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Women Participation in Acha Processing
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farm production, marketing and intra house hold distribution of food. They play a lead role in post-harvest activities such as shelling of grains, storage and processing (Adebola, Oladimeji, Koichi & Tadasu 2015; Odurukwe, Matthews, Njoku, & Ejiogu, 2006; Satyavathi, Bharadwaj, & Brahmanand, 2010).
In transforming agriculture in the country, due attention should be given to women processors who add value to food crops (Adenugba & Raji, 2013; Adeoye, Oyeleye, Ajibade, Daud, Alabi, & Amao, 2018; Onyemauwa, 2012; Yusuf, Akinola, Abdulsalam, Shuaibu, Yusuf &
Yusuf, 2014). The demand for acha and its products have increased in both the national and international markets. This is as a result of the increased use of the crop for food and other uses.
There is, however, paucity of information on the socioeconomic factors influencing women participation in acha processing in Jaba Local Government Area (LGA) of Kaduna state, Nigeria. This study is thus conducted to: (i) identify the methods of acha processing and types of labour employed; (ii) identify the sources of funds for processing; (iii) determine factors influencing rural women‘s participation in acha processing; (iv) determine the profitability of processing and (v) to identify the challenges processors encountered in the study area.
Literature Review
Acha cultivation and the center of origin is around the central delta of the Niger and neighboring areas dating back to 5000BC. The crop is the most important of the diverse group of wild and domesticated Digitaria species that are harvested in the savannah of West Africa; the smallest seed of all species of millets (Seignobos and Tourneux, 2002). Acha is a low input demanding crop which tolerates a wide range of soils, but not waterlogged clayed soils (Philip & Itodo, 2006). The uniqueness and potential of acha in contributing significantly to whole grain diets in Africa and, its proper utilization would lead to improvement in economic status of the producers and processors in (Chinwe et al., 2015).
Agro processing is commonly known to contribute significantly to the improvement of income and food availability which enhance the sustainability of smallholder farmers‘
livelihoods (Mhazo, Mvumi, Nyakudya & Nazare, 2012). Several researchers conducted empirical studies on the participation of women in agricultural activities such as; Mirtorabi, Hedjazi, & Hosseini (2012) study on factors influencing rural women‘s participation in food processing activities and the results of the study indicated that rural women‘s participation in processing depended on variables such as the level of education, family size, animal ownership, and using extension services. The results also showed rural women with low level of literacy were more involved in food processing activities than women with higher levels of education.
Rural women are important for increasing agricultural production and productivity, they participate in food and cash crops and manage mixed agricultural operations, involving crops, livestock, fish farming and processing (FAO, 2011; Imeh & Odibo, 2013; Onyemauwa, 2012). Similarly, the study conducted by Okolo, Omoregbee & Alufohai (2018) on women‘s involvement in shea butter production showed age, income and household size influenced their participation. Kalyani, Krishnamurthy, Rao & Kumari (2011) result showed the women were of poor background, had limited access to resources and their overall rate of participation in agriculture was higher than that of men. There are many enormous challenges and constraints faced by women in agriculture such as use of modern methods for processing,
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financing, policy and lack the power to make decisions without their husbands consent (Anyakoha & Mbanefoh, 2000; Godfrey, 2010; Fischer & Qaim, 2012; Odini, 2014; Pala, 2013; Rahman, 2008).
Methodology
The study was conducted in Jaba LGA of Kaduna State. The study area is located in the Guinea Savannah zone of Nigeria, between Latitude 80 001 - 80 21N and Longitude 90 261 -90 281W. It shares boundaries with Kachia LGA to the North, Kagarko LGA to the West, and Jema‘a LGA to the East. Jaba LGA is made up of 13 districts which include: Ankung, Bitaro, Chori, Daddu, Dura, Fada, Fai, Forgyei, Kurmin Jatau, Ngumkparo, Sabon Gari, Sab-Zuro and Samban. The major crops produced in the area are: ginger, acha, maize, sorghum, rice, yam, groundnuts, cocoyam and cassava (KADP, 2019).
Sampling procedure and sample size
A three-stage sampling procedure was used to select one hundred and twenty respondents for the study. The first stage was the purposive selection of four (4) districts (Ankung, Bitaro, Dura and Fada) out of the 13 districts in the LGA. The second stage involved random selection of four (4) villages; one village from each of the selected districts where acha is intensively cultivated and processed. Lastly, random sampling was used to select thirty (30) respondents from each of the selected villages to give a total of 120 respondents. A structured interview schedule was used to elicit information from processors. Descriptive statistics was used to achieve objectives i, ii and v, while regression and gross margin was used to achieve objectives iii and iv respectively.
Regression model
Regression model as used by Adeoye et al. (2018), Akinwekomi et al. (2017), Okolo et al.
(2018) and Onyemauwa (2012) among others was used. Mathematically, the multiple regression analysis is presented by the formula as expressed below:
Y a0 + b1X1 +b2X2 + b3X3 +b4X4 +b5X5 +b6 X6 +b7X7 +b8X8 + b9X9 + U
Where Y= Participation in processing (threshing, de-stoning, milling, winnowing).
a0 = Intercept X1 = Age (years)
X2 = Marital status (1= married, 0 = single) X3 = Household size (No of persons) X4 = Level of education (years) X5 = Processing experience (years)
X6 = Access to extension services (1 = Yes, 2 = No) X7 = Access to credit (1 = Yes, 2 = No)
X8 = Membership of association (1 = Yes, 2 = No) X9 = Access to market (1 =Yes, 2 = N0
U = Error term
b1-9 = Regression coefficients Gross Margin
This is the difference between the Enterprise Gross Revenue and Total Variable Cost.
GM = GR – TVC Where:
GM = Gross margin
GR =Gross return Total revenue generated TVC = Total Variable Cost incurred in the processing of acha
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Results and Discussion
Methods of Acha Processing and Type of Labour Employed
The result of the study conducted showed the women processed acha manually. Once dry, the plant is threshed to separate the grain from the stems. Threshing is traditionally done manually with sticks. The removal of the hulls from paddy acha by the women is by pounding the grains with a pestle in a mortar, and then winnow several times to separate the grain from the hulls. This is an indication that processors in the study area do not have access to modern equipment/ machines for processing. This probably can account for low productivity of the enterprise.
Similarly, Akinnagbe & Ayibiowu (2020); Awerije (2014); Cruz, Béavogui, Dramé, &
Diallo (2016); Gyang & Wuyep (2005); stated that manual de-hulling/threshing has low output, required a lot of physical energy, skill and experience to ensure that all grains are removed from the panicles with minimal spillage. Furthermore, the result showed majority (65%) used family labour while 35% employed hired labour for acha processing.
Table 1: Distribution of respondents according to method of acha processing and type of labour employed
Acha Processing Method Frequency Percentage
Manual 120 100
Mechanical
Total 120 100
Type of labour used*
Family 78 65.0
Hired 63 35.0
*Multiple response Source: field survey 2018
Sources of Fund for Acha Processing
The amount of credit available to women farmers from financial houses or organizations is either very small or not available. The annual income of most women is generally low and this could have significant impact on the participation of women in agricultural activities (Food and Agricultural Organization [FAO], 2007). The result of this study revealed 98% of the women source funds for their processing activities from their personal savings, while about 2% got their processing funds from friends. This implied that funds for acha processing is sourced through personal savings. That is, there is no credit scheme available for processing in the study area. This result revealed the processors lack access to credit and availability of credit is an important factor in agro-activities. Ekong (2010); Yusuf (2017) asserts that credit is a very strong factor that is needed to acquire or develop any enterprise;
its availability could determine the extent of production/processing capacity.
Table 2: Distribution of the respondents based on sources of funds for acha processing
Source of Funds Frequency Percentage (%)
Friends 2 1.7
personal savings 118 98.3
Total 120 100
Source: field survey 2018
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Factors Influencing Rural Women Participation in Acha Processing
The adjusted coefficient of determination (R2) square for the results is 0.57, while the F- ratio was 13.57. This implied that the independent variables in the model explained about 57% of variation in the dependent variable. The model was well fitted to the data since the F-ratio value (13.57) was highly significant (PP<0.01), the regression result was statistically reliable. Table 3 showed six variables significantly influenced participation of rural women in acha processing activities. Age (0.033) significantly influenced participation of women at p<0.05; this result implied that, the older processors are, the more they process acha. In a study by Khoza, Senyolo, Mmbengwa, Soundy, & Sinnett (2014) and Okolo et al. (2018), their results indicated the relationship between the age of the farmer and the decision to participate. Increasing age of the household head, up to a certain level is more likely to increase the level of participation. However, after the farmer reached a particular age, the level of participation is less likely to decrease.
Education was found to be significant to participation at 1% level of probability with a negative coefficient. The negative coefficient (-0.061) implied that as level of education increases, level of participation in acha processing decreases. This result conforms with earlier findings of Adesope, Nwakwasi, Matthews-Njoku, & Chikaire, (2010); Tologbonse, Jibrin, Auta, & Damaisa (2013), that the higher the educational level of the farmer, the higher the chances of getting better paying jobs or the higher the tendency to be involved in none agricultural activities. The coefficient of marital status was positive (0.612) and significant (p<0.05) suggesting that women‘s marital status influence their level of participation in acha processing. This is possible since family members can assist or support them in the processing activities. This result did not conform to earlier studies Akinwekomi et al. (2017); Chikezie, Chikaire, Osuagwu, Ihenacho, Ejiogu, & Oguegbuchulam (2012);
Itari, Anthony & Okeme (2015), whose result on marital status revealed that respondents who are single are more likely to engage in agribusiness.
Extension contact was positive (0.203) and significant at 10% level of probability. This implied that, the more informed the processors are, the more their participation in acha processing. Access to agro-processing-related training influence the extent of participation.
Similarly, Khatun & Roy (2012) also found the evidence of the positive effect of training by extension agents. Access to market coefficient was positive (0.133; p<0.01) which signifies;
the more the processors have access to ready market, the higher their participation in acha processing.
Table 3: Factors influencing rural women participation in acha processing
Variables Coefficient Standard error Significance Age of Processors 0.033 * 0.009 3.667 Marital status 0.612 * 0.211 2.900 Household size 0.009 NS 0.011 0.818 Level of education -0.061 * -0.061 * -3.813 Processing experience 0.013 NS 0.014 0.929 Extension contact 0.203 *** 0.107 1.897 Access to credit 0.223 NS 0.284 0.785 Cooperative membership 0.582 *** 0.500 1.164 Access to market 0.133 * 0.025 5.320
*, ** and *** implies significant at 1%, 5% and 10% respectively
NS = Not R2 = 0.61 Adjusted R2 = 0.57 F- ratio = 13.57
The Average Cost and Return of Acha Processing
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The average acha paddy bought by processors in the study area at a mean market price of N48, 750.00 contributed 86.35% of the total variable cost (TVC). The total cost (TC) of labour was N7, 500.00 contributing 13.29%, followed by the cost of transportation (N300.00) which contributed 0.53%. The gross revenue was N62, 000.00, while the total variable cost was N56, 450.00 on the average, with an average gross margin of N5, 550.00. The average gross margin per sampled respondent was found to be N46.25kobo while the average gross return per sampled respondent was N516.67kobo. The gross return per naira invested was N1.098 on the average. It means that putting all the variable costs of processing into consideration, acha processing is profitable in the area. This result concurred with the finding of Abdurrahman, Nasiru, Iliyasu, Ja‘afar, & Baidu (2015) that, the profitability index of acha was high in Bauchi State, suggesting that any effort to improve in the production would enhance economic capacity of the stakeholders.
Table 4: An average cost and returns of acha processing in the study area
Source: Field survey 2018
Constraints mitigating Acha Processing
All the respondents (100%) listed lack of machines as one of the problems of acha processing. This limits the ability of the processors to increase the quantity of acha.
Similarly, Umuhoza (2010) observed women generally participate in manual processing of coffee. Acha processors used their previous savings to finance acha processing which did not permit large scale processing. Lack of credit (97.5%) ranked next to lack of modern machines indicated as one of the challenges the respondents encountered. This is in agreement with what Chikwendu and Adekoya (2008); Onuwa, Obisesan, Mailumo &
Okeke-Agulu (2013); Abdurrahman et al. (2015) reported that, women‘s lack of land ownership right has hindered their access to bank loans and other forms of resources.
The high cost of labour for processing acha (90%) was ranked third. Due to the tedious nature of acha processing, hired labour is expensive because only those who are able, fit and are willing to participate in the processing. This result is in consonant with finding of Soniia (2015), compared to men, women had shortage of funds to invest in agriculture generally.
The marketing system in the study area is not standardized, instability of agricultural product prices all contributed to the challenges encountered by the processors. This is confirmed by Oyewole (2017) that rural women are faced with constraints in their processing activities.
Table 5: Distribution of the respondents according to challenges encountered
Items Amount (N) / kg % of total cost
Gross revenue 62,000 Variable cost
Cost of paddy acha 48,750.00 86.35 Cost of processing 7,500.00 13.29 Cost of transportation 300.00 0.53 Total variable cost 56,450.00
Gross margin 5,550.00
Average gross margin per respondent 46.25
Average gross return per respondent 516.67
Returns on naira invested 1.098
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Challenges of acha processing Frequency Percentage Lack of modern machines 120 100
Lack of Credit Facility 117 97.50 High Cost of Labour 108 90.00 Lack of information 93 77.50 Poor marketing system/price 78 65.00 *Multiple response
Source: field survey 2018
Conclusion and recommendations
The analysis of rural women participation in acha processing, revealed age, education and marital status have significance influence on participation. Acha processing was found to be profitable as revealed by this study, therefore it has the potential of increasing processors income and providing enough food for the increasing population of people in Africa.
However, there are inadequate, inefficient machines/equipment for processing of acha. This has negatively affected the production and processing of the commodity.
Thus, it is recommended government and well-meaning Nigerians should assist by providing modern machines for acha processing in order to reduce drudgery and encourage processing.
Low interest rate credit facility should be made accessible to acha processors to enable processors to expand their business. More research should be conducted on acha processing and latest findings be communicated to the rural acha processors. Efficient and effective marketing system that will ensure stability of agricultural product prices be established to reduce losses incurred by the processors.
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