"Demand and Consumption of Bananas (Musa spp.) in Tanzania"

"Demand and Consumption of Bananas (Musa spp.) in Tanzania" | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report "Demand and Consumption of Bananas (Musa spp.) in Tanzania" William George This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4475344/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Bananas (Musa spp.) are a vital component of the diet, serving as a significant source of nutrition and income for people across urban and rural areas. The study was conducted to investigate the demand and consumption of bananas in Tanzania. The study utilized cross sectional data (2021) and time series data (2000–2021). The demand and consumption models were used as analytical tools. Key variables including price, income levels, and preferences were examined to understand their influence on banana demand and consumption. Results shows that elasticity of own price of banana was negative ranging from − 0.11 to -0.13 and statistically significant at 10% level. Cross-price elasticity with respect to cassava ranged from 0.06 to 0.08 and for sweet potatoes was 0.24. The income elasticity was positive ranging from 0.04 to 0.07 but significant. The elasticities of banana demand vary between urban and rural areas. Consumers in urban and rural areas was responsive to price stimuli. Price elasticities ranged from − 0.33 to -1.15 in urban areas and from − 0.48 to -1.01 in rural areas. The income elasticities were positive and significant for all regions. It is concluded that banana consumption was attributed to relative food prices, income, changing tastes and preferences. Consumption demand elasticity income Musa spp 1.0 Introduction Bananas (Musa spp.) are one of the most important staple foods in Tanzania, contributing significantly to food security, income generation, and rural livelihoods [ 1 ]. Tanzania is among the top banana-producing countries in Africa, with diverse varieties cultivated across different agro-ecological zones [ 2 ]. Bananas are not only consumed fresh but also processed into various products such as banana chips, juice, and flour, adding value to the crop and expanding market opportunities [ 3 ]. The consumption of bananas in Tanzania is deeply ingrained in the cultural and dietary practices of both urban and rural populations [ 4 ]. Bananas are rich in essential nutrients such as potassium, vitamins, and dietary fiber, making them a valuable component of a balanced diet [ 5 ]. Additionally, bananas serve as a source of income for millions of smallholder farmers, especially in rural areas where agriculture is the primary livelihood [ 2 ]. Despite their importance, several challenges threaten the sustainability of banana production and consumption in Tanzania. These include pests and diseases such as Fusarium wilt (Panama disease) and Banana Xanthomonas Wilt (BXW), which can devastate banana plantations and reduce yields [ 6 ]. Moreover, inadequate infrastructure, post-harvest losses, and market inefficiencies hinder the development of the banana value chain [ 2 ]. The crop occupies a strategic position in Tanzania’s economy contributing greatly to the internal exchange economy and providing national food security. However, despite the decline in consumption of bananas, the consumer demand for bananas in Tanzania and the magnitude to which the major determinants, namely price, income and preference influence its demand and consumption is not yet fully understood. This applies to the consumer demand of many other food crops ([ 7 , 8 ]. Demand is a multivariate relationship, that is, it is determined by many factors simultaneously. Some of the most important determinants of market demand for a particular product are own price, consumer income, prices of other commodities, consumer’s taste and preferences. Income distribution, total population, consumer’s wealth, credit availability, government policy and past levels of demand and income [ 9 ]. A complex web of interactions, however, between policies, income and prices contribute to access to food and therefore, food security [ 10 ]. While knowledge of the average price response of populations may be sufficient to determine a market-clearing price, it will not enable one to know how different parts of a population will change their consumption following a price change [ 10 ]. Furthermore, the absence of information on commodity substitution limits a planner’s ability to influence the consumption of specific groups through price changes without introducing disincentives, costly general subsidies, or targeting schemes that would strain the administrative resources of a country. Consumption research is therefore, as important as food security research because it can answer two basic questions. First, had the decrease/increase in consumption in recent years been a response to availability, change inn taste and preferences or to relative prices of banana substitute? If it is the later, they are reversible and subject to change through price policy. If it is due to changing tastes and preference, then it may prove difficult to “turn back the clock”. Second, how to consumption patterns vary by income groups? The objectives of the study were therefore, to analyze the consumption trends of bananas in Tanzania; to determine consumption in relation to prices and income; identify the possible implications of consumption on production and vice-versa and suggest policies regarding consumption relationship and food security. 2.0 Materials and Methods 2.1 Research design The study adopted both cross-sectional data (2020/21) and time series data (2000–2021). The cross-sectional data provides a snapshot of banana consumption and demand at a particular point in time across selected major towns of Tanzania. It allowed researchers to analyze variations in consumption patterns among different population segments. In addition, cross-sectional data helped to identify factors such as income levels, preferences that influence banana consumption at a specific point in time. On the other hand, time series data helped to analyze the factors evolved over time and their impact on consumption. By combining both types of data, researchers can better understand the complex interactions between these factors and their influence on banana demand. 2.2 Data sources The data were obtained from Ministry of Agriculture, Food Security and Cooperatives and the 2020/21 Tanzania Household Budget Survey Report. The primary data were collected through survey using a structured questionnaire to obtain information from the respondents. 2.23 Analytical methods The general form of the demand function was specific as shown in Eq. 1: $$DEMAND=f\left(Prices, Income, Family size, Dummy, Tastes\right)\dots \dots \dots \dots \dots \dots \dots .\dots \dots \dots \dots .\left(1\right)$$ A theoretical demand curve was based on the assumption that consumers seek to maximize utility subject to an income constraint (Koutsoyiannis, 1988). Thus, consumers are faced with a problem of choosing the specific goods and services that best satisfy their needs with the limits imposed by income. Quantity adjustments do not take place instantaneously because of imperfect knowledge, rigidities in consumer habits. Consumer uncertainty, etc. to account for the adjustment process, lagged demand for banana was included as an exploratory variable. A linear trend was included to account for changing tastes and preferences within the population. The constant elasticity demand function was used in the specification of the model. This captured the problem of simultaneous change of all the determinants, through this made it difficult to assess the influence of each individual factors separately. The model was also dynamized to express the generally accepted idea that current decision was influenced by past behavior. Banana, being a non-durable commodity, past purchases do reflect a habit which is acquired by buying and consuming the commodity in the past, so that the level of purchases in previous periods influences the current (and future) patterns of demand. Since capital accumulation in third world countries, Tanzania in particular, is negligible, lagged income will not be considered in the distributed lagged dynamic constant elasticity model to be specified. The assumption of “no money illusion” postulated by the traditional theory of the consumer has been taken note of by expressing the demand function as a homogenous function of degree zero (Koutsoyiannis, 1988). $${Q}_{x}={b}^{0}{\left(\frac{{P}_{x}}{CPI}\right)}^{{b}^{1}}+{\left(\frac{{P}_{y}}{CPI}\right)}^{{b}^{2}}+{\left(\frac{Y}{CPI}\right)}^{{b}^{3}}\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \left(2\right)$$ Where: \({Q}_{x}=\) Quantity demanded \(CPI=\) Consumer Price Index \(Y=\) Income \(P\left(x,y\right)=\) Prices $${b}^{0} - {b}^{3}= \text{C}\text{o}\text{e}\text{f}\text{f}\text{i}\text{c}\text{i}\text{e}\text{n}\text{t}\text{s}$$ If the prices and income change by the same proportions, the quantity demanded of a commodity will not change because constants appear in both numerator and denominator which cancel out. The new quantity demanded will be the same as the initial one, that is, there is no money illusion in the behavior of the consumer. The quantity of a commodity consumed in any period is assumed to be equal to total domestic production plus imports less exports and waste. In case of bananas in Tanzania, the imports and exports are negligible. Therefore, total consumption is the total production less wastes: $${DB}^{t}={\partial }_{0}+{\partial }_{1}ln{RPB}^{t}+{\partial }_{2}ln{RPCa}^{t}\left({lnRPS}^{t}\right)+{\partial }_{3}ln{Y}^{t}+{\partial }_{4}{DB}^{t-1}+{\partial }_{5}T+{\partial }_{6}DV+{U}^{t}\dots \dots \dots \dots \left(3\right)$$ Where: \({DB}^{t}=\) Quantity of bananas consumed per capita in Thousands of metric tons, in year \(t\) \({RPB}^{t}=\) Retail price of bananas in Shillings per metric ton, in year \(t\) \({RPCa}^{t}=\) Retail price of cassava in Shillings per metric ton, in year \(t\) \({RPS}^{t}=\) Retail price of Sweet potatoes in Shillings per metric ton, in year \(t\) \({Y}^{t}=\) Per capita income in shillings, in year \(t\) \({DB}^{t-1}=\) Quantity of bananas consumed per capita in thousands of metric ton, in year \(t-1\) \(T=\) Tastes and preferences trend value, 2000 = 1, 2001 = 2, etc. \(DV=\) Dummy variable to reflects the 1978-80 war (1978-80 = 1, 0 otherwise). \({U}^{t}=\) Random error term \({\partial }_{0}-{\partial }_{6}=\) Variable coefficients \(t=\) Current year. 3.0 Results and Discussion 3.1 Regression coefficients for banana demand function Table 1 shows that coefficient (elasticity) of own price of banana was negative and statistically significant at 10% level. In the short run, it ranged between − 0.11 and − 0.13. This implies that, other factors being equal, an increase of 10% in the price of bananas would reduce the demand between 1.1 and 1.3%. The low elasticities are not unreasonable given the fact that banana is taken to be superior to its close substitutes (root crops and cereal crops) especially among the urban dwellers. Also, an increase in its price will reduce demand in favour of the substitutes by only small percentage. Cross-price elasticity with respect to cassava ranged from 0.06 to 0.08 in the short run and that for sweet potatoes was 0.24 in the short run. Apparently, consumers prefer cassava to sweet potatoes, which in turn preferred to maize as substitute for bananas in that order. Table 1 Estimated time series regression coefficients for banana demand functions in Tanzania Variables Equation 1 Equation 2 Equation 3 Constant 2.61 2.18 1.81 \({\left(2.78\right)}^{**}\) \({\left(2.98\right)}^{***}\) \({\left(2.61\right)}^{**}\) Price of banana -0.13 -0.11 -0.11 \({\left(-2.44\right)}^{**}\) \({\left(-2.09\right)}^{**}\) \({\left(-2.09\right)}^{**}\) Price of cassava 0.08 0.06 - \({\left(1.15\right)}^{NS}\) \({\left(1.34\right)}^{NS}\) Price of sweet potatoes - - 0.24 \({\left(1.83\right)}^{**}\) Income 0.04 0.04 0.07 \({\left(1.32\right)}^{NS}\) \({\left(1.23\right)}^{NS}\) \({\left(3.29\right)}^{**}\) Lagged banana demand 0.34 0.47 0.61 \({\left(1.38\right)}^{*}\) \({\left(2.69\right)}^{**}\) \({\left(4.07\right)}^{***}\) Tastes and preference -0.01 -0.008 -0.001 \({\left(-2.03\right)}^{**}\) \({\left(-1.98\right)}^{**}\) \({\left(-1.99\right)}^{**}\) Dummy Variable 0.05 \({\left(0.75\right)}^{NS}\) Adjusted coefficients of determination 0.86 0.87 0.86 \(*, **, ***\) : Significant at the 10%, 5% and 1% levels, respectively, Values in parentheses are students’ t values, Eq. 1–3 are best probable fitting equations. The income elasticity was positive but significant. It ranged between 0.04 and 0.07 in the short run. Therefore, as income increases, the demand for bananas increases. This conform to a priori economic theory. The coefficient for lagged consumption of banana ranged between 0.34 and 0.61 and was significant \(\left(P=0.17\right)\) . The trend variable has a negative and significant coefficient at 6% 8% levels. This suggests a negative growth rate in consumption attributed to decline in production. The income elasticity was positive but significant. It ranged between 0.04 and 0.07 in the short run. Therefore, as income increases, the demand for bananas increases. This conform to a priori economic theory. The coefficient for lagged consumption of banana ranged between 0.34 and 0.61 and was significant \(\left(P=0.17\right)\) . The trend variable has a negative and significant coefficient at 6% 8% levels. This suggests a negative growth rate in consumption attributed to decline in production. The coefficient for Dummy Variable (DV) was positive and insignificant coefficient. This implies that during periods of war in Tanzania (1978/1980), the demand for bananas, just like other food commodities and necessities, increased. Probably, people diverted their incomes to ensure that they had enough food. The explanatory power of the demand model for bananas, the adjusted coefficient of determination \(\left({R}^{2}\right)\) , implied that 86–90% of the variation in the dependent variable is explained by the explanatory variables (own price, substitute food prices, income, dummy variables (war). 3.2 Elasticity of banana demand function in rural and urban areas Field survey data indicated that, consumers were responsive to price stimuli. An increase in price was accompanied by a decrease in the per capita consumption. Price elasticities ranged from − .0.33 to -1.15 in urban areas and − 0.48 to -1.01 in rural areas (Table 2 ). Table 2 Elasticities of banana demand functions in urban and rural areas of Tanzania Dar es Salaam Mwanza Mbeya Arusha Tanga Urban urban rural Urban Rural Urban Rural Urban Own price elasticity -1.1 -1.15 -1.01 -0.79 -0.54 0.33 -0.48 -0.71 Income elasticity of demand 0.77 1.05 0.65 0.63 0.85 -0.33 0.49 0.64 Adjusted R-square 0.42 0.41 0.55 0.38 0.30 0.54 0.46 0.32 A 10% increase in own price of bananas reduced consumption by 3.3 to 11.5%. Short run income elasticities were found to be positive for all regions and major towns and significant \(\left(P=0.1\right)\) . As incomes increases therefore, the demand for banana increases. The income elasticities therefore, imply that a 10% increase in the per capita income is postulated to induce increased per capita consumption of banana between 4.5% − 8.5% in the short run. The explanatory power of the presented demand model, the adjusted coefficient of determination ranges between extremes of 0.30 and 0.55. Among urban consumers, banana was found to be necessity food item with the exception of Mwanza town. Where banana features as luxury food item, it implies that banana may not be a staple food crop in that area. Conclusion This study provides valuable insights into the demand and consumption of bananas in Tanzania. The analysis revealed the significant importance of bananas as a staple food in both urban and rural areas, serving as a source of nutrition and income for millions of people. Through time series regression analysis, we identified key factors influencing banana demand. tastes and preferences are changing from banana to cassava and sweet potatoes and relative food prices do not contribute much to changing tastes and preferences. Additionally, the estimation of elasticities highlighted variations in demand responsiveness between urban and rural contexts. The findings underscore the importance of tailored interventions to promote sustainable banana consumption in Tanzania. Improving market infrastructure, and addressing socio-economic disparities are critical steps towards ensuring food security and improving livelihoods for banana farmers and consumers. Moreover, value addition and market diversification strategies can unlock new opportunities for income generation and economic growth in the banana sector. Moving forward, policymakers, stakeholders, and development partners must prioritize evidence-based decision-making and collaborative efforts to address the challenges facing the banana value chain. By implementing targeted interventions informed by the findings of this study, Tanzania can unlock the full potential of its banana sector, contributing to enhanced food security, economic development, and improved well-being for its citizens. This study serves as a foundation for policy formulation aimed at promoting sustainable agricultural development and addressing food security challenges in Tanzania and beyond. Declarations Data availability The author confirms that all data generated or analyzed during this study are included in this manuscript paper. Furthermore, primary and secondary sources and data supporting the findings of this study were publicly available at the time of submission. Funding No funding was received Author Information Authors Information Department of Economics, The University of Dodoma, P.O. Box 259, Dodoma, Tanzania William George Contributions WG: Conceptualization, Methodology, Software, Data curation, Writing- Original draft preparation, Visualization, Investigation, Writing- Reviewing and Editing. Corresponding author Correspondence to William George, Corresponding author’s email: [email protected] Ethics approval and consent to participate This article does not contain any studies with animals or humans performed by any of the authors. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Food and Agriculture Organization (FAO). (2017). Banana market review. Retrieved on 20.10. 2020 from http://www.fao.org/3/a-i7403e.pdf National Bureau of Statistics (NBS). (2020). Tanzania agricultural sample census 2019/2020 . Dar es Salaam, Tanzania: Author. Nkuba, J., Ndunguru, G., Maerere, A., & Rajabu, C. A. (2018). Assessment of banana post-harvest handling and processing practices in Tanzania: A case of Kagera, Mbeya and Kilimanjaro regions. Journal of Stored Products and Postharvest Research, 9(2), 19–27. Msinjili, K. N., Talipouo, N., Dizyee, K., & Vinceti, B. (2019). Socio-economic impact of banana bacterial wilt disease (BBW) on banana farming households in Tanzania. Agricultural Sciences, 10(08), 1104–1117. Mrema, G. C., Masunga, G. S., & Msolla, S. (2016). Banana diversity in Tanzania: Implications for conservation and improved livelihoods. Agriculture & Food Security, 5(1), 1–15. Swai, I. S., Minja, R. J. A., & Mbega, E. R. (2019). Status of banana diseases and pests, management practices and challenges in Tanzania. African Journal of Agricultural Research, 14(22), 975–986. Purcell, C.J. and Raunikar, R. (2018). Price Elasticities from Panel Data. American Journal of Agricultural Economics, 53(2): 216–221. Timmer, P., Falcon, W.P. and Pearson, S.R. (2010). Food Policy Analysis . An Econometric analysis of the Tanzanian and Uganda Economies. FAPU Research Series Working Paper No. 92 – 1. Koutsoyiannis, A. (2020). Modern Microeconomics . Second Edition. Longman, Essex, England. Anonymous, 1988. Food Security of the Poor . International Food Policy Research Institute. Pp.67–69. Massachusetts Washington, DC. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Nov, 2024 Reviews received at journal 08 Nov, 2024 Reviews received at journal 06 Oct, 2024 Reviewers agreed at journal 01 Oct, 2024 Reviewers agreed at journal 28 Sep, 2024 Reviewers agreed at journal 27 Sep, 2024 Reviewers agreed at journal 08 Aug, 2024 Reviewers agreed at journal 07 Jul, 2024 Reviewers invited by journal 05 Jul, 2024 Editor assigned by journal 18 Jun, 2024 Submission checks completed at journal 14 Jun, 2024 First submitted to journal 25 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Tanzania is among the top banana-producing countries in Africa, with diverse varieties cultivated across different agro-ecological zones [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Bananas are not only consumed fresh but also processed into various products such as banana chips, juice, and flour, adding value to the crop and expanding market opportunities [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe consumption of bananas in Tanzania is deeply ingrained in the cultural and dietary practices of both urban and rural populations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Bananas are rich in essential nutrients such as potassium, vitamins, and dietary fiber, making them a valuable component of a balanced diet [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, bananas serve as a source of income for millions of smallholder farmers, especially in rural areas where agriculture is the primary livelihood [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite their importance, several challenges threaten the sustainability of banana production and consumption in Tanzania. These include pests and diseases such as Fusarium wilt (Panama disease) and Banana Xanthomonas Wilt (BXW), which can devastate banana plantations and reduce yields [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, inadequate infrastructure, post-harvest losses, and market inefficiencies hinder the development of the banana value chain [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe crop occupies a strategic position in Tanzania\u0026rsquo;s economy contributing greatly to the internal exchange economy and providing national food security. However, despite the decline in consumption of bananas, the consumer demand for bananas in Tanzania and the magnitude to which the major determinants, namely price, income and preference influence its demand and consumption is not yet fully understood. This applies to the consumer demand of many other food crops ([\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDemand is a multivariate relationship, that is, it is determined by many factors simultaneously. Some of the most important determinants of market demand for a particular product are own price, consumer income, prices of other commodities, consumer\u0026rsquo;s taste and preferences. Income distribution, total population, consumer\u0026rsquo;s wealth, credit availability, government policy and past levels of demand and income [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A complex web of interactions, however, between policies, income and prices contribute to access to food and therefore, food security [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile knowledge of the average price response of populations may be sufficient to determine a market-clearing price, it will not enable one to know how different parts of a population will change their consumption following a price change [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, the absence of information on commodity substitution limits a planner\u0026rsquo;s ability to influence the consumption of specific groups through price changes without introducing disincentives, costly general subsidies, or targeting schemes that would strain the administrative resources of a country.\u003c/p\u003e \u003cp\u003eConsumption research is therefore, as important as food security research because it can answer two basic questions. First, had the decrease/increase in consumption in recent years been a response to availability, change inn taste and preferences or to relative prices of banana substitute? If it is the later, they are reversible and subject to change through price policy. If it is due to changing tastes and preference, then it may prove difficult to \u0026ldquo;turn back the clock\u0026rdquo;. Second, how to consumption patterns vary by income groups? The objectives of the study were therefore, to analyze the consumption trends of bananas in Tanzania; to determine consumption in relation to prices and income; identify the possible implications of consumption on production and vice-versa and suggest policies regarding consumption relationship and food security.\u003c/p\u003e"},{"header":"2.0 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research design\u003c/h2\u003e \u003cp\u003eThe study adopted both cross-sectional data (2020/21) and time series data (2000\u0026ndash;2021). The cross-sectional data provides a snapshot of banana consumption and demand at a particular point in time across selected major towns of Tanzania. It allowed researchers to analyze variations in consumption patterns among different population segments. In addition, cross-sectional data helped to identify factors such as income levels, preferences that influence banana consumption at a specific point in time. On the other hand, time series data helped to analyze the factors evolved over time and their impact on consumption. By combining both types of data, researchers can better understand the complex interactions between these factors and their influence on banana demand.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data sources\u003c/h2\u003e \u003cp\u003eThe data were obtained from Ministry of Agriculture, Food Security and Cooperatives and the 2020/21 Tanzania Household Budget Survey Report. The primary data were collected through survey using a structured questionnaire to obtain information from the respondents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.23 Analytical methods\u003c/h2\u003e \u003cp\u003eThe general form of the demand function was specific as shown in Eq.\u0026nbsp;1:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$DEMAND=f\\left(Prices, Income, Family size, Dummy, Tastes\\right)\\dots \\dots \\dots \\dots \\dots \\dots \\dots .\\dots \\dots \\dots \\dots .\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eA theoretical demand curve was based on the assumption that consumers seek to maximize utility subject to an income constraint (Koutsoyiannis, 1988). Thus, consumers are faced with a problem of choosing the specific goods and services that best satisfy their needs with the limits imposed by income. Quantity adjustments do not take place instantaneously because of imperfect knowledge, rigidities in consumer habits. Consumer uncertainty, etc. to account for the adjustment process, lagged demand for banana was included as an exploratory variable. A linear trend was included to account for changing tastes and preferences within the population. The constant elasticity demand function was used in the specification of the model. This captured the problem of simultaneous change of all the determinants, through this made it difficult to assess the influence of each individual factors separately. The model was also dynamized to express the generally accepted idea that current decision was influenced by past behavior. Banana, being a non-durable commodity, past purchases do reflect a habit which is acquired by buying and consuming the commodity in the past, so that the level of purchases in previous periods influences the current (and future) patterns of demand.\u003c/p\u003e \u003cp\u003eSince capital accumulation in third world countries, Tanzania in particular, is negligible, lagged income will not be considered in the distributed lagged dynamic constant elasticity model to be specified. The assumption of \u0026ldquo;no money illusion\u0026rdquo; postulated by the traditional theory of the consumer has been taken note of by expressing the demand function as a homogenous function of degree zero (Koutsoyiannis, 1988).\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${Q}_{x}={b}^{0}{\\left(\\frac{{P}_{x}}{CPI}\\right)}^{{b}^{1}}+{\\left(\\frac{{P}_{y}}{CPI}\\right)}^{{b}^{2}}+{\\left(\\frac{Y}{CPI}\\right)}^{{b}^{3}}\\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\dots \\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Q}_{x}=\\)\u003c/span\u003e \u003c/span\u003e Quantity demanded\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(CPI=\\)\u003c/span\u003e \u003c/span\u003e Consumer Price Index\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(Y=\\)\u003c/span\u003e \u003c/span\u003e Income\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(P\\left(x,y\\right)=\\)\u003c/span\u003e \u003c/span\u003e Prices\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${b}^{0} - {b}^{3}= \\text{C}\\text{o}\\text{e}\\text{f}\\text{f}\\text{i}\\text{c}\\text{i}\\text{e}\\text{n}\\text{t}\\text{s}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIf the prices and income change by the same proportions, the quantity demanded of a commodity will not change because constants appear in both numerator and denominator which cancel out. The new quantity demanded will be the same as the initial one, that is, there is no money illusion in the behavior of the consumer. The quantity of a commodity consumed in any period is assumed to be equal to total domestic production plus imports less exports and waste. In case of bananas in Tanzania, the imports and exports are negligible. Therefore, total consumption is the total production less wastes:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${DB}^{t}={\\partial }_{0}+{\\partial }_{1}ln{RPB}^{t}+{\\partial }_{2}ln{RPCa}^{t}\\left({lnRPS}^{t}\\right)+{\\partial }_{3}ln{Y}^{t}+{\\partial }_{4}{DB}^{t-1}+{\\partial }_{5}T+{\\partial }_{6}DV+{U}^{t}\\dots \\dots \\dots \\dots \\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({DB}^{t}=\\)\u003c/span\u003e \u003c/span\u003e Quantity of bananas consumed per capita in Thousands of metric tons, in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({RPB}^{t}=\\)\u003c/span\u003e \u003c/span\u003e Retail price of bananas in Shillings per metric ton, in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({RPCa}^{t}=\\)\u003c/span\u003e \u003c/span\u003e Retail price of cassava in Shillings per metric ton, in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({RPS}^{t}=\\)\u003c/span\u003e \u003c/span\u003e Retail price of Sweet potatoes in Shillings per metric ton, in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Y}^{t}=\\)\u003c/span\u003e \u003c/span\u003e Per capita income in shillings, in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({DB}^{t-1}=\\)\u003c/span\u003e \u003c/span\u003e Quantity of bananas consumed per capita in thousands of metric ton, in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t-1\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(T=\\)\u003c/span\u003e \u003c/span\u003e Tastes and preferences trend value, 2000\u0026thinsp;=\u0026thinsp;1, 2001\u0026thinsp;=\u0026thinsp;2, etc.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(DV=\\)\u003c/span\u003e \u003c/span\u003e Dummy variable to reflects the 1978-80 war (1978-80\u0026thinsp;=\u0026thinsp;1, 0 otherwise).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({U}^{t}=\\)\u003c/span\u003e \u003c/span\u003e Random error term\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\partial }_{0}-{\\partial }_{6}=\\)\u003c/span\u003e \u003c/span\u003e Variable coefficients\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(t=\\)\u003c/span\u003e \u003c/span\u003e Current year.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Regression coefficients for banana demand function\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that coefficient (elasticity) of own price of banana was negative and statistically significant at 10% level. In the short run, it ranged between − 0.11 and − 0.13. This implies that, other factors being equal, an increase of 10% in the price of bananas would reduce the demand between 1.1 and 1.3%. The low elasticities are not unreasonable given the fact that banana is taken to be superior to its close substitutes (root crops and cereal crops) especially among the urban dwellers. Also, an increase in its price will reduce demand in favour of the substitutes by only small percentage. Cross-price elasticity with respect to cassava ranged from 0.06 to 0.08 in the short run and that for sweet potatoes was 0.24 in the short run. Apparently, consumers prefer cassava to sweet potatoes, which in turn preferred to maize as substitute for bananas in that order.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated time series regression coefficients for banana demand functions in Tanzania\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquation 1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEquation 2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEquation 3\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(2.78\\right)}^{**}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(2.98\\right)}^{***}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(2.61\\right)}^{**}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrice of banana\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(-2.44\\right)}^{**}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(-2.09\\right)}^{**}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(-2.09\\right)}^{**}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrice of cassava\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(1.15\\right)}^{NS}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(1.34\\right)}^{NS}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrice of sweet potatoes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(1.83\\right)}^{**}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(1.32\\right)}^{NS}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(1.23\\right)}^{NS}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(3.29\\right)}^{**}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLagged banana demand\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(1.38\\right)}^{*}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(2.69\\right)}^{**}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(4.07\\right)}^{***}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTastes and preference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(-2.03\\right)}^{**}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(-1.98\\right)}^{**}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(-1.99\\right)}^{**}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDummy Variable\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(0.75\\right)}^{NS}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted coefficients of determination\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(*, **, ***\\)\u003c/span\u003e\u003c/span\u003e : Significant at the 10%, 5% and 1% levels, respectively, Values in parentheses are students’ t values, Eq.\u0026nbsp;1–3 are best probable fitting equations.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe income elasticity was positive but significant. It ranged between 0.04 and 0.07 in the short run. Therefore, as income increases, the demand for bananas increases. This conform to a priori economic theory. The coefficient for lagged consumption of banana ranged between 0.34 and 0.61 and was significant\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(P=0.17\\right)\\)\u003c/span\u003e\u003c/span\u003e. The trend variable has a negative and significant coefficient at 6% 8% levels. This suggests a negative growth rate in consumption attributed to decline in production.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe income elasticity was positive but significant. It ranged between 0.04 and 0.07 in the short run. Therefore, as income increases, the demand for bananas increases. This conform to a priori economic theory. The coefficient for lagged consumption of banana ranged between 0.34 and 0.61 and was significant\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(P=0.17\\right)\\)\u003c/span\u003e\u003c/span\u003e. The trend variable has a negative and significant coefficient at 6% 8% levels. This suggests a negative growth rate in consumption attributed to decline in production.\u003c/p\u003e \u003cp\u003eThe coefficient for Dummy Variable (DV) was positive and insignificant coefficient. This implies that during periods of war in Tanzania (1978/1980), the demand for bananas, just like other food commodities and necessities, increased. Probably, people diverted their incomes to ensure that they had enough food. The explanatory power of the demand model for bananas, the adjusted coefficient of determination\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left({R}^{2}\\right)\\)\u003c/span\u003e\u003c/span\u003e, implied that 86–90% of the variation in the dependent variable is explained by the explanatory variables (own price, substitute food prices, income, dummy variables (war).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Elasticity of banana demand function in rural and urban areas\u003c/h2\u003e \u003cp\u003eField survey data indicated that, consumers were responsive to price stimuli. An increase in price was accompanied by a decrease in the per capita consumption. Price elasticities ranged from − .0.33 to -1.15 in urban areas and − 0.48 to -1.01 in rural areas (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eElasticities of banana demand functions in urban and rural areas of Tanzania\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDar es Salaam\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMwanza\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMbeya\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eArusha\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTanga\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eurban\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003erural\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwn price elasticity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.71\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome elasticity of demand\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-square\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eA 10% increase in own price of bananas reduced consumption by 3.3 to 11.5%. Short run income elasticities were found to be positive for all regions and major towns and significant\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(P=0.1\\right)\\)\u003c/span\u003e\u003c/span\u003e. As incomes increases therefore, the demand for banana increases. The income elasticities therefore, imply that a 10% increase in the per capita income is postulated to induce increased per capita consumption of banana between 4.5% − 8.5% in the short run. The explanatory power of the presented demand model, the adjusted coefficient of determination ranges between extremes of 0.30 and 0.55. Among urban consumers, banana was found to be necessity food item with the exception of Mwanza town. Where banana features as luxury food item, it implies that banana may not be a staple food crop in that area.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides valuable insights into the demand and consumption of bananas in Tanzania. The analysis revealed the significant importance of bananas as a staple food in both urban and rural areas, serving as a source of nutrition and income for millions of people. Through time series regression analysis, we identified key factors influencing banana demand. tastes and preferences are changing from banana to cassava and sweet potatoes and relative food prices do not contribute much to changing tastes and preferences. Additionally, the estimation of elasticities highlighted variations in demand responsiveness between urban and rural contexts.\u003c/p\u003e\u003cp\u003eThe findings underscore the importance of tailored interventions to promote sustainable banana consumption in Tanzania. Improving market infrastructure, and addressing socio-economic disparities are critical steps towards ensuring food security and improving livelihoods for banana farmers and consumers. Moreover, value addition and market diversification strategies can unlock new opportunities for income generation and economic growth in the banana sector.\u003c/p\u003e\u003cp\u003eMoving forward, policymakers, stakeholders, and development partners must prioritize evidence-based decision-making and collaborative efforts to address the challenges facing the banana value chain. By implementing targeted interventions informed by the findings of this study, Tanzania can unlock the full potential of its banana sector, contributing to enhanced food security, economic development, and improved well-being for its citizens. This study serves as a foundation for policy formulation aimed at promoting sustainable agricultural development and addressing food security challenges in Tanzania and beyond.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author confirms that all data generated or analyzed during this study are included in this manuscript paper. Furthermore, primary and secondary sources and data supporting the findings of this study were publicly available at the time of submission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors Information\u003c/p\u003e\n\u003cp\u003eDepartment of Economics, The University of Dodoma, P.O. Box 259, Dodoma, Tanzania\u003c/p\u003e\n\u003cp\u003eWilliam George\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWG: Conceptualization, Methodology, Software, Data curation, Writing- Original draft preparation, Visualization, Investigation, Writing- Reviewing and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to William \u0026nbsp;George, Corresponding author\u0026rsquo;s email: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with animals or humans performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFood and Agriculture Organization (FAO). (2017). Banana market review. Retrieved on 20.10. 2020 from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fao.org/3/a-i7403e.pdf\u003c/span\u003e\u003cspan address=\"http://www.fao.org/3/a-i7403e.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Bureau of Statistics (NBS). (2020). \u003cem\u003eTanzania agricultural sample census 2019/2020\u003c/em\u003e. Dar es Salaam, Tanzania: Author.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNkuba, J., Ndunguru, G., Maerere, A., \u0026amp; Rajabu, C. A. (2018). Assessment of banana post-harvest handling and processing practices in Tanzania: A case of Kagera, Mbeya and Kilimanjaro regions. Journal of Stored Products and Postharvest Research, 9(2), 19\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMsinjili, K. N., Talipouo, N., Dizyee, K., \u0026amp; Vinceti, B. (2019). Socio-economic impact of banana bacterial wilt disease (BBW) on banana farming households in Tanzania. Agricultural Sciences, 10(08), 1104\u0026ndash;1117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMrema, G. C., Masunga, G. S., \u0026amp; Msolla, S. (2016). Banana diversity in Tanzania: Implications for conservation and improved livelihoods. Agriculture \u0026amp; Food Security, 5(1), 1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwai, I. S., Minja, R. J. A., \u0026amp; Mbega, E. R. (2019). Status of banana diseases and pests, management practices and challenges in Tanzania. African Journal of Agricultural Research, 14(22), 975\u0026ndash;986.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePurcell, C.J. and Raunikar, R. (2018). Price Elasticities from Panel Data. American Journal of Agricultural Economics, 53(2): 216\u0026ndash;221.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimmer, P., Falcon, W.P. and Pearson, S.R. (2010). \u003cem\u003eFood Policy Analysis\u003c/em\u003e. An Econometric analysis of the Tanzanian and Uganda Economies. FAPU Research Series Working Paper No. 92\u0026thinsp;\u0026ndash;\u0026thinsp;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoutsoyiannis, A. (2020). \u003cem\u003eModern Microeconomics\u003c/em\u003e. Second Edition. Longman, Essex, England.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnonymous, 1988. \u003cem\u003eFood Security of the Poor\u003c/em\u003e. International Food Policy Research Institute. Pp.67\u0026ndash;69. Massachusetts Washington, DC.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-food","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discoverfood","sideBox":"Learn more about [Discover Food](https://www.springer.com/44187)","snPcode":"","submissionUrl":"","title":"Discover Food","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Consumption, demand, elasticity, income, Musa spp","lastPublishedDoi":"10.21203/rs.3.rs-4475344/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4475344/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBananas (Musa spp.) are a vital component of the diet, serving as a significant source of nutrition and income for people across urban and rural areas. The study was conducted to investigate the demand and consumption of bananas in Tanzania. The study utilized cross sectional data (2021) and time series data (2000\u0026ndash;2021). The demand and consumption models were used as analytical tools. Key variables including price, income levels, and preferences were examined to understand their influence on banana demand and consumption. Results shows that elasticity of own price of banana was negative ranging from \u0026minus;\u0026thinsp;0.11 to -0.13 and statistically significant at 10% level. Cross-price elasticity with respect to cassava ranged from 0.06 to 0.08 and for sweet potatoes was 0.24. The income elasticity was positive ranging from 0.04 to 0.07 but significant. The elasticities of banana demand vary between urban and rural areas. Consumers in urban and rural areas was responsive to price stimuli. Price elasticities ranged from \u0026minus;\u0026thinsp;0.33 to -1.15 in urban areas and from \u0026minus;\u0026thinsp;0.48 to -1.01 in rural areas. The income elasticities were positive and significant for all regions. It is concluded that banana consumption was attributed to relative food prices, income, changing tastes and preferences.\u003c/p\u003e","manuscriptTitle":"\"Demand and Consumption of Bananas (Musa spp.) in Tanzania\"","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-01 08:28:28","doi":"10.21203/rs.3.rs-4475344/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-14T05:19:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-09T03:34:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-06T08:37:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260124188416131762213989268406819001953","date":"2024-10-01T15:01:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59269157265362616927288362116360868325","date":"2024-09-28T21:29:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32086118445392625666159724759893082686","date":"2024-09-27T10:10:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198278272867851844953605082137073249594","date":"2024-08-08T10:11:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1485499967653286787753683043409020694","date":"2024-07-07T19:10:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-05T16:07:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-18T05:33:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-14T06:22:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Food","date":"2024-05-25T06:09:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-food","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discoverfood","sideBox":"Learn more about [Discover Food](https://www.springer.com/44187)","snPcode":"","submissionUrl":"","title":"Discover Food","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f795a3b2-df1b-4e2c-8cf9-f1a31e087cda","owner":[],"postedDate":"July 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T05:08:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-01 08:28:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4475344","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4475344","identity":"rs-4475344","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}