ZA. The dietary intake and practices
of adolescent girls in low- and middle-income countries: a systematic review. Nutrients.
2018;10(12):1–14. https://doi.org/10.3390/nu10121978.
[26] Bhargava M, Bhargava A, Ghate SD, Rao RSP. Nutritional status of Indian adolescents
(15–19 years) from National Family Health Surveys 3 and 4: Revised estimates using
WHO 2007 Growth reference. PLOS ONE. 2020;15(6):1–24. https://doi.org/10.1371/
journal.pone.0234570.
[27] Kebede D, Prasad RPCJ, Asres DT, Aragaw H, Worku E. Prevalence and associated fac-
tors of stunting and thinness among adolescent students in Finote Selam Town,
Nothwest Ethiopia J Health. Pop Nutr. 2021;40:44.
[28] Otekunrin OA, Otekunrin OA. Nutrition Outcomes of Under-five Children of Small-
holder Farm Households: Do Higher Commercialization Levels Lead to Better
9

Nutritional Status? Child Ind Res. 2022;15(6):2309–34. https://doi.org/10.1007/
s12187-022-09960-2.
[29] Ochieng J, Afari-Sefa V, Lukumay PJ, Dubois T. Determinants of dietary diversity and
the potential role of men in improving household nutrition in Tanzania. PLoS ONE.
2017;12(12):1–15. https://doi.org/10.1371/journal.pone.0189022.
[30] Ponce X, Ramirez E, Delisle H. A more diversified diet among Mexican Men may also be
Atherogenic. J Nutr. 2006;136:2921–7.
[31] Sangroula RK, Subedi HP, Tiwari K. Factors associated with the Nutritional status
among male workers of iron and steel industries in Bara District. Nepal J Nutr Metab.
2020;2020:1–8. https://doi.org/10.1155/2020/7432716.
[32] Maps of World. (Accessed 10 June 2022). https://wwww.mapsofworld.com/lat_long/
nigeria-lat-long.html; 2021.
[33] Ogun State official Website. (Accessed 5 November 2022). https://archive.
ogunstate.gov.ng/ogun-state/; 2022.
[34] NigeriaGalleria. Oyo State, Nigeria. (Accessed 5 November 2022) https://www.
nigeriagalleria.com/Nigeria/States_Nigeria/Oyo/; 2022.
[35] World Health Oganization. Physical status: the use and interpretation of anthropometry.
Report of a WHO Expert Committee. World Health Organization technical report series.
1995;854:1–452.
[36] Centre for Disease Control and Prevention (CDC). Defining Adult overweight and
Obesity. (Accessed 29 September 2022). https://www.cdc.gov/obesity/basics/
adultdefining.html#:~:text=If%20your%20BMI%20is%20less,falls%20within%20the
%20obesity%20range; 2022.
[37] Otekunrin OA. Agricultural commercialization and nutritional status of smallholder cas-
sava farming households in South-West Nigeria. Unpublished PhD Post-Data Seminar
presented in the Department of Agricultural Economics and Farm Management.
Abeokuta, Nigeria: Federal University of Agriculture; 2021.
[38] Kennedy G, Berado A, Papavero C, Horjus P, Ballard T, Dop M. Proxy measures of house-
hold food consumption for food security assessment and surveillance, comparison of the
household dietary diversity and food consumption scores. J Pub Health Nutr. 2010;13
(12):2010–8.
[39] Kennedy G, Ballard T, Dop M. Guidelines for measuring Houdehold and Individual
Dietary Diversity Nutrition and Consumer Protection Division, Food and Agriculture
Organization of the United Nations. https://www.fao.org/3/i1983e/i1983e.pdf; 2010.
[40] Steyn NP, Nel J, Labadarios D, Maunder EMW, Kruger HS. Which dietary diversity indi-
cator is best to assess micronutrient adequacy in children 1 to 9 y? Nutrition. 2014;30
(1):55–60. https://doi.org/10.1016/j.nut.2013.06.002.
[41] Uzosike TCJ, Okeafor I, Mezie-Okoye M. Dietary Diversity, Nutritional status and
Academic performance of pupils in public primary schools in Port Harcourt Metropolis.
J Com Med Pry Health. 2020;32(2):42–56. https://doi.org/10.4314/jcmphc.v32i2.4.
[42] World Health Oganization. Overweight and Obesity. (Accessed 3 October 2022).
https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight; 2021.
[43] Gujarati DN, Porter DC. Basic EconometricsFifth edition; 2009.
[44] Long JS, Long JS. Regression Models for Categorical and Limited Dependent Variables.
1st ed, 7 London, UK: Sage Publications; 1997; 114–45.
[45] Greene WH. Econometric Analysis. 7th ed. Pearson: London, UK; 2012.
[46] Booroah VK. Logit and probit: Ordered and multinomial models. Quantitative Applica-
tions in the Social Sciences; Series No. 138. Thousand Oaks, CA, USA: Sage Publications;
2002.
[47] Obayelu AE. Households’ food security status and its determinants in the North-Central.
Food Econ. 2012;9(4):241–56. https://doi.org/10.1080/2164828X.2013.845559.
[48] Otekunrin OA. Investigating food insecurity, health and environment-related factors,
and agricultural commercialization in Southwestern Nigeria: evidence from smallholder
farming households. Env Sci Poll Res. 2022;29(34):51469–88. https://doi.org/10.
1007/s11356-022-19544-w.
[49] Obayelu OA, Adeleye OR. Explaining Child Nutritional Status in rural Nigeria: Socioeco-
nomic Dimensions. J Hunger Environ Nutr. 2021;16(6):829–46. https://doi.org/10.
1080/19320248.2020.1781011.
[50] Rajendran S, Afari-Sefa V, Shee A, Bocher T, Bekunda M, Dominick I, et al. Does crop
diversity contribute to dietary diversity? Evidence from integration of vegetables into
maize-based farming systems. Agric Food Secur. 2017;6:1–13. https://doi.org/10.
1186/s40066-017-0127-3.
[51] Megbowon ET, Mushunje A. Assessment of food security among households in Eastern
Cape Province, South Africa: evidence from general household survey, 2014. Int J Soc
Econ. 2018;45(1):2–17. https://doi.org/10.1108/IJSE-07-2016-0187.
[52] Jebessa GM, Sima AD, Wondimagegnehu BA. Determinants of household dietary diver-
sity in Yayu Biosphere Reserve. Southwest Ethiopia Ethiop J Sci Technol. 2019;12(1):
45–68. https://doi.org/10.4314/ejst.v12i1.3.
[53] Sambo TA, Oguttu JW, Mbombo-Dweba TP. Analysis of the dietary diversity status of
agricultural households in the Nkomazi Local Municipality. South Africa Agri Food
Secur. 2022;11:1–22. https://doi.org/10.1186/s40066-022-00387-0.
[54] Turner RK, Daily GC. The ecosystem services framework and natural capital conserva-
tion. Environ Res Econ. 2008;39:25–35. https://doi.org/10.1007/s10640-007-9176-6.
[55] Fanzo J, Mattei F. Ensuring agricultural biodiversity and nutrition remain central to ad-
dressing the MDG1 hunger target. In: Burlingame B, Dernini S, editors. Sustainable diets
and biodiversity: directions and solutions for policy, research and action. Rome: Food
and Agriculture Organization; 2012. p. 44–53.
[56] Moodie R, Stuckler D, Monteiro C, Sheron N, Neal B, Thamarangsi T, et al. Profits and
pandemics: prevention of harmful effects of tobacco, alcohol, and ultraprocessed food
and drink industries. Lancet. 2013;381:670–9. https://doi.org/10.1016/S0140-6736
(12)62089-3.
[57] Alleyne G, Binagwaho A, Haines A, Jahan S, Nugent R, Rojhani A, et al. Embedding non-
communicable diseases in the post-2015 development agenda. Lancet. 2013;381:
566–74. https://doi.org/10.1016/S0140-6736(12)61806-6.
[58] Johnston JL, Fanzo JC, Cogill B. Understanding sustainable diets: a descriptive analysis
of the determinants and processes that influence diets and their impact on health, food
security, and environmental sustainability. Adv Nutr. 2014;5(4):418–29. https://doi.
org/10.3945/an.113.005553.
[59] FAO, IFAD, UNICEF, WFP, WHO. The State of Food Security and Nutrition in thpe
World 2020. Transforming Food Systems for Affordable Healthy Diets. Rome: FAO;
2020http://www.fao.org/publications/sofi/2020/en/.
[60] Otto MC, Padhye NS, Bertoni AG, Jacobs Jr DR, Mozaffarian D. Everything in modera-
tion—dietary diversity and quality, central obesity and risk of diabetes. PLoS One.
2015;10(10):e0141341. https://doi.org/10.1371/journal.pone.0141341.
[61] Khamis AG, Ntwenya JE, Senkoro M, Mfinanga SG, Kreppel K, Mwanri AW, et al. Asso-
ciation between dietary diversity with overweight and obesity: A cross-sectional study
conducted among pastoralists in Monduli District in Tanzania. PLoS ONE. 2021;16(1):
1–17. https://doi.org/10.1371/journal.pone.0244813.
[62] Adeloye D, Ige-Elegbede JO, Ezejiomofor M, Owolabi EO, Ezeigwe N, Omoyele C, et al.
Estimating the prevalence of overweight and Obesity in Nigeria in 2020: a systematic
review and meta-analysis. Anals Med. 2021;53(1):495–507. https://doi.org/10.1080/
07853890.2021.1897665.
[63] Wahab KW, Sani MU, Yusuf BO, Gbadamosi M, Gbadamosi A, Yandutse M. Prevalence
and determinants of obesity – a cross-sectional study of an adult Northern Nigerian pop-
ulation. Int Arch Med. 2011;4(1):10. https://doi.org/10.1186/1755-7682-4-10.
[64] Forouzanfar MH, Afshin A, Alexander LT, GBD 2015 Risk Factors Collaborators. Global,
regional, and national comparative risk assessment of 79 behavioural, environmental
and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic anal-
ysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1659–724.
https://doi.org/10.1016/S0140-6736(16)31679-8.
[65] Iftikhar M, Yasin MA, Ali R, Khan MI, Iftikhar N. Factors affecting the household dietary
diversity pattern in rural areas of Southern Punjab. Pakistan Int Trans J Eng. 2020;11(7):
1–12. https://doi.org/10.14456/ITJEMAST.2020.124.
[66] Magaji BD, Oladimeji YU, Sunday H, Abdulkarim AG. Dietary diversity score and its de-
terminants among rural households in Panshekara, Kano. J Agric Econ Environ Soc Sci.
2020;6(2):46–56.
[67] Maila G, Audain K, Marinda PA. Association between dietary diversity, health and nutri-
tional status of older persons in rural Zambia. South Afr J Clin Nutr. 2021;34(1):34–9.
https://doi.org/10.1080/16070658.2019.1641271.
[68] Okoye BC, Abass A, Bachwenkizi B, Asumugha G, Alenkhe B, Ranaivoson R, et al. Effect
of transaction costs on market participation among smallholder cassava farmers in Cen-
tral Madagascar. Cogent Econ Fin. 2016;4:1–26. https://doi.org/10.1080/23322039.
2016.1143597.
10

Dietary diversity, nutritional status, and agricultural commercialization: evidence from adult men of rural farm households

Recommended

Recommended

More Related Content

Similar to Dietary diversity, nutritional status, and agricultural commercialization: evidence from adult men of rural farm households

Similar to Dietary diversity, nutritional status, and agricultural commercialization: evidence from adult men of rural farm households (20)

More from Olutosin Ademola Otekunrin

More from Olutosin Ademola Otekunrin (20)

Recently uploaded

Recently uploaded (20)

Dietary diversity, nutritional status, and agricultural commercialization: evidence from adult men of rural farm households