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Vertical Farming is a cultivation method that offers a range of solutions for future global challenges. To be able to feed the entire world in 2050, we’ll need to produce 70% more food according to the United Nations. At the same time, due to erosion and climate change, scientists expect that the world’s arable land per capita will shrink by 20% (Bayer, September 2021). Realizing that there is a need for a solution to feed the entire population, vertical farming seems to offer promising solutions:

• Farmers can grow their crops year-round
• Crops are unaffected by adverse weather
• Zero use of chemicals and pesticides
• Efficient use of space by layering up the plants
• Reduce water usage by 95%
• Cuts down transportation costs and emissions that come along
• Farmers can control the nutritional values

Sensors, smart energy systems, colored LEDs, AI, and machine learning all can contribute in making vertical farming financially feasible. Because vertical farming is currently very costly and most of the time unprofitable. This is due to high initial costs such as the price of the land. In urban areas where at the moment the majority of the vertical farms are located, are usually very expensive. Moreover, vertical farms have high operational costs which according to pure greens is composed of 57% of labor costs and 12% of energy costs. Labor costs are high because these farms require experts in the field and close monitoring of the plants. Energy costs come from the LEDs used to create artificial light.

Despite the fact that vertical farms can be used for almost any crop available, high operational and initial costs force farmers to grow only a selection of exclusive crops. Those crops are at the moment only sold in gastronomic restaurants or sold at very high prices in the retail stores.

However, there is a belief that advancements in technology and the increasing expertise in the understanding of vertical farming, are seeding new breakthroughs for efficient vertical farms. I am of the opinion that soon, vertical farms will become profitable and mainstream. Partly because; firstly, technologies such as machine learning allow farmers to bolster efficiencies, reducing waste of resources and thus reducing costs of production. Secondly, sensors and cameras retrieve data about the crops’ status in their growth cycle (Piechowiak, 2019). By collecting this data, farmers can identify the best possible moment to harvest. This technology has already shown proof of significant reduction in food waste (avoid rotting), waste of time, and many other benefits. Thirdly, improved LEDs also help to cut costs, by increasing energy efficiency through drastic reduction of heat waste throughout the entire vertical system (Bayer, September 2021). Moreover, the LEDs are editable in a way farmers can change their color in order to adapt to the crops’ specific nutritional needs.

Realizing that machine learning, LEDs, and sensors are still subject to developments and growth, I believe these can provide vertical farming a way to challenge its struggle to be financially feasible. In addition to these technologies, economies of scale will also define the profitability of vertical farming. For that reason, I do not believe that vertical farms in shipping containers that are already marketed, will make radical changes in the farming industry. Or at least, not making vertical farming an attractive source of revenue. Therefore, farms including Bowery in the US (https://boweryfarming.com) or the German company &Ever (https://and-ever.com), seem to be promising business models. Nevertheless, there is much to debate about and as our environment and capacity to feed people is coming under high pressure, vertical farming is rising as a complementary way of farming in the pursuit of sustainability, food waste and food supply.

References

Bowery. (2021) Bowery: Vision. Retrieved from https://boweryfarming.com.

Eckhardt, J. (2021) Urban Farming: Growing Vegetables in Cities. Bayer Global. Retrieved from https://www.bayer.com/en/af/news-stories/urban-farming-growing-vegetables-in-cities?gclid=Cj0KCQjw-4SLBhCVARIsACrhWLU5971yYhLkLdTABOIAjs7auWbzjXrR7iGjx6IdRjCMaDRV_5mDjKUaAhjZEALw_wcB&gclsrc=aw.ds.

Piechowiak, M. (2019) Why is Vertical Farming Bad? Vertical Farming Planet. Retrieved from https://verticalfarmingplanet.com/why-is-vertical-farming-bad-9-disadvantages/.

&Ever (2021) &Ever: Technology, &Ever MegaFarm. Retrieved from https://and-ever.com.

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Nowadays, rational investors are challenged by inaccuracies of ESG-data, where data is unstructured and subject to many data gaps. In a world transforming into being more sustainable, the challenge of business leaders is to understand how to create meaningful Key Performance Indicators and select adequate frameworks for ESG reporting. This is where artificial intelligence comes in. AI will give these leaders a vital tool for implementing next-generation KPIs and enabling them to illustrate how they want to create long-term value through trust.

Advanced analytics and AI can change the way we understand the data by creating trust, integrity, consistency and openness. Which at the moment is a major issue related to ESG-data according to Boillet (2020) author at EY. This trust is vital for companies because people’s trust in the firm will impact their position towards the company. These people can be in the first instance investors. But also suppliers, customers, employees and many other stakeholders.

Currently, companies and capital markets already use numerous systems to demonstrate a tangible relationship connecting behavior that instills trust and delivering the financial results that are wished. This relation between trust and financial performance also concerns investors and asset managers; which at the moment majorly focus on traditional financial metrics. Platforms such as Arabesque AI are allowing investors to customize and automate the ESG-data up to their requirements. To understand how these platforms really make a difference, below are listed a couple of capabilities that AI brings to ESG:

• AI offers real-time information that traditional sources simply cannot compete with.
• The amount of data collected through AI will evolve over the years, as the adoption rate and the adeptness will increase.
• AI makes it possible to analyze and forecast risks related to human rights issues among suppliers way faster, which makes it possible to identify risks sooner and increase the accuracy of the ‘Social’ and ‘Governance’ metrics in ESG.
• AI is able to aggregate information related to ESG which is at the moment provided in unstructured and inconsistent reports. This will enable decision-makers to make accurate comparisons and key performance indicators.
• Resolve the issue related to data gaps between conventional ESG rating agencies.

To make this analysis nuanced, note that AI brings also a large number of challenges, including its cost and its struggle to identify unreliable sources in accordance with fake news. But I want to let this part open for debate.

References:

Arabesque (2021) Arabesque AI. Retrieved from https://www.arabesque.com/ai/

Boillet, J. (2020) How AI will enable a better understanding of long-term value. EY. Retrieved from https://www.ey.com/en_us/assurance/how-ai-will-enable-a-better-understanding-of-long-term-value.

Boillet, J. and Cobey, C. (2021) How do you teach AI the value of trust? EY. Retrieved from https://www.ey.com/en_nl/digital/how-do-you-teach-ai-the-value-of-trust.