Optimization of arable land use towards meat-free and climate-smart agriculture: A case study in food self-sufficiency of Vietnam

Vladimir Kuzmanovski, Daniel Ellehammer Larsen, Christian Bugge Henriksen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


UN Sustainable Development Goals and the Paris agreement for climate change indicate that a transition to sustainable and healthy diets is necessary. Additionally, the fact that agricultural sector is responsible for near a quarter of global greenhouse emissions (IPCC 2019-special report on climate change), such transition will require substantial dietary shifts, including reduction of sugar and red meat consumption. Vietnam, with more than 95 millions of population, have a challenge to significantly reduce the rice consumption and convert some of the land used for it to production of more legumes. However, correct allocation of arable land for cultivation of particular crops' combination that would ease the transition, and comply with recommendations for healthy nutritional intake, is a challenge of the society. We approached the problem of arable land allocation with mathematical optimization, in particular stochastic evolutionary computing. Arable land allocation to crops' combination is evaluated through three objectives: food self-sufficiency, climate efficiency and crop diversity. Candidate solutions (crops' combinations) were analysed through the non-dominated Pareto front with prioritizing the objective of food self-sufficiency of Vietnam. The results suggest significant change in production of certain crops. As such, sugar cane and rice are required to be reduced on expense of increased production of soybeans, maize, brassicas, and nuts. Therefore, the current surplus of produced carbohydrates would be reduced while proteins increased, which leads to balanced production of macronutrients.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
Number of pages9
ISBN (Electronic)9781728108582
Publication statusPublished - 1 Dec 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Big Data - Los Angeles, United States
Duration: 9 Dec 201912 Dec 2019


ConferenceIEEE International Conference on Big Data
Abbreviated titleBig Data
CountryUnited States
CityLos Angeles


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