Access of Households to Arable Land and Nutritional Status of Children Aged 6–59 Months in Rural Areas of South Kivu, Case of the Health Zone of Minova, Eastern DRC

Abstract

Already knowing enough about the determinants of malnutrition, this study set itself the objective of verifying the influence of access to arable land on the nutritional status of children aged 6 to 59 months in a rural Health Zone of the DRC in South Kivu (Minova) with very high prevalence of malnutrition (62% CM and 7.1% AM). A cross-sectional quantitative study conducted on a representative sample of 424 children aged 6 to 59 months selected using the Lynch formula by probabilistic stratum sampling; using a structured questionnaire. Malnutrition (acute and chronic) assessed on the basis of WHO growth standards served as the dependent variable and access to arable land considered according to the FAO definition was the main independent variable. Chi-square or Ficher tests were used to compare proportions and logistic regressions were used to determine the factors associated with malnutrition; the significance threshold set at 5%. The frequency of less than 3 meals per day and the low socio-economic level of households were associated with chronic malnutrition (p-value 0.046 and 0.007). Exclusive breastfeeding and unimproved source of drinking water were associated with acute malnutrition. Finally, no statistically significant association was found between access to arable land and the nutritional status of children aged 6 to 59 months. How land production and household incomes are allocated for other needs would also be part of the problem.

1. Introduction

Globally, nearly two billion people, or about 30% of the population, suffer from invisible hunger, that is, deficiencies in micronutrients and other macronutrients [1]. Efforts to achieve food security for all in order to combat malnutrition are hampered by emerging issues that threaten the food system [2]. Undernutrition is the single most important risk factor for mortality and morbidity in developing countries [3].

One in three children (200 million worldwide) does not realize their full physical, cognitive, psychological, and/or socio-emotional potential due to poverty, poor nutrition, poor health, insufficient care, and stimulation associated with other risk factors for early childhood development [4, 5, 6]. Scientific studies carried out on maternal and child health have revealed that 45% of cases of death of children under five are directly or indirectly due to malnutrition [4]; The cost of prematurely developed diseases and deaths directly caused by hunger in the world is estimated at 30 billion per year according to the FAO.

In Africa, there was a time when land seemed almost inexhaustible, but population growth and market development are creating increased competition for land resources, especially near villages and towns and in rural areas. High-value productive areas. According to the FAO, about 65 percent of agricultural land in Africa is degraded, costing the continent nearly $68 million each year and affecting 180 million people, mainly poor rural populations already struggling to meet their needs. With demographic pressure on natural resources such as access to land and water, to which are added the indirect effects of the Covid-19 pandemic and climate change, deforestation, recurrent armed conflicts and the rate of high unemployment favoring the rural exodus; the number of people suffering from malnutrition in Africa and the DRC is likely to double or even triple over the next few years. A profound change in the global food and agriculture system is, therefore, necessary to hope to feed millions of people who suffer from malnutrition hunger today and the 2 billion additional people that the world will have by 2050 [7], Asia and Africa are the most affected.

In the Democratic Republic of Congo, evidence from nutritional surveys of major DHS and MICS studies report alarming prevalence of deficiency malnutrition in chronic and acute forms, while paradoxically the country has about 80 million hectares of arable land, including Barely 10% developed and occupies the second place in the world in terms of cultivable arable land after Brazil and an unprecedented hydrographic network [8].

Despite some progress recorded in the prevention and fight against malnutrition, the DRC is to date deviating in order of severity, the first country in the world with such a high number of people affected by food insecurity.

The incidence of poverty for the whole country is very high (71.34%) if we compare it to that of the other countries of Central Africa [9]. Deficiency malnutrition in all its forms remains a worrying public health problem [10] with 43% chronic malnutrition and the prevalence of GAM ranging from 6.5 to 15% in the provinces and an average of 2% acute malnutrition strict; ranking the DRC as 1 of 10 countries that account for 60% of the global burden of wasting in children under 5.

In South Kivu, about 9% of children aged 6–59 months only have access to a minimum acceptable diet and 14.9% for the Minova Health Zone [11] and the 2018 MICS survey revealed 48% chronic malnutrition (about one in two children), a proportion far above the national average (43%).

Still in South Kivu, in the territory of Kalehe, the prevalence of stunting in children under five in the ZS of Minova was estimated at 62.1% in 2018 against 51% in 2020 according to the results of the surveys conducted by the NGO Graines and the National Nutrition Program as well as global acute malnutrition at 7%.

Recurrent armed conflicts force people to move outside their natural living environment (rural exodus) in search of peace and employment to urban centers that are poorly prepared for rapid urbanization and demographic explosion.

It should also be noted that the large land concessions in South Kivu and Minova in the territory of Kalehe are strongly solicited by personalities and businessmen who want to invest in land. This seems to constitute a kind of heaviness for the rural population who rely only on agricultural life for their survival, yet without financial means to compete with purchases or limit sales.

A study carried out in 2008 by the INGO ACF in the territory of Kalehe revealed that access to land for women and other inhabitants of Minova is very limited and constitutes one of the major sources of conflict between herders and farmers. According to this study, the total cultivated areas vary between 0.02 and 12.7 Ha, with an average of 1.5 Ha for the ZS of Kalehe and 1.7 Ha for the ZS of Minova per agricultural practitioner [12].

The survey conducted by the National Nutrition Program in South Kivu with the support of UNICEF, WFP and FAO found that the average cultivated area per inhabitant in the Minova Health Zone varies between 13.1 and 30 ares.

Having found no studies focused on in-depth research into the causes of the persistence of malnutrition in this area despite the joint interventions carried out by Humanitarian Organizations; we estimated that there would be a problem of household access to arable land, which would influence these high prevalence of malnutrition alongside other underlying factors. Thus, this justified our study to explore the association between access to land and the nutritional status of young children in rural areas, given that for the low-income peasant population; it has been affirmed by certain literature that agriculture represents an important part of the food ration of families [13, 14] while other authors think that the simple fact of having access to arable land does not guarantee advances a good nutritional status.

In view of the above, our study mainly addressed the following research question: “Is there an association between having or not having access to arable land and the nutritional status of children aged 6–59 months in rural areas, the case of the Minova Health Zone given the high prevalence of stunting and acute malnutrition?” If not, what are the other factors associated with this malnutrition in the Zone?

Generally, this study aims to verify the influence of rural households’ access to arable land on the nutritional status of children aged 6–59 months in order to better guide control strategies and actions to improve their health.

Specifically, this involves identifying the proportion of households without access to arable land in this health zone; assess/determine the level of exposure of households without arable land to acute and chronic malnutrition in children under 6–59 months; determine the prevalence of chronic and acute malnutrition among the target population of Minova Health Zone and other associated factors.

Three hypotheses have thus been formulated to verify these objectives: the proportion of households in the Minova Zone that do not have access to arable land will be close to 50%; the prevalence of chronic and acute malnutrition will be significantly higher in households without access to land compared to those with access; then the large size of households and closely spaced births will be one of the main factors associated with acute and chronic malnutrition in children aged 6–59 months in this area considering the low purchasing power of the population.

2. Methodological framework

2.1 Materials and method

This cross-sectional quantitative study was conducted in 11 Health Areas randomly selected out of the 21 in the Minova Health Zone (Bobandana, Bulenga, Cheya Chebumba, Kalungu, Karango, Kishinji, Minova, Muchibwe, Numbi and Ruhunde) in the province of Sud Kivu (Eastern DRC) during the period from June to November 2021.

The study population is made up of children aged 6–59 months residing in the households of the chosen Health Areas; the size of our sample was 375 children but we added 15% or 426 children in total surveyed to prevent the risk of non-response and possible incomplete data when cleaning the database.

Inclusion criteria: to be a resident child aged 6–59 months in the survey household whose father or mother has given consent for anthropometric measurements and is willing to answer the questionnaire. The mother of each selected child was also concerned by the measurement of the Brachial Circumference.

Was excluded from the study, the child from 6 to 59 months concerned whose parent did not give the consent for his participation or residing less than 6 months in the household.

2.2 Sample and sampling technique

Systematic and proportional stratified sampling was used at several stages in strict compliance with statistical standards (Health area at 1st stage, Village at 2nd stage).

In the village, households with children aged 6–59 months were identified with the help of parcel surveys from Community Relays then the choice of children was made by the technique of systematic random drawing with reference to the table of proportion of Health Areas.

The sampling interval was equal to the number of children aged 6–59 months in the village divided by the number of children to be surveyed in this village.

The sample size was calculated with the following Lynch formula:

$$\begin{array}{l}n=\frac{\mathit{NZ}{\alpha }^2\bullet P\bullet \left(1-P\right)}{N\bullet a^2+Z{\alpha }^2\left(1-P\right)}\\ =\frac{211163\bullet {1.96}^2\bullet 0.43\left(1-0.62\right)}{211163\bullet {0.05}^2+{1.96}^2\left(1-0.62\right)}\\ =\frac{211163\bullet 3.8416\bullet 0.43\bullet 0.38}{211163\bullet 0.0025+3.8416\bullet 0.38}\\ n=375\\ n=375\text{Respondents}\end{array}$$E1

where N = total population of the 11 health care; n = sample; Z alpha = constant = 1.96 (for 95% CI); a = the margin of error; P = the prevalence of malnutrition in the health zone; P = 62% of Chronic Malnutrition [15].

2.3 Malnutrition in its chronic and acute forms was the dependent variable of our study

After exclusion of outliers, the two forms of malnutrition were defined according to WHO growth standards.

Regarding the Independent Variables, we considered:

Access to arable land:

Defined according to the FAO as the set of processes by which citizens, individually or collectively, acquire the rights and opportunities allowing them to occupy and use land (for production and for economic and social purposes), whether on a temporary or permanent basis [16, 17].

These processes include participation in formal and informal markets, access to land through family or social networks, including transmission of land rights by in heritance and within families, and allocation of land by the state and other authorities, with control over them.

According to the Provincial Inspection of Agriculture of South Kivu (direction of production and protection of plants), an agricultural household must have on average an area of 50 ares (1/2 hectare) for the cultivation of cassava and at least 30 ares for other speculations such as (legumes, cereals, vegetables, potatoes) in order to consider that he has access to arable land to meet his most basic needs.

Conveniently, to express the area of arable land owned by a household during this study; the head of the household of the child surveyed presented the land purchase document, or he expressed it according to locally recognized conventional measures (kamba moya = one hectare of land, nusu ya kamba =1/2 hectare; kipandé = between 30 and less than 50 ares).

The other independent variables were evaluated taking into account their standard values (birth weight in Kg, child’s age in months, male or female sex, arm circumference in millimeters, exclusive Breastfeeding in 6 months, the daily frequency of meals greater than or equal to 3, their composition of at least four essential food groups, the level of education attained by the head of the household, and his profession, the size of the household, the marital status of the parent of the child, religion, birth interval, child vaccination status, source of drinking water, socio-economic status or constituted wealth index.

2.4 Data collection

The subjects of study were randomly identified by a systematic technique of drawing from households after having constituted a sampling base as indicated above. The biological mothers (or father of the child) served directly as respondents to the questions asked.

To identify the survey households, the interviewers used the home visit notebooks and local count notebooks from the Village Community Relays. Data was collected electronically on Android tablets using Open Data Kit (ODK) and stored remotely on the server. This collection in the field was facilitated by qualified staff, with medical and non-medical profiles (two nutritionists, a public health graduate, a primary school teacher and two agricultural engineers) making up three paired teams; all supervised by the Principal Investigator. A refresher course preceded the collection to strengthen the investigators’ understanding of the mastery of the tools and the technique, followed by a pre-survey.

High-precision anthropometric equipment provided by UNICEF, including SECA electronic scales, Shakir measuring rods and strips, was used to take various measurements (weight, height, MUAC) in addition to the search for nutritional edema.

In terms of ethical consideration, the request for consent was read and requested from each respondent before completing the questionnaire and taking measurements. No act contrary to the ethics and methodology of the research was practiced. Parents of children suspected of malnutrition were advised to take them to the Health Centers of their choice for confirmation and appropriate action.

2.5 Data analysis

The data collected from a structured and digitized questionnaire with the Kobo toolbox platform were analyzed with Stata version 14.

Descriptive statistics (medians and interquartile ranges (IQR) for continuous variables, and frequencies with percentages for variables categorical) were used to describe the study sample according to the shape of the distribution and then the chi-square test was used for comparison.

To determine the associated factors, we constructed logistic regression models (uni-varied and multi-varied) and to introduce the variables into the multi-varied analysis, the step-by-step selection method with a forced entry of plausible factors was used.

The measures of association were reported by the unadjusted odds ratios (ORna) and the adjusted odds ratios (ORa) with their 95% confidence intervals, ie the significance level set at 5%.

3. Results of the study

The majority of people in the Minova Health Zone (86.60%) is self-employed and live in self-employed activities (agriculture, livestock, petty trade, etc.). 39.10% of households include more than 7 people and the majority of houses are built of boards (44.12%). Mostly monogamous (74.19%), almost a third of the population has no level of education (34.82%) and the Protestant religion is dominant, followed by Catholics.

The median age of our respondents was 26 years and for children under five, this was 24 months, dominated by the 24–59 month age group (Table 1).

Variables	n (%) or Med (min–max) or Avg (SD)
Profession of Head of Household (n = 418)
Non-employees	362 (86.60)
State employees/Private sector	56 (13.40)
Age of respondent in years (n = 424)	26 (14–70)
Gender of Respondent (n = 424)
Female	401 (94.58)
Male	23 (5.42)
Respondent’s marital status (n = 424)
Singles	98 (23.11)
Separared	22 (5.18)
The bride and groom	304 (71.70)
Level of education of the respondent (n = 424)
No studies	148 (34.82)
Primary studies	135 (31.85)
Studies high school and more	141 (33.33)
Household size (n = 422)
Median size (Min–Max)	7 (2–19)
1–7 peoples	257 (60.90)
More than 7 people	165 (39.10)
Age of children in months (n = 422)	24 (6–59)*
Children from 6 to 11	10.43
Children from 12 to 23	27.01
Children from 24 to 59	62.56
Gender child (n = 424)
Girls	52.36
Boys	47.64
Intergenerational interval in months (n = 422)	19.92 (13.60)**
Less than 2 years old	54.26
2 years or more	45.70
Number of children under 5 in the household (n = 424)	2 (1–6)*
1 child	33.65
2 and more	66.35
Religion practiced by respondent (n = 424)
Catholic	24.06
Protestant	53.77
Kimbanguist	1.42
Muslim	2.59
Other religions	18.16
Tribe/nature of study population (n = 423)
Non-aboriginal	39
Aboriginal	61
Last 3 months household income (n = 313) Median (Min–Max)
	45 (1–600)*
≤ 100$	78.91
>100$	21.08
Socio economic status, n = 313
Poor	52.08
Medium	41.85
Rich	6.07

Table 1.

Sociodemographic characteristics of households surveyed in the Minova health zone.

^*

Med = Median; n = number; % = percentage in column.

^**

Average = mean; SD = standard deviation.

The results of the analyses show in the histogram in Figure 1 that 52.6% of households in Minova do not have access to arable land. There was also a high prevalence of acute malnutrition in households without access to land (9.1%) compared to those with access (5.1%) but this difference was not statistically significant.

Figure 1.

Distribution of malnourished children according to access to land in the Zone (p value GAM = 0.269 and p value CM = 0.337).

Regarding the mode of acquisition of arable land by households, sharecropping and inheritance are the most dominant means of acquisition found in Minova with 39.49 and 26.15%, respectively. With regard to the crops cultivated, 44.30% of farmers practice mono-cropping. With regard to provisions, only 8% of households have food stocks in their homes. 29% practice livestock farming, dominated by backyard animals and 84.63% of households spend an average of between two and twenty thousand Congolese francs, or $1 to $10 per week, to supplement market needs, which indicates a kind of precariousness in living conditions (Table 2).

Variables	% or Med (min–max)
Area of land owned by households in hectares (n = 195)	1 (0.01–15)
Less than one hectare of arable land	45.13
One to two hectares of arable land	28.72
More than two hectares of arable land	26.15
Means/mode of household arable land acquisition (n = 195)
Purchase	9.74
Legacy	26.15
Rental	21.54
Metayage	39.49
Others means	3.08
Types of crops planted in the fields (n = 307)
Various crops (manioc/potatoes, market gardening)	55.70
Non-varied crops (only one type in the field)	44.30
Weekly market spending by household (n = 397)	5.25 (1–50)
From 1 to 10 dollars	84.63
From $ 11 to 20 dollars	12.59
From $ 21 to 50 dollars	2.77
Households with food stocks (provisions), n = 423
No provisions	91.96
Available provisions	8.04
Types of food stocks (household provisions), n = 33
At least 3 types of food (legumes, staples, oil/animal food)	30.30
Two types of food in stock (basic food + other)	15.15
Only one type of feed in stock	54.55
Household livestock practice (n = 423)
No	70.69
Yes	29.31
Type of farming practiced by the household (n = 124)
Backyard breeding (poultry, guinea pigs, rabbits)	54.84
Small livestock (goats, sheep, pigs)	33.87
Mixed breeding (all species)	10.48
Breeding of large livestock (cattle)	0.81

Table 2.

Agricultural practices of surveyed households in the Minova Health Zone.

Table 3 shows that the prevalence of global acute malnutrition in the Minova Health Zone is 7.92% based on the weight/height ratio associated with other measures taken among children aged 6-59 months, and that chronic malnutrition (stunting) affects at least one out of every two children in the area, i.e. 50.38%. In addition, 7.91% of children were born with a low birth weight according to the WHO reference and more than half of children (65.48%) were exclusively breastfed for six months. Only 5.45% of households have access to a diversified diet (4-star foods) that can meet their essential nutrient intake needs. A significant proportion of households (40.63%) consume their livestock products occasionally.

Variables	% or Med (min–max)
Diagnosis of GAM with Weight-Height, Nut Edema, PB (n = 404)	7.7
Weight-height index (n = 404)	0.03 (−4.96; 3.91)
Acute global malnutrition (<−2 Z-score)	7.20
Normal child ≥−2Z-score)	92.8
Nutritional edema (n = 309)
Yes	7.12
No	92.88
Branchial perimeter (n = 401)	143 (110–180)
Acute global malnutrition (<125)	6.56
Normal child (≥125)	93.44
Weight-for-age index (n = 401)	1.06 (5.7; 2.43)
Underweight	24.01
Normal child	75.99
According to the Size-Age Index (n = 401)	−2.03 (−5.92; 2.87)
Growth delay	50.38
Normal growth	49.62
Children who were exclusively breastfed (n = 424)
No	36.32
Yes	63.68
Continuous breastfeeding at 24 months or older (n = 423)
No	34.52
Yes	65.48
Birth weight (kg) (n = 417)	3 (1–5.8) Moy 3.201
Child born with low weight (<2.500 kg)	7.91
Child born with normal weight (2.500–3.500 kg)	66.43
Child born overweight (>3.500 kg)	25.66
4 star food consumption in the household (n = 422)
Consume the 4 star meal	5.45
Does not consume 4 stars	94.55
Frequency of consumption of livestock products (n = 124)
Regularly (1 week to 1 month)	23.96
Occasionally (3–6 months)	40.63
Rarely	35.42
Meal frequency (24-hour recall) (n = 423)
<3 meals	85.31
≥3 meals	14.69
Nutritional status of mother and child by PB (mm), n = 410	260 (150–442)
Thinness (≤ 230 mm)	11.79
Good nutritional status (≥230 mm)	88.21
Source of drinking water for the household (n = 423)
Developed water point	86.05
Undeveloped water point	13.95
Complete child immunization by age (n = 420)
Child not fully vaccinated by age	20.24
Child fully vaccinated according to age	79.76

Table 3.

Nutritional status of children under 5 years of age in households surveyed in the Minova Health Zone.

GAM = global acute malnutrition, Min = minimum value, Max = maximum value, Med = median.

The results of the univariate analysis presented in the following Table 4 show that the factors associated with acute malnutrition in children aged 6-59 months are low birth weight, household socioeconomic status, daily meal frequency, exclusive breastfeeding, and immunization status.

Variables	% de GAM	ORna (95% CI)	p
Access to land
Yes (n = 138)	5.8	1
No (n = 175)	9.1	1.63 (0.64–4.55)	0.269
Exclusive breastfeeding
Yes (n = 205)	3.4	1
No (n = 108)	15.7	5.28 (1.98–5.53)	<0,001
Food consumption at 4 stars
Yes (n = 37)	2.7	1
No (n = 276)	8.3	3.27 (0.50–138.48)	0.227
Child sex
Girls (n = 168)	8.9	1
Boys (n = 145)	6.2	0.67 (0.25–1.71)	0.367
Frequency of meals per day
<3 (n = 267)	10.7	1.51 (1.13–5.98)	<0,002
≥3 (n = 46)	6.5	1
Reproductive interval in months
Less than 2 years (n = 164)	8.5	1.19 (0.47–3.12)	0.68
2 years or more (n = 138)	7.2	1
Birth weight (kg)
<2.500 kg (n = 23)	26.1	3.97 (1.12–12.34)	0.007
2.500 kg and more (n = 208)	8.2	1
Branchial perimeter of the mother (mm)
<230 mm (n = 33)	9.1	1.23 (0.22–4.51)	0.745
≥230 mm (n = 280)	7.5	1
Household size
≤7 people (n = 187)	6.4	1
>7 people (n = 126)	9.5	1.54 (0.61–3.87)	0.311
Socio economic status
Low (n = 162)	6.8	13.31 (1.89–576.09)	0.001
Average (n = 131)	9.2	18.55 (2.66–797.22)	0.001
High (n = 19)	5.3	1
Childhood immunization by age
Up to date (n = 256)	6.3	1
Not current (n = 57)	14	2.45 (1.85–6.45)	0.046
Drinking water source
Fitted (n = 219)	5	1
Undeveloped (n = 94)	13.8	3.03 (1.19–7.79)	0.007
Number of children under 5 years old
1 child (n = 87)	8	1
More than one child (n = 226)	7.5	0.93 (0.35–2.75)	0.876
Level of education of the respondent
Without (n = 104)	10.6	3.05 (0.86–13.50)	0.053
Primary (n = 102)	8.8	2.49 (0.66–11.40)	0.128
High school and above (n = 107)	3.7	1
Tribe
Non-aboriginal (n = 118)	10.2	1.73 (0.68–4.36)	0.196
Aboriginal (n = 195)	6.2	1

Table 4.

Factors associated with acute malnutrition (AM) in children 6–59 months of age in the Minova health zone (simple logistic regression).

GAM = global acute malnutrition; ORna = unadjusted odds ratio; CI = 95% confidence interval.

After adjustment by multivariate analysis (Table 5), only the variables exclusive breastfeeding, low birth weight and source of drinking water remained associated with acute malnutrition.

Variables	ORa (95% CI)	p
EBM less than 6 months	3.03 (1.19–7.79)	<0.001
Low birth weight	3.50 (1.09–12.15)	0.049
Undeveloped water source	3.21 (1.10–9.32)	0.032

Table 5.

Multivariate analysis: factors associated with acute malnutrition (am) in children 6–59 months of age in the Minova health zone.

GAM = global acute malnutrition; ORa = adjusted odds ratio; CI = confidence interval; EBM = exclusive breastfeeding.

With respect to chronic malnutrition, the results of the bivariate analysis (Table 6(a) and (b)) reveal that meal frequency and household socioeconomic status are significantly associated with chronic malnutrition in children aged 6–59 months in the study area (p < 0.05 with 95% CI).

(a)
Variables	% de CM		ORna (95% CI)	p
Access to land
Yes (n = 138)	47.1		1
No (n = 175)	52.6		1.24 (0.78–2.00)	0.337
Exclusive breastfeeding
Yes (n = 215)	48.4		1
No (n = 98)	54.1		1.26 (0.76–2.09)	0.349
Consumption of food at 4 stars
Yes (n = 37)	48.6		1
No (n = 276)	50.4		1.07 (0.51–2.27)	0.845
Frequency of meals per day
<3 (n = 267)	48.7		3.03 (1.19–7.79)	0.007
≥ 3 (n = 46)	58.7		1
Child sex
Girls (n = 162)	53.7		1
Boys (n = 151)	53.6		0.87 (0.54–1.39)	0.535
Marital status of Respondent
Single (n = 74)	43.2		0.70 (0.40–1.22)	0.181
Separated (n = 13)	53.8		1.07 (0.30–3.97)	0.909
Married (n = 226)	52.2		1
Reproductive interval in months
Less than 2 years (n = 164)	48.8		0.92 (0.57–1.49)	0.736
2 years or more (n = 138)	50.7		1
Birth weight (kg)
< 2.500Kg (n = 23)	65.2		1.99 (0.75–5.63)	0.129
2.500–3.500Kg (n = 208)	48.6		1
> 3.500Kg (n = 82)	50		1.06 (0.61–1.82)	0.825
Brachial perimeter of the Mother (mm)
< 230 mm (n = 33)	45.5		0.81 (0.36–1.78)	0.568
> = 230 mm (280)	50.7		1
Household size
<=7 people (n = 187)	47.1		1
> 7 people (n = 126)	54.8		1.36 (0.84–2.20)	0.181
Socio economic status
Law (n = 163)	46.6		5.28 (1.98–5.53)	<0,001
Average (n = 131)	53.4		3.97 (1.12–12.34)	0.006
High (n = 19)	57.9		1
(b)
Variables	% de CM	None	ORna (95% IC)	p
Childhood immunization by age
Up to date (n = 256)	49.6	129 (82.69)	1
Not current (n = 57)	52.6	27 (17.31)	1.12 (0.61–2.09)	0.680
Source of drinking water
Fitted (n = 219)	93.6	114 (73.08)	1
Undeveloped (n = 94)	55.3	42 (26.92)	1.34 (0.80–2.25)	0.232
Number of children under 5 years old
1 Child (n = 284)	14.8	45 (28.85)	1
More than one child (n = 226)	50.9	111 (71.15)	1.11 (0.66–1.88)	0.679
Food stock (Provisions)
No provisions (n = 284)	50.3	141 (90.38)	1.08 (0.47–2.53)	0.831
Available provisions (n = 29)	48.3	15 (9.62)	1
Level of education
Without (n = 104)	54.8	47 (30.13)	1.33 (0.75–2.37)	0.299
Primary (n = 102)	48	53 (33.97)	1.01 (0.57–1.81)	0.957
High school and above (n = 107)	47.7	56 (35.90)	1
Tribe
Non-aboriginal (n = 118)	54.2	54 (34.62)	1.30 (0.80–2.11)	0.262
aboriginal (n = 195)	47.7	102 (65.38)	1
Household land area
Less than one hectare (n = 104)	48.1	54 (73.97)	1.17 (0.50–2.77)	0.688
1 hectare or more (n = 34)	44.1	19 (26.03)	1

Table 6.

Factors associated with chronic malnutrition in children aged 6–59 months in the Minova HZ: simple logistic regression.

CM = chronic malnutrition, ORna = unadjusted odds ratio, CI = confidence interval.

In Table 7, it is found that even after adjustment for all factors by multiple regressions, daily meal frequency and low household socioeconomic status remained statistically associated with chronic malnutrition as found in the bivariate analyses.

Variables	ORa (95% CI)	p
Frequency of less than three meals/day	2.46 (1.85–6.45)	0.046
Low socio economic status	3.97 (1.12–12.34)	0.007

Table 7.

Multivariate analysis: Factors associated with chronic malnutrition in children 6–59 months of age in the Minova health zone.

ORa = unadjusted odds ratio, CI = confidence interval.

Children from households that consumed less than three meals per day were twice as likely to be chronically malnourished compared to other children and those from households with low socioeconomic status were 3.97 times more likely to be chronically malnourished compared to children from households with high socioeconomic status.

4. Discussions from results

4.1 Access to arable land

This study shows that more than half of households, or 54%, live without access to arable land, while agriculture in rural areas is an important source of income for the population, as stated by Coulibaly B. and Berkhout ED. in their studies carried out in Mali and in certain countries of Sub-Saharan Africa [18, 19].

The international NGO ACF in its study conducted in the Minova Health Zone in 2008 confirms that of all the main constraints linked to agriculture in this zone; limited access to arable land alone occupies 22% and the practice of sharecropping comes first as a mode of accession, followed by inheritance. According to studies by the National Nutrition Program of South Kivu and the UNICEF-WFP-FAO agencies, at least 45% of the households surveyed have no means of accessing land and that the purchase of food constitutes up to 75% of object of family expenses. This reality, which is not, however, unique to Minova alone, remains a major concern for the population, especially when it is necessary to take into account the speed of population growth experienced by the country.

4.2 Malnutrition and access to land

With a prevalence of chronic malnutrition of 50.38% in Minova, that is, one in two children, against GAM at 7.7%; malnutrition remains one of the worrying factors in the health of children in terms of the risk of morbidity and mortality. These results are not far from those found in the MISC 2018 surveys (48% chronic malnutrition for South Kivu against 43% for the whole of the DRC), EDS 2014 (MC 53% and MAG 6.5%) and the survey the most recent (2021) from Pronanut the three UN Agencies which found that the prevalence of GAM in Minova in children under five increased from 7.5% in 2018 to 5.8% in 2021, 95% CI (4.1–8.1) and that of MC decreased from 67.6 in 2018 to 51.0 (30.1–34.4) as a result of ongoing joint response interventions in the Area.

Several factors discussed below justify this high prevalence and further require a holistic response to continue to reverse the trends.. Even though some of the articles read claim that food security and hunger presented strong evidence of qualitative and quantitative links between land tenure, household food security and nutritional status [11, 13, 20] and that the reduction or a loss of access to land in an agrarian society leads directly to a reduction in income; access to food and impact on the nutritional status of populations [11, 13, 18, 20, 21, 22]; we found that there is no statistically significant association between having access to arable land and improved nutritional status.

Our results also diverge from the findings of the study carried out by Eide WB and Nahalomo on the situation of adequate food and the nutritional status of people evicted from the land in 2018 in Uganda, which found that out of 187 children followed 1 child in 2 of mothers evicted from arable land had developed wasting. The results of the similar study conducted by Tefft and Kelly had however also found the results close to those of Eide and Nahalomo.

By comparing the nutritional status of children in the rice-growing areas with that of children in the cotton-growing areas Tefft and Kelly 2002 [23] in Mali, Tefft and Kelly found a lower prevalence of wasting and stunting (p-value less than 0.05) among children from households in the irrigated rice-growing areas of Macina and Niono (19–25%) compared to children in the cotton-growing area (35–48%), which signified a positive influence of the access to arable land.

Andrew D Jones in the study on agricultural biodiversity, dietary diversity and nutritional status in low and middle income countries concurred with the findings that, agricultural biodiversity (as a result of access to arable land) was consistently positively associated with improved height-for-age (HAZ) Z-score of preschool children. He says, “A one-unit increase in the number of cultured species was associated with a 0.03 and 0.05 increase in HAZ, respectively, in children aged 24–59 months. He also found that land evictions become a public health problem because limited or non-existent land ownership is linked to about 80% of cases of hunger and under nutrition among people living in rural areas.” The same is true for Azka Rehman et al. in Pakistan who in turn stated that women’s land ownership has a significant positive effect on children’s height/age z-score (HAZ score): if a woman owns land, the height-age score of her child may be 0.94 points higher than that of landless women [24].

Despite this continuation of previous results, our study found rather high proportions of malnutrition among children from households without access to arable land (52.6% of MC and 9.1% of MAG) compared to those with access but this without any association statistically significant.

For us, this difference due to an effect of chance in the two categories of households can be explained on the one hand by the way in which the production of the land and the incomes of the households are affected there for other needs and other apart from the heterogeneity of predictors of malnutrition such as socioeconomic status, breastfeeding and infant feeding practices, which have shown statistically significant as sociations in other studies.

Gamuchirai Chakona and Charlie M. Shackleton also confirm this thinking when they state that intra-household food allocation is one of the important factors affecting the nutritional status of children in South Africa.

4.3 Factors associated with malnutrition

Low birth weight (LBW), non-practice of exclusive breastfeeding and water consumption from undeveloped sources were significantly associated with the occurrence of acute malnutrition in children aged 6–59 months in the ZS of Minova even after adjusting for any confounding factors by multiple logistic regression.

This observation has already been made by several other researchers such as Mbalenhle Mkhize and M. Sibanda in South Africa in their study examining the factors associated with the nutritional status of children under five who found that low Birth weight contributed 25.92% to the occurrence of both acute and chronic malnutrition in children and similar observations were found in numerous articles used [3, 5, 6, 25, 26]. On the other hand, F. Diawara in Mali found in his study conducted in 2006 that only the age of the child, the parity of the mother and the family meal were associated with wasting in children aged 6–59 months with a value p < 0.05.

For our part, we are of the opinion that the low birth weight being a reflection of intrauterine growth retardation due to the prolonged under nutrition of the pregnant woman, the child born of this household is not spared to develop sooner or later, other forms of deficiency malnutrition such as emaciation, especially if other factors coexist such as diarrhea often caused in children by the ingestion of unclean water and early feeding.

Regarding the benefit of exclusive breastfeeding, several studies conducted by Experts have argued that early breastfeeding of the child at the hour following birth, exclusive breastfeeding before the first 6 months after birth and the continuity of breast-feeding until more or less 24 months constitutes a powerful line of defense against any form of infant malnutrition, including cachexia and obesity in adulthood.

In our study, we found in the bivariate analyzes that the further a child moved away from the age of breastfeeding, the more he had the chance of being affected by chronic malnutrition (less than 12 months: 20.59%, between 12 and 24 months: 44.30% and over 24 months: 57.50% with p-value <0.001) which further supports the thesis that breast milk effectively protects young children against different forms of malnutrition [27, 28, 29, 30, 31].

In relation to chronic malnutrition, the results analyzed after adjustment show that in households where children consume less than 3 meals a day, they were 2.46 times more likely to be affected by chronic malnutrition compared to those who have a frequency superior.

This result meets the opinion of several Experts in Nutrition (chrono nutrition) who affirm that the more a diet is adequate (quality, quantity, frequency); the more the child is protected from the risk of under nutrition although this is divergent from the results found by Stephen Kofi et al. in Ghana [19, 32].

This we, this is true by the fact that the more the child consumes meals during the day, the more it increases the chance to vary the foods that can bring together the different nutrients that the body needs for its growth. Our study also revealed that in households with low socio-economic status, children were 3.97 times more likely to have chronic malnutrition before age five ORa 3.97, 95% CI (1.12–12.34), p value = 0.007.

Our results thus join those found by D.Karageorgou who cites among the main factors of change in chronic malnutrition, the wealth index with 4% [33].

Célestin Bucekuderhwa and S. Mapatano also demonstrate in their study on understanding the dynamics of food vulnerability in South Kivu that the capacity to take charge is the ability to mobilize human, economic and institutional resources for the benefit of the household; and this ability therefore depends on education, knowledge, culture, time and control over resources, including socio-economic status or income [22].

5. Conclusions

Considering the very high prevalence of multiple forms of deficiency malnutrition in the Provinces of the DRC despite its potential, this study examined the influence of access to arable land on the nutritional status of a sample of children. From 6 to 59 months in the HZ of Minova (South Kivu) with specific reference to cases of chronic malnutrition and acute malnutrition. Malnutrition (chronic and acute) which constituted our outcome was discussed in relation to access to arable land as the main explanatory variable associated with other factors sensitive to nutrition including breastfeeding and feeding practices. Infant and young child feeding, mother’s age, household socio-economic status, level of education of mothers and household heads, water-hygiene-sanitation, vaccination, agricultural practices.

After analyzing the data, it was found that:

• The prevalence of chronic malnutrition and global acute malnutrition were observed to be relatively higher in households without access to arable land compared to those with access, but there was no proven statistically significant association. The simple fact of having access to arable land for a household in Minova therefore does not guarantee the improvement of the nutritional status of children aged 6–59 months residing there.

• 54% of households in Minova do not have access to arable land that can meet their production needs and of consumption;

• The frequency of less than 3 daily meals in the household and the low socio-economic level were significantly associated with the occurrence of chronic malnutrition in children aged 6–59 months. In addition, low birth weight, exclusive breastfeeding and undeveloped source of drinking water are variables that have been significantly associated with global acute malnutrition.

Compared to research perspectives and recommendations, we did not carry out the study on soil fertility in our study to assess the influence that this could have on production per sown area and the content of nutrients in food; this could be an important confounding factor.

We thus suggest to future researchers or organizations to be able to carry out studies on the physicochemical and biological analysis of the soil of the ZS of Minova in order to determine its level of fertility and better orientate on the consequent actions (types of appropriate speculations, bio fortification possibly, etc.).

Thus, we recommend:

• to the Government of the Republic:

  • To structure and ensure the strict application of the structure of food prices throughout the national territory in order to guarantee the minimum of food security to households and contribute to the fight against undernutrition considering that the latter do not have sufficient access to arable land and obtain their supplies from markets.

  • To put in place adequate strategies to improve farmers’ access to arable land and their protection against land misuse (Ministries of Agriculture and that of Land Affairs).

  • Initiate a general grassroots development program alongside the determinants of health already known (improvement of drinking water supply, access to quality health care for all, job creation for young people and facilitation of access). More investment should be made in improving the food system to achieve better nutrition.

  • “No panis nec pax”, no peace, no bread too: having to ensure the safety of people and their property throughout the national territory is one of the prerequisites for the nutrition and health of the population.

• To the community of the Health Zone of Minova:

To understand that despite the efforts made to redress chronic and acute malnutrition, much remains to be done. With or without access to land, we can fight malnutrition in our households.

The fight is essentially based on prevention. Our life practices and the way our household incomes are distributed (their use) greatly depend on it. Let us learn to consume in quantity and quality what we produce locally.