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Katılımcı Tarımsal Dönüşüm ve Çiftçi Piyasası Okulu Yaklaşımlarına Yönelik Kombine Kırsal Girişimlerin Gelir ve Gıda Güvenliğine Etkisi

Year 2024, Volume: 55 Issue: 1, 41 - 50, 31.01.2024

Abstract

Tarımsal üretimi artırmaya yönelik müdahaleler her zaman piyasa stratejilerini fazla dikkate almadan üretime yönelik olmuştur. Bu arka plana karşı, çeşitli kalkınma kuruluşları, sırasıyla
küçük çiftçilerin tarımsal üretim ve pazarlamasını dönüştürmek amacıyla katılımcı tarımsal dönüşüme yönelik kırsal girişimleri ve çiftçi pazarı okulu yaklaşımlarını birleştiren bir proje başlattı.
Bu çalışmanın amacı, katılımcı tarımsal dönüşüme yönelik birleşik kırsal girişimler ile çiftçi pazarı okulu yaklaşımlarının gelir ve gıda güvenliği üzerindeki etkisini incelemektir. Bu çalışmada kesitsel bir araştırma tasarımı uygulanmış ve kullanılan veriler, çok aşamalı rastgele örnekleme prosedürü kullanılarak seçilen 321 çiftçi hanesinden oluşan bir örneklemden toplanmıştır. 321 katılımcının 93'ü katılımcı tarımsal dönüşüm ve çiftçi pazarı okulu için kırsal girişimlere katılan çiftçilerdi ve sırasıyla 100 ve 128'i katılımcı tarımsal dönüşüm müdahaleleri için kırsal girişimlere katılan ve katılımcı olmayan çiftçilerdi. Veriler, niceliksel veriler için tanımlayıcı istatistikler ve eğilim puanı eşleştirme kullanılarak analiz edilirken, nitel bilgilerin analizinde içerik analizi uygulandı. Sonuçlar, katılımcı tarımsal dönüşüm için kırsal girişimlere çiftçi pazarı okulu ve yalnızca katılımcı tarımsal dönüşüm için kırsal girişimlere kayıtlı çiftçiler ile katılmayanlar arasında, gelir ve gıda çeşitliliği düzeylerinde anlamlı bir fark olmadığını, ancak olumlu bir fark olduğunu göstermektedir. Katılımcı tarımsal dönüşüme yönelik kırsal girişimler ve çiftçi pazarı okulu ve yalnızca katılımcı tarımsal dönüşüme yönelik kırsal girişimler müdahalelerinin sonuçları sırasıyla Tanzanya Şilini (TZS) 73.947 ve TZS 51.796 oldu ve gıda çeşitliliği puanları 7.454 ve 7.418 oldu. Çiftçilerin pilot uygulama sırasında karşılaştığı kuraklığın, yaklaşımların etkisinin önemsiz olmasındaki temel zorluk olduğu görüldü. Çalışmamızın sonuçları, katılımcı tarımsal dönüşüm için birleşik kırsal girişimlerin ve tarımsal müdahalelerde çiftçi pazarı okulunun benimsenmesinin, küçük çiftçilerin gelirini ve gıda güvenliğini iyileştirebileceğini göstermektedir. Verimlilik ve arazi kullanım verimliliği açısından ek faydalar üretmek için
iki yaklaşımın desteklenmesi gerekmektedir.

References

  • Adeyanju, D. F., Mburu, J., & Mignouna, D. (2019). Impact of agribusiness training programmes on youth empowerment in Nigeria: The case of Fadama GUYS programme. Proceedings of the book, 24, 207.
  • African Development Bank Group. (2016). Transforming African agriculture. https://www.afdb.org/fileadmin/uploads/afdb/Images/high_5s/Feed_Africa_Feed_Africa.pdf.
  • Andrew, A., Makindara, J., Mbaga, S. H., & Alphonce, R. (2019). Ex-ante analysis of adoption of introduced chicken strains among smallholder farmers in selected areas of Tanzania. International Conference on Social Implications of Computers in Developing Countries, Springer, Cham.
  • Benedetto, U., Head, S. J., Angelini, G. D., & Blackstone, E. H. (2018). Statistical primer: Propensity score matching and its alternatives. European Journal of Cardio-Thoracic Surgery, 53(6), 1112–1117. [CrossRef]
  • Bizimana, J. C., & Richardson, J. W. (2017). Household level food security and nutrition analysis using A farm simulation model (FARMSIM): Case study of Ethiopia [Research report]. https://ilssi.tamu.edu/files/2019/11/
  • Bravo-Ureta, B. E., Greene, W., & Solís, D. (2012). Technical efficiency analysis correcting for biases from observed and unobserved variables: An application to a natural resource management project. Empirical Economics, 43(1), 55–72. [CrossRef]
  • Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys. Discussion Paper Series, 22(1), 31–72. [CrossRef] Carletto, C., Zezza, A., & Banerjee, R. (2013). Towards better measurement of household food security: Harmonizing indicators and the role of household surveys. Global Food Security, 2(1), 30–40. [CrossRef]
  • Christiaensen, L. (2017). Agriculture in Africa – Telling myths from facts: A synthesis. Food Policy, 67, 1–11. [CrossRef]
  • Denmark, A. (2021). Farmer market school: Generic facilitation manual – Level [Annual report]. Copenhagen.
  • FANTA (2006). Household Dietary Diversity Score (HDDS) for Measurement of Household Food Access: Indicator Guide. https://www.fantaproje ct.org/sites/RIPAT-SUA. (2020). Farming for the market- a Pilot Project in Morogoro.
  • Ferris, S., Robbins, P., Best, R., Seville, D., Buxton, A., Shriver, J., & Wei, E. (2014). Linking smallholder farmers to markets and the implications for extension and advisory services. https://meas.illinois.edu/wp-content/uploads
  • FAO. (2013). Guidelines for measuring household and individual dietary diversity. https://www.fao.org/3/i1983e/i1983e.pdf.
  • FAO. (2017). Africa: Regional overview of food security and nutrition-the food security and nutrition–conflict nexus: Building resilience for food security, nutrition and peace. https://www.fao.org/3/i7967e/i7967e.pdf.
  • Frölich, M., & Sperlich, S. (2019). Impact evaluation. Cambridge University Press.
  • Guo, S., Fraser, M., & Chen, Q. (2020). Propensity score analysis: Recent debate and discussion. Journal of the Society for Social Work and Research, 11(3), 463–482. [CrossRef]
  • Hotmaida, R., & Purba, F. (2018). Impact of Indonesia conditional cash transfer program on student achievement. European Journal of Economics, 4(1), 98–109. [CrossRef]
  • Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of pro-gram evaluation. Journal of Economic Literature, 47(1), 5–86. [CrossRef]
  • International Dietary Data. (2022). Household Dietary Diversity Score (HDDS). Data4Diets: Building blocks for diet-related food security analysis. https://inddex.nutrition.tufts.edu/data4diets/data-sources-and-methods
  • IFAD. (2022). United Republic of Tanzania country strategic opportunities programme 2022–2027. https://webapps.ifad.org/members/eb/135/docs/EB-2022-135-R-20.pdf
  • Johnson, S. R., Tomlinson, G. A., Hawker, G. A., Granton, J. T., & Feldman, B. M. (2018). Propensity score methods for bias reduction in observational studies of treatment effect. Rheumatic Diseases Clinics of North America, 44(2), 203–213. [CrossRef]
  • Kennedy, G., Razes, M., Ballard, T., & Dop, M. C. (2011). Measurement of Dietary Diversity for Monitoring the Impact of Food Based Approaches. http://www.foodsec.org/
  • Leroy, J. L., Ruel, M., Frongillo, E. A., Harris, J., & Ballard, T. J. (2015). Measuring the food access dimension of food security: A critical review and mapping of indicators. Food and Nutrition Bulletin, 36(2), 167–195. [CrossRef]
  • Lilleør, H. B., & Lund-Sørensen, U. (2013). Summary and concluding remarks. In Farmers Choice. Evaluating an approach to agricultural technology adoption in Tanzania (H. B. Lilleør & U. Lund-Sørensen, Eds., pp. 7–22). Practical Action Publishing.
  • Lin, J. (2015). Lecture notes on propensity score matching. https://www.yumpu.com /en/document/view/24667905/
  • Minja, E. G., Swai, J. K., Mponzi, W., Ngowo, H., Okumu, F., Gerber, M., Pühse, U., Long, K. Z., Utzinger, J., Lang, C., Beckmann, J., & Finda, M. (2021). Dietary diversity among households living in Kilombero district, in Morogoro region, South-Eastern Tanzania. Journal of Agriculture and Food Research, 5. [CrossRef]
  • Morgan, C. J. (2018). Reducing bias using propensity score matching. Journal of Nuclear Cardiology, 25(2), 404–406. [CrossRef]
  • Nakano, Y., Tanaka, Y., & Otsuka, K. (2018). Impact of training on the intensification of rice farming: Evidence from rainfed areas in Tanzania. Agricultural Economics, 49(2), 193–202. [CrossRef]
  • NEPAD. (2013). Agriculture in Africa: Transformation and outlook. https://www.tralac.org/images/docs/6460/agriculture-in-africa-transformation-and-outlook.pdf.
  • Nithya, D. J., & Bhavani, R. V. (2018). Dietary diversity and its relationship with nutritional status among adolescents and adults in rural India. Journal of Biosocial Science, 50(3), 397–413. [CrossRef]
  • Olounlade, O. A., Li, G. C., Kokoye, S. E. H., Dossouhoui, F. V., Akpa, K. A. A., Anshiso, D., & Biaou, G. (2020). Impact of participation in contract farming on smallholder farmer”s income and food security in rural Benin: PSM and LATE parameter combined. Sustainability, 12(3), 901. [CrossRef]
  • Ozminkowski, R. J., & Brach, L. G. (1998). Economic analysis of interventions of aged populations in “public health and aging.” John Hopkins University Press.
  • Rathnayake, K. M., Madushani, P., & Silva, K. (2012). Use. BMC Research Notes, 5, 469. [CrossRef]
  • Rossi, P. H., & Freeman, H. E. (1993). Evaluation: A systematic approach (5th ed.). Sage Publications, Inc.
  • Ruzzante, S., Labarta, R., & Bilton, A. (2021). Adoption of agricultural technology in the developing world: A meta-analysis of the empirical literature. World Development, 146, 1–16. [CrossRef]
  • Schulte, P. J., & Mascha, E. J. (2018). Propensity score methods: Theory and practice for anesthesia research. Anesthesia and Analgesia, 127(4), 1074–1084. [CrossRef]
  • Smith, H. (1997). Matching with multiple Non-participants s to estimate treatment effects in observational studies. Sociological Methodology, 27(3), 1–21. [CrossRef]
  • Smith, J. (2000). A critical survey of empirical methods for evaluating active labor market policies. Schweizerische Zeitschrift Fr. Volkswirtschaft und Statistik, 136(3), 1–22. http://www.sjes.ch/papers/2000-III-2.pdf
  • Stewart, R., Langer, L., Da Silva, N. R., Muchiri, E., Zaranyika, H., Erasmus, Y., Randall, N., Rafferty, S., Korth, M., Madinga, N., & de Wet, T. (2015). The effects of training, innovation and new technology on African smallholder farmers’ Economic Outcomes and Food Security: A Systematic Review. Campbell Systematic Reviews, 11(1), 1–224. [CrossRef]
  • Taylor, J. E. (2018). Agricultural development impact evaluation. In G. L. Cramer, K. P. Paudel, & A. Schmitz (Eds.), The Routledge handbook of agricultural economics (pp. 548–580). Routledge. Teka, A., & Lee, S. K. (2020). Do agricultural package programs improve the welfare of rural people? Evidence from smallholder farmers in Ethiopia. Agriculture, 10(5), 190. [CrossRef]
  • Triebs, T. P., & Kumbhakar, S. C. (2013). Productivity with general indices of management and technical change. Economics Letters, 120(1), 18–22. [CrossRef]
  • United Republic of Tanzania (URT). (2021). National five year development plan 2021/2–2025/6. Realising competitiveness and industrialization for human development. United Republic of Tanzania (URT). (2022). Morogoro Regional socio-economic profile. https://morogoro.go.tz/storage/app/uploads/WHO. (2008). Indicators for assessing infant and young child feeding practices: Part 1. WHO.
  • Zhao, W., Yu, K., Tan, S., Zheng, Y., Zhao, A., Wang, P., & Zhang, Y. (2017). Dietary diversity scores: An indicator of micronutrient inadequacy instead of obesity for Chinese children. BMC Public Health, 17(1), 440. [CrossRef]

Impact of Combined Rural Initiatives for Participatory Agricultural Transformation and Farmer Market School Approaches on Income and Food Security

Year 2024, Volume: 55 Issue: 1, 41 - 50, 31.01.2024

Abstract

Interventions to increase agricultural production have always been biased toward production without much consideration of market strategies. It is against this background, several development agents initiated a project that combines rural initiatives for participatory agricultural transformation and farmer market school approaches with the aim of transforming smallholder farmers’ agricultural production and marketing, respectively. The objective of this study is to examine the impact of combined rural initiatives for participatory agricultural transformation and farmer market school approaches on income and food security. The present study applied a cross-sectional research design, and the data used were gathered from a sample of 321 farming households, selected using a multi-stage random sampling procedure. Of the 321 respondents, 93 were farmers who participated in rural initiatives for participatory agricultural transformation and farmer market school, and 100 and 128 were farmers who participated in rural initiatives for participatory agricultural transformation interventions and non-participants, respectively. Data were analyzed using descriptive statistics and propensity score matching for quantitative data, while content analysis was applied for analyzing qualitative information. The results indicate that, across farmers enrolled in rural initiatives for participatory agricultural transformation and farmer market school and rural initiatives for participatory agricultural transformation only, and nonparticipants, there was no significant difference in levels of income and food diversity, although there was a positive difference. The results of rural initiatives for participatory agricultural transformation and farmer market school and rural initiatives for participatory agricultural transformation-only interventions were Tanzania Shilling (TZS) 73,947 and TZS 51,796, respectively, with food diversity scores of 7.454 and 7.418. The drought faced by farmers during piloting was found to be the main challenge for the insignificance impact of the approaches. The results of our study suggest that the adoption of combined rural initiatives for participatory agricultural transformation and farmer market school in agricultural interventions is likely to improve smallholder farmers’ income and food security. The two approaches should be promoted to produce additional benefits in terms of productivity and land use efficiency.

References

  • Adeyanju, D. F., Mburu, J., & Mignouna, D. (2019). Impact of agribusiness training programmes on youth empowerment in Nigeria: The case of Fadama GUYS programme. Proceedings of the book, 24, 207.
  • African Development Bank Group. (2016). Transforming African agriculture. https://www.afdb.org/fileadmin/uploads/afdb/Images/high_5s/Feed_Africa_Feed_Africa.pdf.
  • Andrew, A., Makindara, J., Mbaga, S. H., & Alphonce, R. (2019). Ex-ante analysis of adoption of introduced chicken strains among smallholder farmers in selected areas of Tanzania. International Conference on Social Implications of Computers in Developing Countries, Springer, Cham.
  • Benedetto, U., Head, S. J., Angelini, G. D., & Blackstone, E. H. (2018). Statistical primer: Propensity score matching and its alternatives. European Journal of Cardio-Thoracic Surgery, 53(6), 1112–1117. [CrossRef]
  • Bizimana, J. C., & Richardson, J. W. (2017). Household level food security and nutrition analysis using A farm simulation model (FARMSIM): Case study of Ethiopia [Research report]. https://ilssi.tamu.edu/files/2019/11/
  • Bravo-Ureta, B. E., Greene, W., & Solís, D. (2012). Technical efficiency analysis correcting for biases from observed and unobserved variables: An application to a natural resource management project. Empirical Economics, 43(1), 55–72. [CrossRef]
  • Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys. Discussion Paper Series, 22(1), 31–72. [CrossRef] Carletto, C., Zezza, A., & Banerjee, R. (2013). Towards better measurement of household food security: Harmonizing indicators and the role of household surveys. Global Food Security, 2(1), 30–40. [CrossRef]
  • Christiaensen, L. (2017). Agriculture in Africa – Telling myths from facts: A synthesis. Food Policy, 67, 1–11. [CrossRef]
  • Denmark, A. (2021). Farmer market school: Generic facilitation manual – Level [Annual report]. Copenhagen.
  • FANTA (2006). Household Dietary Diversity Score (HDDS) for Measurement of Household Food Access: Indicator Guide. https://www.fantaproje ct.org/sites/RIPAT-SUA. (2020). Farming for the market- a Pilot Project in Morogoro.
  • Ferris, S., Robbins, P., Best, R., Seville, D., Buxton, A., Shriver, J., & Wei, E. (2014). Linking smallholder farmers to markets and the implications for extension and advisory services. https://meas.illinois.edu/wp-content/uploads
  • FAO. (2013). Guidelines for measuring household and individual dietary diversity. https://www.fao.org/3/i1983e/i1983e.pdf.
  • FAO. (2017). Africa: Regional overview of food security and nutrition-the food security and nutrition–conflict nexus: Building resilience for food security, nutrition and peace. https://www.fao.org/3/i7967e/i7967e.pdf.
  • Frölich, M., & Sperlich, S. (2019). Impact evaluation. Cambridge University Press.
  • Guo, S., Fraser, M., & Chen, Q. (2020). Propensity score analysis: Recent debate and discussion. Journal of the Society for Social Work and Research, 11(3), 463–482. [CrossRef]
  • Hotmaida, R., & Purba, F. (2018). Impact of Indonesia conditional cash transfer program on student achievement. European Journal of Economics, 4(1), 98–109. [CrossRef]
  • Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of pro-gram evaluation. Journal of Economic Literature, 47(1), 5–86. [CrossRef]
  • International Dietary Data. (2022). Household Dietary Diversity Score (HDDS). Data4Diets: Building blocks for diet-related food security analysis. https://inddex.nutrition.tufts.edu/data4diets/data-sources-and-methods
  • IFAD. (2022). United Republic of Tanzania country strategic opportunities programme 2022–2027. https://webapps.ifad.org/members/eb/135/docs/EB-2022-135-R-20.pdf
  • Johnson, S. R., Tomlinson, G. A., Hawker, G. A., Granton, J. T., & Feldman, B. M. (2018). Propensity score methods for bias reduction in observational studies of treatment effect. Rheumatic Diseases Clinics of North America, 44(2), 203–213. [CrossRef]
  • Kennedy, G., Razes, M., Ballard, T., & Dop, M. C. (2011). Measurement of Dietary Diversity for Monitoring the Impact of Food Based Approaches. http://www.foodsec.org/
  • Leroy, J. L., Ruel, M., Frongillo, E. A., Harris, J., & Ballard, T. J. (2015). Measuring the food access dimension of food security: A critical review and mapping of indicators. Food and Nutrition Bulletin, 36(2), 167–195. [CrossRef]
  • Lilleør, H. B., & Lund-Sørensen, U. (2013). Summary and concluding remarks. In Farmers Choice. Evaluating an approach to agricultural technology adoption in Tanzania (H. B. Lilleør & U. Lund-Sørensen, Eds., pp. 7–22). Practical Action Publishing.
  • Lin, J. (2015). Lecture notes on propensity score matching. https://www.yumpu.com /en/document/view/24667905/
  • Minja, E. G., Swai, J. K., Mponzi, W., Ngowo, H., Okumu, F., Gerber, M., Pühse, U., Long, K. Z., Utzinger, J., Lang, C., Beckmann, J., & Finda, M. (2021). Dietary diversity among households living in Kilombero district, in Morogoro region, South-Eastern Tanzania. Journal of Agriculture and Food Research, 5. [CrossRef]
  • Morgan, C. J. (2018). Reducing bias using propensity score matching. Journal of Nuclear Cardiology, 25(2), 404–406. [CrossRef]
  • Nakano, Y., Tanaka, Y., & Otsuka, K. (2018). Impact of training on the intensification of rice farming: Evidence from rainfed areas in Tanzania. Agricultural Economics, 49(2), 193–202. [CrossRef]
  • NEPAD. (2013). Agriculture in Africa: Transformation and outlook. https://www.tralac.org/images/docs/6460/agriculture-in-africa-transformation-and-outlook.pdf.
  • Nithya, D. J., & Bhavani, R. V. (2018). Dietary diversity and its relationship with nutritional status among adolescents and adults in rural India. Journal of Biosocial Science, 50(3), 397–413. [CrossRef]
  • Olounlade, O. A., Li, G. C., Kokoye, S. E. H., Dossouhoui, F. V., Akpa, K. A. A., Anshiso, D., & Biaou, G. (2020). Impact of participation in contract farming on smallholder farmer”s income and food security in rural Benin: PSM and LATE parameter combined. Sustainability, 12(3), 901. [CrossRef]
  • Ozminkowski, R. J., & Brach, L. G. (1998). Economic analysis of interventions of aged populations in “public health and aging.” John Hopkins University Press.
  • Rathnayake, K. M., Madushani, P., & Silva, K. (2012). Use. BMC Research Notes, 5, 469. [CrossRef]
  • Rossi, P. H., & Freeman, H. E. (1993). Evaluation: A systematic approach (5th ed.). Sage Publications, Inc.
  • Ruzzante, S., Labarta, R., & Bilton, A. (2021). Adoption of agricultural technology in the developing world: A meta-analysis of the empirical literature. World Development, 146, 1–16. [CrossRef]
  • Schulte, P. J., & Mascha, E. J. (2018). Propensity score methods: Theory and practice for anesthesia research. Anesthesia and Analgesia, 127(4), 1074–1084. [CrossRef]
  • Smith, H. (1997). Matching with multiple Non-participants s to estimate treatment effects in observational studies. Sociological Methodology, 27(3), 1–21. [CrossRef]
  • Smith, J. (2000). A critical survey of empirical methods for evaluating active labor market policies. Schweizerische Zeitschrift Fr. Volkswirtschaft und Statistik, 136(3), 1–22. http://www.sjes.ch/papers/2000-III-2.pdf
  • Stewart, R., Langer, L., Da Silva, N. R., Muchiri, E., Zaranyika, H., Erasmus, Y., Randall, N., Rafferty, S., Korth, M., Madinga, N., & de Wet, T. (2015). The effects of training, innovation and new technology on African smallholder farmers’ Economic Outcomes and Food Security: A Systematic Review. Campbell Systematic Reviews, 11(1), 1–224. [CrossRef]
  • Taylor, J. E. (2018). Agricultural development impact evaluation. In G. L. Cramer, K. P. Paudel, & A. Schmitz (Eds.), The Routledge handbook of agricultural economics (pp. 548–580). Routledge. Teka, A., & Lee, S. K. (2020). Do agricultural package programs improve the welfare of rural people? Evidence from smallholder farmers in Ethiopia. Agriculture, 10(5), 190. [CrossRef]
  • Triebs, T. P., & Kumbhakar, S. C. (2013). Productivity with general indices of management and technical change. Economics Letters, 120(1), 18–22. [CrossRef]
  • United Republic of Tanzania (URT). (2021). National five year development plan 2021/2–2025/6. Realising competitiveness and industrialization for human development. United Republic of Tanzania (URT). (2022). Morogoro Regional socio-economic profile. https://morogoro.go.tz/storage/app/uploads/WHO. (2008). Indicators for assessing infant and young child feeding practices: Part 1. WHO.
  • Zhao, W., Yu, K., Tan, S., Zheng, Y., Zhao, A., Wang, P., & Zhang, Y. (2017). Dietary diversity scores: An indicator of micronutrient inadequacy instead of obesity for Chinese children. BMC Public Health, 17(1), 440. [CrossRef]

Details

Primary Language English
Subjects Agricultural Engineering (Other)
Journal Section Research Articles
Authors

Rogers LUMENYELA This is me 0000-0003-2103-6806

Emanuel MALİSA This is me 0000-0001-9874-1082

Christopher MAHONGE This is me 0000-0001-8180-2950

Suzana NYANDA This is me 0000-0003-3301-1630

Early Pub Date January 29, 2024
Publication Date January 31, 2024
Published in Issue Year 2024 Volume: 55 Issue: 1

Cite

APA LUMENYELA, R., MALİSA, E., MAHONGE, C., NYANDA, S. (2024). Impact of Combined Rural Initiatives for Participatory Agricultural Transformation and Farmer Market School Approaches on Income and Food Security. Research in Agricultural Sciences, 55(1), 41-50.

Content of this journal is licensed under a Creative Commons Attribution NonCommercial 4.0 International License

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