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  • 1 Department of Food Chain Management, Faculty of Economics and Social Sciences, Hungarian University of Agriculture and Life Sciences, , Práter K u. 1., H-2100, Gödöllő, , Hungary
  • | 2 Faculty of Education and Psychology, Eötvös Loránd University, , Kazinczy street 23–27., H-1075, Budapest, , Hungary
Open access

Abstract

The contribution of food production to the environmental burden is considerable, therefore, numerous countries have been trying to create a sustainable food supply chain to ensure food and nutrition security. The scope of this study was to analyse the association between water footprint and healthiness based on dietary records. Furthermore, it was aimed to create a classification of integrative dietary indicators of sustainable nutrition. With these methodological aims, the dietary records of 25 healthy adults were assessed. The dietary quality scores and dietary water footprint were calculated and Spearman's rank correlation was tested between them. The indicator nutrients were classified based on their advantageous or disadvantageous health impact and association with water footprint. There was a significant positive correlation between the meat consumption and water footprint, while significant negative correlations were found between the dietary quality score and water footprint and dietary quality score and meat consumption (P < 0.05). Protein, energy, sodium, and saturated fatty acids as integrated indicator nutrients could be identified for both dietary quality and water footprint. The improvement in dietary quality could simultaneously decrease the dietary water footprint. The integration of environmental impact into the analysis of diets could be the future direction in the counseling practice of nutritionists.

Abstract

The contribution of food production to the environmental burden is considerable, therefore, numerous countries have been trying to create a sustainable food supply chain to ensure food and nutrition security. The scope of this study was to analyse the association between water footprint and healthiness based on dietary records. Furthermore, it was aimed to create a classification of integrative dietary indicators of sustainable nutrition. With these methodological aims, the dietary records of 25 healthy adults were assessed. The dietary quality scores and dietary water footprint were calculated and Spearman's rank correlation was tested between them. The indicator nutrients were classified based on their advantageous or disadvantageous health impact and association with water footprint. There was a significant positive correlation between the meat consumption and water footprint, while significant negative correlations were found between the dietary quality score and water footprint and dietary quality score and meat consumption (P < 0.05). Protein, energy, sodium, and saturated fatty acids as integrated indicator nutrients could be identified for both dietary quality and water footprint. The improvement in dietary quality could simultaneously decrease the dietary water footprint. The integration of environmental impact into the analysis of diets could be the future direction in the counseling practice of nutritionists.

1 Introduction

The nutritional pattern affects not only human health but the environment as well, however, the size of its effect considerably differs depending on the type of diet. Agriculture and livestock farming put a great burden on the environment – especially the production of animal based foods – by pressures such as greenhouse gas emission (GHGE), land and water use, and pollution among others (Fischer and Garnett, 2016; Vanham et al., 2019). In order to decrease this environmental burden, shifting the population nutrition is thought to be a possible direction, therefore, several countries have involved sustainability – thus environmental consideration – into their official food-based dietary guidelines (Fischer and Garnett, 2016). According to the review of Harris et al. (2020), the difference in the dietary WFP of different dietary scenarios could be up to 30% that is especially important considering that food production contributes to 70% of the total anthropogenic water footprint and is the major source of water pollution (Fischer and Garnett, 2016). That is one of the reasons why research has put focus on this issue recently (Tompa et al., 2020b; Harris et al., 2020). Non-communicable diseases (NCDs) are responsible for the 2/3rds of total death numbers in Hungary. According to the database of the Global Burden of Disease (GBD), the dietary factors among lifestyle risk factors are the major contributors to the development of NCDs (Institute for Health Metrics and Evaluation (IHME), 2019). Thus, the measurement of the adequacy and healthiness of nutrition is particularly important in order to optimise diets to prevent NCDs. In the field of sustainable nutrition (SN), it is often carried out by describing it with a simple and complex score value (dietary quality scores (DQSs)). It is also a reasonable measurement since SN is characterised by numerous metrics and DQSs can make it easier to understand the complex nature of it. The purpose of DQSs is to classify foods, meals, and diets as advantageous or disadvantageous in the aspect of health. In the recent period, the application of DQSs in the field of SN has become a well-accepted method in the integrated analysis of environmental impact and healthiness of nutrition, however, the application of them still holds some challenges due to the complex nature of metrics (Hallström et al., 2018).

2 Materials and methods

The purpose of this study was to analyse the association between dietary water footprint and dietary quality (i.e. healthiness). Furthermore, it was aimed to classify dietary indicators (i.e. nutrients) based on methods published in the international scientific literature (van Dooren et al., 2017; Hallström et al., 2018), while considering the specific Hungarian aspects. These methods could be applicable in the analysis of diets, so they could bring the environmental considerations into the counseling practice of nutritionists as well as serve as an initial step to further research in the field of sustainable nutrition focused on the Hungarian population. Twenty-five healthy and adult (18+ years old) volunteers were recruited by an online questionnaire from 08/05/2019 to 01/16/2020, they could participate anonymously and by accepting GDPR consent. The group of this study included 7 male and 18 female participants, their average age was 28 and they also self-reported their diet: 19 stated not to follow any particular diet and 6 kept plant-based diet. At the time of data collection, 11 individual held college or university degree, while 14 had high school degree; 24 of them lived in the capital or other cities and 1 in a village.

2.1 Determination of dietary quality scores

To measure the healthiness of the participants' nutrition, their 3-day dietary records were validated and evaluated with the NutriComp DietCAD (NutriComp Bt., Budapest) diet analysis software that is specific for the characteristics of Hungarian nutrition. By making this analysis, the energy, nutrients, and total meat intake values were determined in the dimension of amount/day/capita averaged for the 3 days. The estimated intake values to calculate DQSs were further processed. Among several methods to calculate DQSs, algorithms that are based on the ratio of nutrient intake values and dietary recommended intake values (RDIs) were applied. To categorise nutrients as qualifying (i.e. advantageous) or dis-qualifying (i.e. disadvantageous) in the aspect of health, the classifications were based on the review of Hallström et al. (2018), as these nutrients were considered as indicators for good or bad dietary quality as applied by several international studies (Hallström et al., 2018). The problematic definition of nutrients as advantageous or disadvantageous can be resolved by considering their population intake level and the health risks or benefits related to this intake level. The RDI components of algorithms were based on the published recommendations of the Hungarian Diet and Nutritional Status Survey for healthy adults (Nagy et al., 2017; Sarkadi Nagy et al., 2017; Schreiberné Molnár et al., 2017). The nutrients classified as advantageous were the followings: total protein (g and energy intake share in %), dietary fibres (g), vitamin C (mg), calcium (mg), iron (mg), all in the dimension of amount/day/capita. The RDI of protein as nutrient that contributes to energy intake can be described by a range, so the algorithm results in optimal values between the minimum and maximum limit of the range and starts to decrease above and below it. The algorithm of protein (energy intake share in %) in the DQS:
p(x)={2xxrefmin+xrefmax+0.2,ifx<xrefmin1,ifxminxxrefmax2xxrefmin+xrefmax+2.2,ifx>xrefmax
where: p is the subscore referring to protein, x is the amount of protein in the diet, xrefmin minimum limit of the RDI range, and xrefmax maximum limit of the RDI range. In the case of other advantageous nutrients, the algorithm increases up to 150% of the RDI but not above, since the intake of excessive doses cannot be considered as advantageous and may cover the low intake of others. The algorithm of advantageous nutrients in the other DQS:
NA(x)={xxref,ifxxref1.51.5,ifx>xref1.5
where: NA is the subscore refers to advantageous nutrients, x is the amount of advantageous nutrient in the diet, and xref RDI of the advantageous nutrient. The nutrients classified as disadvantageous were the followings: energy (kcal), sodium (mg), saturated fatty acids (g and energy intake share in %), and added sugars (g and energy intake share in %), all in the dimension of amount/day/capita. The determination of the ideal value of energy intake was more sophisticated, since it was necessary to consider the participants' physical activity level beside their individual parameters: gender, age, body weight and height. The physical activity level coefficients were calculated based on the published paper by the European Food Safety Authority (EFSA, 2017), for which the data were acquired from the initial online questionnaire filled out by the participants. The algorithm of disadvantageous nutrients in the DQS was:
NDA(x)=1(xxref1)
where: NDA is the subscore refers to disadvantageous nutrients, x is the amount of dis-advantageous nutrient in the diet, and xref RDI of the disadvantageous nutrient. Furthermore, the total meat intake as a sustainable dietary quality indicator was also assessed, since the animal-based foods, especially meats, are usually the largest contributors to the dietary WFP (Harris et al., 2020). Besides, according to the database of the GBD, the high processed and red meat intakes can be identified as individual dietary risk factors for the development of NCDs (IHME, 2019).

2.2 Estimation of dietary WFP

WFP is a term that refers to the volume and way of application of used water resources for the production of a certain product; thus, it is often called as “virtual water”. There are three types of WFP classified: green, blue, and grey. Green water is from precipitation and stored in the soil, wherefrom plants absorb it; it is especially relevant for agricultural products. Blue water is from the surface or groundwater stores and used for irrigation and industrial and domestic applications. Grey water is the amount of water used to dilute the pollution of used water to meet specific water quality standards (Barilla Center for Food and Nutrition, 2015). In this study, in the accordance with the methods applied in the field of SN, the total WFP values (including green, blue, and grey) were calculated by considering the total WFP values of each food categories as products (Hoekstra and Mekonnen, 2012; Vanham et al., 2013; Harris et al., 2020; Vanham, 2020). To estimate the dietary WFP of diets, the data on food intake from the dietary records were compiled with water footprint values of food categories. The WFP values were acquired from the database of Barilla Center for Food and Nutrition, in which hundreds of published data and databases are systemised and averaged at the level of food categories (Barilla Center for Food and Nutrition, 2015). As the result of this analysis, the dietary WFP of the participants in the dimension of L/day/capita could be estimated.

2.3 The integrative analysis of dietary quality and WFP

From the several methods to integrate the aspects of dietary quality and environmental impact (Hallström et al., 2018), the following approach was selected: (1) At first, the dietary quality as scores values, dietary WFP values (L/day/capita), and total meat intake (gram/day/capita), which were considered as variables, were separately calculated,. (2) Then, correlation analyses were carried out between these variables. Based on the type of scales and non-normal distribution of variables (Shapiro-Wilk test, P < 0.05), Spearman's rank correlation was applied at the significance level of P < 0.05. Excel 2016 was used for the calculation of algorithms and Jamovi statistical analysis software for the correlation analyses (Jamovi, 2020; R Core Team, 2019). Our sample size (n = 25) was appropriate to determine the significance values of Spearman's rank correlation results according to an assessment of correlative statistical test (May and Looney, 2020). The sample could not be regarded as representative; however, it was suitable for our methodological aims. In the studies published in the field of SN, the search for indicator nutrients integrating environmental impact and dietary quality is a common aim (Hendrie et al., 2016, Saarinen et al., 2017; van Dooren et al., 2017). Therefore, a classification on the principles of the followings was also created: (I.) Type of the nutrient: (1) advantageous in the aspect of health, (2) disadvantageous in the aspect of health (Hallström et al., 2018) and (II.) Type of correlation between the nutrient and WFP: (a) significant and positive, (b) significant and negative, (c) no significant correlation (Table 1).

Table 1.

Association of nutrients and water footprint of diets (n = 25). Spearman's rank correlation

Type of nutrientType and direction of the correlation
(1) Advantageous in the aspect of healthprotein***dietary fibres, iron, vitamin C, calcium,
(2) Disadvantageous in the aspect of healthenergy***, sodium*, saturated fatty acids***added sugars
(a) significant (–) correlation with water footprint(b) significant (+) correlation with water footprint(c) no significant correlation

Significance levels: P < 0.001***, P < 0.01**, P < 0.05*

3 Results and discussion

The associations between the DQS, total meat intake, and WFP of the 25 participants were analysed. There was a positive significant correlation between total meat intake and WFP (Fig. 1A), while a negative significant correlation between DQS and WFP (Fig. 1C) as well as total meat intake and DQS (Fig. 1B). The mean WFP volume of the analysed dietary records was 2,629 L/day/capita (±879) that is lower than the results (4,053 L/day/capita) of the only analysis focused on this region of Europe calculating with total WFP values (Vanham et al., 2013). This difference can be explained by the fact that they estimated the nationally typical food intake based on databases, while this study analysed dietary records, an observation already pointed out by Vanham (2020). Besides, these results are not representative and included women in majority and 6 plant-based diets that are typically lower in WFP (Harris et al., 2020). There was an inverse correlation between the DQS and WFP (Fig. 1C) that suggests that the improvement of dietary quality would simultaneously decrease the dietary WFP. WFP was in a positive correlation with total meat intake (Fig. 1A), which is not surprising considering that meats usually have the greatest contribution to dietary WFP (Harris et al., 2020). Based on these results, it would be reasonable lowering meat intake, while increasing the intake of other animal- and plant-based protein sources. Furthermore, the DQS and total meat intake also showed an inverse correlation (Fig. 1B) that suggests that diets higher in meat content could be lower in dietary quality.

Fig. 1.
Fig. 1.

A. Spearman's rank correlation between water footprint and total meat intake. B. Spearman's rank correlation between dietary quality scores and total meat intake. C. Spearman's rank correlation between water footprint and dietary quality scores. D. Spearman's rank correlation between water footprint and total protein intake

Citation: Acta Alimentaria 50, 4; 10.1556/066.2021.00070

Table 1 shows no nutrients in 1a and 2a groups, meaning that there was no negative significant correlation between water footprint and nutrient density values. Protein is classified in group 1b as the only advantageous nutrient in a positive correlation with WFP (also see Fig. 1D). Group 2b are the disadvantageous nutrients in positive correlation with WFP: energy (r = 0.690, P < 0.001), sodium (r = 0.477, P = 0.017) and saturated fatty acids (r = 0.668, P < 0.001). 1c group contains dietary fibres, vitamin C, calcium, and iron (mg/day), while 2c consists of added sugars, from which none showed significant correlation with WFP (P > 0.05). The aim of the nutrient classification was to identify the integrative, environmental impact and dietary quality indicator nutrients. According to our results, the disadvantageous nutrients were mainly identifiable as indicator nutrients based on their significant, positive association with WFP. Therefore, these nutrients could be regarded as negative indicators for the aspects of both environmental impact and dietary quality, and lowering the intake of foods high in them could be recommended. Among the environmental pressures, the correlation between GHGE and energy density (Hendrie et al., 2016; van Dooren et al., 2017) or sodium and saturated fatty acids (van Dooren et al., 2017) in diets has been described in other studies. Protein should be emphasised as an indicator nutrient, however, it shows a controversal direction: it has been shown a positive, significant correlation with GHGE in other studies (Saarinen et al., 2017; van Dooren et al., 2017) and with WFP in this present one. On the other hand, it has also been classified as advantageous here and in other studies (Masset et al., 2014; Hallström et al., 2018) as well (Fig. 1D). Our research team has already detected this association regarding green water footprint and protein for the most consumed food products in Hungary (Tompa et al., 2020a). The quality and origin of protein play a key role here; based on these results, the modification in the quality of dietary protein could also be recommended by decreasing the intake of meat-based protein while increasing the intake of other animal- and plant-based protein.

4 Conclusions

In this present study, we aimed to develop a SN assessment tool that could be applied in the counseling practice of nutritionists and a methodology that would contribute to the basis of further studies. Our results suggest that a shift in the nutritional pattern could simultaneously direct to lower dietary WFP and better dietary quality, thus it could also contribute to the prevention of NCDs.

Acknowledgements

The study was supported by the Doctoral School of Food Sciences at the Hungarian University of Agriculture and Life Sciences. The project is supported by the European Union and co-financed by the European Social Fund (grant agreement no. EFOP-3.6.3- VEKOP-16-2017-00005).

References

  • Barilla Center for Food and Nutrition (2015). Double pyramid 2015: Available at: https://www.barillacfn.com/m/publications/dp-2015-en.pdf (last accessed: 24 April 2020).

    • Search Google Scholar
    • Export Citation
  • EFSA (2017). Dietary reference values for nutrients summary report. EFSA Supporting Publications, 14(12): e15121E. https://doi.org/10.2903/sp.efsa.2017.e15121.

    • Search Google Scholar
    • Export Citation
  • Fischer, C.G. and Garnett, T. (2016). Plates, pyramids and planets: developments in national healthy and sustainable dietary guidelines: a state of play assessment .FAO, Róma; FCRN, University of Oxford, Oxford. p. 71. Available at: http://www.fao.org/3/i5640e/I5640E.pdf(last accessed: 24 April 2020).

    • Search Google Scholar
    • Export Citation
  • Hallström, E. , Davis, J. , Woodhouse, A. , and Sonesson, U. (2018). Using dietary quality scores to assess sustainability of food products and human diets: a systematic review. Ecological Indicators, 93: 219230.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harris, F. , Moss, C. , Joy, E.J.M. , Quinn, R. , Scheelbeek, P.F.D. , Dangour, A.D. , and Green, R. (2020). The water footprint of diets: a global systematic review and meta-analysis. Advances in Nutrition, 11(2): 375386.

    • Search Google Scholar
    • Export Citation
  • Hendrie, G.A. , Baird, D. , Ridoutt, B. , Hadjikakou, M. , and Noakes, M. (2016). Overconsumption of energy and excessive discretionary food intake inflates dietary greenhouse gas emissions in Australia. Nutrients, 8(11): 690.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoekstra, A.Y. and Mekonnen, M.M. (2012). The water footprint of humanity. Proceedings of the National Academy of Sciences of the United States of America, 109(9): 32323237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IHME (2019). GBD Database. Cause of death in both sexes at all ages and related risk factors in Hungary. Institute for Health Metrics and Evaluation IHME, University of Washington. Available from: https://vizhub.healthdata.org/gbd-compare/ (last accessed: 01 March 2021).

    • Search Google Scholar
    • Export Citation
  • Jamovi (2020). The jamovi project, jamovi. (Version 1.2) (Computer Software). Available at: https://www.jamovi.org. (last accessed: 01 March 2021).

    • Search Google Scholar
    • Export Citation
  • Masset, G. , Soler, L.G. , Vieux, F. , and Darmon, N. (2014). Identifying sustainable foods: the relationship between environmental impact, nutritional quality, and prices of foods representative of the French diet. Journal of the Academy of Nutrition and Dietetics, 114(6): 862869.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • May, J.O. and Looney, S.W. (2020). Sample size charts for spearman and kendall coefficients. Journal of Biometrics & Biostatistics, 11(2): 511. https://doi.org/10.37421/jbmbs.2020.11.440.

    • Search Google Scholar
    • Export Citation
  • Nagy, B , Nagy-Lőrincz, Z , Bakacs, M , Illés, É. , Sarkadi Nagy, E. , Erdei G. , and Martos, É. (2017). Országos Táplálkozás és Tápláltsági Állapot Vizsgálat – OTÁP2014. IV. A magyar lakosság mikroelem-bevitele. (Hungarian Diet and Nutritional Status Survey – HDNSS 2014 II. Micro nutrient intake of the Hungarian population). Orvosi Hetilap, 158(21): 803810.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • R Core Team (2019). R: a language and environment for statistical computing. (Version 3.6) (Computer software). Available at: https://cran.r-project.org/. (last accessed: 01 March 2021).

    • Search Google Scholar
    • Export Citation
  • Saarinen, M. , Fogelholm, M. , Tahvonen, R. , and Kurppa, S. (2017). Taking nutrition into account within the life cycle assessment of food products. Journal of Cleaner Production, 149: 828844.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sarkadi Nagy, E. , Bakacs, M. , Illés, É. , Nagy, B. , Varga, A. , Kis, O. , Molnár, E. , and Martos, É. (2017). Országos Táplálkozás és Tápláltsági Állapot Vizsgálat – OTÁP2014. II. A magyar lakosság energia- és makrotápanyag-bevitele. (Hungarian Diet and Nutritional Status Survey – HDNSS 2014 II. Energy and macro nutrient intake of the Hungarian population). Orvosi Hetilap, 158(15): 587597.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schreiberné Molnár, E. , Nagy-Lőrincz, Z. , Nagy, B. , Bakacs, M. , Kis, O. , Sarkadi Nagy, E. , and Martos, É. (2017). Országos Táplálkozás- és Tápláltsági Állapot Vizsgálat – OTÁP2014. V. A magyar lakosság vitaminbevitele (Hungarian diet and nutritional Status Survey – HDNSS 2014 II. Vitamin intake of the Hungarian population). Orvosi Hetilap, 158(33): 13021313.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tompa, O. , Kiss, A. , and Lakner, Z. (2020a). Towards the sustainable food consumption in central Europe: stochastic relationship between water footprint and nutrition. Acta Alimentaria, 49: 8692.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tompa, O. , Lakner, Z. , Oláh, J. , Popp, J. , and Kiss, A. (2020b). Is the sustainable choice a healthy choice?—water footprint consequence of changing dietary patterns. Nutrients, 12(9): 2578, 19 pages.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Dooren, C. , Douma, A. , Aiking, H. , and Vellinga, P. (2017). Proposing a novel index reflecting both climate impact and nutritional impact of food products. Ecological Economics, 131: 389398.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vanham, D. , Hoekstra, A.Y. , and Bidoglio, G. (2013). Potential water saving through changes in European diets. Environment International, 61: 4556.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vanham, D. , Leip, A. , Galli, A. , Kastner, T. , Bruckner, M. , Uwizeye, A. , … Hoekstra, A.Y. (2019). Environmental footprint family to address local to planetary sustainability and deliver on the SDGs. Science of the Total Environment, 693: 133642.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vanham, D. (2020). Water resources for sustainable healthy diets: state of the art and outlook. Water, 12(11): 3224, 15 pages.

  • Barilla Center for Food and Nutrition (2015). Double pyramid 2015: Available at: https://www.barillacfn.com/m/publications/dp-2015-en.pdf (last accessed: 24 April 2020).

    • Search Google Scholar
    • Export Citation
  • EFSA (2017). Dietary reference values for nutrients summary report. EFSA Supporting Publications, 14(12): e15121E. https://doi.org/10.2903/sp.efsa.2017.e15121.

    • Search Google Scholar
    • Export Citation
  • Fischer, C.G. and Garnett, T. (2016). Plates, pyramids and planets: developments in national healthy and sustainable dietary guidelines: a state of play assessment .FAO, Róma; FCRN, University of Oxford, Oxford. p. 71. Available at: http://www.fao.org/3/i5640e/I5640E.pdf(last accessed: 24 April 2020).

    • Search Google Scholar
    • Export Citation
  • Hallström, E. , Davis, J. , Woodhouse, A. , and Sonesson, U. (2018). Using dietary quality scores to assess sustainability of food products and human diets: a systematic review. Ecological Indicators, 93: 219230.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harris, F. , Moss, C. , Joy, E.J.M. , Quinn, R. , Scheelbeek, P.F.D. , Dangour, A.D. , and Green, R. (2020). The water footprint of diets: a global systematic review and meta-analysis. Advances in Nutrition, 11(2): 375386.

    • Search Google Scholar
    • Export Citation
  • Hendrie, G.A. , Baird, D. , Ridoutt, B. , Hadjikakou, M. , and Noakes, M. (2016). Overconsumption of energy and excessive discretionary food intake inflates dietary greenhouse gas emissions in Australia. Nutrients, 8(11): 690.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoekstra, A.Y. and Mekonnen, M.M. (2012). The water footprint of humanity. Proceedings of the National Academy of Sciences of the United States of America, 109(9): 32323237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IHME (2019). GBD Database. Cause of death in both sexes at all ages and related risk factors in Hungary. Institute for Health Metrics and Evaluation IHME, University of Washington. Available from: https://vizhub.healthdata.org/gbd-compare/ (last accessed: 01 March 2021).

    • Search Google Scholar
    • Export Citation
  • Jamovi (2020). The jamovi project, jamovi. (Version 1.2) (Computer Software). Available at: https://www.jamovi.org. (last accessed: 01 March 2021).

    • Search Google Scholar
    • Export Citation
  • Masset, G. , Soler, L.G. , Vieux, F. , and Darmon, N. (2014). Identifying sustainable foods: the relationship between environmental impact, nutritional quality, and prices of foods representative of the French diet. Journal of the Academy of Nutrition and Dietetics, 114(6): 862869.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • May, J.O. and Looney, S.W. (2020). Sample size charts for spearman and kendall coefficients. Journal of Biometrics & Biostatistics, 11(2): 511. https://doi.org/10.37421/jbmbs.2020.11.440.

    • Search Google Scholar
    • Export Citation
  • Nagy, B , Nagy-Lőrincz, Z , Bakacs, M , Illés, É. , Sarkadi Nagy, E. , Erdei G. , and Martos, É. (2017). Országos Táplálkozás és Tápláltsági Állapot Vizsgálat – OTÁP2014. IV. A magyar lakosság mikroelem-bevitele. (Hungarian Diet and Nutritional Status Survey – HDNSS 2014 II. Micro nutrient intake of the Hungarian population). Orvosi Hetilap, 158(21): 803810.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • R Core Team (2019). R: a language and environment for statistical computing. (Version 3.6) (Computer software). Available at: https://cran.r-project.org/. (last accessed: 01 March 2021).

    • Search Google Scholar
    • Export Citation
  • Saarinen, M. , Fogelholm, M. , Tahvonen, R. , and Kurppa, S. (2017). Taking nutrition into account within the life cycle assessment of food products. Journal of Cleaner Production, 149: 828844.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sarkadi Nagy, E. , Bakacs, M. , Illés, É. , Nagy, B. , Varga, A. , Kis, O. , Molnár, E. , and Martos, É. (2017). Országos Táplálkozás és Tápláltsági Állapot Vizsgálat – OTÁP2014. II. A magyar lakosság energia- és makrotápanyag-bevitele. (Hungarian Diet and Nutritional Status Survey – HDNSS 2014 II. Energy and macro nutrient intake of the Hungarian population). Orvosi Hetilap, 158(15): 587597.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schreiberné Molnár, E. , Nagy-Lőrincz, Z. , Nagy, B. , Bakacs, M. , Kis, O. , Sarkadi Nagy, E. , and Martos, É. (2017). Országos Táplálkozás- és Tápláltsági Állapot Vizsgálat – OTÁP2014. V. A magyar lakosság vitaminbevitele (Hungarian diet and nutritional Status Survey – HDNSS 2014 II. Vitamin intake of the Hungarian population). Orvosi Hetilap, 158(33): 13021313.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tompa, O. , Kiss, A. , and Lakner, Z. (2020a). Towards the sustainable food consumption in central Europe: stochastic relationship between water footprint and nutrition. Acta Alimentaria, 49: 8692.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tompa, O. , Lakner, Z. , Oláh, J. , Popp, J. , and Kiss, A. (2020b). Is the sustainable choice a healthy choice?—water footprint consequence of changing dietary patterns. Nutrients, 12(9): 2578, 19 pages.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Dooren, C. , Douma, A. , Aiking, H. , and Vellinga, P. (2017). Proposing a novel index reflecting both climate impact and nutritional impact of food products. Ecological Economics, 131: 389398.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vanham, D. , Hoekstra, A.Y. , and Bidoglio, G. (2013). Potential water saving through changes in European diets. Environment International, 61: 4556.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vanham, D. , Leip, A. , Galli, A. , Kastner, T. , Bruckner, M. , Uwizeye, A. , … Hoekstra, A.Y. (2019). Environmental footprint family to address local to planetary sustainability and deliver on the SDGs. Science of the Total Environment, 693: 133642.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vanham, D. (2020). Water resources for sustainable healthy diets: state of the art and outlook. Water, 12(11): 3224, 15 pages.

 

The author instruction is available in PDF.
Please, download the file from HERE.

Senior editors

Editor(s)-in-Chief: András Salgó

Co-ordinating Editor(s) Marianna Tóth-Markus

Co-editor(s): A. Halász

       Editorial Board

  • L. Abrankó (Szent István University, Gödöllő, Hungary)
  • D. Bánáti (University of Szeged, Szeged, Hungary)
  • J. Baranyi (Institute of Food Research, Norwich, UK)
  • I. Bata-Vidács (Agro-Environmental Research Institute, National Agricultural Research and Innovation Centre, Budapest, Hungary)
  • J. Beczner (Food Science Research Institute, National Agricultural Research and Innovation Centre, Budapest, Hungary)
  • F. Békés (FBFD PTY LTD, Sydney, NSW Australia)
  • Gy. Biró (National Institute for Food and Nutrition Science, Budapest, Hungary)
  • A. Blázovics (Semmelweis University, Budapest, Hungary)
  • F. Capozzi (University of Bologna, Bologna, Italy)
  • M. Carcea (Research Centre for Food and Nutrition, Council for Agricultural Research and Economics Rome, Italy)
  • Zs. Cserhalmi (Food Science Research Institute, National Agricultural Research and Innovation Centre, Budapest, Hungary)
  • M. Dalla Rosa (University of Bologna, Bologna, Italy)
  • I. Dalmadi (Szent István University, Budapest, Hungary)
  • K. Demnerova (University of Chemistry and Technology, Prague, Czech Republic)
  • M. Dobozi King (Texas A&M University, Texas, USA)
  • Muying Du (Southwest University in Chongqing, Chongqing, China)
  • S. N. El (Ege University, Izmir, Turkey)
  • S. B. Engelsen (University of Copenhagen, Copenhagen, Denmark)
  • E. Gelencsér (Food Science Research Institute, National Agricultural Research and Innovation Centre, Budapest, Hungary)
  • V. M. Gómez-López (Universidad Católica San Antonio de Murcia, Murcia, Spain)
  • J. Hardi (University of Osijek, Osijek, Croatia)
  • K. Héberger (Research Centre for Natural Sciences, ELKH, Budapest, Hungary)
  • N. Ilić (University of Novi Sad, Novi Sad, Serbia)
  • D. Knorr (Technische Universität Berlin, Berlin, Germany)
  • H. Köksel (Hacettepe University, Ankara, Turkey)
  • K. Liburdi (Tuscia University, Viterbo, Italy)
  • M. Lindhauer (Max Rubner Institute, Detmold, Germany)
  • M.-T. Liong (Universiti Sains Malaysia, Penang, Malaysia)
  • M. Manley (Stellenbosch University, Stellenbosch, South Africa)
  • M. Mézes (Szent István University, Gödöllő, Hungary)
  • Á. Németh (Budapest University of Technology and Economics, Budapest, Hungary)
  • P. Ng (Michigan State University,  Michigan, USA)
  • Q. D. Nguyen (Szent István University, Budapest, Hungary)
  • L. Nyström (ETH Zürich, Switzerland)
  • L. Perez (University of Cordoba, Cordoba, Spain)
  • V. Piironen (University of Helsinki, Finland)
  • A. Pino (University of Catania, Catania, Italy)
  • M. Rychtera (University of Chemistry and Technology, Prague, Czech Republic)
  • K. Scherf (Technical University, Munich, Germany)
  • R. Schönlechner (University of Natural Resources and Life Sciences, Vienna, Austria)
  • A. Sharma (Department of Atomic Energy, Delhi, India)
  • A. Szarka (Budapest University of Technology and Economics, Budapest, Hungary)
  • M. Szeitzné Szabó (National Food Chain Safety Office, Budapest, Hungary)
  • S. Tömösközi (Budapest University of Technology and Economics, Budapest, Hungary)
  • L. Varga (University of West Hungary, Mosonmagyaróvár, Hungary)
  • R. Venskutonis (Kaunas University of Technology, Kaunas, Lithuania)
  • B. Wróblewska (Institute of Animal Reproduction and Food Research, Polish Academy of Sciences Olsztyn, Poland)

 

Acta Alimentaria
E-mail: Acta.Alimentaria@uni-mate.hu

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2020
 
Total Cites
768
WoS
Journal
Impact Factor
0,650
Rank by
Nutrition & Dietetics 79/89 (Q4)
Impact Factor
Food Science & Technology 130/144 (Q4)
Impact Factor
0,575
without
Journal Self Cites
5 Year
0,899
Impact Factor
Journal
0,17
Citation Indicator
 
Rank by Journal
Nutrition & Dietetics 88/103 (Q4)
Citation Indicator
Food Science & Technology 142/160 (Q4)
Citable
59
Items
Total
58
Articles
Total
1
Reviews
Scimago
28
H-index
Scimago
0,237
Journal Rank
Scimago
Food Science Q3
Quartile Score
 
Scopus
248/238=1,0
Scite Score
 
Scopus
Food Science 216/310 (Q3)
Scite Score Rank
 
Scopus
0,349
SNIP
 
Days from
100
submission
 
to acceptance
 
Days from
143
acceptance
 
to publication
 
Acceptance
16%
Rate
2019  
Total Cites
WoS
522
Impact Factor 0,458
Impact Factor
without
Journal Self Cites
0,433
5 Year
Impact Factor
0,503
Immediacy
Index
0,100
Citable
Items
60
Total
Articles
59
Total
Reviews
1
Cited
Half-Life
7,8
Citing
Half-Life
9,8
Eigenfactor
Score
0,00034
Article Influence
Score
0,077
% Articles
in
Citable Items
98,33
Normalized
Eigenfactor
0,04267
Average
IF
Percentile
7,429
Scimago
H-index
27
Scimago
Journal Rank
0,212
Scopus
Scite Score
220/247=0,9
Scopus
Scite Score Rank
Food Science 215/299 (Q3)
Scopus
SNIP
0,275
Acceptance
Rate
15%

 

Acta Alimentaria
Publication Model Hybrid
Submission Fee none
Article Processing Charge 1100 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription fee 2021 Online subsscription: 736 EUR / 920 USD
Print + online subscription: 852 EUR / 1064 USD
Subscription fee 2022 Online subsscription: 754 EUR / 944 USD
Print + online subscription: 872 EUR / 1090 USD
Subscription Information Online subscribers are entitled access to all back issues published by Akadémiai Kiadó for each title for the duration of the subscription, as well as Online First content for the subscribed content.
Purchase per Title Individual articles are sold on the displayed price.

Acta Alimentaria
Language English
Size B5
Year of
Foundation
1972
Publication
Programme
2021 Volume 50
Volumes
per Year
1
Issues
per Year
4
Founder Magyar Tudományos Akadémia    
Founder's
Address
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 0139-3006 (Print)
ISSN 1588-2535 (Online)

 

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Jun 2021 0 0 0
Jul 2021 0 0 0
Aug 2021 0 0 0
Sep 2021 0 0 0
Oct 2021 0 0 0
Nov 2021 0 33 27
Dec 2021 0 10 6