TY - GEN

T1 - Optimization of arable land use towards meat-free and climate-smart agriculture

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

AU - Kuzmanovski, Vladimir

AU - Larsen, Daniel Ellehammer

AU - Henriksen, Christian Bugge

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

U2 - 10.1109/BigData47090.2019.9006264

DO - 10.1109/BigData47090.2019.9006264

M3 - Article in proceedings

AN - SCOPUS:85081378782

SN - 978-1-7281-0858-2

SP - 5140

EP - 5148

BT - 2019 IEEE International Conference on Big Data (Big Data)

A2 - Baru, Chaitanya

A2 - Huan, Jun

A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

A2 - Lee, Kisung

A2 - Ye, Yanfang Fanny

PB - IEEE

Y2 - 9 December 2019 through 12 December 2019

ER -