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Ayoade M. Adetoye1 and Dare Akerele2

1Environmental and Economic Resource Centre, EERC, Abeokuta, Ogun State, Nigeria 2Department of Agricultural Economics and Farm Management, Federal University of Agriculture Abeokuta, PMB 2240, Alabata Road, Abeokuta, Ogun State, Nigeria

Acknowledgements The authors express their appreciation in respect of the grant support provided by the African Economic Research Consortium (AERC) to conduct the study.

Influence of Farm Production Diversity and Land Use Patterns on Food Security and

Nutrition among Farm Households In Nigeria

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Influence of Farm Production Diversity and Land Use Patterns on

Food Security and Nutrition among Farm Households In Nigeria

Author

Ayoade M., ADETOYE

Environmental and Economic Resource Centre

EERC Building, Opposite Federal University of Agriculture Abeokuta

Ogun State, Nigeria

Email: am.adetoye@eerce.org

Tel: +2347031557937

Dare AKERELE

Senior Lecturer

Department of Agricultural Economics and Farm Management,

Federal University of Agriculture, Abeokuta, Nigeria.

PMB 2240, Alabata Road,

Abeokuta Ogun State, Nigeria

Acknowledgement

The author gladly expresses his profound gratitude for the fund support received from the

African Economic Research Consortium (AERC) towards the course of the research.

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Acronyms

CBN – Central Bank of Nigeria

DDS – Dietary Diversity Score

DFH – Department of Family Health

DHS – Demographic and Health Survey

DQI – Dietary Quality Index

FAO – Food and Agriculture Organization

FEWS NET – Family Earning Warning System Network

FOS – Federal Office of Statistics

FVS – Food Variety Score

GDP – Gross Domestic Product

ICF – International Classification of Functioning, Disability and Health

IFAD – International Fund for Agricultural Development

IFPRI – International Food Policy Research Institute

LGA – Local Government Area

MDAT – Mozambican Diet Assessment Tool

NBS – National Bureau of Statistics

NFMH – Nigeria Federal Ministry of Health

NII – Nutrient Intake Index

NPC – National Population Commission

NPC – National Population Commission

PRB – Population Reference Bureau

UN – United Nations

UNICEF – United Nation Children Education Fund

USAID – United State Agency for International Development

WFP – World Food Programme

WHO – World Health Organization

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1.0 Introduction

Although substantial milestones have been made to ensure improvements in the global food

and nutrition security, hunger and malnutrition are still in common place, and a complex global

problem (Sibhatu, 2015). Even though substantial progresses (millstones) have been achieved

at the global level towards food insecurity, malnutrition and hunger reduction over the past few

decades, however, the proportion of food insecure and malnourished population are still

unacceptably high, especially in Asian and African countries (Godfray et al, 2010; Dubé et al,

2012; IFPRI, 2014). Close to 800 million people are still classified as chronically hungry,

meaning that they do not have enough access to calories (FAO, IFAD, WFP, 2015). Africa

alone houses more than 25% of the world’s hungry and undernourished population (FAO,

2015). Approximately 2 billion people worldwide suffer from micronutrient malnutrition

(hidden hunger), mostly due to low intakes of essentials vitamins and minerals (IFPRI, 2014).

Nutritional deficiencies are responsible for a large health burden in terms of lost productivity,

impaired physical and mental human development, susceptibility to various diseases, and

premature deaths especially among children (Lim et al, 2012).

Global statistics indicate that approximately 795 million people are hungry and chronically

undernourished with over 25% of them located in sub-Saharan Africa (FAO, IFAD, WFP

2015). African alone houses more than one-third of stunted children under 5 years of age, and

approximately 28% of wasted children under 5 years of age (UNICELF 2015). Nutritional

deficiencies are not only the result of low food quantities consumed, but also of poor dietary

quality and diversity. In fact, the level of dietary diversity was shown to be a good indicator of

people’s broader nutritional status in many situations (Ruel, 2003; Headey, 2013). The above

food deprivation and nutritional concerns, including high susceptibility to sickness and

diseases, premature death, impaired physical human development and cognitive functioning,

reduced productivity at adulthood, loss of gross domestic product (GDP) and inter-generational

poverty transmission, among others, (Bain et al. 2013) are closely connected to food insecurity

and nutritional deficiencies.

The problem of food security and nutritional deficiencies in African does not only result from

inadequate consumption of food quantities, but also of consumption of monotonous or less

varied diets. Consumption of more varied diets has been hypothesized/conceptualized, and

documented in some empirical studies, to be positively related to dietary quality (Ruel, 2002),

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and food quantity in terms of access or availability dimensions of food security as well as

reduction of chronic under-nutrition and a plethora of nutrition-related diseases (Arimond and

Ruel 2004; Mallard et al. 2014). This therefore situates dietary diversity at the epicentre of the

linkage between food security, nutrition and health.

Available studies have shown that the majority of households in Nigeria are poor, consuming

relatively cheap, energy dense foods and low-quality monotonous diets derived largely from

cereals or tubers (Akerele, 2015) - a situation that has contributed to the slow progress in food

insecurity and malnutrition reduction in the country. Most food insecure and malnourished

households in Nigeria reside in the rural areas. Issues of monotonous diet is also commonly

reported among the rural poor, particularly among farm households. Moreover, of all the farm

household member, children have been reported to experience severe food insecurity (Nnakwe

and Onyemaobi, 2013).

More diverse diets tend to be associated also with lower rates of overweight, obesity, and other

nutritional problems of rising magnitude in many parts of the world (Popkin and Slining, 2013).

Increasing dietary diversity is therefore an important strategy to improve nutrition and health.

This implies that agricultural production also needs to be diversified, so that a wide range of

different types of foods are available and accessible also to poor population segments (Pingali,

2015). It thus implies that diversification of agricultural production among farm households is

often perceived as a useful approach to improve dietary diversity and nutrition (Khouryet al,

2014; Pinstrup-Andersen, 2007; Jone et al, 2014). Several recent development initiatives have

promoted smallholder diversification through introducing additional crop and livestock species

with the intention to improve household nutrition (Burlingame, 2012; Fanzo, 2103). In

addition, farm diversity can help to increase agrobiodiversity too, this approach is also welcome

from environmental perspectives (Frison, 2006; Deckelbaum, 2006; Burlingame, 2012;

Sibhatu et al, 2015).

Although farm level diversity is being projected as a way of improving nutrition and the quality

of diets, the preponderance of food insecurity and malnutrition that is experienced even among

farm households including those who diversified seems to challenge this submission. This

therefore, raises an important question of whether agricultural transformation that prioritises

farm production diversity can be a veritable pathway for enhancing dietary diversity (quality)

and nutrition among farm households. Also, market price situation (dynamics) and whether the

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farmer is a net-food consumer, operates in autarky or is a net-food seller and whether a farmer

more oriented towards farm diversity or specialization also play important role in food security

and nutrition. Consequently, this study would seek to examine whether food surplus farmers

(taking advantage of price signal to increase farm income), or those with more diverse farm

production are well-off in terms of diet quality/diversity and nutrition as well as what a trade-

off between farm diversity and specialisation holds for food security will need more unpacking.

Even though encouragement of agricultural production diversity is increasingly being

recognised as a possible avenue to reduce food insecurity and malnutrition through increased

supply of varied foods, and, by extension consumption of more diverse diets, some other

complexities (challengers) need to be addressed. The livelihoods of rural dwellers depend

greatly on (land-based) agricultural production. Most of these farmers make little or no

conscious efforts to enhance land quality and sustainability and there also seems to be limited

government actions to help them to maintain the quality of their farm land (soil condition). The

start points to addressing this problem is to empirically examine whether the existing land

use/cropping patterns being adopted by farm households can enhance soil nutrient in a way that

will guarantee sustainable food production. Another issue that is closely connected to land use

sustainability and food security /nutrition relates to land tenure security and property right of

the farmers and other socio-economic or cultural factors. When ownership and property on

land secure, farmers may be more likely to adopt practices/land use patterns that enrich soil

fertility, all else equal. One of the key questions this study would seeks to answer would relate

the influence of land tenure security and other socio economic/cultural on sustainable land use

patterns and draw implication for household food security.

Against this background, this study seeks to provide answer to the following questions:

1. What is the food security status of the farm households?

2. Aside the overall food security status of the households, what is the nutritional status

of children among farm households i.e. are they malnourished?

3. Would farm production diversity have significant influence on household food security

and the nutritional status of children among farm households?

4. What influence would farm specialisation have on food security and nutrition compared

to more diverse farms?

5. Would increases in food prices translate to improved diets and nutrition among farm

households?

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6. Are the existing land use/cropping patterns adopted by farm households suitable for

sustainability of food production?

7. What influence would land tenure security and socioeconomic characteristics, among

others, have on sustainable cropping patterns?

1.2 General Objectives

The broad objective of the study is to examine the influence of diversified farm systems and

land use/cropping patterns on household food security status and nutritional wellbeing of

children among farm households in Nigeria.

Specific Objectives

1. To examine the food insecurity status of farm households in terms of food quality

(dietary diversity) and food quantity (measured by food calorie consumption).

2. To assess the nutritional status of children among farm households

3. To examine, having controlled for other relevant variables, the influence of farm

production diversity, food prices and land use/cropping patterns on food security

(dietary diversity and calorie intake adequacy) and child nutrition among farm

households. What influence would farm specialisation have on food security and

nutrition compared to more diverse farms?

4. To examine how sustainable is the existing farm households' land use/cropping

behavioural practices. That is, to assess whether land use/cropping patterns currently

being adopted by farm households are adequate to improve soil fertility, nutrient

recycling and promote sustainable food production.

5. To examine, having controlled for other confounding factors, the relationship between

sustainable land use/cropping patterns and aggregate crop output in rural Nigeria. The

understanding here is to link farmers' land use/cropping behavioural practices to food

security in terms of sustainable food production

6. To examine the influence of land tenure security, socioeconomic factors and farmers'

perception about climate change on sustainable land use/cropping patterns.

1.3 Definition of Key Concepts

Food Security: this is a situation when all people, always, have physical, social and economic

access to enough, safe and nutritious food which meets their dietary needs and food preferences

for an active and healthy life (FAO, 2003). Household food security is the application of this

concept to the family level, with individuals within households as the focused of concern.

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Food Diversity: this implies how diverse the food consumption basket of the household in

terms of different foods or food group consumed. A more diverse diet is highly correlated with

adequacy of intake of calories, macro and micro nutrients.

Food Quality: this could also be used as a proxy for quality of diet consumed.

Farm Household: this refer to households whose occupation are predominantly farming. Their

main livelihoods revolve around farm production activities.

Land Use Pattern: this refers to system of land modification which is determine by physical

attributes of a landscape and socioeconomic factors.

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2.0 Literature Review

Farm households in developing countries strive to achieve food security by consuming and

then selling surplus household produce (Govereh and Jayne, 2003). This is typical of farm

households in Nigeria who experience issues of food insecurity especially during the dry

season. Further this challenge, integrated crop–livestock farming has been identified as an

important mechanism to address rural household food insecurity and poverty in developing

economies (Herrero et al., 2007; FAO, 2010). Globally, this type of integrated farming system

produces about half the world’s food (Herrero et al., 2010). In addition, it reduces the cost of

total farm production by exploiting economies of scope (Chavas and Di Falco, 2012). For

instance, cost savings of 14% have been identified from joint crop and livestock production

rather than producing crops and livestock separately (Wu and Prato, 2006; Asante et al, 2015).

2.1 Food Insecurity in Nigeria

Food security has been defined in various ways by different scholars. According FAO, et al.

(2013) food security is always access to the food needed by all people to enable them to live a

healthy life. A country is said to be food secured when there is always access to food of

acceptable quantity and quality consistent with decent existence for most of the population

(Idachaba, 2004). This means that food must be available to the people to meet the basic

nutritional standard needed by the body. But it should be noted that availability of food does

not mean accessibility to food. Availability depends on production, consumer prices,

information flows and the market dynamics. Abudullahi (2008) defined food security as when

people have physical and economic access to enough food to meet their dietary needs for a

productive healthy life at present as well as in the future. This definition outlines some indices

for measuring the extent or degree of food security to be achieved by any country and the

indices are adequate national food supply, nutritional content, accessibility, affordability and

environmental protection. Absence of food security is food insecurity; food insecurity on the

other hand represents lack of access to enough food and can either be chronic or temporary.

FAO (2010) refers to food insecurity as the consequences of inadequate consumption of

nutritious food bearing in mind that the physiological use of food is within the domain of

nutrition and health. When individuals cannot provide enough food for their families, it leads

to hunger and poor health. Poor health reduces one’s ability to work and live a productive

healthy life. Poor human development destabilizes a country’s potential for economic

development for generations to come (Otaha, 2013). According to FAO, et al. (2013), the core

determinants of food security are availability, accessibility, utilization and stability.

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Food Availability: - Availability of food plays a conspicuous role in food security. Having

enough food in a nation is necessary but not adequate to ensure that people have satisfactory

access to food. Over the years, population has increased faster than the supply of food thus

resulting in food unavailability per person.

Food Accessibility: - The ability to have access to food depends on two major conditions: -

Economic access and physical access. Economic access depends on one’s income, the price of

food and the purchasing power of the people. Physical access depends on the availability and

quality of infrastructure needed for the production and distribution of food. Lack of economic

access to food is because of the increase in the rate of poverty.

Food Utilization: - Food utilization is measured by two outcomes indicators which reflect the

impact of inadequate food intake and utilization. The first outcome is measured by under-five

years of age nutrition level while second measurement is quality of food, health and hygiene.

According to FAO measuring the nutritional status of under-five years of age is an effective

approximation for the entire population. The indicators for the measurement of under-five years

of age are wasting (weight for height); underweight (weight for age) and stunting (height for

age). Most times, progress in terms of having accessed to food is not always accompanied by

progress in the utilization of the food. A more direct indicator of food utilization is underweight

because it shows improvement more promptly than stunting and wasting whose improvement

can take a longer time to be noticeable.

Stability: it has to do with exposure to short-term risks which have a way of endangering long-

term progress. Key indicators for exposure to risk include climate shocks such as droughts,

erosion and volatility in the prices of inputs for food production. The world price shocks lead

to domestic price instability which is a threat to domestic food producers as they stand the

chance of losing invested capital.

Nigerian farmers are mainly smallholders farming mainly for subsistence, this makes it difficult

for them to cope with changes in the prices of inputs, and it also lowers their ability to adopt

new technologies thereby resulting in reduced overall production. Changing weather patterns

as a result of climate change have played a part in reducing food supply, for instance flood in

the southern parts of the country and drought in the northern parts leads to substantial losses in

production and income. The interplay of all these variables determines whether an individual,

household, state or nation is food secured or not. This is because sustainable food security at

the household level does not guarantee sustainable food security at the state or national level.

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2.2 Food Insecurity

Food insecurity exists when people lack sustainable physical or economic access to enough

safe, nutritious, and socially acceptable food for a healthy and productive life. Food insecurity

may be chronic, seasonal, or temporary. Food insecurity and malnutrition result in catastrophic

amounts of human suffering. The World Health Organization estimates that approximately 60

percent of all childhood deaths in the developing world are associated with chronic hunger and

malnutrition. In developing countries, persistent malnutrition leaves children weak, vulnerable,

and less able to fight such common childhood illnesses as diarrhoea, acute respiratory

infections, malaria, and measles.

The root cause of food insecurity in developing countries is the inability of people to gain

access to food due to poverty. While the rest of the world has made significant progress towards

poverty alleviation, Africa, Sub-Saharan Africa continues to lag. Projections show that there

will be an increase in this tendency unless preventive measures are taken. Food security on the

continent has worsened since 1970 and the proportion of the malnourished population has

remained within the 33 to 35 percent range in Sub-Saharan Africa. The prevalence of

malnutrition within the continent varies by region. It is lowest in Northern Africa with 4 percent

and highest in Central Africa with 40 percent. (Angela, 2006).

Categories of Food Insecurity

There are three main categories of food insecurity as classified by the Food and Agricultural

Organization of the United Nations.

Acute: Sever hunger and malnutrition to the point that lives are threatened immediately (e.g.

famine)

Occasional: When food insecurity occurs due to a specific temporary circumstance.

Chronic: Ability to meet food needs is consistently or permanently under threat.

2.3 Malnutrition in Nigeria

Malnutrition has many adverse consequences for child survival and long-term well-being. It

also has far-reaching consequences for human capital, economic productivity, and national

development overall. These consequences of malnutrition should be a significant concern for

policy makers in Nigeria, which has the highest number of children under 5 years with chronic

malnutrition (stunting or low height-for-age) in sub-Saharan Africa at more than 11.7 million,

according to the most recent DHS report (NPC and ICF International 2014).

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Nigeria is the most populous nation in Africa with almost 186 million people in 2016 (UNICEF

2017). It has a high fertility rate of 5.38 children per woman, the population is growing at an

annual rate of 2.6 percent, worsening overcrowded conditions. By 2050, Nigeria’s population

is expected to grow to a staggering 440 million, which will make it the third most populous

country in the world, after India and China (Population Reference Bureau 2013). In Nigeria,

37 percent of children under 5 years are stunted. The prevalence of stunting increases with age,

peaking at 46 percent among children 24–35 months. While stunting prevalence has improved

since 2008 (41 percent), the extent of acute malnutrition (wasting or low weight-for-height)

has worsened, from 14 percent in 2008 to 18 percent in 2013 among children under 5 years

(NPC and ICF International 2009 and 2014). Women’s nutrition is also of concern in Nigeria,

facing the double burden of malnutrition: prevalence of undernutrition is 11 percent and

prevalence of overweight/obesity is 25 percent (NPC and ICF International 2014). One driver

of Nigeria’s high rate of growth is that childbearing begins early in Nigeria. By age 19, 41

percent of adolescent girls had begun childbearing in 2013, which is an increase from 38

percent in 2008. This has serious consequences because, relative to older mothers, adolescent

girls are more likely to be malnourished and have a low birth weight baby, who is more likely

to become malnourished and be at increased risk of illness and death than those born to older

mothers (National Population Commission and ICF International 2014). The risk of stunting is

33 percent higher among first-born children of girls under 18 years, and as such, early

motherhood is a key driver of malnutrition (Fink et al. 2014). Children in rural areas are more

likely to be stunted (43 percent) than those in urban areas (26 percent), and the pattern is similar

for severe stunting (26 percent in rural areas and 13 percent in urban areas). The North West

has the highest proportion of children who are stunted (55 percent), followed by the North East

(42 percent) and North Central (29 percent). At the state level, Kebbi has the highest proportion

of stunted children (61 percent), while Enugu has the lowest proportion (12 percent). A

mother’s level of education generally has an inverse relationship with stunting: stunting ranges

from a low of 13 percent among children whose mothers have a higher education to a high of

50 percent among those whose mothers have no education. A similar inverse relationship is

observed between household wealth and stunting. Children in the poorest households are three

times as likely to be stunted (54 percent) as children in the wealthiest households (18 percent)

(NPC and ICF International 2014). Prevalence of early initiation of breastfeeding decreased

from 38 percent in 2008 to 33 percent in 2013, while children who received a pre-lacteal feed

increased from 56 percent in 2008 to 59 percent in 2013. In addition, prevalence of breastfed

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children 6–23 months receiving a minimum acceptable diet decreased from 9 percent in 2008

to 4 percent in 2013 (NPC and ICF International 2014). The causes of malnutrition and food

insecurity in Nigeria are multifaceted and include poor infant and young child feeding

practices, which contribute to high rates of illness and poor nutrition among children under 2

years; lack of access to healthcare, water, and sanitation; armed conflict, particularly in the

north; irregular rainfall; high unemployment; and poverty (Nigeria Federal Ministry of Health,

Family Health Department 2014). Although chronic and seasonal food insecurity occurs

throughout the country, and is exacerbated by volatile and rising food prices, the impact of

conflict and other shocks has resulted in acute levels of food insecurity in the North East zone

(FEWS NET, 2017). An estimated 3.1 million people in the states of Borno, Yobe, and

Adamawa received emergency food assistance or cash transfers in the first half of 2017 but,

because much of the North East zone has been inaccessible to aid agencies, the number who

need assistance is likely much greater (FEWS NET, 2017). Diet-related non-communicable

diseases are also on the rise in Nigeria due to globalization, urbanization, lifestyle transition,

socio-cultural factors, and poor maternal, fatal, and infant nutrition (NFMH, Family Health

Department 2014).

2.3.1 Primary Causes of Undernutrition and Possible Solutions in Nigeria

Poor infant Feeding Practices: Two-thirds of all new-borns do not receive breast milk within

one hour of birth, only 13% of infants under six months are exclusively breastfed and during

the important transition period to a mix of breast milk and solid foods between six and nine

months of age, one-quarter of infants are not fed appropriately with both breast milk and other

foods (UNICEF 2009). To prevent poor infant feeding practices in Nigeria, women and their

families should be supported to practice optimal breastfeeding and ensure timely and adequate

complementary feeding. Breast milk fulfils all nutritional needs of infants up to six months of

age, boosts their immunity, and reduces exposure to infections (WHO, 2009).

High Disease Burden: 13% of child deaths under five are due to diarrhoea, undernutrition

increases the likelihood of falling sick and severity of disease. Moreover, undernourished

children who fall sick are much more likely to die from illness than well-nourished children.

Parasitic infestation diverts nutrients from the body and can cause blood loss and anaemia

(UNICEF 2009). Incidence of high disease burden can be reduced by preventing and treating

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childhood infection and other disease. Hand-washing, deworming, zinc supplements during

and after diarrhoea, and continued feeding during illness are important.

Limited Access to Nutritious Food: Fewer than 10% of households are food insecure as

defined by available calories per capita, Achieving food security, however, means ensuring

quality and continuity of food access, in addition to quantity, for all household members,

dietary diversity is essential for food security, high rates of micronutrient deficiencies in

Nigeria indicate that dietary diversity may be low (FAO 2009). Possible solution includes

involving multiple sectors agriculture, education, transport, gender, the food industry, health

and other sectors, to ensure that diverse, nutritious diets are available and accessible to all

household members.

2.4 Farm Production Diversity among Households in Nigeria

Farm production diversity is concerned with the switch from subsistence food production to

commercial agriculture which is applicable to both crop and livestock production. Crop

diversification refers to growing many crops at the same time. Many households in developing

countries have been found to diversify their income sources allowing them to spread their risk

and smoothen consumption. This is often necessary in agricultural based peasant economies

because of risk of such variability in soil quality, household and crop diseases, price shocks,

unpredictable rainfall and other weather-related events (Ibrahim et al., 2009).

In Nigeria, crop and livestock production systems are practiced by 75% of smallholders.

However, the choices available to farmers are limited by the availability of resources such as

land, labour and capital. In recent years, the options for farmers have also been limited by

climatic conditions, especially erratic rainfall, drought, high temperatures and floods

(Agyemang-Bonsu et al., 2008; Griebenow and Kishore, 2009; Ellis-Jones et al., 2012). These

result in low crop yields and, in extreme cases, crop failure. This has a cascading debilitating

effect on livestock production (Ellis-Jones et al., 2012; Ndamani and Watanabe, 2015), because

pasture and crop residue, which play important roles as livestock feed in most rural settings,

become limited. This leaves resource-poor farm households suffering from income instability,

inadequate food availability and increased poverty. The relevance of agricultural

diversification in reducing production and marketing risks, as well as household income

instability and its implications for food security and poverty has long been acknowledged

(Ellis, 1998; Little et al., 2001; Joshi et al., 2004; Mainik and Rüschendorf, 2010; Yan et al.,

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2010; Chavas and Di Falco, 2012; Kim et al., 2012; Tasie et al., 2012; Ogundari, 2013; Sibhatu,

2015).

Nigeria’s Agriculture at present is characterized by weak and inefficient production system,

decade infrastructure and risk and uncertainty. Thus, rural household are therefore constrained

to develop strategies to cope with vulnerability of agriculture production system through

livelihood diversification (Ellis, 2000). Although, most rural households are involved in

agricultural activities such as animal and crop production as their primary source of livelihood,

they also engage in non-farming and income generating activities to improve their primary

source of income. In other words, only very few rural households are involved in just one

activity therefore livelihood diversification plays a very significant role in the socio-economic

life of the rural households (Barrett et al., 2001).

Diversification decisions seem to be driven to a large extent by desperation rather than new

opportunities, especially among the migrants. The share of income from non-agricultural

sources increases considerably and, in fact, drives income growth of the poorest, whose income

from agriculture stagnates. While diversification seems to be beneficial to the poor from this

static perspective, analysis may also give reasons to be concerned. High non-farm growth rates

are achieved through allocating more labour to the local non-farm sector as well as migration

and not by improved ‘diversification productivity’ (Ijaiya et al., 2011). Probably, this does not

imply that diversification cannot provide a pathway out of poverty. Overall, there is still a

positive correlation between income per capita and diversification despite the dichotomy of

diversification. Yet, there are signs that diversification is increasingly desperation-led, which

is why this positive correlation tends to become weaker. Migration seems to be an important

driver of local non-farm diversification, most likely through remittances and returning

migrants. In addition, the expansion of education has certainly enabled or motivated more

individuals to engage in local non-farm activities. These factors may indeed allow some

household to escape poverty through diversification (Lay and Schuler, 2007). The adopted

strategies in diversification of income include non-farm income sources, most importantly

those obtained from other than unskilled labour. These are associated with increased income

and enormous income mobility especially upward earnings mobility. In contrast, those

households that have neither access to non-farm activities nor enough productive non-labour

assets to devote themselves entirely to on-farm agricultural production, typically must rely on

a low return strategy of complete dependence on the agricultural sector and often find

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themselves caught in a dynamic stochastic poverty trap (Barrett et al., 2001). Reardon et al.

(2001) suggested that policies aimed at the rural sector which must be oriented towards

providing incentives that stimulate households to participate in rural non-farm jobs, as well as

the capacity of households to respond to such incentives. The challenge that was encountered

was the share of income from non-farm enterprises which tends to be greater among higher

income rural households (International Food Policy Research Institute, IFPRI, 2003).

Therefore, programs to develop existing non-farm rural enterprises will be probably directed

to assist higher-income households more than the poor. Occupational diversification challenges

conventional wisdoms about poverty reduction in rural areas of low-income countries (Ellis,

2004). The problem of diversification decisions seems to be driven to a large extent by

desperation rather than new opportunities, about migration (Lay and Schuler, 2007). If trend of

these problems continues, the farmers will have less money to carter for their household, and

generally remaining in the ultimate vicious cycle of poverty (Olarinde and Kuponiyi, 2005).

According to Ibrahim and Umar (2008), the diversification of income source of the farming

household heads can help to reduce the risk associated with income from a single source

especially a very risky enterprise such as agriculture. Poor infrastructure will continue to be a

disincentive to farmers diversifying in other activities due to high transaction costs coupled

with other constraints such as poor assets base, lack of credit facilities, lack of awareness and

training (Wanyama et al., 2010; Khatun and Roy, 2012). Babatunde and Quaim (2009) revealed

that entry barriers for households disadvantaged to participate in higher-payment off-farm

activities need to be overcome and the dire need to promote crop and livestock activities, which

currently benefit the poor more than the rich. Obayelu and Awoyemi (2010) showed that

majority of the rural household heads are engaged in farming activities as the major source of

income with attendant low income and this is expected to enhance increased access to credit in

the rural areas where most of the poor reside. Adepoju and Obayelu (2013) showed that non-

farm income plays a very important role in augmenting farm-income as almost three-quarters

of the respondents adopted a combination of farm and nonfarm strategy. This is an indication

that farming alone is not an adequate source of revenue for the rural households. Schwarze

(2004) posited that poor households tend to have more income sources and a more evenly

distribution of the income between these sources. This has less momentum as most rural

households diversified income sources (Babatunde and Quaim, 2009). According to Amare

and Belaineh (2013), despite the high level of participation in non/off-farm activities, the

contribution of non/off-farm income to total household income is small compared to farm

income.

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Apart from livelihood Diversification, Off-farm work offers an alternative approach towards

reducing income variability. Off-farm income refers to that portion of household income that

is generated outside the farm. It is classified into three categories: the agricultural wage

employment involving labour supply to other farm, non-agricultural wage employment

including formal and informal non-farm activities and self-employment such as own business

(Babatunde et al., 2010). There has been a substantial growth in household participation in off-

farm work in recent time ad as a result, activities in the off-farm sector have witnessed a boom

in the manufacturing, agro-based and service sectors (Ibekwe et al., 2010). The reasons for this

boom attributed for the growth in off-farm engagement include: declining farm incomes and

the desire to insure against agricultural production risks, off-farm work has traditionally been

viewed by operators and their spouses as an action necessary to save the farm by providing

resource to pay farm bills or to repay debt (Mishra et al., 2002). The growth of off-farm income

has reduced the income inequality in agriculture and contributed to the catch-up of farmer’s

income with those of non-farm population.

In addition, the key benefits of agricultural diversification can then be classified into three

categories: economic, social and agronomic (Johnston et al., 1995). Economic benefits include

seasonal stabilization of farm income to meet other basic needs such as education, household

food security and mitigating risk. Indeed, most farms are involved in multi-output diversified

enterprises due to the presence of the significant uncertainty that is inherent in agriculture (Lin

et al., 1974; Chavas and Di Falco, 2012). In such cases, agricultural diversification can reduce

the risk exposure of farm households by optimizing income from a range of activities that are

subject in different ways to varying weather and market conditions. The social benefits include

more stable employment for farm workers and resources throughout the year. This can result

in sustainable incomes through efficient use of resources and exploitation of comparative

advantage, for example, in India (Joshi et al., 2004). Conservation of soil and water resources,

reduced disease, weed and insect infestation, reduced erosion, increased soil fertility and

increased yields are key determinants among the agronomic benefits of diversification

(Caviglia‐Harris and Sills, 2005; Iiyama et al., 2007; Mainik and Rüschendorf, 2010; Assante,

2015).

Despite the significant contribution of agricultural diversification in developing countries in

Asia and some parts of Africa in managing production risks, studies on agricultural

diversification in Nigeria are minimal and have largely focused on incomes and livelihoods

(Knudsen, 2007; Lay and Schüler, 2008; Aneani et al., 2011; Fausat, 2012; Senadza, 2012;

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Akerele, 2013). Studies on diversification of farm production purposely to ensure food security

are still very few with exception of such study in Nigeria as at now. Diversification studies on

livestock are also minimal, particularly in sub-Saharan Africa (Devendra and Ibrahim, 2004;

Ibrahim et al., 2009) and non-existent in Nigeria. However, given that crop production is highly

correlated with livestock production in sub-Saharan Africa, with nearly 60% of crops produced

with livestock, this study extends the literature on diversification by examining influence of

farm diversification on food security and land use patterns among farm households in Nigeria.

2.5 Land Tenure and Property Right Issues

Land tenure security exists when landowners and users including smallholder farmers in Sub

Saharan Africa enjoy clearly defined and enforceable rights to land, whether such rights are

based on formal law or customary practices.

Land tenure is an important part of social, political and economic structures. It is

multidimensional, bringing into play social, technical, economic, institutional, legal and

political aspects that are often ignored but must be considered. Land tenure relationships may

be well-defined and enforceable in a formal court of law or through customary structures in a

community. Alternatively, they may be relatively poorly defined with ambiguities open to

exploitation.

Land tenure constitutes a web of intersecting interests which include:

a. Overriding interests: when a sovereign power (for instance a nation or community has

the powers to allocate or reallocate land through expropriation among other factors)

b. Overlapping interests: when several parties are allocated different rights to the same

parcel of land (e.g., one party may have lease rights, another may have a right of way,

etc.)

c. Complementary interests: when different parties share the same interest in the same

parcel of land (e.g., when members of a community share common rights to grazing

land, etc.)

d. Competing interests: when different parties contest the same interests in the same parcel

(e.g., when two parties independently claim rights to exclusive use of a parcel of

agricultural land, Land disputes arise from competing claims.)

Land tenure is often categorized as:

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i. Private: the assignment of rights to a private party who may be an individual, a married

couple, a group of people, or a corporate body such as a commercial entity or non-profit

organization. For example, within a community, individual families may have

exclusive rights to residential parcels, agricultural parcels and certain trees. Other

members of the community can be excluded from using these resources without the

consent of those who hold the rights.

ii. Communal: a right of commons may exist within a community where each member of

the community has a right to use independently the holdings of the community. For

example, members of a community may have the right to graze cattle on a common

pasture. Non-members of the community are excluded from using the common areas.

iii. Open access: specific rights are not assigned to anyone and no-one can be excluded.

This typically includes marine tenure where access to the high seas is generally open to

anyone; it may include rangelands, forests, where there may be free access to the

resources for all.

iv. State: property rights are assigned to some authority in the public sector. For example,

in some countries, forest lands may fall under the mandate of the state, whether at a

central or decentralized level of government.

In practice, most forms of holdings may be found within a given society, for example, common

grazing rights, private residential and agricultural holdings, and state ownership of forests.

Customary tenure typically includes communal rights to pastures and exclusive private rights

to agricultural and residential parcels. In some countries, formally recognized rights to such

customary lands are vested in the nation state or the President “in trust” for the citizens.

2.6 Land tenure and Food Security

Availability, access, and utilization of food depend on land tenure security of farmers this

implies that for food to be consistently available in markets and for people to have the financial

means to access safe and nutritious food, there must be an enabling environment for sustained

agricultural growth for smallholders and large-scale investors alike. as seen in today’s high-

income countries, such an environment includes land tenure security. The United States agency

for international development (USAID) has noted that: “Without squarely addressing tenure

security and property rights issues, investments in food security programming may be

compromised and, indeed, be undermined.” (USAID. 2013)

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Vulnerability to food insecurity in rural areas is higher when there is no security of land tenure.

Moreover, in countries with mixed tenure systems, whether statutory and/or customary, user

rights (including access/use and management of natural resources) are often neither recognized

nor respected. Governments and public agencies whose mandate it is to promote investments,

frequently consider land as unoccupied and therefore, as being transferrable to national or

foreign investors. Such land is, in most cases, collectively utilized and essential for people´s

access to food. Pastoralists, indigenous peoples and fishermen are some of the people most

affected by the lack of recognition of their rights over such resources. From a right to food

approach, guaranteeing access to natural resources for the most vulnerable populations is the

first essential step for realizing this human right. To ensure such access, government, policies

and legal frameworks should address territorial rights over natural resources. Furthermore, it

is crucial that participatory mapping of rural areas is a recognized government priority in order

to identify: i) the effective use and management of land and ii) the populations that are the most

vulnerable to food insecurity. Land titling schemes have often been considered the best option

for providing security of tenure. It is evident, however, that land titling does not always provide

the most appropriate solution (IFAD, 2008). Private ownership can exclude some segments of

the population, thereby obstructing the collective use of such land. Additionally, in some

contexts land titling schemes can be a source of conflict. Ultimately, participatory community

land delimitation and public registry of those rights might constitute an adequate way for

communities to decide on land use to ensure their livelihoods and have a stronger footing in

dealing directly with outside investors. Uncompensated forced eviction commonly occurs

because of the State’s desire to satisfy the aims of national or foreign investors, and particularly

in countries where governance is weak. Since forced eviction strongly affects peoples’ access

to food, it should be considered only in exceptional cases and should be strictly compliant with

the conditions established by law. In cases of arbitrary evictions affecting peoples’ access to

natural resources and food, individuals should be able to claim justice, remedial action and

effective reparation through adequate mechanisms of recourse.

2.7 Research Findings on Food Security and Land Rights in Africa

Recent research highlights the critical relationship between food security and security over

land. There is a growing body of evidence suggesting that securing land and resource rights for

men and women has a positive impact on food security and broader development outcomes,

such as household investment, agricultural productivity, women’s empowerment, nutrition,

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and more robust rental markets for farmland. The existing literature highlighting the positive

impact of strengthened land tenure on food security outcomes is discussed below.

Studies in Malawi of a pilot land redistribution program based on a willing buyer/willing seller

model illustrate the substantial increase in food availability, and thus food security, when

formerly landless or near landless households acquire land or substantially more land. An early

study of the pilot found that beneficiary households: Invested a greater proportion of their land

in hybrid crops (24 percentage points more); and Had a higher yield for maize, the staple crop

(yield was twice as much) (Chirwa, 2008). These figures are in comparison to nearby

households that did not receive land from the pilot. at the end of the pilot, an independent

impact evaluation found that, among 15,000 beneficiary households, there were significant

increases in the production of all food crops, particularly a 236 percent increase in maize yields

(per hectare), increasing household food availability (Simtowe, et al., 2011). Household food

security (measured by meals eaten in the lean season and months of reserve food) and income

from agricultural activities continued to increase after the pilot.

A study in Ethiopia also bridges the two disciplines, measuring how land tenure security and

land tenure reforms affect and are affected by household food security (Hagos and Holden

2013). That study assessed the impacts of how a regional land registration and certification

program contributed to increased food availability and, thus to food security in a food deficit

region of northern Ethiopia. Comparing child nutrition impacts 8 to 12 years after

implementation of the land program, the study found that land certification appears to have

contributed to increased caloric intake and more so within female-headed households either

through enhanced participation in land rental markets or increased owner investment in land

and productivity. Results also showed that those households who accessed additional land

through rental markets (made possible in part through widespread land certification) had a

significantly higher body mass index.

Meanwhile, a study from Zambia that compared caloric intake among children whose families

had access to land and those that did not revealed dramatic impacts. researchers found that

children under 10 in households who lost access to agricultural land within the previous 5 years

received fewer daily calories (a decrease of 243 calories, or 11 percent of the average daily

calorie intake) compared to same-aged children whose households did not lose access to

agricultural land within the same period.

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However, a full-scale land program need not be the only type of land tenure intervention that

can contribute to improved food security. advocacy and public education around land rights

can also make a difference. For example, SNV supported a national farmers’ advocacy program

in Mali, empowering and providing tools for farmers to engage in the national land policy

process, where there are significant commercial demands for agricultural land (Traore, 2012)

2.8 Land Rights, Household Investments, And Agricultural Productivity

Secure land and resource rights provide positive incentives to invest in and conserve valuable

resources, including land, pastures, and forests. Conversely, when these rights are insecure,

people have more limited incentives to invest labour and capital to improve soil, plant perennial

crops, manage rangelands, and invest in irrigation. The relationship between tenure security

and land related agricultural investment is widely documented. (Bodnar and Hilhorst 2014).

2.8.1 When land rights are secure, farmers invest more in their land and agricultural

productivity improves.

In Thailand, land titling increased investment, input use, and yields (Feder and Onchan 1987).

In Ethiopia, land certification led to land productivity increases of 40 to 45 percent in the Tigray

Region, and soil and water conservation investments rose by 30 percent in the Amhara Region

(Deininger et al., 2008). In Rwanda, investment doubled in farmers’ soil conservation (Ali et

al., 2011). In rural Benin, communities that participated in a process to map and recognize land

rights, were 39 to 43 percent more likely to shift their crop investments from subsistence to

long-term and perennial cash crops, and tree planting (Goldstein et al., 2015) Additional studies

from Nicaragua, Peru, Cambodia, and Vietnam found statistically significant effects of land

titling interventions on agricultural investment and productivity (Lawry et al., 2016).

2.8.2 When land rights are insecure, investment, productivity, and yields fall.

In Uganda, when plot ownership and control was disputed, yields were 20 percent lower; with

eviction risk, yields were 37 percent lower (Mwesigye and Matsumoto 2016). In Burkina Faso,

productivity dropped 40 percent when households had concerns regarding land disputes

(Linkow, 2016). In Malawi, the probability of investing in conservation was approximately 14

percent lower on informally rented plots where tenure was less secure than on inherited or

purchased plots (Lovo, 2016).

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2.8.3 When land rights are secure, household participation in land markets increases.

Research has shown that better functioning land markets including rental markets positively

impact agricultural productivity and food security by allocating land more efficiently to the

most productive users and allowing less productive farmers to migrate or work in other sectors.

In Ethiopia, household land certification increased participation in the leasing and amount of

land rented out (Deininger et al., 2011). Another study from Ethiopia found that female-headed

households with access to formal land rights are more likely to engage in land rental activities

as landlords (Holden et al., 2011). In the Dominican Republic, insecure property rights

decreased land rental activity. The same study suggests that improving tenure security would

increase the total area rented to the poor by 63 percent (Macours et al., 2010)

2.8.4 Women’s land rights and food security.

Women make up approximately 43 percent of the agricultural labour force and produce a

significant portion of the food grown in the developing world. While land is the most important

resource for those dependent on agriculture for their livelihoods, women consistently have less

access to land than men, and women’s land rights are less secure. At the same time, research

shows that if women had the same access to resources for agricultural production as men, they

could increase yields on their farms by 20 to 30 percent; this could raise total agricultural output

in developing countries by 2.5 to 4 percent, and in turn reduce the number of undernourished

people in the world by 12 to 17 percent (FAO, 2011). This and other evidence support the

global consensus that closing the gender gap in secure access to land for women is fundamental

to food security and women’s empowerment (Van den Bold et al., 2013).

2.8.5 When women have secure land rights, agricultural investment and production

increase.

In Rwanda, women whose land rights were formalized were 19 percent more likely to engage

in soil conservation, compared to 10 percent among men (Ali et al., 2014). In rural Benin,

women were historically unlikely to invest in soil fertility by leaving their land fallow; but this

gender gap disappeared in communities where female-headed households mapped and

documented their parcel boundaries. In these communities, female-headed households were

just as likely as male-headed households to leave their land fallow (Goldstein et al., 2015).

Gender-sensitive allocation of micro-gardens in India increased use of credit and inputs like

fertilizer (Santos et al., 2013). In Ethiopia, land certification led to increased productivity on

plots owned by women (Bezabih and Holden 2010).

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2.8.6 When women have secure access to land, nutrition outcomes improve.

In Ethiopia, an increase in land allocated to women decreased household food insecurity by 36

percent (Espinosa, 2014). Studies from Nicaragua and Honduras have found that increases in

female landholdings are associated with increases in household food expenditure and child

educational attainment (Katz and Chamorro 2002). In Vietnam, children in households where

women own land are up to 10 percent less likely to be sick (Menon, 2014). In Nepal, in

households where women own land, children are 33 percent less likely to be severely

underweight (Allendorf, 2006).

2.8.7 Secure land tenure provides solid ground for food security

Land tenure arrangements create incentives for investing labour and resources over the long

term and adopting and using new technologies and sustainable land management practices.

When rights to land are secure, there is:

• Greater incentive to manage and conserve the land;

• Greater incentive to make long-term improvements to the land and other land-related

investments;

• Less potential for conflict and arbitrary eviction;

• Opportunity for land rental and sales markets to transfer land to more productive uses

and users; and

• If combined with cost-effective systems of land administration, opportunity to reduce

the cost of credit by leveraging the land as collateral.vi in addition, land tenure security

builds household resilience to climate, environmental, financial, and health shocks by

providing families a safety net.

2.9 Land Use/Cropping Patterns and Soil Quality

Drought, desertification and other types of land degradation currently affect more than 2 billion

people in the world (Gabathuler et al., 2009). The situation might worsen due to unsustainable

use of soil and water under present scenarios of climate change (Gabathuler et al., 2009;

Muluneh et al., 2014). Soil loss is a worldwide risk and adversely affects the productivity of

all-natural ecosystems as well as agricultural, forest and rangeland ecosystems (Pimentel, 2006;

Perkins et al., 2013; Lemenih et al., 2014; Van Leeuwen et al., 2015). Changes in soil quality

affected by accelerated erosion are significant and have resulted in decreased production and

land abandonment (Pimentel et al., 1995).

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In Nigeria, the demand for agricultural produce is continuously rising due to the geometric rise

in population; this has resulted in the intensification of cultivable land in an attempt to increase

agricultural productivity (Akinbile and Adekunle, 2000). Nigeria like most developing

countries is an agrarian society where vast percentage of the population is involved in several

agricultural activities. The rural population in the country represents a strong and virile

productive force in subsistence agriculture. They play an important role in the management of

land, agricultural forestry and water resource. Agricultural production in the developing

economies depends on land use intensity and resource allocation (Raufu, 2010). Efficient land

utilization and management practices ensure achievement of farm level objectives in term of

economic viability, food security and risk aversion (Pinstrup et al., 1995; Krusemen et al, 1996;

and Udoh and Akintola, 2002). With the ever-increasing Nigeria’s population, the pressure on

land has become so prominent that land which was initially regarded as a free-gift of nature

tends to be most highly priced factor of production (Gomez, 1993). The alternative features to

this, are the intensive use of the few plots of land which usually would result in land nutrient

exhaustion or degradation, low yield restricted farms and continuous poverty following low

productivity.

In recognition of the important of land as a farm resource, most agricultural policies and

programmes in Nigeria were aimed at improving accessibility to fertile land by farmers through

provision of irrigated lands, land reclamation and development of Fadama. For instance, the

River Basin Development Authority was mandated to increase land size in the country through

extensive irrigation programme. The on-going Fadama development programme in the country

is an attempt to increase crop productivity through improvement in marginal or less productive

lands. Thus, the availability and productivity as well as utilization of land have become priority

objectives of most recent agricultural policy in the country.

The accessibility of most agricultural lands especially in the southern part of the country

depends largely on land tenure system and the extent of competition by non-agricultural land

uses (Udoh 2000). Land as a factor of production is a critical input in agricultural production.

The criticality is imposed by its availability, accessibility, quantity and quality. In Nigeria’s

agriculture, the quality factor stands out as a major determinant of land productivity. This is

due to the problems associated with sourcing artificial amendments that can improve the

productivity of land especially by majority poor subsistent farmers that dominate the arable

crop production landscape (Raufus, 2010).

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The results of exploiting land-use systems without consideration of the consequences on soil

quality have been environmental degradation. Agricultural use and management systems have

been generally adopted without recognizing consequences on soil conservation and

environmental quality, and therefore significant decline in agricultural soil quality has occurred

worldwide (Imeson et al., 2006). Soil erosion and diffuse soil contamination are the major

degradation processes on agricultural lands because of expansion and intensification of

agriculture. Other non-agricultural uses, such as industrial and urban uses, also have important

negative consequences on soil quality, due to local contamination, soil sealing, and changes in

the dynamics of the landscape systems. The concept of soil quality (Doran and Jones, 1996;

Karlen et al., 1997) is useful to assess the condition and sustainability of soil and to guide soil

research, planning, and conservation policy. However, some authors (Sojka and Upchurch,

1999; Davidson (2000) noted that soil quality is a valid and important concept that is not

amenable to a simple and universal definition, and that will make a distinctive and crucial

contribution to soil management. The importance of soil quality lies in achieving sustainable

land use and management systems, to balance productivity and environmental protection.

Unlike water and air quality, simple standards for individual soil-quality indicators do not

appear to be enough because numerous interactions and trade-offs must be considered. For

assessing soil quality, a complex integration of static and dynamic chemical, physical, and

biological factors need to be defined in order to identify different management and

environmental scenarios. Also, the consequences of any decline in soil quality may not be

immediately experienced. The soil system does not necessarily change because of changing

external conditions or use, because soil has the capacity of resistance (or resilience) to the

effects of potentially damaging conditions or misuse or to filter out harmful materials added to

it. In part, this capacity of the soil in buffering the consequences of inputs and changes in

external conditions arises because the soil is an exceedingly complex and varied material with

many diverse properties and interactions between soil properties. It is this complex dynamic

nature which often makes it difficult to distinguish between changes as a result of natural

development and changes due to non-natural external influences. Soil-quality assessment,

based on inherent soil factors and focusing on dynamic aspects of soil system, is an effective

method for evaluating the environmental sustainability of land use and management activities

(Nortcliff, 2002).

Therefore, the sustainability of soils is key to addressing the pressures of a growing population,

the sustainable management of soils can contribute to healthy soils and thus to a food-secure

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world and to stable and sustainably used ecosystems, good land management is of economic

and social significance, and this includes soil management, particularly for its contribution

towards economic growth, biodiversity, sustainable agriculture and food security, which in turn

are key to eradicating poverty and allowing women’s empowerment, it is urgent to address

issues such as climate change, water availability, desertification, land degradation and drought,

as they pose global challenges

2.10 Dietary Diversity in Nigeria

Dietary diversity can be measured as the number of foods consumed across and within food

groups over a reference period- is widely recognized as being a key dimension of diet quality.

It reflects the concept that increasing the variety of foods and food groups in the diet helps to

ensure adequate intake of essential nutrients and promotes good health. There is ample

evidence from developed countries showing that dietary diversity is indeed strongly associated

with nutrient adequacy, and thus is an essential element of diet quality (Foote, et.al., 2004).

Food consumption pattern not only affect an individuals’ wellbeing but also have implications

for the society (Henry-Unaeze and Okonkwo 2011). It is documented that the choice of which

food to eat, where to eat and when to eat are intensely personal and influenced by several

factors which in turn influence an individual’s needs (Harwalin, 2002). Young adults have

special nutrients needs for growth and have been shown not to meet the dietary

recommendation for their age (Bonnie et al., 2004). Globally, there is evidence that

approximately two billion people suffering hidden hunger which has devastating effects and

significantly contributes to the global burden of disease (Kraemer, 2010). Anthropometric

measurements are vital tools used for proper monitoring and management of human health and

also useful in the determination of the relationship between various body measurements and

medical outcomes (Krishan, 2008). The few studies that have validated dietary diversity against

nutrient adequacy in developing countries confirm the well-documented positive relationship

observed in developed countries. A consistent positive association between dietary diversity

and child growth is also found in several countries. Finally, recent evidence from a multicounty

analysis suggests that household-level dietary diversity is strongly associated with per capita

consumption (a proxy for income) and energy availability, suggesting that dietary diversity

could be a useful indicator of household food security (defined in relation to energy

availability).

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The rationale for emphasizing dietary diversity in developing countries stems mainly from a

concern related to nutrient deficiency and the recognition of the importance of increasing food

and food group variety to ensure nutrient adequacy. Lack of dietary diversity is a particularly

severe problem among poor populations in the developing world, because their diets are

predominantly based on starchy staples and often include little or no animal products and few

fresh fruits and vegetables. These plant-based diets tend to be low in several micronutrients,

and the micronutrients they contain are often in a form that is not easily absorbed. Although

other aspects of dietary quality, such as high intakes of fat, salt, and refined sugar, have not

typically been a concern in developing countries, recent shifts in global dietary and activity

patterns resulting from increases in income and urbanization are making these problems

increasingly relevant for countries in transition as well (WHO/FAO 1996).

2.10.1 Dietary Diversity Measure

Dietary diversity is usually measured by summing the number of foods or food groups

consumed over a reference period. The reference period usually ranges from one to three days,

but seven days is also often used, and periods of up to 15 days have been reported (Drewnowski

et al. 1997). Common measures of dietary diversity used in developed countries include

measures based on a simple count of foods or food groups (Krebs-Smith et al. 1987; Lwik, et

al., 1999), while others take into consideration the number of servings of different food groups

in conformity with dietary guidelines. Examples of this latter approach include the dietary score

developed by Guthrie and Scheer (1981), which allocates equal weights to each of four food

groups consumed in the previous 24 hours: milk products and meat/meat alternatives receive

two points for each of two recommended servings, and fruits/vegetables and bread/cereals

receive one point for each of four recommended servings (total = 16 points). A modification

of this approach developed by Kant et al. (1991, 1993) evaluates the presence of a desired

number of servings from five food groups (two servings each from the dairy, meat, fruit, and

vegetable groups and four servings from the grain group) over a period of 24 hours. This score,

called the serving score, allocates a maximum of four points to each food group for a total score

of 20. The authors also use a simple five-point scale called the food group score, which is a

simple count of food groups consumed in one day (using the same five food groups). Finally,

Krebs-Smith et al. (1987) used and compared three different types of dietary diversity measures

(which they refer to as dietary variety): (1) an overall variety score (simple count of food items),

(2) a variety score among major food groups (six food groups), (3a) a variety score within

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major food groups, counting separate foods, and (3b) a variety score within major food groups,

counting minor food groups. All dietary measures are based on a three-day recall period.

However, in developing Countries, Single food or food group counts have been the most

popular measurement approaches for dietary diversity in developing countries, probably

because of their simplicity. The number of servings based on dietary guidelines was not

considered in any of the developing country studies reviewed. In China (Taren and Chen 1993),

Ethiopia (Arimond and Ruel 2002), and Niger (Tarini, et.al., 1999), researchers used food

group counts, while in studies in Kenya (Onyango, et.al., 1998), and in Ghana and Malawi

(Ferguson et al. 1993), they used the number of individual foods consumed. Studies in Mali

(Hatloy, et.al., 1998), and Viet Nam (Ogle, et.al., 2001) used both single food counts (Food

Variety Score) and a food group count (Dietary Diversity Score).

Also, studies done at the household level also used dietary diversity indicators that included

either individual foods or food groups (Hoddinott and Yohannes 2002; Hatloy et al. 2000). A

study in Mozambique used a weighting system, which scored foods and food groups according

to their nutrient density, the bioavailability of the nutrients they contain, and typical portion

sizes (Rose et al. 2002). For example, foods that were usually consumed in small amounts

(condensed milk) were given a lower score than foods with similar nutrient content that were

consumed in larger amounts (fluid milk).

2.10.2 Association Between Dietary Diversity and Nutrient Intake in Developing Countries

A study in Mali specifically validated dietary diversity against nutrient adequacy (Hatloy, et.al.,

1998). The study used two types of diversity scores: one based on a simple count of number of

foods (food variety score [FVS]) and one based on eight food groups (dietary diversity score

[DDS]). Both measures were computed from a quantitative dietary assessment using direct

weighing for two three days. Nutrient adequacy was measured using the NAR/MAR method

described previously (Schuette, et.al.,1996). This carefully conducted study documents a

significant association between nutrient adequacy (MAR) and both measures of dietary

diversity: the correlation coefficients between nutrient adequacy and FVS and DDS were 0.33

and 0.39, respectively.

A useful contribution of this study is the comparison of the two diversity measures in a

regression analysis, which shows that DDS (based on food groups) is a stronger determinant

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of nutrient adequacy than FVS (based on individual foods). Thus, in this context, increasing

the number of food groups has a greater impact on nutrient adequacy than increasing the

number of individual foods in the diet.

An additional methodological contribution of the study is the sensitivity specificity analysis

carried out to identify best cut-off points to predict nutrient adequacy for both diversity

indicators. In this sample, the cut-off points of six for food-group diversity and for food variety

provided the best sensitivity and specificity combinations to predict nutrient adequacy.

Although these findings are highly context specific, they provide useful methodological

guidance for similar studies to be conducted in other populations.

The study in Viet Nam, which included adult women, used a similar methodology to validate

the same diversity measures (FVS and DDS) against nutrient intake and nutrient density (Ogle

et.al 2001). FVS and DDS were derived from a seven-day food frequency questionnaire and

included more than 120 foods and 12 food groups, respectively. The findings confirm a positive

association between the two measures of diversity and intake of a variety of nutrients. Women

in the highest tercile of FVS those who had consumed 21 or more different foods in 7 days had

a significantly higher intake of most nutrients studied than those from the lowest tercile who

had consumed 15 or fewer foods. Similarly, women with a food group diversity greater or equal

to eight (out of a maximum of 12 groups) had significantly higher nutrient adequacy ratios for

energy, protein, niacin, vitamin C, and zinc than women with lower food group diversity ( The

authors also measured a variety of nutritional status indicators anthropometry, haemoglobin,

serum ferritin, retinol, retinol binding protein and C-reactive proteins and reported only weak

associations between women’s nutritional status and the dietary diversity measures).

Two other studies that have looked at the association between diversity measures and nutrient

intakes confirm the positive association between dietary diversity and intake of a variety of

nutrients (Onyango, et al., 1998; Tarini, et al., 1999). An additional study, conducted in Ghana

and Malawi, is probably the only one that documents only weak, and in some cases negative,

associations between diversity and certain nutrients (Ferguson et al. 1993). In this study,

analysis of the association between diversity and nutrient intakes was not a primary objective,

and the findings are reported only briefly.

Although, a study in Mozambique evaluated a rapid assessment tool named the Mozambican

Diet Assessment Tool (MDAT) to determine whether households could be classified accurately

into three categories of dietary quality (defined in this study as synonymous to dietary

31 | P a g e

diversity). The tool was applied at the household level and gathered information on all

individual foods consumed by all household members in one day. Each food received a score

of 14, based on its nutrient density, the bioavailability of the nutrients it contains, and typical

portion sizes (foods received a lower score if consumed in small amounts compared to foods

of similar nutrient value consumed in larger amounts). Total scores below 12 points were

considered very low dietary quality (term used by authors), 1219, average, and 20 or higher,

adequate. The association between this rapid assessment tool and a Diet Quality Index (DQI)

score was computed from data from a quantitative household-level 24-hour recall was tested.

Findings show that households classified by the rapid assessment tool as having acceptable

diets had higher mean intakes of energy, protein, and iron than those qualified as having poor

or very poor diets. Findings for vitamin A intakes, however, were in the opposite direction.

However, Akerele and Odeniyi (2015) examined the extent of food consumption diversity and

the factors influencing demand for diverse foods in Nigeria using micro-data on 18191

households. The transformed versions (logistic transformation) of Berry and Entropy measures

of dietary diversity were used as regressands in the econometrics models employed for

analysis. They found out that low-income households and households whose heads are females

or without formal education have lower than the norm in terms of diversity in food

consumption. Also, income, food prices (captured by food price index), access to remittance,

educational attainment up to secondary school, sex of household head and spatial factors are

important determinants of demand for varied diets. Income improvement strategy, renewed

emphasis on nutrition education especially in secondary schools, efforts to curtail food price

inflation and sensitively-guided gender-based interventions are advocated, among others.

Findings call for evaluation of the extent to which policy actions in agriculture and other

relevant sectors weaken or advance diet diversity in order to devise holistic strategies for

nutrition and health.

Ukegbu (2014) assessed nutritional status of lactating women in Umuahia, Nigeria This study

was conducted on 240 randomly selected women attending post-natal clinics with their infants

(0-6 months) in four health facilitates in Umuahia North Local Government Area (LGA) of

Abia State, Nigeria using structured, validated and pre-tested questionnaire. Body Mass Index

(BMI) was used to assess nutritional status by taking height and weight measurements using

standard procedures. Dietary intake was assessed using 24-hour recall and a validated food

frequency questionnaire. Mean nutrient intake was calculated and expressed as percentages of

32 | P a g e

FAO/WHO recommended values. Data was analysed using descriptive statistics and Pearson

correlation coefficient was used to determine association between BMI and nutrient intake. She

found out that the prevalence of overweight and obesity were 52.10 and 18.30%, respectively.

Cereals/cereal based dishes (1430) and leafy/non-leafy vegetables (1079) were consumed more

frequently while legumes were less frequently consumed daily. Energy (2279.01±446.79kcal),

protein (50.02±12.23g), calcium (339.21±186.35mg) and vitamin A (698.52±615.50 µgRE)

intake were lower than recommendations. However, there was no significant correlation was

found between BMI and energy intake (p=0.793). She concluded that intake of some essential

nutrients was lower than recommendations. Intervention programs such as nutrition education

and dietary diversity should be emphasized during antenatal and lactation period to improve

better health and nutrition outcomes.

2.11 The Concept and Determinants of Agricultural Diversification

Agricultural diversification refers to the shift from the dominance of one crop to production of

several crops on a farm or in a region, to meet the ever-increasing demand for food (Petit and

Barghouti, 1992). In other words, agricultural diversification has meant that the farmers now

concentrate on new areas of agriculture, growing alternative crops, rearing new breeds of

livestock and adopting a new farming system (Joshi et al., 2006).

Chaplin (2000) and Vyas (2006) perceived diversification as consisting of three stages. The

first stage reflects a cropping system that shifts away from monoculture. The second stage

involves the cultivation of more than one enterprise producing a variety of crops to meet the

market at different times of the year. The third stage is mixed farming involving a shift of

resources from one crop (or livestock) to a larger mix of crops (or livestock) or a mix of crops

and livestock. For this reason, Joshi et al. (2006) described agricultural diversification as

connoting crop mix, enterprise mix and activity mix at the household level aimed at increasing

household income and profit. This study focuses on the second and third stages in

diversification, as defined here, as mean of achieving food security among farm households in

Nigeria. Thus, crop diversification refers to the cultivation of two or more crops with the

available productive resources. Similarly, livestock diversification is the rearing of two or more

livestock types by the farmers given their available resources. Crop–livestock diversification

is defined as the production of one or more crops and livestock with the available resources.

The determinants of diversification can largely be classified into demand- and supply-side

categories. Specifically, such a classification includes per capita income, urbanization and

33 | P a g e

population growth (Chand, 1996; Ryan and Spencer, 2001; Smith et al., 2001; Joshi et al.,

2007), on the demand-side, whereas, on the supply-side, there are farm-level factors,

household-level factors, biophysical factors, risk factors, infrastructure or institutions,

technology and resource endowments. The characteristics of the decision-making households

are largely determined by household-level factors such as age, gender, education, household

dependency ratio, capital and off-farm income activities (Guvele, 2001; Shezongo, 2005;

Birthal et al., 2007; Van den Berg et al., 2007; Ashfaq et al., 2008; Rahman, 2008; Ibrahim et

al., 2009; Abro, 2012; Tasie et al., 2012; Sichoongwe et al., 2014; Asante et al, 2017). Relevant

farm-level factors include the number of crops cultivated and the livestock managed by farm

households, the total land area cultivated, total value of outputs, types of crops cultivated, hired

and family labour, use of complementary technologies and quantity of fertilizer used (Weiss

and Briglauer, 2000; Benin et al., 2004; Rahman, 2008; De and Chattopadhyay, 2010; Dzanku

and Sarpong, 2010; Mesfin et al., 2011; Senadza, 2012; Sichoongwe et al., 2014).

Diversification could also be influenced by other socioeconomic variables like farmers’ access

to markets, distance to markets, access to extension, access to credit, membership of

associations and proximity to research and extension institutions and availability of advice

(Chand, 1996; Smith et al., 2001; Estache, 2003; Joshi et al., 2007; Kankwamba et al., 2012).

Several techniques have been employed to explain specialization or diversification of

commodities or activities in a given time and space by a single indicator with unique strengths

and weaknesses. Common among these are: the index of maximum proportion, the Berry index

(BI), Herfindahl index (HI) and its related transformations, Simpson’s index, the Ogive index

and the Entropy index and its associated modifications (Kelley et al., 1995; Chand, 1996;

Pandey and Sharma, 1996; Joshi et al., 2006). However, among these techniques, the Simpson

index of diversity, the BI, HI and the Ogive index have been widely employed in estimating

agricultural diversification (Mekhora and Fleming, 2004; Joshi et al., 2006; Ashfaq et al., 2008;

Ibrahim et al., 2009; Fausat, 2012; Ogundari, 2013).

2.12 Theory/Concepts and Measures of Food Diversity

This empirical study derives from the traditional demand theory as extended by Jackson (1984)

in his work on the hierarchy demand and Engel's curve for commodity variety. He argued that

the homothetic preferences implicit in the traditional consumer choice model, as reflected by

smooth indifference curves that are convex to the origin (Lancaster, 1990), presumes that

diverseness in consumption is only influenced by prices and that income increase has no impact

34 | P a g e

on demand for variety. This traditional theoretical approach is unsuitable for modelling

consumer demand for diversity as diverseness in consumption cannot alone be ascribed to price

changes. Consequently, Jackson, drawing on Maslow hierarchy of needs, proposed a

hierarchical model of consumer demand (model of hierarchy of purchase) by which income

effect could be expected on diversity. In Jackson conception, consumer behaviour is

characterised by the following: at low level of income, only a limited number of foods is

bought; and as income grows, the range of purchased foods expands. It is also presumed that

variety increases independently of consumer's level of income such that no food leaves the

consumption bundle set at any given time. These attributes lay the groundwork for the utility

maximisation problem. Assuming separability between food and non-food items, Jackson

conceptual approach to food consumption diversity began by specifying utility maximization

problem for food items 𝑐𝑖 as follows:

𝑉(𝑐)𝑖 = 𝑣(𝑐1, 𝑐2, 𝑐3, ………………. 𝑐𝑁); 𝑠. 𝑡. ∑ 𝑝𝑖𝑁𝑖=1 𝑐𝑖 = 𝑌 𝑎𝑛𝑑 𝑐𝑖 ≥ 0 (1)

where 𝑐𝑖 is the quantity of food item 𝑖 and; 𝑝𝑖 represents the price of 𝑖𝑡ℎ food commodity;

Y is the total food expenditure and N is the total number of food items. The Lagrangian function

(L) can be stated as:

𝐿 = 𝑣(𝑐1, 𝑐2, 𝑐3, ………………. 𝑐𝑁) + 𝝀(𝒀 − ∑ 𝑝𝑖𝑁𝑖=1 𝑐𝑖) (2)

where λ is the Lagrangian multiplier. Using the Karush-Kuhn-Tucker (KKT) conditions, L is

first maximized with respect to all the choice variables 𝑐𝑖, and the related KKT conditions are:

𝜕𝐿

𝜕𝑐𝑖=

𝜕𝑣

𝜕𝑐𝑖− 𝜆𝑝𝑖 ≤ 0 (3)

𝑐𝑖(𝜕𝑣

𝜕𝑐𝑖− 𝜆𝑝𝑖) = 0 (4)

and 𝑐𝑖 ≥ 0 (5)

Then L is minimized with respect to λ with the related KKT conditions stated as

(6)

(7)

and

λ ≥ 0 (8)

35 | P a g e

Solving the above equations leads to Marshallian demand functions1 represented

mathematically as:

𝑐𝑖 = 𝑐𝑖 (Y,p) (9)

If condition (4) is satisfied, then 𝑐𝑖 = 𝑐𝑖 (Y,p) = 0 implies that there should exist commodity 𝑖

with zero-consumption at the optimum, given the budget constraint. From equation 9, and with

concept of cardinality, the number of different items purchased by the consumer at given prices

can be stated as:

𝑀(𝑐) = {𝑖|𝑐𝑗(𝑝, 𝑌) > 0} (10)

Assuming the Stone-Geary type additive preferences function {U(c) = 𝛴𝑖u(ci)}, M denotes the

commodity diversity that consumer demands. If 𝑀(𝑐) = {𝑖|𝑐𝑗(𝑝, 𝑌) > 0) is defined as set of

foods in a purchased set at given prices, then by the cardinality of M, the number of discrete

(distinct) food items demanded (purchased) can be a function of price vector and income (total

expenditure) (Jackson, 1984). This leads to the count measure of food consumption variety

expressed as the total number of distinct food items in the purchase set of the consumer

(household). The set of M (the number of purchased goods) is a monotonically increasing

function of income (total expenditure); and increases asymptotically at a decreasing rate,

resulting in non-linear Engel curves.

Based on Jackson’s hierarchical model of consumer demand, another measure of food

consumption variety which tends to assess diversity not only by the number of foods but also

by the rates of food consumption can be constructed. In this context, the rates/frequency of

consumption of a particular food item is assumed reflect in the concentration, distribution or

share of consumers’ food expenditure dedicated to the particular food item among the different

food categories. Consequently, another measure of food diversity that accounts for the

concentration (Thiele and Weiss, 2003; Liu et al., 2014; Rizov et al., 2014) were constructed.

1If all ci is greater than zero, the KKT condition suggests (from equation 3) that λ =

𝜕𝑣

𝜕𝑐𝑖 . With 𝑝𝑖 > 0 and presuming consumer's

non-satiation such that 𝜕𝑣

𝜕𝑐𝑖 > 0, then λ > 0. If λ > 0, then KKT condition suggests (from equation 7) that budget constraint holds

as equality with 𝑐𝑖 > 0 corresponding to the interior solution under the classical constrained utility maximization problem.

Likewise, for the case in which 𝑐𝑖 = 0, the KKT condition suggests that 𝜕𝑣

𝜕𝑐𝑖 pi ≤ λ with positive 𝑝𝑖 and non-satiation.

36 | P a g e

The two mostly employed methods of such food diversity measures is Berry index and Entropy

index. The higher the values, the greater the degree of diversity in food consumption for the

two indices. If a household consumes a single food item or a classified food-group, the Berry

diversity index is zero and comes close to unity if the household spread food spending equally

among several foods (Liu et al., 2014). The Berry Index (BI) (Berry, 1971) for a given

household is specified as:

𝐵𝐼 = 1 − ∑ 𝑤𝑖2𝑁

𝑖=1 (11)

where 𝑤𝑖 is the expenditure share of food commodity 𝑖. 𝑤𝑖 = 𝑝𝑖𝑐𝑖

∑ 𝑝𝑖𝑐𝑖𝑁𝑖=1

and 𝑝𝑖𝑐𝑖 is the expenditure

on food commodity 𝑖 over the reference period. N is the total number of food items. Berry

index is equal to one less the Harfindahl index (∑ 𝑤𝑖2𝑁

𝑖=1 ). Likewise, the Relative Entropy Index

(𝑅𝐸𝐼𝑗)2 for each household is as stated as:

𝑅𝐸𝐼𝑗 = − ∑ 𝑤𝑖𝑗ln (𝑤𝑖𝑗

𝑁𝑖=1 )

ln (𝑁) (12)

The Relative Entropy index derives from the Entropy Index − ∑ 𝑤𝑖𝑗ln (𝑤𝑖𝑗𝑁𝑖=1 ) (Shannon,

1948).

37 | P a g e

3.0 Methodology

3.1 Data and Sources of Data

The data used for this study were extracted from two main sources. The first data set was the

household level panel data for 2012/2013 and 2015/2016 post-planting and post-harvest

agricultural seasons. The data were collected by the World Bank in collaboration with the

National Bureau of Statistics (NBS), Nigeria. The panel survey was targeted to cover a total of

4,481 households selected from rural and urban areas of the 36 states of the country. The data

covered different aspects of household livelihoods2. Included data are the socioeconomic

characteristics of the household and household head such as household size, age, sex, marital

status, education of household head, location (rural-urban), season (post-planting or post-

harvest seasons), whether or not a household engages in agriculture as main source of income,

quantity of different foods consumed by the households, quantity of food purchased, value of

each food purchased, and expenditure on specific non-food items, and safety nets (this include

cash transfers and free food distribution), land use systems, land security, soil characteristic,

cropping systems/land use pattern, farm level characteristics (like irrigation systems, land

preparation), input used (fertilizer, cost of seeds, labour), among others. Also, data on food

consumption and purchases (expenditures) were collected over a recall of period of 7 days,

expenditure data on some non-food items either were reported on weekly and monthly basis

(frequent non-food purchases), or over a period of 6 months or 1 year (non-frequent non-food

purchases). The value of each of the food consumed by a household was extrapolated from the

corresponding value of the food purchased3.

The second aspect of the data used include the retail price of some specific foods collected by

the NBS across the 36 states of the country, and in months and years corresponding to the

household panel survey. The food items included in the data are imported rice, local rice, maize,

sorghum, millet, beef (meat), fish, egg, yam, garri, beans, and palm oil. These specific food

items are very critical to household food security in the country as they constitute important

components of household diets. However, the study chose to consider the direct influence of

average price of some of the food groups (7 of them) considered the most commonly consumed

food groups among households, other than the food price index. In order to construct a measure

of dietary diversity, food items were grouped into twelve (12): cereals, root and tubers, milk

2More details about the dataset and information therein can be accessed via http://econ.worldbank.org/WBSITE/EXTERNAL/EXT DEC/EXTRESEARCH/EXTLSMS/0, contentMDK:23512353~pagePK:64168445~piPK:64168309~theSitePK: 335899700.html. 3 Extrapolation for the value of each food item consumed involved multiplying the value of food purchased by the quantity of food consumed and then dividing the product (outcome) by the quantity of food purchased.

38 | P a g e

and dairy, egg, fish/sea foods, meat, pulses, fruits, vegetables, sweeteners, fat and oil and

miscellaneous group (Swindale and Bilinsky, 2006). The percentage contribution of other food

groups (e.g. vegetables, fruits, beverages etc) whose average prices were not included in the

estimation is very low. Definition of food group showing individual food item in each group is

presented in Table 1.

Table 1: Definition of Food Aggregates

Food Groups Specific Food Items

Cereals Guinea corn, Maize, Unshelled maize, Shelled Maize, Millet, Rice,

Unshelled rice, Wheat, Sesame seeds, and Acha.

Beans and Pulses Beans, Groundnut, Unshelled Groundnut, Shelled Groundnut,

Bambara nut, Pigeon pea, and Soy beans

Roots and Tubers Yam, White yam, Yellow yam, Water yam, Three leave yam, Potato,

Sweet potato, Cassava, Cocoyam, and Plantain.

Fruits Pear, Mandarin, Grape Fruit, Guava, Cashew, Pumpkin Fruit, Carrot,

Pawpaw, Avocado Pear, Banana, Mango, Pineapple, Orange, Water

Melon.

Vegetables Cucumber, Cabbage, Lettuce, Garden egg, Ginger, Okra, Onion,

Pepper, Sweet Pepper, Small Pepper, Pineapple, Pumpkin leaves,

Pumpkin, Green Vegetables, Tomato, Chill.

Fat and oil Oil bean, Cotton seed, Melon, Unshelled melon, Palm oil, Agbono,

Pumpkin Seed, Other Fatty Food Crop.

Beverages Cocoa, and Zobo.

Meats Calf male, Calf female, Heifer, Steer, Cow, Bull, Ox, Donkey,

Horse, Goat, Sheep, Pig, Chicken (local), Chicken layer, Chicken

broiler, Chicken cockerel, Turkey, Duck, Rabbit, Guinea fowl,

Camel, Other animals.

Milk and dairy Cow.

Eggs Egg (from layers), and Local eggs

Fish Fish

Sweeteners Sugarcane.

Others Cashew nut, Palm tree, Coconut, Palm nut, and Kola nut

Source: Extracted from LSMS Panel Data of 2012/2013 and 2015/2016.

3.2 Data Analysis

Primarily, the study seeks to assess dietary diversity as well as farm production diversify.

Analysis will feature simple count of foods consumed by the farm households (from the foods

purchased or consumed from what the households produced) as well as the simple count foods

produced. This is to evaluate the count attribute of food diversity. In addition, composite

measures of food diversity such as Berry/Simpson index and Entropy index will be employed

39 | P a g e

to assess dietary diversity as well as farm production diversity. The reason for the composite

indices is to capture the distribution dimensions of food diversity and to be able to utilise the

various diversity measures in econometrics analyses to ascertain robustness of results. In order

to be able to evaluate the quantity dimension of household food insecurity, the FGT poverty

index as adopted and used as food insecurity index (Orewa and Iyangbe, 2010; Akerele et al.,

2015) will be employed. The food insecurity index decomposes food insecurity status into three

parts, namely: incidence (head count), depth/gap/intensity and severity of food insecurity.

Anthropometric measures such as height for age z-scores (HAZ), and weight for age z-scores

(WAZ) and weight for height z-scores (WHZ) will be used as indicators of child nutritional

outcomes to capture the rates of stunting, wasting and overweight respectively. Moreover,

relationship between land use pattern, crop output, and other related variables required for each

of the specific objectives were presented under this section. The section presents the approach

employed in the analysis of the specific objectives of the study. Descriptive and measurement

details of the variables used in the study analysis is presented in Table 5.

3.2.1 Food Insecurity Status of Farm Households

Here, we seek to examine the food insecurity status of farm households in terms of food quality

(dietary diversity) and food quantity (measured by food calorie consumption). For the this we

objective, the daily per capita calorie requirement is used, and a household is defined as a group

of people living together and eating from the same pot. The lower limit of the FAO

recommended daily caloric intake for an adult aged man (30-60 years) of 2500 kilocalories

which is slightly below the FAO 2730 kcal target for developing countries by 2010 (FAO,

1996) is used as a threshold for food security status.

In developing food insecurity profile, the study adopted the Foster, Greer and Thorbecke

(FGT), (1984) class of poverty measure and modified it into food insecurity index. In is defined

as:

1

1 q

i

i

Z YP

N Z

=

− =

(13)

Where, N = total number of respondents; Yi = household per capita daily calorie intake; Z =

food insecurity line (2,500Kcal); q = number of households with per capita daily calorie less

than Z; α = food insecurity Aversion Parameter Index which takes on the values of 0, 1 and 2

representing head count or incidence of food insecurity, food insecurity gap (depth) and

severity of food insecurity respectively). The measure relates to different dimensions of the

incidence of food insecurity:

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If α = 0, FGT is reduced toH

PN

= ,

which is the proportion of the population that falls below the food insecurity line. This is called

the head count or incidence of food insecurity. If α = 1, FGT becomes:

(14)

Which is the depth of food insecurity. It is the percentage of income required to bring everyone

below the food insecurity line up to the poverty line. If α = 2, FGT becomes:

2

1

1 q

i

i

Z YP

N Z

=

− =

(15)

Which is the severity of food insecurity. It is indicated by giving longer weight to the extremely

(core) food insecure. It is achieved by squaring the gap between their calories and the food

insecurity line to increase its weight in the overall food insecurity measure.

3.2.2 Dietary Diversity Measures and Model Specification

As earlier mentioned, the Berry index and Relative Entropy measure are used in evaluating the

degree of diversity in food consumption. The values of both Berry and relative Entropy index

range between zero and unity. The higher the value of the index, the greater the degree of

diversity in food consumption. If a household consumes a single food item or a classified food-

group, the Berry diversity index is zero and comes close to unity if the household's total food

spending is spread equally among several foods. Likewise, the Relative Entropy index yields a

score of zero if household consumes a single food item and becomes higher with greater levels

of food diversification. The Berry Index (𝐵𝐼𝑗) (Berry, 1971) for each household

is specified as:

𝐵𝐼𝑗 = 1 − ∑ 𝑤𝑖𝑗2

𝑁

𝑖=1

(16)

Where 𝑤𝑖𝑗 is the expenditure share of food commodity i consumed by household j. 𝑤𝑖𝑗 =

𝑇𝑖𝑗

∑ 𝑇𝑖𝑗𝑛𝑖=1

and 𝑇𝑖𝑗 is the amount on money (in Naira) spent on food commodity 𝑖 by household 𝑗

over the reference period. N is the total number of food items. For this study, N=133 if index

is constructed from individual foods or 13 if constructed from food-groups. The Relative

Entropy Index (𝑅𝐸𝐼𝑗)2 for each household is as stated as:

2

1

1 q

i

i

Z YP

N Z

=

− =

41 | P a g e

𝑅𝐸𝐼𝑗 = − ∑ 𝑤𝑖𝑗ln (𝑤𝑖𝑗

𝑁𝑖=1 )

ln (𝑁) (17)

The Relative Entropy index derives from the Entropy Index − ∑ 𝑤𝑖𝑗ln (𝑤𝑖𝑗𝑁𝑖=1 ) (Shannon,

1948). The Entropy Index has an undesirable feature in that it is undefined when there are zero

food expenditures. For this limitation, the index cannot be estimated directly. Rather, it is

computed by replacing the zero expenditures with discretionary (very small) expenditures.

However, if zero expenditures are replaced with very small arbitrary values, the Entropy Index

approaches its maximum value of ln(𝑁) (Bellù and Liberati, 2006). The maximum value ln(𝑁)

is therefore used as the denominator in the Relative Entropy Index formulation (equation 13)

to obtain an index whose value ranges between zero and 1. The ratio of the entropy of a source

to the maximum value it could have while still restricted to the same symbols will be called its

relative entropy (Shannon, 1948). Given that the values of the dietary diversity measures fall

within zero and unity, one may be doubtful of the normality assumption. In addition, one may

be interested in an estimator that ensures the predicted values for the measures (Berry and

Relative Entropy Index) are within the interval of zero and one. The study follows the

conventional logistic (logit model) transformation (Greene, 1997, p.227) of the Berry and

Relative Entropy as used by Thiele and Weiss (2003). Consequently, the transformed measures

(variable) become 𝑇𝐵𝐼𝑗 = ln (𝐵𝐼𝑗

1−𝐵𝐼𝑗) and 𝑅𝐸𝐼𝑗 = ln (

𝑅𝐸𝐼𝑗

1−𝑅𝐸𝐼𝑗) for Berry and Entropy measures

respectively. Where TBI is the transformed Berry Index and TREI is the transformed Relative

Entropy Index. TBI and TREI (for individual foods and on food-group basis) are used as

response variables in the econometric models employed, and the statistical test (independent

sample t-test3) of difference of means between the dietary diversity of some selected vulnerable

households and other household groups conducted in this study. Whether the between-

household variations in the degree of food consumption variety can be explained by some key

economic decision variables, household demographic characteristics and community/spatial

factors are analysed using a panel data logistic regression model was considered with the final

choice of fixed effects variants of the binary outcome models selected based on econometrics

Hausman and Likelihood ratio tests.

3.2.3 Econometrics Estimation Procedure for the Dietary Diversity

Food Count Index

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For the simple food counts index of food consumption diversity, specification of empirical

(econometrics) model beings with a supposition that a count (outcome) variable 𝑦𝑗 is random

in a given time interval, having a Poisson distribution with probability density specified as:

𝑃(𝑦𝑗 = 𝑚𝑗) =𝑒𝑥𝑝𝜆𝑗𝜆𝑗

𝑚𝑗

𝑚𝑗 𝑚 = 1,2,3, … … … … . 𝑁 (14)

where 𝑚𝑗 is the realized value (outcome) of the random variable. In its empirical realization,

𝑦𝑗 represents the number of food groups consumed/purchased by household j out of N=12 food

groups. The list of food item included in each of the food groups is presented in Table 1.

Poisson model is a one-parameter distribution with mean and variance of 𝑦𝑗 equalling 𝜆𝑗. To

include a set of regressors (𝑋𝑗) into the analysis, and to fend-off negativity of mean 𝑦𝑗, the

parameter 𝑦𝑗 of the Poisson model is stated as:

𝐵𝐼𝑗 = 𝛼 + 𝛽1𝑋1𝑗 + 𝛽2𝑋2𝑗 … … . + 𝛽𝑘𝑋𝑘𝑗 + 𝜀𝑗 (16)

The standard error robust option was selected during model estimation to correct for possible

over-dispersion in the data (Cameron and Trivedi, 2010).

Berry Index and Entropy Index Measure

The BI was used as response variables in the econometrics (regression) models employed for

analysis. The dietary diversity model is specified for the Berry Index as:

𝑇𝐵𝐼𝑗𝑣 = 𝐸(𝑦𝑖|𝑋𝑖 = 𝜆𝑗 = exp(𝛼 + 𝛽1𝑋1𝑗 + 𝛽2𝑋2𝑗 + ⋯ + 𝛽𝑘𝛽𝑘𝑗) (17)

and for the transformed Relative Entropy Index as

𝑇𝑅𝐸𝐼𝑗𝑣 = 𝐸(𝑦𝑖|𝑋𝑖 = 𝜆𝑗 = exp(𝛼 + 𝛽1𝑋1𝑗 + 𝛽2𝑋2𝑗 + ⋯ + 𝛽𝑘𝛽𝑘𝑗) (18)

where α and β1 to βk are parameters to be estimated, 𝑋1 to 𝐾𝑘 are the explanatory variables while

𝜀𝑖 is the error term assumed to be normally distributed with zero mean and constant variance.

Similarly, the regression models were estimated using the fixed effects model while correcting

for potential heteroskedasticity using the robust standard error option in the STATA (14.2)

software that was used for the analysis. List and description of the dependent variables (food

count, berry index, and entropy index), and explanatory variables (farm production diversity:

crop diversification index, livestock diversification index, and crop-livestock diversification

index; socioeconomic characteristics, and food prices) in the (random effects and panel

43 | P a g e

Poisson) regression models are indicated in Table 2. Likewise, the detail approach to farm

production diversification index estimation: crop, livestock and crop-livestock diversification

indices is also presented as follows:

3.2.5 Farm Production Diversification Computation

The Harfindahl Index (HI) is employed to compute the crop diversification index (CDI), the

livestock diversification index (LDI) and the crop–livestock diversification index (CLDI).

These indices are obtained by subtracting the estimated HI from one. Accordingly, the indices

have direct relationships with diversification and make the interpretation of the empirical

results straightforward, such that a zero value indicates specialization and a value greater than

zero signifies some measure of diversification. For standardization, the revenues (which are

influenced by quantities produced, quantities sold, prices, the weather and transaction costs)

from each crop or livestock enterprise were used to compute the HI. To compute the HI, we

first calculate the revenue shares of the crops and/or livestock involved in the total crop and/or

livestock revenue for each sample farmer. The revenue share is expressed as:

𝑆𝑘 =𝑅𝑘

∑ 𝑅𝑘𝑛𝑘=1

(19)

where 𝑆𝑘 denotes the revenue share occupied by the kth crop or livestock in the total revenue

for a farm’s crops, the livestock and/or the crop–livestock enterprises, respectively; 𝑅𝑘 denotes

the revenue from the kth crop or livestock enterprise for a farmer; ∑ 𝑅𝑘𝑛𝑘=1 denotes the total

farm revenue for the crops, the livestock and/or the crop–livestock enterprises, respectively; k

= 1,2,…, n (number of enterprises involved in all farmers’ farming operations).

The Herfindahl index is specified as:

𝐻𝐼𝑘 = ∑ 𝑆𝑘2

𝑛

𝑘=1

(20)

The value ranges from zero to one. A value approaching 1.0 indicates specialization whereas

smaller values reflect increasing diversification, stability of income and sustainability of land

use pattern (Spio, 1996; Udoh, 2000). For a high level of diversification, the value of the HI is

likely to be small. For the special case of equal revenue shares among a total of n enterprises

for a sample farmer, the values of the revenue shares would be equal to 1/n and the value of

44 | P a g e

the HI would also be 1/n. The HI is computed for crops (𝐻𝐼𝐶), livestock (𝐻𝐼𝐿) and crop–

livestock (𝐻𝐼𝐶𝐿) farming systems. It follows that CDI, LDI and CLDI are then obtained as:

CDI = 1 – 𝐻𝐼𝐶; LDI = 1 – 𝐻𝐼𝐿; and CLDI = 1 – 𝐻𝐼𝐶𝐿.

The data consists of farmers who diversified farm production and those who did not. Farmers

are said to be diversified in crops, livestock and integrated crop–livestock farming systems

when they obtain a CDI, LDI and CLDI > 0.5, respectively. Hence, to examine the causal

relationship between agricultural production diversity and the dietary diversity (in terms of

quantity dimension of food security evaluated in terms food calorie adequacy, and dietary

diversity), panel data regression models expressed in equation 16 – 18 was considered with the

final choice of random effects variants, selected based on econometrics Hausman and

Likelihood ratio tests.

3.2.6 Analysis of the Land Use/Cropping Patterns among Farm Households

In order to examine how sustainable is the existing farm households' land use/cropping

behavioural practices, Soil Nutrient Intake Index employed by Udoh (2000) and Lawal et al.

(2009) was proposed to be used to assess the sustainability of land use/cropping patterns among

the farming households. However, the data did not provide information on crops that were

intercropped but rather provided information on the cropping systems adopted by the individual

farm household.

According to Udoh (2000), the Nutrient Intake Index (NII) was estimated to reflect how crop

diversity pattern can affect nutrient depletion and sustainability of farmland. In his work, he

measured NII as a ratio of crop configuration to number of crops in combination. He further

estimated the crop configuration which was derived by assigning different weights to different

classes of crop in combination and summing the weighted value for each farm and then dividing

the value by the number of crops in such combination. The assigned weights to the respective

classes were based on nutrient depletion ability of crops in an environment where nutrient

augmenting input like fertilizer is inadequate (Fageria and Baligar, 1993). It is expected that

the yield of crops in combination would be affected if the combined crops were mostly of the

same class of crops (Fageria and Baligar, 1993; Ali, 1996; Udoh, 2000). For instance, a

combination of melon/maize/yam would not deplete soil nutrient as the case of

cassava/yam/cocoyam mixture.

45 | P a g e

The index is expected to reflect how patterns of crop diversity can affect nutrient depletion and

sustainability of farmland. The higher the value of the Nutrient Intake Index, the higher the

probability that crop combinations can affect nutrient depletion, land degradation and

sustainability of farmland (Fageria and Baligar, 1993; Ali, 1996; Udoh, 2000; Lawal et al.,

2009). Nutrient intake index is meant to capture the vulnerability of farm total output to

different crop combination. The index value ranges between 1 and 4. The higher the NII the

more the likelihood that crop combinations can affect nutrient depletion, land degradation and

sustainability of farmland (Fageria and Baligar, 1993; Ali, 1996; Udoh, 2000).

However, the LSMS data only reports the type of cropping system engaged by individual

households across different crops. Although the data indicates the type of cropping system

employed but the crop combination in each system was not reported. Furthermore, it was

observed in the data, that the most households employ almost similar cropping system for all

the crop grown with exception in some cases. Hence, we capture land use intensity (a proxy to

nutrient intake intensity) based on each of the identified cropping system. The identified

cropping system was scored based on their agronomic history to soil nutrient consumption.

Table 3 show the summary of the classification and the attached weight based nutrient

consumption of the land use/cropping system. Since land use intensity (LUI) is also a function

of density of crop combination, a final measure of the land use intensity by the individual farm

household was determined as the product of the number crop in combination and the weight

assigned to each of the classified cropping system.

𝐿𝑈𝐼 = 𝐶𝑆 ∗ 𝑁 (21)

The weight of the cropping systems ranges from 1 to 4. This is in consonance with the NII

computed by index, but it does not reflect truly reflect the LUI of the individual farm plot.

Thus, multiplying the value by the number of crops in combination gives a true reflection of

the LUI. The higher the value, the more the likelihood that crop combinations can affect

nutrient depletion, land degradation and sustainability of farmland (Fageria and Baligar, 1993;

Ali, 1996; Udoh 2000). This approach enables us to capture land use intensity among farmers

particularly when only the data on number of crops combine is known but the specific crop in

combination is not known. Although, the situation is not likely to occur when a primary data

is involved. However, for multiple cropping like strip, mixed and intercropping, the Multiple

Cropping Index or Multiple Cropping Intensity (MCI) was used for their estimation. The MCI

was proposed by Dalrymple (1971). It is the ratio of total area cropped in a year to the land

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area available for cultivation and expressed in percentage (sum of area planted to different

crops and harvested in a single year divided by total cultivated area times 100).

𝑀𝐶𝐼 =∑ 𝑎𝑖

𝑁𝑖=1

𝐴∗ 100 (22)

Where, n is total number of crops, 𝑎𝑖 is area occupied by ith crop and A is total land area

available for cultivation.

Table 2: Land Use Pattern/Cropping System Classification

Cropping System Weight Remarks/Rationale

Alley 1 Usually, alley cropping system revitalizes the soil

through conversion of tree litres into organic nutrient.

(Rana and Rana, 2011)

Monocropping 2 Monocropping poses less intensity on the soil. Usually,

it is less nutrient consuming (Fageria and Baligar, 1993)

Relay cropping 3 This is used to make up nutrient depletion rate. Hence, it

is generally nutrient consuming. (Rana & Rana, 2011)

Strip cropping, mixed,

and intercropping

4 Nutrient uptake is generally more in intercropping

system compared o other forms (Dalrymple, 1971)

3.2.7 Relationship Between Crop Output and Land Use/Copping Patterns

One of the specific objectives of the study is to examine, having controlled for other

confounding factors, the relationship between sustainable land use/cropping patterns and

aggregate crop output in rural Nigeria. The understanding here is to link farmers' land

use/cropping behavioural practices to food security in terms of sustainable food production. In

order to examine such relationship, crop outputs were first converted to the grain equivalent

metric (a composite index for the aggregate crop outputs). Grain or Cereals equivalent for crop

products as indicated by the FAO is presented on Table 3. The cereals (CE) was used as the

dependent variable while the nutrient intake index, and other farm level and socioeconomic

determinants were used as independent variables in a linear panel data econometrics analyses

with fixed effect model, selected as the final choice model for the estimation based on

econometrics tests such as Hausman, Lagrange Multiplier tests, among others. The empirical

model specification is presented as follows:

The 𝐺𝐸 was used as response variables in the econometrics (regression) models employed for

analysis. The final model structure specified for the estimation is as follows:

47 | P a g e

𝐺𝐸𝑗 = 𝐸(𝑦𝑖|𝑋𝑖 = 𝜆𝑗 = exp(𝛼 + 𝛽1𝑋1𝑗 + 𝛽2𝑋2𝑗 + ⋯ + 𝛽𝑘𝛽𝑘𝑗) (22)

where α and β1 to βk are parameters to be estimated, 𝑋1 to 𝐾𝑘 are the explanatory variables while

𝜀𝑖 is the error term assumed to be normally distributed with zero mean and constant variance.

The regression models were estimated using the fixed effects model while correcting for

potential heteroskedasticity using the robust standard error option in the STATA (14.2)

software that was used for the analysis. List and description of the dependent variables (farm

level characteristics, and the socioeconomic characteristics can be found in Table 4.

Table 3: Grain/Cereals Equivalent Conversion Factor for Crop Products

Crop products Food item C. Factor

Cereals Guinea corn, Maize, Unshelled maize, Shelled Maize,

Millet, Rice, Unshelled rice, Wheat, Sesame seeds, and

Acha.

1.00

Fruits Pear, Mandarin, Grape Fruit, Guava, Cashew, Pumpkin

Fruit, Carrot, Pawpaw, Avocado Pear, Banana, Mango,

Pineapple, Orange, Water Melon.

0.14

Pulses Beans, Groundnut, Unshelled Groundnut, Shelled

Groundnut, Bambara nut, Pigeon pea, and Soy beans

1.06

Starchy roots Yam, White yam, Yellow yam, Water yam, Three leave

yam, Potato, Sweet potato, Cassava, Cocoyam, and

Plantain.

0.25

Sugar, sweeteners Sugarcane 1.08

Tree nuts Cashew nut, Palm tree, Coconut, Palm nut, and Kola nut 0.77

Vegetable oils Oil bean, Cotton seed, Melon, Unshelled melon, Palm oil,

Agbono, Pumpkin Seed, Other vegetable oil foods.

2.72

Vegetables Cucumber, Cabbage, Lettuce, Garden egg, Ginger, Okra,

Onion, Pepper, Sweet Pepper, Small Pepper, Pineapple,

Pumpkin leaves, Pumpkin, Green Vegetables, Tomato,

Chill.

0.08

CE = Cereal equivalent /Grain equivalent. Extracted from Rask and Rask (2014).

48 | P a g e

Table 4: Definition and Descriptive Statistics of the Variable Used for Analysis (N = 10,719, Panel Group = 4,482)

Variables and Their Definition Mean Standard Deviation Minimum Maximum

Age of the Household head 51.19 15.96 0.00 112.0

Urban farm households dummy (1 if farm household lives in the urban area, 0 otherwise) 0.203 0.402 0.00 1.00

Households size reported by the household head (number of people eating from the same pot) 6.978 3.279 1.00 35.00

Gender of the household head (1 if female, 0 if male). 0.840 0.366 0.00 1.00

Marital status of the household head (1 if the household head is married) 0.604 0.489 0.00 1.00

Spouse living with the household head dummy (1 if household head is living with the spouse) 0.761 0.426 0.00 1.00

Household head that attained higher Degree (MSc/PhD) (1 if yes, 0 otherwise) 0.007 0.860 0.00 1.00

Household head that attained a full Degree (HND/BSc) (1 if yes, 0 otherwise) 0.015 0.125 0.00 1.00

Household head that attained OND/NCE dummy (1 if yes, 0 otherwise) 0.194 0.138 0.00 1.00

Household head that attained Secondary school education dummy (1 if yes, 0 otherwise) 0.090 0.286 0.00 1.00

Household head that attained Primary school education, dummy (1 if yes, 0 otherwise) 0.117 0.321 0.00 1.00

Natural log of average price of cereals 4.914 0.158 4.41 5.67

Natural log of average price of roots and tubers 5.446 0.184 5.02 6.52

Natural log of average price of pulses 5.677 0.122 5.26 6.00

Natural log of average price of fat and oil 5.090 0.143 4.92 5.27

Natural log of average price of vegetable 5.840 0.851 4.83 6.08

Natural log of average price of egg 5.644 0.735 3.91 6.97

Natural log of average price of beef 7.310 0.221 5.75 8.87

Natural log of average price of other food group 5.076 0.129 4.92 5.24

Crop diversification Index 0.850 0.356 2e-07 1.00

Animal diversification Index 0.606 0.488 2e-07 1.00

Harvest season/surplus season, Dummy (1 if yes, 0 otherwise) 0.507 0.499 0.00 1.00

Soil slope dummy (1 if the soil slope is flat, 0 otherwise) 0.121 0.326 0.00 1.00

Farmers’ perception of soil quality (1 if the said the soil is good, 0 otherwise) 0.126 0.332 0.00 1.00

Land ownership status dummy (1 if the land is owned by the farm household, 0 otherwise) 0.367 0.482 0.00 1.00

Land use pattern dummy (1 if the farmer practice alley cropping, 0 otherwise) 2.383 4.789 0.00 32.00

Irrigation dummy (1 if the farm is irrigated, 0 otherwise) 0.005 0.073 0.00 1.00

Tractor used dummy (1 if the farmer used tractor for land preparation, 0 otherwise) 0.003 0.57 0.00 1.00

Value of fertilizer used during production (Naira) 18.89 417.24 0.00 15,000

Amount spent on hired labour (Naira) 16,381 772,370 0.00 6e+07

Farm size (area of the farm plot measured with GPS). The unit was measured in square meter 133.80 3228.95 0 301,800

Source: Computed from the LSMS Panel Data

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3.2.8 Analytical Procedure for Land Use/Cropping Pattern and Land Tenure Security

The last specific objective of the study is to examine the influence of land tenure security,

socioeconomic factors and farmers' perception about climate change on sustainable land

use/cropping patterns. Again, a linear panel data econometrics model will be employed with

Hausman, Lagrange Multiplier tests performed to select between fixed and random effects

model. To achieve this objective, the nutrient intake index (which a proxy measure of

sustainable land use patterns) will be used as dependent variable while land tenure security,

other variables will be included as regressors. Likewise, a seasonal dummy (1 for post-harvest,

0 for planting) will be introduced as part of the covariates for explaining the nutritional

outcome.

𝐿𝑈𝐼𝑗 = 𝐸(𝑦𝑖|𝑋𝑖 = 𝜆𝑗 = exp(𝛼 + 𝛽1𝑋1𝑗 + 𝛽2𝑋2𝑗 + ⋯ + 𝛽𝑘𝛽𝑘𝑗) (23)

3.3 Pros and Cons of the Outcome Variables

On food security, dietary diversity/quality and nutrition: the outcome variables for this study

are calories consumption which is used as a proxy for the food quantity dimension of household

food insecurity. The advantage of using this method is that it can be a conservative measure of

acute food insecurity especially among poor households. Since food insecurity is counted

through (by counting) food calories. It does not account for the quality of household diets.

Hence, measure of food diversity/quality is needed to appraise the quality dimension of food

security. This necessitate measurement of dietary diversity. Both calories and food

consumption diversity measure are unable to address the aspect of chronic food insecurity. To

capture this, anthropometric measures such as the HAZ, WAZ and other anthropometric

measure are required. The limitation of the outcome variables on food security and nutrition

are that they may not be able to fully account for the food stability dimensions of food security.

On land uses pattern/sustainability: although the Land Use Intensity maybe used to assess the

sustainability of land use/cropping patterns among the farming households, however, it only

reflects how patterns of crop diversity can potentially affect nutrient depletion and

sustainability of farmland. It cannot better ascertain the level of soil nutrient

capability/depletion compared to a situation involving soil nutrient test.

50 | P a g e

Results and Discussions

Results of the factors influencing dietary diversity (food count) among households are

presented in Table 5. The results indicate a positive relationship between the measures of farm

diversity and that of dietary diversity. This implies farm households’ who have a higher

diversity index in crop and animal productions have a better chance of having a diverse diet.

This is due to the subsistence nature of these farming households who produce different crops

or animals; they end up consuming a greater part of it within the household. This finding is in

line with that reported by Akerele and Shittu (2017) and Dillion et al. (2015) in Nigeria.

Similarly, other findings outside the Nigeria such as Kopmair et al. (2016), Sibhatu et al.

(2015) and Jones et al. (2014). This therefore could mean that encouraging farming households

to diversify into different crop and animal production could help in improving their nutrient

intake.

Urban households were also found to consume more diverse foods than rural households. This

finding agrees to what Akerele and Odeniyi (2015) reported in Nigeria. This can be explained

in terms of the advantage that urban households enjoy in terms of access to larger markets

where they can easily get all their food needs and also the fact that urban farming households

are closer to larger urban markets where they can easily sell their farm produce thereby

enhancing their income earning ability. Although increase in household size may raise

consumption of more varied foods in the households, it has a depressing effect on the relative

abundance (distribution) of the various food varieties. This shows that farming households may

have the required number of food groups in their daily consumption but the relative

contribution of each of the food group to the total diet may be low. This has noted by Powell

et al. (2017) and kumar et al. (2017) may be due to challenges farming households may face

in getting enough of a particular food due to low income therefore they are forced to take only

what they can afford from each food group or resort to low quality diet.

Similar findings hold for the gender of household head as male headed household have a better

chance of eating diverse food groups however its relative abundance favour their female

counterpart. This underscores the role women play in household food distribution and therefore

they tend to allocate more of their income to food consumption within the household compared

to a household headed by a male. This supports the findings of Ochieng et al. (2017) in Malawi

in terms of food count but contradicts the findings of Akerele and Odeniyi (2015) who reported

a negative relationship for both food count and relative abundance. Household heads who are

married were found to consume less food groups when compared to those that were not

51 | P a g e

married. However, the case was different for the relative abundance of the various food groups

consumed. This is not surprising as the addition of an extra person for a married household

would affect the household food decision making thereby influencing the component of the

household diet.

Furthermore, age of the household head played a significant role in the relative abundance of

the various food groups consumed though it did not affect the number food group in household

diet. The findings indicate a positive marginal effect implies that increase in age will have

positive effect on the diversity of the farming household diet. This increase in relative

abundance could be a result of changes in the food consumption need of the household as a

result of growth. This result is consistent with the findings of Dillion et al. (2015) who also

reported a positive relationship between dietary diversity and age of the household head.

Primary school education has an increasing effect on dietary diversity while for other

educational level above primary school except for polytechnic education, positive influence

was also reported in terms of the number of food groups consumed but no significance was

reported for the relative abundance. This means that an educated household head with the

exception of polytechnic education attainment is more likely to have a diverse diet when

compared to an uneducated household. This can be explained based on the role education plays

in enlightenment and as noted by Codjoe et al. (2016), it enhances the knowledge of farmers

on the importance of balanced of diet. Akerele and Odeniyi (2015) reported similar findings of

this study however with only primary and secondary education significantly affecting dietary

diversity and also Koppmair et al. (2015) and Ochieng et al. (2017) in a study carried out in

Malawi and Tanzania, respectively.

Furthermore, food prices of some food commodities were found to affect the dietary diversity

as it was observed that as the prices of tuber, pulses, beef and fat and oil increased the number

of food groups consumed increased and its relative abundance except for that of fat and oil

which was not significance for relative abundance. This actually looks awkward, but this may

be that as food price increases for the aforementioned commodities, households shift their food

consumption to a cheaper food alternative within the same food group. This shift according to

Akerele and Shittu (2017) and Rashid et al. (2011) could lead to households consuming less

nutritious food and this may have a long-term effect on the productivity of the farming

household.

52 | P a g e

This was however not the case for the prices of vegetable, cereals, egg and other food item as

it was observed that increase in the price of this food commodities have negative influence on

the dietary diversity of the farming household while the price of other food item only affected

the food count, the price of cereals only had a depressing effect on the relative abundance. The

reason for this can be that as prices of these commodities go up households tend to look for

cheaper alternative outside the food group thereby making their diet less diverse. Rashid et al.

(2011) however argued that these food commodities exert a negative influence because of their

relative importance in household diet therefore household would rather drop other food

commodity in other to maintain the consumption of this food commodity as the price increases.

53 | P a g e

Table 5: Panel Poisson and Linear Regression of the Farm Households Dietary Diversity in Nigeria.

Dependent Variable Food -group Count TBEI TREI

Independent Variable list Coefficient R.S.E. z-value Coefficient R.S.E. z-value Coefficient R.S.E. z-value

Crop diversity index 0.029*** 0.008 3.32 0.064*** 0.006 10.44

Animal diversity index 0.065*** 0.006 11.02 0.041*** 0.004 10.93

Season (1 if planted in dry season) 0.022 0.032 0.69 -3.5e-05 0.020 -0.00

Urban farm household (1 if yes) 0.098*** 0.006 15.35 0.069*** 0.004 16.62

Household Size 0.002** 7.8e-04 2.36 -0.002*** 5.3e-04 -4.25

Gender of the HH head 0.049*** 0.007 -6.40 -0.021*** 0.005 -4.37

Marital Status of the Household head -0.025*** 0.006 4.40 0.009*** 0.004 2.58

Age of the Household head 2.38e-03 1.5e-04 1.57 2.0e-04** 9.4e-05 2.14

Higher degree dummy 0.072*** 0.025 2.91 0.014 0.016 0.89

Full degree dummy 0.030* 0.018 1.67 0.018 0.011 1.63

Polytechnic education dummy 0.019 0.017 1.06 -0.001 0.010 -0.13

Secondary education dummy 0.017* 0.009 1.88 -0.003 0.006 -0.58

Primary education dummy 0.055*** 0.008 7.16 0.010* 0.006 1.93

Log of average price of cereals -0.027 0.016 -1.64 -0.25** 0.005 -2.43

Log of average price of tuber 0.263*** 0.014 18.14 0.076*** 0.010 6.44

Log of average price of pulses 0.091*** 0.025 3.67 0.075*** 0.012 4.78

Log of average price of fat and oil 5.976*** 0.969 6.16 -0.867 0.016 -1.36

Log of average price of vegetable -0.328*** 0.029 -11.17 -0.228*** 0.638 -10.75

Log of average price of egg -0.366*** 0.003 -9.95 -0.020*** 0.021 -7.92

Log of average price of beef 0.109*** 0.011 9.59 0.057*** 0.002 7.01

Log of average price of other food item -6.649*** 1.084 -6.13 0.786 0.008 1.11

Income 7.52e-09 2.8e-08 0.27 5.9e-09 0.711 0.35

Constant 4.739*** 0.546 8.67 1.275*** 1.6e-08 3.51

Log pseudolikelihood -22778.50

Wald chi2 (22) 592746.70 1323.78

F-value 0.000 0.000

R-square 0.133

TBEI = Transformed Berry Index, TREI = Transformed Relative Entropy Index. ***, **, * indicates significance level at 1%, 5%, and 10% respectively.

54 | P a g e

Assessment of the existing farm households' land use/cropping behavioural practices.

Table 6 shows the distribution of various land use pattern among farm households in Nigeria.

The distribution revealed that most households (21.02%) engage in multiple cropping system

like mixed, strip and intercropping. Although the data did not reveal any information about the

time of planting within the year but rather provides a list crop grown by individual farm

households. Thus, it is difficult to ascertain the intensity of land us. However, according FAO

(2018), most farm households in Nigeria engage in multiple cropping being a smallholder

farmer. This is because farmers own 0.5 hectares of land on average, predominantly managing

mixed crop-livestock systems, including also fish farming. The practice indicates unstainable

land use, especially with the traditional farming systems (slash and burn) which has contributed

adversely to unhealthy soil for farming. Although, some farm households recorded sustainable

practice like alley cropping but the percentage was very low. The overall indication of the land

use pattern among farm households is low productivity, declining soil nutrient and poor income

which consequently result in food insecurity.

Table 6: Results of the Land Use/Cropping Pattern among Farm Households in Nigeria

Group Cropping pattern Frequency Percentage Land Use Index

0 No response 7,520 70.16 ****

1 Alley cropping 17 0.16

2 Monocropping 894 8.34

3 Relay cropping 35 0.33

4 Mixed, strip, and intercropping 2253 21.02

Mean = , n =10,719. **** has no estimated figure since no cropping system was identified

4.2 Farm output, Land Use Pattern, Socioeconomic and Farm Level Characteristics of

Farm Households in Nigeria.

Table 7 presents the result of the analysis of the relationship between farm output, land use

pattern, socioeconomic characteristics and farm level characteristics. The result shows that the

use of tractor is significant and negatively affects farm output among the farming households.

Although tractor use have been noted to enhance agricultural production (Amadi and Ekezie,

2016; Lamidi and Akande, 2013; Musa et al. 2012), this contrary finding may be due to the

fact that farmers who use tractors are able to clear large expanse of land for production however

credit to effectively crop this area may not be available as most of them prepare in anticipation

of the getting credit and when it is available, the management skills to manage such large

expanse of cultivated land may be lacking which may lead to losses or reduction in output. The

coefficient of farm size was also found to have a depressing effect on farm output. It therefore

55 | P a g e

means that households with larger farm size are not likely to contribute more to farm output

this according to Oseni et al., 2014 may be because of improper management which may lead

to underutilisation of this farmlands for crop cultivation. This same direction of relationship

was reported by Bello et al. (2016), Oseni et al. (2014) and Adesoji and Farinde (2006) in

Nigeria and Carletto et al. (2011) in Uganda.

Similarly, the pattern of land use by the household was also found to negatively influence farm

output. This implies that the system of land use adopted by the household is not sustainable in

maintaining the nutrient retaining ability of the soil thereby leading to decrease in the farm

output. This may be caused by increasing demand for land due to urbanisation and other

economic activities which has led to farming households being constrained in their use of land

thereby cultivating same continuously with the attendant effect being nutrient depletion from

the soil without time for replenishment (Bamire, 2010; Ogundare, 2016). An increase in

household size was also found to reduce farm output. This result agrees with the findings of

Osanyilusi and Adenegan (2016); Akinola and Adeyemo (2013) and Obasi et al., (2013) and

could be due to the presence of children and old members within the household who may not

be able to contribute to farming activities.

Similar findings hold for farming household heads that were living together with their spouse.

The negative influence of the presence of spouse in the household on farm output could be

because of the presence of an additional person in the individual which might cause the

household head to devote part of the time to be spent on the farm with the spouse. This finding

agrees with that of Ayoola et al. (2011) however, it contradicts the findings of Ojiako et al.

(2017) who reported a positive relationship. Farm output was also found to be significantly

better in the harvest season when compared with the planting season. This is not farfetched as

farm output tends to be more in the postharvest season when compared to the planting season

when they could have sold most of their harvest or even consumed it.

The role of education in determining farm output was significant as it was observed that

farming household heads with educational attainment at secondary and polytechnic level have

a better chance of having better farm output when accessed with those who had no education.

This finding is in line with that of Anyanwu (2013) and Onogwu et al. (2016) who also noted

that increase in education will increase farm output. However, it didn’t favour those with full

degree as they were likely to have lesser farm output as also confirmed by Anibogu et al., 2015

who noted a negative relationship as education increases. This could be because household

56 | P a g e

head that are more educated tend to pay less attention to farming activity as they are likely to

take it as a part time job.

Table 7: Panel Regression Showing Relationship Between Farm output, Land Use Pattern,

Socioeconomic and Farm Level Characteristics of Farm Households in Nigeria.

Variable Coefficients Robust S.E. t-value

Land ownership status -39.423 86.444 -0.46

Cost of fertilizer 0.019 0.016 1.22

Irrigation dummy (1 if the plot was irrigated) -41.331 98.541 -0.42

Tractor dummy (1 if tractor was used) -995.45** 483.134 -2.06

Farm size -0.002* 0.001 -1.85

Land use pattern -65.975*** 8.405 -7.85

Urban households (1 = yes, 0=otherwise) -485.656 453.569 -1.07

Cost of hired labour -1.94e-05 2.6e+5 -0.74

Household size -324.628** 119.592 -2.71

Harvest/surplus season dummy (1 if yes) 10.649** 5.902 1.80

Gender of the household head 140.853 152.210 0.93

Marital status 173.100 132.177 1.31

Age of the household head 0.415 1.628 0.25

Spouse living with household head dummy -287.240* 172.533 -1.66

Higher degree dummy 54.754 86.419 0.63

Full degree dummy -146.864** 71.013 -2.07

Polytechnic education dummy 204.013* 119.135 1.71

Secondary education dummy 248.162* 143.053 1.73

Primary education dummy 26.754 45.777 0.58

Constant 566.107*** 186.444 3.04

R-square 0.398

F-value 12.47

Prob. > F 0.000

Source: LSMS Panel Data (2012/2013 and 2015/2016). ***, **, * indicates significance level at

1%, 5%, and 10% respectively.

4.3 Influence of land tenure security, socioeconomic factors and farmers' perception

about soil characteristics on sustainable land use/cropping patterns

The estimated parameters of the influence of land tenure security, socioeconomic factors and

farmers' perception about soil characteristics on sustainable land use/cropping patterns is as

shown in Table 5. The result revealed that lands that are flat are more likely to be used

sustainably for production unlike other soil slope types. This could be because of the believe

57 | P a g e

that the steeper the slope of a land the higher the risk of a land being predisposed to soil erosion

and loss of soil nutrient (Teshome, 2014).

The coefficient of land ownership was positive and significant among the factors affecting

sustainable land use pattern. This shows that farming household tend to use lands belonging to

them more sustainable for production when viewed in relation to land rented, leased or gotten

through other means. The temporary nature of other land ownership structure could be the

reason for the non-sustainable use of the land by the farmers. This according to Adamu (2014)

limits their use of the land as they may not be able to introduce permanents crops which can

serve as shade and help in soil fertility. The effect being that farming households keep moving

from one farmland to the other when their current farm land can no longer sustain their

production (Oyekale, 2012a).

Farmers were found to engage in more sustainable land practices in the planting season when

compared to the postharvest season. This might be because of the importance of planting

operations on farm output therefore farming households pay more attention on practices that

ensure sustainability during this period when compared to the postharvest season when they

might have harvested their crop. Furthermore, the age of the household head was observed to

improve the sustainable use of land for agricultural production. This could be because the

experience of the household head in farming operations tends to increase with age thereby

making them better positioned in sustainable use of land because of the trainings they might

have received, and knowledge gained over time. This is in agreement with the findings of

Nwaiwu et al. (2013) in Nigeria.

It was further reported that household head with full degree were less likely to make use of

land sustainably for agricultural production. This is contrary to the finding of Oyekale (2012b)

who reported a positive relationship and quite surprising because of the role education plays in

informing and enhancing knowledge the knowledge of the farmers. However, this trend may

be because educated household heads are likely to specialise in specific crops which are

believed to be more profitable and therefore pay less attention to cropping patterns such as

mixed cropping, crop rotation which could enhance the sustainable use of the land.

58 | P a g e

Table 8: Influence of land tenure security, socioeconomic factors and farmers' perception about

soil characteristics on sustainable land use/cropping patterns

Variable Coefficients Robust S.E. z-value

Farm Income -7.38e-08 5.21e-07 -0.14

Soil slope dummy (1 = flat, 0 = otherwise) 0.767*** 0..219 3.49

Soil quality perception 0.009 0.211 0.05

Land ownership status dummy 0.713*** 0.109 6.49

Irrigation dummy -0.525 0.638 -0.82

Use of tractor dummy -0.318 0.567 -0.56

Cost of fertilizer (Naira) 1.2e-04 1.4e-04 0.81

Crop diversification dummy 0.007 0.132 -0.05

Seasonal dummy (1 = post-harvest, 0 = planting period) -0.269*** 0.097 -2.77

Household size 0.016 0.015 1.05

Gender of the household head -0.015 0.201 -0.73

Marital status 0.013 0.121 0.11

Age of the HH head 0.005* 0.003 1.80

Spouse living with household head dummy 0.261 0.181 1.44

Higher degree dummy -0.055 0.529 -0.10

Full degree dummy -0.689** 0.315 -2.19

Polytechnic education dummy -0.131 0.314 -0.42

Secondary education dummy -0.037 0.167 -0.22

Primary education dummy -0.068 0.144 -0.47

Constant 1.712*** 0.282 6.08

R-square 0.008

Wald Chi2 89.38

Prob > Chi2 0.000

Source: LSMS Panel Data (2012/2013 and 2015/2016). ***, **, * indicates significance level at

1%, 5%, and 10% respectively.

59 | P a g e

References

Abro, A.A. 2012. Determinants of crop diversification towards high value crops in Pakistan.

International Journal of Business Management & Economic Research 3:536–545.

Agyemang-Bonsu,W.,Kojo,W.,Minia,Z.,Dontwi,J.,Dontwi,I. K., Buabeng, S.N., Baffoe-

Bonnie, B., Agyemang-Yeboah, F., Buabeng, S.N., Ofori, E., Gyasi, E.A., Karikari, O., Dugan,

E., Nelson, W., Naa Debei Agbey, A., Sagoe, R., Damptey, P., Tutuah Mensah, A., Anim-

Kwapong, G.J., and Frimpong, E.B. (eds.) 2008. Ghana Climate Change Impacts, Vulnerability

and Adaptation Assessments. Environmental Protection Agency, Accra.

Akerele, D., Momoh, S., Aromolaran, A. B., Oguntona, C. R. B. and Shittu, A. M. (2013).

Food Insecurity and Coping Strategies in South-West Nigeria. Food Security.5; 407–414.

Published by Springer, Netherlands.

Akerele, D. (2015). Household Food Expenditure Patterns, Food Nutrient Consumption and

Nutritional Vulnerability in Nigeria: Implications for Policy. Ecology of Food and Nutrition.54

(5); 546-571.

Akerele, D, Shittu, A.M., Ogunmola, O. O., Adekanmbi, A. A, Adekunle, C. P. (2017). Poverty

transition in rural Nigeria: Do household idiosyncratic, farm and geo-spatial factors influence

movers and stayers? Trop. Agric. (Trinidad) 94 (2): 185-199

Ali M (1996): “Quantifying the Socio-Economic Determinants of Sustainable Crop Production

an Application to Wheat Cultivation in the Tarui of Nepal” Agric. Econ. 14: 45 – 60

Aneani, F., Anchirinah, V., Owusu-Ansah, F., and Asamoah, M. 2011. An analysis of the

extent and determinants of crop diversification by cocoa (Theobroma cacao) farmers in Ghana.

African Journal of Agricultural Research 6:4277– 4287.

Arimond, M. and Ruel, M. (2004). Dietary diversity is associated with child nutritional status:

Evidence from 11 demographic and health surveys 2004., J. Nutr. 134: 2579-2585.

Arimond M, et al. (2010) Simple food group diversity indicators predict micronutrient

adequacy of women’s diets in 5 diverse, resource-poor settings. J Nutr 140(11): 2059–2069.

Ashfaq, M., Hassan, S., Naseer, M.Z., Baig, I.A., and Asma, J. 2008. Factors affecting farm

diversification in rice–wheat. Pakistan Journal of Agricultural Science 45:91–94.

Bain, L. E., Awah, P. K., Geraldine, N., Kindong, N. P., Sigal, Y., Bernard, N., Tanjeko, A. T.

(2013). Malnutrition in Sub – Saharan Africa: burden, causes and prospects, Pan African

Medical Journal.15: 120. doi:10.11604/pamj.2013.15.120.2535

Bellù, L. G. and Liberati, P. (2006). Describing income inequality Theil index and entropy

class indexes, Agricultural Policy Support Service, Policy Assistance Division, FAO.

Available at: www.fao.org/tc/easypol.

Benin, S., Smale, M., Pender, J., Gebremedhin, B., and Ehui, S. 2004. The economic

determinants of cereal crop diversity on farms in the Ethiopian highlands. Agricultural

Economics 31:197–208.

60 | P a g e

Berry, C. H. (1971). Corporate growth and diversification, Journal of Law and Economics 14:

371-383.

Birthal, P.S., Joshi, P.K., Roy, D., and Thorat, A. 2007. Diversification in Indian Agriculture

towards High-Value Crops: The Role of Smallholders. IFPRI Discussion Paper 00727.

Burlingame B, Dernini S, eds (2012) Sustainable Diets and Biodiversity: Directions and

Solutions for Policy, Research and Action (Food and Agriculture Organization and

Biodiversity International, Rome).

Cameron, A. C. and Trivedi, P. K. (2010). Micro-econometrics using STATA. Revised Edition,

Stata Press, United State of America.

Caviglia‐Harris, J.L. and Sills, E.O. 2005. Land use and income diversification: Comparing

traditional and colonist populations in the Brazilian Amazon. Agricultural Economics 32: 221–

237.

Chand, R. 1996. Diversification through high value crops in western Himalayan region:

Evidence from Himachal Pradesh. Indian Journal of Agricultural Economics 41:652– 663.

Chaplin, H. 2000. Agricultural diversification: a review of methodological approaches and

empirical evidence. Available at Web site: http://www.ilr.uni-bonn.de/agpo/rsrch/Idara/Farm/

wyewp2.doc (verified 22 January 2013).

Chavas, J.P. and Di Falco, S. 2012. On the role of risk versus economies of scope in farm

diversification with an application to Ethiopian farms. Journal of Agricultural Economics

63:25–55.

De, U.K. and Chattopadhyay, M. 2010. Crop diversification by poor peasants and role of

infrastructure: Evidence from West Bengal. Journal of Development and Agricultural

Economics 2:340–350.

Deckelbaum RJ, Palm C, Mutuo P, DeClerck F (2006) Eco nutrition: Implementation models

from the Millennium Villages Project in Africa. Food Nutr. Bull 27(4):335–342.

Devendra, C. and Ibrahim, M. 2004. Silvopastoral systems as a strategy for diversification and

productivity enhancement from livestock in the tropics. The importance of silvopastoral

systems in rural livelihoods to provide ecosystem services. Second international symposium of

silvopastoral systems. Universidad Autónoma de Yucatán, Mérida, 8–22.

Dubé L, Pingali P, Webb P (2012) Paths of convergence for agriculture, health, and wealth.

Proc Natl Acad Sci USA 109(31):12294–12301.

Dzanku, F.M. and Sarpong, D. 2010. Agricultural diversification, food self-sufficiency and

food security in Ghana–the role of infrastructure and institutions. In G. Djurfeldt, E. Aryeetey,

and A.C. Isinika (ed.). African Smallholders: Food Crops, Markets and Policy. CAB

International, Wallingford, UK, 189–213.

Ellis, F. 1998. Household strategies and rural livelihood diversification. The Journal of

Development Studies 35:1–38.

61 | P a g e

Ellis-Jones, J., Larbi, A., Hoeschle-zeledon, I., Dugje, I., Teli,I., Buah, S., Kanton, R.,

Kombiok, M., Kamara, A., and Gyamfi, I. 2012. Sustainable intensification of cereal-based

farming systems in Ghana’s Guinea savanna: constraints and opportunities identified with local

communities. IITA report. International Institute for Tropical Agriculture, Ibadan, Nigeria.

Estache, A. 2003. On Latin America’s infrastructure privatization and its distributional effects.

Available at Web site at SSRN: http://ssrn.com/abstract=411942 or http://dx.doi.org/

10.2139/ssrn.411942

Fageria N.K., Baligar V.C. (1993). Growth and Mineral Nutrition of Field Crops. Chectarerles

Allen Jones Publication, London. 208p.

Fanzo J, Hunter D, Borelli T, Mattei F, eds (2013) Diversifying Food and Diets: Using

Agricultural Diversity to Improve Nutrition and Health (Routledge, London).

FAO (2003). Trade Reforms and Food Security – conceptualizing the linkages, Food and

Agriculture Organization of the United Nations, Rome, 2013.

Food and Agriculture Organization (FAO). 2010. An international consultation on integrated

crop-livestock systems for development: The way forward for sustainable production

intensification. Integrated Crop Management 13:1–63.

FAO (2011). Guidelines for measuring household and individual dietary diversity, Food and

Agriculture Organization (FAO) of the United Nations, Rome, Italy.

FAO (2015). The State of Food Insecurity in the World. Meeting the 2015 international hunger

targets: taking stock of uneven progress. http://www.fao.org/3/a-i4646e.pdf

FAO, IFAD, WFP (2015) The State of Food Insecurity in the World 2015 (Food and

Agriculture Organization of the United Nations, Rome)

Fausat, A.F. 2012. Income diversification determinants among farming households in

Konduga, Borno state, Nigeria. Academic Research International 2:555–561.

Frison EA, Smith IF, Johns T, Cherfas J, Eyzaguirre PB (2006) Agricultural biodiversity,

nutrition, and health: Making a difference to hunger and nutrition in the developing world.

Food Nutr Bull 27(2):167–179.

Godfray HCJ, et al. (2010) Food security: The challenge of feeding 9 billion people. Science

327(5967):812–818

Govereh, J. and Jayne, T.S. 2003. Cash cropping and food crop productivity: Synergies or

trade‐offs? Agricultural Economics 28:39–50.

Greene, W. H. (1997). Econometric Analysis, 3rd Edition, Prentice Hall, Englewood Cliffs,

NJ.

Griebenow, G. and Kishore, S. 2009. Mainstreaming environment and climate change in the

implementation of poverty reduction strategies. Environment Department, Paper No. 119. The

World Bank, Washington, DC.

62 | P a g e

Guvele, C. 2001. Gains from crop diversification in the Sudan Gezira scheme. Agricultural

Systems 70:319–333.

Herrero, M., González-Estrada, E., Thornton, P.K., Quiros, C., Waithaka, M.M., Ruiz, R., and

Hoogenboom, G. 2007. IMPACT: Generic household-level databases and diagnostics tools for

integrated crop-livestock systems analysis. Agricultural Systems 92:240–265.

Herrero, M., Thornton, P.K., Notenbaert, A.M., Wood, S., Msangi, S., Freeman, H.A., Bossio,

D., Dixon, J., Peters, M., and Van De Steeg, J. 2010. Smart investments in sustainable food

production: Revisiting mixed crop-livestock systems. Science 327:822–825.

Ibrahim, H., Rahman, S., Envulus, E., and Oyewole, S. 2009. Income and crop diversification

among farming households in a rural area of north central Nigeria. Agro-Science: Journal of

Tropical Agriculture, Food, Environment and Extension 8:84–89.

IFPRI (2014) Global Nutrition Report 2014: Actions and Accountability to Accelerate the

World’s Progress on Nutrition (International Food Policy Research Institute, Washington, DC).

Iiyama, M., Maitima, J., and Kariuki, P. 2007. Crop-livestock diversification patterns in

relation to income and manure use: A case study from a Rift Valley community, Kenya. African

Journal of Agricultural Research 2:058–066.

Jackson, L. F. (1984). Hierarchic demand and the engel curve for variety, The Review of

Economics and Statistics 66(1984): 8-15.

Jackson, L. F. (1984). Hierarchic demand and the engel curve for variety, The Review of

Economics and Statistics 66(1984): 8-15.

Johnston, G., Vaupel, S., Kegel, F., and Cadet, M. 1995. Crop and farm diversification provide

social benefits. California Agriculture 49:10–16.

Jones, A. D., Shrinivas, A. and Bezner-Kerr, R. (2014). Farm production diversity is associated

with greater household dietary diversity in Malawi: Findings from nationally representative

data, Food Policy 46(2014): 1-12.

Joshi, P.K., Gulati, A., and Cummings, R.W. (eds). 2007. Agricultural Diversification and

Smallholders in South Asia. Academic Foundation, New Delhi, India.

Joshi, P.K., Gulati, A., Birthal, P.S., and Tewari, L. 2004. Agriculture diversification in South

Asia: Patterns, determinants and policy implications. Economic and Political Weekly 39:2457–

2467.

Joshi, P.K., Gulati, A., Birthal, P.S., and Tewari, L. 2004. Agriculture diversification in South

Asia: Patterns, determinants and policy implications. Economic and Political Weekly 39:2457–

2467.

Joshi, P.K., Joshi, L., and Birthal, P.S. 2006. Diversification and its impact on smallholders:

Evidence from a study on vegetable production. Agricultural Economics Research Review

19:219–236.

Kankwamba, H., Mapila, M., and Pauw, K. 2012. Determinants and spatio-temporal

dimensions of crop diversification in Malawi. Project Report produced under a co-financed

research agreement between Irish Aid, USAID and IFPRI, Paper.

63 | P a g e

Kelley, T., Ryan, J., and Patel, B. 1995. Applied participatory priority setting in international

agricultural research: Making trade-offs transparent and explicit. Agricultural Systems 49:177–

216.

Khoury CK, et al. (2014) Increasing homogeneity in global food supplies and the implications

for food security. Proc Natl Acad Sci USA 111(11):4001–4006.

Kim, K., Chavas, J.P., Barham, B., and Foltz, J. 2012. Specialization, diversification, and

productivity: A panel data analysis of rice farms in Korea. Agricultural Economics 43:687–

700.

Knudsen, M.H.2007. Making a living in the cocoa frontier, Western Ghana: Diversifying

incomes in a cocoa economy. Geografisk Tidsskrift – Danish Journal of Geography 107:29–

44.

Lancaster, K. (1990). The economics of product variety: A survey, Marketing Science 9(3):

189-206.

Lawal AF (2001). “Socio-Economic Effects of Flood Disaster on the Riparian Communities

Around Jebba and Shiroro Dams in Niger State, Nigeria”. Paper Presented at the 19th Annual

Conference of Agricultural Society of Nigeria. Badeggi

Lay, J. and Schüler, D. 2008. Income Diversification and Poverty in a Growing Agricultural

Economy: The Case of Ghana. 39th German Development Economics Conference, Zürich.

Lay, J. and Schüler, D. 2008. Income Diversification and Poverty in a Growing Agricultural

Economy: The Case of Ghana. 39th German Development Economics Conference, Zürich.

Lim SS, et al. (2012) A comparative risk assessment of burden of disease and injury attributable

to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: A systematic analysis for

the Global Burden of Disease Study 2010. Lancet 380(9859):2224–2260

Lin, T.-F. and Schmidt, P. 1984. A test of the Tobit specification against an alternative

suggested by Cragg. The Review of Economics and Statistics 66:174–177.

Little, P.D., Smith, K., Cellarius, B.A., Coppock, D.L., and Barrett, C. 2001. Avoiding disaster:

Diversification and risk management among East African herders. Development and Change

32:401–433.

Liu, J., Shively, G. E. and Binkley, J. K. (2014). Access to variety contributes to dietary

diversity in china, Food Policy 49(2014): 323-331.

Mainik, G. and Rüschendorf, L. 2010. On optimal portfolio diversification with respect to

extreme risks. Finance and Stochastics 14:593–623.

Mallard, S.R., Houghton, L.A., Filteau, S., Mullen, A., Nieuwelink, J., Chisenga, M., Siame,

J. and Gibson, R.S. (2014). Dietary diversity at 6 months of age is associated with subsequent

growth and mediates the effect of maternal education on infant growth in urban Zambia.

Journal of Nutrition 144 (11): 1818-1825.

64 | P a g e

Mekhora, T. and Fleming, E. 2004. An analysis of scope economies and specialisation

efficiencies among Thai shrimp and rice smallholders. Working paper series, Graduate School

of Agricultural and Resource Economics, University of New England.

Mesfin, W., Fufa, B., and Haji, J. 2011. Pattern, trend and determinants of crop diversification:

Empirical evidence from smallholders in Eastern Ethiopia. Journal of Economics and

Sustainable Development 2:78–89

Ndamani, F. and Watanabe, T. 2015. Influences of rainfall on crop production and suggestions

for adaptation. International Journal of Agricultural Sciences 5:367–374.

Nnakwe N. and Oyemaobi G. (2013). Prevalence of Food Insecurity and Inadequate Dietary

Pattern among Households with and without Children in Imo State Nigeria. International

Journal of Sociology and Anthropology. Vol. 5(9), pp 402 – 408, December, 201.3

Ogundari, K. 2013. Crop diversification and technical efficiency in food crop production: A

study of peasant farmers in Nigeria. International Journal of Social Economics 40: 267–287.

Ogundari, K. 2013. Crop diversification and technical efficiency in food crop production: A

study of peasant farmers in Nigeria. International Journal of Social Economics 40: 267–287.

Olutayo I.B. (2009) Poverty and Income Diversification among Household in Rural Nigeria;

A Gender Analysis of Livelihood Pattern. Conference page No 41 of 2nd Institute de Estudos

Socias Economics (IESE) Conference on Dynamics of Poverty and Patterns of Economic

Accumulation in Mozambique at Maputo, Mozambique 22-23 April,2009.

Orewa, S. I. and Iyangbe, C. (2010). The struggle against hunger: the victims and the food

security strategies adopted in adverse conditions, World Journal of Agricultural

Sciences 6(6): 740–745

Pandey, V. and Sharma, K. 1996. Crop diversification and self-sufficiency in food grains.

Indian Journal of Agricultural Economics 51:644–651.

Petit, M. and Barghouti, S. 1992. Diversification: Challenges and opportunities. World Bank

Technical Paper 1:0253–7494.

Pingali, P. (2007). Westernization of Asian diets and the transformation of food systems:

implications for research and policy, Food Policy 32(3): 281-298.

Pinstrup-Andersen, P. (2009). Food security: definition and measurement, Food security 1(1):

5-7.

Popkin BM, Slining MM (2013) New dynamics in global obesity facing low- and middle-

income countries. Obes Rev 14(Suppl 2):11–20.

Rahm, M.R. and Huffman, W.E. 1984. The adoption of reduced tillage: The role of human

capital and other variables. American Journal of Agricultural Economics 66:405–413.

Rahman, S. 2008. Determinants of crop choices by Bangladeshi farmers: A bivariate probit

analysis. Asian Journal of Agriculture and Development 5:30–14.

Rahman, S. 2008. Determinants of crop choices by Bangladeshi farmers: A bivariate probit

analysis. Asian Journal of Agriculture and Development 5:30–14.

65 | P a g e

Rana S S and M C Rana. 2011. Cropping System. Department of Agronomy, College of

Agriculture, CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur, 80 pages.

Rizov, M., Cupak, A. and Pokrivcak, J. (2014). Demand for the food diversity in Central and

Eastern European Countries: an evidence from Slovakia,

http://econpapers.repec.org/paper/agsaiea14/172978.htm.

Ruel, M. T. (2002). Is dietary diversity an indicator of food security or dietary quality? A

review of measurement issues and research needs, Food Consumption and Nutrition Discussion

Paper No. 140.

Ryan, J.G. and Spencer, D. 2001. Future Challenges and Opportunities for Agricultural R&D

in the Semi-Arid Tropics. International Crops Research Institute for the Semi-Arid Tropics,

Patancheru, India.

Senadza, B. 2012. Non‐farm income diversification in rural Ghana: Patterns and determinants.

African Development Review 24:233–244.

Shannon, C. E. (1948). A mathematical theory of communication in, The Bell System

Technical Journal 27: 379-423, 623-656.

Shezongo, M. 2005. Women’s property rights in Zambia. The Strategic Litigation Workshop.

Johannesburg, South Africa. www.cohre.org/womenlitigation/zambia.doc.

Sibhatu, K.T., Krishna V.V., Qaim, M. (2015). Farm Production Diversity and Dietary

Diversity in Developing Countries. Pnas Journal 112(34): 10657 – 10662

www.pnas.org/lookup/suppl/doi:10. 1073/pnas.1510982112/-/DC Supplemental

Sichoongwe, K., Mapemba, L., Ng’ong’ola, D., and Tembo, G. 2014. The Determinants and

Extent of Crop Diversification among Smallholder Farmers: A Case Study of Southern

Province, Zambia. IFPRI Working Paper International Food Policy Research Institute,

Washington, DC.

Smith, D.R., Gordon, A., Meadows, K., and Zwick, K. 2001. Livelihood diversification in

Uganda: Patterns and determinants of change across two rural districts. Food Policy 26:421–

435.

Spio K.C. (1996). “Inter-Cropping-the Hidden Revolution: A Solution to Land Scarcity and Household

Food Security” In Conference Issue: Paper presented at the 3rd Annual Conference of the Agricultural

Economic Association of South Africa, Agrekon 35(4): 266 – 270.

Swindale A. and Bilinsky, P (2006). Household Dietary Diversity Score (HDDS) for

Measurement of Household Food Access: Indicator Guide.

Tasie, C., Offor, U., and Wilcox, G. 2012. Determinants of off-farm income diversification in

Rivers state, Nigeria. Wudpecker Journal of Agricultural Research 1:331–334.

Thiele, S. and Weiss, C. (2003). Consumer demand for food diversity: evidence for Germany,

Food Policy 28(2003): 99-115.

Udoh E.J. (2000). “Land Management and Resource Use Efficiency among Farmers in South

Eastern Nigeria: Ph.D Thesis Dept. of Agricultural Economics, University of Ibadan, Nigeria

66 | P a g e

Van den Berg, M.M., Hengsdijk, H., Wolf, J., Van Ittersum, M. K., Guanghuo, W., and Roetter,

R.P. 2007. The impact of increasing farm size and mechanization on rural income and rice

production in Zhejiang province, China. Agricultural Systems 94:841–850.

Vyas, V. 2006. Diversification in agriculture: Concept, rationale and approaches. In N.A.

Mujumdar and U. Kapila (eds). Indian Agriculture in the New Millennium: Changing

Perceptions and Development Policy Vol. 2. Academic Foundation, New Delhi, p. 245–256.

Weiss, C.R. and Briglauer, W. (2000). Determinants and dynamics of farm diversification. FE

Working paper 0002/Universität Kiel, Department of Food Economics and Consumption

Studies.

Wu, S. and Prato, T. 2006. Cost efficiency and scope economies of crop and livestock farms in

Missouri. Journal of Agriculture and Applied Economics 38:539–553.

Yan, J., Wu, Y., Zhang, Y., and Zhou, S. 2010. Livelihood diversification of farmers and

nomads of eastern transect in Tibetan Plateau. Journal of Geographical Sciences 20:757– 770.

UNICELF (2015) Levels and trends in child malnutrition UNICEF – WHO – World Bank

Group joint child malnutrition estimates. Key findings of the 2015 edition. Available at

http://www.unicef.org/media/files /JME_2015_edition_Sept_2015.pdf

UNICEF (2012) Integrated Social Protection systems: Enhancing equity for children. UNICEF

Social Protection Strategic Framework

UN/WFP (2007). World Hunger Series2007: Hunger and health. The United Nation World

Food Programme. Published by Earthscan in the UK and USA in 2007. WFP ISBN: 987 92

920