Profit efficiency analysis of red onions production in Sironko district of Uganda

Abstract

This study assessed the profit efficiency of red onions smallholder farmers in Sironko district of eastern Uganda. A structured questionnaire was employed to collect data from 216 randomly selected red onion producers. Data analysis was done using descriptive statistics, gross margin, and the Cobb-Douglas type of stochastic profit frontier function model. Results showed that red creole and Afri seed were the only varieties of red onions that were grown in the study area. Onion production was characterized by limited access to credit, extension services, and farmers’ group membership. The gross margin (GM) and returns on investment (ROI) analyses revealed that red onion production was profitable in Sironko district. An average of UGX 884,500 (~USD 241) for Afri seed producers and UGX 1,724,414 (~USD 469) for red creole were obtained per acre of land. Although the farmers made profits, their profitability could be improved by up to 19% through harnessing factors that influence profit efficiency. These factors were cost of seeds (p < 0.01), cost of fertilizer (p < 0.1), cost of pest control (p < 0.05), cost of weeding (p < 0.1), farming experience (p < 0.1), extension services (p < 0.1) and selling price (p < 0.1). The study recommends that to improve the profit efficiency in onion production, the focus should be on the provision of tailored agricultural extension services and affordable credit facilities to the farmers. Further, appropriate policy instruments and agri-business strategies such as agricultural subsidies and price support schemes are needed to spur onion profitability.

Keywords:

• profit efficiency
• stochastic frontier approach
• red onions
• Uganda

PUBLIC INTEREST STATEMENT

Red onion is one of the food ingredients mostly consumed all over the world. In Uganda, red onion is produced under rainfed or irrigated and is the most traded in the local markets and consumed for medicinal purposes or as a food spice in households, restaurants, hotels, and schools among other places. Despite its incredible importance in the country, the productivity of red onion is still low compared to the world’s average production, hence low profit for farmers. This may be associated with the fact farmers have limited access to skills related to modern agricultural practices, and limited storage facilities, among other challenges. The findings from this study will inform producers, the government, and other stakeholders on what should be done to address this issue of low production.

1. Introduction

Onions (Allium Cepa L.) are cool-season biennial crops belonging to the family, Liliaceae (Boukary et al., 2012). It is the most consumed food ingredient globally, making it a crop with unique market opportunities in agribusiness (Ren et al., 2020). This vegetable typically cultivated throughout the world as an annual crop is said to have originated in Southwest Asia (Jo et al., 2020)., With a global production of two million metric tonnes, onions are a commercially significant crop on all continents and are fourth among the most consumed vegetables after tomato, cabbage, and watermelon (Ddamulira et al., 2019). China is recognized as the major producer of onions around the world with an annual production of 22.3 million metric tons (MT). In China, the good quality and affordable prices of onions produced make them competitive on the international market (Joachim, 2018). Other top onions producers in the world are India, USA, Iran, Egypt, Turkey, Brazil, Pakistan, the Republic of Korea, and the Russian Federation (Pareek et al., 2017). In terms of productivity, the Republic of Korea comes on the top with 66.16 metric tons per hectare followed by USA (56.26 metric tons per hectare), Spain (53.31 metric tons per hectare), and Netherlands (51.64 metric tons per hectare). Based on their colors, onions are classified into red onions, white onions, and yellow onions, with red onions being the most predominant and highly consumed (Nile et al., 2021). Onions can be consumed as raw as many other vegetable species. As a spice, they add flavor to meals and also carry some restorative components important for health. The crop is rich in carbohydrates, proteins, water-soluble vitamins, calcium, iron, and other nutrients (Li et al., 2020). Onions are also produced for medicinal purposes as they contain health benefits like anticancer properties, antiplatelet activity, antithrombotic activity, antiasthmatic activity, and antibiotic effects (Liubov, 2019).

In 2013, onion production in the East African Community (EAC) was estimated at 497,843 metric tons (Kilimo, 2017). Statistics showed that in 2020, there was an increase of 50% in onion consumption in EAC since 2013 and the consumption was estimated to be 743,795 metric tons (Kilimo, 2017; USAID, 2021). The trade of onions in EAC is relatively low. However, onions are the most traded compared to other horticultural crops such as tomatoes due to the fact that onions can be easily transported over long distances and have a long shelf-life. Uganda comes first in onions production followed by Tanzania while Kenya and Rwanda’s production is even below consumption (Barnett et al., 2011) and contributes 47% of onions produced and 41% of onions consumed in East Africa (Nyangau et al., 2022). Different varieties including Jamba, Red Creole, Red cornet, Red Bombay, Afri seed, Red Pinoy, Red snack Pearl, Red passion, and Texas Grano are commonly grown mainly in the district of Rakai, Kigezi, Mbale, Sironko, Tororo, and Mbarara, and onions production in Uganda occupies 3,700 hectares (ha) with an estimated yield of 147,000 metric tons per hectare (Bua et al., 2017; Mohammed, 2022; Okiror, J. F., & Muchunguzi, P. 2019).

In Uganda, onions are grown as a major source of income and are among the top vegetables mostly consumed in Uganda (Mugizi & Matsumoto, 2021). The nation contributes 47% of the total onions produced and 41% of the total onions consumed in East Africa (Nyangau et al., 2022). However, there is limited access to information on the commercial production of onions, and this limits farmers’ access to technologies needed to increase production. The other challenge faced by most onion farmers includes a shortage of post-harvest handling practices and a decrease in onion yield due to pests such as thrips in case of late application of pesticides and/or the use of resistant varieties (Ddamulira et al., 2019).

Despite its socio-economic importance to the country, onion production remains low in Uganda at just five metric tons per hectare. This is only 20% of the world average of 19.7 tons per hectare (Ddamulira et al., 2019). Thus, the level of domestic production has failed to meet the increasing domestic and regional demand (Tumuhe et al., 2020); a fact that has resulted in remarkable volumes of onions being imported from Tanzania to fill up the supply gap (Dijkxhoorn et al., 2019). Official records showed that Uganda imported up to 18,000 metric tons of onions in 2018 (Dijkxhoorn et al., 2019). This volume of imported onions is very high despite Uganda having very favorable climate conditions for onion production. This is not helped by the fact that poor storage facilities in the country contribute immensely to onion post-harvest losses, especially during rainy seasons. This pushes onion farmers to sell their produce at giveaway prices, thus explaining the minimum return on onion production which have a negative effect on the profit efficiency of onion farmers (Dijkxhoorn et al., 2019).

Bakhshoodeh and Shahnushi (2009) concluded that technological advancement, intended to increase smallholder farmers’ efficiency, would be profitable only if farmers are sufficiently efficient in allocating their available resources. However, it remains unclear how the uptake of modern technologies has improved the productivity and profit efficiency of this crop. In light of this, it was, therefore imperative to explore the profit efficiency of onion production. This study specifically characterized red onions farmers, determined the profitability of red onion production, and ascertained the factors that influence the profit efficiency of red onions production in Sironko district, one of the high potential production areas located in eastern Uganda.

2. Materials and methods

2.1. Study area

This study was conducted in Sironko district in eastern Uganda (Figure 1). This district was purposively selected given the fact that it is one of the leading districts in red onion production in Uganda (Bua et al., 2017). Sironko main town is located at 34°15‘E and 01°14‘N. The district has a total area of 421 km² and lies between 1,299 metres and 1,524 metres above sea level. It is a highland area with steep slopes, intensely cropped hillsides, and high population densities. The region has experienced outmigration for many years due to the scarcity of land. The average annual precipitation is about 1500 mm. The temperature variability in the area is influenced by altitudinal differences, with the average maximum temperature of 27 − 32°C, and minimum temperature of 15 − 17°C, with an annual average temperature of 21.5°C. The beautiful green scenery has woodlots in the valleys, meandering rivers, streams, escarpments, and waterfalls over the cliffs. Over 80% of the green beauty is crop vegetation, bananas, coffee and seasonal crops, maize, onions, beans, and horticultural crops. Most people in Sironko district are mainly peasants living on small farm holdings. Hand tools such as machetes, hoes, and axes, play major roles in opening up fields for cultivation. Due to the hilly terrain and the fragmented nature of the plots, mechanization may not be optimal in the area

Figure 1. Map of Sironko district, eastern Uganda showing the various sub-counties.

2.2. Sampling procedure and data collection

A cross-sectional survey design was employed for this study. This design was adopted because it is cost-effective, time-saving, good for establishing conclusions across the whole population, and does not require follow-up (Jeong, 2021). A multistage sampling procedure was utilized in the selection of a representative sample using a list of red onions farmers that were obtained from the district agricultural production officer. In the first stage, Sironko district was purposively selected based on its predominance in onion farming. In the second stage, four sub-counties (Namaguli, Namugabwe, Masaba, and Zesui) were purposively selected based on their relative concentration of onion farmers. In the final stage, simple random sampling was used to select 54 onion farmers from each sub-county to give a total of 216 onion farmers that were interviewed. Primary data were collected using a structured questionnaire that was administered to selected red onion farmers in the study area. A pretest was done in Mbale district prior to the actual data collection to test for the validity and reliability of the data collection instruments. Relevant modifications were made to the questionnaire to ensure that all required data for the study is captured. Enumerators were trained on how to administer the questionnaire to farmers. The questionnaire comprised two main sections: The first section collected data on the socio-demographic, institutional, and production characteristics of the respondents, while the second section collected data on the profitability of onions farmers and factors that influence the profit efficiency of onions production in the study area. The inclusion criteria were that farmers produced red onions in at least one of the two production seasons (rain season and dry season) of the year 2021. The sample size n was calculated using Yamane’s simplified formula expressed as in equation (1)(1) $\mathrm{n}=\frac{\mathrm{N}}{1+\mathrm{N}{\mathrm{e}}^2}$(1) :

(1) $\mathrm{n}=\frac{\mathrm{N}}{1+\mathrm{N}{\mathrm{e}}^2}$(1)

Where n= sample size, N= total size of onion farmers, and e = error of precision

The target population in Sironko district is 5000 (Source: Farmer profiling done by HORTIMAP Project) and the error of precision is at e = 7%,

$\mathrm{n}=\frac{5000}{1+5000\left(0.07\ast 0.07\right)}=196.07\sim 196$

Ten percent (10%) of the 196 respondents which is 20 was added to cater for the non-response and this resulted in a total adjusted sample of 216 respondents.

2.3. Data analysis

The collected data were edited for any anomalies during data capture and entered into SPSS v25 and STATA 14 for analysis. Prior to analysis, the data were cleaned for errors in entry. The data were then summarized, tabulated, and analyzed to achieve the objectives of this study. The results were presented in the form of tables and inference was drawn from the study. Descriptive statistics were employed to characterize the red onions farmers in the study area. An independent sample t-test was used to compare numerical farmers’ characteristics by onion variety cultivated, while, the categorical chi-square test of association was used to test the association between onion varieties cultivated and categorical farmers’ characteristics.

Gross margin analysis was employed for the second objective to determine the profitability of onion production in the study areas. Gross margin (GM) analysis is a method of computing the profitability of small-scale enterprises. It is a useful planning tool in situations where fixed capital is a negligible portion of the farming enterprises in the case of small-scale production systems (Gindi et al., 2019). Profitability describes the financial benefits realized when the revenue generated from a business activity exceeds the expenses as seen in equation (2)(2) $G{M}_{ij}=\mathrm{T}{\mathrm{R}}_{\mathrm{i}\mathrm{j}}-\mathrm{T}\mathrm{V}{\mathrm{C}}_{\mathrm{i}\mathrm{j}}\hspace{0.17em}$(2) . It is calculated as total revenue minus total expenses. Mathematically, the GM employed in this study is expressed as in equation (2)(2) $G{M}_{ij}=\mathrm{T}{\mathrm{R}}_{\mathrm{i}\mathrm{j}}-\mathrm{T}\mathrm{V}{\mathrm{C}}_{\mathrm{i}\mathrm{j}}\hspace{0.17em}$(2) :

(2) $G{M}_{ij}=\mathrm{T}{\mathrm{R}}_{\mathrm{i}\mathrm{j}}-\mathrm{T}\mathrm{V}{\mathrm{C}}_{\mathrm{i}\mathrm{j}}\hspace{0.17em}$(2)

Where $ij$ is to the j^th observation of the i^th farmer, GM is the gross margin in Uganda shillings, TR is the total revenue generated from the sales of onions (in Ugandan shillings) estimated following equation (3)(3) $\mathrm{T}\mathrm{R}={\sum }^{\mathrm{P}\mathrm{Q}}$(3) and TVC is the total variable cost in Ugandan shillings estimated following equation (4)(4) $\mathrm{T}\mathrm{V}{\mathrm{C}}_i={\mathrm{W}}_{\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}}{\mathrm{Y}}_{\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}}+{\mathrm{W}}_{\mathrm{s}\mathrm{e}\mathrm{e}\mathrm{d}}{\mathrm{Y}}_{\mathrm{s}\mathrm{e}\mathrm{e}\mathrm{d}\mathrm{i}}+{\mathrm{W}}_{\mathrm{p}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{d}\mathrm{e}}{\mathrm{Y}}_{\mathrm{p}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{d}\mathrm{e}\mathrm{i}}+\dots +{\mathrm{W}}_{\mathrm{n}}{\mathrm{Y}}_{\mathrm{n}}$(4) .

(3) $\mathrm{T}\mathrm{R}={\sum }^{\mathrm{P}\mathrm{Q}}$(3)

Where: P is the price per kilogram of onion, and Q is the quantity of onions sold (in kilograms).

(4) $\mathrm{T}\mathrm{V}{\mathrm{C}}_i={\mathrm{W}}_{\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}}{\mathrm{Y}}_{\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}}+{\mathrm{W}}_{\mathrm{s}\mathrm{e}\mathrm{e}\mathrm{d}}{\mathrm{Y}}_{\mathrm{s}\mathrm{e}\mathrm{e}\mathrm{d}\mathrm{i}}+{\mathrm{W}}_{\mathrm{p}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{d}\mathrm{e}}{\mathrm{Y}}_{\mathrm{p}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{d}\mathrm{e}\mathrm{i}}+\dots +{\mathrm{W}}_{\mathrm{n}}{\mathrm{Y}}_{\mathrm{n}}$(4)

Where W is the price of the variable inputs and Y is the quantity of variable inputs.

The Return on Investment (ROI) was calculated to estimate the level of profitability (Ojiakor et al., 2018). The higher the ROI, the higher the profitability. ROI is expressed as in equation (5)(5) $\mathrm{R}\mathrm{O}\mathrm{I}=\hspace{0.17em}\frac{\mathrm{G}\mathrm{M}}{\mathrm{T}\mathrm{V}\mathrm{C}}$(5) :

(5) $\mathrm{R}\mathrm{O}\mathrm{I}=\hspace{0.17em}\frac{\mathrm{G}\mathrm{M}}{\mathrm{T}\mathrm{V}\mathrm{C}}$(5)

Where GM and TVC are as stated in equation (2)(2) $G{M}_{ij}=\mathrm{T}{\mathrm{R}}_{\mathrm{i}\mathrm{j}}-\mathrm{T}\mathrm{V}{\mathrm{C}}_{\mathrm{i}\mathrm{j}}\hspace{0.17em}$(2) .

Stochastic Profit Frontier (SPF) was used for objective 3 which was to determine the factors that affect the profit efficiency of onion production in the study area. It specifically measured the relationship between the GM of onions production and the factors of production used (Ogunniyi, 2011). Earlier studies determined technical/profit efficiency using a deterministic production function with parameters computed using mathematical programming techniques. However, there were shortcomings in this approach such as the statistical inference of the parameters and resulting efficiency estimates and the inadequate characteristics of the assumed error term (Ngombe, 2017). To overcome that deficiency, the Stochastic frontier production function (parametric approach) was developed thereafter to estimate efficiency (Ogundari & Ojo, 2006). “The frontier production function model is estimated using maximum likelihood procedures. This is because it is considered to be asymptotically more efficient than the corrected ordinary least square estimators” (Muhammad Lawal et al., 2009). The Cobb-Douglas (CD) function was employed to estimate the contributing factors to the GM from onion production and all the inefficiency factors used in the model had reasonably low correlations. The model is as specified in equation (6)(6) ${\mathrm{Y}}_{\mathrm{i}}={\mathrm{A}\mathrm{X}}_1^{{\mathrm{b}}_1}{\mathrm{X}}_2^{{\mathrm{b}}_2}\dots \dots \dots {\mathrm{X}}_{\mathrm{n}}^{{\mathrm{b}}_{\mathrm{n}}}{\mathrm{e}}^{{\mathrm{u}}_{\mathrm{i}}}$(6) :

(6) ${\mathrm{Y}}_{\mathrm{i}}={\mathrm{A}\mathrm{X}}_1^{{\mathrm{b}}_1}{\mathrm{X}}_2^{{\mathrm{b}}_2}\dots \dots \dots {\mathrm{X}}_{\mathrm{n}}^{{\mathrm{b}}_{\mathrm{n}}}{\mathrm{e}}^{{\mathrm{u}}_{\mathrm{i}}}$(6)

The profit function was linearized by transforming it into the following logarithmic form as shown in equation (7)(7) $log\mathrm{Y}=log\mathrm{a}+{\mathrm{b}}_{1}log{\mathrm{X}}_1+{\mathrm{b}}_{2}log{\mathrm{X}}_2+{\mathrm{b}}_3\mathrm{l}\mathrm{o}\mathrm{g}{\mathrm{X}}_3+{\mathrm{b}}_4\mathrm{l}\mathrm{o}\mathrm{g}{\mathrm{X}}_4+{\mathrm{b}}_{5}log{\mathrm{X}}_5+\mathrm{e}{\mathrm{v}}_{\mathrm{i}}-{\mathrm{u}}_{\mathrm{i}}$(7)

(7) $log\mathrm{Y}=log\mathrm{a}+{\mathrm{b}}_{1}log{\mathrm{X}}_1+{\mathrm{b}}_{2}log{\mathrm{X}}_2+{\mathrm{b}}_3\mathrm{l}\mathrm{o}\mathrm{g}{\mathrm{X}}_3+{\mathrm{b}}_4\mathrm{l}\mathrm{o}\mathrm{g}{\mathrm{X}}_4+{\mathrm{b}}_{5}log{\mathrm{X}}_5+\mathrm{e}{\mathrm{v}}_{\mathrm{i}}-{\mathrm{u}}_{\mathrm{i}}$(7)

Where Y is the unit profits from onion production (UGX/acre), X₁ is the Land size (Fixed Input), Y is the unit profit from onion production (UGX/acre), X₂ is the unit cost of seedling (UGX/kg), X₃ is the unit cost of fertilizers (UGX/kg), X₄ is the unit pest control cost (UGX/Man-day), and X₅ is Unit weeding cost (UGX/Man-day), A is the intercept, ${\beta }_1\to {\beta }_5$ are Coefficients of the respective variables to be assessed, U_i is the farm characteristics related to production efficiency and v_i is the disturbance term.

The profit inefficiency is calculated by using the inefficiency effects model specified in equation (8)(8) ${\mathrm{U}}_{\mathrm{i}}=\hspace{0.17em}{\delta }_0{\sum }_{\mathrm{h}=1}^{\mathrm{n}}{\delta }_{\mathrm{h}}{\mathrm{X}}_{\mathrm{h}\mathrm{i}}$(8) .

(8) ${\mathrm{U}}_{\mathrm{i}}=\hspace{0.17em}{\delta }_0{\sum }_{\mathrm{h}=1}^{\mathrm{n}}{\delta }_{\mathrm{h}}{\mathrm{X}}_{\mathrm{h}\mathrm{i}}$(8)

Where, $\delta { }_0$ represents the constant term, δ_h represents the parameters to be estimated, U_i represents the profit inefficiency and X_hi represents the vector of hth independent variable of the ith farm including farming experience (years), household size(number), Education level(years), Farm size(acres), Marital status (Single, married, widow, widower), Gender (Male or female), access to credit, access to extension service, farmers’ group membership, land acquisition(freehold, family land, community land, hired land, or personal land), methods used in onions production (direct sowing, transplanting or both), participation in non-farming activities, and selling place (farmgate or market place). The measurement and a priori expectations of these variables are presented in Table 1.

Table 1. Description of variables used in profit efficiency analysis

Variables	Units of Measurement	A priori expectation	Citation
Household characteristics
Age	Years	+/-	(Teklewold et al., 2006)
Gender	Male=1 and Female=2	+	(Chikezie et al., 2012)
Farmers’ education level	Years	+/-	(Mdoda et al., 2022; Tadesse et al., 2021)
Farming Experience	Years	+	(Tasila Konja et al., 2019)
Household size	Number of persons	+	(Berhan, 2015)
Marital status	Single, married, widow, widower	+/-	(Khan, 2015)
Level of income	UGX	+	(Haque et al., 2011)
Production and market variables
Farm size	Acres	+/-	(Abdulkadir, 2015; BAREE, 2012)
Tenancy	%	+	(Tanko et al., 2012)
Capital	UGX	+	(Nigatu et al., 2019)
Price of Organic manure, urea, MP, TSP	UGX/Kg	+/-	(Nigatu et al., 2019)
Human labor	UGX/Man-day	+	(Adeoye, 2016)
Price of Seedling	UGX/quantity	+/-	(Ddamulira et al., 2019)
Price of Weedicide	UGX/container	+/-	(Adeoye, 2016; Gindi et al., 2019)
Pesticides	UGX/container	+/-	(C. A. Wongnaa & Mensah, 2018)
Quantity of output	Kg/acre	+	(Fentie, 2012; Van Vliet et al., 2013)
Quantity of output sold	Kg	+/-	(Van Vliet et al., 2013)
Total cost of production	UGX	+/-	(Dalchiavon et al., 2019)
Total revenue for produce	UGX	+/-	(Dijkxhoorn et al., 2019; Pareek et al., 2017)
Total Profit	UGX	+	(C. Wongnaa et al., 2019)

3. Results

3.1. Socio-economics characteristics of red onions farmers

The findings in Table 2 present a comparison of onion farmers’ characteristics by onion variety (Afri-seed and Red Creole) grown. Results showed that 18% of the farmers cultivated Afri seed while the rest were engaged in red creole production. Overall, the mean age of the onion farmers was 39 years old. The age did not vary across the Afri seed producers and red creole producers. The red onion farmers had an overall mean farming experience of 15 years with red creole producers being significantly more experienced than Afri seed producers. Overall, the average household size was seven individuals with Afri seed producers having significantly more household members than red creole producers. The average overall education level was about 7 years which indicates that most of the farmers at least completed primary school with Afri seed producers having significantly slightly lower education levels than red creole producers. This implies that red onions farmers in the study area can read, write, easily adopt new agricultural techniques, and keep a record of their farming activities.

Table 2. Comparing farmers’ characteristics based on the variety of onions grown (N = 216)

		Overall (N=216)	Afri seed (n=39)	Red creole (n=177)	t-stat	p-value
Numerical Variables		Mean (SD)
Age (years)		39.36(12.73)	41.92(12.02)	38.79(12.84)	1.394	1.165
Farming experience (years)		15.14(11.19)	11.92(7.08)	15.85(11.80)	−1.997**	0.047
Household size		6.59(3.62)	7.64(3.06)	6.36(3.70)	2.024**	0.044
Education level (years)		7(3.23)	6.05(3.28)	7.27(3.18)	−2.146**	0.033
Owned size of landholding (acres)		2.08(1.84)	1.30(1.04)	2.25(1.93)	−2.976***	0.003
Rented size of landholding (acres)		0.14(0.33)	0.33(0.49)	0.80(0.31)	4.922***	0.000
Total farmland owned (acres)		1.48(1.50)	1(0.93)	1.59(1.58)	−2.238	0.260
Total farmland rented (acres)		0.12(0.30)	0.29(0.41)	0.10(0.22)	3.171***	0.002
Land size under onion production (acres)		0.91(0.65)	1.26(0.69)	0.84(0.62)	3.794***	0.000
Categorical variables	Category	Frequency (%)			χ2	p-value
Gender	Male	158(73.1)	29(74.40)	129(72.9)	0.360	0.851
	Female	58(26.9)	10(25.60)	48(27.10)
Marital status	Married	197(91.20)	36(92.30)	161(91.00)	0.720	0.788
	Otherwise	19(8.80)	3(7.70)	16(9.00)
Access to credit	Yes	47(21.80)	23(59.00)	24(13.60)	38.717***	0.000
	No	169(78.20)	16(41.00)	153(86.40)
Access to extension service	Yes	27(12.50)	0(0.00)	27(15.30)	6.799***	0.009
	No	189(87.50)	39(100.00)	150(84.70)
Belong to farmers’ associations	Yes	44(20.40)	17(43.60)	27(15.30)	15.819***	0.000
	No	172(79.60)	22(56.40)	150(84.70)
Acquisition of land	Family land	101(46.80)	30(76.90)	71(40.10)	17.394***	0.000
	Hired land	64(29.60)	5(12.80)	59(33.30)
	Personal land	51(23.60)	4(10.30)	47(26.60)
Sources of labour	Family labour	10(4.60)	2(5.10)	8(4.50)	144.484***	0.000
	Hired labour	167(77.30)	4(10.30)	163(92.10)
	Community labour	39(18.10)	33(84.60)	6(3.40)
Decision on labour charges	Fixed amount	188(87.00)	37(94.90)	151(85.30)	2.589*	0.108
	By bargaining	28(13.00)	2(5.10)	26(14.70)
Method used in onion farming	By direct sowing	5(2.30)	0(0.00)	5(2.80)	166.409***	0.000
	By transplanting	170(78.70)	3(7.70)	167(94.40)
	Both	41(19.00)	36(92.30)	5(2.80)
Sources of onions seeds	Buying from market	173(80.10)	34(87.20)	139(78.50)	3.587	0.166
	Buying from fellow farmers	2(0.90)	1(2.60)	1(0.60)
	Retail shops	41(19.00)	4(10.30)	37(20.90)
Categorical variables	Category	Frequency (%)			χ2	p-value
Specialized only in onions	Yes	35(16.20)	24(61.50)	11(6.20)	72.039***	0.000
	No	181(83.80)	15(38.50)	166(93.80)
Involved in non-farm activities	Yes	119(55.10)	24(61.50)	95(53.70)	0.799	0.371
	No	97(44.90)	15(38.50)	82(46.30)

Note: For continuous variables, the means are presented with the standard deviation in brackets, while for categorical variables, the frequencies are presented with percentages in brackets

Source: Survey data & Author‘s calculation, 2022. *Significant at 10% probability level. **Significant at 5% probability level. ***Significant at 1% probability level

The mean of owned land size of red onions farmers in this study was found to be 2.08 acres with Afri seed producers owning significantly fewer acres of land than red creole producers. On average, red onion farmers had about 0.14 acres of rented land with red creole producers having statistically significant (p < 0.01) more rented land than Afri seed producers. Overall average, the rented land size under agricultural production and total land size under onion production were 0.12 acres and 0.91 acres respectively. Afri seed producers were found to have statistically significant (p < 0.01) more rented land sizes under agricultural production than red creole producers. Similarly, Afri seed producers had significantly (p < 0.01) more land under onion production than red creole producers (Table 2).

The findings indicated that about 73.1% and 91.2% of red onion producers were males and married respectively, with no significant association between gender or marital status and the two onion varieties grown. The study findings also showed that the majority (78.2%) of onion producers did not have access to credit with a significant (p < 0.01) association between access to credit and the two onion varieties grown. Only 13% of the onion farmers had access to agricultural extension services with a significant (p < 0.01) association between access to agricultural extension services and the onion varieties. About 20.4% of the respondents were only involved in the farmers’ group with significantly (p < 0.01) more Afri seed farmers belonging to the farmers’ group than Afri seed farmers (Table 2).

Over 47% of the onion farmers produced red onions using family land, 30% were using hired land and 24% used personal land with a statistically significant (p < 0.01) association between land acquisition and the varieties grown. Results also showed that the majority (77%) of the onions farmers relied on hired labour, 5% used family labour, and 18% used community labour in red onion production with a significant (p < 0.01) association between sources of labour and the varieties grown. About 87% of the onion farmers paid labour with a fixed amount per day, while 13% bargained on the labour charges with a significant (p < 0.1) association between the decision on labour charges and the two varieties produced (Table 2).

Results also indicated that 2%, 79%, and 19% of the onions farmers produced onions using the method of direct sowing, transplanting, and/or both methods (direct sowing and transplanting) at once respectively, with significantly (p < 0.01) association between methods used in onions production and varieties grown. Ninety-four percent of red creole producers employed the transplanting method in red onions production while 92% of Afri seed producers employed both methods. About 80%, 1%, and 90% purchased onion seeds from the market, from fellow farmers, and from retail shops, respectively, with no significant association between the sources of onion seeds and red onion varieties grown. Results also show that 16% of the red onions farmers specialized only in onions production with significantly (p < 0.01) more Afri seeds producers specializing only in onions than red creole producers. About 55% of the red onions farmers were also involved in non-farm income-generating activities with no significant association between participation in non-farm income-generating activities and the varieties grown.

3.2. Production costs and returns on red onions production

Table 3 presents the summary statistics of costs incurred and returns to onion production for the two varieties cultivated. Revenue and costs were evaluated during the survey based on the prices which were prevailing in the previous production seasons of 2021. Results indicated that the overall mean cost of red onions seeds was UGX 157,546 (~USD 43) with the cost of seeds being on average significantly (p < 0.01) higher than red creole seeds’ cost. Overall, the fertilizer cost was found to be the highest in red onions production in the study area with an average cost of UGX 487,646 (~USD 133) with no significant differences in the cost of fertilizers for Afri seed producers and red creole producers. The mean cost of pesticides was UGX 32,829 (~USD 9) with significantly (p < 0.1) Afri seeds producers spending more money on pesticides than red creole producers. Red onions farmers spent an overall average of UGX 10,667 (USD 3) on drying and storage labour charges with, Afri seeds farmers significantly (p < 0.05) used up using the least amount on labour for drying and storage than red creole producers.

Table 3. Costs and returns (average values) of red onions production

Variables	Mean (SD)			t value	p-value
	Overall (N=216)	Afri seed (n=39)	Red creole (n=177)
Cost of seeds (UGX)	157,546(110,046)	259,102(90,640)	135,169(101,148)	7.051***	0.000
Cost of fertilizers (UGX)	487,646(407,893)	502,654(435,813)	484,339(402,710)	0.253	0.800
Cost of pesticides (UGX)	32,829(62,138)	48,756(95,113)	29,319(51,912)	1.777*	0.077
Cost of herbicides (UGX)	7,273(14,666)	11,538(18,875)	6,333(13,450)	2.021**	0.045
Cost of labour for drying/storing (UGX)	10,667(29,580)	615(3,843)	12,881(32,324)	−2.369**	0.019
Cost of nursery bed preparation and management (UGX)	31,852(26,505)	32,692(23,640)	31,667(27,154)	0.218	0.827
Cost ploughing (UGX)	85,704(77,265)	59,128(41,350)	91,559(82,053)	−2.399**	0.017
Cost of transplanting (UGX)	77,560(52,755)	81,744(59,861)	76,638(51,200)	0.546	0.586
Cost of weeding (UGX)	114,502(105,082)	86,359(100,595)	120,703(105,309)	−1.858*	0.065
Cost of pest control (UGX)	8,611(7,820)	7,077(9,988)	8,949(7,247)	−1.356	0.176
Cost of harvesting (UGX)	53,903(74,966)	87,385(149,249)	46,525(41,931)	3.143***	0.002
Cost of transportation from farm to home (UGX)	53,375(99,481)	100,628(219,021)	42,963(33,592)	3.354***	0.001
Marketing costs (UGX)	22,219(74,004)	30,000(57,807)	20,504(77,151)	0.725	0.469
Quantity sold (Kg)	1,552(1,072)	1,278(924)	1,613(1,095)	−1.774*	0.078
Selling price (UGX/Kg)	1,773(328)	1,746(345)	1,779(325)	−0.558	0.578
Total cost (UGX)	1,143,686(718,660)	1,307,679(967,574)	1,107551(649,239)	0.078	0.116
Total revenue (UGX)	2,716,449(1,872,653)	2,192,179(1,583,084)	2,831,966(1,915,328)	−1.944*	0.053
Gross Margin (UGX)	1,572,763(1,538,854)	884,500(1,190,221)	1,724,414(1,577,629)	−3.149***	0.002
ROI	1.50(1.44)	0.67(1.10)	1.56(1.46)	−3.323***	0.001

Source: Survey data & Author‘s calculation, 2022. *Significant at 10% probability level. **Significant at 5% probability level. ***Significant at 1% probability level

The results also showed that the overall mean cost of nursery bed preparation and management was UGX 31.852 (~USD 9) and the cost of nursery bed preparation and management did not vary across Afri seed producers and red creole producers. The overall average cost of ploughing was UGX 85,704 (~ USD 23) and Afri seeds producers had significantly (p < 0.05) spent the least amount on the cost of ploughing than red creole producers. The total cost of transplanting was UGX 77,650 (~USD 21) with no significant difference in the cost of transplanting to the Afri seeds producers and red creole producers. The red onions farmers spent on average UGX 114,502 (~USD 39) on weeding with Afri seed producers spending significantly (p < 0.1) the least amount on weeding than red creole producers. The findings also revealed that farmers spent on average UGX 8,611 (~USD 2) on pest control with no significant difference in the cost of pest control between Afri seed producers and red creole producers. The overall mean cost of harvesting was UGX 53,903 (~USD 15) with Afri seeds producers having significantly (p < 0.01) spent a higher amount than red creole. Overall average, the cost of transportation of red onions from the farm to home was UGX 53,375 (~USD 15), and the Afri seed producers significantly (p < 0.01) spent the least amount than red creole producers. The overall mean marketing costs were UGX 22,219 (~USD 6), and marketing cost was the sum of the costs of packing, packaging material, tax, transport, loading and offloading, sorting, and grading. Marketing costs did not vary across the Afri seed producers and red creole producers in the study area.

The overall average quantity of red onions sold was 1,552 kg with Afri seed producers selling significantly (p < 0.1) the least quantity of red onions than red creole producers. On average a kilogram of red onions was sold at UGX 1,777 (~USD 0.5) with no significant differences in the selling price between the two varieties. Similarly, the total cost of red onions production amounted to UGX 1,143,686 (~USD 311), and the total cost did not vary across the Afri seeds producers and red creole producers. Results indicated also that the total revenue in red onion production was UGX 2,716,449 (~USD 739) with Afri seed production having significantly (p < 0.1) generated the least revenue than red creole production. On average, red onions production generated a profit of UGX 1,572,763 (~USD 428). There was a statistically significant difference in the gross margins of the two onion varieties. The gross margin (GM) obtained was significantly (p < 0.01) lower for Afri seed production than red creole production. Overall, results implied that onion production was profitable with an overall average return on investment (ROI) of 1.50 with Afri seed production having significantly (p < 0.01) lower ROI than red creole production. This is in agreement with the findings of Dossa et al. (2018) who reported a positive net operating income for onion production in Benin implying that onion production is a profitable enterprise.

3.3. Stochastic frontier analysis of onion production profit efficiency

Factors that affect onions production were determined using an estimated stochastic profit frontier and are presented in Table 4. These results are for respondents who got a profit greater than one Ugandan shilling. Results show that most explanatory variables have an impact on the profit efficiency of onions production. The cost of seeds positively significantly (p < 0.01) influences the profit efficiency of red onions production and shows that a unit increase in the cost of seed fertilizers is more likely to lead to an increase in profit efficiency in red onions production. Similarly, the cost of pest control and weeding were significant (p < 0.05), and (p < 0.01) respectively which indicate that a rise in one Ugandan shilling in these inputs was associated with an increase in the profit efficiency of red onion production.

Table 4. Maximum likelihood estimates of the stochastic frontier profit function (n = 192)

Variables	Coefficients	Standard error	P>|z|
Profit efficiency
Constant	3.160***	0.631	0.000
Land size	0.143	0.117	0.220
Cost of seeds	0.294***	0.113	0.009
Cost of fertilizers	0.136*	0.073	0.062
Cost of pest control	0.173**	0.087	0.048
Cost of weeding	0.314***	0.104	0.003
Inefficiency model
Constant	−9.729*	5.164	0.060
Years of farming experience	−0.758***	0.282	0.007
HH Size	0.382	0.243	0.116
Education level	0.420**	0.185	0.024
Marital status	−2.228	1.453	0.125
Gender	−1.373	1.088	0.207
Access to credit	6.143***	2.132	0.004
Access to extension service	−11.681***	3.996	0.003
Belong to farmers’ associations	3.458**	1.392	0.013
Land acquisition	−1.414	1.264	0.263
Methods used in onion farming	2.322	1.744	0.183
Involved in non-farm activities	2.396**	1.154	0.038
Selling place	−7.808***	2.593	0.003
Variances parameters
Usigma constant	0.089	0.520	0.863
Vsigma constant	−0.334***	0.114	0.003
Sigma-u	1.046***	0.272	0.000
Sigma-v	0.846***	0.048	0.000
Lambda	1.236***	0.286	0.000
Log-likelihood	−256.2683

Source: Survey data & Author‘s calculation, 2022. *Significant at 10% probability level. **Significant at 5% probability level. ***Significant at 1% probability level

On the other hand, findings revealed that the coefficients of the dependent variables of the inefficiency such as farming experience positively significantly (p < 0.01) influenced the efficiency of red onions production and indicated that if farming experience increases by one unit, the more likely the profitability of onion farming will increase. Results also indicated that the access to extension services influenced positively (p < 0.01) the efficiency of red onions production was statistically significant and exerted a positive influence on the profit efficiency of onions farmers in the study area. This shows that as one unit rise in extension services, the more likely the profit efficiency of red onions production increases. In addition, the place where farmers sold onions produced (farmgate) positively significantly (p < 0.01) influenced the profit efficiency of red onions production and shows that the more farmers sell their harvest to the wholesalers or retailers at the farm gate, the more their profit efficiency is increased

Conversely, results showed that education level negatively influenced the profit efficiency of red onions production, with farmers having a high level of education significantly (p < 0.05) less likely to increase the profit efficiency. Results also indicated that access to credit negatively significantly (p < 0.01) influenced the profit efficiency of red onion farmers and indicated, which implies that an increase in access to credit is more likely to decrease the profit efficiency in onion production. Participation in farmers’ associations was as well found statistically significant at (p < 0.01) and negatively influence the profit efficiency of onion production. The more farmers are members of farmers ‘associations, the less likely they increase their profit efficiency. Results also showed that red onions farmers who were engaged in non-farm income-generating activities were significantly (p < 0.05) less likely to increase to profit efficiency of red onion production.

Table 5 shows the profit efficiency scores of the sampled onions farmers. The results indicate that the estimated sigma value was 0.846 and was statistically significant at 10%. More so, the estimated model had a log-likelihood value of −256.2683. About 53% of the respondents fell in the modal profit efficiency score of above 0.90. The mean profit efficiency of onions farmers was 0.81 which implies that onions farmers have a scope of increasing profit efficiency of onions farmers by 19%.

Table 5. Profit efficiency distribution of onions production in Sironko district

Efficiency scores	Frequency	Percentage
<0.40	20	10.42
0.40–0.70	15	7.81
0.71–0.80	16	8.33
0.81–0.90	39	20.31
>0.90	102	53.12
Total	192	100
Mean	0.8064 0.0051 0.9764 0.2353
Minimum
Maximum
Std. Dev.

Source: Field survey (2022) computed using Stata version 14

4. Discussion

4.1. Characteristics of onions farmers

This study aimed to characterize red onions farmers in Sironko district. The findings were presented to provide a brief description of the sampled respondents. The mean age reported in this study indicates that onion farmers in the study area were middle-aged, energetic, and active which could result in effective production. These findings are in line with similar research done on marketing shea butter in Kaduna state (Omotayo et al., 2021). Similarly, most of the onion farmers had inherent experience in onion farming. These results concur with those of other studies. For instance, Okello et al. (2019) reported an average farming experience of 18 years for rice and cassava farmers in Gulu and Amuru Districts. The household sizes were averagely large and typical of rural areas of Uganda (UNHS, 2016). Most of the respondents had completed primary seven, and this implies that they can easily read and write. This result corroborates the findings of Gabriel et al. (2021) who reported that the majority of tomato producers in Uganda had seven years of formal education. The average mean experience coupled with the mean education level implies that farmers in Sironko district can be able to adopt new technologies and research findings that are related to onion production. Onion farmers in Sironko allocated about half of their total land to onion production. This is typical of cash crops, especially in smallholder settings where the average land size is usually less than five acres (de Haas, 2017).

Majority of the onion farmers were male and married. This implies that red onions farmers are dominated by men in income-generating activities. This is a high manifestation of gender inequality in distribution and correlates with the finding of Mdoda et al. (2022) who reported that 84.1% of the respondents were male in their analysis done on Spinach Production under Small-Scale irrigated agriculture in the Eastern Cape Province, South Africa. Results in this study also showed that access to credit was very low among onion farmers. This could be the reason why they operate their activities on a small scale while rural credit could enable them to acquire seeds, agricultural tools, fertilizers, pesticides, and other inputs and even expand their business of onion farming (Chandio et al., 2018; Yasin, 2020). Similarly, only about one out of ten onion farmers had access to extension services. This shows that there was a low frequency of contact or exchange between onion farmers and extension workers. The low access to extension services correlates with the findings of (Musaba & Chibalani, 2021) for cocoa production in Zambia. In addition, the majority of the sampled farmers don’t belong to any farmers’ groups. Not belonging to a farmer group could affect negatively onion farming reason being that the farmers’ associations are the main mean used by extension officers to deliver a bunch of information about new farming technologies and market opportunities (Norton & Alwang, 2020). The results show that most of the respondents used either family land or personal land in owned production. This low use of rented land is in line with the findings of Musimu (2018) who reported that bean producers in Mbeya Tanzania rely majorly on household member-owned land. The findings indicate also that over eight out of ten onion farmers had other income-generating activities (including other agricultural enterprises) besides onion production. Specifically, more than half were involved in off-farm activities in addition to farming. This could be explained by the fact that farmers try not to rely only on one crop as there are risks that may arise in onion production. The majority of onion seeds were purchased from the input shops and the variety Red Creole is most preferred by the respondents over Afri seed.

4.2. Profitability of onions production in the study area

It was observed that between the two varieties grown (red creole and Afri seed) in Sironko district, within the four sub-counties, red creole is the most grown variety and productive with the higher gross margin. The seeds cost, harvesting cost, and transportation of onions to home were statistically significant at 1% level, and ploughing cost, drying/storing cost, and herbicides cost were significant at 5% level whereas weeding cost, and pesticides cost was significant at 10% level. This shows that those factors are critical in red onions production in the study area. Although the cost of fertilizers was not significant, Table 3 revealed that Afri seed and red creole farmers invested a big amount in fertilizers. This is a clear indication of the importance of fertilizers in onion production in the study area and could be explained by the fact that the use of fertilizers is a long-time investment in soil fertility. The results agree with the findings of Dalchiavon et al. (2019) who indicated in their study conducted on economic opportunity for investment in the Soybean and Sunflower crop system in Mato, Grosso Brasil that costs of inputs impacted those crops with fertilizer ranking first with 26% of the total variable costs.

Based on the results presented in Table 3, there is a confirmation of the profitability of onions production in Sironko district looking at the result of the gross margin supported by the result of return on investment. This indicates that more than 60% of the amount invested was gained by Afri seed producers and red creole producers and this corroborates similar results reported by (C. Wongnaa et al., 2019). All the findings stated above indicated that farmers engaged in red creole and Afri seed in Sironko district are making a remarkable profit.

4.3. Determinants of profit efficiency of onions production

Results of the stochastic profit frontier showed that 14 factors were found statistically significant and influence the profit efficiency of onions production in the study area. The coefficients of the physical estimated parameters such as cost of fertilizers, seeds, weeding, and pest control conform to a prior expected positive sign which implies that a unit rise in those factors will increase the profitability of onion production, ceteris paribus. These results contradict those of C. A. Wongnaa and Mensah (2018) who reported that an increase in the costs of fertilizer, pesticide, herbicide, and seeds affected significantly and negatively the profitability of maize production in Ghana. However, results of the analysis done by Nigatu et al. (2019) indicated that an increase in the cost of fertilizer leads to an increase in the yield of onion production in Dembiya District of Amhara Region, Ethiopia.

The estimated coefficients of the inefficiency model provide some explanations for the relative efficiency levels among individual farms. Since the dependent variable of the inefficiency function represents the model of inefficiency, a positive sign of an estimated parameter implies that the associated variable has a negative influence on efficiency and a negative sign indicates the reverse. Therefore, it was also observed that an increase in farming experience will lead to an increase in the profit efficiency of onions production. This implies that onion farmers with more years of experience operate significantly at a higher level of profit efficiency, which means that the more they are experienced the more they perform better. These results are in line with those of other studies. For instance, Tasila Konja et al. (2019) reported that farming experience was significant and affect positively the profit efficiency of certified groundnut seed production in Northern Ghana.

Furthermore, it was observed that the rise in education level in the study area will lead to an increase in the profit inefficiency of onions farmers. This could be explained by the fact that educated people in Sironko district could be engaged in other activities than farming. This result contradicts the findings of other studies by Tadesse et al. (2021) and Mdoda et al. (2022) who reported that as a farmer is exposed to agricultural and innovative techniques used in crop production, the productivity of their production increases as well.

Results revealed also that access to credit decreases the profitability of onions farmers. This could be simulated to the fact that they are not guided well on how to handle credit money. It could also be explained by the high-interest rate fixed by banks or other institutions or microfinance institutions that push onion farmers not to contract credit money. This result is in line with the findings of (Mdoda et al., 2022) who reported a significant and negative effect of access to credit on Spinach Production under Small-Scale Irrigated Agriculture in the Eastern Cape Province, South Africa as well as the findings of Maniriho et al. (2020) who stated in his study that access to credit was significant and negatively affected the technical efficiency, thus the profit efficiency of onion production in volcanic highlands in Rwanda.

The access to extension services was expected to be signification and influence positively the profitability of onion production and the findings revealed a negative relationship between extension services and profit inefficiency of onions production. The negative and significant relationship with profit inefficiency justifies the contribution of extension workers in onions farming which help red onion farmers to boost the ability of farmers to use the optimal use of available resources which in turn improves productivity and thereby raises the profitability of onion production (Abrha et al., 2020). Conversely, Taye et al. (2018) in their study indicated that farmers who had access to extension services do not apply new techniques and advice given by the extension workers.

Participation in farmers’ associations is a pertinent factor that was predicted to be significant and positively influencing profit efficiency of onion farming in the study area as farmers’ associations facilitate agricultural extension, help to supply farm credit among farmers in the group and contribute to providing supply and marketing services. However, the research findings indicate that farmers’ associations are significant but have a negative correlation with the profit efficiency in the study area. In contrast, the findings of Kaibartya et al. (2018) showed that group networks enable beans producers in Mbeya, Tanzania to improve their productivity, as well as their profitability.

It was also found that a rise in onion producers who are engaged in other businesses (non-farm activities) results in a decrease in the profit efficiency of onion production. This could be simulated by the fact that they allocate their time and money to non-farm income-generating activities. Odoh et al. (2019) reported that farmers invest in other activities due to the fact that the income received by farmers who depend solely on farm ventures is inadequate and that reason pushes them to engage in other activities to increase their revenues.

Furthermore, it was also found that the selling place (farmgate) was significant and negatively affect the profit inefficiency of red onion production. This could be explained by the high amount of marketing costs paid by farmers when selling onions to the marketplace.

5. Conclusion and policy implications

The study examined the profit efficiency of onion production in Sironko district. The findings indicated that onion producers experienced in onion production completed primary school and were most of a young age which, enable them to be versatile in farming activities. Additionally, the findings revealed that onion production is dominated by men and this is a manifestation of gross inequality in gender distribution in the onion value chain. Onion production in the study area is characterized by limited access to credit, and extension services and most of the producers do not belong to any farmers’ associations. The limited access to credit and extension services could lead to poor adoption of productivity-enhancing technologies. Although red onion production is generally profitable for both Afri seed and red creole producers, it is obvious that the seeds of Afri seed are expensive compared to red creole while the yield, thus the profitability of red creole is higher compared to Afri seed. Furthermore, the study indicated that factors such as the cost of seeds, cost of weeding, extension services, access to credit, and selling places were observed to have significant effects on profit efficiency.

This study recommends that women should be sensitized, encouraged, and empowered by the Government to venture into red onion production. Credit facilities should be made available to onion farmers by financial institutions. Producers are also encouraged to use credit obtained judiciously for the expansion of onion production and not for other purposes. Furthermore, Government and other relevant stakeholders should make extension services available to onion producers so as to sensitize them on the adoption of innovative techniques and research findings. Also, the profit efficiency of onion production in Uganda could be improved through adequate use of available resources, an improved state of technology, and agricultural policies that would encourage the enlightenment of onion farmers.

Acknowledgments

Our heartfelt thanks to Regional Universities (RUFORUM) for mentoring, building our capacity in Agriculture, and allowing us to share our research experience.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was funded by the MasterCard Foundation (TAGDev Project) through the Regional Universities Forum for Capacity Building in Agriculture (RUFORUM).

Notes on contributors

Elias Munezero

Elias Munezero The first author Elias Munezero has completed an MSc. in Agri-Enterprises Development at Gulu University, Uganda and holds a BSc. in Plant Production from the University of Burundi. He is currently executing an insect-based project in Burundi that entails the production of maggot meal from black soldier fly.

Caleb. I. Adewale

Caleb I. Adewale The second author, is currently a Research Assistant Intern at Alliance Bioversity International & CIAT, Uganda. He has completed an MSc. in Agri-Enterprises Development at Gulu University, Uganda and holds a Bachelor’s Degree in Agriculture (Agricultural Economics & Farm Management) from the University of Ilorin, Nigeria.

Daniel Micheal Okello

Daniel Micheal Okello The third author, holds a MSc. Degree in Agricultural Economics from Makerere University. He is a part-time assistant lecturer in the department of Rural Development and Agribusiness, Gulu University. He is currently finalizing his doctoral studies at Gulu University.

Basil Mugonola

Basil Mugonola (PhD) The fourth author, is Associate Professor of Agricultural economics in the Department of Rural Development and Agribusiness of Gulu University.