Hemp Growing

The third highest ranking concern of the Northern and Southern San Joaquin Valley and Low Desert regions was “water quality ”, while “regulations on water quality” was the third highest ranking concern for the Intermountain region. The third highest ranking concern of the Coastal and ierra Nevada regions was “regulations on chemical use”. The acramento alley ranked “input costs” in the top three concerns. Growers were asked about their top management challenges for the agronomic crops they grow. While several stood out, such as weed control and irrigation/water management ranking as the top challenges , there was relatively strong representation across categories. Soil management, disease control, and harvest operations ranked lowest. When broken down by the top 8 agronomic crops, the highest-ranking management challenges differed among crops . Irrigation/water management was the top management challenge for alfalfa and corn silage growers, while weed control was the top management challenge for dry beans, sunflower, and cotton growers. For rice growers, irrigation/water management and weed management were tied for first as the top management challenge. Nutrient management was the top management challenge for wheat and corn grown for grain.Slightly more than 2/3rds of responses were from California’s Central Valley which is where most of the state’s agricultural production is located . In 2017, counties with the highest total cropland were Fresno , Kern , Tulare , Merced , and San Joaquin . According to USDA NASS data from 2018, the district containing Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare had the highest total gross value of agronomic crops in 2018 , vertical grow rack system while the district containing Butte, Colusa, Glenn, Sacramento, Solano, Sutter, Tehama, Yolo, and Yuba counties had the second highest gross value of agronomic crops in 2018 .

Therefore, since 67% of our respondents represent these two districts, the geography of our respondents appears to be representative of where much of the agronomic crop production is taking place in the state. Representation is lacking most in Imperial county, which is a large producer of agronomic crops, particularly hay crops. In 2018, Imperial county had 341,229 acres in agronomic crops, ranking second in the state for gross value of alfalfa production . However, it is important to note that the average farm size in Imperial County in 2018 is much larger than the state average of 348 acres, meaning that less people may be working larger areas of land. Therefore, looking only at acreage and economic value in agronomic crops may not be representative of how many people in our target population work in a particular region. Regarding the top three field crops grown by respondents, agriculture census data from 2017 shows that rice represented 541,000 planted acres in California, alfalfa 720,000 acres, and wheat 480,000 acres in 2016 . Grain corn represented 420,000 planted acres, silage corn 315,000 acres, cotton 218,000 acres, dry beans 50,000 acres, and sunflower 46,600 acres. Therefore, crop representation in our needs assessment survey roughly follows the area planted throughout the state. The results of our survey indicate that respondents are skewed slightly younger than the distribution of the industry population. According to the 2017 Census of Agriculture Online, the average age of California farmers is 59 years old . Nearly 60% of survey respondents were 54 years old or below, and the greatest number of respondents fell between the ages of 35-44 . This may be related to the mode of survey delivery. Online surveys may be bias towards younger respondents with higher income and education .

Additional drawbacks to online surveys include the fact that the survey presentation may vary based on browser settings and variations in hardware which may increase the likelihood of response error . In addition, the flexibility of the internet and ease with which false identities can be created can make survey results unreliable . Online surveys do have the advantage of allowing for large-scale and inexpensive data collection. With online surveys, costs per response decrease as sample size increases, while for surveys sent through postal mail, costs tend to increase significantly as sample size increases . Research comparing electronic surveys to postal surveys has confirmed that electronic survey content results may be no different than postal survey content results yet provide advantages of fast distribution . Qualtrics Survey Software allowed us to customize survey questions based on the respondent’s primary vocation. While paper surveys can also indicate that a set of questions are only for people that select a particular answer choice in a previous question, the online survey allows for a more customized experience through format and response control . Electronic surveys can also yield a significantly higher response rate than paper surveys . Because the objective of this study was to reach the broadest audience possible, an online survey that could be completed on a computer or mobile phone platform was selected.Water-related issues were clearly the most prominent in our survey responses, representing 4 out of the top 5 concerns listed by respondents. Specifically, regulations on water use and water costs were the two concerns that had the highest number of respondents expressing that they were “very concerned”.

The Sustainable Groundwater Management Act , signed into law by Governor Brown in 2014, requires groundwater-dependent regions to stop over drafting groundwater and develop plans to balance pumping and recharge . Since this is the first time groundwater use is subject to regulations in California, growers are expectedly concerned about changes that will occur as a result. Groundwater contributes % of California’s water supply in an average year, and up to % or more during dry years, while some agricultural and disadvantaged communities rely on groundwater for up to 100% of their water supply . This suggests that tensions will grow in the future as groundwater pumping regulations are enacted. Water is generally the most-limiting input for crop production, and therefore impacts on cost, availability, or quantity will limit the capacity for growers to manage this resource. California has approximately 2.8 million ha of irrigated land, which produces nearly 90% of the harvested crops in the state . A decrease in water availability because of new regulations has implications for maintaining the same area under irrigation into the future. Impacts of water decline were already being felt before SGMA was signed into law. Due to increasing incidence of prolonged drought, California saw a decline of more than 200,000 acres of irrigated land between 2004 and 2006, while nearly 250,000 acres had to be idled in 2014 alone . It is projected that an additional 500,000 to 1 million acres of land in the San Joaquin Valley alone may have to be retired due to SGMA . Uncertainty and difficulty around water resource planning and management is amplified by increasing unpredictability of weather patterns. Annual rainfall varies greatly in California – more notably than other parts of the country – which makes predicting rain fall year to year a challenge . For these reasons, it was unsurprising that irrigation and water management were ranked as a top management challenge for survey respondents.A large portion of respondents expressed that they are “very concerned” about regulations on chemical use, such as pesticides, fertilizers, and herbicides. Given new or impending bans on agrochemicals in California, vertical farming racks it makes sense that growers are concerned about finding alternatives. California’s recent ban on chlorpyrifos – an inexpensive and effective pesticide used nationwide since 1965 – highlights this issue. Chlorpyrifos exposure has been linked to harmful health effects, including neurodevelopmental disorders . In 2015, the Environmental Protection Agency proposed a federal ban for chlorpyrifos on all food crops, but soon after, the federal government under the Trump administration concluded the science was “unresolved” and removed the ban. Regardless, California, along with Hawaii and New York, decided to move forward with banning chlorpyrifos. In California users were required to stop using these products on December 31, 2020. Other states continue to use these agrochemicals, leaving California growers to feel like they are at a competitive disadvantage.

The ban on chlorpyrifos has and will likely continue to be felt where it was most heavily used. This includes Fresno, Tulare, Kern, and Kings counties, all of which have strong representation in our survey. The period between 1991-2012 saw large increases in chlorpyrifos use in these four counties . During this time, 7.2 million pounds of chlorpyrifos was used in Fresno county, 6.1 million pounds was used in Tulare county, 5.4 million pounds were used in Kern counties, and 3.2 million pounds was used in Kings county . Effects will also be felt heavily in alfalfa production since chlorpyrifos is the most popular side-spectrum insecticide for management of key alfalfa pests, such as the alfalfa weevil and aphids . In our survey, 5% of alfalfa growers and consultants said that they were “very concerned” about regulations on chemical use. There are other impending regulations on neonicotinoids in California, which are commonly used on cotton, corn, and grains. However, recent evidence has linked these chemicals to honeybee die off and declining pollinator health . Because of the high solubility of neonicotinoids in water, it has also been found that they readily leach into water bodies and can persist over multiple years, which has implications for aquatic species . In July 2018, the California Department of Pesticide Regulation announced that they will not consider applications of any new uses of neonicotinoid insecticides until re-evaluation of the chemicals are completed . An addendum was published in January 2019, and the investigation is ongoing . This prospective ban is particularly worrisome to certain stakeholders. A recent study found that in 2011 between 79-100% of maize acreage in the USA were treated with neonicotinoids . In addition to chemical bans, there are significant challenges with getting new products registered in California. California is unique in that tens of thousands of residents live near intensively farmed areas and the production is often labor-intensive. Therefore, the effect of pesticide use at the agricultural-urban boundary and the potential effect on farmworkers are key evaluation factors for product registration by the California DPR, while there is not as much emphasis on these factors at the federal level by the U.S. EPA . In addition, federal pesticide law mandates that the U.S. EPA consider the economic benefits of a pesticide when deciding whether to register it. California law does not allow consideration of economic benefits in the decision to register a pesticide unless there is no feasible alternative. Therefore, the financial advantages of using a certain pesticide cannot outweigh the health risks of use under California law. This is beneficial for communities, farmworkers, and consumers – yet, it may seem unfair to growers and input suppliers when market competitors have access to chemicals that they do not.Weed control was ranked as the top management challenges by growers and consultants. Current estimates of losses on global crop production show that weeds cause the largest losses , followed by insects , and diseases . Total weed control costs in the U.S. are more than $11 billion a year, most of which is spent on herbicides . The direct annual cost to monitor and control invasive plants in California is estimated at around $82 million . One of the most widely used herbicides is glyphosate. Although the Environmental Protection Agency has repeatedly stated that glyphosate is safe, California has led the charge in holding Monsanto accountable for Roundup’s link to cancer in humans and the death of important insects. As early as 2017, California added glyphosate to its list of carcinogens under Proposition 65 and the state has a growing number of cities and counties banning or restricting glyphosate. To date, more than 40 communities in the state of California have restricted the use of glyphosate in some capacity . Yet, weed management as a category in our survey was broad and could mean many things – new weed species, herbicide resistance, drift issues, or preventing the use of certain herbicides, Therefore, UCCE must work directly with agronomic crop producers to determine future directions of weed management research. Herbicide resistance is a growing concern in cropping systems throughout the state, particularly in rice .

Together, these rapidly expanding species cover more than 15 million acres throughout California . The phenological development of medusahead and yellow starthistle is in part what makes these invaders so successful. Medusahead, and particularly yellow starthistle, mature late in the annual growing season . These species germinate after the first fall rains, with smaller germination events sometimes occurring later in the wet season . Although germination timing is similar to that of the surrounding grassland community, medusahead does not produce seed heads until late April or May, after most naturalized annuals have completed their life cycle . Yellow starthistle commonly produces seed heads in May and June; it begins flowering in June and can continue beyond October . In fact, many of yellow starthistle’s developmental stages generally extend well into the summer dormant period distinctive to Mediterranean climates . The later development periods enable medusahead and yellow starthistle to take advantage of late spring and early summer rains when they occur. When late-season moisture is present, medusahead and yellow starthistle will continue to grow after potential competitors have stopped, allowing them to dominate and dramatically alter the vegetation structure .Prescribed livestock grazing is commonly proposed as a low-cost, if not profitable, option to manage weedy species on rangelands. Prescribed grazing is the controlled implementation of the timing, vertical grow room design frequency and intensity of grazing to achieve specific goal, such as weed control. Small-scale grazing studies have examined the effects of livestock type , grazing intensity and grazing season on individual weed species .

These studies have consistently demonstrated that properly timed and intensive grazing can reduce medusahead cover by 30% to 100% and yellow starthistle flower heads by 75% to 90% . Experimentally manipulated livestock grazing has also been shown to enhance herbaceous diversity and native plant richness in vernal pools, interior annual grasslands and coastal grassland sites . However, there is little published work examining pasture-scale implementation of prescribed grazing to manage invasive weeds.Across California, rangeland managers have reported that livestock grazing can be managed to control medusahead and yellow starthistle . These findings are experiential rather than experimental — that is, based on direct implementation, observation and site-specific fine-tuning of intensity, season and frequency of livestock grazing to achieve specific goals. A recent scientific review of conservation effectiveness of rangeland management practices highlighted a critical need for the monitoring and reporting of practice effectiveness at the pasture scale . Collaborative, on-theground management implementation and monitoring will enable managers and researchers to better assess effectiveness and practicality of conservation practices such as prescribed grazing to control invasive weeds. Our objective was to assess the effect of a “real” prescribed grazing regime implemented by ranch personnel on medusahead and yellow starthistle populations on a Bureau of Land Management grazing allotment known as the Bear Creek Unit of the Cache Creek Natural Area.The Bear Creek Unit, located in Northern California’s interior coast range in Colusa County, is an 11,090-acre BLM-managed land that consists of a patch-mosaic of annual grasslands, blue oak woodlands and serpentine chaparral plant communities. The climate is Mediterranean, with hot, dry summers and mild, wet winters. Mean annual precipitation is 24 inches, and mean annual air temperature is 61°F .

Sites examined in this study ranged from approximately 1,200 to 1,600 feet in elevation. For this study, we targeted the annual grassland and blue oak woodland plant communities, as they provided the majority of forage on the management unit, and were dominated by the target weeds. In the study area, soils were largely formed from residuum of sandstone and shale , with a small inclusion of soils formed from alluvium . Common nonnative annual grasses include soft chess , slender oat and ripgut brome . This area also supports various native forbs, including miniature lupine , Ithuriel’s spear , owl’s clover Chuang & Heckard, mariposa lily and tidytips . Native grasses are widely scattered in the area, with purple needle grass being the most prominent native perennial grass. Medusahead and yellow starthistle are common across the landscape, with an emerging population of barb goatgrass also present.Until August of 2001, the Bear Creek Unit was continuously grazed throughout the growing season under grazing leases. The BLM, which acquired the Bear Creek Unit in 1999, terminated grazing in 2001 in an attempt to enhance native plant cover. In the 4 years following cessation of grazing, BLM monitoring teams reported increased invasive weed cover and high accumulations and persistence of vegetative litter, or thatch . In fall of 2006, average residual dry matter across the unit was estimated to be 4,200 pounds per acre. In working toward invasive weed control — one of BLM’s top management priorities — the BLM collaborated with local stakeholders to reintroduce grazing on the Bear Creek Unit in 2006. To target medusahead and yellow starthistle, we implemented a moderately stocked, rotational cattle grazing system across 11 paddocks, ranging from 80 to 600 acres in size. Paddocks were generally grazed January through May using cows calving between January and March — cattle on and off dates, stocking densities and paddock rotations were made at the discretion of the site manager based on factors such as drinking water availability, forage availability and cattle conditions . From 2006 to 2011, cattle numbers ranged from 318 to 520, averaging 392 total cows during the study period.

Grazing event duration ranged from several days up to 2 weeks, with two grazing events per paddock: one grazing from late November to February to reduce weed thatch, and allow alternative species to establish ; and one grazing event from March to June to target late-flowering invasives . By October of 2009, we estimated average RDM across the unit to be 1,400 pounds per acre, or approximately one-third the RDM observed under initial ungrazed conditions.Prior to reintroduction of cattle grazing, we established permanent paired plots in each of the 11 paddocks. Permanent plots were chosen in a random stratified manner to ensure sample sites were representative for each pasture. Exclosure plots measured 8 feet by 8 feet and were livestock proof. To examine shifts in plant species cover and abundance over the course of the study, we began monitoring plant community composition in June of 2006. At each set of permanent grazed and ungrazed paired plots, we estimated percent basal cover by species within a 10-ft2 hoop. Ocular estimates of herbaceous composition were collected after peak standing crop for both grazed and ungrazed plots in June of 2006, 2009 and 2011. This resulted in a total of 22 observations for each year, and 66 total observations for the study period. To determine if grazing management at the Bear Creek Unit significantly impacted medusahead and yellow starthistle over the course of the study, we used linear mixed effects regression to examine trends in cover of these species between grazed and ungrazed treatments. The dependent variables observed were percent medusahead and yellow starthistle cover, and the independent variables were treatment , year and the interaction between treatment and year. Within each treatment, we also examined changes in cover between the baseline and final evaluations for the most commonly occurring species: medusahead, yellow starthistle, soft chess, filaree , red brome , ripgut brome, slender oat and a composite functional group composed of several thatch-loving species including red brome, ripgut brome and slender oat. We used linear and generalized linear mixed effects regression models to test for differences in percent observed species cover between 2006 and 2011. For all analyses, site identity was included as a random term to account for repeated measurements . Standard diagnostic tests were used to check assumptions of linearity, normality and constant variance. Analyses were performed using STATA/SE 13.0 statistical software . To examine changes in overall plant community composition, clone rack we used non-metric multidimensional scaling . NMDS is an ordination technique widely used to examine patterns in multidimensional data and, unlike other ordination methods, makes few assumptions about the data. Species cover values were log-transformed, and NMDS scores were calculated based on a Bray-Curtis dissimilarity matrix . Analysis was conducted in the R software environment using the metaMDS routine from the vegan package . The metaMDS function selects several random start positions to find a global solution, so that it does not become trapped at local optima. The final configuration is rotated via principal components so that the first dimension explains the greatest amount of variance. NMDS was run for 2 through 6 dimensions, with the optimal number of dimensions selected via examination of a scree plot, which displays stress versus dimensionality for each solution .

To examine whether overall plant community composition significantly differed between grazed and ungrazed treatments, we used blocked multiresponse permutation procedures . MRBP provides a non-parametric test of multivariate differences between pre-defined groups, such as “grazed” and “ungrazed” plots . Observations were blocked by plot pair identification number, and species cover data were log-transformed. Our analyses showed that prescribed grazing applied to Bear Creek Unit did not impact yellow starthistle cover. Trends in basal cover of yellow starthistle did not significantly differ between grazed and ungrazed treatments , with no significant changes in yellow starthistle cover for either treatment between baseline and final evaluations . The lack of response to grazing may be due to a mismatch in the timing of grazing and the post-bolting/pre-flowering phenological stages of yellow starthistle. Since yellow starthistle matures and produces seeds later than other species, including medusahead, grazing late in the annual growing season is particularly important for effective suppression . In addition, yellow starthistle populations commonly exhibit multiple life forms simultaneously . This diversity creates an additional obstacle to suppression, because individual plants are not all susceptible to grazing at the same time. During this study, timing of cattle removal was dictated by real management considerations such as availability of water and desirable forage for livestock, which were both limited by May in most years. As a result, cattle were likely not present during the post-bolting/pre-flowering phenological stages when grazing can reduce yellow starthistle cover and seed production .Following baseline botanical evaluations, medusahead cover within grazed treatment plots fell by roughly half in 2009. Additionally, in 2009, medusahead cover in the grazed treatment was significantly lower than that observed in the ungrazed treatment . However, by the final evaluation , medusahead cover for both grazed and ungrazed treatments converged to similar levels. As with yellow starthistle, research has shown that grazing late in the growing season is critical to successful medusahead control . However, medusahead develops earlier in the spring than yellow starthistle and does not exhibit yellow starthistle’s diversity of life forms. Medusahead’s earlier maturing phenology narrows the window for grazing to achieve suppression. Although managerial constraints in this study made it impossible to graze late enough into the season to impact yellow starthistle, medusahead populations were impacted in several years. The differential reduction of medusahead cover in the grazedtreatment between the periods 2006 through 2009 and 2009 through 2011 is potentially explained by three interacting factors: timing and amount of rainfall; timing of cattle removal each spring; and ability of medusahead to recover from grazing and produce seed. During the period 2006 through 2009, when medusahead cover was significantly reduced in the grazed treatment , late spring and/or total annual rainfall were substantially lower than reported long-term averages in 2007 and 2008. Lack of late season precipitation created dry soil moisture conditions at the end of the grazing season, which potentially diminished the ability of medusahead to recover from grazing and produce new seed heads , which is why the plant reduction created in 2008 is apparent in 2009. With the exception of 2006 and 2010, cattle were removed from the management unit between May 22 and 27. The lower late season rainfall, and resulting depleted soil moisture levels, may have created a multi-year window of opportunity in which the timing of grazing overlapped with the most susceptible phenological stages of medusahead development. In contrast, late spring rainfall during the period 2009 through 2011 was well above the reported long-term average, which potentially enhanced the ability of medusahead to respond to postgrazing conditions. Although the timing of cattle removal was similar to that of the 2006–2009 period , this late season rainfall enabled medusahead plants to recover from grazing disturbances and produce new inflorescences.

Researchers and advocates in Uganda used data on deforestation, costs and benefits of tobacco farming and other issues to develop public support for effective tobacco control policies and for the Framework Convention on Tobacco Control. In February and March 2004, researchers with the Environmental Action Network conducted a survey among farmers in Uganda focusing on deforestation and economic and health status. Researchers interviewed government officials on the same issues. Findings revealed that farmers in Uganda suffer worsening poverty and poor health associated with tobacco growing. The project is a best practice to retrieve and organize data on the social and environmental costs of tobacco growing. In the U.S., tobacco farmers and tobacco control advocates committed to reducing disease caused by tobacco and ensuring the prosperity and stability of tobacco farmers, their families and communities. Beginning in 1994, national groups such as the National Black Farmers Association and the Campaign for Tobacco-Free Kids, state groups such as the Coalition for Health and Agricultural Development in Kentucky and the North Carolina Council American Cancer Society, and regional groups such the Burley Tobacco Growers Cooperative and the New England Society of Public Health Education participated in meetings with representatives of all groups affected by tobacco, provided expertise to educate participants of similar and opposing positions, vertical farming supplies and encouraged tobacco dialogue to strengthen alliances between farmers and health advocates. Cooperation and commitment to promote tobacco farmer prosperity and public health renders false the dichotomy between policies for tobacco agriculture development and policies directed at the reduction of tobacco use.

The common ground established by farmers and health groups in the U.S. is a best practice that could be used to build partnerships for tobacco farmer welfare and tobacco control in developing countries. Conclusion Tobacco farming contributes to poverty and insufficient economic development in developing countries. Farmers under contractual obligations to tobacco companies or farm landlords are vulnerable to leaf downgrading, suppressed tobacco prices, and inflated prices for inputs. Bonded labor prevents farmers from receiving earnings to cover costs for inputs, food requirements, and health care needs. Child labor undermines children’s education and threatens their health and physical growth, pushing children into a cycle of poverty. Tobacco farming involves wood use for curing and pesticides and fertilizers that destroy forests and pollute soils and water tables. Tobacco farming erodes the lives of present and future generations of farmers, harming human and land capital, key assets for rural development, that could otherwise be devoted to healthy crops and environmentally friendly agriculture.Ecological interactions are increasingly recognized as being highly contingent on their context, shaped by forces that are both historical and contemporary as well as biotic and abiotic . For example, variation between years and sites can have profound influences on the outcomes of field experiments in community ecology . If we want the results of ecological experiments to be general, and not unique to a particular site or time, we need to better explore and understand these and other contingencies. Understanding such contingencies is also crucial for successfully restoring ecosystems. One emerging theme is the phenomenon of priority—how differences in arrival times by different species may have profound effects on the long-term trajectories of communities .

Such priority effects were the centerpiece of initial definitions ofassembly theory, and are currently being explored as potential management techniques in ecological restoration, in particular to assist the establishment of less-competitive species in communities . A number of experimental studies on perennial herbaceous plants have shown that a 1- to 3-week priority can significantly affect initial community structure . In other words, initial community structure is contingent on the relative arrival times of species. This includes research in our study system , where we have extended this concept to show that even small initial priority effects of native perennial grasses over exotic annual grasses can multiply over several years to result in substantially greater cover by the natives . Priority effects may be particularly relevant for testing the mechanisms underlying the competitive advantage of invasive annual plants over native perennials. In many western US ecosystems, these invasives have become community dominants . It has been posited that this competitive advantage is driven by the earlier germination and initially higher growth rates of the annuals . Several short-term priority experiments suggest that this is the case . Most of these studies were carried out at a single site and in a single planting year, and we do not know how the strength and consequences of this priority effect differ though space and time. The structure of communities may also be dependent on conditions in the year in which they were established . Ecologists , and restoration practitioners have noted differences in project outcomes and results from experiments initiated in different years, but these have not been subject to controlled experiments where putative drivers of year differences are manipulated. Community structure may be also contingent on site conditions, and the relative abundances of different species may change over relatively small environmental gradients .

It is likely that these differences are due to a combination of site effects, year effects or differences in restoration practices , but these different factors have rarely been examined together in controlled, replicated experiments. Here we report the results of experimental tests of how seeded native perennial grass cover is influenced by competition with exotic annual grasses, the relative timing of seed arrival , rainfall addition and geographical location . We also tested the interactions among priority, rainfall addition and site effects.Over the previous 6 months , we had collected seeds of local provenance at each of the three sites from four native perennial grasses and four exotic annual grasses . For a few of these 24 provenances for which local reproductive populations could not be located, we purchased seeds from local native seed providers. We made some adjustments at the species level to match local sites: for the annual Avena species, we collected and sowed A. fatua in Davis and the very similar A. barbata at McLaughlin and Hopland; for the annual Vulpia species, we collected and sowed V. myuros at Davis and McLaughlin, and the similar species V. bromoides at Hopland. At each site, we established five blocks, each with two replicates of the following four planting treatments: natives sown alone , natives sown together with exotics , natives sown and exotic sown 2 weeks after the next germinating rain and exotics sown alone 2 weeks after the next germinating rain . Planting treatments were implemented in an additive design . In a splitplot design, blocks were divided in half, with one half designated for rainfall manipulation . Each experimental plot was 1.25 m on a side, and each was separated from adjacent plots by 1 m. Prior to planting, all sites were tilled to control weeds, both before and 1–2 weeks after the first germinating rains in the fall. Within 1 week of the second tilling, we did the first sowing . Each plot was lightly raked, sown and then raked again to increase seed–soil contact. There was a second germinating rain on 24 November. Two weeks later, the plots designated to receive a second sowing where sown. Unusually, there had been little rain in the intervening 2 weeks, and there was no rain in the 5 weeks that followed the second sowing. Therefore, to simulate an early season rain that was more similar to a normal year, weed rack the four treatments designated for rainfall manipulation in each block were watered with the equivalent of 1.25 cm of rain immediately after the second sowing pass .

Over the following weeks, plots were weeded of volunteer forbs. Because grasses are difficult to reliably identify at the seedling stage and because there were volunteer seedlings of sown species at two of the three sites , we only weeded the obvious non-sown grass species. Nonetheless, there were significantly greater exotic grass densities in the plots deliberately sown with exotics than in those without . Surveys were carried out after the main winter rain had ceased in the spring, at the time of peak flowering. For the Davis and Hopland sites, this was 26–31 May 2012. The phenology of the grasses was delayed at the higher elevation McLaughlin site, which was surveyed 8 June 2012. The areal cover of each seeded species was visually estimated for each plot. We also recorded the cover of common non-sown exotic grasses.For each of the following analyses, linear mixed-effects models were specified with the lme function from the R software package ‘nlme’ . Block was included in all of the models as a random effect. Where necessary, variance structures were specified using the VarIdent function to address violations of homogeneity of variance . ANOVA tables were generated by calling the anova command from the ‘stats’ package . Due tothe nested nature of the design we tested the effects of each factor with sequential sums of squares.It is not surprising that the success of sown native grasses was greatly reduced when sown together with exotic annual grasses , and that in general, cover by exotic annual grasses and native perennial grasses were strongly negatively correlated . In grassland restoration projects in the Central Valley of California, the presence of exotic annuals represents perhaps the greatest challenge to successfully establishing native perennial grasses, and aggressive pre-sowing control of exotics is now considered a sine qua non for restoration. Conversely, one of the most effective means of preventing the dominance of exotic annuals is the establishment of cover by native perennial grasses . Together, these processes result in strong negative correlations between exotic and native grasses. The magnitude of the competitive suppression of natives by exotics, however, varied across the three sites. Site effects are a complex array of interacting differences, including different means and patterns of rainfall and temperatures, different intensities and identities of weed challenge, and different herbivore pressures. We can only suggest which are the important drivers, but note that in the coolest site , where native grasses achieved little cover in the first year even when planted alone , they were significantly less affected by the sown exotic annual grasses , which also had reduced cover . Although in practice weed control often seeks to greatly reduce the challenge of exotic annuals for at least the first year of native planting, our results show that even a much briefer respite can have a profound effect. When exotic annual grasses were seeded just 2 weeks after germinating rains for the natives, their ability to suppress these natives was greatly reduced . This provides experimental support for the suggestion that one of the ways the exotic annual species outcompete natives in California grasslands is their demonstrated earlier germination and faster growth . The fact that the tE treatment had nearly as much exotic cover as the NE treatment strongly suggests that the late sowing did not itself greatly reduce eventual exotic cover, but that this occurred only in the presence of natives, i.e. as a priority effect. There are also reasons to believe that these differences in community structure arising from initial differences in our experimental treatments have long-term consequences . Vannette and Fukami made several predictions about the strength of priority effects that apply in this system . In particular, they suggested that priority effects would be greater under higher resource availability . In our system, however, watering reduced the strength of priority effects. This was not because of increased resource availability perse, but rather because the watering treatment effectively reduced the duration of the priority treatment. Greater temporal priority usually results in stronger priority effects . This experiment was initiated in a year when there was a 4-week drought following a few weeks of germinating rains in November . Our watering treatment suggests that one of the reasons that the priority effect was so strong in our experiment was this early wet season drought that allowed sown natives to grow for almost a full month before exotics germinated. When this drought was partially alleviated by watering, the strength of the priority effect was significantly reduced . We would predict that in a year with more consistent fall rain, these priority effects would be milder. Indeed, in a very similar experiment carried out in 2008, this was the case .

Amaranthus palmeri decreased the yield of no. 1 and jumbo grades at all densities greater than 1 plant m−1 row when compared with weed-free sweetpotato yields . Similarly, D. sanguinalis at 1 plant m−1 row decreased the weight of sweetpotato jumbo grade when compared with the weed-free check . Digitaria sanguinalis densities greater than 2 plants m−1 row decreased no. 1 grade sweetpotato yield relative to the weed-free check, with 16 plants m−1 causing the greatest loss of no. 1 and jumbo grades. These findings further demonstrate the negative impact of A. palmeri and D. sanguinalis on sweetpotato yields at low weed densities. Interspecific competition is also reflected in biomass reduction of one or both plant species competing with each other Interactions between year, crop versus no crop, and weed density were not significant ; therefore, means pooled over years were obtained for density and crop versus no crop combinations for each weed species. Biomass per meter of row of A. palmeri and D. sanguinalis increased with increasing weed density . The presence of sweetpotato reduced overall biomass per meter of row for both weed species at densities of 1, 2, and 4 plants m−1 row. Furthermore, sweetpotato reduced the rate of bio-accumulation for D. sanguinalis, as can be seen when comparing the slopes of biomass accumulation of both weeds . We believe that this was an effect of weed height, as A. palmeri quickly establishes and reduces the light reaching the sweetpotato canopy, vertical farming racks whereas D. sanguinalis does not exceed the sweetpotato canopy height as quickly as A. palmeri and is therefore less competitive with sweetpotato for light.

The impact of A. palmeri on light interception with the sweetpotato canopy has been documented by others . Individual weed biomass of A. palmeri and D. sanguinalis was similar across weed densities when grown with sweetpotato . Individual weed biomass for A. palmeri and D. sanguinalis, however, was lower for all weed densities when grown in the presence of sweetpotato compared with weeds grown without sweetpotato. The reduced individual biomass and biomass per meter of row for both weeds, when grown with sweetpotato, indicate that interspecific interference is occurring between sweetpotato and weeds. Crop biomass reductions are generally associated with increased weed competition and yield losses . However, in this study, although weed biomass was lower when grown with sweetpotato, increased weed density did not reduce sweetpotato biomass, despite the reduction in sweetpotato yield at the same densities.Individual dry biomass of each weed species growing without sweetpotato decreased as weed density increased . In the absence of sweetpotato, individual dry biomass ofboth weeds was fit to a linear-plateau model. Individual weed biomass was greatest for both weeds at the lowest density. Amaranthus palmeri and D. sanguinalis individual plant biomass decreased 71% from 1 to 3 plants m−1 of row and 62% from 1 to 4 plants m−1 of row, respectively, and remained unchanged at densities above 4 plants m−1 row for both weeds . This finding was similar to the trend observed in peanut for A. palmeri. We believe that the reduction in individual weed biomass for A. palmeri and D. sanguinalis at lower weed densities when grown without sweetpotato is due to increasing intraspecific competition as weed density increases. At the higher densities of both weeds, the impact of intraspecific competition has limited effect on further decreasing individual weed biomass.

The established threshold is the density at which all weeds achieve maximum accumulated biomass before intraspecific competition begins. Further biomass increases would require densities resulting in weed mortality due to intraspecies competition, and such densities were not evaluated in this study. This study demonstrates that A. palmeri and D. sanguinalis have the ability to reduce yield at densities as low as 1 to 2 plants m−1 row. Sweetpotato competes with A. palmeri or D. sanguinalis, resulting in reduced weed biomass. This observation suggests that sweetpotato with rapid canopy establishment and dense growth habit may provide additional competition with weeds and reduce yield loss, as proposed by Harrison and Jackson . Future studies should establish critical weed-free periods for these weeds in sweetpotato, investigate competitiveness of resistant weed biotypes with sweetpotato, and determine weed interference with sweetpotato under varying management practices .Dittrichia graveolens Grueter, commonly known as stinkwort, is a member of the Asteraceae, or sunflower, family. This plant is native to the Mediterranean region of Europe, occurring as far east as Turkey, Afghanistan and Pakistan . Stinkwort is an erect, fall flowering annual that can grow about 2.5 feet tall. Its foliage has sticky glandular hairs covered in resin. The resin emits a strong aromatic odor that resembles the smell of tarweeds. The flower heads are 0.2 to 0.3 inch in diameter and consist of short yellow ray flowers on the outer edge and yellow to reddish disk flowers in the center. Stinkwort is closely related to fleabanes, horseweed , goldenasters and telegraph weed , but it also closely resembles the tarweeds .

From a distance, stinkwort can resemble Russian-thistle , also called tumbleweed. Because it is fairly unattractive and nondescript in appearance, stinkwort initially passed unnoticed by many botanists and weed managers, and it was not included in the 1993 edition of The Jepson Manual of California flora . In its native range and some introduced regions, stinkwort inhabits riparian woodlands, margins of tidal marshes, vernal pools and alluvial floodplains, although it has not yet invaded these wildland areas in California. In California and other introduced areas of the world, stinkwort is most often found in disturbed places, such as overgrazed rangelands, roadsides, pastures, wastelands, vineyard edges, gravel mines, levees, washes and mining sites, although in California it is seldom found in rangelands or pastures . Stinkwort grows best on well drained, sandy or gravelly soils and thrives in areas with hot, dry summers but can also do well along the margins of wetlands. In addition, this plant tolerates a variety of soil types and survives under a range of soil conditions, temperatures and precipitation regimes . When adequate moisture is available, stinkwort can even survive on serpentine or saline soils. In Europe, this plant was shown to tolerate and to possibly hyper accumulate heavy metals, including mercury, zinc and copper .While stinkwort is native to the Mediterranean region, including Egypt and other areas of North Africa, this species has also been introduced to several European countries where it is not native. Within the last two decades, this weed has been spreading rapidly along the highways of Central Europe. In summer 2008, stinkwort was detected for the first time in Slovenia and Austria . Outside of Europe, stinkwort has been reported as an invasive species in Australia and South Africa . Stinkwort is not considered as a palatable species to animals. In fact, it is reported to cause poisoning in livestock . Although livestock mortality is rare, it appears to be due to enteritis caused by the barbed pappus bristles on the seed, which can puncture the small intestine wall . Stinkwort can also cause contact allergic dermatitis in humans . However, impacts to wildlife, natural ecosystems and working landscapes have not been broadly characterized. This is likely due to its very recent introduction and expansion in California and to the lack of published information on the species elsewhere in the world.The first record of Dittrichia graveolens in California is a collection made in 1984 near Milpitas in Santa Clara County . Although the initial mechanism and time of introduction of stinkwort in the state are not documented, many of the earliest collections were made in the south and east San Francisco Bay Area . Stinkwort has since spread to numerous counties in California, and many additional herbarium collections have been made throughout the state . Using the Consortium of California Herbaria records, we determined the rate of stinkwort’s spread since the first discovery in Santa Clara County. Based on collection date and location data from herbarium records, this weed invasion appears to have had only a brief lag period and to have expanded at an exponential rate over the past 18 years .

This has caused increased concern among resource managers across the state. Although it is still uncommon in many places where it is found, stinkwort has been reported in 36 of the 58 California counties . Stinkwort seeds are likely spread by wind, on the fur and feathers of mammals and birds and on motor vehicles and equipment, thus moving along transportation corridors. While the primary expansion has moved radially from the original infestation in Santa Clara County, vertical grow rack unconnected populations have also been discovered in San Diego and Riverside counties . This is likely due to either separate introductions or long-distance movement on vehicles.Stinkwort has very high seed viability, with an average of about 90% of the seeds capable of germination at the time they disperse from the plant. There does not appear to be primary dormancy in the seeds, which is defined as a seed that is dormant at the time it disperses from the plant . These traits, combined with the small seed size, suggest that seed longevity in the soil should be relatively short, perhaps 2 to 3 years. Seeds are capable of germinating at nearly any time of year in the field, but they typically germinate throughout winter and early spring following periods of precipitation. We have shown that germination is limited by soil moisture, rather than soil temperature or low light conditions . When seeds germinate in winter, the plants remain as small rosettes until mid-May. During late spring and summer, they develop into pyramid- or sphere-shaped plants that superficially resemble Russian-thistle. What makes stinkwort’s life cycle rather unusual is that it matures muchlater in the season than most annuals, even other late-season winter annuals . For example, yellow starthistle begins to send up a flowering shoot in April, begins flowering in late June, and — like most late-winter or summer annuals — has completed its life cycle by September or October. In contrast, stinkwort begins to bolt in mid-May , grows most of its branches and leaves between June and September and flowers and produces seeds from September to December. Flowering in stinkwort appears to be controlled by photoperiod , as all plants initiate flowering at the same time regardless of when they germinated . Aside from the tarweeds, there are few other late-season winter annual species with a similar life cycle in the native California flora. Some other weedy species, such as Russian-thistle, horseweed Cronquist and yellow starthistle , have similar life history strategies, with only Russian-thistle and horse weed flowering within the same time frame as stinkwort. In contrast to stinkwort, Russian-thistle is a summer annual that germinates in spring.The environmental and economic impacts of stinkwort in California have not been fully realized and are largely unknown. Our greenhouse studies have shown that stinkwort is dramatically suppressed when grown under shaded conditions, even at 50% light . Thus, like yellow starthistle, stinkwort is not expected to be competitive in understory communities of woodland and forest ecosystems. However, stinkwort can form dense infestations along highways and in open disturbed areas. In addition, while the establishment of this weed in undisturbed wildlands and rangelands is currently very limited in California, invasion of such areas over time is likely based on the pattern of spread in Australia. We are now conducting studies comparing the below ground growth and development of stinkwort with two other common grassland annual species: yellow starthistle and virgate tarweed . Yellow starthistle is an invasive winter annual, and virgate tarweed is a native species that, like stinkwort, is a late-season winter annual. The goal is to determine whether stinkwort shares the characteristics of yellow starthistle and virgate tarweed that allow them to compete with shallow-rooted grasses. These characteristics are a rapid rate of root growth and deep soil root penetration. Initial results indicate that while stinkwort does eventually grow roots as deep as yellow starthistle and virgate tarweed, this occurs several weeks after these other grassland annuals grow their roots. Thus, it may be that stinkwort will not be a significant invasive plant of rangelands, except in years when there is significant late-season rain or when competitive winter annual species are removed by overgrazing. Nevertheless, we have observed stinkwort in open riparian systems, where water is not a limiting factor and a slow-growing shallow root system will not limit its competitive ability.

Awan et al. did observe a decrease in plant height from pendimethalin treated plots in a dry-seeded system with no recovery by the final evaluation.Pendimethalin is currently not available for water-seeded rice; however, these results support the introduction of pendimethalin in California water-seeded rice. These results indicate rice injury is reduced with a post-emergence application after the 3- to 4-leaf stage rice in a water-seeded system compared to an application at 1- to 2-leaf stage. Pendimethalin is not a stand-alone herbicide and will need to be accompanied with other available herbicides to achieve season long weed control. In general, most rice cultivars tested were relatively tolerant to pendimethalin when treated after the 3-leaf stage rice; furthermore, cultivars with lower seedling vigor scores may become more injured from a pendimethalin post-emergence application. The results provide supporting data for registration of pendimethalin in water-seeded rice and provide a base knowledge from which further work should be conducted to enhance its use in this system.The authors would like to acknowledge the California Rice Research Board and BASF for providing funding for this project, the California Rice Experiment Station for their support in field management, and the various past and present lab members who assisted with this project, in particular Saul Estrada and Dr. Alex R. Ceseski. The authors also acknowledge the D. Marlin Brandon Rice Research Fellowship by the California Rice Research Trust, the Horticulture and Agronomy Graduate Group scholarships including the Bert and Nell Krantz Fellowship and the Jack Pickett Agricultural Scholarship, and the Department of Plant Sciences, UC Davis for the award of a GSR scholarship funded by endowments, indoor weed growing accessories particularly the James Monroe McDonald Endowment, administered by UCANR, which supported the student.

Rice is a staple crop produced worldwide of cultural and economic value . The export exchange of rice has become a prominent market for many countries worldwide . In the US, the export value rice production was nearly 1.7 billion USD in 2022 . Therefore, worldwide rice production must be upheld to current or superior standards to continuously fulfill the global rice demands. There are various common rice production systems used worldwide like transplanted paddies, dry-seeded seasonally flooded and continuously flooded systems . Water-seeded rice is not common worldwide but is the primary method in some geographical areas such as the Sacramento Valley of California . Waterseeded rice is the practice of seeding pregerminated seeds onto fields with a 7- to 15-cm flood, then, typically continuously flooded for the remaining of the season. The water-seeded rice production is popular in areas with ample water for irrigation or where early flood occurrence and poor drainage lead to continuously flooded fields . The flood in water seeded rice helps to control weedy rice, weedy grasses and non-aquatic weed species . However, flood-adapted and herbicide-resistant weeds have further intensified the weed management challenges in many rice fields . Historically, there has been a limited number of herbicide modes of action available for water-seeded rice . Continuous rice cultivation is common in many growing regions because of soil types and economic limitations . Overuse of the same herbicides and continuous rice cultivation have selected for herbicide-resistant weeds which reduce weed control with the currently available herbicides. To support herbicide resistance management, additional herbicides would be beneficial for growers to practice herbicide mode of action rotations . Pendimethalin is a mitotic inhibiting pre-emergence herbicide from the dinitroaniline chemistry that halts seedling growth shortly after germination . In previous surveys and preliminary greenhouse work, pendimethalin has been successful in controlling herbicide-resistant grass populations . Therefore, pendimethalin was evaluated for rice response in water-seeded rice to understand its applicability in this system .

Results from Becerra-Alvarez and Al-Khatib, in chapter 2 of this dissertation, demonstrated rice injury from pendimethalin was reduced in a post-emergence application at the 4-leaf stage rice and in a capsule suspension formulation within 1.1 to 3.4 kg ai ha-1 . However, at the suggested rice stage timing, many grasses have already emerged and control with pendimethalin is reduced. Therefore, if applied post-emergence in herbicide mixtures to control the emerged grasses, then, greater season-long weed control can be achieved. Additionally, herbicide mode of action mixtures are important strategies for herbicide resistance management which help delay resistance development and can control herbicide-resistant populations . It is hypothesized that the residual pendimethalin soil activity when applied postemergence at 4-leaf stage water-seeded rice could assist in control of late-emerging grasses. Economically important late-emerging grasses in California rice include bearded sprangletop [Leptochloa fusca Kunth ssp. fascicularis N. Snow] and watergrass populations. Bearded sprangletop is characterized as a late-emerging grass weed when compared to barnyardgrass [E. crus-galli Beauv] . While the majority of watergrass will emerge early in the season, there are subpopulations that can emerge later and are characterized as prolonged emergence throughout the season . Populations of multiple-resistant late watergrass [E. phyllopogon Koss] have demonstrated evidence of biphasic emergence with the majority emerging early in the season followed by late-emerging cohorts within the population . There is potential benefit from a pendimethalin post-emergence application for control of late-emerging grasses in water-seeded rice. Preliminary field studies evaluating water-seeded rice response were conducted on a continuous 10-cm flood with application onto the water and demonstrated timing after the 3- to 4-leaf stage reduced injury . However, some growers lower the flood depth to encourage rice seedling establishment, or when irrigation water is limited that year.

Decreasing the flood depths can influence pre-emergence herbicide rice injury in water-seeded rice as observed with available herbicides . Therefore, knowledge of rice response as affected by pendimethalin applications at different flood depths in water-seeded rice is important to develop appropriate application methods and recommendations. The objective of the field study was to evaluate the weed control and rice response of a post-emergence application of pendimethalin alone and in mixtures with currently available herbicides. The objective of the greenhouse study aimed to characterize rice response from pendimethalin applications at two flood depths.The study was conducted at the Rice Experiment Station in Biggs, CA in 2022 and 2023. The field soil is characterized as an Esquon-Neerdobe , silty clay, made up of 27% sand, 39% silt, and 34% clay, with a pH of 5.1, and 2.8% organic matter. During the off-season months, the field stubble was burned in spring 2022 prior to a pass with a single offset stubble disc. Field preparation for both years consisted of one pass with a chisel plow to dry the upper soil surface and then two passes with a single offset disc, followed by a land plane to smooth the soil surface. A granule fertilizer starter mixture application of ammonium sulfate and potassium sulfate was applied at 336 kg ha-1 . Then, a corrugated roller was used to pack the soil and eliminate large clods on the soil surface. Individual 3-m wide by 6-m long plots surrounded by 2.2-m wide shared levees were made after fertilizing and prior to flooding to prevent contamination from adjacent treatments in a replication. Seeds of the rice cultivar ‘M-209’ were pregerminated in water. For disease control, a 5% sodium hypochlorite solution was used for the first hour, then drained and refilled with only water for the remaining 24 hours. The seed was then drained until dry up to 12 hours, and seeded at 170 kg ha-1 both years onto the field with a 10-cm standing flood. The flood was maintained the whole season with the exception of a temporary lowering for the post-emergence herbicide treatments but was reflooded back to 10 cm 48 hours after the application. Copper sulfate crystals were applied by plane at 17 kg ha-1 three days after seeding for control of algae. Standard agronomic and pest management practices were followed based on the University of California rice production guidelines . Seeding dates were May 23, 2022 and May 31, 2023. The herbicides and adjuvants used in the field study are outlined in Table 1. Pendimethalin with 0.4 kg L-1 of active ingredient, vertical grow rack system was applied alone and in mixture with foliar active herbicides at the four-leaf stage rice. The pendimethalin application rates were 1.1, 2.3 and 4.6 kg ai ha-1 . The selection of these rates was based on preliminary studies on pendimethalin rates and timings, where 1.1 and 2.3 kg ha-1 were most appropriate rates for water-seeded rice as a post-emergence application . The 4.4 kg ha-1 rate was included in this study to provide rice response data at 2X of the proposed rate for waterseeded rice. The treatment herbicide mixtures with each pendimethalin rate were propanil, cyhalofop-butyl , and bispyribac-sodium . The applications were carried out with a CO2 backpack sprayer calibrated to deliver 187 L ha-1 at 206 kPa traveling at 4.8 km h-1 . The sprayer boom was 3-m wide equipped with six flat-fan 8003VS tips . At time of herbicide applications, the flood water was lowered 24 hours before treatment and reflooded back to 10 cm 48 hours after the treatment. A non-treated control and a grower standard treatment of clomazone applied at day of rice seeding were included for comparison . The treatments were arranged in a randomized complete block design with four replications both years. A follow-up herbicide application of propanil plus triclopyr was applied for sedge and broadleaf control at the midway of full tiller formation rice stage on all treatments except the non-treated . The treatments with pendimethalin alone had a follow-up treatment of cyhalofop plus florpyrauxifen-benzyl at the mid-tiller stage to control all remaining weeds after the initial assessment date . The metabolites have not been labeled of environmental concern and for the most part the pendimethalin parent molecule remains intact when bound to organic matter . In plants, metabolites are also not common and the majority remain as pendimethalin parent molecule when absorbed . The metabolites are also not documented as of concern to the environment by the US EPA ; however, quantifying metabolites helps in understanding the partitioning behavior of an herbicide in an agricultural or environmental system.Visual weed control was recorded for Echinochloa spp., bearded sprangletop, ricefield bulrush [Schoenoplectus. mucronatus Palla], smallflower umbrella sedge , ducksalad , water hyssop and redstem on 14, 24 and 56 days after pendimethalin treatment , on a scale of 0 to 100, where 0=no control and 100=complete control. Weed density counts for Echinochloa spp., sedges and broadleaves were conducted 30 DAT by sampling twice in each plot with a 30-cm by 30-cm quadrat and data scaled to a meter squared area for presentation. Bearded sprangletop counts were conducted for the whole plot after heading of the grass due to a relatively low population density in the field. Visual rice injury assessments were conducted at 20 DAT and 40 DAT by observing present symptomology, which included chlorosis and stunting on a scale of 0 to 100, where 0=no injury and 100=plant death. Rice tiller counts were conducted at 75 days after seeding by sampling twice in each plot with a 30-cm by 30-cm quadrat and data scaled to a meter squared area for presentation. Plant height was recorded at 100 DAS. Rice grain yield was collected both years and adjusted to 14% moisture. The rice grain was harvested from a 2-m by 6-m area in the plots with a small-plot combine on November 2, 2022 and October 30, 2023 .A greenhouse study was conducted at the Rice Experiment Station in Biggs, CA to characterize rice growth as affected by two flood depths after a pendimethalin application. The greenhouse study allowed more accurate management of flood depths than feasible in the field study and direct side by side treatment comparison. Plastic containers with 34-cm by 20-cm by 12-cm dimensions, with openings for drainage were filled with soil from the field study and placed inside larger 58-cm by 41-cm by 31-cm plastic containers, with no drain holes. ‘M-206’ rice seeds were pregerminated by placing the seeds inside cloth bags, and submerging in five gallon buckets for 24 hr. Then, the seeds were air-dried and ten seeds were placed on the soil surface of each smaller container, which would later be thinned to five evenly spaced plants per plot.

The growth benefits in height, canopy formation and leaf numbers per plant is a reflection of the nutrient use efficiency from the cover cropping treatments. In contrast, some previous researchers have observed higher mean number of leaves and heavier stem dry weight from a bare soil than when a rye cover crop used suggesting that rye cover cropping treatments resulted in broccoli marketable yield losses. The negative consequences from cover cropping may have been from cover crop vegetable intercropping, hence live competition for available resources. In this experiment I did not observe any negative consequences of cover cropping on any of the three year growth or yield components of the subsequent vegetable crop. Broccoli shoot biomass determination from destructive crop sampling at harvest time showed that there was no significant broccoli shoot biomass gain from cover cropping for the first year rotation. The observation once again suggests that a single year cover cropping rotation is not sufficient enough to benefit dry mass accumulation by a subsequent vegetable crop. Shoot biomass gain from cover cropping of the latter years was consistent with the observation of increased soil and crop nutrition. The increase in crop biomass with increasing years of cover cropping reveals that repeated cover cropping results in the buildup of cover crop effects. As for crop shoot dry biomass, vertical cannabis broccoli marketable yields were not significantly different between the cover cropping and fallow treatments for the first year cropping. Such responses were seen in almost all broccoli growth parameters and suggest that a one-year cover cropping rotation is of no net and ultimate benefit to broccoli. Increase in marketable yield from cover cropping was significant in the subsequent study years.

Similar to the higher marketable yield observed from cover crop residue supplemented tomatoes , I observed vigorous growth, higher shoot biomass accumulation and higher marketable yield of cover crop residue supplemented broccoli.In contrast, Hoyt observed a reduction in yield of broccoli planted into desiccated barley cover crop and attributed it to lower soil temperatures in the cover crop treatment. The reduction of soil temperature with the use of cover crop mulches and residues has been discussed as a possible limitation delaying crop harvest for several days . However, I observed that broccoli crops grown following summer cover cropping were heavier and had vigorous crop appearances compared these on a fallow field. These benefits however were more eminent after the second year of cover cropping rotations, indicating a buildup effect of the cover crops. Broccoli seems to have benefited from previous summer cover cropping during its second and third year trials. Higher fresh broccoli marketable heads were obtained during the second year from both harvest times and the total marketable heads. An increase in marketable yields starting the second year indicates the necessity of repeated cover cropping rotations to be beneficial. The increase in both marketable head numbers and fresh weights of the marketable heads occurred during the third year, further revealing the importance of longer and repetitive cover cropping rotation. The generally lower yield for the 2009 trial, relative the previous year, however was due to crop damage by other herbivorous pests and unexpected flowering, reducing number and fresh weights of marketable broccoli heads. Yet relative yield comparisons were valid.The more inclusion of the cover crops in the cropping rotations, the higher was the crop yield benefit both in number and weights of marketable broccoli yield. These findings confirm the recommendations that vegetable farmers can grow cover crops during the off-season and benefit from the harvest of the subsequent crop.

Ngouajio et al. suggests that cover crops can be used in diverse cropping conditions as they are compatible with both organic and conventional farming practices by either incorporating or using them as surface mulches. Improvements in soil physical, chemical, and biological environment from the use of cover crops are the reasons for the improved yields of subsequent crops, although crop yields may vary from crop to crop and agroecological regions. The positive response of the subsequently grown crop is also attributed to the transfer of nutrients from cover cropping and less immobilization nutrients . Similar to our findings, Hively and Cox ; Fageria et al. 2005 observed a higher corn yield following white clover and red clover cover crops. Marketable yield of sweet corn was approximately doubled by hairy vetch in 2 of 3 years compared to an unfertilized, no cover crop control . Burket et al. observed a 58% higher average broccoli yield when grown with no fertilizer N, but following a legume cover crop. In general, the response of broccoli as a vegetable crop to cover cropping rotations was positive associated with nutrient, growth and yield output of the crop. If properly managed, then it is most likely that the cover cropping system can sponsor its own soil fertility, crop protection and productivity. Such low input farming systems with improved crop productivity and profitability can be easily adopted by farmers and becomes very useful in organic farming systems where the use of synthetic fertilizers in not acceptable. Cover crops in farming systems improve soil health, reduce environmental pollution, and improve crop yields and maintain sustainability of crop production . Such sustainable production of agricultural products achievable through cover cropping must be based on holistic agricultural management that encourages interdependent and diverse properties. For higher cover crop use efficiency farmers should also deal with selection of appropriate cover crop species with desirable socioeconomic considerations and ultimate vegetable crop yield improvement. It must also involve lower production costs with no adverse effect on crop health and the environment.Management of weeds is a challenge in crop production.

Weeds can interfere with the cultivated crop by competing for light, water and nutrients, which can lead to reduced yields and reduced economic return on investment to the grower . The approach for integrated management of weeds consists of combined inputs from cultural, mechanical, biological and chemical control methods. Cultural practices like clean seed, clean equipment, and proper field preparation are commonly integrated with mechanical practices like tillage, mowing or cultivation for control of weeds. Biological practices are less common in weed management. Therefore, chemical control is the following option to integrate for weed management . Chemical practices are the use of herbicides to prevent weed emergence or to cease growth of weeds until plant death, in most cases. Herbicides continue to be important tools to integrate in weed management programs because of their cost effectiveness, rapid action and flexibility with management, when used appropriately, which have allowed for increased crop yields to be achieved . A successful weed management program can be accomplished when cultural, mechanical and chemical management are integrated. In the California rice production system, herbicide resistance has been a continuing challenge due to continuous rice cultivation year after year, a historically limited number of herbicides available and the overuse of the available herbicides for weed control . From 2015 to 2021, there were 661 suspected herbicide-resistant weed reports and nearly 53% of watergrass populations recorded multiple-resistance to up to four modes of action . The presence of herbicide-resistant weeds leads to a reduction in weed control with the available herbicides and reduced yield. The most recent herbicides registered in California rice include pyraclonil in 2024, florpyrauxifen-benzyl in 2023, benzobicylcon in 2017, carfentrazone in 2006 and clomazone in 2004 . However, grow racks these herbicides have varying degrees of control over different weed species and producers are limited in control options . There is a need for new herbicide tools to maintain the viability of the current herbicides for future years by practicing herbicide rotations and mixtures . However, the registration of new modes of action in a crop or region is influenced by many factors like the crop injury potential, weed control efficacy, environmental concerns or lack of economic incentive by the manufacturing companies . Because not many new herbicide modes of action have been developed recently and herbicide resistance is increasing, new potential rice herbicides can be evaluated from other cropping systems or by revaluating or reformulating older chemistries. There has been success in introducing herbicides from larger agronomic crops to high value specialty crops through the Interregional Project Number 4, a US federal program . Similarly, evaluating older chemistries for new crops can be successful; however, the environmental effects are of greater concern because old chemistries tend to be less environmentally safe . Various characteristics are important to consider when evaluating a potential herbicide for a new crop like crop safety, weed control spectrum and persistence in the environment.

To ensure a greater potential for success when evaluating new herbicides, a hypothesis-driven research approach should be taken. Pendimethalin [N–2,6-dinitro-3,4-xylidine] is a mitotic inhibiting herbicide from the dinitroaniline chemistry that inhibits seedling growth shortly after germination . Pendimethalin controlled herbicide-resistant grass populations in the greenhouse and has relatively few reports of resistant weed populations . Preliminary greenhouse work indicated pendimethalin was effective in controlling several recently collected herbicide-resistant grasses from California rice fields . Therefore, pendimethalin could be a valuable addition for management of herbicide-resistant weedy grasses. Pendimethalin is registered for use in dryseeded rice and commonly applied to the soil surface after drill-seeding rice relatively deep in the soil . In dry-seeded systems; however, rice injury from pendimethalin is influenced by soil moisture, where higher soil moisture leads to greater injury levels . Characterization of pendimethalin in water-seeded rice, where moisture is always present, has not been evaluated because of the perceived risk of rice injury . There is no previous research that has evaluated pendimethalin formulations at different rates and timings in water-seeded rice. Therefore, the objective of these studies was to evaluate and characterize pendimethalin in water-seeded rice. The objective of this chapter is to review the literature on pendimethalin, pendimethalin use in rice production systems and background related to characterizing pendimethalin for water seeded rice. This review will provide greater background to the research studies outlined in the following dissertation chapters. The review will begin with a history and background of the dinitroaniline chemical family, pendimethalin use in rice, environmental fate of pendimethalin in rice production systems, and future directions for characterization in water-seeded rice.The dinitroaniline chemistries have been available herbicides since the 1960’s. The first dinitroanilines were synthesized by the Eli Lilly Research Laboratories and included trifluralin, benefin, nitralin, isopropalin, oryzalin, profluralin, butralin, ethalfluralin, fluchloralin andprosulfalin . Trifluralin was the first commercialized compound in the US and was used in soybean and cotton as a pre-plant incorporation for grass weed control at rates of 1,000 to 2,000 g ai ha-1 . Currently, trifluralin and pendimethalin are the most commonly used dinitroanilines in the US and worldwide for weed control in cereals, cotton, soybeans, vegetables, ornamentals and fruit and nut trees . Pendimethalin was developed by American Cyanamid in the 1970’s, previously named penoxalin . BASF would later purchase the American Cyanamid’s agrichemical business and take possession of pendimethalin in the 2000’s. Pendimethalin was moderately less volatile than trifluralin, which lead to relatively greater soil persistence and longer weed control activity .Characteristics of Dinitroanilines. The physical, chemical and biological properties of herbicides can help broadly predict their behavior in the environment, weed control efficacy and handler safety. The 2,6-dinitroaniline chemical structure is the base structure that defines the compoundsin the dinitroaniline chemical group . The additional chemical structures on the base structures will affect the specific characteristics of each compound. The common characteristics in all dinitroaniline compounds is a low water solubility, typically <1 parts per million , and most are soluble in organic solvents. The nitro groups decrease the water solubility by creating hydrogen bonds with alkyl groups of other compounds like soil or organic sediments, which creates lipophilic aggregates . The lipophilic nature appears to make the compounds susceptible to bioaccumulation in the environment; however, they have a high affinity for organic sediments or organic matter in the soil . Most compounds are non-ionizable, except for oryzalin . These characteristics tend to lead to the classification of dinitroanilines including pendimethalin as low risk for contaminating surface or ground water and low risk to environment contamination and human health .

The sugar beet cyst nematodes were not even as much sensitive as the RKN to the cover crop treatments was not variable among the cropping treatments, although were slightly higher in cowpea and marigold cover crops at the ABH sampling of the second year and the ACCP and ABH samplings of the third year. The increase in SCN mainly at the ABH samplings than at other sampling periods, may indicate that broccoli is a host to the SCN. Potter and Olth of actually show that broccoli is a potential host to the cyst nematodes. Infection of broccoli roots and broccoli root gall formation was very minimum and unaffected by the cropping treatments. Based on my current over all findings therefore, the usefulness of cowpea and marigold as offseason cover crops does not confirm their nematode suppression potentials in the subsequent winter broccoli crop.There are various reasons documented for variation in nematode suppressing efficiency of cover crops. Ploeg and Maris state that the life cycle of Meloidogyne incognita complete between average soil temperatures of 16°C and 30°C on tomato, but only at 30°C on marigold . Furthermore, motility of M. incognita J2 and its subsequent root penetration may decrease with decreased soil temperatures below 18°C . These findings suggest that the effectiveness of cover crops to suppress nematodes depends on the condition under which they are utilized. Ploeg and Maris further suggested the need for information on thermal-time relationships of plant parasitic nematodes to predict geographical distributions, nematode population dynamics and effects of cover crops on the subsequent crops. Effectiveness of a cover crop for the purpose of nematode suppression may also depend on the type of target nematode itself.

Wang and McSoreley pointed out that Iron Clay‘, cowpea failed to suppress root-knot nematodes where there were mixed species of Meloidogyne. Ploeg and Maris also identified nematode suppression of marigold being influenced by crop plant variety, nematode species, hydroponic trays and soil temperature. Marigold while suppressive to root-knot nematode, it enhanced the population densities of other nematodes such as stubby-root, spiral and sting nematodes on the other hand. Therefore, the evidence suggests that the type of nematode can determine the effectiveness of a cover crop.Others observed that nematode suppression of cover crops may depend on how the cover crops were utilized. Wang and McSorley observed that cover crop mulch was more effective than live crops. On the other hand, Ploeg and Maris state that live marigold suppress nematodes, because of the release of alpha-terthienyl, a toxic chemical compounds from its live roots that have nematicidal characteristics . These nematicidal compound released by active, living marigold roots may not be available if marigold is used as an organic mulch . Since my research was based on the off season cover cropping system and employed their residues as surface mulch and soil incorporation, the observation of poor or no nematode suppression can be justified. Similarly, Ploeg did not observe any significant suppression from preceding vegetable crops or amending a planting site with marigold plant parts. Furthermore, while cowpea incorporation as a green manure has been observed to suppress Meloidogyne incognita , the suppression was short-lived, and the numbers of M. incognita were not different from a fallow treatment . Another factor determining cover crop effectiveness was the type of the subsequent vegetable crop that may determine the potential incidence of plant parasitic nematodes.

If the subsequent indicator crop is a nematode susceptible plant, it may be possible to detect nematode suppression of cover crops, otherwise, the effects of the cover crops can be masked if the indicator crop is nematode resistant. However, if the vegetable crop isresistant to nematodes by itself, nematode suppression potential of a cover crop could be masked. Accordingly, I may not have observed any significant nematode suppression by the cover crops, because the broccoli used in this research was resistant or a poor host to most nematodes. Most broccoli cultivars contain sulphur compounds such as methanethiol, dimethyl sulphide, methyl thiocyanate, dimethyl disulphide, dimethyl trisulphide, dimethyl tetrasulphide that may be toxic to nematodes . The presence of nematode antagonizing organisms such as bacteria and fungi in a soil may also contribute to the reduction of nematode population densities , regardless of nematode suppressive treatments. Kerry observed that the second-stage juveniles of root-knot nematodes encumbered with spores of the bacterium Pasteuria penetrans are less able to invade the roots of host than the unencumbered nematodes. The most significant outcome of the cover cropping treatment was the enhancement of saprophytic nematodes. Saprophytic nematode populations were significantly enhanced at ACCP sampling of the second year and the ACCI sampling of the third year in the cover cropped plots, relative to the fallow plots. Since these nematode populations became higher at after cover crop incorporation, the increase in saprophytes may have come from the accumulation and decomposition of cover crop residues. The relatively lower saprophytic nematode populations in the fallow plots may have been associated to the lower input of organic matter from such cropping system.Therefore, the results confirm that preceding vegetable crops with cover crop could enhance beneficial saprophytic nematode populations. Saprophytic nematode population density for the first year was not significantly different for the cropping treatments, indicating that a one year cover cropping rotation is not sufficient to enhance populations of free-living nematodes. On the other hand, the increase in saprophyte population with repeated years of cover cropping suggests that there is accumulative effect of the cover cropping treatments.

The results clearly demonstrate that cover-cropping rotations must be repeated for several years in order to provide significant contributions to enhance saprophytic populations. The sharp decline in saprophyte populations at the ABH sampling of the third year might have been due to a complete decomposition and degradation of the organic matter to a level that no longer sustained high saprophytic populations at this stage. An increase in bacterial-feeding nematode population densities following soil treatment with sunn hemp as organic mulch was also observed by Wang and McSorley . Free living bacterial feeding taxa of nematodes constitute more than 60% of the nematode community . The presence of high population densities of saprophytic nematodes may provide an added advantage in soil biology . Saprophytic nematodes are useful in mineralization of plant nutrients and nutrient cycling and can be used as sensitive indicators of ecosystem change . Langat et.al. suggested that bacterivorous nematodes respond quickly to increased food supply. Therefore, cover crops play an indirect role of increasing population levels of the beneficial free living nematodes. The structure of the nematode assemblage offers an interesting instrument to assess changes in soil conditions . Enhancement of saprophytic nematodes and the mineralization and nutrient cycling benefit that such nematodes can provide to the subsequent crop indicates the profitability of cover cropping rotations. In general, the use of cowpea or marigold cover crops as an off-season cropping rotation may not provide a viable alternative as a nematode suppression strategy. Hence, the use of cover crops for nematode suppression must be considered carefully, accounting for the target nematode, how the cover crops are to be used, and the environmental conditions of the field. However, pipp mobile systems these cover crops can be used as off-seasoning cropping rotations to effectively enhance beneficial saprophytic nematode population densities in the subsequent vegetable crop. They do so as their residues decompose supporting nematode food webs. The increase in population levels of saprophytes and feeding on nutrientimmobilizing bacteria and fungi promotes nutrient mineralization and nutrient cycling. It is believed that with more knowledge about the mechanisms stimulating a beneficial nematode community, we may develop cover crop management plans to maximize the desirable effects associated with free living nematodes . Traditional insect pest management approaches utilize pesticides, but these are known tobe environmental pollutants , and in some cases carcinogenic . Insect pests may also develop resistance to insecticides making them inefficient . Those shortcomings of broad spectrum insecticides, encouraged attempts to replace them by ―soft‖ microbial based insecticides such as Bacillus thuringiensis . Yet, insect pests developed resistance to the soft insecticides as well and some ―soft‖ insecticides can be injurious to parasitoids . Therefore, there is an increasing demand for environmentally friendly and economical alternative pest management strategies . Many researchers have suggested crop diversification and cover crops as alternative insect pest management tactics . Cover cropping systems may adversely affect insect pests and, if effective could be used as an alternative insect pest management strategy as they are ecologically benign, minimize reliance on pesticides, reduce chemical exposure, and increase consumer confidence in food production . Although cover crops could potentially interfere with vegetable insect pests, not all cover crops are equally efficient in suppressing vegetable insect pests.

For example, sweet clover cover crop suppressed broccoli pest populations, but not pests of tomato or pepper . There are also concerns that cover crops used as simultaneous inter planting may compete with the main crop for growth resources and reduce vegetable crop yield . Consequently, this research was designed to evaluate the effectiveness of off-season summer cover cropping as an ecologically desirable pest management strategy for the subsequent winter vegetable crop of broccoli. It specifically evaluated the effect of two summer cover crops on population densities of broccoli insect pests and beneficial arthropods.A three-year field study was conducted from 2007-2009 at the University of California South Coast Research and Extension Center in Irvine, CA on a loamy-sandy soil. Three summer cropping treatments were employed: 1) French marigold , 2) cowpea , seeded at 56 kg/ha, and 3) a summer dry fallow as the untreated control. Each treatment plot was 12 m long x 10.7 m wide and laid out into 14 planting rows. The cover crops were direct-seeded in the last week of June in the center of the planting rows of each plot, watered through drip-tubing and grown for three months. The fallow control plots did not receive water during the summer. Each cover crop treatment plot was planted with the same cover crop in each of the three years of study. Plots were separated from each other with a 3 m wide buffer bare ground. The three treatments were replicated four times in a completely randomized design. At the end of the summer cropping period , the cover crops were mowed at the soil line, chopped, and the residues left on the ground. Concurrently, alternate rows of each of the cover crop treatments were incorporated into the soil at about 0.4 m intervals using a hand-pushed rotary tiller in preparation for broccoli transplanting. The fallow plots were not tilled. Plots for cover crop and broccoli planting are shown in Figure 1a. At the beginning of the subsequent cropping season , broccoli seedlings were transplanted in double rows into the tilled strips of the summer cover crop and fallow plots at an inter and intra-row spacing of 13 and 35 cm, respectively . Broccoli transplants were drip irrigated and fertilized with emulsified fish meal at 5 gallons/acre rate. Broccoli was chosen because it is a high-value vegetable crop that is sensitive to weeds, insect pests, nematodes , and requires high soil nutrients . All plot treatments were maintained in the same location for all three years of study in order to assess a cumulative effect of cover crops over time.Plants were non-destructively sampled for major broccoli insect pests beginning 15 days after broccoli transplanting and continuing every two weeks until broccoli harvest. On each sample date, 20 randomly-selected plants per plot were assessed visually for thepresence of insect pests following methods used by Costello and Altieri and Hooks and Johnson . The larvae of the insects actively feeding on the vegetable crop were identified to species level and recorded as the number of individuals per plant. Population density of each species was computed as average number of larvae per broccoli plant.Arthropod parasitoids were assessed by rearing field collected broccoli insect pests in the laboratory, beginning at 21 DAT and continuing once every two weeks until broccoli harvest. Five plants from the interior three rows of each plot were randomly selected and searched for insect larvae. Larvae were collected and placed in clear plastic cups with greenhouse grown broccoli leaves as source of food. Individual larvae were reared in the lab and the fate of each larva recorded.

Agronomic crops have distinct and uniform morphology, but for roadside invasive species, the high variation in plant morphology and the non-uniform backgrounds will lead to more significant detection errors. Previous studies and discussions supported that computer vision can replace human observers in species detection. However, we still need human observers to create a training dataset. Training, testing, and validation datasets are the essential components of a deep learning model, with the most time-consuming task being image labeling. A larger training dataset can increase model performance, but the size would determine the amount of labor for a single project. Abdulsalam & Aouf suggested that 1,000 images of a particular species are required to achieve high prediction accuracy. Yan & Ryu proposed that the training sizes would differ depending on the mapping species since they only used 400 training samples for corn, but the model could still perform with high accuracy. Compared to the traditional car survey, the AIbased survey method can be conducted by non-experts once the image detection model is trained. Johnsongrass is a common weed along the roads in the United States and is a good model plant for the AI-based survey method. It is native to the Mediterranean and North Africa and was introduced to the United States in the early 1800s . Johnsongrass is a perennial grass and can colonize and spread nearby landscapes through the underground rhizome system . The mature plant can grow up to 2.5 meters in height, and the height of the mature plant can vary based on the local condition . McWhorter reported that the reproductive stage of johnsongrass started around a month after seed emergence, cannabis drying system and the maximum rhizome growth was about 60 meters in 5 months. The flowering part of johnsongrass is a diffuse panicle, which is the primary feature in the image identification process.

The flowering head is orange and purple at the mature stage. Johnsongrass is a weedy relative of the cultivated sorghum , which compete for the same limiting resources, and the presence of johnsongrass will cause yield loss in sorghum or other crop fields . Kansas and Texas are the top two states in cultivated sorghum production regarding planting acreage . As a weedy relative, johnsongrass is widespread in sorghum fields and along the roads around Nebraska, Kansas, and Texas to conduct an automated road survey of johnsongrass, examined the cost-effectiveness, and discussed the potential application of the johnsongrass population map.The performance of the YOLOv2 model in detecting johnsongrass in GSV images was tested using a total of 2,040 test images. Based on the threshold value of 0.6 for presence , the confusion matrix shown in Table 1 was created. The YOLOv2 model achieved a recall value of 85% in the GSV testing dataset. Dang et al. reported similar recall values in their study which the average recall value of the YOLOv3 detection model on 12 different weed species was 87.93%. However, there is still about a 15% chance that johnsongrass could be undetected by our model when johnsongrass is present in the image. In the testing dataset, there were 153 images were classified as FN in the confusion matrix. FN in this project is the image that contained johnsongrass and was identified and labeled by human observers, but the model was not able to output the same result. Individuals that were at pre-flowering stage were not considered as FN since the training dataset only contained mature johnsongrass. The precision for the YOLOv2 model was 0.74, which is lower than the recall . Both the precision and FPR include false positive detection in the calculation. FPR implies that the model could wrongly detect other plant species as johnsongrass, with a 30% chance. Among the group of incorrect detection , FP had twice the number of images compared to FN in the test dataset . Sincemost studies in weed detection were conducted in the crop field, and their models were applied to distinguish weeds from the crop, the precision values were high and were about 85% to 95% . Yan & Ryu applied a CNN model on GSV images to detect roadside crop type, and the results denoted that most crops had detection precision above 90%, but only rice had a 76% precision. The study also reported that the misclassification of rice was more frequent in low-resolution images or if the object was far away from the camera . In our research, the quality of images and the distance between the target object and the camera might contribute to the high FP value. Future work will focus on decreasing the rate of FP in the image where johnsongrass is absent. More CNN models will be tested on the johnsongrass training database to compare the precision and accuracy of different models. More roads with high-resolution images from Google can help improve this survey method’s accuracy. The overall accuracy of the YOLOv2 model was 77.5% for detecting johnsongrass in the GSV images. This index provided an overall evaluation based on total correct detection and the total number of test images. Ringland et al. and Yan & Ryu both conducted image detection models on GSV images to survey different types of crop production along the roads but with different CNN networks from our model. The accuracy of detecting general crops like alfalfa , almond , corn , and rice in the GSV images could reach 92% . An explanation of high accuracy on crops is that major crops always have unique morphology or patterns because of domestication, row spacing, and field layout that might help to increase performance in computer vision. For roadside weedy species like johnsongrass, morphological variations under different environmental conditions were reported in many studies, and the variation could lead to low precision and overall accuracy . There were several challenges in the labeling process, and they can explain most of the incorrect detections. In some annotated training images, the target species were partially occluded by other objects, including other invasive species, traffic signs, and fences. In this case, we could only label either the flowering part or the basal part of johnsongrass. In this project, and for johnsongrass specifically, the panicle part of the plant would be labeled in most cases since we could not differentiate the basal part of johnsongrass from other grass species. The growing stages of the target species were another challenge in the labeling process. The juvenile stage of johnsongrass has no panicles and looks similar to many other grass species. Only the mature johnsongrasses were included in the training dataset, so the model was unlikely to detect individuals at their early vegetative stage.The trained model was applied to 269,489 images collected from Google Street View. In Figure 6, the red points denote the potential location of johnsongrass predicted by the model. The model identified a total of 2,031 images as having johnsongrass. The predicted distribution of johnsongrass suggested that johnsongrass is less widespread in Nevada than in the other states in this study. The location shown on this map is only the prediction. The johnsongrass individual might not be found in that location depending on the growing season since most images were taken 2 to 3 years ago. Deus et al. conducted a Google Street View study that surveyed E. globulus , and their results mentioned that environmental stresses could lead to variability in species abundance in a short period, from one to two years. Recent studies and our results suggested that integrating GSV and a deep learning image detection model can map species on a much larger scale. Yan & Ryu integrated GSV and other deep learning algorithms and produced cropping system maps of Central Valley in California and the state of Illinois. Another roadside crop survey in Thailand covered 572 km of road and examined about 57,000 panoramas . Our study covered more areas , longer roads , growing tray and more panoramas than studies used a similar road survey method . Future research will focus on the survey in other states in the US, and our goal is to survey all the roads in the US.YOLO has been tested in many studies to detect multiple plant species in a single image . Johnsongrass was the only detection target in this study, but other invasive species can be mapped by using our methods. A larger-scale species distribution map can be combined with environmental factors or land use to determine the conditions suitable for spreading the species. An example would be the habitat suitability model, which predicts how well species thrive and spread in a location given environmental conditions . According to Crall et al. , even though the habitat suitability model is a key tool for invasive species risk management, the model requires location data on a large spatial scale. Our method can provide a more prominent presence/absence dataset than the traditional local dataset. Habitat suitability models based on a larger scale can yield a more robust conclusion. As noted, the sampling created by our method is a biased sampling of the environment under which the species can thrive as we only search for species along the roadside habitats. AI-based surveys can provide accurate location data to build and test invasive species dispersal models. The AI-based mapping approach can only detect roadside weedy species. A dispersal model can be applied based on the johnsongrass location map. A typical dispersal model requires two primary parameters, reproduction rate and spread distance, and then for parameterization and calibration, a multiple-time-step map is required . The johnsongrass map was created based on images from a different date. Even though most of the images were taken in recent years, like 2020, a small portion was taken 8 or 10 years ago. Expenses and estimated time for the car survey, the human-based GSV survey, and the AI-based GSV survey were calculated . Expenses for the car survey were calculated based on the same scale as the AI-based survey. The cost breakdown of the car survey was calculated based on regular domestic travel daily expenses. Vehicle rentals and gas estimation are US$ 9,408 and US$ 7,350, respectively, and the accommodation accounts for a more significant portion of the costs, i.e., US$ 25,200 . The total travel time of a car survey per person requires 180 days, estimated based on a daily 750 km drive. Dues et al. conducted a 38-day car survey of 15,000 km of roads in Portugal, and a standard car survey would drive much less than 750 km per day. For the human- and AI-based GSV survey, US$1,890 is required for image purchase from Google, which is US$7 per 1000 images . Labor costs for all three types of surveys were calculated based on the minimum hourly wage in California and the total time spent on each method. In terms of cost, the AI-based GSV survey is 50% less than the human-based GSV survey, and the AI-based GSV survey is only 5.6% of the total cost of the car survey.Compared to car surveys, GSV-based surveys did not require outdoor work and driving, so GSV-based detection can minimize potential worker risks . Studies by Deus et al. and Kotowska et al. reported that the results produced by GSV resemble that produced by the field survey. Compared to the GSV survey by a human observer, the AI-based GSV survey had spent shorter time. Once the detection model is trained, the machine can work 24 hours per day, but on average, a human can process about 6000 ~ 8000 images per day based on our labeling experiences. A more detailed GSV survey might take more time. For example, a human-based GSV survey took 35 hours to examine 2,350 panoramas in Sicily, Italy, to assess invasive species abundance along the roads . As noted, once the training dataset is created, the labor cost of the AI-based GSV surveys is fixed and will not increase as the number of images increases. However, the relation between labor cost and image number in the human-based surveys is linear. As the sampling scales increase, AI-based surveys will outcompete human-based surveys, and the comparison between these two surveys in Table 3 is underestimated. Then in terms of errors, the detection errors by the AI-based model could be consistent, quantified, and improved, while errors in car surveys and humanbased GSV surveys are unable to quantify and inconsistent.

A key finding is that overnight charging currently has the lowest absolute level of CO2 emissions and a relatively low variance compared to other times of charging. This may not imply that an EV solution is categorically preferred on the grounds of net CO2 emissions compared to a baseline HEV, although the research does permit such quantitative comparisons. And it does convey actionable information given the vast temporal optionality currently allowed for charging. Going forward, we can envision a means of aggregating the impacts of individual analyses, meaning that these events will be weighted based on their likelihood and current behaviors. We can furthermore consider behaviors will evolve as time goes by and as larger shares of EVs are realized. However, within a transition period where EV growth and grid dynamics adapt iteratively, this study conducted novel simulations of the primary grid-vehicle scenarios which are reflective of current EV behavior and grid characteristics in recent years. The study’s consultations with experts, literature review, and data analysis revealed that about ¾ of events occur at home, with 50% on a level two charger and 25% on a level one charger. This would suggest, for the near and intermediate term, that EVs will act as a kind of aggregated demand in the evening/overnight hours and that as a block , EVs are more likely to require marginal resources because they act together to force demand projections out of the expected regime. The good news is that, for the foreseeable future, these are, on balance, curing cannabis hours that are moderately lower in terms of marginal resource carbon intensity, since they can be met by intermediate resources . Whereas workplace charging is substantially less common, perhaps the marginal mix or the hourly estimation is reasonable for the effective CO2 signatures.

As one thinks about the scale, and a situation where of EV reach double-digit shares of the fleet, it’s very likely that all the modes and locations of charging will eventually be subject to conditions where marginal assumptions prevail. How the transition period is defined and how the system boundaries between EV growth and grid resources are balanced are important, but open questions. Another essential unknown that will impact effective CO2 emissions from EVs are how predictable EV charging events become, with an emphasis on the high power, coincident peak events. More research into this question can help inform more accurate methods and models for simulating the environmental impacts of EVs. The present framework sets up an approach that will be valuable in estimating future impacts under such conditions. While additional focus and scope lie beyond this study, it is clear that a more complete understanding of popular EV charging profiles and EV driving behaviors will be essential inputs to better decision-making and resource planning. As EV use and charging habits become more predictable and well-known, the relevant data and insights can be critically valuable to utilities. For instance, foreknowledge of EV charging events will be needed at an aggregate level and could be beneficial to grid operators. The reason is that they can better plan and iterate their learning for those types of loads and events for which currently they lack visibility.In the last 40 years, 30 percent of the world’s arable land has become unproductive and 10 million hectares are lost each year due to erosion.1 Additionally, accelerated erosion diminishes soil quality, thereby reducing the productivity of natural, agricultural and forest ecosystems. Given that it takes about 500 years to form an inch of topsoil, this alarming rate of erosion in modern times is cause for concern for the future of agriculture.

This supplement explores the major causes of soil erosion and the social impacts it has on communities, underscoring the importance of agricultural practices that prevent or minimize erosion. Anthropogenic causes of accelerated soil erosion are numerous and vary globally. Industrial agriculture, along with overgrazing, has been the most significant contributor, with deforestation and urban development not far behind.2, 3, 4 Heavy tillage, fallow rotations, monocultures, and marginal-land production are all hallmarks of conventional agriculture as it is variably practiced around the world and significantly encourage accelerated soil erosion. Repeated tillage with heavy machinery destroys soil structure, pulverizing soil particles into dust that iseasily swept up by wind or water runoff. Fallow rotations, common with cash crops around the world and subsidized in bio-fuel production in the U.S., leave land vulnerable to the full force of wind gusts and raindrops. Monocultures tend to be planted in rows, exposing the soil between to erosion, and are commonly associated with fallow rotations. More and more marginal land, land that is steep and particularly susceptible to water erosion, is being planted by farmers either attracted by higher crop prices or forced by loss of productivity on flatter, but already eroded lands. In an increasingly complex global food web, seemingly separate causes of erosion begin to influence each other, magnifying their effects. For example, deforestation of tropical forests in Brazil clears the way for industrial soybean production and animal grazing to feed sprawling urban populations in the U.S. All the while, fertile topsoil is carried away by wind and water at alarming rates. Environmental harms resulting from accelerated erosion are well documented. Decreased soil fertility and quality, chemical-laden runoff and groundwater pollution, and increased flooding are just a few of these detrimental effects. There are, in addition, disproportionate social harms resulting from high rates of erosion that are less obvious, but no less directly linked. Hunger, debt, and disease are serious problems in mostly poor, rural communities around the world that are exacerbated by accelerated erosion.

As global agricultural development and trade have accelerated in the last half-century, mainly via the “green revolution” and the formation of the World Trade Organization , increasing trade pressures have raised export crop production in less developed countries. As a result, farmers mainly in Asia, Latin America, and sub-Saharan Africa are increasingly abandoning traditional farming techniques and locally significant crops in favor of adopting the industrial practices mentioned above that lead to high rates of erosion.5 While development institutions and governments proclaim concerns for the rural environment, agricultural policy supporting high commodity prices and limited credit access continually pushes farmers to intensify land use. Coupled with the fact that the total area of arable land in cultivation in these parts of the world is already very high , land degradation by soil erosion threatens food security by removing from cultivation land sorely needed for domestic food production. The majority of the world’s 868 million undernourished people live in Eastern and Southern Asia and sub-Saharan Africa. One of the international responses to soil degradation in the developing world has been to promote soil conserving tillage practices known as minimumor no-till agriculture. No-till agriculture protects soil by leaving crop residue on the field to decompose instead of plowing it into the ground before planting the next crop. Weed management is addressed with heavy herbicide use to make up for the loss of weed control from tillage. The practice, extensively adopted in the U.S., weed dryer has been popular in Brazil and Argentina, and much effort is being expended to expand no-till to Asia and Africa. There are, however, costs associated with no-till agriculture, both economic and social. First, no-till agriculture is expensive to adopt. Herbicides, seed drills, fertilizers, and other equipment require a high initial investment not possible for poor farmers without incurring significant debt. Second, heavier herbicide use increases human exposure to chemicals and contributes to water and air pollution. Third, weed pressures can change in unexpected ways as reliance on a handful of herbicides breeds resistance. Weed resistance to the popular herbicide, glyphosate, is an increasing concern in conventional agriculture and is leading to development of more harmful herbicides to compensate for glyphosate’s reduced effectiveness. Lastly, no-till agriculture also promotes monoculture cropping systems that, as described above, have a deleterious effect on soil quality. The techniques illustrated in this manual emphasize long-term soil stewardship using an integrated approach to soil health and management. For example, cover crops hold soil aggregates together in the wet season, protecting soil from the erosive effects of rain. Properly timed tillage limits its destructive effects on soil particles and soil structure. Compost promotes a healthy soil ecosystem, improving soil’s structure and its ability to more successfully withstand wind and water erosion. In addition to environmental benefits, agroecological systems are often based on traditional farming practices that promote soil-conserving techniques and varietal choices adapted to the particular region, stemming the tide of land consolidation and commodity crop production. Food security is enhanced and debt risk reduced by way of diverse cropping systems and labor-intensive, rather than input intensive, production methods. And there are public health benefits from eliminating exposure to harmful pesticides and herbicides. In sum, the serious challenge presented by accelerated soil erosion coupled with the uncertainty about whether no-till agriculture’s benefits outweigh its harms underscores the importance of employing an agroecological approach to farming that prevents soil erosion on farms.On vegetable farms in the Salinas Valley, a shrinking farm labor pool and rising minimum wages are driving innovation and adoption of machinery that can automate manual labor tasks — thinning, weeding and, for some crops, harvest. The technology is evolving quickly, led mainly by small engineering firms collaborating with large growers. Automation promises a number of benefits. Foremost, of course, is a reduced dependence on manual labor. But it could help in other ways too — for instance, automated weeding could remedy the declining effectiveness of some herbicides. UC researchers and advisors are helping to advance the basic technologies involved, and also serving as key evaluators of the technology . But the drive to automate also raises decades-old concerns about UC contributions to new technologies that are likely to primarily benefit only large-scale growers, at least in the short term.The automation of thinning and weeding involves two main steps: identifying each plant to be removed and then directing the killing of the undesired plant with a blade or a small dose of herbicide. It replaces work that would otherwise be done by hand with hoes. Figures on the acreage being thinned by machine aren’t available, but the use of automated thinners in some crops, notably lettuce, has been expanding in the Salinas Valley since its introduction in 2012 . The two in widest use in the Salinas Valley, according to several researchers and equipment suppliers, are made by two small northern European firms, Denmark-based F. Poulsen Engineering and Netherlands-based Steketee. Long-running concerns about farm labor cost and availability in Europe have driven automation innovation, and the technology has been more widely adopted there than in the United States, said Richard Smith, a UC Cooperative Extension farm advisor in Monterey County. While the weeding machines are costly — roughly $150,000 to $200,000 — their use appears to be limited more by availability than by price, according to equipment suppliers and UCCE staff. Poulsen and Steketee are small operations with limited production capacity. Britton Wilson of Pacific Ag Rentals, an equipment supplier to Salinas Valley farms, estimated that there are 15 to 20 Poulsen weeders in the United States, a figure Poulsen corroborated. “I’d love to get my hands on more” to meet local demand, he said.A crop like lettuce or broccoli represents a comparatively small market for major farm equipment makers like John Deere and Case IH. About 300,000 acres of lettuce are grown in the United States, for instance, compared with 12 million acres of cotton or 90 million acres of soybeans. As a result, vegetable crop automation is being led by small engineering and fabrication firms as well as growers themselves, often in close collaboration, said Mark Siemens, an associate specialist and associate professor of agricultural and biosystems engineering at the University of Arizona. Because the technology is somewhat modular, it’s possible to address the needs of a particular crop or grower by combining or modifying existing technologies and equipment. An example: Harvest Moon Automation, a four employee engineering firm with several clients in the Salinas Valley, recently received a patent on a modified version of a leafy greens harvester developed in partnership with two Salinas Valley growers. Steve Jens, Harvest Moon’s president, said the new machine uses a camera and pattern-recognition technology to spot foreign objects and diseased or damaged plants as the harvester moves across a field.

The intensification experiment was implemented in an established walnut orchard at the Plant Sciences Field Facility in Davis, CA, USA . The orchard was planted in the spring of 2015 with ‘Chandler’ walnuts. The entire orchard was 0.7 ha in area consisting primarily of Yolo silt loam soils . Orchard management included microsprinkler irrigation and weed-free tree strips maintained with preemergent herbicides. Experimental plots included the orchard alley between seven pairs of trees, approximately 6 m by 40 m. Cover crop programs were based on cereal rye, since it is known to be a competitive, weed-suppressing species that has desirable termination characteristics . Furthermore, this species thrives under various cultural management conditions and has cultivars that are well-adapted to grow as a winter cover crop in Central California. We used ‘Merced’ rye, which is a relatively tall cultivar. The whole experiment was conducted in one orchard over two growing seasons. Cover crops were established in the fall of each year, on November 11, 2019 and November 9, 2020, and terminated in the spring of each study year, on April 24, 2020 and April 9, 2021. Each plot received the same cover crop management program in both years of the experiment. Except for the forage treatment described below, rye was direct-planted with a seed drill at 22.5 kg planted ha-1 , drying rack and cover crop termination was performed with a flail mower. Planting and termination operations were planned to minimize equipment traffic in the orchard, and only one tractor pass was made across each orchard alley at each planting and termination date.

Flail mowers are practical for cover crop termination in California, since these implements are more common than other cover crop termination tools and they minimize crop residue ahead of nut harvest. We had five treatments which represented a range of different cover crop management intensities. The ‘sprayed’ treatment was used as our non-treated control, and the rye planted in these plots was terminated with a glyphosate application when rye plants reached 5 to 10 cm in height. These burn down applications occurred on January 13, 2020 and January 12, 2021, and included a broadcast application of Roundup Weather MAX at 1.607 L ha-1 with a carbon dioxide-propelled backpack sprayer. This treatment mimics a relatively intense commercial management system where orchard alleys are kept weed free. The ‘standard’ treatment included rye with no other cover crop management until termination. The ‘multi-species’ treatment included the base planting of rye and several additional cover crop species. The other cover crop species in the mix were common vetch 4.5 kg planted ha-1 , ‘PK’ berseem clover at 4.5 kg planted ha-1 , daikon radish at 2.25 kg planted ha-1 , and ‘Braco’ white mustard at 2.25 kg planted ha-1 . These seeds were broadcast spread immediately before rye was planted. We used these methods to establish the sprayed and multi-species treatments to minimize logistical challenges and orchard traffic, while also relying on the tractor and seed drill to enhance seed-to-soil contact of our additional cover crop species in the multi-species treatment. The multi-species treatment in this experiment has the same species and approximate planting rates as the multi-species mix in the planting date experiment described below.The ‘boosted’ treatment included a 45 kg ha-1 N top dress with granular urea after rye tillering which were made on February 25, 2020 and February 26, 2021.

The ‘forage’ treatment was managed as a rye hay intercrop. This treatment was planted at a rate of 45 kg planted ha-1 . At planting, we fertilized with 40 kg ha-1 N and 28 kg ha -1 P as granular urea and monoammonium phosphate at planting. We also top dressed with 45 kg ha-1 N after rye tillering. On the same day as topdressing, we broadcast-applied carfentrazone at 73 mL ha-1 with a backpack sprayer as a post emergent herbicide application for broadleaf weed control. The top dress and herbicide applications were applied on February 25, 2020 and February 26, 2021. The forage treatment was terminated with a swather, and the crop material was subsequently baled and removed. Immediately before cover crop termination, we destructively sampled cover crop and weed biomass. We collected biomass samples from two 0.25 m2 quadrat subsamples in each plot. Cover crops and weeds were separated before being dried in forced air drying ovens. Finally, we weighed dry plant biomass. Summer weed emergence was assessed after cover crop termination using point intercept transects. One transect was placed diagonally across the alley in each plot. Transects were 25 m long with 25 points spaced evenly along the transect. Plants were identified visually at each point. These summer weed transects were performed on June 17, 2020 and May 21, 2021, when summer weed emergence and potential cover crop regrowth might be scouted by a grower planning summer weed management. Planting date experiment. The planting date experiment was implemented in a non-bearing almond orchard at the Wolfskill Experimental Orchard near Winters, CA, USA . The orchard was established in the fall of 2017 with alternating rows of‘Nonpareil’ and ‘Aldrich’ almonds.

The entire site was about 1.1 ha in area with primarily Yolo loam soils . Orchard management included microsprinkler irrigation and weed-free tree strips treated with preemergent herbicides. The experiment was laid out as a randomized complete block design with five repetitions. Experimental plots were roughly 25 m long and 12 m wide, comprising five trees in length and two orchard alleys in width. We had five treatments, including a non-treated control and two multi-species cover crop mixes each planted at two different planting dates. The non-treated control had commercial standard vegetation management practices, which included several glyphosate applications throughout the winter months. We used cover crop mixes in this experiment because of their existing use by California orchard growers . Orchard growers frequently choose among cover crop mixes that support a variety of ecosystem services aside from vegetation management, such as pollinator health or improved soil structure, and multi-species cover crops can support some of these multifunctionality goals. Additionally, using different cover crop mixes allowed us to evaluate cover crops with different germination timings and a range of emergence phenologies. The two cover crop mixes used in this study were a ‘multi-species’ mix and a ‘brassica’ mix. The multi-species mix used the same species as the multi-species treatment in the intensification study, and it included a common combination of cover crop functional groups including a small grain, legumes, and mustards . The mix consisted of 10% ‘Braco’ white mustard, 10% daikon radish, 30% ‘Merced’ rye, 20% ‘PK’ berseem clover, and 30% common vetch planted at 56 kg planted ha-1 . Each of the cover crop mixes was planted at a relatively early planting date and a late planting date. These dates were chosen to represent a timely cover crop planting soon after nut harvest and coincidental with the onset of winter rains as well as a later cover crop planting coincidental with nut pruning, sanitation, and other winter management activities. This experiment was conducted in one orchard over three growing seasons. The early planting date occurred on October 15, 2018, October 24, 2019, and November 9, 2020. The late planting date occurred on January 31, 2019, February 10, 2020, and January 21, 2021. Cover crops were direct-seeded with a conventional grain drill. Ground preparation occurred before each planting date. Before the early planting date, the whole orchard received light tillage immediately before a glyphosate burndown. Before the late planting date, cannabis curing late planted plots and the non-treated control received an additional glyphosate burndown but no additional soil disturbance. Cover crops were terminated with a flail mower on April 19, 2019, April 27, 2020, and April 22, 2021. Weed emergence was monitored throughout the cover crop growing season using permanent point intercept transects. Each plot had one transect placed diagonally across one orchard alley. Each transect was 10 m long with 10 points along the transect. Plants were identified at each point along the transect, and monitoring took place weekly while cover crops were growing. This experiment did not have different residue management treatments, so summer weeds were not evaluated. Immediately before cover crop termination, we sampled cover crop and weed biomass using the methodology described above, including two 0.25 m2 quadrat subsamples in each plot.Data analysis. Analyses were performed in R 3.0.3 .

For biomass data from both experiments, we used ANOVA and performed multiple comparisons with Fisher’s LSD. ANOVA was performed by specifying a model with lm and entering it into Anova from the car package . The models we used had treatment, replicate, and their interaction as predictors and either weed biomass or cover crop biomass as a response variable. We inspected ANOVA assumptions visually using plot. Subsequently, weed biomass from the intensification experiment was analyzed with one outlier removed and a square root transformed response variable due to leptokurtosis. However, unabridged and non-transformed data are displayed in the figures. Finally, we performed Fischer’s LSD with LSD.test from agricolae using a significance level of P<0.05 . Summer weed emergence data were analyzed in the same manner but using cover crop regrowth and summer weed emergence as response variables. Weekly transect surveys were analyzed with multiple linear regression. We compared the slope of each regression line in to evaluate the relative rates of weed and cover crop emergence after each plant date. Cover crop emergence was represented as the change in ground cover as observed in weekly observations throughout the first ten weeks following the respective planting date of each treatment. There was only one non-treated plot in each repetition, and we evaluated ground cover following both the early and late planting dates in the same non-treated plots. Weed and cover crop emergence were modeled as functions of treatment, weeks after respective planting, and their interaction. These linear models were created using lm. We created additional linear models using other possible combinations of predictor variables and compared these various models using anova. However, we determined the model described above to be the most parsimonious. Parameter estimates for the slope of each line were compared with Tukey’s HSD using lstrends from the emmeans package . All figures were made with ggplot2 .In the intensification experiment, cover crop biomass varied with management treatment . While year was not a significant predictor of cover crop biomass , we detected an interaction between year and treatment . Furthermore, multiple comparison testing led to different conclusions from each year of the intensification experiment. With data pooled across years, the forage and boosted treatments resulted in higher cover crop biomass than multi-species or standard treatments. Within each year, the boosted treatment alone resulted in the highest cover crop biomass in 2020, while the forage treatment did so in 2021. Cover crop treatment and year both predicted weed biomass. The interaction term was also important . In general, the four cover crop programs resulted in less weed biomass compared to the sprayed treatment and similar weed biomass compared to each other. This conclusion was supported in both years of the study, but we observed less weed biomass overall in 2021. Intensified cover crop programs can increase cover crop biomass, but all of the cover crop programs we tested were similarly effective at reducing weed biomass. Less rainfall in 2021 could have contributed to differences between study years, and we attribute some decrease in boosted cover crop biomass to dry conditions after top dress fertilizer application which likely caused a reduction in plant-available nutrients from the applied fertilizer. Some cumulative effect of two years of cover cropping could have also contributed to these results.In the planting date experiment, cover crop biomass varied with cover crop treatment . Year was not significant , but the interaction between treatment and year was . In 2019 and 2020, the early planting treatments resulted in higher cover crop biomass than the late planting treatments. Differences between cover crop treatments were greatest in 2020, and the multi-species mix also resulted in greater cover crop biomass compared to the brassica mix in this year. There were no differences in cover crop biomass between treatments in 2021. Year , treatment , and their interaction all contributed to weed biomass. While we observed a lot of year-to-year variation, the late-planted multi-species treatment was consistently in the lowest statistical group for weed biomass, and the early-planted brassica treatment was consistently in the highest group.