Determinants of Awareness and Intensity of Use of Maize Grades and Standards among Smallholder Farmers in Northern Uganda

doi:10.21203/rs.3.rs-7670719/v1

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Research Article

Determinants of Awareness and Intensity of Use of Maize Grades and Standards among Smallholder Farmers in Northern Uganda

https://doi.org/10.21203/rs.3.rs-7670719/v1

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Background

Compliance with maize grades and standards remains a significant challenge in Uganda, particularly among smallholder farmers. Despite the guidelines established by the East African Grain Council, adherence within local markets remains limited. Understanding the factors that influence the awareness and adoption of maize grading is crucial for enhancing market quality and improving farmer incomes.

Methods

This study investigated determinants of smallholder farmers’ awareness, utilization, and intensity of use of recommended maize grades and standards in northern Uganda. Primary data were collected from 270 farmers. Probit regression and the Heckman two-stage model were employed to identify factors affecting awareness, use, and the extent of compliance.

Results

The results indicated that awareness and utilization of maize grades and standards were generally low. The key factors influencing awareness included education, gender (male), access to extension services, access to credit, and membership in farmer associations. Farmers’ use of maize grades and standards was positively associated with awareness and access to extension services but negatively affected by proximity to markets and membership in farmer groups. The intensity of use was significantly influenced by the scale of maize production and higher education levels.

Conclusions

The findings highlight the need for interventions that improve farmer awareness and access to grading services. Strengthening extension services to include maize grading messages, improving access to credit facilities, and developing more accessible grading stations are recommended to promote compliance. Such measures would enhance maize quality, improve market participation, and ultimately increase farmer productivity and incomes.

Adoption

Awareness

Heckman two-stage model

Intensity of use

Maize grades and standards

Smallholder farmers

Over the last decade, global maize production has increased significantly, reaching nearly 1.2 billion tons by 2020 [1]. Maize ranks as one of the world’s key staple crops, accounting for more than 30% of the daily calorie consumption of more than 4.5 billion people across 94 developing nations [2]. In East Africa, particularly in Uganda, maize is a central staple food, with an average daily per capita consumption of 136 grams [3]. Although Uganda has prioritized improving maize productivity, production remains largely in the hands of smallholder farmers, especially in the eastern region, which contributes more than half of the nation’s output [4]. Over time, maize has replaced traditional staples such as sorghum and millet [5], becoming critical not only for food security but also as a source of income, with farmers selling surplus harvests to millers [6, 7].

Uganda exports approximately 22% of its annual maize harvest, whereas the remaining 78% is consumed domestically: approximately 60% is processed into flour, 37% is used for animal feed, and 3% supplies breweries [8]. The maize trade is structured around four main categories of traders: informal buyers operating without quality controls, large local traders (40%) who supply millers, Kenyan large-scale traders (20%) offering competitive prices, and institutional buyers such as the World Food Programme (30%) [4]. Approximately 10% of Uganda’s maize is exported to neighboring countries, including Kenya, Rwanda, and South Sudan [9]. Major traders based in urban centers such as Kampala typically source grain from high-production districts such as Nwoya and Kiryandongo to meet miller demand [10].

As trade has expanded, adherence to quality standards has become increasingly important. Smallholder farmers face growing pressure to comply with regulations set by the Uganda National Bureau of Standards and the East African Grain Council. At the regional level, the East African Community (EAC) has harmonized maize grain standards to facilitate cross-border trade, with particular emphasis on safety measures such as monitoring aflatoxin levels [11]. These standards help classify maize quality, ensuring regional consistency and food safety [4, 12]. Nonetheless, compliance with these standards in domestic markets is inadequate, leading to trade barriers. For example, in 2018, Kenya declined over 600,000 metric tons of maize from Uganda due to high aflatoxin levels, and in 2021, another ban was imposed on maize imports from Uganda and Tanzania for similar reasons [13]. This impedes trade, reduces farmer incomes, and causes significant postharvest losses.

Recent studies have explored a range of topics, including improved maize farming techniques and technology adoption among smallholder maize farmers. Bold et al. [14] focused on the barriers to quality upgrading and the role of market incentives and extension services among smallholder maize farmers in Uganda. Ramogodi [15] developed a quality management framework for emerging maize farmers in South Africa, assessing their competencies regarding maize quality and grading standards. Ngwenyama et al. [16] investigated the factors influencing the use of improved maize storage practices in Zimbabwe. A study in Ghana examined the factors affecting both the adoption and intensity of use of hermetic storage bags among smallholder maize farmers [17]. Ahmed [18] examined how the use of inorganic fertilizer and the adoption of improved maize varieties influenced the productivity levels and household consumption expenditures of smallholder farmers in Ethiopia. In South Africa, research by Siphokazi [19] assessed the factors influencing smallholder farmers’ selection of maize varieties, including awareness of permit conditions. Shitaye et al. [20] evaluated the extent of agricultural information use among smallholder farmers in Ethiopia, with a focus on information related to agricultural standards and practices. However, none of the existing studies have explicitly examined the determinants of farmers’ awareness and intensity of use of maize grades and standards. This highlights a clear research gap regarding the specific factors that influence farmers’ knowledge and adoption of grading systems.

The present study sought to address this gap by assessing farmers’ levels of awareness of maize grades and standards, as well as identifying the determinants influencing their awareness, adoption, and intensity of use. The findings provide important insights into the role of grades and standards in agricultural production and trade while offering practical recommendations to improve maize quality, increase farmer incomes, and strengthen food security. Ultimately, this study contributes to the wider body of knowledge on agricultural product standardization and underscores its significance in addressing future food safety and market competitiveness challenges.

Study area

Amuru and Nwoya districts, situated in the Acholi subregion of northern Uganda along the Gulu–Arua road, served as the study area (Fig. 1). Amuru District, positioned at 2°48'59.99"N, 31°56'59.99"E, spans 3,625.9 square kilometers with a population of 247,574, whereas Nwoya District, located at 2°38'06.0"N, 32°00'00.0"E, covers an area of 4,736 square kilometers and has a population of 220,593 [21]. Both districts are predominantly agrarian, with most households engaged in livestock rearing and crop cultivation. Amuru district contributes approximately 45,000–55,000 metric tons, the bulk of which is grown by smallholder farmers, whereas Nwoya district produces an estimated 50,000–60,000 metric tons of maize annually. Although these districts possess significant potential for large-scale agricultural production, small-scale farming remains the dominant system. The major crops cultivated include maize, cotton, tobacco, millet, rice, and cassava. The selection of Amuru and Nwoya districts for this study was informed by their high levels of commercialized maize production and their central role in meeting both local and regional grain demand [22].

Sampling procedure

The study employed a cross-sectional survey design, conducted between November 2022 and August 2023, and utilized a multistage sampling approach. In the first stage, Amuru and Nwoya districts were purposively selected because of their high level of engagement in commercial maize farming. Their proximity to the South Sudan border, which is an important gateway for agricultural commodity exports, also made them strategically relevant to the study. In the second stage, Amuru sub-county in Amuru district and Alero sub-county in Nwoya district were randomly selected. Finally, a random sample of 270 maize farmers was drawn from both sub-counties, and farmer lists obtained from the districts’ agricultural officers were used as the sampling frame. The sample size (n) was determined via the Cochran [24] formula as follows:

$$\:n=\frac{{Z}^{2}Pq}{{e}^{2}}$$

The formula above was calculated on the basis of the following parameters:

p denotes the proportion of maize farmers in Amuru (32.3%) and Nwoya (45%) districts [25], Z represents the confidence level at 95% (corresponding to the standard value of 1.96), e is the margin of error, set at 5%, and n is the sample size. The percentage of farmers in Amuru and Nwoya districts (32.3% + 45%) = 77.3%

$$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:n=\:\frac{{\left(1.96\right)}^{2}\times\:0.773\:\left(0.227\right)}{{\left(0.05\right)}^{2}}=270$$

Data collection

Data were collected via a well-structured, pretested questionnaire that combined both open- and closed-ended questions to obtain comprehensive information from maize farmers in the selected study area. The authors designed the survey questionnaire used in this study for this research. The full English version is available as a supplementary file. The questionnaire was administered by carefully selected and thoroughly trained research assistants to ensure accuracy and consistency in data collection. The open-ended questions provided respondents with the flexibility to express their views in detail, whereas the closed-ended questions enabled them to select from predefined response options for ease of analysis. A pretest was conducted before the main survey to improve the validity of the data collection instrument. Twenty-five maize farmers from a neighboring district were interviewed, and any issues identified, such as redundancies or omissions, were addressed by revising the affected items in the tool. The questionnaire was organized into three main sections. The first section focused on sociodemographic characteristics, including variables such as age (in completed years) and education level (measured in years of schooling). The second section assessed farmers’ awareness of the recommended maize grades and standards, which were captured via a five-point Likert scale, and further examined their knowledge of the East African Community (EAC) recommended grades across different grain attributes. The final section explored farmers’ utilization and intensity of use of maize grading and standardization practices.

Data analysis

Descriptive statistics

Descriptive statistics, including frequencies, percentages, t-tests, and chi-square tests, were employed to analyze the sociodemographic and institutional characteristics of the maize farmers in Amuru and Nwoya districts. In addition, these tools were used to compare the demographic characteristics of maize producers on the basis of their level of awareness of maize grades and standards.

Probit regression model

To identify the determinants of farmers’ awareness of the recommended maize grades and standards, a probit regression model was applied, with the dependent variable being binary: awareness status (0 for unaware, 1 for aware). The independent variables (X1–X8) included factors such as education, age, and market access and are defined in Table 1. The model is expressed in Eq. (3).

$$\:Yi\left(\text{0,1}\right)\:=\:\beta\:0\:+\:\beta\:1X1\:+\:\beta\:2X2\:+\:\beta\:3X3\:+\:\beta\:4X4\:+\:\beta\:5X5\:+\:\beta\:6X6\:+\:\beta\:7X7\:+\:\beta\:8X8\:+\:\epsilon\:i\:\:$$

where:

Y = Awareness (1 = Aware, 0 = not aware)

$\:{\epsilon\:}_{i}$ = Error term

Awareness was assessed via a set of Likert-type questions designed to capture farmers’ perceptions and understanding of maize grades and standards. Each respondent was asked multiple questions related to maize grading, with responses recorded on a five-point Likert scale: 5 (highly aware), 4 (moderately aware), 3 (neutral), 2 (less aware), and 1 (not aware). For each participant, an awareness score was calculated by averaging their responses across the relevant questions. A mean score greater than 3 was classified as “aware,” indicating sufficient knowledge or recognition of maize grades and standards, whereas a mean score less than or equal to 3 was categorized as “not aware.” This binary classification served as the dependent variable for the probit regression analysis, enabling a systematic evaluation of the factors influencing farmers’ awareness.

Heckman two-stage model

The Heckman two-stage model was applied to account for potential selection bias in analyzing both the adoption and intensity of use of maize grades and standards among farmers. In the first stage (Eq. (4)), a probit model estimates the likelihood of a farmer adopting grades and standards, thereby identifying the factors influencing this initial decision. In the second stage (Eq. (5)), the intensity of use was modeled among those who had adopted, with the inverse Mills ratio generated in the first stage incorporated to correct for nonrandom selection. This two-step approach provides unbiased estimates of the determinants of both adoption and intensity of use, reflecting the sequential nature of farmers’ decision-making regarding maize grades and standards. The independent variables used in the Heckman two-stage model are defined in Table 1.

$$\:Pi\left(\text{0,1}\right)=\:\beta\:0\:+\:\beta\:1X1\:+\:\beta\:2X2+...\dots\:\dots\:\dots\:\dots\:..\:\beta\:nXn\:+\:\epsilon\:i$$

$$\:Yi=\:\beta\:0\:+\:\beta\:1X1\:+\:\beta\:2X2+...\dots\:\dots\:\dots\:\dots\:..\:\beta\:nXn\:+\:\epsilon\:i$$

where Pi (ranging between 0 and 1) indicates the probability that a farmer utilizes maize grades and standards, with 1 representing utilization and 0 representing non-utilization, while Y denotes the extent or intensity of utilization. Utilization of maize grades and standards was measured by asking farmers whether they participated in maize grading and standardization, with responses captured as "yes" or "no." This binary response was coded as 1 for utilization and 0 for non-utilization, allowing the analysis to identify farmers who adopted the practice. The extent or intensity of use was determined by asking those who indicated participation to specify the quantity of maize they graded and standardized during the last planting season, recorded in kilograms (KG). This approach enabled the study to assess the utilization of maize grades and standards and the intensity of use among adopters.

Table 1
Description of the Explanatory Variables and their *a priori* expectations
Variables	Description of variables	A priori signs			Reference
Variables	Description of variables	Awareness	Decision to Utilize	Intensity of use	Reference
Age Distance to market Education Household size Access to Credit Level of awareness Quantity Produced Farm size Membership in association Market information Extension service Gender	Age of Farmers (Years) Distance from farm gate to market (in km) Years spent in school (years) Number of people in farmers’ households Credit access by farmers (Yes = 1, otherwise = 0) Awareness of maize grading (Yes = 1, No = 0) Quantity of maize produced in kg Size of farm cultivated by farmers in hectares Membership in a group (Yes = 1, otherwise = 0) Access to market information(Yes = 1, No = 0) Access to extension service (Yes = 1, No = 0) Gender (Male = 1, Female = 0)	+/- + + +/- +/- N/A + N/A + + + +	+ + + +/- + + + + +/- N/A + N/A	+ + + +/- + +/- +/- + +/- N/A + N/A	[26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37]

Socioeconomic characteristics of maize farmers by awareness

The findings in Table 2 revealed that male farmers (51.4%) were more aware of maize grades and standards than were female farmers (48.6%), with a significant gender difference. Compared with unmarried heads, married household heads (87.2%) also presented greater awareness (12.8%), suggesting the need for gender-sensitive interventions to increase awareness among female and unmarried farmers. These results align with those of Ragasa et al. [38], who reported that gender and marital status influence access to agricultural services, particularly among male-headed households. Moreover, farmers with market access (85.3%) and extension services (78.0%) had significantly greater awareness of maize standards, indicating that improving market linkages and strengthening extension services are crucial for increasing farmers' knowledge, as corroborated by Kudi et al. [39], who also reported that access to extension services increased awareness of improved maize varieties.

Farmers who were aware of maize grades and standards had access to credit (82.6%), belonged to a farmer group, and received inputs from government programs, thus highlighting the role of financial support and collective action in raising awareness. However, those growing improved maize varieties were less aware, suggesting a need for targeted education. These findings align with those of the study of Obayelu & Ajayi [40], which also revealed positive links between cooperative membership and technology awareness.

Table 2
Comparison of categorical variables for maize farmers’ socioeconomic characteristics by awareness of grades and standards
Variables	Overall N = 270	Aware N = 109	Not Aware N = 161
	Freq (%)	Freq (%)	Freq (%)	ꭓ²	Sig
Gender
Male	121 (44.8)	56 (51.4)	65 (40.4)	3.182*	0.074
Female	149 (55.2)	53 (48.6)	96 (59.6)
Marital Status
Married	237 (87.8)	95 (87.2)	142 (88.2)	0.066	0.797
Unmarried	33 (12.2)	14 (12.8)	19 (11.8)
Ready Access to Market
Yes	217 (80.4)	93 (85.3)	124 (77.0)	2.840*	0.092
No	53 (19.6)	16 (14.7)	37 (23.0)
Access to Credit
Yes	210 (77.8)	90 (82.6)	120 (74.5)	2.428	0.119
No	60 (22.2)	19 (19.4)	41 (25.5)
Access to Extension Services
Yes	177 (65.6)	85 (78.0)	92 (57.1)	12.500***	0.000
No	93 (34.4)	24 (22.0)	69 (42.9)
Membership in Farming Association/Cooperative Society
Yes	199 (73.7)	81 (74.3)	118 (73.9)	0.035	0.852
No	71 (26.3)	28 (25.7)	43 (26.7)
Sources of farm inputs
Governments	29 (10.7)	17(15.6)	12 (7.5)	4.885*	0.087
NGOs	55 (20.4)	19 (17.4)	36 (22.4)
Agro dealers	186 (68.9)	73 (67.0)	113 (70.2)
Maize varieties grown
Improved varieties	145 (53.7)	64 (58.7)	81 (50.3)	2.340	0.310
Local varieties	104 (38.5)	36 (33.0)	68 (42.2)
Both varieties	21 (7.5)	9 (8.3)	12 (7.5)
Source: Field survey 2023; ꭓ² denotes Pearson chi-square coefficient; * and indicates significance at 1% and 5%, respectively

The results from Table 3 show that the average age of maize farmers was 38 years for the “aware” group and 37 years for the “not aware” group, indicating that the farmers were relatively young, an age conducive to the labor-intensive nature of maize production. The mean household size of the farmers was seven members, which could help reduce labor costs, similar to the findings of Adenegan et al. [41]. The average educational attainment was seven years of schooling. A t-test for mean equality revealed a statistically significant difference in educational level between the two groups (p < 0.05). The mean farming experience of 8 years supports efficient farm management decisions, in line with the findings of Girei et al. [42].

Table 3
Comparison of continuous variables for maize farmers’ characteristics by awareness of grades and standards
Variable	Mean			Mean Difference	T-Stat	Sig
Variable	Overall N = 270	Aware N = 109	Not aware N = 161	Mean Difference	T-Stat	Sig
Age	37.8	38.3	36.7	1.6	-1.530	0.575
Years of education	7.8	7.9	7.7	0.2	− 0.278**	0.042
Household size	7	7	7	0.2	-0.210	0.876
Years of experience	8.2	8.1	8.4	-0.3	0.247	0.608
Source: Field survey 2023; ** denotes p value < 0.05

Status of utilization and intensity of use of maize grades and standards

The results in Table 4 show that 59.6% of the farmers are not aware of maize grades and standards. Only 31.1% of the surveyed farmers utilized maize grades and standards, whereas a substantial 68.9% did not. This suggests that the adoption rate of maize grading and standardization practices is relatively low among the sampled farmers, likely due to low awareness, a perceived lack of benefits, or potential barriers to implementation. Among the farmers who do utilize maize grades and standards, the mean quantity of maize graded and standardized per farmer is 559.66 kg. The intensity of use ranges widely from a minimum of 100 kg to a maximum of 16,320 kg, indicating significant variability in how extensively individual adopters use grading and standardization.

Table 4
Status of utilization and intensity of use of maize grades and standards among farmers
	Freq	Percent
Awareness of maize grades and standards
Aware	109	40.4
Not aware	161	59.6
Utilization of maize grades and standards
Yes	84	31.1
No	186	68.9
	Mean (S.D)	Min – Max
Intensity of use (Quantity of maize graded and standardized in KG)	559.66 (2119.406)	100-16320
SD: Standard deviation in parentheses

Factors influencing farmers’ awareness of the recommended maize grades and standards

The results presented in Table 5 indicate the factors influencing farmers’ awareness of the recommended maize grades and standards. The analysis revealed that education had a significant effect (p < 0.10), with each additional year of schooling increasing the likelihood of awareness by 1.6%. This suggests that more educated farmers are more inclined to seek information on improved agricultural technologies. These findings are consistent with those of Muatha et al. [28], who demonstrated that formal education enhances awareness of agricultural innovations, and Ado et al. [43], who reported a positive association between education and climate change awareness.

Access to extension services was also found to exert a strong influence on awareness (p < 0.01). Farmers with access to extension services were 24.4% more likely to be aware of maize standards than their counterparts were. This highlights the critical role of extension in disseminating agricultural knowledge and innovations. Similar observations were made by Ullah et al. [30] in their study on wheat varieties, as well as Meijer et al. [36], who emphasized the importance of extension agents in facilitating technology adoption.

Gender also played a significant role in awareness, with male farmers being 12.2% more likely to know about maize standards than females were. These findings support those of Ofuoku & Campus [37], who noted greater perceptions of climate change among male-headed households. Credit access is more likely to increase awareness by 13.2%, as access to financial resources enables farmers to explore practices that can enhance productivity, echoing Ullah et al. [30] on the role of credit in agricultural innovation. Additionally, membership in farmer associations positively influences awareness, as these groups facilitate information dissemination. This finding is consistent with those of Buckland & Campbell [44] and Alhassan et al. [34], who reported that participation in farmer groups improves access to agricultural knowledge and adaptation strategies.

Table 5
Marginal effects of the probit regression on factors influencing awareness of recommended grades and standards in maize
Variables	Coefficient (SE)	Marginal effect (SE)	p values
Age	-0.006(0.007)	-0.002(0.002)	0.385
Education	0.045(0.025)	0.016(0.009) *	0.077
Access to extension service	0.682(0.195)	0.244(0.065) ***	0.000
Access to credit	0.369(0.223)	0.132(0.079) *	0.098
Membership of association	0.480(0.219)	0.172(0.077) **	0.029
Access to market information	0.212(0.266)	0.076(0.095)	0.426
Marital status (married)	-0.272(0.252)	− 0.097(0.090)	0.282
Gender (Male)	0.340(0.166)	0.122(0.580) **	0.041
Constant	-1.327(0.500)		0.008
Log-likelihood	-169.337
p value	0.001
Number of observations	270
Pseudo R²	0.0701
GOF Pearson Chi² (242) = 259.35; Prob > Chi² = 0.2117
Mean VIF	1.22
Source: Field survey, (2023); *, and * infers significance at 1%, 5%, and 10%, respectively

Determinants of farmers’ utilization of maize grades and standards and their intensity of use

Determinants of farmers’ utilization of maize grades and standards

Table 6 presents the factors influencing farmers’ utilization of maize grades and standards. The results show that farmers who were aware of maize grading and standards were significantly more likely to apply them, highlighting the positive relationship between awareness and adoption. This finding is consistent with that of Sunny et al. [45], who reported a similar effect of environmental awareness on the adoption of solar irrigation. Proximity to markets also had a positive influence, with farmers located closer to markets being more likely to adopt maize grading and standards. Reduced transportation costs and easier market access likely made adoption more attractive, an observation aligned with Kelebe et al. [27], who reported that greater market distance hindered the adoption of biogas technology. These results suggest that reducing market access barriers can enhance the uptake of agricultural innovations.

Interestingly, a negative but significant relationship was found between farmers’ association membership and the adoption of maize grading and standards. Group dynamics, such as divergent interests, limited collective knowledge, or weak market demand for graded maize, may discourage adoption, an outcome also noted by Simtowe et al. [46]. In contrast, access to extension services significantly increased the likelihood of utilizing maize grading and standards. Extension agents provide vital training, information, and support that facilitate informed decision-making. This finding echoes earlier evidence by Martey et al. [47], who reported a similar effect for drought-tolerant maize.

Table 6
Marginal effects of the probit regression model on the determinants of the utilization of maize grades and standards
Variables	Coefficient (SE)	Marginal effect (SE)	p values
Education	-0.028(0.295)	-0.006(0.006)	0.345
Household size	0.026(0.332)	0.006(0.007)	0.426
Access to credit	0.108(0.269)	0.023(0.058)	0.687
Membership in association	-0.417(0.250)	-0.089(0.053) *	0.095
Awareness	1.312(0.209)	0.281(0.036) ***	0.000
Distance to the market	-0.038(0.016)	-0.008(0.003) **	0.020
Access to extension service	0.958(0.324)	0.205(0.066) ***	0.003
Constant	-0.805(0.471)		0.087
Log-likelihood	-103.841
P value	0.000
Number of observations	270
Pseudo R²	0.3085
GOF – Pearson Chi² (236) = 261.15; Prob > Chi² = 0.1252
Mean VIF	1.21
Source: Field survey, (2023); *, and * implies significance at 1%, 5%, and 10%, respectively

Determinants of the intensity of use of maize grading and standardization among farmers

The determinants of the intensity of use of maize grades and standards are presented in Table 7. The inverse Mills ratio (IMR) was negative and statistically significant, indicating a negative correlation between unobserved factors influencing the decision to utilize and those affecting the intensity of use. This confirms the presence of selection bias and justifies the use of the Heckman two-stage model, which effectively corrected for this bias and ensured the reliability of the estimates. The results show that the quantity of maize produced had a positive and significant effect on the intensity of grading and standardization, suggesting that higher production levels increase farmers’ likelihood of engaging in grading practices. This finding is consistent with that of Mahoussi et al. [32], who reported that increased maize yield encouraged the adoption of improved maize seeds.

Conversely, household size exhibited a negative relationship with the intensity of use, with smaller households being more likely to adopt grading practices, possibly due to reduced financial pressure. This contrasts with the findings of Orinda et al. [29], who reported that larger households positively influence the adoption of sweet potato technologies. Similarly, membership in farmers’ associations negatively affects maize grading, potentially because of conflicting priorities or limited collective interest, a result that aligns with those of Rahman & Majumder [26], who reported similar findings in relation to agricultural technology adoption. Education significantly increased the intensity of use, with more years of schooling positively associated with greater participation in maize grading. Educated farmers are better positioned to recognize and respond to the benefits of grading. This is supported by Kebedom & Ayalew [48], who reported that years of schooling positively influence the intensity of the use of coffee technology packages in Ethiopia. Finally, awareness of maize grades and standards was also positively linked to greater utilization, confirming that knowledge enhances adoption, as similarly demonstrated by Zondo [31] in the case of organic fertilizer use.

Table 7
OLS regression model results of the intensity of use of maize grading and standardization among farmers
Variable	Coefficients	Std error	p value
Quantity of maize produced	0.220 **	0.900	0.015
Household size	-0.836 **	0.402	0.038
Membership of association	-1.141 ***	0.362	0.002
Land size for maize prod.	0.039	0.087	0.656
Education	0.507 *	0.280	0.071
Age	0.514	0.536	0.339
Source of input (NGO)	-0.436	0.372	0.242
Awareness	1.238 *	0.756	0.102
Constant	6.607	2.366	0.006
Lambda (IMR)	-3.719	0.584	0.000
P value	0.000
Adjusted R2	0.7256
Mean VIF	2.44
Source: Field survey, (2023); , , and ** indicates significance at 10%, 5%, and 1%, respectively

The study revealed that a proportion of the farmers in both districts lacked awareness of maize grading and standardization, and overall participation in these practices remained low. Analysis of determinants revealed that the probability of awareness increased with higher levels of education, access to credit, availability of extension services, male gender, and membership in farmer groups or associations. With respect to utilization, farmers’ likelihood of adopting maize grades and standards increased with greater awareness and access to extension services but declined with increasing distance to markets and with association membership. The intensity of use was positively influenced by larger maize production volumes, higher education levels, and awareness, whereas it decreased with larger household sizes and membership in farmer groups and associations.

To increase the utilization of maize grades and standards among Ugandan farmers, several policy measures are recommended. First, efforts should strengthen farmer education by incorporating training on maize grades and standards into both formal and informal learning platforms, given the strong association between education and adoption. Second, extension services should be expanded and equipped to deliver targeted messages on maize grading, recognizing their critical role in promoting awareness and utilization. Improved access to credit is also essential, as it allows farmers to invest in the technologies and practices needed to meet grading standards; thus, tailored credit schemes for smallholder farmers should be scaled up. Additionally, establishing grading and standardization centers closer to farming communities would help mitigate logistical barriers related to market distance. Furthermore, while membership in farmer associations increased awareness, it had a negative relationship with actual utilization. This suggests the need for capacity building within associations to address internal challenges, encourage active participation, and increase the demand for graded maize.

This study is not without limitations. Its cross-sectional survey design restricts causal inference since the data were collected at a single point in time. Moreover, focusing specifically on the Amuru and Nwoya districts may restrict the extent to which the findings can be applied to other areas with different agricultural practices or economic contexts. Therefore, future studies should consider conducting comparative analyses across diverse regions within Uganda or similar agricultural settings. Such research would help capture regional differences, identify effective approaches, and support the development of more inclusive and tailored policy measures aimed at enhancing the livelihoods of smallholder farmers.

Ethics approval and consent to participate

The study was approved by the Gulu University Research Ethics Committee (GUREC-2022-403). Participation in this study was voluntary. Informed consent was obtained from all participants before the study. The informed consent form was first read and interpreted to the participants before their consent was obtained. The participants were assured of the confidentiality of the information they provided.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Clinical trial number

Not applicable.

Funding

This work was supported by the MasterCard Foundation grant to the Regional Universities Forum for Capacity Building in Agriculture (RUFORUM) through the project “Transforming African Agricultural Universities to Meaningfully Contribute to Africa’s Growth and Development (TAGDev).

Author Contribution

ONK: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Funding acquisition, Conceptualization. CIA: Writing – review & editing, Writing – original draft, Methodology, Formal analysis. DMO: Writing – review & editing, Writing – original draft, Methodology, Conceptualization, Supervision. BM: Writing – review & editing, Writing – original draft, Methodology, Conceptualization, Supervision.

Acknowledgement

The authors gratefully acknowledge the Regional Universities Forum for Capacity Building in Agriculture (RUFORUM) for their mentorship and for giving us the opportunity to carry out this study.

Data Availability

The data are available from the corresponding author upon reasonable request.

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No competing interests reported.

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Determinants of Awareness and Intensity of Use of Maize Grades and Standards among Smallholder Farmers in Northern Uganda

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Abstract

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Introduction

Materials and methods

Study area

Sampling procedure

Data collection

Data analysis

Results and Discussions

Socioeconomic characteristics of maize farmers by awareness

Status of utilization and intensity of use of maize grades and standards

Factors influencing farmers’ awareness of the recommended maize grades and standards

Determinants of farmers’ utilization of maize grades and standards and their intensity of use

Determinants of the intensity of use of maize grading and standardization among farmers

Conclusion and Policy Recommendations

Declarations

Clinical trial number

Funding

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1