All pollen samples were stored at −20°C after collection. Our collection protocols were as follows, during hand collection, we placed a 50-mL centrifuge vial at the base of inflorescences with dehiscent anthers and used tweezers to tap or brush the dehiscent flowers, allowing pollen to fall into the collection vial. During the water collection method, we flipped plants upside down and dipped them into a 100-mL graduated cylinder filled with 50 mL of distilled water to wash all the pollen off the plant. The water sample containing pollen was then transferred into a 50-mL centrifuge vial for storage. During bag collection, we placed brown paper bags on the plants and loosely tied the base of the bag using twine. We then flipped the plants upside down and lightly shook them for 10 s to encourage dehiscence of pollen, after which we untied the twine from the base of the bag to remove the plant, and quickly sealed the bag to prevent pollen loss. After the first trial was completed and it appeared that bag and water collection methods would likely be less successful and/or efficient than hand collection, we focused on comparing the efficiency of vacuum to hand collection in the second trial. We retrofitted small paper cups to act as filters inside a hand-held vacuum by reducing the height of the cup to 2 cm and poking four holes along the cup’s sides to allow a small amount of airflow through the filter. Retrofitted cup filters were then placed inside the body of the vacuum, between the nozzle and the motor, intercepting and storing all of the vacuumed particles. Pollen was then collected by vacuuming the leaves and inflorescences of male plants, after which the filter was carefully removed and transferred to plastic containers for storage.Using a stopwatch, we recorded the time spent collecting pollen for each method. For the treatments that did not involve water collection,ebb and flow rolling benches we mixed each pollen sample into 50 mL of distilled water, and then vortexed the samples for 30 s to create a liquid suspension with a consistent distribution of pollen grains.

To quantify the relative grain density in each sample, we used visible light spectroscopy, employing the absorption reading as a response variable. We pipetted 2 mL of the vortexed suspension into a 3-mL cuvette and then used the light spectrometer to quantify the proportion of light that was reflected by the sample. We ultimately chose 425 nm as the reflectance wavelength for the absorption reading by testing multiple wavelengths on the first sample and then identifying the wavelength region that corresponded to the peak in the absorption curve. To verify that our light spectroscopy readings were truly indicative of the amount of pollen in each sample, we pipetted 5 μL of the suspension in each cuvette onto a glass slide and used a light microscope at 10× magnification to count the number of grains contained in the sample. Counting was done using a hand-held tally counter , and counting extended across the entire length and width of the slide cover . To determine if any of the pollen grains had burst and, if so, what proportion of the total sample they represented, we counted the number of burst grains on each slide using a second hand-held tally counter. All data used in this paper are provided Appendix S1.All analyses were run in R version 3.6.0 . To test if spectroscopy readings were correlated with microscopy-derived pollen count data, we used a linear regression model. We used the adjusted R2 value and correlation of the linear model to evaluate how well reflectance predicts pollen counts. A strong positive relationship was confirmed , and so we used this method to compare yield and efficiency for the four different collection protocols. We compared the effectiveness of the collection methods using repeated measures analysis of variance , where method was a fixed effect, collection event was the repeated measure, and plant ID treatment was used as an error term. We log-transformed the response variables, i.e., the spectroscopy reading and efficiency , to satisfy the assumption of normally distributed residuals.

We then compared transformed estimates of pollen yield and collection efficiency with a repeated-measures multivariate ANOVA  followed by individual repeated-measures ANOVAs when factors were found to be statistically significant, using the manova and aov functions. Any experimental factors that were determined to be statistically significant underwent subsequent post-hoc analysis using Tukey’s honest significant difference test function in the R stats package, version 3.6.0.Visible light spectroscopy readings strongly predicted microscopy-derived pollen counts in liquid samples , indicating that this method accurately quantifies pollen abundance. Correlation between the two variables was high , implying that there was a strong linear relationship between pollen counts and light spectroscopy readings. On average, only 0.88% of pollen grains had burst. Both trial 1 and trial 2 showed significant differences between collection protocols in the initial repeated measures MANOVAs . Trial 1, which compared hand, bag, and water collection, showed significant differences between methods for relative yield , with post-hoc analysis revealing that hand collection yielded significantly more pollen than the other two methods . Water collection resulted in a somewhat higher yield than bag collection . Collection yield did not differ across time points, nor was there a time point by-method interaction . Collection efficiency was not affected by collection method, time, or their interaction . Trial 2, which compared hand collection to vacuum collection, showed no significant influence of collection method, time, or their interaction for pollen yield or collection efficiency , implying that increases in collection time directly resulted in increases in relative yield .Artificial selection for preferential traits in wind-pollinated species like cannabis critically depends upon effective and efficient methods for pollen collection and storage so as to prevent unintended genetic contamination of selected lines . Similarly, methods for pollen handling are also essential in cannabis production, where growers have conflicting needs: to maximize yield of the current crop, pollen must be excluded from production plants, but to generate future crops, pollen is essential. A gap in the literature comparing the relative success and efficiency of pollen collection methods highlighted the need to explore the often laborious process of mass collection of pollen for controlled cross-fertilization. A key step is to determine the best method for the controlled capture of pollen. Here we compared the yield and efficiency of multiple collection methods , and also compared two approaches for quantifying the relative pollen yield of different methods.

We found that light spectroscopy was an effective method for quickly and easily quantifying the abundance of pollen when suspended in distilled water. Light spectroscopy is a much faster method for quantifying pollen abundance than microscopy and is successful in predicting the pollen abundance in a collection sample. We anticipated this result, as light spectroscopy has often been used for measuring the abundance of particles in a suspension , and variations have previously been used on pollen . Hand collection resulted in a higher pollen yield than water or bag collection in our first trial, but the efficiency with which they collected pollen did not differ. In the second trial, hand collection and vacuum collection did not differ in their yield or efficiency, implying that they are equally suitable for pollen capture. Bag and water collection did require significantly less time for pollen capture; however, the substantially lower yield inhibits their application as an effective method of controlled capture. We further note that while our vacuum device did not outperform hand collection, improvement of the design to engineer a better filtration system and tailor suction power to individual growers’ needs could improve yield and efficiency. Ultimately, the results of these experiments serve as an important early step in the establishment of a practical framework for breeding cannabis,rolling grow benches as well as other economically valuable wind-pollinated crops.The coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2 . COVID-19 was declared as a global pandemic by the World Health Organization . The main cause of the disease was a previously unknown coronavirus which was first identified and reported in Wuhan, China in late December 2019 . The possible zoonotic nature of the disease and viral spillover have made it more serious due to the transmission patterns from animals to humans and with the progression of the COVID-19 pandemic, transmisson from humans to animals and spill back events to other animal species were reproted . Calssification of coronaviruses within the family Coronaviridae is shown in Fig. 1. The possible transmission patterns of SARS-CoV-2 from human-to-animal and animal-to-human are shown in Fig. 2. Based on the available data at the time of this review, COVID-19 is believed to have emerged in a seafood market in Wuhan, China . Bats could be the proximal origin of SARS-CoV-2 and pangolins could be a potential intermediate host because of high genome sequence similarities of isolated SARS-related viruses with the SARS-CoV-2 genome . However, the origins of SARS-CoV-2 were recently reviewed elsewhere , yet the host range and intermediate hosts of SARS-CoV-2 remain unknown . SARS-CoV-2 is not the first coronavirus to cross species and infect humans leading to the first pandemic in history to be caused by a coronavirus. Previously, two highly pathogenic coronaviruses, severe acute respiratory syndrome coronavirus 1 and Middle East respiratory syndrome coronavirus infected humans and caused severe diseases . As a result of the uncontrollable spread of COVID-19, countries imposed lock downs, postponed or banned international travel, and limited exports and imports to control the transmission of SARS-CoV-2 , 2020. There were significant concerns on whether food and animal products may contribute to the transmission of SARS-CoV-2 and whether the disruption of the agricultural production chain including livestock production systems would significantly harm the global economy. The effect of the COVID-19 pandemic on agricultural production, including crop and animal products, depends on the product, the location, and the economic status of the impacted location . The current pandemic has had serious impacts on animal production, animal health and welfare, global food safety, and the global economy. Impacts include the disruption of food supply chain, shortage of labor, reduced access to markets and veterinary health services, in addition to movement restrictions and limitations on international trade.

The COVID-19 pandemic has also resulted in ominous impacts on food security, leading to hunger and increased poverty in resource-limited countries . Milk and meat industries, animal and animal-product processing industries such as slaughterhouses, and poultry sectors were negatively impacted during the course of the COVID-19 pandemic. Smashing of eggs, dumping of milk, inhumane culling of animals, and disruptions of animal feed supply chain have resulted in crisis in the global economy . In the current review, we highlight different routes of transmission of SARS-CoV-2 in animals and humans, possible ways COVID-19 can disrupt the animal production chain, and effects of COVID-19 pandemic on animal health and welfare, diagnosis and treatment of diseases, and the global economy. We also provide recommendations for the prevention and control of the COVID-19 pandemic and for boosting up animal production and ultimately global economy. Since SARS-CoV-2 is primarily a respiratory, not a food borne, pathogen, the risk of food borne transmission of COVID-19 is negligible . The possibility of SARS-CoV-2 being transmitted via feces is much lower than enteric viruses which are transmitted via the fecal-oral route which may be explained by the lower relative amounts of infectious viruses in feces . In addition to the droplet transmission, the National Health Commission of the People’s Republic of China confirmed that aerosol transmission of SARS-CoV-2 is possible in special circumstances including long exposure to high concentration in a closed environment . Additionally, it was recently reported that aerosole transmission plays a role in the spread of COVID-19 . Food industry premises can be considered a closed workplace setting where infected workers can transmit the virus to their co-workers through air due to the close proximity. Viral particles of SARS-CoV-2 can also end up on surfaces of food preparation areas, meat, dairy, or other animal products. In addition, SARS-CoV-2 viral particles can also be present on swab samples from isolation wards, other hospital wards, sewage treatment units, and nursing homes . The deposition on surfaces can lead to subsequent hand-to-mouth, hand-to-nose, or hand-to-eye transmission .