Cannabis Grow

Results from our study are limited in generalizability, though complement work by others on examining the impact of tobacco free policies on US college campuses. This includes a recent study from 2020 of small colleges in Massachusetts that found that a college with a smoke-free policy had significantly more antismoking attitude than a control campus, but did not have lower rates of smoking itself . Relatedly, a separate earlier study from 2005 that analyzed undergraduates in Texas found that campuses with preventive education programs had lower odds of smoking, whereas designated smoking areas and cessations programs were associated with higher odds of smoking . Collectively, these prior studies and our own work helps to better characterize knowledge, attitudes and behaviors of college campus communities toward smoking, as well as the smoke-free policies attempting to discourage smoking, which in turn should aid in the development of more targeted approaches to educate college-aged populations about the health harms of tobacco and also enable better implementation of anti-tobacco policies in these critical populations.This study was exploratory in nature and collected social media messages for which latitude and longitude coordinates could be collected from the Twitter API, but this data collection methodology is limited to collecting messages from Twitter users that enabled geolocation, a specific limitation to generating a more generalizable dataset on Twitter as it is estimated that only 1% of all tweets are geocoded . Hence, cannabis drying room the dataset used in this study after filtering for keywords was small and likely biased, limiting the generalizability of results.

This method of data collection may have introduced bias in the types of tweets collected, thereby limiting the generalizability of findings as the majority of Twitter users do not geolocate their posts. Potential sampling biases for Twitter include oversampling for certain geographic areas , filtering for specific features , and the limitations of the Twitter public streaming API in lieu of other data collection approaches . Future studies should examine the use of multiple Twitter APIs to generate a more representative Twitter dataset and compliment findings with other traditional sources of data to generate findings that are more robust and generalizable, as well as use complementary Twitter and social media datasets made publicly available by other researchers. Specific to identification of Twitter users and conversations associated with colleges and universities, using keyword searches, and selecting accounts affiliated with higher education should be explored in future studies. Also, inclusion criteria required tweets to be posted from college campuses, which would not have accounted for variability in smoking related tweets from off-campus housing or areas/neighborhoods at the borders of campus properties where college students may reside. Furthermore, though the study design permitted searches of the Twitter API to return different volume of tweets for different keywords, there was a smaller number of original keywords for substances containing marijuana/cannabis than those for e-cigarettes or products containing tobacco due to our purposeful filtering for tobacco and alternative tobacco product keywords .

Additionally, the majority of tweets analyzed for this study were from 2015, a period prior to major public scrutiny about default privacy settings for location sharing on Twitter . Finally, this study is an ecological study and should therefore be considered hypothesis generating and not generalizable to individuals on college campuses until further studies among individuals confirm these correlational findings.The study of personality has a long and rich history, and has been studied in across the lifespan, from temperament in early infants to coping ability at the end of life . How personality during adolescence, a period of dramatic physical, emotional and psychosocial development , relates to behaviors in later life, is of particular interest. While there are likely numerous moderating and mediating factors, individual differences in adolescent personality have been shown to be important predictors of adult life outcomes, including social competence, academic and professional achievement, and physical and mental health and longevity . From a clinical perspective, understanding individual differences in personality development is important for informing mental health treatment efforts and preventing substance misuse in adolescents , as personality may be modifiable by clinical intervention . Although debated , the five-factor model is one of the most widely accepted and utilized hierarchical structures for measuring personality, as it strikes a balance between specificity and generalizability that is more difficult to achieve using lower-order constructs .

The five-factor model consists of extraversion, emotional stability , conscientiousness, agreeableness, and openness. While this factor structure has been well-described in adults, the stability of, or change in, these personality constructs during childhood and adolescence is still debated . First, a meta-analysis of early longitudinal research found all Big Five personality factors increased substantially during adolescence and young adulthood . This pattern, referred to as the “maturity principle,” suggests that an increase in these personality traits across adolescence and early adulthood reflects adaptations to newly evolving social roles . More recently, several large-scale studies show declines in at least one, and up to all Big Five personality factors early in adolescence, with subsequent increases in late adolescence and early adulthood . This pattern has been referred to as the “disruption hypothesis,” and suggests that adolescence is a key period for personality development . However, there are inconsistencies between these reports in regards to which of the Big Five personality factors support the disruption hypothesis. For example, Borghuis, Denissen et al. found emotional stability and extraversion declined in early adolescence , before increasing, while agreeableness increased throughout . Conversely, Van den Akker, Deković et al. found continued declines in emotional stability and extraversion into young adulthood , but found agreeableness decreased , before increasing.How personality development varies between male and female youth also remains unclear. While the degree and practical significance of sex differences in personality are still debated, they continue to be supported by large multi-national studies . Given significant sex differences in the timing of neurobiological development during adolescence, it could be expected that sex differences in personality may also emerge during this developmental period , and understanding these effects may provide insight into the emergence of psychiatric disorders in youth . Past findings in adolescents and young adults largely suggest that female youth report greater extraversion, openness, agreeableness, and conscientiousness, and less emotional stability, compared to male youth . However, the timing of these sex-specific effects is less clear. For example, Soto, John et al. found the biggest differences in emotional stability between male and female youth emerged between the ages of 10 to 15, and then persisted well into adulthood; however, Göllner, Roberts et al. reported no sex-differences in emotional stability in early adolescence . Similarly, while several studies support the notion that sex differences in extraversion emerge during adolescence and persist in young adulthood , others found no sex differences in extraversion in mid-to-late adolescence . While these are not the only studies to investigate personality development, or sex differences therein, they highlight two major inconsistencies in past studies: 1) it is unclear which if any personality traits support the “disruption hypothesis”, and 2) there are discrepancies regarding the timing, or developmental course, of sex differences in personality. One important factor that could contribute to this effect is researchers’ choice of analytic modeling strategy. For example, vertical grow rack system two previous studies used polynomial growth parameters and reported that extraversion either decreased linearly from 9 to 20 years of age , or showed quadratic growth, with early decreases and subsequent increases from ages 12 to 22 . However, another study found that changes in personality with age could not be adequately fit with traditional growth parameters ; when reporting mean level changes, they found extraversion decreased between ages 10 to 15 before largely leveling off into young adulthood .

In the current study, we seek to address past discrepancies in the sex specific development of personality by using data-driven non-linear modeling strategies, and comparing them to more traditional growth models, to test our hypothesis that analytic strategy plays a role in the conclusions draw from developmental studies. Another area of research the current dataset is well-suited to investigate is the association between personality and substance use. Alcohol and marijuana are the most commonly used substances by youth and are associated with a myriad of cognitive and neural alterations . A recent review and meta-analytic work suggests that alcohol use is associated with low conscientiousness, agreeableness,and emotional stability, and high extraversion , with longitudinal work confirming that increases in conscientiousness and emotional stability with age are associated with decreases in problematic alcohol use . Similarly, in adolescents, higher extraversion and lower conscientiousness, openness and agreeableness are associated with alcohol use , and high adolescent extraversion, in particular, may be a common predictor of future alcohol use . Marijuana use is also associated with lower emotional stability, agreeableness and conscientiousness in adolescents and young adults . However, when combining alcohol with other illicit substance use , greater openness has been shown to be associated with heavy substance use in young adulthood . Meanwhile, when simultaneously examined in a sample of young adults concurrently using alcohol and marijuana, marijuana use was associated with greater openness, but alcohol use with lower openness . Similarly, extraversion has been shown to be differentially related to substance use, with higher extraversion associated with more alcohol use but less marijuana use . Taken together, these findings highlight the importance of simultaneous modeling of both alcohol and marijuana use in the same sample. Work looking at sex differences in the association between substance use and personality in adolescents and young adults is limited. Cross-sectional studies have found that the association between reduced emotional stability and marijuana use was greater in female adolescents than male adolescents , and that the negative association between alcohol use and emotional stability and conscientiousness in young adults is more predominant in women . However, robust longitudinal studies that allow for the assessment of substance use alongside potential non-linear developmental changes in personality are needed. Using seven waves of longitudinal data from the National Consortium on Alcohol and Neurodevelopment in Adolescence dataset, the current study sought to examine personality changes across adolescence and young adulthood. In addition to replicating previous literature on personality development in a robust, longitudinal, multi-site national cohort, the study had the following five goals: First, we aimed to use generalized additive mixed effect models to empirically assess non-linear change in personality across age and compare these effects to more traditional linear mixed effects models with polynomial growth trajectories . We hypothesized added model flexibility would yield findings that better coalesce past literature, and choice of modeling strategy would partially explain past discrepant reporting.Second, we examined sex differences in personality development. In addition to replicating previous reports of greater extraversion, openness, agreeableness, and conscientiousness, and less emotional stability, in female youth , we expected our non-linear modeling strategy would again help provide clarity in regards to the timing of sex differences in personality development. Third, we examined the association between substance use and personality. While previous studies generally suggest greater alcohol and marijuana use is associated with lower agreeableness, conscientiousness and emotional stability , associations with extraversion and openness may vary based on substance . Therefore, we simultaneously modeled both alcohol and marijuana use, and hypothesized they would be differentially related to reported extraversion and openness. Fourth, we examined the sex-specific association between substance use and personality. Based on limited past literature, we hypothesized that the association between substance use and lower emotional stability and conscientiousness would be more prominent in female youth . Finally, while previous reports highlight the importance of comprehensively assessing personality , this is not always feasible in large multi-site studies, such as NCANDA. Thus, here, we demonstrate the ability of an abbreviated measure, the Ten-Item Personality Inventory , to obtain robust results largely consistent with prior literature.Data were analyzed from participants of the NCANDA study. Youth between 12 and 21 years of age were recruited at five sites across the United States: Duke University, Oregon Health & Science University , University of Pittsburgh, SRI International, and University of California San Diego . Adults provided informed consent, while adolescents and their parents provided informed assent and consent, respectively, and all study procedures were approved by the respective institutional review board for each site.

We were not able to examine the quantity of marketing items . Lastly, our findings may not be applied to RMDs around adolescents’ homes, adolescents in private schools, or jurisdictions outside of California. With the dynamics in marijuana retail environments and government surveillance and law enforcement, the findings in the early stage of recreational marijuana commercialization may also lack generalizability to the most recent regulatory and retail contexts.Epidemiologic data indicate US young adult smokers use marijuana in greater amounts that their non-smoking peers. In 2009, 34.6% of smokers aged 18 to 25 reported past-month cannabis use compared with 8.9% of young adult nonsmokers. Depending on definitions of use, tobacco use increases the risk of cannabis use from 2 to 52 times in adolescents, and 3 to 6.4 times in adults. Demographic differences have been observed in patterns of tobacco and marijuana involvement among young adults. Older youths, males , students in vocational schools, and those living in the Northeast and in small metropolitan areas are more likely to use tobacco or cannabis. There is a need to examine more detailed patterns of tobacco and marijuana use to understand the complex relationship between these two substances. The internet is increasingly used in survey research of substance use with benefits over face-to-face interviews including broader reach; greater inclusion of low-incidence or “hidden” populations; rapid, best way to cure cannabis convenient input by respondents; and reduced bias in response to sensitive, potentially stigmatizing topics including illicit substance use.

Young adults remain the age group most likely to use the internet , and they are less likely, compared to other age groups, to present to traditional research settings for studies of health behavior . Our prior research has demonstrated the reliability and validity of anonymous online surveys of young adult tobacco and cannabis use. Analyzing data from an anonymous online survey of young adult smokers with national coverage, the present study examined the prevalence of past-month marijuana use, frequency among past-month marijuana users, and the frequency of co-using tobacco and marijuana. The large sample permitted analyses by gender, age, ethnicity, geographic region, urban/rural designation, student status, household income, daily smoking status, and by whether or not respondents resided in a state where marijuana is legal for medicinal use.Data for the present study were taken from a national cross-sectional survey using a convenience sample of young adult smokers. Briefly, young adults between the ages of 18 and 25, who reported smoking at least one cigarette in the past 30 days, were recruited online between 4/1/09 and 12/31/10. Three recruitment methods were used: 1) a paid advertisement campaign on Facebook; 2) a free campaign on Craigslist; and 3) a paid email advertising campaign through a survey sampling company. Participant entries could be tracked to which advertisement type they viewed . Only entries from advertisements targeting tobacco use were used in the present study so as not to inflate the prevalence of marijuana use in this population. Advertisements invited young adults to participate in a 20-minute online survey on tobacco use with a chance to win a prize in a drawing worth either US $25 or $400.

Advertisements contained a hyperlink directing potential participants to the study’s institutional review board -approved consent form, which mentioned assessment of marijuana use; to a screener for eligibility criteria; and to a secure online survey with data encryption for added security. Computer IP addresses were tracked, and only one entry was allowed from a single computer to prevent duplicate entries from the same person; however, multiple entries were allowed from the same internet connection .During the recruitment period, the online survey received more than 6423 hits, and 6176 people gave online consent to determine eligibility; of these, 3512 were eligible and deemed to be valid cases. Of eligible and valid cases, 2998 completed information about demographic and tobacco use only, and 1808 completed the entire 20–30 minute survey. Those who completed the survey differed from those who didn’t on some demographic variables, but the differences were small . The majority of the sample was male , Caucasian , living in an urban area , not currently a student , and smoked marijuana daily . Among current smokers, the overall prevalence of marijuana use was 53%. There was a significantly higher prevalence of marijuana use among males compared with females; among those aged 18 to 20 compared with those aged 21 to 25; among those with higher household income; among those living in urban versus rural areas; and among non-daily versus daily smokers.

There were no differences in prevalence of recent cannabis use by ethnicity, census region, residence in a medical marijuana state, or student status. Among past-month marijuana users, the median number of days using marijuana was 18.0 in the past 30 days . Non-students used marijuana on significantly more days than students, and daily smokers used on significantly more days than non-daily smokers. There were no differences in the number of days using marijuana in the past month by gender, age, ethnicity, household income, region, urban versus rural residence, or residence in a medical marijuana state. The proportion of days using both substances out of all past-month using days was a median of 45.5% . There was a higher proportion of tobacco and marijuana co-use among Caucasian respondents compared with those of other ethnic groups, among those residing in the Northeast compared to other census regions, among those residing in rural versus urban areas, among non-students, and among daily versus non-daily smokers . There were no differences in percentage of days with co-use by gender, age, household income, or residence in a medical marijuana state. The findings from this online anonymous survey of young adult smokers with national coverage indicate a greater prevalence of marijuana use than has been reported in epidemiological studies using household interviews. For example, in 2009, the US Substance Abuse and Mental Health Services Administration National Survey on Drug Use and Health reported that 34.6% of past-month smokers age 18 to 25 used marijuana, compared with 53.1% reported in the present study. The present sample was recruited online, primarily through social media, and the survey was completely anonymously, potentially allowing for reduced bias in reporting of illegal substance use . High prevalence of use was observed across demographic groups and regions, suggesting the issue of marijuana and tobacco co-use is of national relevance. The highest prevalence of marijuana use was observed among males, younger people, those with a higher household income and living in urban areas, and non-daily tobacco smokers. Consistent with previous epidemiological studies, young adult males tended to use marijuana at higher levels than young adult females, and young adults tended to reduce substance use as they reached developmental milestones of emerging adulthood, including leaving home, obtaining stable employment, cannabis drying kit and starting a family. Greater use among those in urban areas and from wealthier households reflects factors related to availability and is also consistent with national trends from household survey data. Notably, although daily tobacco smokers were slightly less likely to use marijuana than non-daily smokers, when they did use, they used it more frequently. There was a two-fold greater frequency of use among daily smokers compared with non-daily smokers and elevated frequency of use among non-students. Non-students and daily smokers also had greater co-use. Given the potential for detrimental effects of co-use among daily smokers, these findings support the broadening of interventions for daily tobacco smokers to consider use of both substances. Future research should examine the potential for substitution or compensatory effects during attempts to quit either substance. Study limitations include convenience sampling and self-reported data; however, face-to-face surveys often similarly rely on self-reported drug use, and we have previously demonstrated strong reliability and validity of tobacco and marijuana online surveys with young adults. The survey completion rate in this study was comparable to online survey studies with young adults but lower than that typically seen in nationally representative surveys. For example, weighted response rates for the 2010 SAMHSA-sponsored National Survey on Drug Use and Health were 88.8% for household screening and 74.7% for household interviewing.

Our respondents could leave the survey at any time; methods considered to encourage completion would have compromised participant anonymity. Sampling procedures and online data collection could have led to higher prevalence of marijuana use and co-use than is typical of representative surveys that have procedures to increase response rates .Marijuana is the most widely used illicit intoxicant and a significant public health concern for adolescents. Almost half of 12th graders have tried marijuana, with 5% reporting daily use . Early marijuana involvement can be particularly problematic, as use before age 15 is associated with a seven fold increased risk of developing a substance use disorder in the future . Concomitant alcohol and marijuana use is common, as 58% of adolescent drinkers also use marijuana . Animal studies have demonstrated cellular changes associated with chronic cannabis exposure, especially in prefrontal, hippocampal, and cerebellar regions among mice , rats , and primates . Two studies reported both gray and white matter abnormalities in several brain regions among young adult marijuana users , although findings reported by Aasly and colleagues may have been attributable to alcohol use. In contrast, Block and colleagues, in a study excluding individuals with histories of heavy drinking, did not find structural brain abnormalities among cannabis users . Recent functional neuroimaging studies on adults have found prefrontal, hippocampal, and cerebellar functioning abnormalities among marijuana users . However, the long term effects of chronic cannabis use, as opposed to acute effects, are less characterized. In a meta-analysis examining 11 studies, Grant and colleagues found that chronic cannabis use was associated with persistent but subtle deficits in learning and memory, but not in other cognitive domains. Other studies have demonstrated persisting deficits in processing speed, attention, working memory, visuospatial skills, and executive functioning . However, some studies found no persisting cognitive deficits among adults with histories of heavy marijuana use , and one study found that observed neurocognitive deficits normalized within a month of abstinence . Because neuromaturation continues through adolescence , results based on adults cannot necessarily generalize to adolescent marijuana users. White matter develops into the late 20s . Concurrently, gray matter volume peaks around ages 12–14 then decreases, due largely to synaptic pruning in the striatum and frontal lobe anterior to the motor strip , frontal poles, and lastly in the dorsolateral prefrontal cortex , which also is late to myelinate . Furthermore, adolescence may be a period of vulnerability to the neurocognitive effects of drug and alcohol use . For example, CB1 cannabinoid receptor levels in animals peak in early adolescence , cannabis-exposed adolescent rats are more vulnerable to learning impairments compared with exposed adult rats , and early adolescent onset of use is associated with increased morphometric and cognitive abnormalities in adult marijuana users . Despite the high prevalence of marijuana use, few studies have examined neurocognitive functioning in heavy marijuana using adolescents . Recently, we examined hippocampal volume and asymmetry and verbal memory among 63 adolescents . Similar to Tzilos and colleagues , we found that marijuana and alcohol using adolescents did not significantly differ from controls in hippocampal volume. However, we did find that the correlations between hippocampal asymmetry and verbal learning were abnormal among the marijuana users compared with the non-drug using controls. More specifically, increased right greater than left hippocampal asymmetry was associated with improved verbal learning among the controls, while no significant correlations between structure and function were found among marijuana users. Consistent with the adult literature , functional neuroimaging studies have found abnormal frontal, temporal, and parietal activation patterns among adolescent marijuana users compared with controls in response to verbal working memory and spatial working memory tasks. With few exceptions , neuropsychological studies focusing on adolescent substance abusers have found persisting cognitive deficits associated with heavy marijuana use. In an inpatient treatment study, marijuana-dependent adolescents demonstrated short-term memory decrements after 6 weeks of abstinence compared with polydrug users and controls . Marijuana using adolescents have also demonstrated increased perseverative responding on a problem solving task compared with control adolescents . A longitudinal investigation by Tapert and colleagues followed 47 polysubstance users and 26 normal controls over 8 years, from ages 16 to 24.

There is good evidence that foreign regulations have affected export demand for transgenic crops, but there is mixed evidence of price premia for traditional non-GM grains. For example, after the United States started growing GM corn, EU corn imports from the United States dropped from 2.1 million metric tons in 1995 to just under 22,000 metric tons by 2002 [USDA, Foreign Agricultural Service 2003b]. Notably, however, the gap in U.S. corn sales to the EU was filled by Argentina, a transgenic producer that only grows varieties approved by the EU . On the other hand, imports of U.S. corn byproducts to the EU have dropped only slightly since 1995 . The U.S. GM soybean export share in Europe has suffered as well, declining by more than 50 percent since 1997 . Price premia exist for non-U.S. corn in Japan and the Republic of Korea, traditional soybeans in Japan, and non-transgenic corn at elevators in the U.S., typically ranging from 3 to 8 percent . However, there is little evidence for price differentials between the GM and non-GM product in the canola market . The global market for rice differs from the market for soybeans in that the majority of rice sold is for human consumption rather than for animal feed. As a result, the market-acceptance issue is likely to be a key determinant of the success of transgenic rice adoption in California . As can be seen in Table 1, the export market for California rice accounts for approximately one-third to one-half of total annual production with Japan and Turkey as the major destinations. California Japonica rice imported by Japan is channeled through a quota system that was negotiated at the Uruguay Round in 1995. Most of California’s rice exports are purchased by the Japanese government and used for food aid and for other industrial uses, vertical grow racks including food and beverage processing .

Only a small portion of this imported high-quality rice is released into the domestic Japanese market . Turkey is reportedly attempting to severely restrict imports of transgenic crops through health regulations, despite importing corn and soybeans from the United States , while Japan requires labeling of 44 crop products that contain more than 5 percent transgenic material as one of the top three ingredients . Currently, several varieties of HT and viral resistant rice have entered the Japanese regulatory system for testing but have not yet been approved for food or feed use . As an illustration of potential market resistance, Monsanto suffered setbacks in Japan in December 2002 when local prefecture authorities withdrew from a collaborative study to develop a transgenicrice cultivar after being presented with a petition from 580,000 Japanese citizens . In 2002, China imposed additional restrictions on transgenic crops, including safety tests and import labeling . However, this action may be nothing more than a trade barrier to reduce soybean imports from the United States. In addition, China is worried that introducing biotech food crops may jeopardize trade with the EU. Nevertheless, China is not taking a back seat in transgenic crop research, as it has a major ongoing research program on biotech rice and other crops and is predicted to be an early adopter . There is also some skepticism in the United States with regard to GM crops. Aventis was sued in 2000 over accidental contamination of taco shells by transgenic corn that was not approved for human consumption, resulting in an expensive food recall. The company subsequently decided to destroy its 2001 LibertyLink® rice crop rather than risk its potential export to hostilenations . Kellogg Company and Coors Brewing Company have publicly stated that they have no plans to use transgenic rice in their products due to fears of consumer rejection, and several consumer and environmental groups favor labeling of foods made from transgenic crops . For most food and beverage products manufactured by these companies, however, rice accounts for a small input cost share, resulting in little financial incentive to support GM crop technology.

In May 2004, Monsanto announced that it was pulling out of GM wheat research in North America, partly due to consumer resistance. This has important implications for commercialization of GM rice because both grains are predominantly food crops. Many California rice farmers are concerned over the confusion regarding GM crops and do not want to jeopardize export market sales. This fear has been exacerbated by Measure D on the November 2004 ballot in a major rice-producing county that would have prohibited farmers from growing GM crops.13 A 2001 survey of California growers performed by the University of California Cooperative Extension showed that, of the respondents, 24 percent planned to use transgenic varieties, 37 percent would not, and the remainder were undecided . Of those growers who answered “no,” 78 percent responded that market concerns were a reason. Nevertheless, if profitability at the farm level increases, it is likely that a subset of California producers will adopt the technology . Presumably, those with the most significant weed problems and hence the highest costs would be the first to adopt.UCCE produces detailed cost and return studies for a wide variety of crops produced in California, including “Rice Only” and “Rice in Rotation.” The studies are specific to the Sacramento Valley region where virtually all California rice is produced. Figures on herbicide applications are based on actual use data as reported by DPR and UC Integrated Pest Management Guidelines . The most recent study completed for rice is by Williams et al. and is used as the basis for this study. As the potential adoption of transgenic rice is unlikely to significantly change farm overhead expenses on average, we focus on returns and operating costs per acre as reported in the sample-costs document. However, given weed-resistance evolution, changing regulations from DPR, and changes in the 2002 Farm Bill, the baseline cost scenario is adjusted here to account for changes in herbicide-use patterns, prices of herbicides and rice, and projected government payments. Using information from the 1999 pesticide use report compiled by DPR, the 2001 sample costs assume applications of bensulfuron and triclopyr, both broadleaf herbicides, on 25 and 30 percent of the acreage, respectively, and applications of the grass herbicides molinate and methyl parathion on 75 and 45 percent, respectively, of the acreage.

These figures are updated using data from Rice Pesticide Use and Surface Water Monitoring, a 2002 report by DPR, as interpreted by the authors. We maintain the assumption of two applications of grass herbicides, although we increase the treated acreage to 80 and 60 percent with one application composed of 40 percent molinate and 40 percent thiobencarb and the other composed of propanil on 60 percent of the acreage. Broadleaf control was adjusted to one application of triclopyr on 45 percent of the total rice acreage. Finally, vertical cannabis all cash operations are assumed to be financed at a nominal interest rate of 10.51 percent in accordance with the UCCE sample-costs document . As such, any change in the cost structure directly affects interest on operating capital, though the magnitude tends to be small. Overall, these updates result in a per-acre cost increase of $17.69 over the 2001 cost study. Estimated farm-level revenues are adjusted as well. To more accurately represent the current world rice market , we assume the market price per cwt at harvest is the average price from 1986 through 2002 of $6.50 with average yields at 80 cwt per planted acre. Government payments are divided into two components: direct payments and countercyclical income-support payments as described by USDA, ERS . In accordance with the 2002 Farm Bill, direct payments are calculated at 85 percent of average yields at $2.35 per cwt. Williams et al. estimate that growers of approximately 95 percent of planted acres have received this payment in the past, so the total direct payments are multipliedby 0.95. Countercyclical income-support payments are calculated using the ERS formula, which we can summarize as 85 percent of average yields at $1.65 per cwt. Incorporation of these changes results in a $28.01 increase in gross revenue per acre over the 2001 UCCE sample-costs study. The original and adjusted costs and returns per acre are reported in Table 2. Given the public nature of experimental data on LibertyLink® rice grown in California and the full cooperation of Bayer CropScience through phone interviews and email correspondence, we use this transgenic variety as the basis for our analysis . We assume a price for Liberty® herbicide of $60 per gallon16 and an application rate of 0.446 pounds of AI per acre [500 grams AI per hectare ] in accordance with the company’s projected label recommendations . To fully represent the fact that weed infestations will differ across plots, scenarios for transgenic cultivation are presented for both one and two applications of the herbicide on 100 percent of the acreage.The latter result is a direct consequence of the cost differential between ground and air applications of herbicides; ground applications of glufosinate cost approximately $12 per acre while air applications range from $6 to $7.25 per acre . The savings in chemical costs, however, drive the overall cost savings associated with transgenic rice and are explained using the information provided in Table 3. While the price of glufosinate per pound of AI is greater than all of the chemicals under consideration with the exception of triclopyr, the application rate per pound of AI is only 6 to 13 percent of the average herbicide control system. This decreases the cost of herbicide materials per acre by almost 62 percent as shown in the last column Table 2. When these results are combined, net returns over operating costs increase in the range of $45.89 to $74.90 per acre depending on the herbicide application rate, or $0.57 to $0.94 per cwt. Thus, this baseline scenario, which assumes perfect substitutability between medium-grain transgenic LibertyLink® rice and conventional varieties in terms of market acceptance and yields, predicts considerable economic incentives for rice growers to adopt transgenic varieties with similar characteristics due to their increased profitability. It is important to recognize, however, that these results are based on average costs over the entire Sacramento Valley rice growing region and utilize aggregate data to estimate the conventional herbicide weed-management regime. Individual growers, of course, will most likely differ in regime from these averages depending on the characteristics of the specific operation. Those growers with “superior” land, as defined by lower aggregate weed-management costs, would benefit the least from adoption of transgenic rice while those with marginal land or serious weed-resistance problems tend to benefit more from the herbicide-management cost savings offered by the transgenic system and are hence most likely to adopt. To further investigate these issues, the assumption of perfect chemical substitutability, which essentially drives the assumption of identical yields, can be relaxed. A severely infested plot with a large, resistant seed bank of watergrass or some other weed would likely experience yield increases with adoption of a transgenic control system. Such yield gains have been observed in practice for HT soybeans and HT canola in the range of 0 to 20 percent . However, yields are not necessarily guaranteed to increase for all plots. Under generally ideal conditions, a yield drag of between 5 and 10 percent for medium-grain cultivars of LibertyLink® rice relative to conventional varieties has been observed in California rice field trials. This is consistent with similar field trials of HT soybeans. Such losses would decrease revenues and would thus reduce the increased profitability of adoption of this new technology. Yield drag should not be an issue with most growers given the advanced, widespread state of weed resistance to currently licensed chemicals for rice weed control in the Sacramento Valley. However, it is important to note that, in the short run, a few producers could actually experience a slight yield drag if the new technology was adopted; this is not expected to persist in the long run.17 A fall in demand for California rice due to consumer concerns, coupled with increased supply as a result of productivity gains, could cause rice prices to decline over time and decreasing net returns in the presence of yield changes. Similarly, a price premium for non-transgenic rice varieties could erode net-returns differences between traditional and HT cultivars but benefit conventional rice producers.

To guarantee qualitative rigor, in this case, defined by the points at which no new themes are generated from the interviews, three open-ended questions were asked for each objective, adding up to a total of nine questions in addition to basic biographical questions. The interview questions, which are provided in the Appendix, were standardized with the same nine questions were asked for every participant. In addition, following semi-structured interviewing best practices, we asked participants to elaborate on answers and we dedicated more time to certain topics depending on interviewee expertise. These additional questions and structural flexibility enhanced feasibility and accommodated participant preferences.The first step of thematic analysis was interview coding, in which the interview transcripts were run through the software ATLAS.ti , an AI-enabled qualitative data analysis software. An open coding method was used within the framework of grounded theory in which the textual data is used to uncover the responses of individuals to changing conditions and the subsequent consequences . A procedural characteristic of grounded theory is that data analysis must occur at the same time as data collection; the coding and analysis of the first interview should incorporate details of all potentially relevant information into the design of the following interviews . The similarities between the first interviews confirmed the efficacy of the interview questions.

For the first research objective about compatible motivations, vertical growing systems the coding followed a simple coding process: when reviewing the first few participant transcripts, the data from the first set of three questions was cross-checked to identify recurring codes . Then, each additional transcript analysis added to existing codes, as well as potentially generating new ones. Detecting, classifying, and counting the presence of motivation-related codes quantified qualitative data, providing a more rigid content analysis . On the other hand, a thematic framework was developed and the data was indexed against the framework for the second and third objectives . This inductive thematic framework enabled comprehensive indexing and comparative analysis between the interviewees, allowing for the mapping of patterns. Because this study is about the complexity of stakeholder relations and how subjective perceptions leave tangible impacts, we used a structured yet non-mathematical approach for multi-level cognitive maps to answer the second research objective about stakeholder interactions. Cognitive maps are an example of soft organizational research , representing mental models of stakeholders and the processes by which they gather information and make informed decisions that help them reach personal goals . Cognitive mapping has been employed in many fields, including policy development and healthcare, to examine organizational decision making . Precision weeding is a suitable technology for this methodology because its stakeholders have varying, complex assumptions about weed management issues and the role of agtech as a solution. First, individual stakeholder maps were created from the individual interviews . Then, the individual cognitive maps were combined for each stakeholder group .

To combine individual stakeholder maps to create stakeholder group maps, similar themes were overlaid, links were added between themes that individual interviewees contributed, and clustering was identified in the stakeholder group maps . To answer the third research objective about grower user journey, we employed a similar soft OR approach to code concepts and present them in a “swim lanes” format. “Swim lanes” are a common industry method to map out the customer experience: how customers learn, interact, and respond pre-purchase to post-purchase .The key results of this research revealed the motivations behind adopting precision weeding technologies, the financial, R&D, and social exchanges and collaborations between the stakeholders, and the user journey for growers using the products and/or services of precision weeding startups. The key results included the varied motivations between the stakeholders and thus varied understandings of precision weeding’s value, the controversial role of government in accelerating precision weeding technologies, and the user journey of growers adopting precision weeding technologies.For the first objective about compatible motivations, the questions the interviewees answered differed slightly based on their stakeholder group. The motivations answered why precision weeding technologies should be within the future of weed management: why startups are developing, why growers are adopting, why VCs and CVCs are investing, and why government agencies are supporting precision weeding technologies.

Eleven indexed motivations were found in responses to the first objective. Fig. 1 shows the frequencies of these 11 indexed motivations per stakeholder group. The most common motivator was labor concerns, which was cited by 13 out of 17 interviewees . Three participants added additional details about the labor pressures of organic farming in California, five participants spoke about the competitive labor market, three stakeholders mentioned budget difficulties due to California’s increasing minimum wage, and two participants addressed the role of precision weeding technologies in increasing the efficiency of labor. The interviewee Gr3 said that “as you lose your herbicide, you got [sic] to rely more on hand labor [and] mechanical labor.” While they did not think that precision weeding technologies will ever completely replace hand labor, they speculate that growers will be able “to do a lot of heavy lifting with these newer mechanical weeders.” Following labor concerns, the second-most common motivator was cost, which was cited by 12 interviewees . The motivator of ‘meeting specific field conditions/needs’ was cited by seven interviewees . Precision weeding technologies were also recognized for their ability to address specific field conditions or needs such as varying soil conditions, banding, and thinning. Four interviewees noted precision weeding’s potential to ‘transform agriculture’ and to provide positive ‘returns on investments.’ Within the category of ‘transform agriculture,’ five interviewees, primarily startups, addressed precision weeding’s potential to add value and increase farm profitability through sensors, additional data collection, advanced computation abilities, and automation. The motivators of ‘more weeding options’ and ‘environmental sustainability’ were acknowledged by a few interviewees . According to growers Gr3 and Gr4, the motivator of having additional weeding options through precision weeding technologies is partially a result of increased pesticide regulation in California, which may cause growers to lose access to certain types of pesticides. The government stakeholder added concerns about “glyphosate-resistant varieties out there…these weeds are mutating and they’re resistant…then these new formulations come out…these spray-resistant weeds are mutating and getting worse and worse. I would love to see anything that can do targeted spraying or manual weeding come out to the front.” Other interviewees added that weeds eventually adapt to weed management tools and thus effective regimes vary both temporally and in terms of products used. Three interviewees explicitly mentioned ‘environmental sustainability’ but the umbrella of environmental sustainability includes the benefits of fewer inputs and chemicals, cited by six interviewees , animal and human health outcomes, cited by three interviewees , soil conservation, cited by one interviewee , and following the United Nations’ Sustainable Development Goals, also cited by one interviewee . The motivation for aesthetics was evoked by three out of seven of the growers and refers to the negative impact of weeds on the aesthetic or visual appeal of the fields . Interviewee Gr2 shared that “growers like their fields to look nice and so [weeds] are also removed for aesthetic reasons” and interviewee Gr3 added that weeding is also a preventative measure so that harvesters do not accidentally harvest weeds in addition to the crops. Though there was a consensus across all stakeholder groups about the importance of labor concerns, other motivators were more polarized . For the most part, grow rack only growers expressed concerns about weeds harboring diseases, pests, and viruses, weeds competing with crops for resources, and the aesthetic value of weeding. In addition, only growers mentioned–under the motivator of ‘more weeding options’–that precision weeding adoption was partly driven by concerns that increased regulation in California could cause growers to refuse access to herbicides.

To answer the second sub-question regarding collaborative models between stakeholders, the comprehensive indexing of themes revealed that the average number of constructs, defined as key words and/or concepts that address stakeholder interactions and limitations, for all individual interviewees was 60. Excluding labels and descriptors, the startup stakeholder group produced an average of 59 constructs, growers had 58 constructs, and VCs had 63 constructs. All stakeholder groups produced a similar range of constructs, indicating the universality of the interview questions asked. Growers identified several blockers to adoption, such as competition between growers, old-school mentalities, and a lack of connection between startups and growers . Precision weeding requires the bandwagon effect for growers to want to try new technologies. However, competition between growers might hinder the bandwagon effect because growers may not wish to share their competitive advantages with their neighbors. This stakeholder group also asserted that many growers view working with startups as a high-risk endeavor, citing the high capital expenditures of most precision weeding machinery and the history of unsuccessful agtech startups. Because of these perceived risks, the multiple farm managers who work at one company may disagree with one another and prevent adoption. Startups also added that concerns about startup longevity are especially intensified because most traditional agricultural companies, such as John Deere, have been around for decades or centuries . Furthermore, growers perceived old-school mentalities and a potential lack of on-paper education as a blocker to the adoption of precision weeding technologies . Some may view new technologies as unnecessary and the mark of ‘true’ growers as putting in the hard work twelve hours a day, seven days a week. In addition, because many startups compare their products’ efficiencies and costs to hand crews, some growers fear automation replacing their jobs. Although many growers want to own their own equipment, they may be reluctant to hire specialized staff to run the equipment. Startups and VCs also mentioned and expanded upon the growers’ urge to own their own equipment, coming into conflict with the weeding-as-a-service business model that some startups have ventured into. Interviewee V2 also added that “eventually farmers need to own the equipment [because of] timing. As your operations become larger, timing becomes absolutely critical. As you grow different crops in variable environments, you need the machine. You may be in a field and discover; I need the machine right now and you phoned the service guy and he’s got three farms ahead of you.” Other startups have turned to the weeding-as-a-service business model to abate prohibitive capital costs and to ensure the machinery out on the fields are up-to-date with the startups’ latest developments . S3 illustrated this point by saying “the first-generation spray that we’ve built is like the iPhone 1, and technology is changing so fast that I know in three months I’m going to have iPhone 3 coming out.” S3 added that startup-centric reasons for the service model include allowing the startups to have constant access to new data and the ability to quickly relay failure points to the R&D teams. In addition, the weeding-as-a-service model provides a more intimate experience between the startups and the growers, enabling startups to conduct in-depth customer discovery for their current products and future ideas. The startup interviewees brought up the limitation that some startups lack connections to growers . Many proposed that startups need to hire employees who have worked in the agricultural industry and have local connections, while some also brought up that startups could develop strategic alliances with a committee of growers. Another startup limitation was the long timelines for hardware research and development, raising concerns about financial runways and funding. Some startups asserted collaboration between startups could alleviate runway fears as many startups have complementary products; consolidation will save time and effort. According to VCs, startup-university and grower-university relationships are often difficult to navigate and are not always advantageous . With startup-university partnerships, patent battles may sometimes emerge, particularly if the startup’s distinguishing technology directly spun out of university-sponsored research. In terms of how growers interact with universities, university research topics and trial designs are usually limited in scope and not perfectly aligned with the goals of the growers. For both parties, interviewees agreed that portfolio support from VCs to startups includes hiring and marketing support, business acumen and advice, connections to lawyers, accountants, and other startup founders, advancing governance to create stable and mature companies , and financial advising. VCs also mentioned hands-on, agriculture-related support such as matching startups with growers for field trials, building a grower advisory board, and helping with plot designs and trialing systems.

In both studies, great lengths have been taken to develop rigorous physics-based vehicle models, including consideration of architectures, power train, overall accessory loads, and sensitivity to drive cycle and external ambient temperatures. Similar attention to detail has been paid to developing practical and representative EV charging profiles, reasonable mapping of standard drive cycles to real-world trips and travel behavior, and high-fidelity analyses of existing grid dispatch methods based on real-world data. By making the datasets and source codes publicly available, it is the authors’ hope that such methodologies can be expanded, and new regional applications and business use cases can be explored. To recap again here for context, the Phase I study introduced a methodology that accounts for energy and emissions during the use phase of vehicle emissions across a range of light-duty car types, including ICEV, HEV, and EV. This comparative approach enabled a head-to-head assessment of the vehicle technologies relative to a variety of private vehicle use cases. The Phase I effort revealed that EVs can contribute to reduced emissions, drying curing weed but their quantitative benefits are highly sensitive to when and how the vehicles are charged. This factor was shown to deliver results that could have a variance in the same order as the mean emissions. These initial results highlighted the importance of probing deeper into the interplay between charging profiles and vehicle classes.

The study further revealed that driving cycles and use cases are of secondary importance, which can also contribute substantially to the variance in emissions for a given vehicle type and charging profile. A third factor is the overall limit of an EV battery capacity, which is more of a determinant of whether a given EV can actually substitute for a comparable ICEV. It should be noted that EV battery range did limit any drive cycles undertaken in the passenger car comparisons, and the model can readily accommodate EVs of any specified range. Phase I revealed a few “higher order” factors that influence the relative environmental benefits, but a major takeaway is that the timing and duration of a vehicle charging event under the marginal emissions assumptions can affect the environmental impacts by up to 100%. In this Phase II effort, we deepen our investigation to include additional use cases of priority interest, while applying the original methodology developed in Phase I. The timing of this study happens to overlap with the commercial release of several high-profile light duty full-size electric pickup trucks, courier vans, transit buses, and school buses which have been publicized broadly in the media and studied extensively in research and development circles. Thus, Phase II stands to illuminate new insights via investigation of potentially important public and private use cases that leverage these new electric vehicle offerings, as a means of reducing emissions and energy. These use cases show particular promise because many small businesses operate on fairly predictable cycles and return to a central base at the end of the workday.

This Phase II study reveals that the trends observed in Phase I not only continue to be relevant but are in fact more pronounced and important. For example, the sensitivity to the time of charging is greater, accounting now for a variance in excess of 100%, as explained above. First of all, while the daily mileage experienced by a fleet vehicle may not be significantly greater than commuter-type sedan applications, due to the increased energy intensity of these larger vehicles, the energy consumption on a daily basis is considerably higher. Secondly, we forecast that fleet vehicles used for commercial purposes are likely to use Level 3 Fast Charging methods, for economic reasons, which can further intensify the variance associated with regional marginal assumptions. Finally, while previous studies have discussed this phenomenon in subjective terms, benefits for the larger vehicle classes and associated business cases have not, to our knowledge, been quantified in this way. Meaning they have not taken the approach of considering vehicle, power train and use-case characteristics in view of the larger system of charging profiles and upstream grid factors. Few studies that we are aware of have taken the full spectrum approach, leveraging specification data on new vehicles, considering rigorous energy consumption, physics-based models, real-world characteristics of a grid, dispatch, and probable electric vehicle charging profiles in a contemporary manner. These methodologies and some of the simulated results should have considerable value to fleet operators, small business owners, service-oriented, vehicle operations, as well as officials that do utility planning resource planning, and charging infrastructure. In addition, these methodologies can be extended quite broadly to consider the local grid context in other regions, as well as refined use cases that match vehicle types to a growing set of electrified transportation applications.

These studies reveal a few takeaways and signal a few notes of caution. These fleet and MD/HD vehicles are more energy-intensive and will consume more energy per day compared to LDVs, even though they may travel the same number of miles. For this reason, charge management is much more critical. A related need is more timely communication between vehicle fleets, charging service providers, and utilities to ensure charge management is transparent and mutually beneficial. Furthermore, because these larger vehicles are likely to utilize Level 3 Fast Charging in the interest of overall economic and value proposition, there is renewed attention to “get it right” from the CO2 perspective as well. The study implies that one approach that may mitigate adverse consequences is to schedule overnight charging of Medium and heavy-duty vehicles at predictable locations. Doing so could enable grid dispatch operators to increase intermediate loads with the use of highly efficient combined cycle plants or via the release of stored energy from renewables. This would obviously require more advanced planning and data acquisition but could readily be achieved through well-orchestrated pilot programs involving fleet operators, charge managers, and utility operators.Another potential benefit that seems impactful from the use of light-duty, electrified service trucks and vans is that as EVs become more prevalent, the randomization of charging events could potentially become advantageous by spreading out uncertainty and lowering the potential for adverse peaks. Again, if this information were made available to grid operators, more optimal planning decisions could be made. This would not only yield more cost-effective dispatch on a daily basis, it would also reduce emissions and ensure that longer-term investments into local electric vehicle supply equipment , grid distribution, transmission, and generation assets become optimized from the standpoint of society as well as individual consumers and businesses. Primary contributions of this effort are therefore the development of new methodologies, integration of sub-system models and independent data sources, and enhanced tools for quantifying CO2 impacts associated with vehicle electrification. The Phase II study refines the methodology and assesses EV use cases that show particular near-term promise.Given evidence for the prevalence and the human and economic burden of non-communicable diseases, behaviors that may contribute to the incidences of such diseases are of increasing academic, political and societal concern. We debate the extent to which interventions based on behavioral theory work in the real world to contribute to addressing these concerns. This arises from a live debate at the Annual Meeting of the International Society of Behavioral Nutrition and Physical Activity held in Hong Kong in June 2018. The debate reported in this article was conducted using the same format as the ‘live’ debate, our cases were each written independently then exchanged simultaneously, and the same process repeated for the responses. Once this process was completed, we authored the joint conclusion comprising points of agreement, and areas where we disagree.Given epidemiological research associating multiple chronic disease risk with participation in health-related behaviors, industrial grow health organizations and stakeholders have sought to develop behavioral interventions that lead to practically-significant changes in these behaviors and concomitant reduction in disease risk.

Behavioral scientists propose that interventions based on theories from the behavioral sciences, particularly psychology, will be optimally effective in evoking behavior change. Despite an expanding evidence base demonstrating the efficacy of theory-based interventions in promoting sustained change in health-related behavior, my colleague will suggest that the role of behavioral theory is overstated, particularly when it comes to ‘real world’ effectiveness. In particular, he will argue that what works in ‘ideal world’ carefully-controlled conditions are not effective in ‘real world’ contexts where upscaling, logistic, cultural, and implementation factors pervade. He will cite the lack of change in physical activity participation and rates of chronic disease and obesity as evidence that interventions based on behavioral theory are not effective. Here I deconstruct these ‘straw person’ arguments, and contend that interventions based on theory can and do work in promoting behavior change in real world contexts.Behavioral theory is a broad term for a set of pre-specified ideas or predictions aimed at explaining behavior. Behavioral theories come from multiple disciplines , and identify multiple determinants or mechanisms of behavior including beliefs, motivation and intentions, individual differences, social influence, and environment and demographics. A substantive body of research has identified the effectiveness of theory-based interventions targeting change in modifiable determinants or mechanisms. For example, syntheses of evidence have indicated that interventions targeting change in social cognitive beliefs and motivation, social support and norms, and planning to be effective in promoting behavior change in randomized controlled trials. Similarly, interventions based on health-risk communications have been successful in promoting behavior change, with graphic images on tobacco products a prominent example. Research targeting change in determinants derived from social-ecological theories, encompassing environmental, community, and policy factors, have also been shown to be effective. Interventions based on choice architecture, sometimes referred to as ‘nudging’, have demonstrated effectiveness in changing behavior in laboratory and field settings. In addition, interventions adopting specific strategies such as self-monitoring, prompting social support, planning, behavioral skills, and affective appeals have been found to be particularly effective. Taken together, primary studies and research syntheses indicate that theory-based interventions are effective in changing behavior in laboratory and ‘real world’ contexts. In the interests of balance, it would be remiss not to acknowledge a number of caveats to this evidence. Meta-analyses and systematic reviews have also indicated that stated theoretical basis leads to no difference in intervention effectiveness, and, in some cases, even reverse effects. Similarly, there is research demonstrating that adoption of particular behavior change strategies does not lead to greater intervention effectiveness. So how can these two streams of evidence be reconciled? Inadequate mapping of theory on to intervention components may be a moderating factor. A distinction has been made between theory-inspired and theory-based interventions. Prestwich et al. indicated that ‘theory-inspired’ interventions provide insufficient specification of links between theory and intervention strategies. Theory-inspired interventions, therefore, pay ‘lip service’ to behavioral theory, but fail to link intervention components with relevant theoretical determinants. There are also problems with inadequate reporting of such links, which hinders researchers’ ability to evaluate the effect of theoretical basis on intervention effectiveness. There is therefore a need for researchers to become more effective in matching theoretical determinants of behavior with intervention content, and for greater transparency when reporting intervention content.If interventions based on behavioral theory work in changing behavior in ‘real world’ contexts, how have they not stemmed the tide of non-communicable disease pandemics, as my colleague will contend? Knowledge and implementation of effective interventions, whether or not they are based on theory, seems to have had limited impact in changing population-level participation in health behaviors and reducing incidence of chronic disease. Although there is substantive evidence that behavioral interventions are effective in changing behavior across multiple contexts, populations, and behaviors, and, arguably, those based on theory having greater effectiveness despite some of the aforementioned limitations, such knowledge is seldom translated to population-level change. This is largely because many behavioral interventions implemented at the community or even population level are relatively short lived, under-funded, or fail due to poor implementation, up-scaling, or translation. Funding is a key issue; many behavioral interventions receive initial investment that is not sustained. Interventions need sufficient funding for the necessary networks and providers required to implement the intervention in practice. Even though economic evaluation of many behavior change interventions has demonstrated their cost effectiveness, investment in behavioral interventions pales compared to investment in procedures aimed at treating disease. It is unrealistic to expect health care providers to identify, assimilate, and implement research findings reported in scientific outlets.

Together, the route data and power train models determine the energy consumption in each simulation. Our methodology affords access to established data and extends prior vehicle propulsion energy and emissions analyses. In Phase I of the EVALUATE project, the team utilized a 5- cycle EPA dyno schedule and fuel economy label weighting protocol and has continued to do so for the two light-duty vehicles investigated herein. In addition, owing to the larger classes investigated in the present study, Phase II has also called for independently derived travel demands and data from the literature for representative use cases. For more details on the theory, model development, source data, and applications, please refer to [1, 14].As discussed, we developed physics-based power train models developed in [1, 14] to accommodate target vehicle technologies of interest. In each vehicle category, we established characteristics for the baseline vehicles . We then proceeded to develop an electrified power train model for each vehicle category. To facilitate a direct comparison among vehicles using dissimilar energy sources, we identify vehicle specifications for a given vehicle classification and hold constant key parameters such as vehicle power output, capacity to sustain the required torque and speed, vehicle footprint, passenger and cargo capacity, auxiliary power requirements, and so forth. Table 2-1 below depicts some of these operative specs. In developing our model and conducting simulations, growing tray we have made every effort to represent real-world vehicle characteristics across the categories of interest.

For example, when an electrified variant has a greater gross vehicle weight than a comparable internal combustion vehicle within its class, we reflect that vehicle weight difference in the analysis. This is especially important at larger vehicle classes because EVs in these classes have proportionately heavier batteries, which then incur additional energy consumption. In this way, we provide simulated comparisons based on actual vehicles in the marketplace.Five distinct driving cycles comprise EPA test and labeling protocols for light-duty passenger cars and pickup trucks. The three 23°C tests include a derivative of the Urban Dynamometer Driving Schedule known as the Federal Test Protocol , the high acceleration aggressive driving schedule identified as the Supplemental FTP , and the Highway Fuel Economy Driving Schedule . The 35°C drive cycle is the Air Conditioning Supplemental FTP driving schedule referred to as SC03. The -7°C cold weather test schedule repeats the original FTP at the reduced temperature. For the light-duty pickup and cargo van investigated in the study, we have adopted the EPA “5- cycle” protocol and created an approach whereby a weighted mix of driving schedules is obtained to approximate major modes . With the original development of the vehicle architecture models, and assumptions around the weighted driving cycle protocols, the team’s next step was to develop a MATLAB/Simulink code that generated a series of energy consumption values based on inputs of vehicle type and driving cycle. These intermediate outputs were then combined to generate effective fuel economy values, analogous to the EPA 5-cycle approach, for the stipulated categories . This was done and a set of energy consumption outputs were generated.

Using the example of the light-duty pickup truck and van, representative outputs are depicted in Table 2-2 and Table 2-3.The next step was to define representative driving schedules for a range of small business and fleet needs serving a large urban area, such as metro Atlanta. For the light-duty pickup and van, we follow the approach used in Phase I, where we select two urban commutes of 80.5 km and 32.2 and a suburban vehicle use case of 48.2 km . For urban commutes, presumably into and out of a city like Atlanta, it is reasonable to employ the EPA “combined” rating and protocol to determine energy consumption for these trips. For the suburban errand use case, it is reasonable to employ the EPA “city” rating and protocol. This is summarized in Table 2-6 below. Shown in Figure 2-2 is a notional depiction of a baseline vehicle’s instantaneous power and cumulative energy for an example drive cycle. Figure 2-3 depicts a few of the standardized EPA dyno schedules that are fed into a 5-cycle weighting determination.While EPA protocols have been applied for LDV cars, pickups, and vans, we have relied upon a combination of physical models and the literature to define driving schedules for moving trucks and refuse trucks. Section 2.4 provides a more comprehensive discussion of the driving schedules assumed in this study for the moving trucks and refuse trucks. In short, we have relied upon experimental field data, as reported in peer-reviewed literature. We have also corroborated these observations against our physics-based models, in areas such as weight, aerodynamic drag coefficient, frontal area, and rolling resistance.Constructing representative charging profiles for commercial fleet EVs is a distinct and, in many ways, a simpler exercise than doing the same for personal EVs.

In our Phase I report, we consulted four primary data sources to explore observed charging behavior for personal EVs from which we manufactured representative charging profiles: a synthetic dataset generated by a separate Georgia Tech research team evaluating the benefits and challenges of smart charging algorithms; the Georgia Power Electric Vehicle Rate scheme; a ChargePoint dashboard portal and database that has been aggregated for workplace charging on the Georgia Tech campus since 2015; and a verbal consultation with Escalent, a third party research firm. These independent data sources were corroborated and used to develop four representative charging profiles for personal EV charging events. The obvious difference between charging behavior for personal EVs and commercial EVs is the shift in emphasis from convenience charging to charging schedules constrained more severely in space and time by the demands of business. Fleet vehicles have operational obligations that must be fulfilled punctually and reliably. Commercial EV charging behavior is therefore primarily a function of business characteristics. To develop charging profiles for commercial EVs, we envisioned conceivable business use cases for each vehicle type included in Phase II . In order to improve our understanding of daily vehicle usage for these vehicle-application combinations, we reviewed U.S. national vehicle miles traveled data from the Vehicle Inventory and Use Survey. This revealed that daily VMT for LDV are less than 50 miles about 82% of the time, and less than 100 miles about 93% of the time. Similarly, daily VMT for MD in the class 3-6 range are less than 50 miles 68% of the time, and less than 100 miles about 84% of the time. Such empirical data was useful in developing realistic daily driving demands that are explored in this study. Light trucks and vans may most typically be deployed by residential service businesses such as lawn care, and pest control or by electricians and plumbers. It is assumed that the on-road driving cycles for these vocations are somewhat similar. As such, these business use cases were clustered into a representative category labeled “Residential Home Services.” Representative driving cycles were then used to calculate the combined energy consumption rates of light trucks and vans belonging to residential service fleets, as described in the previous section. Similarly, we reasoned local moving trucks would have a fairly consistent mixture of on-roaddriving cycles and produced a combined energy consumption rate for a composite medium duty truck, drying and curing bud as described in the previous section. We then used a distribution of vehicle miles traveled  to calculate a series of cumulative energy consumption totals comparable to a diverse variety of business operations and scales. Modeled VMT figures were determined to be reasonable assumptions based on real-world operational data for service vans from NREL’s FLEET DNA, which publishes 29 days of dynamometer driving data from four service vans in operation in the United States as shown in Figure 2-4.A similar approach was employed for refuse trucks. Driving cycles for refuse trucks are less variable than for other vehicles, but distances traveled might vary more depending on service area and distance to the landfill and are generally greater.

We used higher vehicle miles traveled numbers for the calculation of total cumulative energy consumption for refuse trucks. It is in the nature of the businesses described by each use case that the vast majority of on-road activity occurs at predictable, well-defined times. From experience, “business hours” for residential home service businesses are very likely always during the day, between around 8 AM to 6 PM. This is similarly true for moving businesses, although with perhaps slightly less consistency to accommodate the occasional client requiring moving services outside of typical hours due to scheduling conflicts or other miscellaneous logistical reasons. Refuse truck service is even more regular, with well-defined service schedules and routes. Regardless, if business activity always or most often occurs during certain segments of the day, it leaves fewer segments of the day available for charging events. In terms of charging location, we reasoned further that most businesses with EV fleets would have two options for where to charge their vehicles: at the business home base or at a public EV charging station in the field.It was under these assumptions that five commercial EV charging profiles were developed. Three charging profiles are representative of a business that charges their EVs at their business’ “home base,” some central garage, parking lot, or depot where vehicles return at the end of each day and domicile overnight, with each of the three profiles having a different “after hours” start time for the charging event . Two charging profiles are representative of a business that charges vehicles during the workday using charging equipment in the field . To facilitate comparisons, we designed the charging profiles and only reported simulated outputs if the EV battery was recharged to 100%. We assumed businesses would tend to opt for the lowest charging level that fulfills charging demand within the time constraints of the business’ activity. For the main batch of simulations, Level 2 charging systems were used for light trucks and vans, and Level 3 charging systems were used for moving and refuse trucks. Level 2 was sufficient to completely recharge the batteries of light trucks and vans using every charging profile at all VMT levels, except for field charging starting at 3 PM at 50 and 100 miles traveled per day. Level 3 was sufficient to completely recharge the batteries in every scenario. The Level 2 charging profiles are shown in Figure 2-5 as examples, with on-times of 5PM, 8PM, 1AM for the charging that occurs at the location of the “Business,” and on-times of 12PM and 3PM for charging that occurs at locations in the “Field,” respectively. Please note that the time axis has been shifted with a start at 8AM to accommodate charging cycles that extend past midnight on a given day. The five charging profiles using Level 2 and Level 3 charging systems, four or three VMT levels depending on the use case, and four vehicle types enable the very broad applicability of our simulations and findings to many different business operations and scales.This second phase of research leveraged the extended grid modeling and optimization work described in our Phase I report. We used the same merit-order dispatch estimation framework based on actual data reported by the Southern Company Balancing Authority . These data are high-resolution and provide high detail of individual plants and generating units for all technologies. The methods used to develop the dispatch model and generate the marginal grid emissions assumptions are described in detail in [29]. With similar motives to Phase I, demonstrating the nuanced implications of emissions assumptions for quantifying abatement and comparing EVs to other vehicle technologies requires the development of a series of grid emissions assumptions.Monthly average and hourly emissions were assembled for August, October, and December. Hourly and marginal hourly emissions were collected for representative days from each of those months. Grid characteristics, including total load, demand curves, and to some extent available generation resources, evolve seasonally. In warm climates , demand for electrical power and subsequently grid emissions intensity are highest during the summer months and particularly in the evenings as people return home, turn on their lights, stoves, and televisions, and crank up their air conditioning units. The SERC grid experiences similar fluctuations in the winter months, and less extreme fluctuations in the shoulder months of spring and autumn. Using example emissions from summer, winter, and shoulder months affords the exploration of emissions variations within a 24-hour day at different times of the year and their effects on total cumulative emissions from EV charging events.

A stand life of 3 to 5 years is common in the Central Valley of California and other warm, long growing-season areas of the Southwest. A stand life of 5 to 7 years is common in much of the Northwest, and some alfalfa stands remain in production in excess of 10 years. As suggested by the principles outlined above, it is unwise to rely solely on glyphosate applications for weed control throughout the life of a transgenic alfalfa field. This practice would encourage weed shifts and resistance, and over time weed control would diminish in most cases. Once an herbicide is rendered ineffective as a result of resistant weeds, its usefulness as a weed control tool may be greatly diminished. After a resistant weed population has gained a foothold, it is practically impossible to eliminate it due to the presence of a weed seedbank.Most alfalfa producers apply an herbicide to alfalfa during the dormant season to control winter annual weeds that infest the first cutting. It is strongly recommended that growers not rely solely on glyphosate for their winter weed control program for the duration of the stand. They should rotate to another herbicide or tank mix at least once in the middle of the life of a stand, and perhaps twice if the stand life is over 5 years .Fortunately, cannabis growing systems all of the herbicides currently registered in alfalfa—and there are several to choose from—havea different target site of action than does glyphosate. The soil-residual herbicides applied during the dormant season to established alfalfa [such as hexazinone , diuron , metribuzin , and pendimethalin ] would be appropriate herbicides for a rotation or tank-mix partner.

The rotation herbicide or tank-mix partner of choice depends on the weeds present in the field and their relative susceptibility to the herbicides. Paraquat is another candidate for rotation, but paraquat, like glyphosate, lacks residual activity and is applied late in the dormant season. By rotating paraquat with glyphosate, growers could potentially be selecting for early-emerging weeds that may be too large to control at the typical timing for these herbicides. In addition, they could be selecting for late emerging weeds that germinate after the application. Rotate Herbicides Early in Stand Life So Glyphosate Remains Effective Weed control during the last year of an alfalfa stand is often challenging because the stand is typically less dense and competitive and also there are fewer herbicide options from which to choose. There are significant plant-back restrictions associated with many of the soil-residual herbicides used in alfalfa, so glyphosate is a good choice for controlling weeds in the final year of RR alfalfa field. The preference to use glyphosate in the final year of an alfalfa stand underscores the importance of rotating herbicides earlier so that glyphosate will remain effective and continue to control the majority of the weeds. Consider a Soil-Residual Herbicide for Summer Annual Weed Control Summer annual grass weeds such as yellow and green foxtail , barnyardgrass , cupgrass , and jungle rice , and less frequently, broadleaf weeds like pigweed or lambsquarters , can be problematic in established alfalfa. These weeds emerge over an extended time period whenever soil temperatures and moisture are adequate, typically from late winter or early spring throughout the summer.

Weeds may emerge between alfalfa cuttings, so several applications may be necessary in California’s Central Valley for a foliar herbicide without residual activity like glyphosate to provide season-long control. Multiple applications of a single herbicide during a season is cited as promoting weed resistance.Therefore, growers should not rely solely on glyphosate for summer grass control for multiple seasons. It remains to be seen how many applications of glyphosate will be required for season-long summer grass control. In some of the long growing season areas of California, as many as two to three applications per season may be needed in older, thinner stands. Rather than making multiple applications of glyphosate, a better approach may be to apply a pre-emergence soil-residual dinitroaniline herbicide like trifluralin or pendimethalin , or possibly EPTC , and follow up with glyphosate later in the season as needed for escapes. Not only is this approach more in line with management practices to avoid weed shifts and resistance, but it may be more economical as well, compared with multiple applications of glyphosate. The practice of rotating herbicides or applying tank mixtures is recommended for both dormant applications aimed at winter annual weeds and for spring/summer applications intended to control summer annual weeds. For example, rotating to hexazinone for winter annual weed control for a year does nothing to prevent weed species shifts or the evolution of resistance in the summer annual weed spectrum. Herbicides for summer annual weed control should be rotated as well. Frequency of Rotation Depends on Weed Species and Escapes There is no definitive rule on how often herbicides should be rotated. Our suggestion to rotate or tank mix at least once in the middle years of the life of a stand may need to be modified depending upon actual observations of evolving weed problems. The key point, which cannot be overemphasized, is the importance of diligent monitoring for weed escapes.

Producers should stay alert to the appearance of weed species shifts and evolution of resistant weeds. If the relative frequency of occurrence of a weed species increases dramatically, chances are that it is tolerant to glyphosate and immediate rotation of herbicides or a tank mix is advised. If a few weeds survive among a weed species that is normally controlled easily with glyphosate, it could be an indication of weed resistance, assuming misapplication and other factors can be eliminated as possible causes. However,in these situations, it is imperative to prevent reproduction of a potentially resistant biotype. Treat weed escapes with an alternative herbicide or other effective control measure.Reservoir operation is based on a series of rules that determine the amount of water that is stored and released under different system conditions. These reservoir operation rules determine how reservoir water is allocated during periods of droughts, normal climate, or wet climate. Methods used for the optimal operation of reservoirs can be classified into two main categories: classic algorithms, and evolutionary and metaheuristic algorithms. Although classic methods are relatively simple, they have limitations such as the possibility of not achieving global optima, convergence to local optima, and being hindered by high dimensionality . Evolutionary and metaheuristic algorithms are generally inspired by natural phenomena. One of the advantages of the latter algorithms is that they generally converge to near-global optima for any well-defined optimization problems. In addition, they can solve multiobjective problems. The main disadvantage of evolutionary and metaheuristic algorithms is the long processing time needed to converge to a solution. This has led many researchers to search for and produce newer, dry marijuana computationally more efficient, evolutionary and metaheuristic algorithms. Many classic and metaheuristic optimization techniques have been recently developed and applied in various aspects of water resources systems such as reservoir , hydrology , water-resources management , irrigation , power plants , structures , distribution networks , aquifers , infrastructures , and algorithmic developments . None of these works dealt with the application of the weed optimization algorithm in water resources systems, or, in particular, to solve reservoir optimal operation. Concerning the application of evolutionary and metaheuristic algorithms to reservoir operation, Esat and Hall resorted to the genetic algorithm to optimize reservoir operation for energy production and water for irrigation. Oliveira and Loucks employed the GA to evaluate rules concerning the operation of multi-reservoir systems. Sharif and Wardlaw implemented the GA in several multi-reservoir systems and obtained solutions very close to those calculated with dynamic programming . Ahmed and Sarma compared the GA’s performance with that of stochastic dynamic programming and reported that the GA was superior in calculating desired solutions for optimizing multiobjective reservoir operation. Tospornsampan et al. applied the simulated annealing algorithm to optimize the operation of a multi-reservoir system. Kumar and Reddy implemented the ant colony optimization metaheuristic algorithm to optimize the operation of a multiobjective reservoir.

Bozorg Haddad et al. introduced the honey-bee mating optimization metaheuristic algorithm to reservoir operation. Bozorg Haddad et al. used the HBMO and nonlinear programming for the design and operation of a single and multiple reservoir system. Wang et al. introduced the multi-tier interactive GA for long-term optimization of reservoir operation. Jothiprakash et al. used the GA and stochastic dynamic programming for the operation of a five-reservoir system in Kodaiyar, India. Ostadrahimi and et al. calculated operation rules of a multi-reservoir system using the multipopulation approach in multi-swarm particle swarm optimization algorithm. Ngoc et al. applied the constrained GA to derive optimal operation principles of multiobjective reservoirs. Bozorg Haddad et al. applied the bat algorithm to determine the optimal operation of reservoir policies for Karoon 4 and four-reservoir system in continuous domain. Bozorg Haddad et al. used the water cycle algorithm to determine the optimal operation of reservoir policies of the Karoon 4 reservoir. They also compared the results of the WCA with those obtained with the GA and NLP. The following works have implemented the WOA in a variety of engineering optimization problems, but not to reservoir operation as of yet. Mehrabian and Lucas introduced the WOA. They solved two engineering problems and compared the results with the GA, memetic algorithm , particle swarm optimization algorithm, shuffled frog leading algorithm , and the simulated annealing algorithm. Their results showed a relatively superior performance by the WOA. Mehrabian and Yousefi-Koma applied the WOA to optimize the location of piezoelectric actuators on a smart fin. Mallahzadeh et al. tested the flexibility, effectiveness, and efficiency of the WOA in optimizing a linear array of antenna and compared the computed results with those of the PSO algorithm. Sahraei-Ardakani et al. used WOA to optimize the generation of electricity. Roshanaei et al. applied the WOA to optimize uniform linear array used in wireless networks, such as commercial cellular systems, and compared their results with those from the GA and least mean square . Mallahzadeh et al. used the WOA to design vertical antenna elements with maximal efficiency. Krishnanand and Nayak compared the effectiveness of the WOA, GA, PSO algorithm, artificial bee colony , and artificial immune by solving five basic standard mathematical problems with multivariate functions. Zhang et al. used heuristic algorithm concepts for developing the WOA. They introduced the WOA with crossover function and tested the new algorithm on standard mathematical problems and compared the results of the developed WOA with those of the standard WOA and PSO. Sharma et al. used the WOA to solve dynamic economic dispatch . Their results showed that the WOA algorithms reduced production costs relative to those obtained with the PSO and AI algorithms and differential evolution . Jayabarathi et al. implemented the WOA for solving economic dispatch problems. Kostrzewa and Josiński introduced a new version of the WOA and tested their algorithm on several standard mathematical problems. Abu-Al-Nadi et al. applied the WOA for model order reduction in linear multiple-input-multiple-output systems . Sang and Pan introduced the effective discrete WOA to solve the problem of flow shop scheduling with average stored buffers, and compared their results with the hybrid GA , hybrid PSO algorithm , and the hybrid discrete differential evolution algorithm . Saravanan et al. applied the WOA to solve the unit commitment problem for minimizing the total costs of generating electricity. They compared their results with those calculated with the GA, SFLA, PSO algorithms, Lagrangian relaxation , and the bacterial foraging algorithm. Barisal and Prusty used the WOA to solve economic problems on a large scale with the aim of minimizing the costs of production and transfer of goods subject to restrictions on production, market demand, the damage caused to goods during transportation, and to alleviate other calamities. The reviewed literature established that the WOA has not been applied to optimize reservoir operation. This study introduces the WOA to the field of reservoir operation and compares its results with those obtained with the GA, linear programming , and NLP. Several comparative examples are solved to measure the performance of the WOA against those of well-established optimization methods. Establishing hedgerows of native perennial grasses, shrubs, or trees around farms requires long-term planning and care to ensure success. This effort includes developing a farm plan; selecting, analyzing, designing, and preparing the site for planting; choosing appropriate plants; and initiating a plan for weed and rodent control.

In CT systems, however, residues may be present at the time of herbicide application and may decrease the herbicide’s effectiveness as the residues intercept the herbicides, reducing the amount of herbicide that can reach and kill germinating weed seeds . Since most preemergence herbicides can be surface applied and then incorporated into the soil by rain water or sprinkler irrigation, incorporation should not be an issue in CT systems. It may be that the increased organic matter on the soil surface binds up some of the herbicide, so a grower may need to increase application rates in order to achieve adequate control. Cover crops left on the surface present a different situation for preemergence herbicides. Cover crop mulches are seldom uniform; it is common to see thick mulch and bare ground in the same field. Researchers have observed that in areas with a thick mulch, the mulch may block an herbicide from reaching underlying weeds but may be sufficient by itself to control weeds, whereas in areas of the same field where the mulch is thin or nonexistent, the herbicide can reach the weeds and provide effective control . A planter implement also moves mulch and crop residue away from the seed line, creating a relatively clean zone for good herbicide action where it is needed most.Post emergence herbicides work equally well in CT and conventional tillage systems, cannabis protective tray though it should be noted that residues on the soil surface in a CT system may interfere with effective herbicide contact with emerging seedlings.

Hartzler and Owen suggest that growers wait until weeds become established and then control them with postemergence herbicides since the timing of weed emergence is less uniform in CT than in conventional systems. A grower should not wait too long to apply treatment, however; weeds that emerge together with the crop may cause greater yield losses than those that emerge later in the growing season. Similarly, crop emergence and development may be less uniform in CT systems than in conventional tillage systems, particularly for plantings made during cool periods of the year and in fields that have a lot of surface residue. In spring and summer plantings, growers can expect this difference in weed emergence timing to be much less. Adoption of CT has increased as a result of the development of HTCs that allow post emergence herbicides to be applied during the growing season with a relatively low risk of crop injury. However, when post emergence herbicides are to be aerially applied, growers should not wait so long as to allow the crop canopy to close, since the crops might then intercept the aerially applied herbicide, reducing the contact between the herbicide and the weeds under the crop canopy. Correct identification of the best time frame for post emergence herbicide application is critical in CT systems. Again, the complexity of cropping systems in California makes it difficult to provide a blanket recommendation on the optimal time frame for application.Increased persistence of residual herbicides may be a concern in CT systems. For example, Vargas and Wright observed crop injury that was attributable to the persistence of prythiobac sodium in a CT study.

Staple was applied to cotton but the following tomato and corn crops suffered considerable stand loss due to herbicide carryover . Among the tillage systems compared, injury was most severe in the completely no-till system. Similar carryover of another herbicide, sulfonylurea , applied in corn caused crop injury in the subsequent wheat crop. In the absence of the soil mixing that usually comes with tillage, residual herbicides may not be diluted sufficiently in the soil profile, and this may lead to injury of the subsequent crop. When selecting herbicides for a CT system, then, it is important to make choices that minimize losses to subsequent crops in the rotation. Some herbicides, such as Smetolachlor , are less likely to persist into the following crop.Herbicide-tolerant crops have made it easier for growers to begin to transition to CT systems in California and other states. The advantage of HTCs, mainly Roundup Ready crops, is the ease with which a grower can apply glyphosate over the top of the crop with excellent crop safety and weed control. As a result, production costs have also decreased as growers reduce the number of trips across the field, herbicide applications, cultivations, and hand weeding operations. By reducing cultivation and eliminating hand weeding, growers have reduced their costs by $25 to $150 per acre depending upon weed species and density. UCCE cotton cost studies indicate an average savings of $60 per acre with Roundup Ready cotton compared to the costs of growing conventional cotton . The potential for weed resistance to specific herbicides is always a concern with herbicide programs, and that concern increases with HTCs in a CT system. Roundup Ready promotes the continuous use of glyphosate, and we probably can expect that to induce shifts in weed species or the development of glyphosate-resistant weeds in HTC fields. Glyphosate-resistant horseweed or marestail, has been reported in no-till Roundup Ready corn-soybean rotations in the Midwest and mid-Atlantic regions of the United States , in the cotton producing areas of the mid-South , and recently in California on canal banks with a history of repeated glyphosate use . The elimination of tillage takes away an important tool for managing herbicide-resistant weeds. Studies by Wright and Vargas in the San Joaquin Valley have already shown weed shifts in Roundup Ready fields, with increases in annual morning glory over conventional tillage plots . Although CT systems are most often practiced in conjunction with HTCs, conventional varieties, herbicides with different modes of action, or tillage may also be needed to manage herbicide resistant weeds. Another concern as acreages of HTCs in CT systems increase is growers’ greater reliance on post emergence herbicides applied prior to planting and during the cropping season. Herbicides such as glyphosate and carfentrazone are sometimes applied by airplane or helicopter. With more than 200 different crops grown in the San Joaquin Valley, the potential for herbicide drift and associated damage to non-target crops is very high. Even ground applications of herbicides carry with them some risk of movement to sensitive non-target crops. Herbicide drift management is an important issue when using HTCs in a CT system.Any material that blocks light will suppress or prevent the growth of weeds. Layers of organic mulches such as municipal yard waste, straw, hay, or wood chips, for example, can be used for control of annual weeds . Thicker layers provide better results. Organic mulches break down over time, and the original thickness can typically reduce by 60 percent after one year. Coarse green waste works better than dry organic residue as a mulch. Organic residue mulches are rarely used in vegetable production in California because they are so costly to obtain, cannabis storage as well as to haul and to spread. Organic mulches are used particularly in areas close to cities that have implemented programs to collect organic wastes and either to compost the material or to sell it as green waste for agricultural use. Cover crops can be grown and then undercut and left on the same beds to form organic mulch . Plants that are used to produce this type of organic mulch include various cereals, clovers, vetches, and fava beans . Two advantages of growing the mulch in place are that it is rooted and so will not blow away in windy locations and that it does not have to be transported and spread. Organic mulches provide some weed control, depending on their thickness and ability to block light, besides offering other benefits to row crops.

Thick mulches have created some difficulties in direct-seeded crop fields , but less so with transplant fields. Cover crop mulches are currently the subject of a great deal of research on crops in California’s interior valleys, but at present their use is not widespread. Small grain cover crops are being tried by some growers in CT processing tomato fields in western Fresno County. The cover crops are planted after tomato harvest in the fall, allowed to grow over the winter, and killed in late winter with a post emergence herbicide such as glyphosate. Considerable residue is present on the beds in this system , but this can also have the negative effect of interfering with post emergence herbicides, resulting in weed escapes. Such weed escapes have been observed in a tomato CT system with small grain cover crops in Fresno County .Herbicide resistance is the inherited ability of a plant to survive and reproduce following exposure to a dose of herbicide that would normally be lethal to the wild type. In a plant, resistance may occur naturally due to selection or it may be induced through such techniques as genetic engineering. Resistance may occur in plants as the result of random and infrequent mutations; there has been no evidence to date that demonstrates herbicide-induced mutation. Through selection, where the herbicide is the selection pressure, susceptible plants are killed while herbicide resistant plants survive to reproduce without competition from susceptible plants. If the herbicide is continually used, resistant plants successfully reproduce and become dominant in the population. The appearance of herbicide resistance in a population is an example of rapid weed evolution . Research on early cases of herbicide resistance showed that resistant plants were found infrequently in weed populations before use of the herbicide. In some cases this was because the resistant plant was not as fit as other plants in the population and therefore would not persist in large numbers. Recent research, however, has shown that in some cases resistance doesnot come at a cost , so resistant plants may be just as capable of surviving to reproduce as are susceptible plants. These results suggest that the frequency of resistant plants in a population might be high even before the herbicide selection pressure is applied. Herbicides are active at one or more target sites within a plant. Target sites are enzymes, proteins, or other places in the plant where herbicides bind and thereby disrupt normal plant functions. One example is an enzyme—acetolactate synthase —that is involved in making branched-chain amino acids. Some classes of herbicide bind to the enzyme, causing dysfunction of the enzyme and reducing the synthesis of certain amino acids that are necessary for protein synthesis. These ALS herbicides differ in chemical structure but are active at the same target site. Plants resistant to ALS herbicides have altered acetolactate synthase that does not bind the herbicide. Often, a resistant weed that has been selected by pressure from one herbicide will be resistant to all herbicides that act on that herbicide’s target site. When a plant expressing resistance to a herbicide also demonstrates resistance to other herbicides that target the same plant process even though the plant has not been exposed to the other herbicides, the resistance is termed cross-resistance. For example, a population of yellow starthistle in Washington State evolved resistance to Picloram, a picolinic acid herbicide. When that population was subsequently exposed to Clopyralid, another picolinic acid herbicide, it also expressed resistance. Most cases of herbicide resistance in weeds involve a single mutation or modification in some function so that the weed is resistant or cross-resistant. Rarely does a single plant express resistance to several herbicides that affect different target sites. When a weed that has been exposed to herbicides that attack different target sites expresses resistance to more than one of these herbicides, that is termed multiple resistance. Plants of rigid ryegrass in Australia have multiple resistance to a number of herbicides in the cyclohexanedione, sulfonylurea, dinitroaniline, triazine, substituted urea, and triazole classes to which the weed has not been exposed. These classes include all of the herbicides currently registered in areas where this weed is a problem. The mechanisms of multiple resistance in rigid ryegrass include changes to the herbicides’ sites of action and the detoxification of herbicides by plant enzymes called cytochrome P450 mixed-function oxidases . This family of enzymes is similar to those found in many insects resistant to insecticides.Herbicide resistance was first reported in 1970. Common groundsel in a Washington tree nursery was shown to be resistant to herbicides in the triazine chemical class. Since that time, plants of 61 species have evolved resistance to the triazine herbicides. Resistance did not evolve in plants as early as it did in insects or fungi due to fundamental differences in the life cycles and genetics of plants, insects, and fungi.

Climate change could also influence food safety; it is predicted to increase the prevalence of mycotoxin contamination, with new areas becoming at risk and current hotspots having more frequent and severe episodes . Consequently, research into multiple avenues for reducing mycotoxins in human and animal food should be a high priority. Because many diseases and pests are highly mobile and climate change will result in changes in cropping systems, global approaches to management must be deployed across national boundaries. In 17 / Molecular Plant-Microbe Interaction sterms of food security, the effects of climate change may hit developing nations hardest, as these countries will have less nimble crop improvement programs and therefore will be less able to respond to climate change within existing breeding programs. Studying pathogen evolution on a global scale will allow developed and developing countries alike to better anticipate and respond to emerging and potential threats. Investments and collaborations with developing countries are critical to secure future harvests worldwide.The foundational research described above will provide a plethora of possibilities, including immediately implementable opportunities, for improving plant health in the field. No single intervention will provide a complete solution to disease problems; rather, each intervention should be considered as a component of integrated agricultural systems aimed at providing sustainable, high quality yields. This integration will require considerable coordination between academic, government, industrial drying rack and private sector entities. Most developed countries have long traditions of translational research, for example through the Agricultural Experiment Stations in the US; however, state support for the continuum of foundational to translational research has progressively been eroded in both the US and UK as well as elsewhere.

This trend must be reversed if the beneficial impacts of foundational research on global food security are to be realized. There is a fiscal “valley of death” between innovation and effective deployment at scale. This valley should be spanned by adequate public sector funding mechanisms to support pre-competitive translational research, possibly by stimulating collaborations of academic and government labs with small and large biotech and plant breeding companies, both nationally and internationally. It is also critical to have mechanisms, such as the cooperative extension system, to engage with end-users to increase the adoption of newly available solutions. Professional societies such as the American Phytopathological Society and the British Society for Plant Pathology should continue as effective advocates for funding translational research and implementation at the local, national, and global levels. One is the handling of unprecedented amounts of data. Tools are needed to acquire, curate, query, store, and distribute vast datasets as well as integrate plant health information with other datasets, such as climate data, soil characteristics, agricultural activities, and crop performance. We are transitioning from a data poor to a data rich situation. We should be more concerned about false positives than false negatives because the new technologies will present far more potential leads than can be pursued. Consequently, intelligent algorithms based on machine learning are needed to enable decision making in the context of precision, data-driven agriculture. These computational needs are far from unique to the plant health area. Bio-informaticians and computer scientists who are tackling these challenges in other areas need to be recruited to the plant health area. Another key to successful implementation will be the two-way knowledge exchange and partnerships with all constituents in the food production and distribution chain, particularly growers, extension personnel, pest control advisors, and breeding company staff as well as consumer advocates and policy decision makers.

Translational research to enhance food security in developing countries was given specific consideration at the workshop. Developing countries face additional substantial challenges compared to developed countries. The US and UK plant health research communities have been engaged in mutually reinforcing collaborations and cultivation of the rising generation of plant health researchers as part of programs funded by USAID and DIFD as well as foundations such as the Bill and Melinda Gates Foundation and the CGIAR Consortium. This has resulted in significant cross-fertilization of ideas and exchanges of expertise and experience. In an era in which population growth, climate change, and emerging diseases demand a more global focus, models for integrating developing country partners as effective and equal collaborators are essential. These should be developed by scrutinizing extant and past collaborations for effectiveness, capacity-building of national systems, outcomes, and impact. Strategies for focusing, integrating, and evolving such efforts are necessary to leverage the collective expertise and resources. Such efforts should result in a more responsive, integrated and proactive global community. Although many developing countries in the tropics and subtropics have enhanced their human and infrastructural capacities over the past few decades, these efforts must be improved and accelerated because current capacity is inadequate to tackle the environmental, agroecological, socioeconomic and biodiversity complexities faced by agricultural systems in these regions. As an example, the USAID-supported CRSP and Innovation Lab efforts have trained ~3,500 developing country MSc and PhD scientists in the past 30 years. However, there have been limited efforts to evaluate and leverage this investment by tracking the alumni pool and supporting them in their home countries. The opportunity to access this quiescent expertise should be exploited to integrate these and a rising pool of researchers and other actors in collaborative efforts, and to generate a more global enterprise. Broader collaborations to recruit and support researchers in developing countries would be a major, feasible consequential action.A key component for successful implementation of disease management strategies will be knowledge exchange with farmers. Farmers need to be engaged to ensure that they are aware of innovative approaches and that there is buy-in and adoption. This has to be a two-way process so that researchers are aware of the farmers’ needs and priorities that lead to co-designing of solutions. There are huge opportunities to use information technology to engage with farmers. This is not a substitute for face-to-face meetings, but is complementary to them and keeps contacts active. With resource constraints to reach a very large number of smallholder farmers with poor transport links and few extension personnel, there are opportunities to use mobile phones to crowd source information about plant health priorities and collect feedback on what works and does not work; the CABI Plant wise initiative might serve as a model . Sharing information about potential solutions will target plant health interventions to hotspots where the problems are the most serious. Creation and support of village-based advisers as well as farmer to farmer networks are important because farmers are more convinced when they see another farmer successfully using an approach than if an outsider tells them about it.

GM crops have the potential to make major contributions to food security. In the area of biotic challenges to plant health, they provide means to facilitate control of pests and pathogens for which current control options are inadequate, while greatly reducing the use of chemical protectants and thus reducing the environmental impact of agriculture . GM crops will be increasingly important to prevent a crisis in a more highly regulated pesticide/fungicide world. The commercialization of GM crops has so far been limited to a few crops, focusing primarily on herbicide or insect resistance . GM trait development and deployment should be expanded for traits that directly benefit the consumer and that provide additional sustainability traits, including disease resistance. A more rigorous science based system of risk assessment and an accordingly adjusted regulatory system are needed, to maintain stringent standards where appropriate while lowering the extreme economic cost of making benign and societally beneficial GM crop traits available to farmers and consumers. The ultimate release of GM crops with new traits will depend on advances in research and development, changes in public perception, regulatory requirements, and health and environmental assessments . Adoption will also be facilitated by detailed cost-benefit analyses of economic and societal factors. The opposition to GM organisms is mostly an ideological issue , while consumer antipathy is largely due to a lack of understanding of crop improvement methods and zero tolerance of perceived risk. It is crucial to communicate better with the public and decision makers so as to allay poorly founded concerns about GM technology and to counter emotion-based opposition. It must be effectively conveyed that food crops are the result of breeding that involves a suite of technologies including chemicals, radiation, and molecular tools as well as conventional cross breeding. Transfer of R genes between closely related plants may offer a precedent-establishing an example of GM utilization that a broad sector of the public sees to be low-risk, beneficial, and also achievable but with far more costs, constraints, dry racks for weed and reduced efficacy if done by traditional plant breeding. Genome editing that results in transgene-free genetically improved plants, with useful DNA inserted or deleterious DNA deleted at specific genomic locations, could help promote consumer acceptance of GM crops . Efforts to foster communication among consumers, policy makers, industry representatives, and researchers should be continued so that GM crops benefitting all parties can be more readily brought into use. The potential benefits of GM crops to human health and environmental health need to be communicated more effectively to the public. Novel approaches toward changing public opinion could be deployed through collaborations between social scientists and those engaged in crop improvement. Considerable diplomacy is required because the opposition to GMOs is well funded and organized and resonates with public concerns about the environment and food safety. International collaborations could help provide evidence-based counter-arguments and examples to support a balanced and science-based regulatory policy that would benefit the public, researchers, and the agricultural industry. Britain’s exit from the EU may provide an opportunity for development of its own science-based regulatory framework that is more consistent with those in the US, Australia, and Canada. It is important that farmers and consumers continue to have options for both GM and non-GM crops and food, at a reasonable price. Issues surrounding intellectual property protection of crop cultivars apply to both GM and non-GM crop cultivars. Public sector crop improvement programs could make multiple contributions and may help allay various concerns . It may become essential for governments to enact policies so that publicly beneficial GM crop varieties can become more widely available.There is great potential to increase food production, reduce the environmental impacts of agriculture and enhance global food security, if adequate investments are made. However, this is a time sensitive issue; climate change will likely cause plant health to worsen, reducing food security, and leading to civil strife and mass migrations. Both short term and sustained longer term support for plant health research is necessary to enable both immediate translational implementations and foundational research to address major challenges for which there are currently no solutions. Some funding resources should be designed for flexibility to allow rapid responses to plant health crises when and where they inevitably arise. Global monitoring of the health of major crops modelled on that being conducted for stem rust should be implemented to minimize the vulnerability of the food supply to biotic challenges. There are a broad variety of options for intervention strategies that maximize the evolutionary hurdles that pathogens, pests, or weeds must overcome before they evade control measures. However, development of alternative pest, weed, and disease management strategies is currently not happening fast enough to fill the gap left by losses of chemical protectants due to legislation and evolution of insensitivity. Current and imminent technologies could provide flexible interventions and reduce response times. Control strategies need to evolve at least as fast as the pathogen, pest, or weed; if they do not, then it is an ineffective use of time and resources to pursue those strategies. After further development, genome editing-based allelic replacement and gene insertion hold great promise for accelerating introduction of disease and pest resistance genes into elite cultivars that will be more durable. There are multiple barriers to rapid and effective implementations. These include not only a lack of detailed foundational knowledge but also restrictions on germplasm exchange, legal and financial obstacles to deployment of GM crops, uncertainties surrounding IP and regulatory status of genome editing technologies, a dearth of plant breeders to exploit the wealth of new knowledge and technologies, and inadequate data handling capabilities. The Green Revolution of the last century was largely based on the development of crop cultivars that responded well in terms of yield to high levels of inputs that included fertilizers and control chemicals.

Numerous reviews and reports have documented the global challenges to feeding the growing human population . More people living longer, healthier, more affluent lives will put increased pressure on food production systems. Climate change is predicted to further exacerbate challenges to food production. Furthermore, insufficient food is a major causal factor inciting civil strife. The large investments being made in human health will be of little benefit if people are undernourished. Sustainable increased food production requires both technical and organizational advances. Major, sustained investments in foundational and translational agricultural research are needed. Pathogens, pests, and weeds cause large pre- and post-harvest losses, while beneficial symbionts provide the opportunity to improve yield stability, quantity, and quality . Support for this area of agricultural research is therefore both justified and urgent. Forty researchers from the United States and United Kingdom gathered at the British Embassy in Washington, D.C. for two days in September, 2016, to explore research opportunities focused on the understanding of interactions of plants with pathogens, pests, and weeds as well as with symbionts and other beneficial organisms in the phytobiome. Participants discussed the potential of foundational knowledge generated by such research to enhance the health of plants economically important for agriculture, pipp horticulture racks cost horticulture or forestry in the United Kingdom, United States, and globally.

Research to understand and ameliorate the emergence and spread of resistance of pathogens, pests or weeds to control measures was discussed. In addition, the workshop considered the potential transformative impacts of new technologies, such as high throughput sequencing, synthetic biology, genome editing, and cryo-electron microscopy , on plant health research. This white paper describes the product of these deliberations. Breeding crops for resistance to pests and diseases has tended to be a lower priority than breeding for yield and quality when control chemicals have been available. However, the availability such chemical interventions as well as their efficacies are now becoming limited due to changes in legislation and the evolution of pathogen/pest resistance to control chemicals. Consequently, established cropping systems are highly vulnerable to disruption by adapted pests, weeds, and diseases and there is a pressing need for new interventions . For example, management of insect pests has become much more challenging after recent restrictions on neonicotinoid and organophosphate insecticides in the UK. There are significant problems with herbicide resistant weeds such as black grass and fungicide resistant pathogens such as Septoria leaf blotch. Roundup resistant weeds have emerged in the United States, challenging soil-conservation measures dependent on minimum tillage. Similarly, new strategies for nematode control become essential as soilacting nematicides are phased out.

Durable disease resistance to pests or pathogens can be defined as “resistance that has remained effective over long periods of widespread agricultural use” . This has been a continual and often elusive goal in many disease control programs for decades; however, we now have opportunities to provide more durable resistance based on foundational knowledge and recent technological advances. Long-standing questions as to the molecular and genetic basis of specificity between hosts and pathogens/pests are being answered in increasing detail . There is still much more to be discovered as to how the plant immune system functions and how it can be predictably deployed with minimal side effects on yield and other important agronomic traits. Nevertheless, there is now sufficient foundational knowledge to develop and implement strategies that are likely to provide durable control of pathogens, pests, and weeds as well as to improve yields and yield stability . Although less is known, advances are also being made in the understanding of beneficial biotic interactions, with concomitant opportunities for improving plant health . Many of the recent advances have been enabled by technological innovations and further high impact developments are imminent. In particular, the ever-decreasing cost and increasing output of DNA 5 / Molecular Plant-Microbe Interactions sequencing technologies now enables the genome sequencing of multiple genotypes of many model and non-model plants as well as microbes, pests, weeds and whole communities associated with plants or in soils. Combined with increasing computational resources, these sequences are allowing the characterization of genomic variation, gene expression patterns, the identification of candidate genes for resistance, and pathogen population genetics.

Proteomics, functional screens, ultra-high resolution light microscopy, and cryo-EM are revealing the molecular events involved in resistance and susceptibility . Synthetic biology provides multiple opportunities and approaches for redesigning plant responses, which may allow for more precise control of the plant’s ability to sense and respond to pathogens . Genome editing technologies are greatly enhancing functional investigations and deployment of useful genes . Both synthetic biology and genome editing also provide the opportunity for generating useful genetic variation in numerous crop plants . This workshop report considers the opportunities for advances in foundational research and then the issues involved in translating this knowledge to enhance plant health, particularly in less developed countries.As defined above, durable resistance is an empirical, retrospective attribute that has no single inherent basis. Pathogen and pest populations are highly variable and are evolutionarily driven to overcome plant resistance. Consequently, predicting which new disease resistance genes may be durable is challenging. While some resistance genes are rapidly rendered ineffective by changes in the pathogen, others have proved to be durable. for example, Rps1k in soybean , Xa21 in rice , and H1 in potato for resistance to cyst nematode . Knowledge is needed to implement strategies that maximize evolutionary hurdles for the pathogen to become virulent. Therefore, identification of resistance genes that may prove to be durable requires a comprehensive understanding of pathogen biology, population structure, epidemiology, mechanisms of genetic and epigenetic variation as well as knowledge of plant immune system recognition and signaling to provide predictive outcomes upon manipulation. While it is difficult to predict durability of disease resistance, it is easier to predict, and therefore avoid or minimize, a likely lack of durability based on analyses of pathogen populations. Breeding programs would benefit from avoiding or minimizing the use of narrow-spectrum R genes that are already ineffective against local pathogen races. It is therefore important to define the pathogen/pest component recognized by any to-be-deployed R gene to avoid “pathogen-blind” resistance breeding . Analyzing the durability of resistance genes, including those incorporated into elite germplasm, at the center of pathogen diversity can help predict durability. For R genes that target effectors and for other classes of potential resistance genes that may target other aspects of pathogen biology, it is essential to determine the extent to which the pathogen population is able to evade the targeting of pathogen component and to suppress the defense mechanisms associated with R genes. Even if an R gene is identified that the pathogen cannot be observed to evade or suppress, it is desirable to examine the ability of the pathogen to acquire new genetic or epigenetic variations that enable the pathogen to overcome resistance. R genes that recognize the most conserved and presumably indispensable effectors should be prioritized if it can be ascertained that recognition of such effectors is not masked by other effectors or abrogated by second site genetic variation in the pathogen. Once effective disease R genes have been identified, they should not be deployed individually because widespread plantings will select for variants capable of overcoming single R genes. One approach is to pyramid different R genes; if possible representing different classes , should be pyramided together. This is beginning to occur e.g. soybean–Phytophthora sojae , rice-bacterial blight , pipp racking system and potato-P. infestans interactions. Challenges to implementing this obvious strategy include the efficient identification of sufficient numbers of R genes and ensuring preservation of gene pyramids during breeding that involves crosses. Ideally, pyramids comprised of different combinations of R genes should be deployed in order to diversify selection on the pathogen population in space and time. Furthermore, it is necessary to monitor for any breakdown of individual R genes so that new stacks can be assembled for effective disease control. The use of genome editing to generate stacks of R genes at single chromosomal locations will greatly facilitate the stable deployment of multiple R genes; however, while generating loss of function alleles is now facile, techniques for allele replacements and gene insertions using genome editing need improvement .

Additional layers of disease resistance can also be combined with stacks of PRR and NLR genes. For example, endogenous chemistries may be used to boost signaling, promote the association of beneficial microbes that confer induced systemic resistance, and restrict invading pathogens/pests . There may also be opportunities to amplify responses to help create more durable resistance. Plants employ positive amplification loops mediated in part by membrane proteins that associate with PRRs and endogenous ligand/receptor complexes . The extent to which these associated proteins are limiting and could be manipulated to boost resistance signaling outputs is not known. However, lab experiments with model plants overexpressing some of these components have yielded promising results in priming for stronger immune responses . Both mechanistic studies with tractable model pathosystems and translational trials with crops are needed to determine how well this approach will work. It will also be informative to test whether disease resistance responses mediated by PRR and NLR genes can be reprogramed or amplified using synthetic transcription factors based on engineered TAL effectors or CRISPR/Cas9-based transcriptional activators. Conversely, engineering suppressors of negative immune regulators could also be beneficial for tipping the balance towards plant resistance .Plant defense against pathogens is activated upon pathogen/pest recognition, most commonly via cell-surface PRRs that recognize apoplastic pathogen-derived PAMPs or via intracellular NLR receptors that directly or indirectly recognize pathogen/pest effectors delivered into host cells . Plant breeders have long recruited diverse R genes, which typically encode NLRs, although some encode PRRs or other types of proteins. Elevating disease resistance of crops requires the identification and recruitment of large numbers of diverse resistance genes. This diversity can have multiple sources. Wild relatives of crops are potentially abundant sources of R genes. Most plants carry 100s of NLR-encoding genes that exhibit extensive diversity . Using sequence capture to enrich for NLR genes prior to genome sequencing enables cost-effective interrogation of sequence diversity . Combined with genetic analysis, this can greatly accelerate discovery and recruitment of new recognition specificities . Species outside the primary and secondary gene pools are also potential sources of NLR genes. The discovery of widespread NLR gene pairs, one member of which carries an integrated domain that mimics a host component targeted by pathogen/pest effectors, and the observation that such gene pairs often work when transformed into another plant family, suggests that many such pairs may provide resistance when transferred between distantly related taxa . For example, rice is famously resistant to all rusts; perhaps some of its gene pairs with integrated domains would confer rust resistance if transferred into wheat. The presence of paired NLRs, one with an integrated effector decoy domain , has raised the prospect of replacing one integrated domain with another. For example, removing the Arabidopsis RRS1 WRKY domain and replacing it with another domain targeted by other effectors may be fruitful. However, since such domains are likely to have a role in maintaining the receptor complex in the inactive state prior to interaction with an effector, substitution may perturb intramolecular interactions and result in constitutive activation of defense. It is therefore necessary to better understand the functioning of a diverse set of such NLR pairs and to screen to find pairs that are amenable to substitution of integrated effector decoy domains while retaining function.Engineered R genes have been a long-standing aspiration of researchers in the field and could be a useful source of additional variation. We have not currently reached the point where novel NLRs can be designed to recognize any effector. To be able to design novel recognition capabilities, we need better understanding of the basic mechanisms of NLR protein function. This will be facilitated by structural insights gained by recent advances in biophysical techniques such as cryo-EM . NLRs have two important functions: they must remain “off” and only turn “on” in the presence of a cognate effector. One challenge is that modifications often result in constitutive activity of an engineered NLR; so it is crucial to understand intra-protein domain interactions that inhibit NLR activation prior to effector recognition.