As urban centers encroach on agricultural land, it is increasingly important to study the effects of light pollution on sensitive short day flowering crops such as Glycine max (soybean) and Cannabis sativa. Common responses to light pollution include delayed flower initiation and development, and Cannabis growers additionally speculate a myriad of other detriments as a result of light pollution. We conducted a series of studies with three soybean and ten Cannabis cultivars to elucidate responses to light pollution. Plant were grown under full-night light pollution ranging from 0 to 150 nanomols m-2 s-1 of cool white light or 0 to 40 nmols m-2 s-1 of red light at 660 nm. We found that continuous light pollution as low as 10 nmol m-2 s-1 from cool white LEDs delayed inflorescence initiation and development of the most sensitive Cannabis cultivars, while red light pollution as low as 5 nmol m-2 s-1 caused similar effects. In cultivars that did not experience a delay in inflorescence initiation, other plant characteristics including height and inflorescence development rate were negatively impacted. In soybean, flower delay in response to light pollution varied by cultivar but was reduced or absent in more modern lines, indicating that breeding may have selected against light sensitivity. Future growers must consider tolerance to light pollution during cultivar selection in order to avoid the detrimental impacts to short day crops.
Our research is to define and develop pre-breeding resources as foundational knowledge to underpin breeding of a specialty leafy green crop watercress (Nasturtium officinale; Brassicaceae). This is being achieved by screening a unique, worldwide collection of watercress population to discover and to enhance nutritional traits for health, morphology, and sensory of the indoor controlled environment agriculture (CEA) market. Watercress is a perennial semi-aquatic leafy green vegetable in the Brassicaceae family and is an understudied specialty crop that has important human health benefits. The most abundant secondary metabolite glucosinolate (GLS) in watercress is gluconasturiin, an aromatic GLS, which hydrolyses and releases phenethyl-isothiocyanates (PEITC). PETIC, specifically from watercress, has been proven to have chemo-preventative potentials. Wild germplasm collection harbours natural variations and useful trait discovery opportunities for introgression of novel traits into the existing gene pool. There is limited interdisciplinary research on crop nutrition and breeding for the CEA settings. We found that watercress is well-suited to indoor hydroponic growing. We established the first indoor vertical farm (VF), a controlled growth chamber in a shipping container, at University of California, Davis. Light quality and quantity both serve important roles in watercress growth and development, and a fully controllable vertical farm allows testing a suite of traits of interests with altered LED light regimes. Results showed that VF grown wild watercress possessed significant genotypic differences across treatments, indicating an abundant natural diversity. Altering red to blue LED light ratio and duration may further enhance the anti-cancer GLS compounds as well as nutritional quality profile of this leafy crop.
Traditionally, aquaponic systems are used to produce leafy greens and herbs, while fruits and fruiting vegetables have been considered more difficult to grow due to additional nutrient requirements. When nutrients are not a limiting factor, the possibility of producing more fruit per square foot by increasing planting density is tempting as global populations increase and agricultural land area decreases. This study examined the effects of two different densities on banana peppers (Capsicum annuum L. var ‘Goddess F1’) and pole beans (Phaseolus vulgaris L. ‘Seychelles OG’) in a 20 sq ft grow bed. High densities consisted of 14 and 22 pepper and bean plants respectively, while low densities were 7 and 11 pepper and bean plants. Higher densities of peppers and beans produced more fruits than lower densities, while plant dry biomass of higher densities appeared to be lower than higher densities. Results suggest that higher planting densities of peppers and beans may increase harvestable fruit.
Use of aquaponic systems has the potential to provide sustainable food production in a variety of environments year-round. Unfortunately, little is known about the limitations of aquaponics regarding planting density in a grow bed and year-round growing outside of tropical climates. This study evaluated two different planting densities of kale (Brassica oleracea var. acephala L. ‘Winterbor’) and cilantro (Coriandrum sativum L. ‘Cruiser’) in a 20 sq ft grow bed in a hoophouse grown during winter and early spring in Stillwater, OK, using bluegill (Lepomis macrochirus L.) as the fish species. High planting densities comprised of 54 kale plants and 68 cilantro plants. Low densities contained 36 kale plants and 48 cilantro plants. High planting density reduced fresh weight and chlorophyll content in kale, and chlorophyll content in cilantro. Additionally, total nitrogen content decreased at higher densities of kale while sulfur content increased. Cold weather mitigation was utilized in the form of a secondary plastic covering, extra light sources, and in-line heaters. Results suggest that higher planting density may be feasible for some leafy green and herb species while being detrimental to others and that year-round growing may be possible with the addition of inline water heaters.
Citrus tree roots are vital in nutrient uptake, water absorption, and overall plant health. Soil pH alters the availability and mobility of essential nutrients in the soil, thus influencing root physiological processes; like most plants, citrus trees are particularly vulnerable to changes in soil pH levels. The root apoplast is the plant component that first encounters adverse soil chemical conditions; hence, the conditions in the root apoplast determine a plant's response. This study aims to investigate the physiological responses of citrus tree roots to soil acidification, focusing on the impact of varying soil pH on root morphology, nutrient uptake, and overall root health. A controlled three-month greenhouse study was conducted at the Citrus Research and Education Center (CREC), hypothesizing that soil acidification will alter apoplast and phloem pH, reducing CLas population and root damage. This study was conducted utilizing citrus trees subjected to different soil pH levels. Forty trees were used and divided into four groups by pH treatment. These trees were irrigated thrice a week with pH treatments: 5.5, 6.5, 7.5, and 8.5. Soil acidity and alkalinity were routinely monitored with pH probe sticks. Once soil pH stabilized, feeder root samples were taken for apoplastic and phloem pH experiments. The pH-sensitive fluorescent stains were used for microscopy and vacuum infiltration to collect apoplastic fluids. Parameters such as root length, root surface area, and root diameter were measured to assess the morphological changes in citrus tree roots under different pH treatments. The concentration of essential macro- and micronutrients from the soil, plant tissue, and leachates was also analyzed weekly to evaluate nutrient uptake efficiency. Preliminary results indicate that soil acidification significantly improves fruit yield and feeder root density. By ascribing the specific mechanisms underlying root responses, this research provides valuable insights into the adaptive capabilities of citrus trees. It informs future practices to preserve the health and productivity of citrus groves.
Controlled Environment Agriculture (CEA) systems significantly enhance crop yields per unit area in comparison to traditional open-field farming methods. Moreover, they contribute to reduced water consumption and offer extended and more predictable growing seasons. While CEA systems show promise in meeting urban vegetable demand, the question remains what the required inputs are (water, fertilizer, energy, labor) for different systems (vertical farm, greenhouses) in different climate locations. In this work, an easy-to-use transient energy model that simulates the internal microclimate of CEA systems is developed. The microclimate will include changes in temperature, humidity, water, nutrient, and carbon dioxide while also computing the energy costs associated with conditioning the space and electricity. This model will also accurately map the leaf temperature and hence compute the transpiration water loss accounting for the spectra of different artificial light sources. The energy model will be linked to a functional crop growth model that can simulate the yield of the plant over multiple growth cycles and quantify water and nutrient uptake. The potential of the developed model is demonstrated by performing simulations of year-around greenhouse operation within the U.S. Two climates categorized into hot, and cold based on annual temperature are selected for the simulation of tomato production. Results indicate that supplemental lighting energy requirement ranged between 128-160 kWh/m2-year across the selected climate zones to achieve target yield in a given duration. Overall energy consumption ranges from 200 - 400 kWh/m2-year. Overall, the supplemental lighting requirement makes upto 75 percent of the total required DLI and provides comparable improvements in biomass compared to yield in greenhouses without supplemental lighting. Finally, the model indicates that upto 90 percent of total supplemental lighting requirements require light intensities in the combination of 250 and 500 µmoles m-2 s-1 to satisfy the additional DLI requirement. However, a higher lighting intensity of 1000 µmoles m-2 s-1 is required sporadically at night during winter between October – March in the northern latitudes. Overall, this model integrates energy, temperature, nutrition, and crop yield considerations for various crops and acts as a useful predictive tool for assessing operational costs based on target yield and duration of growth for greenhouses operating in any given climate.
The Modified Energy Cascade (MEC) crop model was originally developed to predict the edible biomass production of bioregenerative life support systems (BLSS) along with BLSS consumption and production of O2 and CO2. Three distinct MEC versions support this original goal and controlled environment agriculture (CEA) on Earth. Cavazzoni built the first MEC for predicting crop growth, transpiration, and productivity of BLSS. Boscheri et al. and Amitrano et al. each developed versions building off Cavazzoni's work. While each of these model versions builds off each other, differences in methodology and assumptions of plant physiology impact the outputs of the model, necessitating a comparison between versions. To describe the effects of input variability and model structure on the outputs of the MEC versions before further development for BLSS and CEA production facilities, four research questions were chosen to guide this evaluation. 1) How much variation in transpiration and yield predictions can be attributed to the model version? 2) How are input variations propagated through the cascading nature of the models? 3) Which model components are highly sensitive or uncertain to which environmental conditions? 4) How well does each model version predict the outcome of lettuce yield and transpiration outcomes of data sets independent from model development? To answer the first three questions, a series of global sensitivity and uncertainty analyses were performed. They revealed that 1) for daily transpiration rate and edible biomass model version alone can explain between 69% and 82% with Amitranos representing the lowest values and Boscheris the highest typically. 2) Even in sequences of identical equations, where each subsequent calculation is identical, variability is gradually reduced with final output variations between 40% - 55% that can be attributed to the prior upstream differences. 3) The Cavazzoni and Boscheri edible yield predictions are highly sensitive to photosynthetic photon flux density (PPFD) and CO2 across calculations while Amitrano’s is more responsive to photoperiod rather than PPFD. 95% of Boscheris transpiration output is driven by relative humidity while the other two utilize a combination of that and photoperiod. Lastly, these models and their performance were evaluated using environmental and yield data from an indoor vertical farming facility and growth chamber experiments. Together these analyses provide the information necessary to continue the development of the MEC for the prediction of resource flows and yield of CEA and BLSS supporting the optimization of electricity usage and circularity processes within closed-loop agriculture.
