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GREENBOX Technology I - Technical Feasibility and Performance in Warehouse Environment
Ankit Kumar Singh1, Richard McAvoy2, Boris Bravo-Ureta3, Xiusheng Yang4,*
Published in Journal of the ASABE 66(5): 1077-1087 (doi: 10.13031/ja.15343). Copyright 2023 American Society of Agricultural and Biological Engineers.
1The Water School, Florida Gulf Coast University, Fort Myers, Florida, USA.
2Department of Plant Science and Landscape Architecture, University of Connecticut, Storrs, Connecticut, USA.
3Department of Agricultural and Resource Economics, University of Connecticut, Storrs, Connecticut, USA.
4Department of Natural Resources, University of Connecticut, Storrs, Connecticut, USA.
*Correspondence: Xiusheng.Yang@uconn.edu
The authors have paid for open access for this article. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License https://creative commons.org/licenses/by-nc-nd/4.0/
Submitted for review on 6 September 2022 as manuscript number PAFS 15343; approved for publication as a Research Article by Associate Editor Dr. Erin Cortus and Community Editor Dr. Shafiqur Rahman of the Plant, Animal, & Facility Systems Community of ASABE on 15 May 2023.
Highlights
Abstract. The surging worldwide population and urbanization have increased food security and safety pressures. Therefore, there is a need to increase food production capacity in urban areas to feed this growing population. We have developed the GREENBOX technology to grow vegetables in individual climate-controlled boxes in urban warehouse environments. A GREENBOX is a thermally insulated modular structure of standard size with an artificial lighting source, a hydroponic nutrient supply system, and environmental controls. GREENBOX units can be used together in various numbers to form different configurations and production capacities. This study was conducted to evaluate the technical feasibility and performance of the GREENBOX technology for urban crop production in warehouse settings commonly found in urban areas. Two model GREENBOX units, constructed with commercially available parts, were located in a high-ceiling headhouse of a laboratory greenhouse complex at Storrs, Connecticut, USA, for the study. Forty-eight (48) heads of Butterhead Rex lettuce (Lactuca sativa) were grown in the model GREENBOX units (24 in each) over a 30-day growing cycle for four seasons. Environmental data, including light, temperature, relative humidity, and carbon dioxide, were collected using iPonic sensors at a frequency of every minute and processed to 15-minute averages. Crop growth was quantified with biomass data, which were wet weight, dry weight, total leaf area, and lettuce head area, using destructive and non-destructive methods every three days. A lysimeter was used to determine the water consumption rate by plants every fifteen minutes. We derived the Daily Light Integral (DLI), Leaf Area Index (LAI), Specific Leaf Area (SLA), productivity, and water consumed per lettuce head, per unit wet weight, and per unit dry weight. Descriptive statistics were used to describe and analyze the results. The DLI in the GREENBOX ranged between 32.48-37.23 mol/m2.d at the lettuce heads' height, higher than the recommended minimum DLI of 6.5-9.7 mol/m2.d. GREENBOX does not rely on external light but solely on the artificial lighting source, regulated at the grower's discretion. The mean temperatures inside were 24.5-26.9°C, falling within the optimal range of 17-29°C for lettuce. The artificial lighting source was a heat source to sustain cultivation. All year, the average relative humidity was 35.53%-58.54%, mostly within the ideal range of 40%-60%. The CO2 concentration inside the boxes fell slightly below the ambient concentration of 350 ppm, between 301.39 and 311.34 ppm over different seasons. Measured growth parameters, including LAI (5.3-6.5 cm2/cm2), SLA (344.3-569.3 cm2/g), and productivity (6.33-7.38 kg/m2), all followed similar patterns, slightly different amongst seasons and higher in warmer seasons. GREENBOX used 1.83-2.69 liters of water per head of lettuce consistently year-round, 95% lower than soil-based irrigation. Lettuce plants were healthy and grew to full size in the 30-day cycle, regardless of the season. Our study showed that the GREENBOX technology was capable of providing desired environmental conditions for growing lettuce crops all year around in the experimental warehouse structure and consequently having a high potential to be applied in food production in mid-latitude urban settings.
Conventional soil-based agriculture has been increasingly taxing on the planet's natural resources. According to the United Nations, 70% of global water use (Kloas et al., 2015) and 20% of fossil fuels (Despommier, 2011) are consumed by crop production. Excessive and protracted use of conventional agriculture techniques mainstreamed due to the green revolution has degraded the environment through soil, air, and water pollution (Kozai, 2018), further exacerbated by climate change (Ortiz-Bobea et al., 2021). Current research estimates that 20%-33% of arable land is degraded (Taghizadeh, 2021; Zareba et al., 2021), and with every 1°C rise in temperature, 10% of arable land is lost (Despommier, 2011). The global population has been increasing from 2.5 billion in 1950 to a projected population of 11.2 billion by 2100 (Pison, 2017), resulting in never before seen pressure on the food production chain (Maisonet-Guzman, 2011). This burgeoning population is not spread out evenly and is concentrated in urban areas. According to the United Nations, more than half the world's citizens live in urban areas, which is projected to be more than 60% by 2030 (Specht et al., 2014). The United Nations has determined since 2007 that the number of residents in urban areas was greater than in rural areas (Taghizadeh, 2021). The socioeconomic and biophysical landscape has been altered due to increasing urbanization (Posivakova et al., 2019), a trend that is only expected to increase. Currently, urban areas occupy about 300-700 thousand square kilometers globally, which is similar to the total cultivated area for fruits and vegetables (Hamilton et al., 2014). Another challenge confronting food production is the decrease in area and productivity of arable land in the face of increasing demand for food (Specht et al., 2014), increasing global food insecurity, and agricultural capacity (Armanda et al., 2019).
Residents of urban regions do not necessarily have access to fresh, nutritious foods. Food deserts are regions with limited access to adequate, safe, and nutritious food for daily needs and healthy living (Hamelin et al., 2002; Olson, 1999; Hendrickson et al., 2006). Food deserts are characterized by an abundance of readily available calorie-dense foods that are nutritionally poor (Chung and Myers, 1999) in the face of a shortage of fresh, nutritious fruits and vegetables. The most commonly found residents of food deserts are economically poor, historically disadvantaged ethnic minorities such as African Americans and Latinos (Cummins et al., 2014), and aged people (Zenk et al., 2005; Raja et al., 2008; Morland et al., 2002). Socioeconomically disadvantaged families tend to have children more than likely to develop obesity and diabetes, which account for $395 billion in medical costs and lost productivity annually (Yang et al., 2018; Hammond and Levine, 2010). The residents of food deserts tend to have poorer health and life outcomes, such as higher poverty rates, lower income, higher rates of foreclosed homes, higher unemployment rates, a higher proportion of people relying on public assistance, and lower educational attainment levels (Dutko et al., 2013). A report demonstrated that in 2018, 11% of the United States population faced food insecurity (Oldani, 2021).
There are four significant challenges confronting feeding our increasing world population: shifting demographics towards urban areas, climate change, increasing waste, and scarcity of natural resources (Xi et al., 2021). Meeting the demand for a rapidly growing population requires increased capacity for food production (Conforti et al., 2011). The FAO estimates that annual world agricultural production needs to increase by 70% between 2005 and 2050 to feed the growing population (Conforti et al., 2011). In response to the current challenges facing feeding the populous urban residents, sustainable food production systems have emerged in urban areas (Shaik et al., 2022). Controlled Environment Agriculture (CEA) is defined as high-yield agriculture protected from the external environment (Armanda et al., 2019; Orsini et al., 2020), and can serve as an excellent solution for feeding the cities of the future (Thomaier et al., 2015). CEA setups commonly consisted of six principal structural elements (Kozai, 2013): (1) a thermally insulated structure, (2) design for vertical stacking, (3) ventilation for heating and cooling, (4) carbon dioxide delivery system, (5) fans to allow for air circulation, and (6) a soilless system for the delivery of the nutrient solution, such as hydroponics (Loi et al., 2020; Goodman and Minner, 2019).
To aid in fresh vegetable crop production in urban settings, the Yang Laboratory at the University of Connecticut (UConn) developed the GREENBOX technology using climate-controlled enclosed spaces with artificial lighting sources, hydroponic nutrient supply systems, and environmental controls (Yang et al., 2017). The GREENBOX was specifically designed for use in warehouse conditions, typically defined with a lower degree of environmental controls, high ceilings, and minimal lighting conditions. Warehouse spaces have the distinct advantage of having minimal requirements of retrofitting or modification to be ready for GREENBOX crop production.
The most prominent aspect of the GREENBOX technology is that it does not require arable or open land that intercepts sunlight and can use existing urban structures for urban vegetable crop production. The implementation of GREENBOX technology enables the ease of mass production of crops in concentrated urban areas. GREENBOX units can be used together in various numbers to form different configurations and production capacities. Each GREENBOX can potentially grow different sets of crops at different growth stages, allowing for a continuous stream of crop production to fulfill consumer needs without being affected by variations in weather and interruptions that the regular food supply chain faces. This method of crop growth presents great potential in the urban agricultural food production landscape. There is a need to understand the viability of GREENBOX technology.
Research Objective
This study was conducted to evaluate the technical feasibility and performance of the GREENBOX technology for urban crop production in warehouse settings commonly found in urban areas. We tested whether the assembled GREENBOX units using commercially available materials could provide optimal environmental conditions to support crop growth year-round over spring, summer, fall, and winter. We also quantified the growth and productivity of the lettuce crop grown in the GREENBOX environment. Finally, we evaluated the water use patterns of the crop grown in the GREENBOX units for a healthy and full-sized crop.
Materials and Methods
Location
The experiments were carried out in the headspace at the Agricultural Biotechnology Laboratory (ABL) greenhouse at UConn, Storrs, Connecticut, United States of America. The ABL Greenhouse (41°48'47" N, 72°15'03" W) was approximately 193 meters above sea level. Connecticut, located on the North American continent on the east coast, generally has a temperate climate, characterized by cold, snowy winters and warm, humid summers (Runkle et al., 2017). Large temperature ranges daily and annual, precipitation equally distributed amongst the four seasons, and considerable variation in weather over a short period are the defining characteristics of Connecticut weather. Over the winter months, the average daily temperature ranges between -3.4°C and -0.4°C, while over the summer months, the average daily temperature ranges between 18.8-21.3°C (Runkle et al., 2017).
The headspace is a semicircular section building with a diameter of about 40 m and a surface area of approximately 400 m2, connected to the various greenhouse bays in the ABL building. The headspace is maintained at between 20.0-23.3°C all year and is not allowed to drop to extremely low or high temperatures. The ambient conditions of the headspace are similar to the warehouse environment. The similarities lie in that they are large volume spaces with high ceilings, sparse windows and lighting, and certain but not sophisticated controls. Neither the geographical location nor headspace is amenable to crop production year-round as-is, without the inclusion of the micro-environment.
Experimental Setup
We assembled two model GREENBOX units using commercially available materials for pilot studies. GREENBOX units were constructed using grow tents, lighting elements, environmental monitoring and control modules, nutrient delivery systems coupled with a lysimeter to monitor water consumption, and a nutrient film technique (NFT) system for hydroponic growth. We used two grow tents (The Original Gorilla Grow Tent 5 x 5, Gorilla Inc., Santa Rosa, California) as the base structure for the GREENBOX units. The growth tent's dimensions were 1.5 x 1.5 x 2.1 m and consisted of an exterior canvas covering made of 1680D material. The grow tents included three access doors and weighed 30 kg. The walls of the grow tents had diamond reflective interior surfaces with desired insulating properties. The two GREENBOX units were placed in the headspace of the ABL, which provided similar environmental conditions in urban warehouses. An illustration of the GREENBOX setup is presented in figure 1 (Singh and Yang, 2021).
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| Figure 1. An illustration of the experimental setup from Singh and Yang (2021) demonstrates the top view (left) and front view (right). The main components of GREENBOX consist of a thermally insulated growing space, an artificial lighting source, a hydroponic fertigation system, and environmental control systems. |
We installed an LED light source (powerPAR PPLF44, Hydrofarm LLC, Petaluma, California) in each GREENBOX unit to facilitate photosynthesis. The light sources were LED line tubes of four feet long, 160 watts with 131 lumens per watt rating efficiency, providing a light of 40,000 K color temperature for a rated diode life of 50,000 hours. We positioned the lighting element 1 m above the plant canopy. LED lights have many advantages over conventional systems, including smaller size, wavelength specificity, longer lifetime, increased durability, and a lower-emitting surface temperature (Rehman et al., 2017). They also have a much longer life of 20,000 to 55,000 hours compared to high-pressure sodium (HPS) lamps of 10,000 hours (Nelson and Bugbee, 2014).
We installed a forced ventilation system comprising a fan at the top and opened air inlets at the tent's bottom. The fan's diameter was about 15 cm, rated 35 watts, and had a manufacturer's rated capacity of 9 m3/min (Hyper Fan GL56701400, Hawthorne Gardening Company, Vancouver, Washington). We maintained the ambient growing conditions inside the GREENBOX by modulating the use of fans and vents.
The main environmental parameters in a controlled environment include light intensity, light duration, air temperature, air moisture content, and aerial CO2 concentration (Fitz-Rodríguez et al., 2010). We monitored the environment inside the two GREENBOX units using one environmental controller (iPonic 624, Link4 Corp., Burbank, California). The controller's sensors were positioned 15 cm above the plant canopy in each GREENBOX.
We placed four Nutrient Film Technique (NFT) channels evenly on a 3 m x 3 m tray stand (model number 706121, Fast Fit Ltd., Hawthorne Gardening Company, Vancouver, Washington) for each hydroponic platform. The nutrient delivery system consisted of a reservoir to hold the nutrient solution, placed on a lysimeter (OHAUS T51 Defender series, OHAUS CORPORATION, Parsippany, New Jersey), a submersible pump (model number AAPW400, Hydrofarm LLC, Petaluma, California) in the reservoir, and associated piping. The lysimeter had a maximum capacity of 30 kilograms. The submersible pump had a capacity of 25 liters per minute (lpm) and had a 24-watt energy consumption rating. The NFT channels, made with UV stabilized plastics, were 10 cm wide, 5 cm deep, and 120 cm long. These channels had holes for inserting transplants 15 cm apart, and we planted six lettuce heads in every channel in the GREENBOX. We placed the NFT channels 8 cm apart to keep a distance of six inches between lettuce heads, forming a 4 x 6 matrix in the two GREENBOX units.
For many reasons, we chose pelleted Rex lettuce (Lactuca sativa) seeds (Johnny's selected seeds, Fairfield, Maine) for crop growth in the GREENBOX. Firstly, lettuce can proliferate under relatively lower photosynthetic photon flux density (PPFD), lower carbon dioxide content, and a higher planting density (Kozai et al., 2015; Liu et al., 2017). Secondly, lettuce has faster growth due to lower stresses associated with water, insects, pests, pathogens, and temperature, with significantly reduced incidences of physiological disorders (Kozai et al., 2015; Liu et al., 2017). Thirdly, manipulation and production of secondary metabolite production and higher economic value per unit weight production of lettuce are possible (Kozai et al., 2015; Liu et al., 2017). Finally, lettuce also fulfills the top characteristic preferences for crops in plant factories, such as having a maximum height of thirty centimeters and having a growth cycle between ten and thirty days (Kozai et al., 2015; Liu et al., 2017).
Lettuce is a cool-season vegetable requiring low temperatures and low light that grows between 5 and 30°C (Frantz et al., 2004). Ideally, the daytime temperatures should range between 20 and 23°C, and the nighttime temperatures should range between 15 and 18°C. Excessively low temperatures can damage plant tissue or stunt growth, while excessively high temperatures can lead to wilting, tissue damage, and bolting. The nutrient solution's ideal pH values should be between 5.5 and 6.0 Standard Units (SU), and the electrical conductivity (EC) should be between 1.5 and 2.0 mS (Holmes et al., 2019). Ideally, lettuce requires between 10 and 16 hours of exposure to moderate sunlight. Exposure to excessively high sunlight can lead to a bitter taste in lettuce. The ambient relative humidity conditions for lettuce range between 40% and 60%, below which can hamper growth, and above which can cause mildew, botrytis, tipburn, or pythium.
Experimental Procedure
We ran growing cycles in spring 2020, summer 2020, fall 2020, and winter 2020-2021, representing four seasons. We ran the experiment for 44 days for each growing period. The first fourteen days were the seedling stage until the lettuce plants were ready for transplant, followed by thirty days of crop growth.
To begin the seedling production stage, we sowed our pelleted Rex lettuce (Lactuca sativa) seeds and let them grow for two weeks until it was ready for transplant in OASIS Horticubes (104 count, OASIS Grower Solutions, Kent, Ohio) with dimensions of 2.54 × 3.18 × 3.81 cm. After saturating it with plain tap water, we placed a single seed for every recess in the OASIS Horticubes placed on a black tray. We covered the seeds with a newspaper and put the covered tray with seeds in the dark inside a GREENBOX unit for 48 hours. After uncovering, the GREENBOX unit's lights were programmed to provide 16 hours of light per day. We irrigated the plants in the seedling stage using a starter fertilizer solution. We formulated the starter nutrient solution by mixing 3.6 grams of "Jack's Nutrients hydroponic 15.5-0-0" (calcium nitrate) and 3.8 grams of "Jack's Nutrients hydroponic 5-12-26" for every ten liters of water. The starter nutrient solution was half the strength of the regular strength solution used for crop production.
We irrigated the seedlings every day by keeping the OASIS Horticubes saturated. The seedlings were ready for transplant after fourteen days when two true leaves, two leaves apart from the cotyledon, had expanded. We selected seedlings randomly from the OASIS Horticubes to transplant into the NFT channels, giving preference to healthier-looking transplants. We prepared the nutrient solution for irrigation, mixing 6 grams of "Jack's Nutrients hydroponic 15.5-0-0" (calcium nitrate) and 6.4 grams of "Jack's Nutrients hydroponic 5-12-26" for every ten liters of water. We circulated a nutrient solution in the NFT system with a target pH of 5.8 SU and a targeted EC of 1.5-2.0 mS.
We checked the nutrient solution's pH and EC every three days. If the pH was below the target pH of 5.8 standard units, we increased the pH by adding an alkali such as 1 M NaOH or 2 M NaOH, and if the pH was above the target pH of 5.8 SU, we added acids such as 1 M HCl or 1 M H2SO4. We administered dosing using a dropper and kept adding an acid or alkali until we reached the target pH of 5.8 SU. If the electrical conductivity was below the target EC of 1.5 mS, we increased the EC by adding fertilizer based on initial calculations. If the EC was above the target of 2.0 mS, we decreased the EC by increasing the water content through dilution with tap water. We replaced the entire nutrient solution in the GREENBOX units every six days by using a wet shop vac to suction out all the present nutrient solutions.
As a preventative measure, we used bio-controls on our crops during the growth cycles. In the GREENBOX units, we placed bran material on the leaves that were the carrier for a predatory mite (Amblyseius Cucumeris) of thrips (Thysanoptera). We set a card (Encarsia Formosa Pupae 100/Card, Hummert International, Earth City, Missouri) on the side of one NFT channel in the two GREENBOX units containing two species of parasitic wasps, Encarsia Formosa and Eretmocerus Eremicus, or sometimes E. Mundus of whitefly (Aleyrodidae).
Data Acquisition
Environmental variables, including light intensity (W/m2), temperature (°C), relative humidity (%), and carbon dioxide (ppm), were collected using sensors on the iPonic 624 controllers. These environmental controllers logged data instantaneously every minute and were accessible via the cloud. We downloaded the data every week from the iPonic cloud. The sensors recorded the data every minute and then exported it in the CSV (comma-separated values) format. All sensors were regularly calibrated following manufacturer recommendations, and we frequently compared the data with ABL greenhouse building data to assure the quality of the data.
We monitored the growth of lettuce biomass through non-destructive and destructive methods. The wet weight is defined as the weight of accumulated fresh biomass right at or after harvest, while the dry weight is the weight of the biomass without water.
We monitored lettuce's development using the lettuce head area (cm2) as a non-destructive proxy for the canopy growth over the growing cycle. The fifth and sixth plants in each NFT channel in the GREENBOX were monitored every three days to evaluate growth using non-destructive methods. We monitored the development of lettuce biomass through destructive methods by using wet weight (g), dry weight (g), and leaf area (cm2). We randomly selected a lettuce plant for destructive testing from each GREENBOX unit except for the fifth and sixth plants on every NFT channel, as they were reserved for monitoring the lettuce head area every three days. We pulled apart the roots and any growing medium attached to the plant before weighing the lettuce immediately after harvest to obtain the lettuce plant's wet weight. We blotted the plant gently with a soft paper towel to remove any free water on the surface. We weighed the plants immediately after harvest because lettuce has a high water composition, and waiting to weigh them could cause them to dry out and lead to inaccurate measurements.
We harvested lettuce on the thirtieth day from the day of the transplant. We obtained the lettuce's dry weight by drying the leaves stored in a brown paper bag in a forced air convection oven at 65°C for six days. Lettuce's total dry weight included non-structural dry weight, including components such as glucose, sucrose, and starch, and structural dry weight, including elements such as cell walls and cytoplasm. We did not measure the root system's weight because it comprised a portion of OASIS Horticubes, which would have led to an inconsistent and skewed analysis. We measured the lettuce head area using the leafscan app installed on a mobile device (iPhone XR, Apple Inc., Cupertino, California) on the fifth and sixth plants on every NFT channel. We obtained and recorded the lettuce head area and the total leaf area of the individual lettuce plant every three days. We obtained the lettuce head area by positioning the camera at a height that could capture the full view of the head and paper with the four reference points within the picture frame. We obtained the lettuce head area of the last two plants per NFT channel for the growing cycle duration in the GREENBOX units. To get the total leaf area for a lettuce plant, after weighing, we pulled apart all the leaves and then individually measured the area of every leaf using the leafscan app using the four reference points for calibration. The four reference points were black dots that formed a square. We placed the leaves under a glass frame to flatten all areas of the leaves measured so that the waves in leaf margins would not impact the measurement. The leafscan app measured the leaf area in square centimeters (cm2) with an accuracy of 0.01 cm2.
We collected the water consumption data using a lysimeter. Using the interface kit, this lysimeter recorded data every fifteen minutes on the computer. This kit consisted of an RS232 to USB A cable connecting the lysimeter to the computer. The lysimeter logged data in real-time using instantaneous weight values in kilograms (kg) with an accuracy of 0.001 kg. We installed the Serial Port Data Collection (SPDC) software (version V2.03, OHAUS CORPORATION, Parsippany, New Jersey). This software would then export the collected data in an excel sheet that lists the date (mm/dd/yyyy), time in 24 hours (hh:mm), and weight in kilograms. We determined the amount of water lost to evapotranspiration, which is a combination of evaporation and transpiration, by finding the difference in water weights over a unit of time by using readings from the lysimeter. We measured the nutrient solution's pH and electrical conductivity every three days using a pH/EC meter. We monitored the nutrient solution's pH and electrical conductivity using a portable pH/EC meter (HI 9813-6N, Hanna Instruments, Woonsocket, Rhode Island). We replaced the nutrient solution every six days. We dipped the probe in the nutrient solution to obtain the pH and EC values and manually logged the data once it read a stable value. The pH/EC meter measured the pH with an accuracy of 0.1 SU and the EC with an accuracy of 0.01 mS. We calibrated the pH/EC meter for every sampling event to ensure accuracy every three days.
Data Processing and Descriptive Statistics Analysis
Lighting was represented by the Daily Light Integral (DLI) (mol/m2.d) by using the light data measured instantaneously (W/m2) and converted to cumulative light accumulated per day. The DLI is defined as the total amount of light received by a plant over 24 hours (Kozai, 2018). It is also defined as a measure of the total photosynthetic photon flux (PPF) density accumulated over one day, which has been extremely useful to assess the irradiance delivered to crops (Faust and Logan, 2018). Other environmental variables such as temperature, humidity, and carbon dioxide were processed to 15-minute averages using data from the iPonic 624 environmental controllers.
We determined the lettuce head area and the total leaf area using the leafscan app. The leafscan app measured the leaf area by running an algorithm that measured the green area compared to the white area. The leafscan app used computer vision algorithms to detect the leaf edge. It then calculated the area inside the contour in pixels. Using the given reference length, it converted the leaf pixel area into the real surface area using the following formula:
(1)
where the reference area was the given reference length squared. We exported the collected data to a CSV file, then assimilated it into an Excel spreadsheet. We recorded the lettuce head area of lettuce and the total leaf area and processed it in cm2.
The leaf area was the summation of all the individual leaves in a plant to observe growth over the growing cycle, obtained using the LeafScan app. We used the wet weight, dry weight, and total leaf area data to obtain crop growth indexes such as the Leaf Area Index (LAI), defined as the ratio of the total leaf area to the lettuce head area, and Specific Leaf Area (SLA), defined as the ratio of the total leaf area to the dry weight of the crop. We derived the GREENBOX productivity using the wet weight values at harvest to determine the total biomass output in kg/m2 of the growing area presented in this paper.
| Table 1. Means and standard errors (SE) light, temperature, relative humidity, and carbon dioxide concentration in the GREENBOX units over the four growing seasons of spring, summer, fall, and winter. The light conditions remained largely invariant during the experiments. | ||||
| Season | DLI (mol/m2.d) | Temperature Over Growth Cycle (°C) | Relative Humidity Over the Growth Cycle (%) | CO2 Concentration Over the Growth Cycle (ppm) |
| Spring | 33.32 | 26.79 ± 0.83 | 55.40 ± 7.02 | 311.34 ± 53.86 |
| Summer | 32.48 | 26.99 ± 0.93 | 58.54 ± 4.77 | 301.39 ± 61.63 |
| Fall | 37.23 | 25.37 ± 1.31 | 39.09 ± 8.24 | 305.38 ± 58.40 |
| Winter | 36.70 | 24.50 ± 1.09 | 35.53 ± 6.27 | 307.81 ± 57.64 |
The water use data was determined from the lysimeter's instantaneous measurements by comparing the weight of nutrient solution values for each 15-minute interval for the four cycles in the GREENBOX. We converted the water use values to report them in the form of water consumed per head of lettuce (l/head), water consumed per unit of wet weight (l/kg), and water consumed per unit of dry weight (l/kg).
We used descriptive statistics to characterize and analyze the environmental data of DLI, temperature, relative humidity, and carbon dioxide at a 15-minute frequency over the thirty-day growing period for all cycles. The means and standard deviations of the light intensity, temperature, relative humidity, and carbon dioxide concentration were evaluated. We also reported dry weight, wet weight, total leaf area, lettuce head area, LAI, SLA, and productivity of the lettuce crop in the GREENBOX units over four growing seasons at harvest (Day 30). We also examined the dynamic behavior of the water use data to understand water consumption patterns over four growing cycles.
Results and Discussion
Environmental Variables
Results from the growing cycles over the year in the GREENBOX units indicated that the lettuce crops were exposed to the desired environmental conditions required to sustain healthy growth to full size at harvest. If any, the exposure to extreme environmental conditions was momentary and did not cause sustained stress on the crop. Table 1 summarizes the average environmental conditions of DLI (mol/m2.d), temperature (°C), relative humidity (%), and carbon dioxide concentration (ppm) over the four growing cycles. Environmental conditions in the two GREENBOX units over the four growing cycles were depicted graphically for the light intensity (W/m2), temperature (°C), relative humidity (%), and carbon dioxide concentration (ppm) in figure 2.
The DLI values in the GREENBOX ranged between 32.48-37.23 mol/m2.d (table 1) over the year, which was significantly higher than the recommended minimum DLI of 6.5-9.7 mol/m2.d (Paz et al., 2019) and could be controlled at the grower's discretion based on different crops. In a study, 16-17 mol/m2.d was identified as optimal for hydroponic production of lettuce, below which a lower head size was observed and above which tipburn was observed (Albright et al., 2000). We noticed very light tipburn in the lettuce crop in one of the experiments from the GREENBOX units, which may have been due to higher light levels over all four seasons, towards the end of the cycle. Although the tipburn was hardly noticeable, we would suggest having light intensity controlled at the recommended optimal value. There were minor fluctuations in the measured light values over the year due to changes in reflections in the internal insulating material, which is reflective by nature. The variation in recorded DLI over the year could be explained by the slight variation in the sensor location during the year due to air movement and human interference. Figure 2a demonstrated that the GREENBOX units received a consistent lighting regime to support photosynthesis and the growth of lettuce crops to maturity. There was a slight decrease in the recorded light received in the GREENBOX units over the spring cycle because the performance of the light in the GREENBOX-I unit was deteriorating and ultimately gave out in the summer and was replaced with a standby light, whose intensity was not as high as the original. Therefore, reducing the average light intensity recorded in the GREENBOX units over the last few days of the growing cycle in the spring and summer. The GREENBOX units did not rely on external light and solely relied on the artificial lighting source. The lack of background or natural lighting explains light levels dropping to zero when the lighting elements were turned off.
In the GREENBOX units, the artificial lighting source acted as the only heat source to sustain cultivation. Previous research had demonstrated that optimal temperatures to grow lettuce crops ranged between 17-29°C (Holmes et al., 2019) in a controlled environment, easily provided by the GREENBOX units (table 1 and fig. 2b). The mean temperatures year-round in the GREENBOX units were 24.50-26.99°C. We observed that the temperature range was minimal in the GREENBOX units, which we could attribute to a higher degree of environmental control and less variability due to an insulated growing space in a warehouse environment that was relatively free from external fluctuations. The average temperatures were higher in the warmer seasons than in the colder ones. In the GREENBOX units, the temperatures increased when the lighting element was turned on for 16 hours a day and then dropped when the lighting element was turned off, as evidenced by figure 2b. The standard deviation measured how spread out a value was around the mean, and the low values in the temperature demonstrated a higher degree of control and lower variation. We noted that the standard deviation values ranged between ± 0.83 and 1.31, indicating the degree of uniformity in environmental conditions provided in the GREENBOX units. Figure 2b demonstrated the temperature values over the cycles, and we noticed that the average values were within 17-29°C, as depicted by a dashed line in the graph.
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| Figure 2. Time series data of the environmental variables of (a) light intensity, (b) temperature, (c) relative humidity, and (d) carbon dioxide concentration in the GREENBOX units for 30-d lettuce growth cycles in spring, summer, fall, and winter seasons. The dashed lines in graphs (b) and (c) depicted the lower and upper limits of the optimal ranges of temperature and relative humidity for lettuce growth, and the dashed line in the CO2 graph (d) described the ambient concentration. |
Figure 2c depicts the relative humidity conditions observed in the GREENBOX units during the growing cycles year-round. The dashed lines represented the ideal limits of ambient relative humidity conditions of 40% and 60%, above and below which were not ideal for crop growth. Excessively high relative humidity values may lead to an increased incidence of diseases, while excessively lower values may lead to stunted growth. We found that the GREENBOX units provided ambient humidity conditions between 40%-60%, which fell in the desirable range for most crops, including lettuce, for most of the growing cycle. Over the year, the humidity ranged from a low of 35.53% ± 6.27% to a high of 58.54% ± 4.77%. Figure 2c indicates that the relative humidity was higher in the spring and summer and lower in the fall and winter. There were periods when the relative humidity in the GREENBOX was below the lower ambient limit in the fall and winter. While we used the exhaust fan to modulate the environment, humidifiers and dehumidifiers may prevent the relative humidity from varying to extreme values, which we may consider for future iterations of GREENBOX units.
Figure 2d depicts the average CO2 concentration in the GREENBOX units. In this study, we chose a reference ambient CO2 concentration of 350 ppm (Amthor, 2001), depicted by a dashed line in figure 2d; this value reflects the expected ambient CO2 conditions around built-up areas, including the ABL area. Over the year, the average CO2 concentration in GREENBOX units ranged from 301.39 ± 61.63 ppm to 311.34 ± 53.86 ppm. We noted that the average CO2 concentration range in the GREENBOX units was lower than the ambient concentration of 350 ppm, as evidenced in figure 2d, which we attributed to lowered levels due to consumption by crops for photosynthesis. The CO2 levels decreased as the crops were photosynthetically active during the daylight hours. However, the CO2 rose during the night as the crops did not consume it. Future iterations of GREENBOX may consider an external supply of CO2 for fertilization purposes (Kozai, 2007).
The GREENBOX units provided the desired conditions to sustain lettuce crop production without exposure to extreme environmental stresses and were within the optimal parameters for the studied environmental variables. The warehouse environment allowed for a higher degree of control and lower fluctuations in the GREENBOX environment, aiding in a higher quality, more controlled environment.
Our pilot study used a headhouse to host the GREENBOX units. It mimics the environment of an urban warehouse with a high ceiling and a temperate environment. However, real warehouses may have different configurations and controls for their original purposes. Therefore, our study may have limitations pertaining to the variable warehouse environments. With different structures and production capacities, the warehouse climate may be somehow different and consequently have an impact on the GREENBOX environment.
Biomass Parameters
The biomass and productivity data were comparable across all seasons, with higher values over spring and summer. Table 2 summarizes the collected biomass parameters of wet and dry weight and the derived parameters LAI, SLA, and productivity (kg/m2). The wet and dry weight represented the cumulative amount of gas exchange in the form of photosynthesis and evapotranspiration over the growing cycle (Kubota, 2016). The mean dry weight of the lettuce crop at harvest ranged between 6.5 g and 8.72 g per head. The dry weight of lettuce heads in the GREENBOX units was the highest in the summer and the lowest in the winter. The mean wet weight per head at harvest varied between 187.35 g in the winter and 231.58 g in the summer. The wet weight of lettuce heads in the GREENBOX units was the highest in the summer and the lowest in the winter, with a variation of over 40 g. Lettuce heads, on average, weigh 181 g at harvest (Tokunaga et al., 2015), which the GREENBOX units were easily able to achieve. Higher temperatures may lead to more evapotranspiration, leading to more biomass accumulation, which can explain the higher weights over warmer seasons.
| Table 3. Water consumption in the GREENBOX units for the four growing seasons. | |||
| Season | Water Consumed per Head of Lettuce (l/head) | Water Consumption per Unit Wet Weight (l/kg) | Water Consumption per Unit Dry Weight (l/kg) |
| Spring | 2.68 | 10.8 | 287.8 |
| Summer | 2.69 | 10.1 | 271.6 |
| Fall | 2.41 | 10.3 | 280.0 |
| Winter | 1.83 | 9.3 | 268.4 |
The LAI quantified the area of leaf material in a canopy and was a unitless ratio of the sum of one-sided leaf area per unit to the corresponding ground area (Bouriaud et al., 2003). For example, a canopy with an LAI of 1 has a 1:1 ratio of leaf area to ground area. Since the LAI for lettuce from the GREENBOX units varied between 5.3-6.5 year-round, it would mean that the lettuce leaf canopy had a ratio of five to six times that of the ground area. The SLA varied between 344.3 cm2/g and 569.3 cm2/g over the year. The SLA determined how much new leaf area develops for each unit of biomass produced. The LAI and SLA in the GREENBOX units did not follow any general trends and had similar values. We observed that the productivity output values varied between 6.33 and 7.38 kg/m2, with the lowest and highest values in the winter and summer, respectively. However, the variation was not very high, with a difference of 1.05 kg/m2, as demonstrated in table 2.
| Table 2. Summary of biomass data in the GREENBOX units over four growing seasons at harvest (Day 30). | |||||
| Season | Dry Weight (g/head) | Wet Weight (g/head) | Leaf Area Index (LAI) (cm2/cm2) | Specific Leaf Area (SLA) (cm2/g) | Productivity (kg/m2) |
| Spring | 8.06 | 217.20 | 5.3 | 495.4 | 6.91 |
| Summer | 8.72 | 231.58 | 6.3 | 413.1 | 7.38 |
| Fall | 8.10 | 201.70 | 5.3 | 344.3 | 6.48 |
| Winter | 6.50 | 187.35 | 6.5 | 569.3 | 6.33 |
Water Use Data
Table 3 presents the water consumed during each growing cycle over the year. The water consumption was presented in the form of water consumed per lettuce head (l/head), water consumed per unit wet weight (l/kg), and water consumed per unit dry weight (l/kg). The water use results accounted for the water consumed by evapotranspiration and crop growth. We found that water use was positively correlated to biomass productivity, with higher levels in the spring and summer. We found the GREENBOX units utilized water between 1.83-2.69 l/head, 9.3-10.8 l/kg wet weight, and 268.4-287.8 l/kg dry weight. A single watering event in soil-based crop growth would utilize nearly 100 times more water when compared to the GREENBOX values. The values of the water consumed per lettuce head were higher in the spring and summer and lowered in the fall and winter. We find that the remarkably consistent water use patterns make it easier to plan GREENBOX crop production in a medium-large setting by using resources efficiently (Singh, 2022). The mean water use of the crop in the GREENBOX units was plotted in figure 3 over the 30 days growing period. The water consumption curve in the GREENBOX units appeared to be smooth but gradually increased with time, with the highest found at the end of the cycle (Smith et al., 2011). The graph demonstrates the actual water use by all the plants in a GREENBOX unit measured using the lysimeter during each growing period. It shows that the water use was higher in warm seasons (spring and summer), and the water use increased most with time in spring.
Conclusions
Our study investigated the technical performance and feasibility of GREENBOX technology for urban crop production. The results from this study demonstrated that the GREENBOX units could provide the desired environmental conditions for growing lettuce in a mid-latitude climate for all seasons. The environmental conditions provided to the crop were in the optimal range with minimal fluctuations and controlled at the growers' discretion. The lettuce plants were grown into a healthy crop ready to consume in 30 days with above-average productivity. Productivity was higher in spring and summer, in general. Water use was comparable to hydroponic facilities and positively related to productivity. Our study has demonstrated that the GREENBOX technology has potential in producing leafy vegetables in warehouse environments for urban consumers.
 |
| Figure 3. The cumulative water consumption per lettuce head in the GREENBOX units for the four seasons during the 30-day growing cycles. |
Acknowledgments
The study was partially supported by a Hatch project (Project No. CONS01000) from USDA NIFA. We are also grateful to the Raudales Laboratory at the University of Connecticut for providing supporting materials and training and to the greenhouse staff (Frederick Petit and Shelly Durocher) for providing critical support for carrying out the experiments over the year. We also want to express our immense gratitude to the Statistical Consulting Services (Dr. Timothy E. Moore) at the University of Connecticut for providing support for statistical help for this project. I am also grateful to George Paul Buss for his help.
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