International Congress on Women in a Global World IV: Struggle for Equality (WGW 2025)

Abstract - The study determined institutional and technological factors that influence the adoption of tenera oil plam production practices with a gender dimension among smallholder farmers in Edo State, Nigeria. Primary data were generated with use of questionnaire administered to 155 males and 137 female respondents. Results show that the level of adoption of tenera oil palm production practices was low for both male and females. Tobi regression result shows that land ownership structure and affordability at 1% significance influenced male adoption of tenera oil palm production practices while age and level of income at 1% significance influenced female in the adoption o. The major roles of male as reported in adopting process were purchase of seedlings, clearing of bush for planting and selling of cut bunches while the major roles of female were periodic weeding, gathering of cut bunches and mulching of palm field. The major constraint faced by male in adoption process were high cost of labour while for females is drudgery nature of the work. The study recommended that the Land Use Act of 1978 should be enforced to help women and non-indigenes to have sizeable farm lands, Government should empower Agricultural Development Programme (ADP) by employing more extension personnel to increase their contacts with the farmers.

Key Words: Gender, Adoption, Variety, Oil, Tenera, Edo

I. INTRODUCTION

Oil palm (Elaeis guineensis) is native to West Africa countries especially Côte d’Ivoire, Ghana, Nigeria and Sierra Leone. They are the major producers of both palm oil and palm kernel oil in the region (Ofosu and Sarpong, 2013). It is one of the most important economic crops in Nigeria (Ajieh, 2013). Its production serves as a major source of income and a means of livelihood to many rural dwellers in the country. Oil palm serves as source of raw material for industries and means of foreign exchange. It is a versatile tree crop with almost all the parts useful and of economic value (Ibitoye et.al, 2011). In the 1960s before crude oil became the dominant income earner for Nigeria, palm oil from the south eastern region of Nigeria was one of the tripods on which the economy of Nigeria stood (Adetola, 2015). With the discovery and dependence of Nigeria on crude oil, the role of agriculture as a whole and palm oil in particular dwindled. Nigeria not only ceased exporting palm oil, but also became a net importer of palm oil even from Malaysia. According to USDA (2017) palm oil which is a product of oil palm amounts to 537,000 metric tons in Nigeria in 1964, dwindles and rises to maximum production of 971,000 metric tons in 2010 and then maintains stagnancy at 970, 000 metric tons since 2011. According to Adetola (2015), the growth in oil palm has stagnated at 930,000 metric tons since 2013 while the consumption of palm oil in Nigeria amounts to 2.0 million metric tons per annum and the shortage in oil palm industry is estimated to be around 1,070,000 metric tons annually.

Edo State is one of the leading States in oil palm production in Nigeria. The main producers of oil palm in the state Okomu Oil and Presco, have continued to increase their plantation area and are on course to meet their 2018 and 2020 targets of growing their respective land area to 12,000 hectares and 20,000 hectares respectively (Nwosu and Okeowo, 2016). In 2015, mature land constituted about 95% of the total land area for both producers combined, growing by 10.8% for Okomu and 46.2% for Presco. Total crude palm oil (CPO) produced during the year increased by 12% to 35,600 tons and 10% to 39,328 tons for both companies respectively (Nwosu and Okeowo, 2016).

The end products of oil palm pass through activities that require the efforts and collaboration of both men and women for maximum output. The role of both could be either complimentary or supportive to achieve high productivity. Issues about gender have taken a global dimension especially in the 20^th and 21^st century (Chukwu and Nwaiwu, 2012). Gender in agricultural production in Nigeria is attracting significant attention especially in the area of processing, marketing, power and right of input ownership and policy. According to United States Agency for International Development (USAID, 2005), gender is a socially constructed role differences between men and women for the purpose of allocating powers, duties, status, responsibilities and role in any given social context. It applies to one sex or the other, and relates to the way each behaves in a given situation, while sex is biologically determined, and cannot normally be changed (Ekenta et al., 2012).

Ironkwe (2011) explained that gender deals with the social relationship between man and woman and how these relationships are negotiated in the production of goods and services. Gender relations manifest in the different roles, priorities, opportunities and limitations of men and women in a social setting. Gender in the adoption of improved production technologies of oil palm as being postulated in this research examines the awareness, interest, evaluation, trial and subsequent constant use (adoption) of the technologies by men and women, and the possible impediments therein.

The involvements of women in agriculture in Nigeria have attracted great attention (Odurukwe et al., 2006). Women in Nigeria form an active and reserve labour force but rarely own the means of production (Rahman, 2004). They are involved in all aspects of agricultural activities; making of ridges, yam moulds, yam staking, weeding, fertilizer application, harvesting, processing, storage and marketing (Ekenta et al., 2012). Several researcher agrees that much of the work done is by women living in the rural areas; women who constitute more than one third of the total population in developing countries produce most of the food for domestic consumption and millions of women work as farmers, farm workers and natural resource managers (Sulo et al., 2012 and Onyemobi, 2000). In doing so, they contribute to national agricultural output, maintenance of the environment and family food security (Brown et al., 2001).

The adoption of tenera production practices is a continuous process which requires the farmer to be aware of the practices, indicate interest in it, make evaluations, try out the technology in a small scale and make a decision to adopt the technology over a long period. According to Nin et al. (2003), technical change in the form of adoption of improved agricultural production technologies has been reported to have positive impacts on agricultural productivity growth in the developing world. Adopting the improved oil palm recommended technologies such as nursery planting, tenera improved seedlings, 9 metres triangular planting space, weed control by hoeing, weed control by cover crop, weed control with Glyphasate and Diuro mixture, disease control with Dithane m45, pest control with Cypermettin application of NPK 20:10:10, use of harvesting chisel and harvesting with machete are expected to improve the productivity of oil palm.

The adoption of the improved technologies with high potentials to produce palm oil and palm kernel, becomes very vital to re-engineering the declining level of palm oil production and its foreign earnings in the country. Further, if the roles of men and women in their different capacities and abilities are explored, and the improved technologies adopted, oil palm production will be enhanced and this will improve farmers’ well-being, income, and food security at the family level and the nation at large. Against this background, the research was conducted to analyze factors influencing the adoption of improved oil palm production technologies in Edo State, Nigeria.

II. RESEARCH METHODOLOGY

The stud was carried out in Edo State located on longitude 06^o 04`E and 06<sup>o</sup> 43`E and latitude 05^o44` N and 07<sup>o</sup>34` N. It occupies a land area of about 17,802 square kilometres but only 400 square kilometres is used for oil palm production in the State. The State is classified as a rainforest Zone. The rainy season starts from April till about the end of October. The annual rainfall is between 1750mm and 2100mm with an average rainfall of 1920mm. The temperature is between 22°C and 30°C with an average annual relative humidity of 82% which is very suitable for oil palm production (Ekunwe et al., 2016). With Benin City as the capital, the state has a population of 3,778,860 people. In 2018, the projected population was 4, 467,410 with a population growth rate of 2.7% per annum (NPC, 2006). It is made up of three major ethnic groups; namely the Bini’s, Esan and Afemai. However, the State has a high presence of residents from across the country and the world because of its cosmopolitan tendencies. Benin City the capital has a history of being one of the foremost destinations of Europeans during their exploration of Africa continent many centuries ago. Some of the flash points have remained enviable tourists’ attraction for the state. It is notable for the production of cassava, oil palm, plantain, maize, yam, melon, coconut and vegetables.

Multi stages sampling comprising purposive, random and stratified sampling techniques were used in selecting Local Government Areas and Villages. From the sampling frame in each village, 10% of respondents were selected. A total of 155 males and 137 females making 292 respondents were sampled for the study. The primary data used were collected through the administration of structured questionnaire, focus group discussion and key informant interview. Data were analyzed with descriptive statistics and Tobit Regressions Analysis.

Tobit regressions analysis

Tobit regression is an inferential statistical tool used in this study to determine the socio-economic, institutional and technological variables that influence the adoption of improved oil palm production technologies. It is often used when there are multiple dependent variables and multiple independent variables. It is expected that the socio-economic, institutional and technological variables will have either positive or negative influence on the adoption of improved Oil palm technologies in the study area. Tobit model used by Ayanlere (2016) was adapted for this study as specified. Adoption was measure by adoption ratio as specified by Orebiyi (2005) and Onemolease and Alakpa (2009) respectively.

V = β₀ + β₁X₁ + β₂X₂ + β₃X₃ + β₄X₄ + β₅X₅ + β₆X₆ + β₇X₇ + β₈X₈ + β₉X₉ + β₁₀X₁₀ + β₁₁X₁₁ + β₁₂X₁₂ + β_nX_n+ e…...(1)

Where

V = limited dependent variables and is defined as

V = V_j*if V_j > 0 ......................................................(2)

V_j = 0 if V_j* < 0.......................................................(3)

j = 1…155,137

V = (Z-Y_{j )} (Adoption Ratio)..................................(4)

Z = number of recommended tenera production practices

Y_j = number of recommended tenera production practices

adopted by jth farmers

X₁ – X₁₅ = Independent variables

X₁ = Age (Years)

X₂ = Educational Level (Years)

X₃ = Farm Size (Hectares)

X₄ = Household Size (Number)

X₅ = Land Ownership (inherited, purchased, rented and gift)

X₆ = Tenera Farming Experience (Years)

X₇ = Level Income (N/K)

X₈ = Extension contact (Frequency of contact)

X₉ = Relative advantage (Farmers’ Perception; Has advantage, Yes =1; Otherwise, No = 0)

X₁₀ = Compatibility (Farmers’ Perception; Compatible - Yes =1; Otherwise – No = 0)

X₁₁ = Affordability (Farmers’ Perception; Affordable - Yes =1; Otherwise – No = 0)

X₁₂ = Complexity (Farmers’ Perception; Complex - Yes =1; Otherwise – No = 0)

e = Error term

β₀ = constant

β₁ - β₁₂ = Regression coefficient

Weighted average score (WAS)

Weighted average is an average where each value is assigned a specific weight or frequency. A weighted average takes different scores, or grades, with assigned weights, or percentage. Weighted average score was used to achieve objective (iv) of the study.

Weighted Avg_x = W₁X₁ + W₂X₂ +….. WnXn ………. (5)

Where

W= Relative weight of the levels of adoption (Awareness = 1, Interest = 2, Evaluation = 3, Trial = 4, Adoption = 5)

X= Nominal value of the adoption levels of tenera oil palm variety

III. RESULTS AND DISCUSSION

Table 1: Socio-economic Characteristics

Table 2: Distribution of farmers’ levels of adoption of tenera production practice among gender

WAS = Weighted Average Score; MS = Mean Score. A = Awareness; I = Interest, E = Evaluation; T = Trial and A = Adoption

Table 3: Multiple Responses of the Roles of Gender in Tenera Oil Palm Adoption

Table 3 indicates that both men and women farmers played several different specific roles in the adoption of improved oil palm production technologies in the study area. Most predominantly, the men farmers played the following roles; purchase of bunches, clearing of bush for planting, selling of bunches, clearing of bush for already grown plants and mapping of the farm land for planting. On the other hand, women farmers played the roles of periodic weeding of newly planted palm, gathering of already cut bunches, mulching of the palm in the field, making hedges around the planted palm and application of chemicals. Roles are specific functions expected of individuals to play at a particular time. The adoption of oil palm production technologies involves various activities which are carried out as roles by individuals involved in the adoption process. The roles of women farmers are associated with household responsibilities while the roles of men farmers are viewed to be providing for the family and engaging in productive ventures for the family’s well-being. A woman who is seen as playing the roles expected of a man is sometimes nick named “the super woman” while a man seen as playing the roles socially expected of a woman is considered to be weak. These social ideas of the roles of men and women affected the roles played by both of them in the adoption of oil palm production technologies. The males played the following major roles; purchase of bunches (85.1%), clearing of bush for planting (69.6%) and selling of bunches (57.4%). On the other hand, female played the roles of periodic weeding (68.6%), gathering of already cut bunches (63.5%) and mulching of (59.8%).

Table 4: Decision Making by Gender in the Adoption of Improved Oil Palm Technologies

Table 4 depicts the areas of decisions by men and women in the adoption of improved oil palm production technologies. It reveals that men farmers majorly took the following decisions in the adoption of oil palm production technology; hectare of farm land to be used, to take loan or credit facility from bank, when to harvest matured bunches, the number of times and period of pruning the palms and the kind of agrochemicals to be used. Similarly, women took the following decisions; the number of persons to be used for labour, when to clear the bush for planting, when to plant the seedlings, the agrochemicals to use and how many times and periods of weeding. Further, the table shows that on the average, men (45%) took more of the decisions as against the women (34%) in the adoption process of improved oil palm production technologies. Decision making in the household is often seen as the responsibility of the men. Even when the woman conceives ideas, she is not expected to take decisions on them unless rectified by the man. In this situation, if a woman has an idea that is productive, worthwhile and beneficial and such idea is not supported by the man, the idea will be relegated. This scenario is prevalent in the study area. The men farmers took the most important decisions in the adoption of improved oil palm production technologies than the women farmers.

Table 5: Tobit Regressions of Socio-economic, Institutional and Technological Factors Influencing Adoption of Improved Oil Palm Production Technologies by Gender

** = Sig @ 5%; *** = Sig @ 1%

The Tobit analysis presented in table 5 reveals that for men, farm size, level of income and relative advantage had positive and significant influence at 5% level of probability while complexity had negative and significant influence at 5% probability. Also land ownership structure, access to credit and affordability had positive and significant influence at the probability of 1%. This means that these variables influenced men in the adoption of the recommended practices. The estimated sigma of 0.32 implies that 32% variation in the adoption of improved oil palm technologies was accounted for by the influence of the independent variables for men farmers. This means that the tendency of a farmer to adopt improved oil palm production technologies is dependent on the independent variables employed. Similarly, for women, marital status and level of income were positive and significantly influenced adoption at 1% level of probability while relative advantage and affordability had positive and significant influence at 5% probability. Further, age and complexity had negative but significant influence at 1% and 5% levels of probability respectively. The estimated sigma of 0.13 implies that 13% of the variations in the adoption of improved oil palm production technologies were accounted for by the independent variables. The positive significant influence of some of the variables for men and women indicate that a unit increase in any of the variables will lead to a unit increase in the adoption of improved oil palm production technologies. Similarly, the negatively significant influence observed of some of the variables shows an inverse relationship. This explains that a unit increase in any of the variables will lead to a unit decrease in the adoption of improved oil palm production technologies. In the regressions analysis, age of the farmers and complexity of the technologies were negatively significant. This implies that the higher the age of the farmer, the lower the adoption of improved oil palm technologies. Similarly, the more complex a technology is perceived by the farmers, the less the tendency of the farmers to adopt it. The socio-economic, institutional and technological factors had influence on men and women farmers in the adoption of oil palm production technologies at different probability levels. However, the factors had more influence on the women as 17% of the factors which is indicated by the value of the sigma influenced the decision of the women farmers to adopt while 13% influenced the decision of the men farmers to adoption. The influences may be positive or negative but as observed in the results, the factors had negative influences. This accounted for the reason the men farmers adopted the technologies more than the women farmers in the study area.

Table 6: Factors that Constrained Farmers in Adopting the Tenera Variety by Gender