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Abstract

Background:

Agroecological methods have the potential to impact nutrition and food security, however, to date there is limited research evaluating this approach.

Objective:

A 5-year participatory research project with farming households in north and central Malawi was designed to train farmers on agroecological practices, alongside raising awareness on nutrition and gender equity. This cross-sectional study aimed to explore the relationships between crop diversity, food security at the household level, and individual diversity for women, within the context of an agroecology, nutrition education, and farmer mentoring program.

Methods:

Participating farmers were trained in and experimented with different farming methods. These farmers subsequently trained other farmers on these short-term agroecological practices and provided mentorship using community-based educational methods designed to address both household food security and nutrition. In year 4 of the intervention, a cross-sectional survey assessed farm practices, food security, and individual dietary diversity of 851 participating households.

Results:

Households with lower crop diversity were significantly less likely to be food secure (odds ratios [OR] = 0.829, P < .001). Women in households with higher crop diversity were more likely to have higher individual dietary diversity (OR = 1.120, P < .01), eat vitamin A rich foods (OR = 1.176, P < .01), and legumes, nuts, and seeds (OR = 1.141, P < .01).

Conclusions:

These findings suggest that within a participatory agroecological training combined with community-based nutrition education with a focus on social equity, crop diversity is associated with less household food insecurity and poorer diet quality for rural farming households. Crop diversity may improve dietary diversity by making nutritious foods more available.

Keywords

agroecology food insecurity agrobiodiversity dietary diversity nutrition gender

Introduction

Smallholder farming households in Sub-Saharan Africa experience high rates of food insecurity and undernutrition.¹ Low agrobiodiversity in farming systems can affect both food security and nutrition, particularly for smallholder farmers who rely on their own production for at least a portion of their food source. Agricultural intensification and access to markets has been the emphasis as a solution for food security; however, diversifying ecosystems has a greater potential to mitigate the effects of climate change by spreading the risk across different crops.^2,3 Diverse farming systems are more resilient to shocks while sustaining key ecological benefits such as pollination, pest control, drought resilience, and soil microorganisms.^4,5 This resilience from diversification of crops is especially important now, as it can also help to mitigate the consequences of climate change in Sub-Saharan Africa where a large percentage of the population consumes a diet focused on a few staple crops, which threaten global food systems and nutrition security.^6,7 Diversifying the food system can address some of the effects of climate change; however, globally, current agricultural tends to focus on specialization of specific crops which makes it difficult for the food system to effectively address poor nutrition.

There have been international policy debates about the food security and nutrition implications of simplified monocrop farm systems that rely on synthetic fertilizer and pesticides, with some calling for agroecology as an alternative approach.^8,9 Agroecology is an approach to farming which mimics natural systems, by increasing agrobiodiversity, recycling organic material, water and energy, and minimizing the use of toxic, fossil-fuel based inputs.¹⁰ As a holistic approach to food production, agroecology integrates social science and other disciplines, as well as traditional and local sources of knowledge.¹¹ Participatory action research, farmer experimentation, and farmer-to-farmer methods of horizontal knowledge sharing are considered crucial methods for “scaling out” agroecology.¹¹ Biodiversity is considered another key element of agroecology, including the integration of crops and livestock, and having a diverse range of varieties and crops grown, which are adapted to local systems.¹²

A positive link between diversified farming systems and dietary diversity has been found for farming households in several studies.^13

-17 While many studies focus on dietary diversity at the household level, others have seen this positive link on dietary diversity at the individual level for women or children.^13,17

-20 There are multiple pathways through which the positive link between dietary diversity and production diversity occurs. One pathway is through direct consumption. An extensive literature demonstrates that for households producing food for their own consumption, dietary diversity depends on farm production diversity, including the number of crops that are grown and species diversity.^14
-16,21,22 In Malawi, for example, farm production diversity has been positively associated with household dietary diversity, with a significantly stronger association for female-headed households.¹⁵ Similar findings have been reported from Zambia, Tanzania, Kenya, and Ethiopia.^23,24 A second, indirect, pathway is through access to local markets.²⁴ For instance, households with relatively good access to markets tend to consume more diverse diets, with their food consumption reliant less on their own production.^24

-27 Research by Hawkes and Ruel suggests other pathways such as the empowerment of women, and lowering food retail prices.²⁸

In addition, previous research has identified gender and generational inequalities (i.e., labor, access to and control over income, and agricultural resources and gender-based violence) as key factors to be addressed in improving food security and household dietary diversity using agroecological methods.^29
-31 Very few studies have looked at the relationship between production diversity and individual dietary diversity in Sub-Saharan Africa, especially for women, who often have a crucial role in agriculture, while also experiencing persistent structural inequalities.³² This study aims to add to the literature by exploring the relationships between crop diversity, food security at the household level, and individual diversity for women, within the context of an agroecology, nutrition education, and farmer mentoring program.

Study Context

Malawi is a landlocked country in south-east Sub-Saharan Africa, with a population of over 16 million.³³ The majority of the population in Malawi are smallholder farmers who rely on agriculture for both food and income.³³ The average crop diversity estimated at 2.3 crops per household.³⁴ Persistent food insecurity and heavy reliance on maize has had multidimensional impacts on families, including low dietary diversity and child malnutrition.³⁵ Over half of the population are considered severely food insecure, with a 17.5% prevalence of undernourishment.³⁶ There are also higher rates of child stunting, estimated at 37% of all children under 5 years in the most recent national survey.³⁷ As well, over half of all women and young children in Malawi suffer from vitamin A deficiency, which results in significant morbidity and mortality.³⁷ Women’s agency and access to agricultural resources is very limited in Malawi, with early marriage associated with low dietary diversity, early pregnancy, and high spousal violence for women.³⁷ Rural Malawian women have less access to education, lower access to land, credit, seeds, and other agricultural resources compared to men.^38
-40 In addition, they are constrained by highly unequal workloads, including agricultural labor, household tasks, and child care responsibilities.^37,41 Social inequalities within households, such as unequal distribution of diverse foods based on age, gender, and other factors is another key determinant of nutrition.^42,43 Given the nature of poverty and food insecurity in the country, various initiatives have been taking place across the country. One such intervention was a 5-year participatory agroecology project.

The Agroecology Project

The participatory agroecology intervention was a 5-year project (2012-2017) with the goal of building resilient, sustainable, low-input farming systems that drew on smallholders’ agricultural knowledge and complex understanding of local environmental conditions. The project provided training to farmers on a variety of short-term sustainable land management (SLM) practices, including crop rotation, legume intercropping, burying legume residues, preparing and applying compost manure, and mulching. These were practices that deliver a quick return investment once implemented, and require less land and other resources.^44,45 Farmers were encouraged to experiment with the practices based on their interest and availability of land and labor. This intervention also used a farmer-to-farmer method of teaching, which included exchanges, field trips, and developing local farmer research teams (FRT) who conducted the project training.⁴⁶ These FRT lead the experimentation of different agroecological farming methods such as legume intercropping. The FRT provided support, mentoring, and training to their farmers peers on using these new farming techniques.⁴⁶

Nutrition training was done for all participating farmers and FRT at least once in each site. Training included learning about the consuming vegetables, fruits, legumes, nuts, fats, grains, and animal-sources foods. In addition to training on the importance of consuming a balanced diet, young child feeding, exclusive breastfeeding, nutrition during pregnancy and lactation, general household hygiene for healthy families (such as handwashing), childcare practices, and the importance of male involvement in childcare were also taught. Nutrition training was conducted for 1 day in a village area, and included drama, songs, presentations, preparing, and sharing recipes. The nutrition training was carried out approximately 2 times a year. In addition, the FRT visited farmers and had informal sharing of information in an unstructured format.

In addition to the nutrition training, multiple times each year, recipe demonstrations (known as “Recipe Days”) were held in both the northern and central regions of Malawi. Participating farmers were invited to take part in Recipe Days and were encouraged to bring their spouses. Men and women learning and preparing recipes together was an important aspect of the participatory approach to training and efforts to improve the household division of labour.⁴⁷ During the training, participants were taught several recipes using crops grown on the project. Some of these recipes included soya milk, soya meat, doughnuts from sorghum, cassava and sweet potato, sweet beer from sorghum and finger millet, fritters from sweet potato and cassava, porridge made from local orange maize, sorghum, finger millet, and soya, and “African cake” made with legumes and different vegetables seasoned with vegetable flour. While experimenting with different recipes, the group was taught about the nutritional value of the recipes and the benefits of including these foods in their diet and their children’s diet. Recipes were selected by local team members and care was taken to use recipes that were nutritious and appealing to local tastes, while ensuring ingredients and equipment needed were accessible to most farmers at home. At the end of the recipe day, each group presented the prepared recipe to the larger group, explained how they made their dish, and the group sampled the items.

Vitamin A was also a major focus area for the nutrition component of this project, due to widespread vitamin A deficiencies in Malawi. Information about the importance of vitamin A and which foods it can be found in was included in all nutrition programming, but there was a heavy focus on increasing cultivation and consumption of local open-pollinated varieties of orange maize as an important source of vitamin A. The project included as an objective examining the potential for local orange maize as one agroecological strategy to address micronutrient deficiencies. Local orange maize was found to be a significant source of carotenoids, with potential to address this micronutrient deficiency.⁴⁸

The focus on local orange maize as an important source of Vitamin A was an exploratory objective of this project, as there was uncertainty about whether farmers would be receptive to including orange maize in their diets. In the early 2000s, hybrid and genetically modified varieties of orange maize had been distributed as food aid during a time of extreme hunger, so there was concern that there would be a stigma associated with choosing to cultivate and consume it. However, from the outset of the project, farmers were eager to learn about and experiment with growing and consuming local orange maize. Information about the benefits of growing and consuming local orange maize was also included in other project trainings, and many recipe demonstrations were done with orange maize so farmers could taste it and experiment with preparing it.

In earlier publications for this project, we found that this project significantly increased crop diversity among agroecology-adopting household.^31,49 We have also found a significant improvement in production diversity and household dietary diversity associated with the intervention.⁵⁰ In another qualitative study, we found that women reported positive impacts from the intervention, including improvements in diets.⁵¹ Our previous analysis also found significant improvements in household food security and wealth.⁵² However, we had not previously examined the impacts on dietary diversity for women.

Due to the participatory, multisectoral nature of this project, it provides a unique opportunity to examine the relationships between agroecology, crop diversity, individual dietary diversity for women, and household food security.

Methodology

This agroecology research project was part of a larger study with a total of 6000 households that had received training in agroecology, nutrition, and social equity.⁵² In July to September 2016, a structured survey was conducted to measure household food security, dietary diversity, crop diversity, and farming practices including agroecological practices. Survey questions were developed based on input from FRT members and project researchers. The survey was originally developed in English and then translated by project partners into 2 local languages—Chichewa and Chitumbuka.

The survey included project village areas randomly selected from the Mzimba and Dedza districts of Malawi. Households were randomly sampled from each village area that was participating in the project. Eight-hundred and fifty-one households were sampled, and these household had participated in the project for either 1, 2, 3, or 4 years.

In the first round of data collection, a research team of 10 enumerators and 2 supervisors were recruited and trained to implement the survey. Survey enumerators and supervisors were trained over a 2-day period to strengthen the quality of the survey and ensure that the questions were asked in a standardized way. The survey questions were pretested, reviewed, and discussed to ensure content validity and clarity and to discuss and potential challenges that may arise during data collection. The survey was conducted in both the local languages (Chitumbuka and Chichewa). Enumerators carried out informed consent prior to carrying out the survey. The research study was approved by the Non-Medical Research Ethics Board at Western University, Canada, and the Ethics Committee of Chancellor College, University of Malawi.

Each farming household was interviewed on household and demographic information (i.e., household assets and number of people living in the household), farming practices, food security, and child feeding practices for children under two. In the case of married couples, both the husband and wife were asked about farming practices, food security, and demographic questions. If it was a polygamous household, a coin was flipped to determine which wife to include. The primary caregiver of the youngest child under two was interviewed for the questions on child feeding practices. Supplementary data collection was completed from December 2017 to February 2018, and the enumerators went back to collect demographic information such as household size, farm size, and women’s education level from project households, as these variables were missing in the previous round of data collection. All the data were entered into a STATA template and analyzed used STATA SE v15.1.

Outcome Variables

The Household Food Insecurity Access Scale (HFIAS) was constructed using the method described to measure food insecurity.⁵³ Questions related to several domains of food insecurity (i) anxiety about food supply; (ii) low food quality, including both a lack of food diversity of their diets and lower availability and access to preferred foods; and (iii) insufficient quantity of food, reflected in lower consumption (Coates et al., 2007). The categorical HFIAS indicator was used, which categorizes households as “Food Secure,” “Mildly Food Insecure Access,” “Moderately Food Insecure Access,” and Severely Food Insecure Access” based on their responses to each of the domains.

For the individual dietary diversity, women indicated if they ate foods from a list of 8 food groups in the past 24 hours; the higher the score, the greater the diversity of foods consumed. These 8 food groups included: (1) fruits and vegetables; (2) meat/fish; (3) eggs; (4) legumes, nuts, and seeds; (5) vitamin A rich foods; (6) dark green leafy vegetables; (7) grains; and (8) dairy. Intake of vitamin A rich foods; eggs; and legumes, nuts, and seeds were kept as binary outcome variables in the analysis.

These self-reported food group were then counted and combined to create the Individual Dietary Diversity Score (IDDS). Individual dietary diversity scores are used to assess nutritional adequacy.⁵⁴ Individual dietary diversity scores have been demonstrated as indicators for predicting of micronutrient adequacy of women’s diets across multiple resource-poor countries.⁵⁵ This IDDS score that was created was similar to the Minimum Dietary Diversity for women of reproductive age (MDD-W) indicator except for the category of organ meats (the consumptions of organ meats in rural Malawi has been found in other studies to be almost nonexistent^56,57), and the combination of pulses and nuts/seeds into one group instead of two separate groups.⁵⁸ Therefore, in this study, we had 8 food groups for the individual dietary diversity instead of the 10 groups used for the MDD-W.

Explanatory Variables

Data were collected from households on household size, year joining the project, district (Dedza or Mzimba), age of wife in years, farm size, wife’s education status, and marital status. A wealth index was created using the Demographic and Health wealth surveys’ index guidelines.⁵⁹ This method involved using principal component analysis to represent a composite measure of a household’s cumulative living conditions. Variables included in the wealth index estimation were ownership of household assets such as radios, bicycles, and motorcycles and ownership of livestock such as cattle, goats, and pigs. Wealth quintiles were generated from the wealth index. Each household asset was normalized using its mean and standard deviation and created into a wealth quintile.

Households were also given a score from 0 to 6 to indicate the number of short-term SLM practices they practiced, to serve as an indicator of the intensity of program involvement. These 6 practices included crop rotation used as a method of pest control, intercropping used as a method of pest control, crop rotation with legumes as a method of soil management, manure or compost used as a method of soil management, mulching used as a method of soil management, and intercropping with legumes as soil management.

Crop diversity was calculated as a count of the crop species grown the previous rainy season (2015-2016) by the household. Crops listed included maize (Zea mays including local yellow maize, local orange maize, local white maize, and maize hybrid), sorghum (Sorghum bicolor), finger millet (Eleucine coracana), groundnut (Arachis hypogaea), common bean (Phaseolus vulgaris), soya (Glycine max), pigeon pea (Cajanus cajan), cowpea (Vigna unguiculata), groundbean (Vigna subterranea), cassava (Manihot esculenta), sweet potato (Ipomoea batatas), “irish” potato (Solanum tuberosum), pumpkin (Cucurbita), and several other minor crops. Households listed grew a maximum of 15 crop species during the rainy season. Crop diversity was also made into a categorical variable with those planting less than the median number of crops (6) categorized as low crop diversity and those planting greater than or equal to the median number of crops categorized at high crop diversity.

Regression Analysis

All regression models controlled for village area (18 village areas) as a random effect, as participation in the project tended to be clustered by village areas. Women’s intake of vitamin A rich foods, women’s intake of eggs, and women’s intake of legumes, nuts, and seeds were assessed as outcome variables using mixed effects logistic regression models, which provided odds ratios (ORs) for the likelihood. We used mixed effects ordinal logistic regression for household food security and IDDS for women. The covariates for these regression models included year joining the project, wealth, household size, district (Mzimba or Dedza), age of wife in years, farm size, and crop diversity. Education was highly correlated with wealth and was therefore removed from the model. All covariates were continuous except for district and wealth. For wealth, the richest category of wealth was used as the reference category and for district, the Dedza region was used as the reference category.

Results

Descriptive characteristics of the survey participants are shown in Table 1 and Table 2. Out of the total of 851 respondents, 553 (65%) were female and 298 (35%) were male. The majority of respondents n = 681 (80%) were married (88% of married households were monogamous, while 12% of married households were polygamous); of the remaining households, 8% were divorced, 11% were widowed, and just 2% reported as single. Northern and central regions of Malawi were evenly represented, with 47% of respondents reporting from northern villages (Mzimba) and 53% from central villages (Dedza). Most participants had lower levels of education, with 34% of men and 52% of women completing only some primary school. Household in this program used on average 2 short-term SLM practices.

Table 1.

Descriptive Statistics.

	Mean ± SD	n	Min	Max
Wife’s age (years)	42.26 ± 14.35	803	17.66	84.50
Husband’s age (years)	44.92 ± 13.91	661	17.17	89.13
Household size	5.33 ± 1.73	851	1	13
Number of people under years of 18	2.65 ± 1.43	851	0	11
Crop diversity (# of different crops planted)	6.00 ± 2.41	851	0	13
Number of short-term SLM practices^a	1.72 ± 1.18	851	0	6
HFIAS	6.67 ± 6.81	850	0	27
IDDS for women	4.42 ± 1.48	443	0	8
Farm size (acres)	2.15 ± 1.37	851	0	20

Abbreviations: HFIAS, Household Food Insecurity Access Scale; IDDS, Individual Dietary Diversity Score; SD, standard deviation; SLM, sustainable land management.

^a Crop rotation used as a method of pest control, intercropping used as a method of pest control, crop rotation with legumes as a method of soil management, manure used as a method of soil management, mulching used as a method of soil management, intercropping with legumes as soil management.

Table 2.

Descriptive Statistics, Cont'd.

	N (%)	n
Marital status		851
Single	13 (1.53)
Divorced	67 (7.87)
Widowed	90 (10.58)
Married	681 (80.02)
Marriage type		681
Monogamous	596 (87.52)
Polygamous	85 (12.48)
District		851
Dedza	447 (52.53)
Mzimba	404 (47.47)
Wealth quintile		845
Richest	169 (20.00)
Richer	169 (20.00)
Middle	167 (19.76)
Poorer	165 (19.53)
Poorest	175 (20.71)
Husband’s education		708
No schooling	54 (7.63)
Some primary school	243 (34.32)
Completed primary school	182 (25.71)
Some secondary school	116 (16.38)
Completed secondary school	113 (15.96)
Wife’s education		842
No schooling	121 (14.37)
Some primary school	440 (52.26)
Completed primary school	173 (20.55)
Some secondary school	89 (10.57)
Completed secondary school	19 (2.26)
Crop diversity		851
Low (<6)	353 (41.48)
High (≥6)	498 (58.52)
Year joining program		851
2012	206 (24.21)
2013	229 (26.91)
2014	370 (43.48)
2015	46 (5.41)
HFIACAT		850
Food secure	291 (34.24)
Mildly food insecure	67 (7.88)
Moderately food insecure	191 (22.47)
Severe food insecure	301 (35.41)

Abbreviation: HFIACAT, household food insecurity access category.

The only items reportedly owned by more than one-third of all participants were bicycles, radios, and mosquito nets. In addition, the majority of project households were food insecure, or in the bottom 3 food security categories (mildly food insecure, moderately food insecure, and severely food insecure).

The average number of crop species grown during the previous rainy season per farm was 6.00 ± 2.41. The most commonly planted crop was maize (98.59% of households), which included local yellow maize, local orange maize, local white maize, and hybrid maize (Table 3). Ten percent of households reported growing local orange maize and 64% reported growing pumpkins, both of which are foods rich in vitamin A. In addition, over 50% of households reported planting groundnuts, beans, soya, and pigeon peas. Sorghum was least prevalent, with less than 3% of households growing it the previous season.

Table 3.

Crop Diversity by Species (n = 851).

	N (%)
Maize (Zea mays)
No	12 (1.41)
Yes	839 (98.59)
Sorghum (Sorghum bicolor)
No	829 (97.41)
Yes	22 (2.59)
Finger millet (Eleusine coracana)
No	589 (69.21)
Yes	262 (30.79)
Groundnut (Arachis hypogaea)
No	311 (36.55)
Yes	540 (63.45)
Common bean (Phaseolus vulgaris)
No	313 (36.78)
Yes	538 (63.22)
Soya (Glycine max)
No	181 (21.27)
Yes	670 (78.73)
Pigeon pea (Cajanus cajan)
No	342 (40.19)
Yes	509 (59.81)
Cowpea (Vigna unguiculata)
No	589 (69.21)
Yes	262 (30.79)
Ground bean (Vigna subterranea)
No	729 (85.66)
Yes	122 (14.34)
Cassava (Manihot esculenta)
No	723 (84.96)
Yes	128 (15.04)
Sweet potato (Ipomoea batatas)
No	373 (43.83)
Yes	478 (56.17)
Potato (Solanum tuberosum)
No	790 (92.83)
Yes	61 (7.17)
Pumpkin (Cucurbita spp)
No	305 (35.84)
Yes	546 (64.16)
Other
No	721 (84.72)
Yes	130 (15.28)

The average IDDS among all women was 4.42 ± 1.48 food groups. About 32% of women consumed foods from vitamin A rich food sources (Table 4). Foods commonly consumed by more than half of the sample included starchy staples, leafy green vegetables, legumes, nuts, and seeds and other fruits and vegetables.

Table 4.

Individual Dietary Diversity for Women by Food Group (n = 443).

	N (%)
Starchy staples
No	3 (0.68)
Yes	440 (99.32)
Leafy green vegetables
No	60 (13.54)
Yes	383 (86.46)
Vitamin A rich foods
No	300 (67.72)
Yes	143 (32.28)
Other fruits and vegetables
No	46 (10.38)
Yes	397 (89.62)
Meat and fish
No	233 (52.60)
Yes	210 (47.40)
Eggs
No	355 (80.14)
Yes	88 (19.86)
Legumes, nuts, seeds
No	201 (45.37)
Yes	242 (54.63)
Dairy
No	389 (87.81)
Yes	54 (9.76)

Ordinal logistic regression was completed to assess the relationship between food security, individual dietary diversity for women, and several other covariates (Table 5). Crop diversity remained statistically significant predictor for household food security (HFIAS) in all 3 model specifications. After including demographic variables in models 2 and 3, poverty was a strong predictor of household food insecurity. Model 2 excluded the wife’s age, wife’s education level, and marital status, as those are often correlated with wealth status, however both model specifications gave similar results. For model 3 with those in the middle (OR = 2.321, P < .001), poor (OR = 3.264, P < .001), and poorest (OR = 5.235, P < .001) wealth quintiles more likely to have higher levels of food insecurity compared to those in the richer quintiles. In addition, within the study context, household size influenced food insecurity (OR = 1.096, P < .05), with larger households more likely to have higher levels of food insecurity. Importantly, those who reported higher crop diversity were also less likely to have higher levels of food insecurity (OR = 0.829, P < .001).

Table 5.

Association Between Crop Diversity and Household Food Security and Individual Dietary Diversity for Women Using Ordinal Logistic Regression, Controlling for Village as a Random Effect.

Variables					Household food security^a			Individual dietary diversity for women^b
Variables					Model 1, odds ratio (SE)	Model 2, odds ratio (SE)	Model 3, odds ratio (SE)	Model 1, odds ratio (SE)	Model 2, odds ratio (SE)	Model 3, odds ratio (SE)
Crop diversity					0.792 (0.024)^c	0.834 (0.027)^c	0.829 (0.027)^c	1.234 (0.049)^c	1.131 (0.046)^d	1.120 (0.046)^d
Farm size						0.949 (0.051)	0.965 (0.051)		0.959 (0.087)	0.983 (0.092)
Wealth quintile				Richest (Ref)
				Richer		1.406 (0.312)	1.436 (0.335)		0.671 (0.204)	0.641 (0.203)
				Middle		2.330 (0.527)^c	2.321 (0.554)^c		0.363 (0.118)^d	0.363 (0.123)^d
				Poorer		3.348 (0.791)^c	3.264 (0.816)^c		0.265 (0.085)^c	0.257 (0.087)^c
				Poorest		5.557 (1.382)^c	5.235 (1.422)^c		0.182 (0.059)^c	0.162 (0.057)^c
Number of short-term SLM practices						1.122 (0.072)	1.077 (0.072)		1.177 (0.097)	1.178 (0.102)
Year joining program			2012 (ref)
			2013			0.913 (0.192)	0.880 (0.192)		0.620 (0.165)	0.675 (0.189)
			2014			1.389 (0.281)	1.323 (0.277)		0.856 (0.212)	0.867 (0.225)
			2015			1.980 (0.693)	2.141 (0.802)^e		0.809 (0.404)	0.850 (0.451)
Household size						1.088 (0.044)	1.096 (0.047)^e		0.931 (0.047)	0.917 (0.049)
District	Dedza (Ref)
District	Mzimba					0.649 (0.118)^e	0.724 (0.141)		1.858 (0.397)^d	1.662 (0.376)^e
Wife’s age							0.997 (0.006)			0.997 (0.007)
Wife’s education		No schooling (Ref)
		Some primary					0.900 (0.203)			1.376 (0.382)
		Completed primary					0.677 (0.181)			1.223 (0.419)
		Some secondary					0.565 (0.177)			1.170 (0.460)
		Completed secondary					0.355 (0.211)			1.514 (0.949)
Marital status		Single (Ref)
		Divorced					2.103 (1.382)			1.296 (0.835)
		Widowed					1.943 (1.252)			1.469 (0.908)
		Married					1.752 (1.077)			1.027 (0.608)

Abbreviation: SLM, sustainable land management; SE, standard error.

^a Household Food Security was a categorical variable from 1 to 4, with “Food Secure” (1), “Mildly Food Insecure Access” (2), “Moderately Food Insecure Access” (3), and Severely Food Insecure Access” (4).

^b Number of food groups consumed, from 0 to 8.

^c P < 0.001.

^d P < 0.01.

^e P < 0.05.

We assessed dietary diversity using ordinal logistic regression and the results in Table 5 show that women in households in the middle to poorest wealth category were more likely to have lower dietary diversity compared to those in the richest wealth quintile, similar to the results for household food insecurity. Crop diversity was positively associated with women’s individual dietary diversity in all model specifications (in model 3 OR = 1.120, P < .01) and continued to be statistically significant after controlling for other key demographic variables.

Although the coefficient for crop diversity and dietary diversity for women is small, the results become more significant when broken down by food group (Table 6). The bivariate analysis using chi-square showed significant differences for women’s intake of vitamin A rich foods, eggs, and legumes, nuts, and seeds between households with low crop diversity (less than 6 crops, or the mean number of crops) and households with high crop diversity, with legumes, nuts, and seeds and vitamin A rich foods being highly significant (P < .001).

Table 6.

Association Between Women’s Consumption by Food Groups and Low and High Crop Diversity^a (Chi-square analysis).

	Low crop diversity (< 6)	High crop diversity (≥ 6)
Starchy staples
No	1 (0.52%)	2 (0.80%)
Yes	191 (99.48%)	249 (99.20%)
Leafy green vegetables
No	28 (14.58%)	32 (12.75%)
Yes	164 (85.42%)	219 (87.25%)
Vitamin A rich foods^b
No	152 (79.17%)	148 (58.96%)
Yes	40 (20.83%)	103 (41.04%)
Other fruits and vegetables
No	26 (13.54%)	20 (7.97%)
Yes	166 (86.46%)	231 (92.03%)
Meat and fish
No	110 (57.29%)	123 (49.00%)
Yes	82 (42.71%)	128 (51.00%)
Eggs^c
No	166 (86.46%)	189 (75.30%)
Yes	26 (13.54%)	62 (24.70%)
Legumes, nuts, seeds^b
No	109 (56.77%)	92 (36.65%)
Yes	83 (43.23%)	159 (63.35%)
Dairy
No	172 (89.58%)	217 (86.45%)
Yes	20 (10.42%)	34 (13.55%)

^a Low and high crop diversity was determined by dividing households into 2 groups based on the median number of crop (6).

^b P < .001.

^c P < .01.

Lastly, multiple variable analysis was completed for the food groups that had a statistically significant association with crop diversity (vitamin A rich foods, eggs, and legumes, nuts, and seeds (Table 7). Crop diversity was found to be associated with women’s intake of vitamin A rich foods and legumes, nuts, and seeds in all model specifications (Table 7). In model 3, crop diversity remained statistically significant for vitamin A rich foods (OR = 1.176, P < .01),and legumes, nuts, and seeds (OR = 1.147, P < .01) after controlling for additional demographic variables. For eggs, crop diversity was insignificant in models 2 and 3 (specifications controlling for household and demographic variables). In model 3, the number of short-term SLM practices used by a household was significantly associated with women’s intake of vitamin A rich foods only (OR = 1.539, P < .001).

Table 7.

Association Between Crop Diversity and Women’s Intake of Vitamin A Rich Foods, Eggs, and Legumes Using Logistic Regression, Controlling for Village as a Random Effect.

Variables		Intake of vitamin A rich foods^a			Intake of eggs^a			Intake of legumes, nuts, and seeds^a
Variables		Model 1, odds ratio (SE)	Model 2, odds ratio (SE)	Model 3, odds ratio (SE)	Model 1, odds ratio (SE)	Model 2, odds ratio (SE)	Model 3, odds ratio (SE)	Model 1, odds ratio (SE)	Model 2, odds ratio (SE)	Model 3, odds ratio (SE)
Crop diversity		1.257 (0.064)^b	1.164 (0.062)^c	1.176 (0.066)^c	1.179 (0.069)^c	1.097 (0.071)	1.094 (0.073)	1.189 (0.051)^b	1.147 (0.054)^c	1.141 (0.056)^c
Farm size			0.951 (0.110)	0.972 (0.119)		1.034 (0.145)	1.001 (0.148)		1.035 (0.111)	1.047 (0.117)
Wealth quintile	Richest (Ref)
	Richer		0.949 (0.354)	1.025 (0.406)		1.251 (0.546)	1.111 (0.512)		0.518 (0.195)	0.467 (0.182)
	Middle		0.702 (0.277)	0.777 (0.325)		0.383 (0.196)	0.357 (0.191)		0.494 (0.189)	0.462 (0.187)
	Poorer		0.654 (0.255)	0.707 (0.297)		0.504 (0.242)	0.570 (0.263)		0.292 (0.109)^c	0.245 (0.099)^c
	Poorest		0.441 (0.179)^d	0.542 (0.245)		0.467 (0.228)	0.495 (0.270)		0.264 (0.099)^b	0.170 (0.073)^b
Number of short-term SLM practices			1.465 (0.154)^b	1.539 (0.172)^b		1.266 (0.163)	1.299 (0.173)		0.955 (0.090)	0.975 (0.097)
Year joining program	2012 (ref)
	2013		1.041 (0.350)	0.960 (0.335)		0.311 (0.142)^d	0.322 (0.152)		1.208 (0.353)	1.444 (0.445)
	2014		1.012 (0.319)^d	0.937 (0.309)		0.615 (0.244)	0.624 (0.257)		1.005 (0.272)	1.118 (0.321)
	2015		0.719 (0.482)	0.774 (0.549		0.267 (0.249)	0.365 (0.347)		1.696 (0.997)	1.383 (0.870)
Household size			0.903 (0.062)	0.911 (0.065)		0.955 (0.082)	1.000 (0.092)		0.908 (0.054)	0.894 (0.056)
District	Dedza (Ref)
District	Mzimba		1.656 (0.455)	1.689 (0.502)		0.999 (0.376)	1.006 (0.400)		0.935 (0.213)	0.882 (0.218)
Wife’s age				0.992 (0.992)			0.987 (0.013)			1.000 (0.009)
Wife’s education	No Schooling (Ref)
	Some primary			0.643 (0.249)			1.757 (0.970)			0.939 (0.310)
	Completed primary			0.785 (0.362)			1.592 (0.994)			0.975 (0.384)
	Some Secondary			0.866 (0.474)			1.993 (1.415)			0.763 (0.364)
	Completed Secondary			0.590 (0.485)			4.335 (4.149)			0.547 (0.415)
Marital status	Single (Ref)
	Divorced			0.564 (0.531)			3.683 (4.846)			0.504 (0.467)
	Widowed			1.297 (1.157)			2.075 (2.684)			0.263 (0.237)
	Married			0.811 (0.696)			2.210 (2.760)			0.215 (0.190)^d

Abbreviation: SLM, sustainable land management; SE, standard error.

^a Binary variable, with 0 being food group was not consumed and 1 being food group was consumed.

^b P < .001.

^c P < .01.

^d P < .05.

Discussion

The results of this study show that higher crop diversity, in combination with community-based nutrition education, is positively associated with individual dietary diversity and household food security. In this study, we saw an association with not only overall dietary diversity but also in vitamin A rich foods, legumes, nuts, and seeds. In particular, the association between crop diversity and dietary diversity was seen even when controlling for intensity of program involvement through involvement in short-term SLM practices. These foods are high in at-risk nutrients such as vitamin A, zinc, B-vitamins.^60,61 Increasing production of these vitamin A rich fruits and vegetables and legumes can be a potential solution used to combat micronutrient deficiencies in sub-Saharan Africa for vulnerable populations such as women and children.⁶² Micronutrient-rich crops such as legumes are also less at-risk than traditional staple crops for environmental shocks due to their shorter cropping cycles, and they can also help to intensify existing cropping systems through methods such as crop rotation or intercropping.⁶²

Relationships between production and dietary diversity are complex, and these relationships will likely be context specific. These relationships are further complicated by the role of markets. Some studies have suggested that access to markets is more effective at improving dietary diversity compared to production diversity.^25,63 Other studies have found that diversifying farm production can be effective in some contexts but may not be the best way to increase diet diversity in all situations.^{24,27,64

-67} While markets may increase cash flow, they may not improve diet or nutrition.⁶⁸ Many studies looking at dietary diversity and markets include sugary foods and drinks, which is biased toward markets because those foods and drinks are generally found in the market.^69,70 In addition, market access can be difficult to measure and may not be feasible in all settings. Although many have focused on agricultural intensification, including links to markets to improve diets, agricultural diversity seems to be most consistently linked to diet diversity.^2,69,70

This study did not include proximity to markets as a variable. Instead, the focus of this study was the role of crop diversity. The study found a significant association of crop diversity both at the household level with food security and the individual level with women’s individual dietary diversity score, which is unique to this study. One further strength of this study is the use of HFIAS to measure food security rather that household dietary diversity, which is often used in other studies as a measure of household food security. This measure strengthens the assessment of crop diversity impacts, as HFIAS tends to measure other important aspects of food security, rather than food availability alone.⁷¹ In addition, while there has been a lot of work on production diversity, diets, and nutrition in smallholder farmer households, only a few studies focus specifically on assessing the relationship between production diversity and nutrition in women. Generally, food security and dietary diversity are assessed for all members of the household together, and not women per se. Another study in Malawi found that low food security is associated with low dietary diversity in pregnant and lactating women, highlighting the importance of addressing low women’s dietary diversity for both maternal and child health outcomes.⁵⁶ This study suggests one possible pathway for impact.

The relationship between dietary diversity and crop diversity may be influenced by other factors such as decision-making and who has control over income.^68,70 This project explicitly addressed gender inequalities as part of the intervention, which may be one explanation for these findings on women’s dietary diversity and crop diversity. While crop diversity was one important factor, it may not be sufficient on its own in enhancing household nutrition. Intra-household gender politics are also often key. In this setting of rural Malawi, gendered access to and control over food and income may have been improved due to community-based nutrition and gender education.³¹

Another limitation of this study was the inability assess direct impact of the agroecology program on crop diversity and the impact of crop diversity on food security and nutrition due to the lack of a counterfactual. However, within this study, we were able to see strong associations between these different factors 4 years into the intervention. This study also had a large sample size, with many farming households across 2 different regions of Malawi.

Crop diversity has multiple benefits beyond the potential to address dietary diversity, and these factors should also be taken into account in considering policy implications for agricultural policies. Current agricultural policy in many countries promote monocrops, fertilizer, pesticides, and reliance on a few crops as a source of income.⁸ Today, 90% of the world’s calories are from only 20% of plant species.⁶ Several recent reviews have noted the importance of considering the environmental, health, social, and economic impacts of food and agricultural systems.^72,73 Diversifying farm production has many positive impacts on ecosystem health⁴ and can be used a way to address global food insecurity by building resilience in the changing climate, especially for small holder farmers.^74,75 Crop diversity may be important also for vulnerable populations (women and children) who are at greater risk for malnutrition.⁷⁶

Conclusion

This study examined the relationships between crop diversity, household food security, and women’s diet quality 4 years into a farmer-to-farmer agroecology mentoring programs combined with community-based gender and nutrition education. Within the context of this program, participants with higher crop diversity had better household food security and diet diversity for women, including higher consumption of Vitamin A rich foods. While more research needs to be done in this area, crop diversity has the potential to improve food security and nutrition of smallholder farm families by making micronutrient-rich foods more available and may build resilience by making farming households more self-sufficient. Crop diversity may help in improving diet quality of women, who are often the most vulnerable members of the household.

Footnotes

Authors’ Note

Rachel Bezner Kerr, Isaac Luginaah, and Catherine Hickey contributed to the study conception and design. Data collection was performed by Lizzie Shumba, Laifolo Dakishoni, and Esther Lupafya. Data analysis were performed by Ibukun Owoputi and Nola Booth. Interpretation of results was performed by Ibukun Owoputi, Rachel Bezner Kerr, Isaac Luginaah, Nola Booth, and Hanson Nyantakyi-Frimpong. The first draft of the manuscript was written by Ibukun Owoputi. All authors contributed to writing and editing subsequent versions of the manuscript. All authors read and approved the final manuscript.

Acknowledgments

This article is based on data from the Sustainable Land Management Surveys, as part of the Malawi Farmer-to-Farmer Agroecology project (MAFFA). The authors thank the Soils, Food and Healthy Communities (SFHC) organization in Malawi, Ekwendeni Hospital, Chancellor College-University of Malawi, University of Manitoba, Trust Beta, Mangani Katundu, for collaborating on the project, and SFHC community promoters in particular for participating, facilitating, and implementing the surveys.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors gratefully acknowledge funding support from Global Affairs Canada of the Government of Canada, the Canadian Food Grains Bank, and Presbyterian World Service and Development.

ORCID iD

Ibukun Owoputi

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Does Crop Diversity Influence Household Food Security and Women’s Individual Dietary Diversity? A Cross-Sectional Study of Malawian Farmers in a Participatory Agroecology and Nutrition Project

Abstract

Background:

Objective:

Methods:

Results:

Conclusions:

Keywords

Introduction

Study Context

The Agroecology Project

Methodology

Outcome Variables

Explanatory Variables

Regression Analysis

Results

Discussion

Conclusion

Footnotes

Authors’ Note

Acknowledgments

Declaration of Conflicting Interests

Funding

ORCID iD

References