Climate Change and Obesity.

Global warming and the rising prevalence of obesity are well described challenges of current mankind. Most recently, the COVID-19 pandemic arose as a new challenge. We here attempt to delineate their relationship with each other from our perspective. Global greenhouse gas emissions from the burning of fossil fuels have exponentially increased since 1950. The main contributors to such greenhouse gas emissions are manufacturing and construction, transport, residential, commercial, agriculture, and land use change and forestry, combined with an increasing global population growth from 1 billion in 1800 to 7.8 billion in 2020 along with rising obesity rates since the 1980s. The current Covid-19 pandemic has caused some decline in greenhouse gas emissions by limiting mobility globally via repetitive lockdowns. Following multiple lockdowns, there was further increase in obesity in wealthier populations, malnutrition from hunger in poor populations and death from severe infection with Covid-19 and its virus variants. There is a bidirectional relationship between adiposity and global warming. With rising atmospheric air temperatures, people typically will have less adaptive thermogenesis and become less physically active, while they are producing a higher carbon footprint. To reduce obesity rates, one should be willing to learn more about the environmental impact, how to minimize consumption of energy generating carbon dioxide and other greenhouse gas emissions, and to reduce food waste. Diets lower in meat such as a Mediterranean diet, have been estimated to reduce greenhouse gas emissions by 72%, land use by 58%, and energy consumption by 52%.

Farmer and farm worker illnesses and deaths from COVID-19 and impacts on agricultural output.

Farmers and farm workers are critical to the secure supply of food, yet this population is potentially at high risk to acquire COVID-19. This study estimates the prevalence of COVID-19 among farmers and farmworkers in the United States by coupling county-level data on the number of farm workers relative to the general population with data on confirmed COVID-19 cases and deaths. In the 13 month period since the start of the pandemic (from March 1, 2020 to March 31, 2021), the estimated cumulative number of COVID-19 cases (deaths) was 329,031 (6,166) among agricultural producers, 170,137 (2,969) among hired agricultural workers, 202,902 (3,812) among unpaid agricultural workers, and 27,223 (459) among migrant agricultural workers. The cases amount to 9.55%, 9.31%, 9.39%, and 9.01% of all U.S. agricultural producers, hired workers, unpaid workers, and migrant workers, respectively. The COVID-19 incidence rate is significantly higher in counties with more agricultural workers; a 1% increase in the number of hired agricultural workers in a county is associated with a 0.04% increase in the number of COVID-19 cases per person and 0.07% increase in deaths per person. Although estimated new cases among farm workers exhibit similar trends to that of the general population, the correlation between the two is sometimes negative, highlighting the need to monitor this particular population that tends to live in more rural areas. Reduction in labor availability from COVID-19 is estimated to reduce U.S. agricultural output by about $309 million.

Cherry Tomato Production in Intelligent Greenhouses-Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality.

Greenhouses and indoor farming systems play an important role in providing fresh and nutritious food for the growing global population. Farms are becoming larger and greenhouse growers need to make complex decisions to maximize production and minimize resource use while meeting market requirements. However, highly skilled labor is increasingly lacking in the greenhouse sector. Moreover, extreme events such as the COVID-19 pandemic, can make farms temporarily less accessible. This highlights the need for more autonomous and remote-control strategies for greenhouse production. This paper describes and analyzes the results of the second "Autonomous Greenhouse Challenge". In this challenge, an experiment was conducted in six high-tech greenhouse compartments during a period of six months of cherry tomato growing. The primary goal of the greenhouse operation was to maximize net profit, by controlling the greenhouse climate and crop with AI techniques. Five international teams with backgrounds in AI and horticulture were challenged in a competition to operate their own compartment remotely. They developed intelligent algorithms and use sensor data to determine climate setpoints and crop management strategy. All AI supported teams outperformed a human-operated greenhouse that served as reference. From the results obtained by the teams and from the analysis of the different climate-crop strategies, it was possible to detect challenges and opportunities for the future implementation of remote-control systems in greenhouse production.