Data-driven GHG reduction in greenhouses in the Netherlands (BSc/MSc)

Short description

A changing climate has ecological, physical and health effects, as well as extreme weather events such as floods, droughts, storms, fires, sea-level rise. Nowadays, CO2 concentration in the atmosphere has reached a record and is expected to increase in the coming year. This has a significant negative social impact on our planet. In The Netherlands, CO2 emissions per capita are equivalent to 8.77 tons per person, and the country is the fourth largest CO2 emitter (per capita) in the EU.
Agriculture emits 17% of carbon dioxide, being the main direct agricultural GHG emissions nitrous oxide emissions from soils, fertilisers, manure, and urine from grazing animals; and methane production by ruminant animals and cultivation. With limited land and a rainy climate, the Netherlands has learned to use land efficiently and innovatively to maximize yields. This has helped to concentrate specialized greenhouse industry in the Netherlands, accounting for 850 greenhouses horticulture farms in 2020, to become the second-largest food exporter in the world.
Given that the Netherlands is home to the largest number of greenhouses in the world, the idea arose to quantify emissions and propose improvements to reduce them and provide effective guidelines on where the most significant results can be achieved.

Objectives

This thesis project will quantify the GHG emissions in greenhouses and according to existing literature, an intelligent design of alternative systems to reduce GHG production will be proposed using data analysis and simulation. The work includes quantifying potential investments and economic savings that could be achieved. Finally, the payback period of the proposed alternatives and the emissions savings can be calculated.

Tasks

The work in this thesis entails:

• To perform a systematic literature review on the different approaches developed to calculate energy use and emissions produced in greenhouses. To collect full-text articles or PDFs from SLRs in the food and environmental sciences fields
• To assess the potential needs (water, energy, CO2) for greenhouses. This stage could be performed demanding data to greenhouses or recovering data form any external source (the internet, data repositories, etc…)
• To develop and propose an intelligent system design for covering needs of greenhouses infrastructure
• To quantify the water, energy and emissions savings for the proposed design

Literature

• Tongwane, M., Mdlambuzi, T., Moeletsi, M., Tsubo, M., Mliswa, V., & Grootboom, L. (2016). Greenhouse gas emissions from different crop production and management practices in South Africa. Environmental Development, 19, 23-35.
• Kramer, K. J., Moll, H. C., & Nonhebel, S. (1999). Total greenhouse gas emissions related to the Dutch crop production system. Agriculture, Ecosystems & Environment, 72(1), 9-16.
• Laborde, D., Mamun, A., Martin, W., Piñeiro, V., & Vos, R. (2021). Agricultural subsidies and global greenhouse gas emissions. Nature communications, 12(1), 1-9.
• Tubiello, F. N., Rosenzweig, C., Conchedda, G., Karl, K., Gütschow, J., Xueyao, P., ... & Sandalow, D. (2021). Greenhouse gas emissions from food systems: building the evidence base. Environmental Research Letters, 16(6), 065007.

Requirements:

• Courses: Programming in Python (INF-22306), (Optional) Big Data (INF-34306)
• Required skills/knowledge: Programming in Python, interest about sustainability, energy transition and CO2 depletion

Key words: Data analysis for Sustainability, Greenhouse/Farm equipment

Contact person(s)
Dr. Tarek Alskaif ( tarek.alskaif@wur.nl), WUR-INF supervisor
Dr. Miguel Ángel Pardo Picazo (mpardo@ua.es), External supervisor