1 Introduction

Agriculture plays an important role in the global economy, contributing 4% of the Gross Domestic Product (GDP) globally and exceeding 25% for Least Developed Countries (LDC) [1]. Its potential for uplifting the incomes of the poorest is more than twice that of other sectors [2]. Recognizing the pivotal role of agricultural productivity in alleviating hunger, the 2030 Agenda for Sustainable Development, under Goal 2, emphasizes the need to double agricultural productivity and incomes of small-scale food producers to achieve zero hunger by 2030 [3]. The imperative to enhance agricultural productivity, which aims to stimulate economic growth and poverty reduction in several ways, has been underscored by numerous global and regional initiatives, reflecting a shared commitment to double or increase production significantly. A prominent illustration of this dedication is evident in the Malabo Declaration, which aims to eradicate hunger in Africa by 2025 [4]. Central to the essence of these initiatives is the pursuit of amplified production, particularly in the realm of food crops. This commitment emanates from the recognition that doubling agricultural productivity is fundamental to addressing the multifaceted challenges surrounding food security and sustenance. As the global population is projected to reach ten billion by 2050, especially with a substantial increase in Sub-Saharan Africa, meeting this demand necessitates heightened agricultural productivity [5, 6].

Accurate measurement of agricultural productivity becomes crucial for countries relying heavily on agriculture. Despite the acknowledged importance of agriculture and the importance of increased productivity [1, 5, 6], the measurement of agricultural produce in standard units in low- and middle-income countries remains a formidable challenge [7]. The lack of a standard system of measurement introduces confusion, mistrust, errors, misrepresentation, and fraud, posing challenges in standardizing varying non-standard units [8]. Understanding these challenges is fundamental to making sound decisions [9]. In response to these challenges, an International System of Units (SI) was developed to ensure worldwide uniformity in measurements, characterized by accuracy, stability, comparability, and coherence [8]. For instance, certain countries worldwide have instituted weights and measures regulations. However, within the global context, the efficacy of these regulations varies, with some being de facto and exerting limited practical influence. Despite the existence of a legislative framework for weights and measures, previous studies have uncovered a notable gap between the regulatory expectations and actual market practices in the measurement of agricultural produce. The current situation highlights a broader global challenge observed in numerous countries with similar regulations in effect.

Empirical evidence from diverse regions with established weights and measures regulations suggests that non-compliance persists within market dynamics, posing potential exploitation risks for farmers. Instances of deviation from standardized measurements undermine the intended protections afforded by regulatory frameworks. In such contexts, the absence of adherence to established standards has given rise to a lack of uniformity in measurements, leading to fluctuating prices and costs. This, in turn, places an undue burden on farmers, compromising their economic interests and overall livelihoods. To illustrate, Tanzania enacted the weight and standards measure Act in 1982. This regulatory framework aimed to address the challenges posed by inconsistent measurement practices in the agricultural sector. Despite the presence of the Weights and measures Act of 1982, it has been revealed that the units of measurement employed in marketing agricultural produce in Tanzania do not conform to the provisions outlined in the Act and its subsequent regulations. The non-compliance with standard units has substantial drawbacks on agricultural productivity and income, as evidenced by various studies. For instance, research on the grain value chain in Tanzania indicated that farmers marketing their produce in non-standard weights and measures receive relatively low margins [10]. Likewise, an investigation conducted along the Irish potato supply chain in the Njombe Region of Tanzania found that the utilization of local units results in unaccounted losses [11]. This echo finding from a study in Ghana, where 60% of respondents perceived compliance with standard weights and measures as a reliable tool for maximizing profits [12]. Adwoa et al. [13] also emphasized that non-compliance with standard weights and measures negatively impacts farmers’ productivity, aligning with the challenges identified in Tanzania.

While various studies have explored the effects of non-compliance with standard weights [10,11,12, 14] few have delved into the extent to which the use of standard weights and measures influences the reported number of non-standard units (e.g., bags, debes, sado) produced—a key numerator in measuring productivity. Furthermore, existing research does not quantify the losses experienced by farmers due to the utilization of traditional measures. It is noteworthy that the literature related to this study reveals a paucity of recent research, particularly within the Tanzanian context.

This study aims to address the magnitude of the influence that standard weights and measures have on productivity and welfare by directly obtaining information from small-holder farmers. It will categorize farmers based on their use or non-use of standard measures, determining the differences in influence among these groups. Furthermore, the study is uniquely tailored to address the issue from the perspective of Tanzanian small-holder farmers, providing a foundation for tailor-made solutions. To achieve this, the study poses the question: to what extent does non-compliance with standard weights and measures influence agricultural productivity and welfare? This research fills a critical data gap by offering insights into the impact of standard weights and measures on the agricultural productivity and welfare of small-holder farmers. The knowledge garnered will guide policy makers and relevant bodies in offering practical solutions to small-holder farmers. The results obtained will serve as a baseline for future research, aiding in the development and improvement of policies related to globalizing small-holders’ agricultural products in terms of weighing, measurement, and presentation in the national and global markets. The remaining parts of the article are organised as follows; “Material and methods” section describes the materials and methods used; “Results” section presents the results of the impact of standard weights and measures on productivity and welfare. “Discussion” section discusses the findings of the study; “Conclusion” section gives the conclusion regarding the implication of standard weights and measures on the productivity and welfare of small-holder farmers, provides the study limitations and recommendations.

2 Material and methods

2.1 The study area

This study was conducted in the Ruvuma region of Tanzania. This area was chosen due to the significant role of maize as a major food and cash crop, with the region being the leading maize producer in Tanzania [15, 16]. Additionally, in the National Sample Agricultural Census of 2019/20, the region had the highest productivity of 2.0 tons/ha which is higher than the national average productivity of 1.5 tons/ha [15]. It islocated at − 11° 00′ 0.00″ S latitude and 36° 00′ 0.00″ E Longitude in the southern part of Tanzania. It shares borders with the Republic of Mozambique in the South, to Morogoro Region in the north, the Lindi Region to the northeast, to the east by the Mtwara Region, Lake Nyasa in the West with Malawi (Fig. 1). The region has a land area of 63,669 km2 [17] and ranks number three after Dodoma and Tabora regions in terms of the largest area of land utilized for agricultural activities [15].

Fig. 1
figure 1

Study areas in the Ruvuma Region

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Maize was chosen because it is a staple food that serves a food and cash crop purpose. Moreover, these small-scale farmers largely depend on it for their livelihood [18]. Additionally, it is an essential food and income generating crop for both rural and urban households in Tanzania [19]. Maize is marketed in the region through interactions involving traders and consumers both within and outside the locality [16]. A substantial proportion of maize transactions (83%) occur within the village, with a notable portion (61%) directed towards middlemen [20]. This marketing paradigm predominantly relies on non-traditional units, utilizing containers of varying sizes and shapes. Despite the region’s status as the primary maize producer, contributing significantly to the national yield, the marketing practices for maize remain entrenched in the use of traditional units of measurement (such as gunia, debe and sados). This persistence in employing customary measurement units stands in contrast to the region’s substantial maize production and poses an interesting dynamic that warrants further exploration and analysis.

2.2 Design and sampling

Cross-sectional research design was employed for this study. According to [21], cross-sectional studies are economical and relatively easy to conduct. Moreover, the design was adopted due to the nature of production which is grown annually. The research focused on maize farm households in the Ruvuma region, with the regional commissioner's office reporting approximately 347,333 such households as of June 2022, of which around 178,000 were actively involved in maize cultivation. A study was based on three stage stratified cluster sample of 557 maize farm-households. The region was stratified into six districts (Table 1), with Mbinga and Songea Urban representing the Town Council and Municipal Council, respectively. To ensure a representative sample, the proportional allocation method was applied [22]. Representation considered factors such as remoteness, transport infrastructure, and proximity to major maize collection centers. At the first stage, a sample of 14 wards with high maize production were randomly selected from the six districts. Second stage involved the random selection of 18 villages from the selected wards. The final stage involved the random selection of 557 maize farm households from respective villages. The allocation method is outlined below:

$${\text{n}}_{{\text{i}}} = \frac{{{\text{nN}}_{{\text{i}}} }}{{\text{N}}}$$
(1)

where n is the sample of maize households in the Ruvuma region; Ni is the population size of respective districts in the region; N is the population of maize households in the Ruvuma region; ni is the sample size of respective districts in the region.

Table 1 Targeted sample and actual interviews conducted
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2.3 Sample size determination

The study used Cochran’s formula for calculating the sample size for this study. Taking a confidence interval of 99% and 5% level-of-precision an optimal calculated sample size for this study was 665 households. However, the study managed to collect responses from 557 households which is equivalent to 83.8% in July and August 2022. The formula used is presented as

$${\text{n}}_{0} = {\text{Z2}} * {\text{p}} * {\text{q}}/{\text{e2}}$$
(2)

where n0 is the sample size; Z is the confidence level; e is the level of precision; p is the estimated proportion of an attribute present in the population; q = 1 − p.

2.4 Data

Data were collected from both primary and secondary sources. Primary data were acquired from a sample of maize farm households’ heads using a structured questionnaire. Secondary data were obtained through review of literature related to the study. Participant observation played a crucial role, as it was employed to assess current practices in measuring and weighing. Investigative observations, such as purchasing samples and conducting rapid tests on the produce to determine the weights of randomly selected samples, were also conducted. This was done to compare and substantiate the reported weights obtained through local measurements with those obtained through standard measurements.

2.5 Data analysis

This study applied a negative binomial regression model and multiple regression model to investigate the effect of standard weights and measures on productivity and welfare respectively. The independent variable, standard weights and measures, was categorized into two groups: coded as 0 for non-use and 1 for use. The dependent variables included productivity, measured as a count variable (annual average number of bags per acre per household), and welfare, measured as a continuous variable (average annual income generated by farmer households from maize farming). A binary logistic model was utilized to assess the influence of demographic variables on the use of standard weights and measures. The Chi-square test was conducted to examine the association between demographic variables and productivity, and welfare, as well as the use of standard weights and measures. Variables demonstrating a significant association with productivity, welfare, and the use of standard weights and measures were incorporated into the model. These significant variables included sex, land size, age, education level, marital status, and bags of maize produced per acre. Descriptive analysis was employed to characterize the demographic features of the respondents.

2.5.1 Binary logistic model

A binary logistic model was employed to investigate the impact of demographic variables on the utilization of standard weights and measures. The exponentials were included to ease the interpretation of the findings in odds ratios. This easily shows how many times the use of standard weights changes for a unit change in demographic variables. The variables considered were; education level, cultivated land in acres and sex. The Binary Logistic Model is specified below

$${\text{Log it }}\left( \pi \right) = \beta 0 + \beta {\text{1X1}} + \beta {\text{2X2}} + \beta {\text{3X3}}$$
(3)

where Log it (π) = log \((\frac{\uppi }{1-\uppi }\)); π is the probability of farmers using standard weights and measures; β0, β1, β2, β3, are the regression coefficients; X1, X2, and X3, are predictor variables (education level, cultivated land in acres, sex).

The Odds Ratio is mathematically expressed as

$$\frac{{{\uppi }_{{1{ }}} /1 - {\uppi }_{1} }}{{{\uppi }_{0} /1 - {\uppi }_{0} }}$$

where \(\frac{{\uppi }_{0 }}{1}-{\uppi }_{0}\) are the odds of the event when the predictor variable is at the reference level; \({\uppi }_{1 }/1-{\uppi }_{1}\) the odds at the level of interest.

2.5.2 Negative binomial model

The impact of weights and measures on the productivity of maize household farmers was analyzed using the negative binomial regression model. The average number of bags per acre was skewed which makes this model appropriate because it deals with counts and over-dispersed data. The results have been presented by coefficients and exponential of coefficients. Exponentials were incorporated to facilitate the interpretation of findings in terms of incident rate ratios. This allows for a clear understanding of how the dependent variable (number of bags per acre) changes for each unit change in the independent variable (standard weights and measures). The variables considered in the model comprise sex, cultivated land in acres, age, and education level. The negative binomial regression model is specified below

$${\text{log }}\lambda {\text{i}} = \beta 0 + \beta {\text{1xi1}} + \beta {\text{2xi2}} + \, \beta {\text{3xi3}}$$
(4)

where λi is the expected outcome (productivity); β0, β1, β2, β3, β4 are the regression coefficients; X1, X2, X3, X4, and X5, are predictor variables (standard weights and measures, sex, cultivated land in acres, age and education level).

2.5.3 Multiple regression model

The effect of standard weights and measures on welfare was obtained by applying a multiple linear regression model. Since income is skewed [23], the income data was log-transformed in order to improve data validity. The interpretation of findings is multiplicative as opposed to the additive rule. Thus, the coefficients are interpreted in exponential form with an expression of [Exp(B)]. The variables included in the model are; sex, education level, cultivated land in acres, age, marital status, and bags produced per acre. The Multiple linear regression model is given as

$${\text{Y}} = \beta 0 + \beta {\text{1X1}} + \beta {\text{2X2}} + \beta {\text{3X3}} + \beta {\text{4X4 }} + \beta {\text{5X5}} + \beta {\text{6X6}}$$
(5)

where Y is the Welfare; β0, β1, β2, β3, β4, β5, β6 are the regression coefficients; X1, X2, X3, X4, X5, and X6 are predictor variables (standard weights and measures, sex, education level, cultivated land in acres, age, marital status, bags produced per acre).

3 Results

3.1 Demographic characteristics of the respondents

The demographic characteristics of the survey respondents are presented in Table 2. The acreage of land under maize cultivation by the households ranged between 0.5 and 25 with an average of approximately 4 acres of land. The productivity of maize indicated a minimum of 1 bag/acre and a maximum of 35 bags/acre with an average of 14 bags/acre. In this context, according to interviews with farmers, a “bag” is equivalent to 100 kg. The annual proportion of the amount of money in Tanzanian shillings generated from maize ranged between 40,000($16) and 15,000,000 ($ 6000) with an average of 2,134,644.81 ($853.9). The age of respondents ranged between 20 and 79 years with an average age of 45, and the majority of the respondents were male (60.1%). The majority of the respondents (80.6%) were observed to have attained primary school education and most of them fell into the working population that is 20–64 years old.

Table 2 Demographic characteristics of the respondents
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3.2 Farmers’ adherence to standards units and traditional measures in agricultural practises

Table 3 shows that among the 557 farm households surveyed, the majority of farmers, accounting for 360 (64.6%), do not employ standard units of measurement such as kilograms and grams. Notably, the number of farmers using standard units is approximately 0.55 times less than those not adhering to such measures. The study further indicates that the prevalent measure for quantifying produce among a substantial (75.4%) of respondents is a 4-L paint container, commonly referred to as “Sado.” Originating from the Sadolin company, a prominent paint seller in Tanzania. The sado is a four-litre paint tin. This container is recognized in the Ruvuma region as “Dumla.” According to the majority of interviewed farmers, the “Sado” is considered to be equivalent to 4 kg. The “Debe’’ is a metallic container estimated to weigh 20 L. This was the second most used traditional measure. “Ndoo” is a plastic bucket weighing 10 and 20 L. However, in this context, the Ndoo refers to a 20-L bucket. In contrast, the least utilized measure, constituting only 1.6% of respondents, is the “Gunia”. This Gunia is a sack or a bag made for the purpose of carrying commodities. This particular sack has a capacity of various sizes according to manufacturer specifications. The commonly used being 50 kg and 100 kg. In this particular study gunia refers to a 100-kg sack. Due to the nature of the material this sack is made of, it can accommodate commodities beyond its required specifications.

Table 3 Farmers' adoption of standard units of measurement
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3.3 Conversion equivalents of traditional units to standard kilogram measurements

In Table 4, it is evident that out of the 557 interviewed households, the majority (64.7%), accounting for 360 respondents, utilize traditional measures for quantifying their produce. At the same time, interviews conducted with households revealed that the farm households have their perceived equivalence of these commonly used traditional measures to kilograms. The survey showed that the sado, commonly utilized by the majority of households, demonstrates equivalence to kilograms within the range of 3 to 5.5. Moreover, the range reported by most of the respondents 260 (61.32%) was 4 to 4.5. Random tests conducted during fieldwork revealed that the same sado, filled with maize and maintaining a consistent heap level, yielded varying weight measurements when placed on the scale. It was also observed that the absence of alignment between reported commonly used traditional measures and their equivalence to kilograms is evident across all measurements. For example, investigative observations revealed that the sados reported to weigh 4 kg, actually weighed 5.5 kg when subjected to the scale. Surprisingly, even though respondents reported that the 20-L bucket weighed 18 kg, sample testing indicated that the weight can go up to 22 kg. Further investigation to identify the cause of this difference revealed that maize from different altitudes and various varieties have distinct weights. For example, some respondents from the farm households of Mbinga District reported that ‘the same variety cultivated at higher altitudes is heavier than its counterpart at lower altitudes. Commenting on the maize variety weight variations, respondents from Madaba District stated that: ‘the local variety is heavier than the hybrid variety’. Other respondents commented that: ‘other exhibited variations in weight are due to seed type regardless of whether it’s local or hybrid ‘. Specifically, several respondents provided examples of seed types and the DK maize variety was reported to be heavier than the Aminika and Tembo varieties. Consequently, the researcher aimed to determine the extent to which these perceptions align with those of the respondents.

Table 4 Traditional measures and farmers reported equivalence to kilogram
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3.4 Demographic factors and adoption of standard weights and measures

Table 5 illustrates the impact of sex, education level, bags of maize produced, and land size on the adoption of standard units of measurement. These variables were chosen due to their observed associations with standard units of measurement when the chi-square test was conducted. Sex, bags of maize produced, and land size exert a significant influence on the adoption of standard units of measurement. Farm households led by females are 0.623 times less likely to utilize standard units of measurement compared to those headed by men. The findings reveal that the likelihood of using standard units decreases with declining productivity levels. That is, other factors remaining constant, farm households with productivity levels in the categories of 1–5 and 6–10 are 0.194 and 0.505 times less likely, respectively, to use standard units compared to farm households with productivity levels in the category of 11–15 bags. The results also indicate that farm households with land sizes ranging between 6 to 10 acres are more inclined to use standard units of measurement compared to those with land sizes of 1–5 acres, assuming all other factors remain constant.

Table 5 Binary logistic regression on the effect of demographic variables on the adoption of standard units of measurement
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3.5 Impact of standard weights and measures on maize productivity

The negative binomial regression was employed to assess the impact of standard units of measurement on maize productivity within farm households. The model yielded statistically significant results χ2(11) = 18.275, p = 0.075. As illustrated in Table 6, the use of standard weights and measures significantly influences maize productivity among farm households. Specifically, farmers utilizing standard weights and measures exhibit 1.195 times more logs of bags compared to those not adhering to standard units. Furthermore, the variables of land size, and education level, included in the model, emerged as significant predictors of productivity. Notably, a decrease in land size is associated with declining productivity. That is, other factors held constant, land size in acres in the category of less than one acre, has 0.707 times less log of maize bags as compared to farm households having a land size in the category of 1-5acres. Education is statistically significant only at the non-formal level of education. When other factors are held constant, farmers with no formal education exhibit 0.632 times less log of bags in contrast to those with primary education.

Table 6 Negative binomial regression on the effect of standard units of measurements on productivity
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3.6 Impact of standard weights and measures on welfare

Multiple linear regression analysis was conducted to examine the effect of standard weights on farmers’ welfare. As indicated in Table 7, the model was expanded by incorporating potential predictors, specifically those demonstrating an association with welfare. Notably, the utilization of standard weights and measures has a positive and significant impact on the income earned by farmers. That is, for every one-unit increase in the use of standard weights and measures, the income of the farmers increases by a factor of 1.145. In other words, a one-unit increase in the use of standard weights and measures increases the income of farmers by 14.5%. For the potential predictors that were added in the model, all had a significant contribution to income though the level of significance did not apply to all levels of categories in some of the variables. Additionally, the contribution of land sizes to farm incomes increased with an increase in land size, assuming other factors remained constant. Notably, positive and significant contributions to education are evident in the categories of primary and secondary education. However, for bags produced per acre, a negative and significant contribution is revealed in the categories of 1–5 and 6–10. Regarding sex, one unit increase in female farmers decreases the income of farmers by a factor of 0.883 as opposed to males’ other factors kept constant.

Table 7 Multi-linear regression analysis on the effect of standard weights and measures on farmers’ welfare
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4 Discussion

This study sought to assess the profound impact of standard weights and measures on both productivity and welfare within the agricultural sector. The primary objective was to analyze the extent to which the utilization of standard weights and measures influences these key aspects. The findings underscore a noteworthy correlation between the adoption of standard units and enhanced maize productivity and the income of farmers. Notably, farmers employing standard measurements in the sale of maize produce demonstrate a significantly higher yield per acre compared to their counterparts who adhere to traditional measuring methods. This pattern suggests a tangible improvement in the measured productivity of farm households when standard units of measurement are employed. Specifically, the utilization of traditional equipment tends to yield additional kilograms when measured against a standard scale. Corroborating our findings, previous research by [7] highlighted instances where bags purchased from farmers exceeded the required market weight. Consequently, traders acquiring large quantities would deduct the excess weight before selling the bags to market vendors. Additionally, a study conducted by [24] on weight practices in Tanzania, specifically in the Iringa market, revealed consistent results. From a randomly selected sample of maize bags in the market, those from Iringa-rural, Songea, and Njombe exhibited significant variations in weight, with a noteworthy discrepancy of 50 kg. These findings further emphasize the detrimental impact of the ambiguous equivalence between traditional and standard units, as well as the observed disparities in weights from randomly tested samples in the field on agricultural productivity.

Despite the compelling evidence highlighting the substantial positive impact of standard units on productivity, it is noteworthy that the majority of farm households persist in measuring their produce using traditional methods. This trend aligns with the observations made by [14] in Ghana, where a significant proportion of farmers were found to eschew standard units in the sale of their produce. Similarly, [10] reported non-compliance with standard units in the marketing of staple crops in Tanzania. It is crucial to acknowledge, however, that the disparity in the use and non-use of standard units in Ghana, as identified by [14], pales in comparison to the findings of this study. It is noteworthy that the quantification of bags produced per acre was reliant on farmers' recall ability, which, as a variable, is subject to various influencing factors.

This study has established that land size plays a crucial role in influencing productivity. A negative and significant effect observed, indicates that productivity declines with a decrease in land size. These findings align with [19] who noted that increment in maize production in Tanzania is a result of expansion in the cultivated area rather than yield. This implies that, attaining zero hunger requires other measures considering land being a scarce resource. This underscores the pivotal role that standard units play in shaping productivity within the agricultural context. Furthermore, our findings highlight a notable correlation between education and productivity, particularly at the primary level. This contradicts the findings of [25], who asserted that education does not wield a considerable influence on productivity. Additionally, non-formal education negatively influences productivity. Our results diverge from [26], who suggested a weak positive statistically significant influence of education, positing that farm household heads with higher education marginally outperformed those with lower educational qualifications. Importantly, the study underscores that the effect of the use of standard units of measurement on productivity surpasses that of education. In essence, while formal education is acknowledged as a factor, it is deemed necessary but not a sufficient influencer to encourage farm households to adopt standard units of measurement. Given that a majority of respondents possess formal education, a prerequisite for using standard units, the study recommends incorporating education and practical training to underscore the importance of employing standard scales in measuring produce and its subsequent benefits on overall productivity.

The other interesting finding from this study is on differences in income generation. The findings indicate that farm households utilizing standard units of measurement generate higher income compared to those relying on traditional units for selling their produce. This aligns with the research by [10] which observed that a majority of maize-selling farmers use volume measurement. Rapid tests conducted revealed that bags weighed more than their indicated weights when measured on a standard scale, consequently reducing farmers' profit margins. Similarly, [14] reported comparable findings, emphasizing that the use of standard weights and measures maximizes farmers' profits.

Examining the variables incorporated into the model, it becomes evident that the increment in the number of bags of maize produced per acre exerts a positive impact on farmers' income. This implies that the significant impact on income arising from the observed differences in traditionally weighed bags, when subjected to a standard scale, becomes more pronounced as the reported volume of bags by farmers increases. In essence, traditionally weighed bags contain more kilograms than what is perceived and reported by farmers. This is in agreement with [7]. Land size and income were observed to be positively correlated. This could be attributed to the increased production as a result of expansion of cultivated land. Noack et al. [27] likewise affirmed that agricultural income increases with increasing farm size. However, with the increasing population and associated pressure exerted on land, its expansion poses a challenge. The results indicated that sex influences the income generated by farmers. This supports the findings of [28] who found that gender influenced the income of maize farmers in Kaduna state, Nigeria. The influence of females was less compared to males in agreement with [28] who noted that male generated higher incomes than females. The observed difference could be due to the comparative advantage that males have in decision making and accessing production resources. The results indicate that training on the role of standard units should be customized to reflect gender roles if the incomes of farm households are to be elevated in aggregate. In addition, education level influences farmers income. The findings corroborate with [29] who found that education positively influences income earned by farmers. The education could be helping farmers to easily comprehend and adapt to the changing technologies and practices. However, the representation of respondents with education level beyond primary was approximately 15%. This suggests that low level of education contributes to reluctance to change. Further points to the need to awareness and sensitization programs in a bid to advance understanding of the essential benefits that compliance with standard units have on their incomes. The study underscores the importance of standard units of measurement in enhancing farmers' welfare. It is noteworthy that a majority of farmers lack written records regarding their income.

The commonly used traditional units and their reported farmers’ equivalence to kilograms revealed mixed results. This is in agreement with the findings on regional variations of non-standard units in Nigeria that were conducted in six geopolitical zones and a cup of milk used as a traditional measure revealed different results in all six zones [7]. Similarly, in this study, the Sado which is a commonly used traditional measure by the majority of farmers in the Ruvuma region, lacked unanimity to what is perceived as an equivalence in terms of kilograms. The lack of unanimity amongst farmers points to preconceived opinions about traditional measures and further creates confusion on what is considered a standard equivalence. Similar findings were reported by [9] who revealed that amongst the problems of using non-standard units of measurement, variations in local standards are the most challenging problem. The sado being accepted and commonly used by the majority does not give justification for being standard as results have revealed. This supports [7], who states that, as much as a bunch of bananas is common in many areas, it is not standard as a bunch of bananas could have many bananas as opposed to other bunches and another bunch could be bigger compared to another bunch. Findings revealed that the same sado keenly filled with maize while maintaining the same heap level, produced different findings in terms of weight when put to scale and the possible causes of this variation were differences in altitude and variety type. Similar to the findings by [30] that seed weight of quercus leucotrichpora seeds were positively associated with altitude. The differences could be due to environmental changes as reported by [31] that seeds produced at low temperatures are 37% heavier in comparison to those produced at high temperatures. This implies that farmers who sell their maize planted at high altitudes, and are a local variety in non-standard units compromise their income and reported productivity since they sell more kilograms for less price. To standardize the value of the produce despite seed type, growth altitude and produce size, all produce needs to be measured using only one standard which cuts across all variables, hence the need for standard weights and measures.

The demographic characteristics of the respondents showed that the majority of the households are matrimonial. This accords with earlier findings by [13] who found that farming activities in Ghana are dominated by married families. An indication that farming is done jointly. More to this, a large proportion of the population of the households falls in the working population. This is in agreement with [11] who revealed that the majority of the farmers in the Njombe region are the active age. This implies that any change made in the current working population could influence change in the future working population. Additionally, male respondents were mostly represented in the study. This is in line with [11], who observed that in the Njombe region, males are largely involved in the sale of Irish potatoes. The majority of the study respondents possess primary education. This conforms with [32], who reported that the majority of the farm households in Ruvuma have primary education. This means that the majority of the farm households can read and write. It further suggests that standard weighing skills could be easily transferred and understood as the possession of formal education eases reading and recording of weighing scale results. The study underscores that training and awareness of the importance of standard units are vital. Considering the matrimonial set-up of most households, the study recommends that, education and any form of sensitization should consider gender roles and much emphasis should be put on the transformation of the youth since the youth are the future generation who will be the source of transfer of knowledge. This will gradually foster the generational change of moving from the traditional units of measurement to the standard use of weights and measures. The impact of adhering to standard weights and measures on farmers’ productivity and welfare is an indication that subsequent contribution to the principal goal of achieving zero hunger by 2030 will be realized, as outlined in Sustainable Development Goal 2.

While the study advances understanding of the crucial role played by standard weights and measures in accurately assessing reported productivity and income, it acknowledges a limitation: the reliance on farmers’ ability to recall for reported productivity and income. Future studies could delve into the reasons behind small-holder farmers’ reluctance to maintain written records. Furthermore, a thorough examination of the factors contributing to low compliance with standard units in the Tanzanian context is strongly recommended.

5 Conclusion

This study undertook a comprehensive analysis of the impact of standard weights and measures on the productivity and welfare of small-holder maize farmers. The findings unequivocally establish a significant positive influence of using standard weights and measures on both productivity and welfare within this demographic. However, a substantial proportion of small-holder farmers persist in eschewing standard weights and measures in the measurement and marketing of their produce, revealing a prevailing reliance on non-uniform traditional measures. Notably, the study highlights a positive correlation between the use of standard units of measurement and enhanced productivity and farm incomes. Several of bags produced per acre, sex and land size emerge as influential factors in the adoption of standard weights and measures. The outcomes affirm that farmers adhering to standard weights and measures in the sale of their produce experience higher yields per acre and increased income compared to those not conforming to these standards. This underscores the vital role of standard weights and measures in elevating both productivity and welfare among small-holder farmers. The study's contribution to existing knowledge lies in providing nuanced insights into the implications of standard weights and measures for this demographic, offering valuable guidance for policymakers and relevant bodies to tailor national solutions.

While the study advances understanding of the crucial role played by standard weights and measures in accurately assessing reported productivity and income, it acknowledges a limitation: the reliance on farmers’ ability to recall for reported productivity and income. Future studies could delve into the reasons behind small-holder farmers’ reluctance to maintain written records. Furthermore, a thorough examination of the factors contributing to low compliance with standard units in the Tanzanian context is strongly recommended. Additionally, since the cause for weight variation amongst seed species planted on different altitudes is subject to nature of species, genetic set up, climatic variations in regions etc. future studies are recommended to ascertain the cause for maize weight variations reported in the region. Empowering small-holder farmers through the agencies and organizations responsible for agriculture, industry, and trade via targeted training and sensitization is important. Such efforts will facilitate a shift away from traditional measuring practices, thereby improving productivity and subsequently elevating income levels among small-holder farmers. This strategic approach aligns with the study's broader implications for fostering positive changes from the grassroots level and contributes to the overarching goal of achieving zero hunger by 2030, as outlined in Sustainable Development Goal 2.