Sixty eight CA colleges were represented in our signal data, though the top 20 accounted for 783 of tweets. Individual colleges exhibited high variation in the proportion of tweets corresponding to each smoking product assessed . Out of the top twenty colleges by tweet volume, the distribution of tobacco-related tweets ranged from 26.1% for CSU Long Beach to 62.2% for CSU San Jose [median [M] = 43.1%, standard deviation [SD] = 10.7%]. Vaping-related tweets were detected from eighteen of these twenty colleges, ranging from 1.6% for CSU Northridge to 21.1% for CSU Fullerton . Finally, the distribution of marijuana related tweets ranged from 28.6% for CSU San Marcos to 61.9% for the University of Southern California .Positive sentiment about tobacco, marijuana, and vaping was detected from 736 tweets. Out of the top twenty colleges by tweet volume, positive sentiment ranged from 55.0% for CSU Long Beach to 95.8% for CSU Los Angeles . When computed as a proportion of all tweets with smoking-related behavior, including neutral tweets without clear user sentiment, positive sentiment ranged from 47.4% for both CSU Fullerton to 80.9% for CSU Santa Barbara . With the exception of CSU Long Beach and CSU Fullerton , all colleges had at least 50% positive sentiment from tweets about smoking . Across product categories, positive sentiment varied with 58.2% for vaping, 66.1% for tobacco, and 70.7% for marijuana.

When calculated as a proportion of all tweets, positive sentiment was 63.9% for vaping, 70.6% for tobacco, and 85.5% for marijuana. The majority of tweets from any product category exhibited either positive or negative sentiment, with only 8.9% of tweets about vaping, 6.3% about tobacco, and 17.3% about marijuana having neutral sentiment. Therefore, while the majority of tweets about any product exhibited either positive or negative sentiment, the data suggests that tweets about tobacco or vaping were much more opinionated than marijuana, which had the highest proportion of neutral sentiment tweets. There were also 502 tweets denoting first-person product use or second-hand observation of another person’s use of smoking products. These reports also ranged by product type, with 40.8% for marijuana, 47.4% for tobacco, and 10.0% for vaping. Out of all tweets, 40.3% of those about marijuana indicated first-person use or second-hand observation, whereas this applied to 48.6% of tobacco-related tweets and 63.3% of vaping-related tweets. Across the top twenty colleges by tweet volume, first-person smoking product use or second-hand observation of another product user ranged from 31.8% from UC Berkeley to 73.7% for CSU San Jose . As the UC system and CSU Fullerton had smoke-free policies in 2015, and the remaining 22 schools in the CSU system did not have smoke-free policies, these tweets were assessed for evidence relating to campus policy violation.

Out of 486 tweets in 2015 indicating first-person smoking or second-hand observation of smoking, 146 were from schools with smoke-free policies. It should be noted that the content of these 146 tweets indicated smoking behavior on campus . As we captured 11 schools with smoke-free policies in 2015 and 19 schools without smoke-free policies , the number of these tweets per school was approximately the same among schools with smokefree policies and those without smokefree policies , potentially indicating a muted effect regarding the implementation of smoke-free policies, at that time, on these college campus populations and their compliance behaviors. Geospatial analysis revealed a distribution of tweets that approximately followed California’s population distribution, with a cluster in the San Francisco Bay Area and a cluster in Southern California, which was dominated by the Los Angeles Basin. However, comparatively fewer smoking-related tweets were captured from colleges in California’s Central Valley region. This distribution may have also been impacted by a low volume of tweets collected and sample bias for higher-population demographic areas based on the data collection process.Based on our use of tweets specifically geolocated for CA 4- year universities combined with a data filtering process to isolate tweets containing smoking-related keywords, 7,342 tweets were obtained for analysis that discussed smoking and also originated from California universities between 2015 and 2019. Within this corpus of social media messages, rates for use of the term “weed” decreased over time, changing from 28% in 2015 to 7% in 2019. Other commonly used smoking-related terms did not exhibit a percentage drop of this magnitude. The mechanisms underscoring the observed decrease in social media messages with this keyword are not clear but may result from evolving word choices to describe marijuana, decreased use of marijuana on CA college campuses, social inhibition of posting marijuana related public messages on Twitter, or some combination thereof. Further, it is unclear how passage of legalized adult-use cannabis Proposition 64 may have impacted these conversations, attitudes, and behaviors, particularly as despite state legalization, some college-aged students may not be of legal age and campus smoke free policies still restrict their use. Manual review uncovered 1,089 tweets explicitly related to smoking behavior and posted within the boundaries of California 4-year universities, with the majority of tweets expressing positive sentiment about smoking products and behavior. Five-hundredand-two of these tweets reported first-person use or secondhand observation of another person’s smoking behavior, with 146 tweets reporting possible violations of smoke- or tobaccofree campus policies that were clearly in place from 2015 but were also in the process of being fully implemented. These tweets indicate early lack of compliance to smoke-free campus policy implementation as self-reported by social media users. For campuses where policies were not in place, rolling benches tweets also reflect general positive sentiment about smoking and reports of smoking behavior, indicating possible barriers to enacting campus smokefree policies that would occur in 2017, when more smoke free campus policies across the California State University system were enacted.

These results provide early indications that smokefree campus policy implementation requires continued attention and sufficient resources to ensure appropriate health promotion, education on policy requirements, and policy enforcement measures in college communities. Overall, our analysis found a higher number of tweets in our corpus identified for tobacco and marijuana products, with comparatively fewer for vaping products geolocated for California university campuses. The majority of geolocated data collected during this study originated in 2015, which may explain the overemphasis on tobacco and marijuana Twitter conversations as vaping products were rising in popularity. Additionally, national debate about marijuana legalization occurred during this time frame, though was not legalized in California for adult recreational use until 2016 and licensure of cannabis retailers was permitted in 2018. As previously stated, national and state discussions relating to marijuana legalization may have influenced the relative social acceptability and volume of marijuana-related Twitter conversations among campus populations. Tweets about vaping had the highest proportion containing first-hand accounts of use or other persons engaged in product use and behavior. The increasing popularity of vaping products throughout the study time period, especially among the college aged population, may partly explain why college students posted about themselves or other people using vaping products in this context, despite having an overall lower volume than other smoking products . The increasing use of more discreet forms of vaping, particularly JUUL , may also have had an impact on social media engagement about vaping behavior, though more research is needed. Also, the associated health risks of vaping were relatively unknown during the study period, though the outbreak of EVALI in 2019 may have generated more attention and possible concern among users about potential health risks of vaping, though these conversations were not detected in this study . Importantly, most tweets that included conversations about tobacco products and behavior expressed positive sentiment. Though unclear from these preliminary results, the influence of “party culture” on college campuses, the opportunities to experiment and initiate with forms of substance abuse behavior, and the immediacy of pleasure from substance use may outweigh concerns, including those relating to long-term health risks, among college students in the United States as observed in this user sentiment . Interestingly, though marijuana tweets exhibited the highest proportion of positive tweets, they also exhibited the highest proportion of neutral tweets and the lowest proportion of tweets with negative sentiment. This finding may suggest relative homogeneity regarding marijuana attitudes, possibly as a consequence of debate regarding marijuana legalization during this time period. As the majority of all sentiment-containing tweets were positive, results from this study may suggest that outreach efforts to raise awareness about the health risks of tobacco and ATPs on college campuses may have limited resonance. However, these preliminary data also suggest discrepancies in sentiment between tobacco products, as well as differences in sentiment toward smoking across California universities. Therefore, policymakers and health promotion advocates should consider tailoring policy implementation and health communication for specific college students in California based upon evidence of latent receptivity toward anti-tobacco approaches and existing community sentiment toward smoking behaviors as detected in this study. Furthermore, future studies should more explicitly assess user reaction and sentiment to debate, communication and implementation of state-level policies that both legalize and restrict use of tobacco and smoking products, as well as how these macro policies interact with campus-specific smoke free policy perceptions for different tobacco, marijuana, and e-cigarette product categories. For example, actionable insights based on preliminary findings from this study indicate that users generally express more positive sentiment about tobacco use and smoking behavior. This may necessitate the use of campus-based health promotion and education activities that focus on reducing appeal of these products, such as restricting any form of marketing and promotion in or near campus communities. This should be coupled with broader state legislation to further restrict marketing and promotion that targets young adults and college communities. Further, perceived penalties for violating smoke- and tobacco-free campus policies may also impact compliance based on socioeconomic factors. For example, one user from UC Riverside tweeted, “other places might be more lenient, but UCs have a shitty tobacco and smoking policy and I got caught and now it’s over” [emphasis added to denote correction of misspelling]. Hence, data-driven approaches to assess receptivity and the impact enforcement has on smoking behavior should be built into smoke free program implementation iteratively. Importantly, the breakdown of smoking-related tweets between numerous college campuses as detected in this study presents challenges with respect to whether the distribution of tweet characteristics accurately reflects distributions in the underlying college populations. Nevertheless, similar work has been conducted which presents correlational evidence between characteristics of geospatially-specific social media posts and characteristics of populations in those areas . Furthermore, as over half of college students in California are between the ages of 18 and 24 , academic and demographic distributions of tobacco consumption within colleges may be the consequence of socioeconomic disparities in childhood and potential effects of these disparities on attitudes about smoking among parents, high schools, and/or neighborhoods that warrant further study .

Indirect access to RMDs through third party adults, such as older friends, relatives, and street dealers, could also increase the availability of and accessibility to marijuana among adolescents. To date, only a few studies have evaluated the association between RMDs’ availability and crime rates using aggregate data at neighborhood level . No studies have focused on RMDs’ proximity and density and marijuana use outcomes in adolescent population. Another major concern on RMDs is their point-of-sale marketing activities, particularly those targeting adolescents. Dispensary storefronts have become the most commonly reported source of advertising among adolescents and adults after recreational marijuana commercialization. In a study conducted by our team in 2018, we observed that nearly three quarters of RMDs in school neighborhoods had marketing activities that were appealing to children. Informed by strong scientific evidence from tobacco literature that marketing activities promote initiation and use of tobacco among adolescents, the American Academy of Pediatrics policy statement “strongly recommends strict enforcement of rules and regulations that limit access and marketing and advertising to youth”. However, empirical evidence regarding the associations between RMDs’ marketing activities and adolescents’ marijuana use outcomes is still lacking. The goal of this study was to provide the first data point on the relationships of proximity and child-appealing marketing activities of RMDs with adolescents’ marijuana use.

We had three hypotheses. First, the proximity of RMDs is positively associated with the likelihood of adolescents’ marijuana use . Second, the presence of child-appealing marketing activities in RMDs is positively associated with the likelihood of adolescents’ marijuana use . Third, the associations between RMDs’ child-appealing marketing activities and adolescents’ marijuana use depend upon the proximity of RMDs . The study findings are expected to inform prevention and regulatory strategies that aim to protect adolescents from the negative influences of RMDs in school neighborhoods.The California Student Tobacco Survey is a repeated cross-sectional and state representative school survey among California middle and high school students . It has been administered biennially since 2001-2 with the purpose of monitoring adolescents’ tobacco use behaviors and perceptions. This study used CSTS 2017-8, which was administered between September 2017 and June 2018. CSTS 2017-8 used a two-stage cluster random sampling approach, where public and nonsectarian schools were first randomly selected from regions and then classrooms in selected schools were invited to participate. All the participating schools were invited to survey all the students in a particular grade. If a school chose not to survey all the students in a grade, five classrooms were randomly selected to participate. Of the 623 schools invited, 359 schools fielded the survey. The final data excluded 26 schools with response rates below certain thresholds, resulting in 151,404 adolescents in 333 schools in CSTS 2017-8.

The survey was administered online in both English and Spanish and completed between 15 and 25 minutes. Details about CSTS 2017-8 sampling approaches and survey implementation have been reported elsewhere. Because California started legal sales of marijuana in RMDs on January 1st, 2018, RMDs presumably had no impacts on adolescents’ reporting on past-month marijuana use before February 2018. Therefore, we restricted our analysis to the 244 school that completed the survey on or after February 1st, 2018. There were 103,914 adolescents surveyed in these 244 schools. After removing adolescents with incomplete information on demographics and marijuana use, the final study sample included 96,011 adolescents. It accounted for 73% of the schools and 67% of the adolescents in the complete CSTS 2017-8 sample.Between June and September 2018, our team collected data on locations and marketing activities of RMDs that had the closest proximity to the CSTS 2017-8 participating schools. Six trained fieldworkers obtained a list of marijuana dispensaries closest to the participating schools from crowd sourced online websites, including Weedmaps, Wheresweed, Leafly, and Yelp. Fieldworkers then made calls to verify dispensaries’ street addresses, operation status, and dispensary classification . We excluded dispensaries that were delivery only, inactive, or classified as medical marijuana dispensaries . When the dispensary closest to a school was excluded for the above reasons, the second closest dispensary was verified until an active RMD with a storefront was identified. We developed and pilot-tested a Web/smartphone-based surveillance tool for dispensary auditing, namely “Standardized Marijuana Dispensary Assessment-Children Focused ”. SMDA-CF assessed physical, economic, and marketing characteristics of RMDs, with special attention to marketing activities appealing to children. SMDA-CF items had moderate to high reliability overall, with a median kappa score of 0.8. After receiving training, fieldworkers audited the call-verified RMDs in teams of two to improve the reliability of data collection. Each visit to an RMD took 10-15 minutes on average.The primary predictors of interest included 1) the proximity of a school to the nearest RMD and 2) the presence of child-appealing marketing activities in the nearest RMD. We also control for the density of RMDs in school neighborhoods. The proximity between a school and its nearest RMD was computed with straight-line method using ArcGIS Version 10.5. In the main analysis, three proximity indicators were assessed: ≤1 mile, 1-3 miles, and > 3 miles. The 1- and 3-mile cutoffs were chosen based on common practices in tobacco and alcohol literature and geographic distribution of the RMDs around schools. The 3-mile buffer represents a distance that is easily reachable by bicycles, vehicles, and public transportation. In addition, the 1- and 3-mile cutoffs provided sufficient sample sizes of schools and adolescents in each cell for statistical analysis. In the sensitivity analysis, we also tested the sensitivity of results to different cutoffs, including 2-, 4-, and 5-mile. Child-appealing marketing activities in SMDA-CF were defined as products, packages, paraphernalia, vertical grow and advertisements that are “characterized by promotional characters , shaped like commercially sold products usually consumed by children , or using bright colors or bubble-like fonts ”. Separate binary indicators were created to indicate the presence of child-appealing products/packages, paraphernalia, and advertisements in RMDs.

To account for the influences of other RMDs that were more distantly located yet close enough to schools, we also considered the density of RMDs in school neighborhoods. It was measured as the weighted number of RMDs within a 3-mile buffer of a school, with weight .7 assigned to the number of RMDs within the 1-mile buffer and weight .3 assigned to the number of RMDs within the 1-3 mile buffer. To avoid double counting and collinearity, the computation excluded the audited RMD itself, which had the closet proximity to the school.Descriptive statistics were computed for individual, RMD, school, and census tract variables. To examine the associations between the proximity and marketing activities of RMDs with adolescents’ marijuana use, a series of multilevel logistic regressions were conducted with the first level of students nested in the second level of schools. All the statistical analyses were conducted using R packages. The three marijuana use outcomes were analyzed separately. Current use was analyzed among all the adolescents included in this study, heavy use was analyzed among current users who used marijuana in the past 30 days, and curiosity was analyzed among never users who never used marijuana before. We took a stepwise approach to test the three hypotheses. We first included proximity indicators in the model , then added indicators of marketing activities simultaneously with proximity indicators , and finally added interaction terms between the proximity and marketing activities along with indicators of proximity and marketing activities . All the regressions controlled for RMD density and individual, school, and census tract characteristics. Sampling weights were applied to all the analyses on adolescents.This study was the first attempt to assess the relationships between objectively measured recreational marijuana retail environments and adolescents’ marijuana use. We audited the locations and point-of-sale marketing activities of RMDs in school neighborhoods and merged auditing data with school survey data on a large sample of adolescents in California. We paid particular attention to child-appealing marketing activities, which were presumably more influential to adolescents than general marketing activities. Instead of aggregating data at zip code or census tract level, we examined individual-level outcomes and simultaneously accounted for between- and within-school variations. Our first hypothesis that a closer proximity of RMDs is associated with a greater likelihood of adolescents’ marijuana use was not supported by the findings. In fact, a closer proximity was found to be associated with lower likelihoods of some outcomes in some model specifications in sensitivity analysis. Although no similar studies on RMDs can be used to compare to our findings, existing evidence on medical marijuana dispensaries did show mixed relationships between dispensaries’ proximity and marijuana use among adolescents. Whether and how the proximity of RMDs in school neighborhoods is associated with adolescents’ marijuana use outcomes deserve further research. Our second hypothesis that the presence of child-appealing marketing activities in RMDs is associated with a greater likelihood of adolescents’ marijuana use was not supported by the findings, either. However, when we examined the third hypothesis , we did find some evidence that child-appealing products, packages, and paraphernalia in RMDs in very close proximity to schools might be associated with a greater odds of current use or heavy use. It is likely that these items were resold or freely distributed to adolescents by third party adults, such as older friends,relatives, street dealers, who resided or worked in school neighborhoods. The interaction effects of RMDs’ proximity and marketing activities were not found on child-appealing advertisements. One plausible explanation is that nearly all RMDs we audited complied with age restrictions by ID check. Adolescents therefore had little chance to see advertisements inside of the RMDs, which could not be taken out by third party adults. It should be noted that the findings on interaction effects were sensitive to the selection of proximity cutoffs and model specifications. This is why we considered the strength of the evidence on interaction effects to be only moderate. Future research is strongly encouraged to add more data points. The findings have policy implications. If the impacts of point-of-sale child-appealing marketing activities depend upon the proximity of RMDs to schools, stronger surveillance may be needed to monitor marijuana-related perceptions and behaviors in schools that have RMDs located near to them. Even though almost all states with legal sales of recreational marijuana prohibit products and advertisements specifically targeting children, our dispensary auditing data demonstrated a wide presence of these prohibited items in school neighborhoods. Actions should be taken to reduce child-appealing marketing activities and prevent adolescents from potential exposure. This study has limitations. First, the cross-sectional examination of associations should not be interpreted as causality. Second, the study sample was restricted to 73% of the CSTS 2017-8 schools that completed the survey on or after February 1st, 2018. The generalizability of the findings to the entire California may be a concern. Third, we audited RMDs after the CSTS 2017-8 was completed in order to have an accurate and complete list of surveyed schools and conduct auditing in a cost-efficient manner. To what extent our observations on RMDs applied to the time when the schools were actually surveyed was unknown. Fourth, the marketing activity predictors were indicators of presence instead of continuous quantity measures due to feasibility considerations in fieldwork.

The more options individual rice growers have to control weeds, the less severe will be the adverse impact of the regulations on grower profits. However, environmental activists, regulators, and the courts view a wide range of available chemicals that have varied environmental risks as undesirable. In recent years, widespread adoption of GM crops such as herbicide-tolerant soybeans and canola and pest-resistant [e.g., Bacillus thuringiensis ] corn and cotton has provided growers with new production alternatives that reduce chemical usage. But the new technologies are not without controversy as some consumers have expressed resistance to purchasing foods made from transgenic materials. In California, environmental groups and organic-rice farmers are also opposed to any cultivation of GM rice in the state. This report examines these issues in the context of California rice production. In particular, we estimate the potential economic impacts of one alternative weed-management strategy, namely, cultivation of HT transgenic rice. Potential grower benefits, measured by net returns over operating costs per acre of first-year adoption, are calculated using a partial-budgeting approach3 based on a representative cost structure. Sensitivity analysis is then utilized to account for the heterogeneity in growing conditions across the state as well as uncertainty regarding yields, technology fees, and government assessments on transgenic seed.

To augment these results, the partial-budgeting approach is applied to data from an independent three-year field trial designed to evaluate alternative herbicide regimes, including one transgenic rice cultivar. Potential environmental benefits of the technology are also discussed. The report proceeds as follows: The next section reviews available information on transgenic rice and describes the potential impacts of grower adoption in California, including market-acceptance issues. We then describe our methodology and present results for a typical Californiarice producer. Next, a range of estimated impacts based on alternative yield differentials and technology fees is presented, followed by a Monte Carlo analysis. The subsequent section provides an economic analysis corresponding to the three-year field study. Environmental regulations for rice production and potential environmental impacts of the new technology are then evaluated, and the final section discusses the limitations of our analysis and concludes.In 2003, California rice growers harvested 495,000 acres of rice, which yielded 39.6 million hundredweight , constituting about 16.5 percent of acreage and 20 percent of total rice production in the United States . The vast majority of California’s rice is of the medium-grain variety while the southern U.S. states primarily produce long-grain varieties. Over the last several years, there has been no discernible trend in California acreage planted or in total volume of production. World rice prices, on average, have been on a decreasing trend4 and, simultaneously, California growers have faced increasing production costs, especially in the area of weed management [U.S. Department of Agriculture , Economic Research Service 2002].

The top three weeds in California rice production are barnyardgrass, watergrass, and sprangletop while various other broadleaf plants, grasses, sedges, and cattails affect production [Gianessi et al.; California Rice Commission 2003]. Interestingly, red rice, a weed of the same genus and species as domesticated rice, is not a major problem in California despite being the number one weed in Louisiana, Arkansas, and Missouri . The combined effect of lower prices and higher production costs has put downward pressure on California rice grower returns and led to considerable research efforts to improve overall weed management through cultural, chemical, and other management means. In California, both chemical and non-chemical techniques are used for weed control . Recently, however, California rice production has experienced what has been called an “epidemic” of herbicide resistance, especially from watergrass, which has resulted in herbicide costs increasing to close to $200 per acre for some growers .5As such, technologies that allow for a small number of applications of chemicals where efficacy is not affected by the resistance problem, as would most likely be the case for HT rice, have the potential to significantly lower this component of rice production costs. There are currently no commercialized GM rice varieties anywhere in the world. However, many transgenic varieties are in the “development pipeline,” including HT, insect resistant , bacterial and fungal resistant, and nutrient-enhancing “Golden Rice,” which produces beta-carotene, a substance that the body can convert to Vitamin A. A non-transgenic but genetically altered variety called Clearfield® IMI by BASF, a mutated HT variety, was released in the United States in 2002 . Approximately 200,000 acres of Clearfield® were planted across the Southeast in the 2003 growing season, accounting for about 8 percent of the seeded area in that region . Countries that are major rice producers and consumers, including China and Japan, are rapidly developing and testing GM rice varieties . For instance, China has approved for environmental release three insect-resistant rice varieties and four disease-resistant varieties and is developing HT, salt-tolerant, pipp racking and nitrogen-fixing cultivars . Many of these varieties have the potential to be of value to producers through reduced disease or pest-control costs and to the environment through reduced use of chemicals, thereby reducing runoff and water pollution. China will likely be one of the first countries in the world to commercialize GM rice. In the United States, the two most widely visible, potentially commercially viable transgenic rice cultivars are Roundup Ready® rice by Monsanto and LibertyLink® by Bayer CropScience . Both are HT varieties—the former is resistant to Roundup® and the latter to Liberty® , both non-selective herbicides able to control a broad spectrum of weeds . Glyphosate is currently registered for rice in California but not widely utilized while glufosinate is not registered [California Department of Pesticide Regulation ]. As such, it is unlikely that local weeds have developed a natural resistance to these chemicals, unlike, for example, bensulfuron methyl . In 1999, LibertyLink® rice cleared biosafety tests by USDA’s Animal and Plant Health Inspection Service but is not commercially available at this time . The primary direct effects of HT transgenic-rice adoption on the cost structure of California rice growers are reductions in herbicide material and application costs and the likely increased cost of transgenic seed.

An HT cultivar differs from conventional seed in that a particular gene has been inserted into the rice plant that renders the species relatively unharmed by a particular active chemical ingredient, thus allowing application of broad-spectrum herbicides directly to the entire planting area . This has the potential to simplify overall weed management strategies and to decrease both the number of active ingredients applied to a particular acreage and the number of applications of any one herbicide, thus decreasing weed-management costs. Reduced chemical use provides the major cost saving for growers. Similarly, herbicide application costs per acre depend on the specific chemical involved and the means of application. Typically, application by ground is 60 to 80 percent more expensive than aerial applications . For this study, other pest-management practices and fertilizer applications are assumed not to change with adoption of HT rice. The cost of transgenic rice seed will be greater than that of conventional seed because companies that sell transgenic varieties typically charge a premium to recoup their research investment costs.8 Based on Roundup Ready® corn and soybeans as a reference point, the technology fee is approximately 30 to 60 percent of conventional seed costs per acre . Seed price premiums are in a similar range for Bt corn varieties . In addition to the technology fee, seed costs for transgenic rice will likely change as a result of the California Rice Certification Act of 2000 signed by Governor Gray Davis in September 2000. With the full support of CRC,9 the CRCA provides the framework for a voluntary certification program run by the industry, offering assurances of varietal purity, area of origin, and certification of non-GM rice . A second, mandatory provision of the CRCA involves classification of rice varieties that have “characteristics of commercial impact,” defined as “characteristics that may adversely affect the marketability of rice in the event of commingling with other rice and may include, but are not limited to, those characteristics that cannot be visually identified without the aid of specialized equipment or testing, those characteristics that create a significant economic impact in their removal from commingled rice, and those characteristics whose removal from commingled rice is infeasible” . Under this legislation, any person selling seed deemed to have characteristics of commercial impact, which would include anytransgenic cultivars, must pay an assessment “not to exceed five dollars per hundredweight.” This fee is currently assessed at $0.33 per cwt with specific conditions for planting and handling divided into two tiers . In addition, the first handler of rice having these characteristics will pay an assessment of $0.10 per cwt . The $0.33 seed assessment is approximately 2.4 percent of average seed costs while the $0.10 fee represents 1.5 percent of average output price. A portion of these assessments is likely to be passed to the grower, depending on the relative elasticities of supply and demand in the seed and milling markets. In addition to generating cost savings, cultivation of HT rice will affect revenues as well. Net returns will be positively correlated with transgenic yield improvements. HT crops are not engineered to increase yields; rather, they are designed to prevent yield losses arising from pest or weed infestation. As such, potential yield gains depend on the degree of the pest and/or weed problem and the efficacy of the HT treatment relative to the alternatives. Many adopters of transgenic corn, cotton, canola, and soybeans have experienced positive yield effects on the order of 0 to 20 percent . However, under more ideal conditions, a yield drag may occur if the cultivar exhibiting the genetic trait is not the highest-yielding variety or if the gene or gene-insertion process affects potential yields . Field tests of LibertyLink® in California have generally found a yield drag of between 5 and 10 percent relative to traditional medium-grain M-202 varieties . Similar results were found for HT soybeans at the time of their introduction . To the extent that a yield drag actually exists in the field, it is expected to quickly dissipate over time as a greater number of varieties with the HT trait become available.Another effect of GM rice cultivation on California growers’ returns is the potential development of price premia for conventional medium-grain rice varieties in world rice markets. Despite the predictions and evidence of producer financial benefits from transgenic crops, there is demand uncertainty in world grain markets, especially in the European Union and Japan . Although challenged by many of the major transgenic-cropproducing countries , the EU has prohibited imports of new GM crops. Many other countries have varying GMcrop threshold labeling regulations, including China, Japan, the Republic of Korea, the Russian Federation, and Thailand . Due to segregation requirements and the higher unit cost of production of non-GM crops, this introduces the potential for a price premium for non-GM rice. As a result, non-adopters may indirectly benefit from the introduction of transgenic rice.

The success of the precision weeding sector is dependent on the compatibility between the startups’ value propositions and the growers’ on-the-ground needs, which in this study is examined within the lenses of objectives, stakeholder interactions, and grower user journeys. This compatibility is heavily impacted by social systems and institutional contexts and individual grower perceptions and characteristics. In a USDA study about the adoption of precision agriculture for commodity row crops like soybeans, researchers found that drivers of adoption include grower participation in USDA programs . Precision weeding adoption has been encouraged by the USDA Conservation Stewardship Program, which provides contract payments for environmental improvements. Technology adoption systems are often highly localized, with stakeholders optimizing their interactions with one another through curated local ecosystems. A study focused on institutional factors that impact startup formation uncovered significant relationships between most county-level variables while much less significant ones between states, implying the importance of local ecosystems . Beyond startup formation, localized networks for innovation can facilitate participatory, co-learning relationships between technology providers and technology users. Case studies of the adoption of decision support systems have shown that these technologies serve as a neutral meeting ground between scientists and growers, enabling the social context of co-learning cycles to influence individual learning .

Government-funded initiatives, as well as local ecosystem dynamics, help shape grower willingness. Individual grower characteristics will also influence the success of precision weeding commercialization. Larger farm sizes are correlated with reduced risk aversion, the specialization of managerial labor, and lower per unit costs for equipment . These characteristics lend to larger farms having a higher level of willingness to adopt new, riskier precision agriculture technologies. In addition, farmers also highly valued financial considerations such as economic returns and capital investment sizes. Characteristics that may impact whether Californian farmers adopt new technologies are their ages and social networks . Though adoption rates are also positively correlated with older age, more experience, and the affordability to take risks, younger farmers may experience the lower cognitive cost of switching and thus be more likely to adopt new technologies . Furthermore, social networks impact the probability of adoption. This correlation can be exhibited through an inverted U-shaped in which the probability of adoption is low when only a few people in the network have adopted or almost all have adopted, but high when about half of the people in the network have adopted . Thus, word-of-mouth could be the key factor in growers becoming aware of precision weeding technology, considering its adoption, and making an adoption decision. Precision agriculture technologies are often used in tandem.

Therefore, precision weeding technology adoption rates could be higher for farmers who also use technologies such as GPSmapping and guidance systems . Grower characteristics and decision-making processes cannot be fully understood without context on California growers and the other stakeholder groups involved in technology commercialization: startups and venture capital firms. In 2019, California had 69,900 farms with an average farm size of 348 acres . This average farm size is smaller than the national average of 445 acres . In 2017, Californian farmers had an average age of 59.2 years, and less than 6 % of all California producers, defined as farmers and ranchers, were aged 24 or younger . Most farms–74 %–were owned by individuals, families, or partnerships. Although 10 % of farms were owned by corporations, less than 2 % were owned by non-family corporations . Most farms are operated by California’s 700,000 seasonal farmworkers . Our study design is limited to growers operating in Central California because of its dominance in Californian and American agriculture. The Central Valley produces over 250 crops with a value of $17 billion per year, an estimated 25 % of the nation’s food, and in 2017, the Central Coast had over 400 vegetable farms and nearly 800 fruit and nut farms . Startup companies are another key stakeholder playing a significant role in California’s precision weeding ecosystem. Precision weeding startups have increased in popularity in the past few decades and have a positive growth trajectory, with the sector expected to grow by $268.75 million with a CAGR of 18.41 % from 2022 to 2026 . Precision weeding startups may be complementary to existing R&D efforts at larger corporations or seeking to disrupt current technologies or business models, potentially eroding monopoly power . However, incumbent agribusiness has retained their advantages of controlling supply chain and distribution standards. In addition, many startups seek out partnerships with incumbents for legitimacy and practical assets, such as manufacturing capacity . While startups often emphasize their value-based approaches and product innovation, they are inhibited by limited resources and look towards incumbents for process innovation . Despite some startups toning down their ambitions after working with incumbents, many argue that VC-backed startups are a more efficient use of capital in comparison to global corporate agriculture R&D expenditures . Investments from venture capital firms enable startups to take on the risky endeavors of developing and trialing novel technologies. In exchange for taking the chance on and providing capital to startups they ascertain as having exponential growth potential, VCs receive equity in the startups. VC investments in agri-food startups have accelerated in the past decade, with “twenty times more capital…invested in new agtech ventures in 2021 than 2012 .” The average seed round for digital and precision agriculture startups is $3.8 million and the average funding increases to $117 million for Series D and later funding stages . Up to 2006, global investments in agtech startups remained less than $200 million per year but then steadily grew until investments began exceeding $3 billion every year in 2010 . The large VC investments in agtech are a reflection of their general ‘pioneering,’ ‘adventurous,’ and ‘disruptive’ culture .

California is a natural geographical focus for our study considering the state’s agtech innovation ecosystem has strong interparty dependence, cannabis drying curing the availability of venture capital, and a forward-thinking and tech-savvy consumer market . In addition to the three stakeholder groups directly involved in commercializing new technologies, government agencies are incentivized to be involved in the agricultural industry for economic reasons. In 2021, California’s top twenty commodities produced over $44 billion in value . For crop commodities, California regulates pesticide use through the California Department of Food and Agriculture and the California Department of Pesticide Regulation. The government historically played a more significant role in agricultural research and development through supporting agricultural research stations and academia as well as supporting philanthropic foundations . Outside of direct research and development financing, the public sector also serves as allies in cleantech startup innovation, particularly with licensing alliances with universities and developing markets through demand-pull policies . In addition, governments play a large role in the mobilization phase in particular because they select participants and set the criteria for funding . With regards to precision weeding, the Governor’s Office of Business and Economic Development has tax credit and sales tax exemption programs for the agriculture and agtech sectors. In the U.S., the USDA, other federal agencies, and state governments all administer public agricultural R&D funding . Although existing research has explored how growers adopt technological advancements through collaboration with commercialization partners, there has been limited focus specifically on the precision weeding sector. The growers’ user journey could be different depending on the startup’s business model . Central California specialty crop growers face unique challenges compared to row commodity growers because specialty crops only account for 10 % of the U.S.’ farm operations but shoulder the complexities of higher risk, variable costs . A crucial gap exists within the body of literature about adoption networks regarding the relationships between stakeholders in the precision weeding ecosystem. Addressing this gap is imperative for elevating the advantages of precision weeding to the forefront of modern agricultural practices and comprehending the commercialization of precision weeding technologies, defined as the entry of such technologies into the mass market. This research study was motivated by the need to better understand how the relationships between stakeholders in the precision weeding ecosystem impact the adoption of the technology. While other stakeholders, such as intermediary organizations like cooperatives, play a role in technology proliferation, we limited the scope of our study to the core groups directly involved in commercialization. The objectives were defined to address specific aspects of these relationships: compatible motivations, investigating whether there are shared motivations among stakeholders, collaborative models, examining the effectiveness of existing collaborative models between stakeholders, and user journey, analyzing the user journey for growers adopting precision weeding technology and how technology adoption is impacted by other ecosystem players.The data collection method employed for gathering textual data to answer the three objectives involved semi-structured qualitative interviews. This method was chosen over other qualitative methods, suchas surveys and focus groups, because open-ended exploratory questions best suit the perception-based and subjective research questions. Though the number of qualitative interviews required to draw legitimate conclusions is contentious, it is widely accepted that most novel information is generated early on in the data collection process in an asymptotic curve and then there is a steep drop off in new information. Analyzing three datasets—the first with 40 interviews and 93 unique codes, the second with 48 interviews and 85 codes, and the third with 60 interviews and 55 codes—researchers found that six to sixteen interviews could reach a median degree of saturation of 69 to 89 %. . Therefore, we conducted 17 interviews to comfortably predict high data saturation. Saturation assesses the rigor of qualitative sample sizes, indicating when additional data adds little or no new information to answering the research questions. Of the 17 interviews, we conducted interviews with seven Californian growers, five venture capital firms/accelerators, four precision weeding startups, and one government agency. A small sample size was effective in revealing the core categories of these lived experiences . Regarding participant recruitment, we used purposive sampling to certify that interviewees had experience within the precision weeding sector and held mid to high-level roles in their respective organizations, ensuring the validity of the data collected. The outreach process was through networking at a relevant in-person conference, cold emailing and social media messaging, and speaking with ‘connectors’ such as UC Cooperative Extension Specialists. In addition, we used the snowball sampling technique, whereby we asked interviewees to recommend additional study participants. Due to logistical constraints, the interviews were conducted over video calls. Prior to the interview and following an explanation of the study objectives, informed consent was obtained via a form through WeSignature or verbally, allowing for voice recordings and transcriptions of the interviews. A caveat is that two of the interviewees’ informed consent forms did not allow voice recordings due to their companies’ legal directives but did allow for the interviews’ content and quotes to be used in this research study. The voice recordings were processed through the software Fireflies.ai to acquire transcripts, which were manually proofread following the interviews. The number of questions asked was based on data and thematic saturation, a criterion used for discontinuing data collection and analysis .

In the near term, planting racks in terms of electric power generation capacity and quality, the argument that more renewables will help accelerate EV adoption while mitigating environmental impacts has not been fully reconciled against space and time considerations. The large and variable rates at which electric power is called for by DCFC events present a challenge to electricity infrastructure planners and developers in identifying optimal strategies; this includes the sizing of distribution equipment, its location, and decisions around the upstream generation mix. Case in point for renewable energy projects, there is often a mismatch in terms of nameplate capacity, capacity factor, and its effective power delivery rate relative to the demands of a fleet of EVs charged at Level 3 conditions. Technological research and policies to address this mismatch require more in-depth study. As the grid decarbonizes over the longer term , the potential adverse impacts of marginal emissions from predominantly fossil fuel resources will subside. However, there will remain a need for decision-support tools to assist at many stages of this transition to a future state. More attention to firmed renewables via long-duration storage or vehicle-to-grid approaches will also be valuable. Because Level 3 Fast Charging is more likely to experience a higher magnitude and variance of emissions, incorporating battery storage infrastructure, either at the utility-scale or as smaller, distributed units, may become an attractive solution for reducing the emissions impact of vacillations between different marginal emissions regimes.

At the utility scale, the grid operator can use battery storage as a reservoir of low-carbon electrical energy for use as a marginal peaking resource. It can charge batteries during periods of low CO2, like during off-peak hours or when renewable generation shares are highest, banking low-carbon electricity and relieving part of the burden currently borne by traditional fossil resources called on to meet marginal demands. This would reduce the fossil fuel dominance of currently observed marginal mixes and therefore reduce the magnitude of vacillations between regimes. Additionally, it may be that greater investments in distributed battery storage infrastructure coupled with EV charging infrastructure could be valuable. Distributed battery storage systems could, in principle, perform a similar function as utility-scale batteries. A key difference is that they would likely be privately owned or jointly owned, suggesting a need for coordination around command and control in order to yield social benefits. Another difference is that it would require many small projects on the distributed side, compared to a few larger utility-scale projects. This can have advantages, such as a reduced need for new transmission investments, but also challenges, such as the need to build out intelligent distribution infrastructure to support it.

Incentivizing private battery storage may be a cost effective method for shifting demand on the electrical power grid off of peak hours, improving the utility’s ability to effectively manage increasing load due to growing EV shares and activity. Further investigation is required to explore the operational nuances and cost components of battery storage systems as described here, but as far as this research can conclude it may be an effective strategy for tackling the fast-charging problem. A fourth, potentially critical, policy implication is the notion that vehicle electrification will not happen in a vacuum as far as the grid is concerned. Thus far in our study, we’ve exclusively looked at new heavy electric loads as additive to existing and future demands. Furthermore, it is also reasonable to assume that most of the electricity generation from renewables will essentially be fully consumed by so-called “baseline” demands for the foreseeable future. In other words, renewables still account for a modest enough share of the total mix that they will be consumed, with or without any EV growth at all. In fact, with the increasing pressure to retire coal plants, much new low-carbon generation is needed to simply offset those resources. However, if EVs could be deployed within a broader frame, that could go a long way toward reducing uncertainties raised by marginal emissions scenarios. Such a broader frame would manage demand, intelligently control EV charging, prioritize overall efficiency gains, and focus on conservation, avoidance, or substitution. In this way, the assumption of average hourly mix or even average daily mix might be more relevant than the uncertainty around a specific marginal resource assumed to meet the incremental kWh required by a particular EV charging session. While the study offers some suggestions for tools and next steps during the near-term and transition period, this broader framing seems to be a difficult hypothesis to test with certainty over the longer term. In addition, it seems there is talk of greater electrification, not less, when it comes to other sectors like residential heat pumps, data centers, industrial heat, or other energy-intensive processes that currently use thermal methods such as fossil fuels. While broader interactions across electric power use segments are beyond the scope of this study, the potential policy implications of those interactions and factors could be profound. For now, it may be reasonable to assert that as EVs are deployed, it is imperative to not only manage the EV charging events in time and space but also consider our latitude to control or influence other large loads on the grid in conjunction with EV deployment growth. Doing so can help ensure that the electrification of transportation results in meaningful decarbonization gains. This Phase II study has built upon a Phase I study that developed a systems methodology to explore important questions about EV growth relative to new vehicle categories and use cases. This report pursued “future work” that was identified during the first research investigation which focused exclusively on light-duty passenger vehicles. The now “present work” has specifically explored fleet and commercial use cases involving medium and heavy-duty vehicles and augmented the findings in depth and breadth. Our study has demonstrated the usefulness of the methodology developed in Phase I, pipp horticulture which integrated vehicle power train, charging profile and grid generation mix sub-systems. The present study investigates an enhanced understanding of MD/HD EV emissions and several promising scenarios and use cases that can help optimize charging schedules and minimize CO2 emissions.

In both phases of our study, we have proposed and investigated rigorous approaches that estimate the variability associated with CO2 and other emissions involving electric vehicles. This required a simulation framework that explored multiple parameters concurrently to yield broad comparisons. In this way, we explore a growing set of electric vehicles as compared to a baseline case . We explore a representative and diverse suite of use cases, driving cycles, and charging profiles for a range of users, including individual commuters, fleets, small businesses, as well as municipal transit and services. The simulations estimate CO2 emissions under a range of scenarios useful to inform decisions, investment, and policy. As noted, prior studies often utilize annualized averages for grid-level C02 emissions to simplify the analysis. In our research review of other tools and dashboards , we observed that a very basic algorithm is typically utilized that does not consider time of day or seasons of the year. We acknowledge such traditional approaches provide a kind of first-order, initial estimation that can be useful to some audiences in some contexts. However, it is imperative to recognize and explain the limitations of this approach, and the risk of relying too heavily on average emissions estimates. The reason is that such estimates are subject to change in the future, and also subject to variability during the present on multiple timescales . This Phase II effort emphasizes the need to focus on Level 3 Fast Charging because this subcategory of charging stands to incur higher rates and potential uncertainty. Not only will better assumptions be needed to estimate emissions resulting from Level 3 charging, but they will also be imperative to inform infrastructure siting to build out charging networks and inform resource planning for the grid at large.Next steps should consider the benefits, tradeoffs, assumptions, and limitations associated with the methodology, practicality, and intent of related research. The authors believe continued attention, in particular during the near-term transition period, can facilitate more direct comparisons of EVs and use cases to other technologies as penetration rates grow. Several summary statements emerge from this body of work. It is clear that at certain modest levels of EV deployment, a weighted average mix of resources may not be illogical or inaccurate in estimating CO2 impacts. However, it can be stated that with significant increases in EV charging, in particular at certain hours of the day and seasons of the year, the assumption of weighted mixes breaks down. The study demonstrates that the breakdown can be quite pronounced for use cases involving greater vehicle miles traveled, for charging sessions occurring in the field, and for charging occurring during periods of peak grid demand. The breakdown also seems pronounced for scenarios involving Level 3 charging. These findings reveal that managing charging events throughout the 24 hours of the day and across LD, MD, and HD use cases in distinct ways should merit greater attention. Furthermore, charge management alone will likely be inadequate as EV shares grow to much greater levels. Future study is anticipated to further inform decision-making around near and intermediate term scenarios including new interactive methods of forecasting both EV demand and grid resources. Historical approaches to dispatch are already underway toward predictive forecasting approaches. Ideally, scenarios will be developed that can better simulate future resources both fossil and non-fossil in order to meet load growth to support electric transportation, as well as additional demand growth from the electrification of other sectors like data centers, heating and cooling, and industrial processes.Together with its Phase I counterpart, this Phase II study explores a systems-of-systems methodology to simulate viable grid-charging-vehicle scenarios of increasing interest for planning and policy-making. Collectively, our team has considered a range of vehicle categories and use cases . The Phase II effort in particular has refined the methods introduced in Phase I and deepened our understanding of several potentially compelling EV applications including electric pickup trucks used by small businesses in service-oriented urban applications, medium-duty fleets, and other specialized uses where vehicles have predictable routes and return to base on a regular basis. The study’s simulation reveals that the CO2 emissions intensity of a battery-electric light truck traveling 20 miles per day could vary dramatically depending on the charging schedule used. Under the study’s marginal resource X grid condition where a specific marginal resource is needed on an hourly basis to meet a particular EV charging event, estimated CO2 emissions could be as much as 42% lower than a baseline ICEV, or as much as 24% higher than the same baseline. This large variance is purely a function of when and how quickly the vehicle is recharged. Thus, this example suggests such tools will be important to ensure environmental benefits are realized. While the simulated use cases yield valuable guidance in their own right, the collective work reveals that such modeling and simulation-based comparisons are generalizable and extendable.

Surveys can help inform growers of emerging herbicide-resistant weed populations while they are still localized; surveys are also often used to encourage adoption of resistance mitigation measures to minimize economic and environmental impacts. Further, surveys combined with population genetic research can determine the evolutionary and geographic origins, and routes of spread, of resistance across an agricultural landscape . Herbicide resistance in California Herbicide resistance has been an important management concern in California flooded rice production for several years . Weeds with resistance to the ALS inhibitors , thiocarbamates and ACCase inhibitors are the dominant weed management problems in most of the Sacramento Valley rice production region. In orchards and vineyards, herbicide resistance is a more recent development and is dominated by resistance to the broad-spectrum postemergence herbicide glyphosate. This herbicide is, by far, the most widely used herbicide in the state in perennial crop production systems, as well as in many roadsides, canal banks and residential and industrial areas. Glyphosate-tolerant cotton, alfalfa and corn are becoming widely adopted in the state, which will further increase selection pressure for additional glyphosate-resistant and -tolerant species. Herbicide resistance in flooded rice. Most California rice is produced in monoculture systems due to impeded soil drainage, which limits rotation to other upland crops .

Rice fields are kept under continuous flood conditions during the growing season, primarily for the control of grass weeds . Although this system favors sedges and other water-tolerant weeds, selective herbicides such as molinate and bensulfuron provided highly effective weed control for several years. However, in the early 1990s, after repeated use, resistance to the ALS-inhibiting herbicide bensulfuron became widespread among weedy species in rice. By 2000, several additionalweed biotypes with resistance to ALS inhibitors, thiocarbamates or ACCase inhibitors had evolved and were causing significant weed management, economic and environmental issues in the rice cropping system. UC researchers, extension personnel and industry partners have devoted considerable efforts to understanding and managing herbicide-resistant weeds in rice. Smallflower umbrella sedge and California arrowhead resistance to ALS-inhibiting herbicides was first reported in California rice fields in 1993 following repeated use of bensulfuron . Field research has shown that California arrowhead is a fairly weak competitor in rice systems and that the ALS-resistant biotypes can be adequately controlled with other registered herbicides. Recently, small flower umbrella sedge biotypes with multiple resistance to the PSII herbicide propanil and to several ALS-inhibiting herbicides were identified in the Sacramento Valley , and research is ongoing to elucidate the mechanisms of resistance and any cross resistance to other rice herbicides. Eared redstem and ricefield bulrush resistance to ALS inhibitor herbicides in rice was reported in 1997. Redstem research has focused on intraand interspecific competition in an effort to develop agronomic solutions to reduce its competition with rice .

Studies have shown that California populations of ricefield bulrush are resistant to most registered ALS inhibitors, whereas populations from other regions are resistant only to one chemical family, the sulfonylureas, in the ALS inhibitor group . Recently, rice field bulrush biotypes with multiple resistance to propanil and bensulfuron were identified in the Sacramento Valley . Late watergrass populations resistant to ACCase inhibitors, ALS inhibitors and the thiocarbamate herbicides in rice systems were reported in 1998 . This resistance to multiple herbicides within an individual plant indicated that using herbicides with different modes of action would be unlikely to provide satisfactory control of the species in the long term. Further complicating the situation in rice, populations of late watergrass and barnyardgrass with resistance to both ACCase inhibitors and thiocarbamates, and thus exhibiting multiple resistance, were reported in 2000. Later research confirmed that the mechanisms of multiple resistance to several herbicide classes are due to metabolic degradation of these compounds . Smooth crabgrass resistance to the synthetic auxin herbicide quinclorac was reported in 2002. Detailed research into the mechanisms of resistance suggested that the cause was an altered sensitivity in the auxin response pathway, leading to ACCase activity, ethylene synthesis and enhanced ability to detoxify cyanide . Although crabgrass is not an important rice weed, quinclorac is used in rice systems for control of other weeds, and resistance to it has been reported in Echinochloa species of rice in California and from other regions. Most importantly, the observed changes in ethylene synthesis and production of toxic byproducts may also relate to the plant’s ability to tolerate abiotic stress. Two implications of this finding include the possibilities that quinclorac-resistant smooth crabgrass has the potential to invade a more diverse range of habitats and become an important weed of rice; and adaptation to the abiotic stress of the flooded environments may predispose Echinochloa phyllopogon or other major rice weeds to evolve resistance to quinclorac in the future. Herbicide resistance in orchard and vineyard cropping systems. The first herbicide-resistant weed in orchard cropping systems was perennial ryegrass, Lolium perenne , reported in 1989 . This ALS inhibitor–resistant biotype was selected on roadsides by the use of sulfometuron and, thus far, has not been a major problem in orchards or vineyards because relatively little of this class of herbicides is used in these crops. However, several ALS inhibitors, including rimsulfuron, penoxsulam, halosulfuron and flazasulfuron, are becoming more widely used in tree and vine crops, and selection pressure for ALS inhibitor resistance may increase in the future. The first case of glyphosate resistance in California was reported in a population of rigid ryegrass in 1998 . However, most confirmed glyphosateresistant ryegrass populations have been identified as Italian ryegrass . Glyphosate-resistant ryegrasses have become widespread and are a major weed problem in orchards, vineyards and roadsides of Northern California .

Research indicated that resistance in ryegrass is not due to metabolism of the herbicide and is instead due to an altered EPSPS enzyme . Glyphosate resistance in these areas has been largely driven by decreases in grower use of other herbicides, especially those under increasing regulatory pressure because of pesticide contamination of ground or surface water. The use of glyphosate-based herbicide programs also increased when the patent on Roundup expired in 2000 and low-cost, generic glyphosate herbicides became readily available. Today, drying and curing buds glyphosate accounts for over 60% of all herbicide-treated acreage in California orchard and vineyard systems . Glyphosate-resistant horseweed, or mare’s tail , was reported in 2005 and is one of the dominant weeds in and around raisin and tree fruit production areas of the San Joaquin Valley, as well as on roadsides and canal banks in the region  that would be required for these larger vehicles and different use cases . In conjunction, the research team assessed likely charging behavior that would be typical of small business in the subject categories. Again, the goal has been to better understand how vehicle use case, charging behavior, and assumptions around the grid, with a particular focus on marginal emissions, may affect the relative pros and cons of EVs as a substitute for the incumbent vehicle technology . A secondary goal of this phase of the effort is to develop guidance and tools to assist stakeholders in understanding the implications of the use case scenarios and the simulation outputs. It is the team’s intent that while the subject of this study has been granular and necessarily regional, the final report and findings can be disseminated to other regions with great effect. In addition, technology transfer activities are anticipated to share these NCST developed tools with practitioners and decision-makers more broadly, so as to maximize the effectiveness of public and private investments in charging infrastructure. The guidance may also provide strategies to businesses seeking to deploy and/or invest in Electric Vehicles, as well as in electric power more broadly. This convergence research has revealed important findings relative to the comparative emissions impact of vehicle charging during various times of the day. Whereas Phase I findings are valuable to an individual vehicle owner, the findings of Phase 2 are of much greater interest to businesses that operate fleets comprised of light-duty pickup trucks, vans, medium-duty delivery/moving trucks, as well as refuse trucks. The pros and cons of replacing a conventional ICEV within the context of larger fleet vehicles that are operated by small and medium public or private businesses are similar in nature, but much greater in magnitude when compared to privately owned and operated cars. Interestingly, this has multiple dimensions including economic as well as environmental . To characterize the differences, the team compares results under five unique emission assumptions, each with its own relevance to the future state of the grid. As an example, the study investigates rather extreme scenarios wherein a specific generation resource is assumed to be dispatched to meet a specific marginal EV charge. In this case, the “effective emissions rate” of that EV charging session is tied directly to a single generation type . For multiple extreme cases, we observe that CO2 emissions can more than double when charged in the early afternoon compared to an identical charging event during the overnight . This finding suggests that it will be essential to adjust and/or coordinate charging schedules to reduce the environmental impacts of EVs. More specifically, to the extent emissions impacts are prioritized among other objectives, individuals and policymakers should be encouraged or incentivized to charge when marginal emissions are lowest whenever possible. This idea also has important implications for the location, type, and ownership models for tomorrow’s charging infrastructure. This not only includes charging equipment, but it also has significant implications on the generation mix, as well as transmission and distribution networks. Translating and operationalizing this type of guidance will require a combination of education, access to rigorous and clear resources, signals between stakeholders , risk management analyses, and behavioral change. In this way, our two studies, taken together can shed light on the critical nature of assumptions involved with serving incremental new electric power demand to charge vehicles. As in Phase I, this study is aimed at comparative analyses that provide insights into how a marginal assumption for CO2 emissions compares. As before, marginal CO2 assumptions generally yield higher CO2 impacts, sometimes a lot higher, than identical simulations that assume weighted average emissions. This variance is broad, ranging from 46% lower to 24% greater, depending on a host of case-sensitive factors. This study manifests the reality that weighted average emissions in the U.S. remain on a declining trajectory due to coal retirements, and the scale-up of combined cycle natural gas plants and renewables. In the Southeast, 2023 is also experiencing the commissioning of new base load nuclear generation, and Georgia is the only state in the country to add new nuclear since 2000. The overall environmental impact of these trends is favorable. However, EV growth as a demand sector for electric power has mixed outcomes because low carbon baseload will be consumed by current demand sectors, and renewables are generally considered non-dispatchable. As such, grid operators will generally deploy flexible generating resources to meet incremental loads like EVs.

Mechanical weed control, such as cultivation, is impractical in a solid seeded perennial crop like alfalfa, and hand weeding is not economical. Alfalfa is grown over extensive acreage in the United States and fields can be large in size; therefore, the overall weed flora available for selection of resistant traits or for weed shifts is plentiful. Perennials like alfalfa, if sprayed repeatedly with the same herbicide, are likely candidates for weed shifts and weed resistance.On the other hand, many weeds do not flourish in an alfalfa field due to its perennial nature and the competitiveness of the crop after establishment. Alfalfa is an aggressive competitor with most weeds, which fail to establish in alfalfa fields due to the crop’s vigorous growth and shading ability. In addition, many weed species do not tolerate the frequent cutting that occurs in alfalfa fields. The lack of soil disturbance once the alfalfa stand is established also reduces opportunities for germination of some weed species. Furthermore, the interval between alfalfa cuttings is short enough that seed production for many weeds is reduced compared with annual crops that allow completion of the weeds’ life cycles.Weed shifts or resistant weeds are unavoidable and will occur eventually with any herbicide used repeatedly, and the same is true with the use of glyphosate . Fortunately, resistance to glyphosate is not as common as resistance to many other herbicides, such as acetolactate synthase and acetyl-CoA carboxylase herbicides that have a single binding site and single target enzyme mechanisms of action .

The relatively low rate of resistance in weeds to glyphosate relative to the widespread use of this chemical has not been fully explained, but may be due to the number orfrequency of mutations that may be required to confer resistance to glyphosate. Two resistance mechanisms, a weak target site mutation, and a reduced glyphosate translocation mechanism have been documented in weed species that have evolved resistance to this herbicide . Regardless of the mechanism, weed resistance to glyphosate is not as common as resistance to other herbicides. However, cases of weed resistance to glyphosate have been documented and are increasing. There is a range of species across the world with documented resistance to glyphosate . Fortunately, most of these species are not common in alfalfa fields. Two weed species in particular have evolved resistant populations in California: Lolium spp. and Conyza sp. . The latter is not important in alfalfa, but ryegrass is frequently found in alfalfa fields. Glyphosate-resistant ryegrass is increasing in the Sacramento Valley and northern San Joaquin Valley of California and may become problematic during fall stand establishment of RR alfalfa. Weed shifts and/or weed resistance have occurred with the other transgenic RR crops released before RR alfalfa . Weed resistance is of greater concern than weed shifts and has occurred in RR soybean, cotton, and corn in less than a decade after their initial release . Alfalfa growers can learn from experience with these crops and in noncrop areas as a preemptive measure to avoid, or at least minimize, the problems with weed shifts and weed resistance. These problems are sure to occur in alfalfa if proper weed management practices are not followed.

Glyphosate-resistant crops have provided growers with an easy-to-use, low-cost, and effective weed management tool. However, the effectiveness of weed control systems using RR crops can make growers complacent in their weed control practices. Even though this technology is highly effective, growers must follow sound weed management principles to prevent short- or long-term weed shifts or weed resistance from occurring. This includes weed identification, crop rotation, attention to application rate, proper timing of application, herbicide rotation, and tank mixtures.Frequent Monitoring for Escapes It is difficult to detect an emerging weed shift or weed resistance problem if fields are not frequently monitored for weeds that escape current weed management practices. Identification and frequent monitoring can detect problem weeds early and guide management practices, including herbicide selection, rate, and timing. Herbicide Rate and Timing It is important to use the appropriate rate and timing for the weeds present. For example, some weeds that are considered somewhat tolerant to glyphosate can be controlled effectively in seedling alfalfa with glyphosate, provided the proper rate is used and the application is made when the weeds are very small. Research in Nebraska over a 7-year period demonstrated a rapid increase in lambsquarters when a low rate of glyphosate was applied, but a higher rate successfully controlled this weed. Just like with traditional weed management programs, the grower must be sure to use the recommended rate for the weed species present and treat at the appropriate time when the weeds are still small. Crop Rotation One of the most effective practices for preventing weed shifts and weed resistance is crop rotation, which allows growers to modify selection pressure imposed on weeds. Continuous alfalfa is not recommended for other agronomic reasons, but especially would be ill advised when it comes to management of resistance and weed shifts. Crops differ in their ability to compete with weeds; some weeds are a problem in some crops, while they are less problematic in others. Rotation therefore would not favor any particular weed spectrum. Crop rotation also allows the use of different weed control practices, such as cultivation and application of herbicides with different sites of action. As a result, no single weed species or biotype should become dominant. The effectiveness of crop rotation to manage weed shifts and resistance is substantially reduced if another RR crop is planted in rotation with RR alfalfa, since the same herbicide and selection pressure would likely occur. Agronomic Practices In addition to crop rotation, several management practices may have an impact on the selection of problem weed populations. If problem weeds germinate at a specific time of year, crop seeding date can be shifted to avoid these weed populations, allowing a vigorous alfalfa crop to develop that is capable of outcompeting weeds. Delaying irrigation after alfalfa cutting can reduce germination of certain summer annual weeds. However, this practice only works on some soil types, and water stress in alfalfa can reduce yields. Harvest management can, in some cases, assist in eliminating or suppressing problem weed populations, but harvests must occur before weed seed production to prevent weed proliferation.Rotation of Herbicides Weed shifts occur because herbicides are not equally effective against all weed species and herbicides differ greatly in the weed spectrum they control.

A weed species that is not controlled will survive and increase in density following repeated use of one herbicide. Therefore, pipp mobile storage systems rotating herbicides is recommended. Rotation of herbicides reduces weed shifts, provided the rotational herbicide is highly effective against the weed species that is not controlled with the primary herbicide. The grower should rotate to an herbicide with a complimentary spectrum of weed control, along with a different mechanism of action and therefore a different herbicide binding site. Weed susceptibility charts are useful to help develop an effective herbicide rotation scheme . In addition, publications on herbicide chemical families are available to assist growers in choosing herbicides with different mechanisms of action . Rotating herbicides is also an effective strategy for resistance management. Within a weed species there are different biotypes, each with its own genetic makeup, enabling some of them to survive a particular herbicide application. The susceptible weeds in a population are killed, while the resistant ones survive, set seed, and increase over time. Using an effective herbicide with a different mode of action from the one to which the weeds are resistant, however, controls both the susceptible and resistant biotypes. This prevents reproduction and slows the spread of the resistant biotype.Herbicide Tank Mixtures For the same reasons that rotating herbicides is effective, tank mixing herbicides is also recommended. The key is to select tank mix partners that have different target sites and that compliment each other so that, when combined, they provide complete or nearly complete weed control.The cost of RR alfalfa seed, including the technology fee, is generally twice or more than that of conventional alfalfa seed. Naturally, growers will want to recoup their investment as quickly as possible. Therefore, considerable economic incentive exists for the producer to rely solely on repeated glyphosate applications alone as a weed control program. Some producers may even be inclined to shave the rates to the minimum amount that would provide acceptable weed control. While relying solely on glyphosate and shaving rates may provide satisfactory results in the short term, it is a risky practice in the long run as it will accelerate weed species shifts and the evolution of resistant weeds. Sound weed management practices should be employed to maintain the effectiveness of the RR technology. Roundup Ready alfalfa is still a relatively new technology, so there has been limited field experience with it to date. The following are some suggestions to consider based upon proven resistance management strategies, our understanding of alfalfa production practices, and our initial experience with RR alfalfa. Ultimately, growers and pest control advisors hold the key to avoiding weed shifts and resistance by reducing selection pressure, which is accomplished by developing a weed management program that does not rely solely on the continuous use of glyphosate. Any management practice that reduces the selection pressure will help avoid weed species shifts and resistance. For Seedling Alfalfa, Use Glyphosate Alone or in a Tank Mix Combination Seedling alfalfa is most vulnerable to weed competition because weeds are often more vigorous and competitive than young alfalfa. Additionally, complete weed control in seedling alfalfa is often difficult to achieve and frequently requires tank mixes of different herbicides to control the broad spectrum of weeds found in an individual field. Yield and stand loss from weed competition, and injury from conventional herbicides, are usually far greater in seedling than in established alfalfa. Numerous field trials throughout the United States have proven the effectiveness of RR alfalfa for stand establishment. Superior weed control with no perceptible alfalfa injury has occurred in most studies. Therefore, it is only logical to use glyphosate for weed control in RR seedling alfalfa for the cost savings, improved weed control, reduced crop injury, superior stand establishment, and the elimination of the small percentage of alfalfa seedlings that do not carry the RR gene. Delayed removal of these nulls may cause weed control problems in the future by creating open spaces for weeds to grow. Ordinarily, 1.0 pound per acre active ingredient of glyphosate is sufficient for weed control during the seedling period. However, a higher rate may be needed if the field contains some of the more tolerant weeds listed in table 1. A tank mix may be advised if especially-difficult-to-control weeds are present. For example, a tank mix of glyphosate with imazamox or imazethapyr may be advised if burning nettle is present, or a tank mix with clethodim will be necessary if the field or surrounding area is known to have glyphosate-resistant ryegrass. Rotate or Tank Mix Herbicides at Least Once During the Life of an Alfalfa Stand Alfalfa stand life varies considerably throughout the western United States depending on the production area, grower practice, and the existence of profitable rotation crop options.

As herbicide-tolerant crops —mainly cotton and corn —have increased, so has interest in CT systems among California growers. Along with the availability of HTCs, several other factors including increased fuel prices, access to better CT, global positioning system technology, and environmental air quality issues have had the combined effect of increasing interest in CT systems in California. Conservation management plans , now required by the San Joaquin Valley Air Pollution Control District , can include HTCs such as Roundup Ready crop varieties and the reduction or elimination of tillage as acceptable practices for dust reduction. The SJVAPCD suggests that the reduction in the number of passes and tillage that accompanies these practices can reduce soil and water losses and mitigate dust problems. Similarly, there is increased interest in testing CT systems in other non-HTC varieties such as tomatoes, wheat, oats, and dry beans in California. Reduced tillage, however, often brings with it changes in weed species and populations, and therefore in weed-management needs, and this is a major concern for the growers who may want to adopt CT systems . Phillips and Young stated that the vital factor for success of no-till row crop production is weed control, and that this depends largely on the proper use of suitable herbicides. For this reason, our focus in this publication is on the weed management issues in CT and we will suggest some techniques for the successful implementation of CT systems in California.Tillage has been a major agricultural weed control technique for several decades, so the development of CT systems that advocate no-tillage or reduced tillage has significant implications for growers.

Tillage affects weeds by uprooting, dismembering, and burying them deep enough to prevent emergence, by changing the soil environment and so promoting or inhibiting the weeds’ germination and establishment, and by moving their seeds both vertically and horizontally . Tillage is also used to incorporate herbicides into the soil and to remove surface residues that might otherwise impede the herbicides’ effectiveness. Any reduction in tillage intensity or frequency, therefore, poses serious concerns with regard to weed management. Weed species shifts and losses in crop yields as a result of increased weed densities have been cited as major reasons why CT systems have not enjoyed widespread adoption. Some other common concerns about weed management under CT include emergence from recently produced weed seeds that remain near the soil surface, interception of herbicides by thick surface residues, lack of disruption of perennial weeds’ roots, and changes in the timing of weed emergence . Reports of weed species shifts, however, have been inconsistent. For example, Cussans reported an increase of some dicot weeds accompanying increased levels of cultivation. Conversely, Wrucke and Arnold reported similar distribution patterns for broad-leaved weeds in both CT and conventional tillage systems. Pollard et al. reported that most weeds showed no consistent response to tillage. Swanton et al. found that tillage was an important factor affecting weed composition: common lambsquarters and redroot pigweed were associated with a moldboard plow system, whereas large crabgrass was associated with no-till.

Derksen et al. suggested that changes in weed communities were influenced more by environmental factors than by tillage system. Childs et al. stated that, over time, small-seeded annual broadleaf weeds and perennial weeds become more prevalent in no-tillage fields. Culpepper , in a survey of six states , reported a shift towards Amaranthus species, annual grasses, winter annuals, and morning glories in glyphosate-resistant cotton CT systems. Shrestha et al. concluded that long-term changes in weed flora are driven by an interaction of several factors: tillage, environment, crop rotation, crop type, and the timing and type of weed management practice. Very little data exist on such weed community dynamics in CT under California conditions. Studies in California have shown that most black nightshade emerged from the top 1 inch of the soil and that effective control of this species could be achieved with deep tillage . Maximum emergence of annual morning glory seeds occurred from the top 3 inches of the soil and a significant reduction in its population was generally observed following cultivation . Wright and Vargas observed increased populations of annual morning glory in cotton under reduced tillage. Further, glyphosate does not provide consistent control of pitted morning glory and other annual morning glory species . These findings suggest that the San Joaquin Valley cotton production systems using Roundup Ready CT technology may still have to rely on some level of cultivation for control of annual morning glory to avoid costly hand weeding. Some other problems associated with reduced tillage include the difficulty in managing perennial weeds such as nuts edge , since control of these species requires integration of cultural, mechanical, and chemical methods . It is clear that CT systems remain problematic in Roundup Ready cotton production systems, but CT systems have been successfully tested in Roundup Ready forage corn in some areas of the San Joaquin Valley .Successful implementation of a CT system depends to a large extent on a good understanding of the dynamics of weed seeds in the soil seedbank. A soil’s weed seedbank is the reserve of viable weed seeds present on the surface and in the soil. The seedbank consists of new seeds recently shed by weed plants as well as older seeds, some of which have persisted in the soil for several years . Different tillage systems disturb the vertical distribution of weed seeds in the soil—in different ways . Studies have found that moldboard plowing buries most weed seeds in the tillage layer, curing weed whereas chisel plowing leaves most of the weed seeds closer to the soil surface . Similarly, in reduced- or no-till systems 60 to 90 percent of the weed seeds are located in the top 2 inches of the soil . The Figure 1 graph shows that most weed seeds remain in the top 0 to 2 inches of the soil in notill systems. These seeds are at a relatively shallow emergence depth, and with suitable moisture and temperature they would seem likely to germinate and emerge more readily than those buried deeper by other tillage systems. In fact, though, weed seeds that are on the soil surface may be more readily eaten by vertebrates and invertebrates , killed by weathering, and more harmed by pathogens than those buried deeper .

Further, CT systems do not bring weed seeds from deeper in the soil profile up to the soil surface. Although CT systems may have more weed seeds at shallow depths in the soil, the weed seedbank can be effectively managed by minimizing processes that replenish the weed seeds and maximizing processes that deplete the seedbank.Shifts in weed populations from annuals to perennials have been observed in CT systems . Perennial weeds are known to thrive in reduced- or no-tillage systems . Most perennial weeds have the ability to reproduce from several structural organs other than seeds. For example, nutsedge and johnson grass , two common weed species in California, generally reproduce from underground plant storage structures: tubers and rhizomes, respectively. Conservation tillage may encourage these perennial reproductive structures by not burying them to depths that are unfavorable to emergence or by failing to uproot and kill them, in contrast to conventional tillage. Most perennial weeds occur in patches, though, and mapping these perennial weed patches and attacking them regularly with herbicide applications or mechanical control could be an effective management strategy in CT systems. Wright and Vargas found that the most effective purple and yellow nutsedge control in cotton was achieved by a combination of glyphosate in a Roundup Ready system that involves mulching seed beds and cultivating two or three times using sweep-type cultivators. Similarly, Shrestha et al. found that cultivation was necessary for successful control of field bindweed in CT blackeye beans . All of this means that some level of cultivation may be necessary for the management of “difficult-to-control” perennial weeds in certain cropping systems in California.Several studies have shown the composition of weed species and their relative time of emergence to differ between CT and soil-inverting tillage systems. Their germination and emergence may be enhanced more by the types of equipment used in soil-inverting tillage systems than by CT equipment. For example, studies in Denair, California have shown a markedly lower emergence rate for wild radish under CT than under soil-inverting tillage . Studies have shown that tillage stimulates the seedling emergence of wild radish . The timing of weed emergence also seems to be species dependent. For example, Bullied et al. found that species such as common lambs quarters, field pennycress , green foxtail Beauv., wild buckwheat , and wild oat emerged earlier in CT than in conventional tillage system. However, redroot pigweed and wild mustard emerged earlier in the conventional system than in the CT. Furthermore, in CT systems the presence of residue on the soil surface may influence soil temperature and moisture regimes that affect weed seed germination and emergence patterns over the growing season ; this may mean that CT practitioners have to change the timing of weed control measures in order to ensure their effectiveness. Soil surface residues can interfere with the application of herbicides, so there is a greater likelihood of weed escapes if residue is not managed properly or if herbicide application timings or rates are not adjusted.Weeds that are present when crops are planted in a CT system will likely need to be controlled with a non-selective burndown herbicide such as glyphosate, paraquat, or glufosinate. Selective herbicides are not typically used for burndown in CT systems, since the objective prior to crop emergence is total vegetation control, and selective herbicides may not control all of the weeds present. For example, common chickweed, shepherdspurse , London rocket , filaree , mustards , and fiddlenecks are common annual weeds that are present on the fallow beds and early cotton stands in CT systems, and these need to be controlled with non-selective post emergence herbicides . The non-selective burndown herbicide can be applied before or after crop planting but prior to crop emergence . Since these herbicides lack residual activity, applications should be scheduled as close to crop planting or emergence as the label will permit in order to minimize further weed emergence prior to crop emergence. Occasionally a burndown herbicide is tank mixed with a residual herbicide; the burndown herbicide is intended to control the emerged weeds and the residual herbicide to prevent weed emergence or growth. These burndown herbicides are usually tank mixed with carfentrazone or oxyfluorfen to control broad leaf weeds. Growers using CT may see this burndown herbicide application as an increase in production costs, considering that tillage would have controlled these emerged weeds in a conventional system. However, they may be overlooking cost savings for fuel, labor, and energy that are realized when a grower practices CT.In conventional tillage systems, crop residues generally are not present at the time of preemergence herbicide application.

The problem has been further compounded by both the loss of many types of herbicide through stricter regulation and the lack of research and development of new modes of action for herbicides; this situation is unlikely to improve over the coming decade. Herbicide resistance arises through two mechanisms. 1) Target site resistance , pipp shelving where the protein functions targeted by herbicides become insensitive to chemical disruption. This can arise through selection for genetic changes resulting either in reduced binding or in increased expression of the target. 2) Non-target site resistance , where the activity of herbicides in weed tissues is reduced to sublethal levels either by neutralizing the herbicide, or through metabolic responses that reduce chemical injury. Our understanding of both types of resistance, their plasticity and evolution, is currently constrained by the lack of genome information for major agronomic weeds. In the case of NTSR, we lack fundamental understanding of the multiple mechanisms associated with this complex quantitative trait. The opportunities for counteracting herbicide resistance can be broadly divided into developing strategies for the better use of existing chemical control measures, changing cultural practices, and developing new approaches to weed control based on new crop traits. In reality, durable weed control will likely require integration of all of these approaches.

Immediate opportunities will be built on foundational research in the biology of major weed species, including the application of the technologies now in place for functional genomics of crops such as genome sequencing, transformation, and editing. Studies should be aimed at understanding the mechanisms underpinning the plasticity of resistance and the molecular basis of NTSR. Outcomes would include better diagnostic and predictive tools for the stewardship of existing products and the identification of new targets for intervention such as ‘resistance-busting’ synergists . Changes in cultural practice, such as alterations in rotations and the use of cover crops as well as precision and robotic weed control, offer the most immediate opportunities for counteracting resistance; these will be best implemented through expanding training programs for agronomists and agricultural engineers. Public funding for field research programs to objectively test the efficacy of different approaches to weed control along with their life cycle analysis will be required to ensure rapid adoption. To date, the use of genetic improvement as a route to weed control has relied on developing resistance to specific herbicides in the crop, the most well-known example being Round-Up Ready technology. This approach was initially projected to be durable; however, it has not proven durable due to over-dependence on a single herbicide and the resulting selection on weeds to develop resistance.

Transformation of crops with new herbicide resistance genes still offers useful opportunities if used carefully in the field. For example, glyphosate-resistant wheat would be a very useful tool to counteract NTSR in wild grasses in Europe. In the longer term, the introduction of novel weed control traits into crops has great potential for future integrated management. There are multiple reports of weed-suppressive crop varieties. The underpinning mechanisms such as allelopathy, plant vigor, and nutrient use efficiency require greater foundational understanding prior to effective translation. Our advancing knowledge of plant pathology may also provide new strategies for weed control, including new herbicides based on microbial pathogens or mechanisms used by them, matched with crops bred to be resistant to these biologicals.Control strategies developed for pathogens are also relevant to controlling weeds that directly parasitize other plants. Parasitic weeds including Striga in sub-Saharan Africa and Orobanche spp. in the Mediterranean can significantly limit crop yields both in the tropics and temperate regions. Traditionally, chemical control of parasitic weeds has been difficult because parasitic plant lifecycles are complex and the host and parasite have similar physiologies . In addition, resistant germplasm has been difficult to develop. This makes introgression of R genes to current commercially desirable crop varieties now possible and hence a priority for future research and control efforts. Host-induced gene silencing targeted against vital parasite genes should be explored as a control strategy where transgenic crops will be accepted .The implementation of any control strategy imposes selection pressures to overcome it. Recent disease outbreaks in plants have been associated with expansions of pathogen geographic distribution and increased virulence of known pathogens, such as in the European outbreak of ash dieback and wheat stem rust in Africa and the Middle East . The scale and frequency of emerging diseases have increased with the globalization and industrialization of food production systems . In the past it has been difficult to monitor for breakdowns in control. Current surveillance and diagnostic systems are reliant on lengthy and costly in-lab processes, such as PCR or ELISA based protocols. Genomic-based surveillance and diagnostic tools are still in their infancy; however, advances in remote sensing and sequencing technologies and increases in computational power are allowing unprecedented opportunities for real-time assessment of pathogen, pest, weed, pipp mobile systems and symbiont populations and the rapid implementation of interventions. Following the influenza paradigm of continual adjustment of the intervention, deployment of control measures should be driven by knowledge of the variability and evolution of pathogen/pest populations . High throughput sequencing is revolutionizing population genetics with further advances on the near horizon. This has stimulated the development of genomic-based surveillance techniques. One example is the development of “field pathogenomics” for surveillance of pathogen populations . This can be based on high-resolution transcriptome data acquired directly from field-collected samples of infected plant tissue. This approach was recently employed to determine the identity and origin of a Magnaporthe oryzae lineage that caused the first severe outbreak of wheat blast in Asia within just six weeks of sample collection . Selective sequence capture of virulence and resistance associated genes could also improve the cost-effectiveness and resolution of field pathogenomics.

Monitoring of human pathogens has capitalized on the recent advances in sequencing technologies; the deployment of portable real-time genome sequencing for surveillance of the Ebola virus in Guinea using the MinION platform provided sequence data that could be immediately exploited for guiding control measures . Similarly, genome surveillance for Zika virus using portable genotyping in Brazil enabled tracking of viral spread into new geographical regions. Widespread deployment of such devices will allow real-time monitoring of plant pathogen variation as long as it is accompanied by adequate reference sequence information. Detailed surveillance of pathogens and pests will reveal their population structure and effector repertoires at the individual and pan species levels. Genome analyses can reveal the center of pathogen diversity, which could be the basis a network of phenotyping centers to analyze germplasm resistance. Furthermore, genomic-based surveillance can also be employed to improve the diagnosis and differentiation of pathogens present that are often misdiagnosed or present in mixed infections . There are several challenges to robust monitoring. Sampling is a major problem. Recent work has shown that adaptive sampling can improve the efficiency of management of some diseases ; however, effective control requires detailed, intensive sampling of host populations which may not be showing symptoms . The distinction between severe, explosive invasions and minor outbreaks which require less expensive intervention is challenging . Foundational research on both theoretical and actual population dynamics on a landscape scale is essential. Therefore, new ideas and technologies are needed to detect pathogens and pests at very low frequency. This is critical for plant hygiene and preventing introductions in the context of increasing global trade. Monitoring for volatiles that are either produced by the pathogen/pest or produced as a consequence to the plant defense response may help with the detection of certain diseases. This could enable the capture of latent diseases and would be deployable in shipping-based trade routes. Latent disease could be detected by machines or dogs. This could be an excellent opportunity for international collaborations to lead the development of diagnostic tools and testing their implementation. Sensitivity is another challenge. Resistance to fungicides is hard to combat because much of the evolution has already happened when detected at the currently typical threshold levels of a few percent. Also, some fungicides are still effective even when some level of resistance exists . Loci likely to be involved in development of resistance are often known when a new fungicide class is introduced; it would be desirable to detect very low levels of change at these loci. The challenge is to find efficient, inexpensive, ways of sampling and to tackle the bio-informatics challenge of heterogeneous samples with many loci being sequenced and examined simultaneously. Monitoring generates very large datasets. Research is needed into methods for efficient data gathering from large numbers of locations and integration with meteorological data to allow accurate epidemiological modelling. Remote sensing from drones or satellites is also providing vast amounts of data with increasing resolution and opportunities for monitoring crop health. Initiatives such as the aggregation of information from CABI “Plant clinics” with specialists able to analyze overall patterns are of great value, but require research in both population biology and social science rather than only biological understanding at the molecular level. Weeds, viruses, nematodes, soil fungi that have limited capacity for movement and produce patches observable from a distance are well suited to remote sensing. Research is needed to link image analysis with data on field performance and genotype, including ground observation of suspicious patches. There is the opportunity for integration of remote sensing with grower observation and response; however, this will require strong partnerships with growers and pest control advisors.As we move towards lower input, sustainable agriculture under changing climatic conditions, it is critical that disease and pest control strategies be considered in the context of the environmental variation and uncertainties resulting from climate change. Climate change models project a range of potential scenarios; as climates change, pathogens, pests and vectors will spread into new areas and new diseases may emerge more frequently. While accurate climate modeling is still under development, the opportunity now exists to investigate how temperature, humidity, CO2 levels, light quality, soil quality, and other environmental factors will affect plant health in the context of diseases and pests. Experimental systems have advanced to the point that they can inform the pathogen/pest layer of climate change models. Investigations can be conducted using high-throughput, sophisticated phenomics approaches to track pathogen and pest interaction with hosts in controlled environmental chambers as well as in field settings . Nonetheless, individual pathosystems need foundational studies before impact will be realized because our current predictive ability on decadal scales is severely limited . There is a dearth of funding for studies of relationships determining long- and medium-term dynamics of plant disease; current understanding of host-pathogen-weather relations rarely extends to comprehension of changes in pathogen populations. Complementary to studies on ecosystem and population dynamics, it is possible to study how environmental conditions that affect immune signaling at the molecular level. In cases where existing resistance genes are functional only within specific temperature ranges, approaches facilitating the expansion of this functional range could be explored. Additional molecular and genetic approaches to optimizing responses to biotic and abiotic stresses should also be investigated .

So, what is the ideal setup for a vertical grow rack in an indoor farm? This comprehensive guide explores every critical component, from structural design and lighting to automation and crop selection, providing actionable insights for farmers, entrepreneurs, and agritech enthusiasts.

Section 1: Core Components of a Vertical Grow Rack System

1. Structural Framework

The foundation of any vertical grow pipp rack is its physical structure. Key considerations include:

• Material: Stainless steel or powder-coated aluminum for durability and resistance to humidity.
• Adjustability: Modular shelves to accommodate different plant heights (e.g., leafy greens vs. fruiting crops).
• Weight Capacity: Ensure racks can support the combined weight of plants, growing media, water, and equipment (aim for 50–100 lbs per shelf).

Example: Gotham Greens uses custom-designed steel racks with reinforced supports to handle hydroponic nutrient tanks and LED fixtures.

2. Lighting System

Lighting is the lifeblood of indoor farming. Vertical racks require layered, energy-efficient illumination:

• LED Grow Lights: Full-spectrum LEDs tailored to crop needs (e.g., blue wavelengths for vegetative growth, red for flowering).
• Light Intensity: 200–400 µmol/m²/s for leafy greens; 600–800 µmol/m²/s for tomatoes or strawberries.
• Light Distribution: Uniform coverage across all tiers using adjustable fixtures (e.g., hanging LEDs or built-in strip lights).

Pro Tip: Use light movers or rotating racks to prevent “hotspots” and ensure even growth.

3. Hydroponic or Aeroponic Systems

Soil-free cultivation is standard in vertical farming. Choose a system that aligns with your crops and space:

• Nutrient Film Technique (NFT): Thin streams of nutrient solution flow through channels (ideal for herbs and lettuce).
• Deep Water Culture (DWC): Roots submerged in oxygenated water (suits fast-growing greens).
• Aeroponics: Mist roots with nutrient-rich spray (maximizes oxygen exposure for plants like basil or strawberries).

Case Study: AeroFarms uses aeroponic vertical racks to grow greens with 95% less water than field farming.

4. Climate Control

Indoor farms demand precise environmental management:

• Temperature: Maintain 68–77°F (20–25°C) for most crops.
• Humidity: 40–70% RH, adjusted for growth stages (higher humidity for seedlings, lower for mature plants).
• CO2 Enrichment: 800–1200 ppm to boost photosynthesis.
• Airflow: Horizontal airflow fans to prevent mold and strengthen plant stems.

5. Automation and Monitoring

Automation reduces labor and ensures consistency:

• Sensors: Track pH, EC, temperature, and humidity in real time (e.g., Arable Labs or ClimateGAN).
• Dosing Systems: Automatically adjust nutrient levels based on sensor data.
• IoT Platforms: Centralized control via apps (e.g., Agrilyst or FarmOS).

Section 2: Designing the Layout for Maximum Efficiency

1. Space Utilization

• Aisle Width: 24–36 inches for easy access and equipment mobility.
• Rack Height: Align with ceiling height (typically 8–12 feet) while leaving space for maintenance.
• Zoning: Separate racks by crop type or growth stage (e.g., germination, vegetative, flowering).

2. Vertical Stacking Strategies

• Density: Balance light penetration and airflow. For leafy greens, 6–8 plants per square foot per tier.
• Crop Rotation: Stagger planting schedules to ensure continuous harvests.

3. Ergonomics and Accessibility

• Height-Adjustable Racks: Use hydraulic lifts or pulleys for easy harvesting.
• Mobile Racks: Implement rail systems to reconfigure layouts as needed.

Section 3: Step-by-Step Setup Guide

Step 1: Assess Your Goals and Space

• Crop Selection: Start with low-maintenance crops like lettuce, kale, or herbs.
• Footprint Analysis: Calculate available space (e.g., 500 sq ft warehouse vs. 10,000 sq ft facility).

Step 2: Choose the Right Hydroponic System

• Small Farms: NFT or DWC for simplicity.
• Large-Scale Operations: Aeroponics for higher yields and resource efficiency.

Step 3: Install Lighting and Climate Systems

• Light Schedule: 16–18 hours/day for leafy greens; 12–14 hours for fruiting plants.
• HVAC Integration: Pair with dehumidifiers and CO2 generators.

Step 4: Automate and Test

• Calibrate Sensors: Ensure pH and EC meters are accurate.
• Run Simulations: Test systems for 1–2 weeks before introducing plants.

Section 4: Real-World Examples of Ideal Setups

1. Plenty Unlimited (USA)

• Setup: 20-foot-tall vertical racks with AI-controlled LED lights and aeroponic misting.
• Result: Grows 400x more produce per acre than traditional farms.

2. Spread Co. (Japan)

• Setup: Robot-automated vertical racks in a 44,000 sq ft facility.
• Result: Harvests 30,000 heads of lettuce daily with 98% less water.

Section 5: Overcoming Common Challenges

1. High Energy Costs

• Solution: Use solar panels or energy-efficient LEDs (e.g., Fluence Bioengineering).

2. Pest and Disease Management

• Solution: Implement UV sterilization and biocontrols (e.g., predatory mites).

3. Labor Intensity

• Solution: Deploy harvesting robots (e.g., Iron Ox or Root AI).

Section 6: Future-Proofing Your Vertical Grow Rack

1. AI-Driven Optimization

• Machine learning algorithms predict crop yields and optimize nutrient formulas.

2. Hybrid Renewable Energy Systems

• Integrate wind, solar, and geothermal energy to cut operational costs.

3. Modular Expansion

• Design racks to easily add tiers or connect to adjacent systems.

Conclusion

The ideal vertical pipp grow rack setup is a symphony of engineering, biology, and technology. By prioritizing modular design, energy efficiency, and automation, indoor farms can achieve unprecedented productivity while conserving resources. Whether you’re a startup in a repurposed warehouse or a corporation investing in agritech, the principles remain the same: plan meticulously, leverage innovation, and adapt continuously.

As vertical farming evolves, so too will the benchmarks for an “ideal” setup. But one thing is certain: the future of agriculture is not just vertical—it’s intelligent, sustainable, and boundless.