Tobacco growing requires heavy applications of fertilizers, and pesticides like methyl dibromide and ethyl-bromide that harm workers and pollute drinking water. In Pakistan, “Up to 48 different chemicals are used between the processes of sowing the seed to its implantation at the sapling stage. Inadequately trained and lacking in proper gear the farmers continue to expose themselves to the dangers of chemical and pesticide exposure year after year.” Nicotine poisoning threatens adults and children who cultivate tobacco . In Mexico children aged 0-14 years who work in tobacco fields are exposed to potentially harmful and toxic amounts of pesticides . Children and adults are harmed by polluted drinking water from pesticide run-off. Most tobacco families in Mexico are financially unable to afford protective clothing and bottled drinking water. Information on injuries, accidents, and fatalities of child laborers in tobacco farming needs to be collected, analyzed and disseminated. Children who work in tobacco fields experience backaches, broken bones, snake bites and other risks. Research is needed on risksfacing child workers and the influence of risks on their educational and psychological development. Researchers can apply Helmut Geist’s multi-method approach of statistical analysis, meta-analytical study and narratives to conduct investigations of child laborers in tobacco growing developing countries. Researchers need to devise measurements to determine soil degradation and downstream effects of pesticides and use the measurements to understand tobacco-related destruction of soil nutrients and pollution of water tables. Research findings could be used to devise workshops and study circles on health and pesticide education, rolling benches for growing building on worker education infrastructure already created by agricultural trade unions. Studies are needed on tobacco industry corporate social responsibility schemes focused on child labor and deforestation.

The studies need to analyze how actual tobacco industry practices contradict corporate schemes and their messages. Research is also needed to understand farmer and consumer perceptions of “ethically produced” cigarettes and how tobacco companies through these cigarettes undermine health policy, pass on misinformation, and build public faith in tobacco. Research is needed on how health policymakers and advocates view and participate in tobacco industry responsibility schemes. Research is needed on the direct links between tobacco industry practices and child labor, deforestation, and other realities of tobacco farming that clash with farmer welfare. Do tobacco companies knowingly purchase tobacco produced with child labor? What evidence is needed to verify that tobacco companies knowingly purchase tobacco produced with child labor? To what extent do companies’ policies and practices allow them to buy leaf produced with child labor? Policymakers and advocates need to examine opportunities for excluding imports of tobacco produced with child labor.Health policymakers and tobacco control researchers need to find a balance between building corporate accountability and recognizing tobacco companies’ efforts to cultivate tobacco and sell cigarettes. How should public health and tobacco control policymakers attempt to make tobacco companies accountable to child labor and other socially disruptive behavior without pressuring companies to move into more vulnerable societies where labor costs are lower and environmental standards are less restrictive or non-existent? What are experiences of tobacco farmers who contract directly with leaf companies and cigarette manufacturers? Is there transparency in contract agreements between farmers and tobacco companies? What remedies exist for tobacco farmers who have been entrapped through debts for marked up inputs from tobacco companies? What is the impact of contract farming on social development and environmental health in tobacco farming communities? Policymakers and researchers need to pressure tobacco companies to publicize details of tobacco farming contracts, average and enforced prices for inputs, and loans granted and collected to ensure fairness in contract arrangements. Cultural attitudes that support child labor need to be examined. What cultural attitudes, practices, and beliefs of tobacco farmers justify or sustain child labor? What cultural changes need to happen to mainstream, standardize, and normalize tobacco growing free from child labor and environmental destruction?

Research is needed on experiences of tobacco farmers and tobacco farm workers, recognizing that these economic groups have contradictory and overlapping interests. How many casual or day laborers work in the global tobacco growing sector? To what extent do farm workers use child labor and harm environments?How can public health policymakers and tobacco control advocates overcome ambivalence toward trade unions of tobacco farmers and farm workers that promote fair and decent work? Do health policymakers, advocates, and researchers develop partnerships focused on food security and sustainable agriculture with tobacco farm worker trade unions that lend support to tobacco industry social responsibility child labor projects? To what extent do health policymakers call upon trade unions that accept tobacco industry money and promote living wages to justify their policy of accepting tobacco money?The best practices for addressing tobacco-related child labor, deforestation and poverty involve equity and inclusivity. Equity in social protections such as quality education, health care, and housing and inclusivity of tobacco farmers in policy making processes and research activities in tobacco farming are major goals of best practices. The aims of best practices are to ensure prosperity and welfare of tobacco farmers, reduce the influence of tobacco companies on child labor and environmental projects, and in cases where tobacco companies financially support projects, obtain commitment from companies to support a program of outside, independent monitoring of compliance with global standards such as the International Labor Organization Convention No. 182 on the Worst Forms of Child Labor, 1999. Best practices to reduce tobacco-related child labor, deforestation and poverty are most effective when balanced with specific country experiences and policy priorities. Child labor in Malawi and child labor in India are different, requiring analyses of local contexts, stakeholder interests, and country needs. Deforestation in tobacco growing sectors in Tanzania and Brazil is not the same. The best practices below need to be examined in specific country contexts and implemented to ensure compatibility between best practices and policy environments.The International Labor Organization, International Program on the Elimination of Child Labor with projects in 88 countries, including many tobacco growing countries, is an example of best practices to address child labor in tobacco growing. The Dominican Republic provides a representative case of ILO-IPEC tobacco related research. In 2004, research was conducted to generate data on the extent and nature of youth and their families working in tobacco plantations in the Dominican Republic. One hundred children performing tobacco-related jobs were interviewed and fifty focus groups discussions were conducted on 35 farms. The main finding of the study is that child laborers perform poorly in school and have low attendance rates in schools because of their involvement in tobacco cultivation. The researchers recommended that non-tobacco agricultural development needs to be created and mechanisms to monitor and inspect child labor on tobacco plantations are required. The study provides a best practice approach to research that could provide basic information on the child labor problem in order to assess the extent and impact of child labor in tobacco growing countries. ILO-IPEC works in partnership with and receives financial support from global tobacco companies through the Elimination of Child Labor in Tobacco Growing Foundation , a tobacco industry funded group, raising the issue that tobacco control policymakers and researchers need to weigh the advantages and disadvantage of involvement with social, development, cannabis dry racks and environmental groups that collaborate with tobacco companies. Beginning in 2002, ECLT financially supported ILO-IPEC projects to reduce tobacco-related child labor in countries such as the Dominican Republic, Indonesia, and Tanzania. ILO-IPEC/ECLT studies appear to document child labor problems in a reasonable manner.

The major weakness of ILO-IPEC/ECLT studies is the absence of information and comment on tobacco companies’tobacco growing practices that harm farmers, children and environments, and companies’ strategies to use corporate social responsibility schemes to build faith in the tobacco and deflect criticism of tobacco companies’ practices. ECLT on its website states that the International Labor Organization plays an advisory role to ECLT. On ILO-IPEC website, ECLT is listed as a donor to ILO-IPEC in 2002-3 and 2006-7. ECLT through ILO involvement obtains legitimacy for ECLT and tobacco companies social responsibility schemes focused on child labor to sidestep labor exploitation in Malawi and other countries where ECLT operates child labor projects. The WHO is not a participant to ILO-IPEC. Industry funded child labor projects create a unique problem for health policymakers and tobacco control researchers that support WHO’s Framework Convention on Tobacco Control. Involvement of health policymakers and researchers in ILO-IPEC/ECLT projects could enhance legitimacy of tobacco industry efforts to promote goodwill and build public faith in tobacco through child labor projects. Refusal of health policymakers and researchers to participate in ILO-IPEC/ECLT child labor schemes creates a gap between the goals of policymakers and researchers to promote farmer prosperity and resources to reduce inequalities and improve living standards on tobacco farms.The hazard rating matrix developed to assess work performed by children in vegetable farming in the Philippines provides a simple tool tobacco control policymakers and researchers could use to assess work performed by children in tobacco cultivation . The hazard rating matrix is a specialized checklist and classification scheme comprised of work environment, materials and equipment used, and contact with social and water. The hazard rating matrix of the degree of safety of working conditions and the intensity of work could allow policymakers and researchers to identify hazardous work of children in tobacco growing that should be banned.78Promoting the creation and dissemination of documentary films about tobacco in Argentina as well as films about tobacco related child labor, deforestation, pesticide pollution and nicotine poisoning in Malawi, Tanzania, Mexico, Brazil, and Bangladesh. The Instituto de Ciencia y Tecnologia Regional in Jujuy, Argentina, coordinates projects to develop leadership among the youth regarding tobacco control through research, identify risk factors such as poverty that factor in the uptake of tobacco use in displaced aboriginal youth, and to raise community awareness and support for improved livelihoods of tobacco farmers in Argentina. In 2004, the Instituto de Ciencia y Tecnologia Regional produced the documentary film “Tabaco, Voces Desde El Surco” on tobacco farmers and workers in Jujuy to educate Argentineans and the international community about the social and environmental costs of tobacco farming. The video is available for viewing on the Internet, providing visual imagery of human experiences of tobacco farming to researchers, policymakers, and individuals with Internet access throughout the world. In the video, a tobacco farmer standing with a hoe in a tobacco field says, “One starts learning from very young when you are eight or nine years old and gets together with friends. We play to put the tobacco leaves on the cane [drying sticks], and in this way you are brought up doing this work. Then, when you are twelve you do the work of an adult.” The video imagery of farming, child labor, and environmental destruction from tobacco farming augments text-based reports and statistical analyses of tobacco work to more fully assess the extent and characteristics of tobacco-related child labor and biodiversity loss. In Malawi, the Guernsey Adolescent Smokefree Project established in 2006 the project “Ana a topa” to support children who work in the tobacco farming sector. Guernsey is a British Crown dependency in the English Channel near Normandy, France. “Ana a topa” involves a partnership between the Guernsey Adolescent Smokefree Project and the Tobacco Tenant and Allied Workers Union of Malawi, the main tobacco farm worker organization in the country. “Ana a topa” is in its beginning stages of a crop diversification scheme that directly supports children in Malawi and a research project with local advocates to assess the frequency of child labor abuses in Malawi.The project is a unique tobacco farmer union-public health group alliance to raise awareness of child labor, reduce the factors that force parents to send their children to tobacco fields instead of schools, and strengthen the tobacco farm worker union’s child labor committees in tobacco farms to confront the child labor problem. The project is cross-national and involves a media campaign in Guernsey to educate youth on the working practices imposed by the tobacco industry on Malawi and the demands placed on children to work in tobacco fields. In Uganda in 2004, the Environmental Action Network developed a project to create a database of information on deforestation and other issues affecting tobacco farmers. The project filled a local knowledge gap on environmental problems relating to tobacco by systematically collecting and organizing data specific to Uganda, allowing researchers and advocates to reduce dependency on data from other countries.

The water flowed by way of gravity into a 125 mL beaker, leaving debris behind. Filter papers were changed periodically as needed. Then, 50 mL were allocated from the filtered sample and placed in storage at -20° C until analysis. A standard for pendimethalin, ACS-grade hexane and MS-grade acetonitrile were obtained from Fisher Scientific. Liquid-liquid extraction methods were modified from USEPA . High pressure liquid chromatography tandem mass spectrometry was employed to analyze for residue in water samples. Fifteen mL samples were extracted three times with 3 mL of hexane and placed on a rotary platform shaker for 5 minutes, then set aside for 15 minutes. Hexane extracts were pooled and 3 mL were then dried under a nitrogen gas stream. Then, volumes of 500 µL acetonitrile were added to the dried sample and vortexed. Volumes of 500 µL 0.4% formic acid was then added and vortexed for a final concentration factor of 15. A Shimadzu LCMS-8040 triple quadrupole mass spectrometer was used equipped with electrospray ionization on positive mode. The desolvation line temperature and heat block temperature were 250° and 400° C, respectively. Nebulizing gas and drying gas were set at a flow of 3 L min-1 and 15 L min-1 , respectively. The mobile phase flow rate was 0.4 mL min-1 and an injection volume of 10 µL. The C18 column was Phenomenex Kinetex polar, 100 by 3.0 mm and 2.6 µm particle size. The multiple reaction monitoring ion transitions for the quantifier ionwere 282.0 > 212.1 m/z in a dwell time of 10 ms and for the qualifier ions were 282.2 > 43.1 m/z and 282.2 > 194 m/z in a dwell time of 5 ms. The limit of detection was 0.006 µg L-1 and the limit of quantification was 0.008 µg L-1. Multiple calibration curves were implemented for the low concentration range and for the high concentration range using Shimadzu LabSolutions and MacCoss Skyline software for small molecules. Method recovery was performed by spiking five non-treated collected water samples with 0.20 µg L-1 of pendimethalin before extraction . A low concentration of pendimethalin below 0.05 µg L-1 was present in the collected non-treated samples, therefore, cannabis dryingn racks the peak areas of the control samples without standard spiking were subtracted from the spiked samples.

The recovery in water samples was on average 79%.A significant increase in sweet potato [Ipomoea batatas Lam.] production area in the southeastern United States has occurred in the past decade, increasing from 33,548 ha in 2007 to 51,800 ha in 2017 . Sweetpotato has proven to be a valuable crop with a national farm gate value of $705.7 million in 2016, up from $298.4 million in 2006 . North Carolina is the largest sweetpotato-producing state, accounting for 54% of U.S. production . North Carolina, California, Mississippi, and Louisiana account for 94% of sweetpotato production in the United States . Unfortunately, due to its prostrate growth habit and relatively slow growth, sweetpotato does not compete well with problematic weeds, resulting in reduced yields . Palmer amaranth and large crabgrass [Digitaria sanguinalis Scop.] are among the top five most common weeds in North Carolina sweetpotato, with A. palmeri being identified as the most troublesome weed . Amaranthus palmeri has been reported to be taller, to have a faster growth rate and greater leaf area, and to produce more overall biomass when compared with other Amaranthus species . Season-long A. palmeri interference is seen in vegetable crops, with reduced yield of 94% in bell pepper , 67% in tomato , 36% to 81% in sweetpotato , with the greater yield losses associated with higher A. palmeri densities. Limited herbicide options exist for use in sweetpotato . Growers rely on PRE herbicides, which do not always provide efficacious weed control and require rainfall for activation. POST herbicide options for A. palmeri control in sweetpotato are limited to between-row applications of carfentrazone or glyphosate . The lack of POST herbicides forces growers to use tillage for control of weeds until row closure, at which time growers have no additional control options for dicotyledonous weeds other than mowing weeds above the cropcanopy and hand weeding, which is a costly control measure . Digitaria sanguinalis is commonly found in fruit and vegetable crops but has not been highly ranked as a problematic weed due to efficacious POST herbicides such as clethodim, fluazifop, or sethoxydim . Although these graminicides can be effective, grasses escaping herbicide application or sprayed after substantial establishment may continue to compete with the crop and reduce yields.

Furthermore, herbicide resistance management for D. sanguinalis should be considered, as resistance to acetyl-CoA carboxylase herbicides, including those registered for use in sweetpotato has been reported . While its impact on sweetpotato has not been reported, season-long, D. sanguinalis reduced yield in bell pepper by 46% , snap bean by 47% to 50% , and watermelon [Citrullus lanatus Matsum. & Nakai] by 82% . A better understanding of the interactions of A. palmeri and D. sanguinalis with sweetpotato would allow for better decision making regarding their control. Thus, the objectives of this study were to determine the effect of five densities of A. palmeri and D. sanguinalis on sweetpotato biomass and storage root yield and quality, the intraspecific response of A. palmeri and D. sanguinalis across five densities with and without sweetpotato, and the effect of sweetpotato on growth of A. palmeri and D. sanguinalis.Field studies were conducted with ‘Covington’ sweetpotato at the Horticultural Crops Research Station near Clinton, NC on a Norfolk loamy sand with humic matter 0.31% and pH 5.9 in 2016 and an Orangeburg loamy sand with humic matter 0.47% and pH 5.9 in 2017. Nonrooted ‘Covington’ sweetpotato 20- to 30-cm-long cuttings were mechanically planted approximately 7.6-cm deep into ridged rows 1 m apart in the entire study at an in-row spacing of approximately 30 cm on June 9, 2016, and June 12, 2017. At 1 d after transplanting, sweetpotato plants were removed by hand in the no-sweetpotato treatments. On the same day, treatment rows assigned A. palmeri or D. sanguinalis were broadcast seeded on the soil surface and lightly raked to a depth of approximately 1.0 cm. After weed seeding, the entire study was irrigated with 1.3 cm of water using overhead irrigation to aid in weed seed establishment. No additional irrigation was applied, in either year, after the initial irrigation event. Treatments consisted of a single weed species at five weed densities grown with and without sweetpotato arranged in a randomized complete block design with three replications . Amaranthus palmeri and D. sanguinalis were hand thinned to treatment densities of 0 , 1, 2, 4, and 8 and 0 , 1, 2, 4, and 16 plants m−1 of row, respectively, when A. palmeri was approximately 8 cm tall, and D. sanguinalis had two expanded leaves. At the time of weed thinning, sweetpotato averaged one to two newly expanded leaves on each plant. Densities of A. palmeri and D. sanguinalis were based on those used in previous research . Plots consisted of two bedded rows, each 1-m wide by 5-m long, with the first row being a weed-free buffer row planted to sweetpotato and the second row a treatment row. Treatment rows were maintained at specific weed treatment densities, and border rows were maintained free of weeds season-long by weekly removal by hand. Season-long rainfall and growing degree day data are presented in Table 1. Two days before sweetpotato harvest, 5 sweetpotato plants and 5 plants of each weed species were randomly harvested at the soil level from each plot to determine aboveground biomass. Samples were placed in 2-ply paper yard waste bags measuring 40 by 30 by 89 cm and fresh biomass was recorded. Samples were then placed in a propane-heated, forced-air drier for 96 h at 80 C. Once dry, samples were removed and weighed immediately to determine dry biomass. To determine fresh and dry sweetpotato and weed biomass on a per plant basis, total sweetpotato or weed biomass within a treatment and replication was divided by the number of plants harvested. To determine dry biomass per meter of row, individual weed biomass was multiplied by sweetpotato plant and/or weed number in 1 m of row, respectively.

Sweetpotato storage roots were harvested at 113 d after transplanting in 2016 and at 107 DAT in 2017. In both years storage roots were harvested with a tractor-mounted two-row chain digger and hand sorted into jumbo , no. 1 , and canner grades and weighed. Total marketable yield was calculated as the sum of jumbo and no. 1 grades. Data for crop biomass, vertical growing weed individual weed biomass, weed biomass per meter of row, yield, and quality were subjected to ANOVA using PROC MIXED in SAS . Treatment, year, and treatment by year were considered fixed effects, while replication within year was treated as a random effect. Year was treated as a fixed effect to further evaluate components of the year by treatment interaction, such as year by weed density and year by crop presence or absence. If the treatment by year interaction was not significant, a contrast statement was used to test for a linear trend for dependent variables with increasing weed density, calculated separately for each weed species. All response variables, except canner yield, were square-root transformed to reduce both data skewness and variance heterogeneity before carrying out the mixed model ANOVA.Marketable yield decreased as the density of A. palmeri or D. sanguinalis increased. No treatment by year interaction for sweetpotato yield was observed ; therefore, data were combined over years. Marketable yield loss associated with A. palmeri density ranged from 50% with 1 A. palmeri plant m −1 of row to 79% with 8 plants m−1 of row, respectively, when compared with the weed-free check . Marketable yield reduction by D. sanguinalis was similar to marketable yield reduction caused by A. palmeri but at higher weed densities. Marketable yield was reduced by 35% and 76% with 1 and 16 D. sanguinalis plants m−1 of row, respectively . Loss of jumbo yield is a significant contributor to overall marketable yield loss at weed densities as low as 1 plant of either species m−1 . Jumbo grade had greater yield loss with 1 plant m−1 for A. palmeri and D. sanguinalis than the no. 1 gradefor both weed species at the same density . Results for estimated marketable yield loss per weed as weed density approaches zero for A. palmeri and D. sanguinalis were 119% and 61%, respectively. The higher estimated marketable yield loss as weed density approaches zero for A. palmeri relative to D. sanguinalis indicated higher competitive capacity of A. palmeri at low densities. These results for A. palmeri are consistent with another study in sweetpotato but higher than in soybean [Glycine max Merr.] , peanut , and corn . Estimated yield loss as weed density approaches zero in the present study indicates that A. palmeri and D. sanguinalis, even at low densities, can greatly reduce sweetpotato marketable yield. The initial yield loss as weed density approaches zero for D. sanguinalis was less than A. palmeri at lower densities. However, sweetpotato yield loss from interference byD. sanguinalis was higher than yield loss reported in snap bean . For parameter A, the asymptote of the regression model estimating the maximum yield loss due to weed density was 87% for A. palmeri and 83% for D. sanguinalis. Meyers et al. estimated a maximum marketable yield loss of 90% at A. palmeri densities of 6.5 plants m−1 of sweetpotato row. Findings from our study further support the findings of Meyers et al. , who also reported the highly competitive nature of A. palmeri with sweetpotato. To reduce interference of A. palmeri and D. sanguinalis, which are commonly reported in sweetpotato, growers should use a combination of efficacious PRE herbicides, as outlined by Meyers et al. , in combination with tillage, hand removal, and mowing . Although POST herbicides for A. palmeri are limited, POST herbicide options for selective grass control in sweetpotato are available and should be used when D. sanguinalis is less than 10 cm to minimize yield loss. If D. sanguinalis resistance is suspected, then alternative methods should be analyzed for control. Growers should not dismiss the impact of either weed, as a single A. palmeri or D. sanguinalis per meter of row reduced marketable yield by 50% and 35%, respectively . Reduction in marketable yield loss was due to a decrease in weight of no. 1 and jumbo sweetpotato grades.

Visual percent rice injury assessments were carried out at 20 DAT and 40 DAT by observing present symptomology, which included stand reduction and stunting, and compared to the non-treated, on a scale of 0 to 100, where 0=no injury and 100=plant death. Rice tiller counts were conducted at 75 DAS by sampling twice within 30-cm by 30-cm quadrat in each plot and data scaled to a meter squared area for presentation. Rice grain was hand harvested from two 1-m2 quadrats in each plot and mechanically threshed . Grain was then cleaned and weighed, and adjusted to 14% moisture.An experiment to compare rice cultivar response to pendimethalin was conducted at the Rice Experiment Station greenhouse in Biggs, CA. A factorial arrangement of treatments in a completely randomized design was implemented. The factors were five cultivars, two formulations, two timings and two rates. The rice cultivars consisted of ‘S-102,’ ‘M-105,’ ‘M- 205,’ ‘M-206,’ and ‘M-209.’ These rice cultivars represent common short-grain and mediumgrain cultivars produced in California. CS and GR formulations were applied at 5 and 10 DAS at1.1 kg ai ha-1 and 2.3 kg ai ha-1 . Three experimental runs were conducted separated by time. The first run was seeded on January 15, 2021, the second run on March 7, 2021 and the third run on April 20, 2021. Field soil with similar characteristics to the field site soil above, was used to fill 34-cm by 12-cm by 12-cm plastic containers, with drainage openings on the bottom, and placed inside larger 58-cm by 41-cm by 31-cm plastic containers, vertical racking with no drainage. Seeds were pregerminated by placing the different cultivar seeds inside cloth bags and in five-gallon buckets completely submerged underwater for 24 h, and then seeds were air dried before sowing.

Twenty seeds were sown in each smaller container by placing the seed on the soil surface in a shallow flood onto the soil surface. The larger containers were immediately filled with water up to 10-cm above the soil level and maintained at that level throughout the study. Starting after the day of seeding, each smaller container was treated as a plot and was set in a completely randomized placement and rerandomized every seven days. Copper sulfate crystals were applied by hand at 13 kg ha-1 three DAS for control of algae in each container for each run. The emerged rice seeds were counted before the pendimethalin applications and at 21 DAT to calculate the percent rice stand survival. At 20 DAT, plant height was measured from the soil surface to the far most extended leaf end in each plot. At 21 DAT, above ground biomass was harvested from each plot and dry biomass was recorded. The greenhouse was maintained at 33/25 ± 2C day/night temperature. A 16-hr photoperiod was provided and natural light was supplemented with metal halide lamps at 400 µ mol m-2 sec-1 photosynthetic photon flux. The CS formulation was applied using a track-sprayer at 187 L ha-1 with a single 8001EVS nozzle by placing container inside the spray chamber with a height of 43 cm from the surface of the floodwater to the spray nozzle. The GR formulation was spread by hand in each respective tub, calculated by the area of the larger plastic container.All statistical analysis was conducted on R with the use of the LMERTEST and EMMEANS packages . Data was subjected to linear mixed effects regression models and mean separation, when appropriate, with Tukey’s HSD at α=0.05. In the field study, the model consisted of the three formulations, three rates, three application timings as fixed factors, and assessment dates as repeated measure, while replications were set as random separately each year. In the greenhouse study, the model consisted of two formulations, two rates, two application timings, and five cultivars as fixed factors, while experimental runs were treated as random.

Normality of distribution were visually examined with quantile-quantile plots and linearity were visually examined by plotting residuals.There was interaction by year for Echinochloa spp. control . In 2020, 330 ± 8 Echinochloa spp. plants m-2 was observed in the non-treated, while in 2021, 180 ± 2 Echinochloa spp. plants m-2 was observed by 56 DAT . The field site previously recorded variations in weed species populations by year caused by differences in weather conditions and soil seedbank . The cyhalofop and propanil application influenced the grass control levels observed in 2020. Interaction effect across formulation with timing were observed for Echinochloa control both years . The interaction of formulations with timings in 2020 demonstrated a reduction in Echinochloa control as application timing was delayed from 5 to 15 DAS with theEC formulation; however, the differences were not observed after application of GR and CS formulations . In 2021, the interaction of formulations with timings demonstrated a decrease in Echinochloa control as application timing was delayed from 5 to 15 DAS with the EC and CS formulation, but again not with the GR formulation . Application rates impacted grass control across timings in 2020 and across formulation in 2021 Interaction of rate with timing in 2020 and rate with formulation in 2021 were observed . The Echinochloa control results are not consistent with Ahmed and Chauhan findings who repeatedly demonstrated an increase in grass control with an increase in pendimethalin rates in a dry-seeded rice system. In the water-seeded rice system, pendimethalin degradation will be increased compared to a dry-seeded system ; therefore, greater pendimethalin rates may be necessary to observe an effect. Transformations on the sprangletop control data did not help meet the assumptions of normality of distribution; therefore, the data is presented as if normality was met. Only pendimethalin timing and rate appeared to affect sprangletop control .

The bearded sprangletop population is minimal and previously observed by Brim-DeForest et al. . Therefore, the control results from pendimethalin may not be comparable to fields with greater sprangletop pressure and because of the population differences each year control levels are unclear. In this study, the flood was continuous and pendimethalin application was into the water. The flood may have also been a factor in suppression of sprangletop .There was treatment interaction by year for visual rice injury but not across assessment dates . Injury differed across formulation, rate and timing . Rice treated at the 15 DAS timing had the lowest injury levels, but differed across formulations . Theresults demonstrate that different formulations resulted in varying rice injury levels, which is similar to the results of Hatzinikolaou et al. who evaluated pendimethalin injury on various grass crop species. In 2020, tiller counts ranged from 30 to 200 tillers m-2 . In 2021, however, tiller counts were higher, ranging from 200 to 500 m-2 . After a GR and CS application, rice tillers were similar across timings; however, after EC application at 15 DAS tillers was higher. The rice treated at 15 DAS produced similar tiller numbers when treated at 10 DAS but not when treated at 5 DAS with pendimethalin applied at the 2.3 and 3.4 kg ha-1 from the EC formulations . Differences in formulations by application timings was evident and resulted in varying injury levels effected by the formulation. The greater weedy grass pressure in 2020 may have been a factor in the increase on visual rice injury and decrease in rice stands compared to 2021. Rice treated with pendimethalin showed increased injury with increasing rates when applied at the 5 and 10 DAS; however, at 15 DAS, injury was similar across rates, which suggests that after rice reaches the 3- to 4-leaf stage, rolling benches pendimethalin injury may not impact rice development. Absorption of pendimethalin can cause greater growth disturbance at earlier seedling stages when the grass seedling coleoptile is emerging at the surface of the soil and comes in contact with the herbicide as demonstrated by Knake and Wax with the grass weed, giant foxtail. Pendimethalin remains on the upper soil surface due to its physico-chemical properties ; therefore, once the seedling growing points are further above the soil surface there is a potential to overcome pendimethalin injury.An interaction in year was observed for grain yield. Interaction effect by formulation with timings were observed for grain yield . In both years, rice grain yield was similar across timings with the GR and CS formulations, but not with the EC formulation . Timing was most influential on grain yield with the EC. Overall, similar grain yield was achieved from rice treated with the GR across all rates and timings in 2020 and similarly in 2021 . The GR is formulated as a slow-release of the active ingredient which results in a reduction of crop injury . These characteristics of the GR may have allowed more rice seedlings to establish by not being exposed to high concentrated levels of the active ingredient at once. There was a rate by timing interaction for grain yield . Rice treated with 1.1 kg ha-1 at all timings produced similar grain yield in both years, which were similar to yield in plots when treated with 2.3 kg ha-1 at 10 and 15 DAS, and with 3.4 kg ha-1 at 15 DAS . Pendimethalin applied to rice at 3.4 kg ha-1 at 15 DAS timing had greater yield by 3,014 kg ha-1 of grain in both years when compared to the 5 and 10 DAS timings at 3.4 kg ha-1 .

The results demonstrate that formulation, rate and timing are important factors affecting grain yield in water-seeded rice with use of pendimethalin. An application of pendimethalin in dry-seeded rice in Bangladesh decreased grain yields by 44% to 50% when pendimethalin was applied 2 DAS compared to the weed-free check . Application timing or soil saturation timing is an important influence on rice injury after a pendimethalin application in dry-seeded systems . In the water-seeded system, application timing is the important factor. While not included in the analysis, the grain yields of the non-treated plots in 2020 were extremely weedy and attempts to harvest failed and yield was recorded as zero. In 2021, thenon-treated plots averaged yields of 2,450 ± 340 kg ha-1 . The yields recorded in this study after pendimethalin treatment were low compared to statewide average yields and potentially affected by the pendimethalin application.Stand reduction was influenced by cultivar, formulation, rate and timing . In general, rice treated at 5 DAS resulted up to 68% stand reduction across cultivars for both CS and GR formulations . At 5 DAS, stand was reduced after application of both formulations for ‘M-105’, ‘M-205’, ‘M-206’ and ‘M-209’ . Only ‘S-102’ at the 5 DAS timing resulted in less than 54% reduction . At 10 DAS, ‘S-102’ and ‘M-206’ did not show stand loss across rates, while ‘M-105’ resulted up to 21% decrease in stand after a 2.3 kg ha-1 application compared to a 1.1 kg ha-1 . However, stand reduction after 10 DAS applications were zero to 29% for all cultivars . Koger et al. observed differential cultivar response from pendimethalin applications on long grain rice in a dry-seeded system. Relative tolerance was attributed to mesocotyl length of seedling rice which may vary by cultivar; however, planting depth is also an important factor in dry-seeded rice for achieving pendimethalin tolerance . In water-seeded rice, a mesocotyl is very short on seedlings because the seeds are placed on the soil surface; however, differences in seedling vigor can be important for relative tolerance to pendimethalin. Ceseski and Al-Khatib observed ‘M-205’ and ‘M-209’ to have greater seedling vigor when compared to ‘M-105’ and ‘M-206’, when drill-seeded in a high clay soil. The cultivar vigor characteristic differences can help understand the observed relative tolerance to pendimethalin across cultivars in this Rice biomass was affected by pendimethalin rate and timing . The higher rate was an important factor in decreasing biomass for ‘S-102’ at the 5 DAS from CS and GR applications at 2.3 kg ha-1 . Dry biomass was reduced by 77% at 5 DAS compared to the 10 DAS timing averaged across formulations, rates, and cultivars. However, biomass reduction was minimal and not significant at 10 DAS, except for ‘M-205’ at 2.3 kg ha-1 GR formulation . Awan et al. observed a decrease in rice seedling biomass in dry-seeded rice when pendimethalin was applied at 2.0 kg ha-1 , but not at 1.0 kg ha-1 . Similarly, in this study biomass reduction was rate-dependent for ‘M-205’. Plant height was no different among treatments and were similar to the non-treated by time of biomass harvest .

The results clearly demonstrated that the marketable yield responses of broccoli closely matched the responses in soil and crop nutrient, crop growth and biomass accumulations. Considering the marketable yields, there was no significant yield difference between cover cropping and fallow treatments for the first year cropping . During this year, the number of marketable heads and fresh weights of the marketable heads from the first, second and total crop harvest were not significantly different from each other for all cropping treatments .Differences between cropping treatments in vegetable marketable yield commenced in the second year cropping year. Interestingly, broccoli gain from cover crops even for the second year study was only with fresh weights of the marketable heads, but not the number of heads . Higher fresh broccoli marketable heads were observed from the first and second harvest from the 2008 crops. Broccoli crops produced higher number of marketable heads and fresh weights of the marketable heads during the third year . The total number and fresh weights of marketable heads from the two harvest periods of crops from a summer cowpea plots for 2008 and 2009 were about 36% and 48% higher, respectively compared to these grown on the summer fallow . The findings in general, suggest the long-term buildup and additive effects of cover cropping rotations on the subsequent vegetable crop.The cover cropping treatments increased soil organic matter contents within the subsequent vegetable crop. However, statistically significant differences in soil organic matter component of the soil was not detected until at the broccoli harvest time of 2008 and following cover crop incorporation in 2009. Since these samplings were both after cover crop or broccoli incorporation, grow rack the higher soil organic matter contents must have been from the decomposition of the cover crop residues as well as broccoli.

A continued practice of cover cropping becomes an investment in building healthy soil over the long term, builds organic matter and by serving as food source to soil organisms , and increasing soil productivity . The initial year similarity in organic matter content levels of cover cropped and fallow plots is probably due to the fact that soil organic matter buildup takes place very slowly.Organic matter of a soil is important in improving soil structure, increase infiltration and cation exchange capacity and serves as efficient storage of nutrients . Upon its breakdown soil organic matter releases available nutrients to plants . However, soil contents of organic matter frequency and type of cultivation , cropping and residue management , or fertilizer N input may also affect soil nutrient status. The soil organic matter contents from cropping treatments were reflected in variation of some soil nutrient contents. As has been shown from soil nutrient analysis, nutrient enhancement from cover cropping was more visible following cover crop residue incorporation. Wagger and Creamer and Baldwin suggest that higher contents of soil nutrients were associated either manure applications or cover crop incorporations. While soil nutrient concentrations oscillated between sampling periods and years, Ca and Na concentrations and soil cation exchange capacities were higher for the cover crop treatments of the second year ABH sampling and at ACCP sampling in the third year. The higher soil nutrient concentration and CEC from the cover crop plots of the 2008 must have been from the accumulation from the previous year crop residue decomposition . However, most of the soil nutrient concentration right after cover crop incorporation of 2008 was not different among the cropping treatments, indicating the probability of nutrient immobilization following residue incorporations.

The latter increase in soil nutrition must have been from the mineralization process following cover crop residue decomposition. The trend suggests that it is possible to buildup up soil nutrient contents with the use of summer cover cropping and allow the subsequent vegetable crop to make use of accumulated soil nutrients. It also suggests that the process of cover cropping rotations must be continuous in order to achieve a continuous improvement in nutrient availability for the subsequent vegetable crop. Similarly, soil NO3 was consistently higher for the cover crop treatments relative to the summer fallow, but not until after cover crop incorporation of 2008. Soil NO3 level declined and was not different among the cropping treatments at ABH sampling of 2009. The decline in NO3 at broccoli harvest was probably depletion due to nitrogen uptake by broccoli. In relatively higher soil NO3 levels in 2009 than in 2008, suggests a nutrient build-up effect from repeated cover cropping and a higher N mineralization with increased years of residue accumulation. Soil SO4, and percent cation saturations were higher for the cover crop treatments, compared to the fallow, but not until 2009. Mn and B were higher in the fallow than in the cover cropped plots at harvest. My results demonstrated the importance of preceding cultivation of vegetable crops with summer cover cropping instead of leaving the land fallow. Following broccoli production after summer cover cropping benefitted the crop in enhancing and increasing soil nutrient availability, enhancing crop growth and marketable yield. The ultimate benefit of cover cropping may also come from pest suppression, enhancing beneficial organisms, increased biodiversity and other indirect benefits of cover cropping. Since soil nutrition is particularly critical for organic food production practices, the use of cover crops could help fulfill this need. I observed that not all soil nutrients are equally enhanced with the use of cover cropping. Besides, not all cover crops are equal contributors to added soil nutrition.

Increases in soil nutrient content, particularly soil NO3 was greater when the cover crop was cowpea than when it was a marigold, probably relating to the nitrogen fixing capability of cowpea. Leguminous cover crops with a biological nitrogen-fixing capability play a much more important role and may reduce dependence of the subsequent crop on synthetic nitrogen fertilizers . However, Franzluebbers et al. 1994; Fageria et al. 2005 all suggest that N supply from the decomposing residues must coincide with the subsequent crop N demand and proper management of residue in order to provide increased efficiency of cover crop use. The N supply from legumes could reduce N application rates below the recommended rate for subsequent vegetable crops . The contribution of N is the primary benefit of leguminous crops resulting in increased crop yields . Therefore, my findings of variable nutrient contribution from different cover crops suggest that the extent of soil nutrient build up is dependent on the type of the cover crops and that proper cover crop compatibility and selection be made based on the requirement of a farm and residue management practices. Although legumes could release fixed N to the soil, leguminous cover crop residues may also transport a large portion of their biomass nitrogen into the seeds if allowed to flower and mature, because the N-fixing symbiosis of the legume shuts down when the crop stops active growth. Therefore, a good management that benefits the subsequent vegetable crop is to kill the legume cover crops in the early- to mid-blossom stage and plant the following cash crop without delay, aside from any period for residue decomposition . Since soil nutrition is somewhat related to soil organic matter accumulations, such benefits must depend on a balanced interaction of organic matter, soil organisms that break down crop residues and nutrient cycling and selection of the cover crop and residue management practices . The increased microbial immobilization of soluble N may require modified fertility management practices that increases nutrient availability to coincide with plant demand . Immobilized nutrients may be subsequently available through mineralization after incorporation . On the other hand, the pattern and timing of mineralization of nutrients depends on the residue quality, soil type, temperature, soil moisture content and timing and method of incorporation . The higher soil Ca and Na under cover crop treatments may also be due to the fact that cover crops may help bring nutrients such as calcium and potassium back into the upper soil profile from deep soil layers and then release them back into the active organic matter when they die and decompose . As for the soil contents, higher N, Mg and Na were detected in the shoots of broccoli grown on the summer cowpea plots compared to the fallow treatments. However, vertical racks these nutrient increases were only in the 2009 crops, but not the 2008, indicating a need for repetitive and multiple-year cover cropping rotations to provide increased nutrient supply to the subsequent vegetable crop. In some cases, while some soil nutrients were higher for the cover crop treatments than in the bare soil, the subsequent crop does not seem to have made full benefit of the improved soil nutrition. My findings were consistent with Baggs et al. where no significant effect of cover cropping was observed on the N content or yield of the subsequent oats crop, regardless of the release of N from decomposing cover crop tissues.

These observations were attributed to non-limiting N in this soil for any benefits to become apparent immediately. It is also possible that mineralization of some nutrients from incorporated residues may be delayed , resulting in conflicting evidence over the ‗fertilizer value‘ of cover crops showed that recovery of N by the subsequent crop is typically less than 30–40%. Cover crops may also reduce available soil NO3 compared with the fallow treatment by 18–44% as a result of low mineralization rates.My observation of low nutrient content in shoots of crops and the many other previous findings suggest that crop nutrient contents do not necessarily match soil N contents. Baggs et al. showed that crop N alone is an adequate indicator of the quality of a cover crop. In some cases a higher N content in crops was observed following a bare ground treatment than the cover crops, suggesting that N was not available for crop uptake following cover crop incorporation and may be delayed until after complete mineralization . Nutrient immobilization from incorporation of residues is short-lived immobilization for soils with comparatively high C:N ratio . Cover crops can provide N to subsequent crops in two ways 1) non-legume cover crops recover and recycle residual fertilizer N, and 2) legume cover crops fix atmospheric N for the later crops . In general while cover crops have the potential to supply nutrients to the subsequent crops, synchronization of N supply from decomposing residues and crop nutrient demands must govern the timing of cover crop kill Creamer and Baldwin, 2000. If not properly managed cover crops create nutrient deficiency as a result of immobilization . This is probably the reason why Schroeder et al. rejected the use of cowpea crop residues as fertilizer N inputs for broccoli. Consistent with nutrient status, crop height growth was highest for those from cowpea, followed by marigold and least for crops grown on the summer fallow. The increase in height of broccoli grown on the cover crops is more prominent after the third week of sampling for all study years, but no height differences were observed between cropping treatments for the initial growth stages . This initial stage indifference in crop height could be due to a growth lag phase and that crops are not able to make immediate use of the added resources. Broccoli canopy spread was similar to the crop‘s height responses in that broccoli on the summer cover crop treatments for all years were relatively of broader canopy, but were most significant for the 2008 cropping year. Canopy growth differences between the study years may have been due to the variation in weather conditions of the different experimentation years. Mean leaf number production and variation between cropping treatments were clearly visible for the 2008 and 2009 cropping seasons than for the 2007 crops. These visible increases in number of broccoli leaves with increasing cover cropping rotations indicate the benefits of multiple cover cropping rations and their buildup effects with increasing use of the system. Regardless of some differences in various growth progressions of the vegetable crop, there were some similarities in their responses to the cropping system treatments. First, crop growth is most enhanced by preceding it with summer cowpea than marigold. Secondly, the taller and the greater the canopy spread of the crops are, the higher are the number of leaves per plant.

Nematicides are used to control plant parasitic nematodes. However, there is health and ecological hazards associated with the use of nematicides, hence the need for risk free, economical, and ecologically desirable alternative methods of managing nematodes . Another simple and practical alternative is the use of nematode suppressive cover crops . Cover crops may suppress nematodes by being a poor host, by having a nematicidal effect, by enhancement of nematode antagonists or beneficial nematodes or serving as a ―dead end‖ trap crop . However, cover crop nematode suppression is dependent on cultivar of the cover crop, soil temperature , nematode species , and how the cover crop is managed. For example, growing marigold as a cover crop consistently lowered root-galling by M. incognita on tomato , while incorporation of marigold crop residue failed to do so . Cover crops grown before the maincrop can also suppress nematodes and protect a subsequent and susceptible vegetable crop . Wang et al. on the other hand observed that incorporated cover crop residues had no effect on parasitic nematodes, but enhanced population densities of bacterivorous nematodes. This research is aimed at assessing the effects of summer cover cropping systems on plant parasitic and free-living nematodes in a subsequent vegetable crop. The approach was to use French marigold and cowpea as summer cover crops and compare them to a summer fallow treatment. Effects of cover cropping were assessed on nematode species composition and density in broccoli during the winter growing season. Managing nematodes with summer cover crops would provide vegetable growers, particularly organic farmers with an easy and acceptable method for pest management and improve traditional nematode management approaches.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. The field site was loamy sand with a history of root-knot nematode infestation.

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. Cowpea was chosen because it is a drought hardy legume, weed drying rack resistant to weeds and enhances some beneficial organisms . Marigold was chosen because it is known to control nematodes . 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. During the third year of the trial I included testing nematode response with susceptible tomato plants. In three of the existing treatment replications, 5-6 tomato seedlings were inter planted into broccoli to observe if cropping treatment differences can be seen on tomato.

At broccoli harvest time , all tomato plants were uprooted and evaluated for tomato root nematode and assayed for gall index.Soil nematode population densities were determined by collecting 14 soil cores from 5-25 cm depth from each plot using a 2.5 cm-diameter Oakfield Model L and LS Tube-Type Soil Sampler. Soil samples were collected at cover crop planting , at cover crop incorporation and at broccoli harvest . In each of these years, the 14 soil cores were pooled for nematode analysis. Nematodes were extracted from a 100 gram sub-sample on modified Baermann funnels for 5 days and the number of nematodes was counted. Ten broccoli plants were removed at harvest from each treatment plot and rated for root-knot nematode galling on the 0 to 5 scale outlined by Taylor and Sasser . One hundred grams of broccoli roots were placed in a misting chamber for 5 days fornematode extraction and the number of second-stage root-knot nematode juveniles was counted. All data were analyzed using a one-way ANOVA analysis and means separated used the student T-test.Soil nematode population levels in the experimental field were generally low in all treatments during all years and all sampling periods. Observed plant parasitic nematodes were root-knot , cyst , and pin nematodes only. On any of these nematodes, there were no significant differences among cropping treatments at any sampling period or trial years . The huge variability of data among replications of each treatment and hence a high standard error made most of the differences statistically insignificant. However, there were some relative variations among the cropping treatments. The root-knot nematode population densities were relatively higher for the ACCI sampling of all years in the cowpea treatment compared to either marigold or the fallow treatment . The sugarbeet cyst nematodes were higher at the ABH sampling for the cowpea, relative to the other cropping treatments . When pooled for the three sampling periods, only RKNs were significantly greater in the cowpea plots for 2007 and 2009, but not 2008 . Neither the SCN nor the pin nematodes were significant for year or cropping treatments . If pooled for the cropping treatments , the RKN were denser for the cowpea treatment compared to marigold or fallow treatments .

The population density of RKN for the cowpea treatment was about 14 times higher than in the RKN population in the fallow treatment . The pooled mean population densities for the other nematode species were not significantly different among the cropping treatments . For the broccoli root analysis, neither of the broccoli nematodes nor the broccoli root gall index was significantly different among the cropping treatments or experimental years . Nematode root-gall formation on broccoli was generally very rare and only appeared during the first year and none during the subsequent vegetable growing years . When data were pooled for the sampling periods, and years, there were more RKN population levels on broccoli roots grown on the summer cowpea field than those grown on either marigold or fallow treatments .The last year trial using nematode-susceptible tomato plants inter-planted did not show any significant variation among the cropping treatments on the population density of any of the nematodes , although there were relatively more j2 RKN in the fallow plots than in either marigold or cowpea cover crop treatments. In general, rolling bench the results reveal that contrary to the hypothesis, the use of cowpea and marigold as an off-season cover cropping do not provide suppression to parasitic nematodes, at least to these observed within this experimental field. In most cases the cover cropping treatments had the same effect on parasitic nematode population densities as the fallow treatments. In rare cases, the cover crops enhanced the population densities of some parasitic nematodes compared to fallow treatment.While the effects of summer cover cropping treatments were not significant on the crop parasitic nematodes, they had significant effects in enhancing saprophytic nematode population densities . Enhancement of saprophytic nematodes started at the ABH sampling in the first year , with no significant differences among cropping treatments for the ACCI sampling. Data on saprophytes was not collected for the ACCP sampling in 2007. At the ABH sampling of 2007, saprophytes were about double on the cowpea treatment compared to the fallow , indicating the stronger enhancement of saprophytes with cowpea cover crop. Population densities of the saprophytes continued to increase in the second and third year compared to the 2007. Higher population were observed in both cover cropping treatments at the ACCP sampling of 2008 compared to the fallow , probably accounting for the previous year cover crop and broccoli crop residues. At this sampling period, saprophyte population were about 5 and 4 times higher in plots that had summer cowpea and marigold, respectively compared to the summer fallow . Regardless of the huge differences in saprophyte population densities among cropping treatments for the ACCI and ABH samplings of 2008, there were no significant differences among the treatments. Saprophyte population densities reached highest peaks following ACCI sampling in 2009 than any other sampling times of all years. At this sampling saprophyte populations were by far greater on the cowpea compared to either marigold or fallow treatments . However, there was a sharp decline in those nematodes for the ABH sampling of 2009 compared to the same time sampling in 2008 . When pooled for the three sampling periods , saprophytic nematode population levels were enhanced by both cover cropping treatments in 2008 and only by the cowpea cover crop in 2009 compared to the fallow system. Over all, cropping treatments had no significant effect in 2007 , indicating that the influence in cover crops on saprophytic population densities cannot be realized within one year of cover cropping rotations. If mean data are pooled just for the cropping treatments , both cowpea and marigold significantly enhanced saprophytes over the fallow treatment.Plant-parasitic nematode population densities in the experimental field were generally low during all years. There were only few species of plant parasitic nematodes, the root knot , cyst , and pin nematodes observed. The root knot and cyst nematodes are classified as the most dominant and economically damaging groups of plant parasitic nematodes . At all sampling times there was a huge variability in nematode population densities within the treatment replications, resulting in a large standard error and often leading to a non-significant effect in spite of large differences in mean values. Accordingly, the cropping treatments did not show significant differences on most nematode responses. Such problem may probably be minimized by using higher replication treatments . Furthermore, both cowpea and marigold cover crops are non-susceptible crops to nematodes and hence the reason for no significant differences among the cropping treatments. Broccoli crop is also a poor host to most nematodes and if planted late in the season when soil temperature is low, the nematode populations would also be low. Although a nematode susceptible crop was introduced by intercropping with broccoli at the third year, nematode population densities were still not variable among the cropping treatments. While cowpea and marigold cover crops are generally regarded as nematode resistant or suppressing plants, their use as off-season cover crop did not guarantee this value under this particular trial conditions. In some cases, RKN population densities were rather higher following cover crop incorporations, particularly cowpea, than under a fallow system. The relatively higher RKN following cowpea residue incorporations may indicate that cowpea still allows some level of nematode multiplication and thus is not resistant against RKN. It is also possible that the cover crops may have suppressed nematodes, but the initial low nematode population level in the field may have made it difficult to make a clear demarcation whether the cover crops suppressed nematode populations or not. Therefore, these responses basically contradict the previous findings that generally regarded cowpea and marigold as resistant and a potential means by which parasitic nematodes can be managed . Wand and McSorley observed that ‗Iron Clay‘ cowpea was susceptible to the same species of nematode it was once identified as suppressing. Chen et al. also showed that an increase in SCN population density in a former nematode suppressant perennial ryegrass treatment.

Adequate water resources along the road provide a suitable environment and resources for seed germination and early seedling growth for invasive species. After the early establishment and the naturalized species overcoming different stresses to produce seeds, the rapid spread of the reproductive offspring makes the species invasive . Human-assisted dispersal potentially creates a longer-range spread than the dispersal mechanisms related to species’ reproductive traits. According to Mortensen et al. , human activities are the main facilitators of the weedy and invasive species spread, and their study indicated that paved roads could spread more weeds than forests and wetlands. Specifically, vehicles are the spreading vectors of long-distance dispersal for weedy and invasive species. The traditional species dispersal model described LDD as a rare event; most cases are seed dispersal by animals , where weed seeds are adhesive to animal fur and travel along with seasonal migration . However, Nathan proposed that human-mediated LDD has become the most important mechanism of LDD in plants and animals, which is a challenge for future LDD prediction. According to Baker , no specific weedy traits or natural dispersal mechanism to help invasive species overcome the large-scale geographical barrier. Nevertheless, human-assisted dispersal can potentially transfer seeds over 100 km away from the parent plants. The 100 km was an approximated cut-off to classify plants as alien species in the model proposed by Richardson et al. . A study in Germany collected seeds from roadside verges, cannabis dryer and their results indicated that nearly 30% of the species collected were by LDD and some species they identified are highly invasive in other countries .

Seeds dispersed by vehicles share common characteristics that might facilitate car-borne dispersal. Zwaenepoel et al. provided another perspective by collecting seed samples from mud attached to the car; the results suggested that carborne floras were pioneer species with small and light seeds. Other traits like large seed production and the ability to reproduce vegetatively are also reported in many studies . Also, a systematic review summarized that about 626 species in 75 families had been identified from cars, and Poaceae is the most frequent family , followed by Asteraceae and Fabaceae . The spread of invasive and weedy species along the road in the real world could be more significant than what has been reported in scientific studies. The large-scale spread of Microstegium vimineum was reported by observation; however, according to a model prediction, the species spread only by natural dispersal is limited . The contrasting results support that human-assisted dispersal leads to an increasing spread rate, and more resources and efforts should be put to roadside vegetation management. Roadside vegetation management is on a large scale, and the local government is the primary agent for management. For example, in California, the California Department of Transportation manages the vegetation along California state highways, and develops projects to protect motorists, cyclists, and potential wildfire spread along the roads . Compared to agricultural weed management, roadside vegetation management has limited tools. The most common practice is mowing, but it requires multiple applications in a short period; therefore, mowing is expensive and ineffective since only the foliar part of the plant is damaged . Herbicide application is used with mowing as the Integrated Vegetation Management . Chemical control could be effective under roadside conditions. For example, herbicide trials conducted in six different regions of Indiana demonstrated that herbicide application could effectively control broad leaf species for more than one year and grasses for months .

Herbicides can significantly lower the cost, but the increasing herbicide-resistant population is another potential concern for roadside weed management . IVM program is important to manage roadside invasive species, and similar to Integrated Weed Management, early detection and monitoring are also main components in IVM. The first step in studying and analyzing the ecological aspect of a specific invasive or weedy species is to conduct a species survey and map the population distribution.A species distribution map is a common approach for evaluating the extent of plant invasion and provides a baseline for the informed allocation of resources and efforts. Botanists usually conduct field surveys to collect plant species, including weedy species. The benefit of this detailed survey is the high accuracy of the species identification and location data, but a detailed survey requires enormous resources. For example, a county-level survey of 3000 km required 35 months, and the researcher had to travel by car, on foot, or even by boat . A typical 3000 km survey is considered small-scale but still time-consuming, labor-intensive, and requires equipment like a vehicle and an accurate GPS positioning system. A field survey is reasonable for species local population examination and species-environment interaction analysis. A car survey can be rapid by applying different sampling or examination methods. According to Shuster et al. , a car survey has a similar probability of finding Alliaria petiolata compared to a survey on foot but requiring four times fewer person-hours. The car survey can involve transects or random sampling sites along the roads base on land uses, soil types, rainfall, and vegetation types . The data collected from the car survey can be used to build a model to understand the relationship between species distribution and environmental factors. A car survey could yield consistent results when various factors are examined in the experimental design. For instance, the traveling speed can vary for different types of roads. For highways, the driver must drive above the minimum speed so that a higher speed can result in lower identification accuracy. Observation accuracy is another factor that affects data consistency. Catry et al. conducted accuracy tests to evaluate the potential human errors. However, it is challenging to include human error rates in the species distribution maps, and in most cases, the human errors in the car survey are unaccountable . A standard methodology for roadside species surveys should be established to yield consistent and comparable data. Some studies do not include all road types in the car survey to save time and reduce costs. For example, Catry et al. excluded the highway or freeway because they believed that the roadsides of the freeway are well-managed and there will be less possibility of having invasive species populations. A survey for all roads in a state will take an unrealistic time to complete, and the cost of traveling will be expensive. For example, California has about 622,000 km of roads, and it will take about 780 days if a driver drives 800 km per day . Thus, government agencies will hesitate to conduct surveys because of limited funding and resources. However, roadside vegetation assessment can help identify the level of invasion and the potential damage. A car survey is not cost-effective for accomplishing a quick assessment on a large scale. Furthermore, a species map can be used in large-scale species dispersal models in which the input data are usually from global or regional databases. Kadmon et al. argued that since randomized surveys on a large scale are rare, those models often rely on incomplete databases with biased data, such as herbaria, natural museums, and user-uploaded entries. The unified database includes data collected from different observers, and we cannot estimate the potential human error if we rely on these databases to run the ecological models. As a result, we need a more systematic approach for large-scale species surveys.Google Street View is a tool in Google Maps and Google Earth that allows users to interact with the panoramas along streets and roads in many countries. GSV and Google Earth are well-developed and well-maintained databases, and the imagery has been online for more than ten years.

Google sends numerous data-collection vehicles on the roads, and those vehicles take 360-degree photos, e.g., cannabis growing systems by installing a rosette camera on the top of the car . According to Anguelov et al. , this project aims to organize a large amount of information, and billions of users can have access to that information. GSV is well-known among ordinary users for educational and recreational activities, but now, these images can be used for ecological studies and vegetation management. For example, GSV was used to map the distribution of the Pine Processionary Moth , in an area of about 45,000 km2 . Their research suggested that this method can be effective if the target species are distinguishable in the GSV images. They also mentioned that the coverage area of GSV still needed to be completed back in 2013. In the past ten years, GSV has been used in several ecological studies. For example, according to Hardion et al. , the integration of ground and aerial images could create a better species distribution map of giant cane , a common grass species along the road, and the species distribution data produced better results in species distribution models compared to the traditional field survey. Additionally, two studies compared GSV and field surveys by car. Studies by Deus et al. and Kotowska et al. reported that the results produced by GSV resemble that produced by the field survey. The two studies surveyed different species of different sizes . Although GSV is more cost-effective than car surveys, studies discussed above have used human observers for plant detection, which is tedious, time-consuming, and not feasible for large-scale mapping. Furthermore, most previous studies reported some common limitations of this imagery database. Most studies suggested seasonality and time differences among the GSV images. The images from different areas were taken in different years or seasons and at different times during the day , which could impact the presence and absence of the survey species or detectability of species within the GSV images . Deus et al. also reported that contrast, ambient light, and sharpness would affect the identification accuracy in some images. Another issue with GSV is that most species were undetectable in their seedling stage when they were small and did not develop distinguishable features . GSV database has been successfully used as a cost-effective method in terms of time and resources, and this benefit can allow researchers to conduct a comparatively large-scale survey in a short period .Artificial Intelligence is a powerful tool to automate tasks, providing insightful solutions for scientific studies in different fields. For example, image detection built with deep learning algorithms can be used in plant species identification. Computer vision methods are successfully used in crop and weed classification to build robotic machines to conduct real-time weed detection in the field . Machine learning includes two main types: unsupervised and supervised learning. Unsupervised learning trains models with unlabeled data, while supervised learning requires labeled data, and most neural networks are examples of supervised learning algorithms . Artificial neural networks are models built based on the nervous system of the living vertebrate . In recent years, neural networks have evolved into the convolutional neural network , which consists of multiple convolutional layers. Many studies have proven that CNNs can reach high accuracy in object detection in images . Dyrmann et al. trained a CNN model to classify young seedlings of 22 species with the average accuracy of 86.2% under controlled indoor conditions. For example, Sugar beet , barley , and Thale Cress could achieve 97% to 98% accuracy . Dyrmann et al. tested another CNN model called DetectNet in overhead images from highly occluded cereal fields, and the model had a recall of 46.3% and a precision of 86.6% for detecting the non-crop plants. The high-occluded fields resemble roadside environments where plants overlap each other. Another well-known fast-detection CNN model called You Only Look Once can be used to detect weeds in outdoor and natural light conditions. Dang et al. tested different versions of YOLO detectors on 12 weed classes at different growth stages in cotton fields. The precisions of 12 weed classes ranged from 81.5% to 98.28%, and the recalls ranged from 78.62% to 97.9% on the YOLOv3 detector . The examples above are all based on overhead images in crop fields, but only a few similar studies have been done under roadside conditions. One example of the roadside condition is a study that integrated GSV Imagery and a CNN network to map the distribution of different crops along the roads and achieved accuracy levels of 92% in California and 98% in Illinois .

Additionally, an EV performing the same trip in August but charging under the Workplace Morning charging profile was found to emit nearly 14% more CO2 per kilometer when using hourly grid emissions profiles instead of annual averages and nearly 10% more instead of monthly averages. This variance is even more pronounced under the marginal scenarios, though not always with the same directionality. Assuming the Hourly Marginal Mix tends to reduce the per-kilometer CO2 emissions of an EV charged with the Workplace Morning profile in the summer months, making that charging profile the most attractive in terms of environmental benefit in some cases. Under Hourly Marginal, Resource X assumptions, the CO2 emitted per kilometer for an EV can vary as much as 58% depending on the time it is charged on a given day. Importantly, almost all simulated EV scenarios realized reduced CO2 emissions per kilometer compared to ICEVs. However, the magnitude of these reductions varies substantially under different emissions assumptions, charging profiles, and seasons. The extreme hourly and seasonal variations in effective emissions rates of EVs found in this study indicate that reliance upon annual or monthly average emissions rates for the modeling of EV environmental benefits is inadequate. Table 3-2 depicts the wide variation in emissions rates from simulation to simulation relative to the ICEV baseline.Average emissions rates at lower resolutions obscure vital information that could otherwise be used to optimize environmental benefits as well as inform policy. Effective communication of hourly or higher resolution of grid CO2 intensity would help the consumer make an informed choice on when to charge their EV to maximize environmental benefits. With the maturation of “smart-charging,” enabling communication between the grid or utility and smart charger units would allow the smart charger to control the rate of charge to minimize effective EV emissions, cannabis drying subject to user-configured constraints involving required time of use, desired battery capacity, and cost.

As expected, the HEV reduced CO2 emissions compared to an ICEV, but it also performed consistently on par or better than many EV scenarios. When annual or monthly average emissions rates are assumed, HEVs already perform better on a per-kilometer basis than EVs beyond a certain distance threshold. These de-facto superiorities of HEVs become less pronounced at certain times when hourly emissions rates are assumed. Failing to understand and incorporate higher-resolution evolutions in grid emissions intensities can lead decision makers to ill-informed conclusions that could be sub-optimal for reducing environmental externalities. It is worth noting that both EVs and grid generating resources are evolving dynamically, placing renewed emphasis on studies that consider environmental impacts during this transition period . There is likely some threshold of EV penetration that will trigger a realignment in marginal emissions trends. As electrical power demand increases at peak charging times when consumers are incentivized to charge their vehicles, marginal resources in addition to those observed in this study, will eventually be required to supply sufficient power. Often, due to the inherent need for dispatch ability, marginal resources are fossil-based or non-renewable in nature. Thus, if additional marginal resources need to be brought online, it could alter grid emissions profiles and lead to shifting environmentally optimal charging periods. Understanding the scaling behaviors of marginal power demand for growing rates of EV adoption will be critical for decision-makers to stay one step ahead of lagging realignments, anticipate them, and communicate optimal charging periods to consumers as well as intelligent infrastructure. An important benefit of the methodology employed in this study is that it can be easily adapted to model additional use cases, charging profiles, emissions profiles, and additional pollutants. To prove the feasibility of such adaptations, SO2 and NOx grid emissions were simulated for the same charging profiles as CO2 for the Suburban Errands trip in August and October.

For these simulations, there were assumed to be no SO2 emissions for the ICEV and HEV baselines. NOx emissions for the ICEV and HEV baselines were calculated using a conversion factor of 0.000167 , as informed by the MoVES model. Figure 3-4 and Figure 3-5 depict pollutants that are greater per kilometer in EVs than ICEVs. SO2 and NOx are examples of pollutants emitted in the production of electrical energy at fossil power plants that are essentially absent from or significantly reduced in vehicular tailpipe emissions. While these additional pollutants should be acknowledged, it is critical to understand the spatial confines of their dispersion. While tailpipe, mobile-source emissions are present anywhere motor vehicles travel, emissions from electricity generation are localized in smaller areas immediately surrounding power plant facilities. Given that power plants are typically located in more rural areas, the per-unit damages from pollutants emitted by electricity generation can be much less. However, there are environmental justice issues inherent in these trade-offs that need to be addressed and explored further.Because of the potential opportunity of vehicle electrification to help decarbonize the transportation sector and improve air quality, the technical findings of this research could have some significant policy implications. To optimize the potential benefits, much greater attention will be required to the incremental difference in EV emissions relative to baseline ICEVs and HEVs. As some of the findings suggest, the individual vehicle and fleet wide improvements may be much lower than expected by some studies as scale-up occurs. However, the findings also provide some suggested means of ensuring that environmental and social improvements can be realized, and at the scales needed. Because this research begins to quantify technical parameters related to both the magnitude and the range of possible emissions impacts as compared to multiple baselines , the study’s findings can be useful for education and awareness by all EV users.

They also have clear implications on policy and public investment, including the urgent need for managed and coordinated charging, and greater attention to resource planning, in terms of generation resources, dispatch decision making, infrastructure funding, and the long-run environmental benefits and impacts for EVs across a range of use cases and time horizons.To investigate the true variability associated with CO2 and other transportation-related vehicle emissions, this study has developed a simulation framework that explores multiple parameters concurrently. The goal has not been to determine with high precision a given case as much as it is to develop a broad comparison among major inputs and factors. In this way, we explore electric vehicles as compared to a baseline case . We explore several driving cycles and charging profiles that represent typical approaches both for residential and workplace charging at various times of the day. And then, we develop various methods for estimating CO2 and other vehicle emissions. As noted, studies that have addressed this previously have often utilized annualized averages to simplify the analysis. In our research review of other tools and dashboards , we confirmed that a very basic algorithm is utilized . We acknowledge such traditional approaches provide a kind of first-order, initial estimation that can be useful to some audiences in some contexts. However, it is imperative to recognize and explain the limitations of accepted approaches, and the risk of relying too heavily on average emissions estimates, as they are highly subject to change in the future, and to variability during the present . In short, new tools and methodologies are needed that can estimate the impact of taking various assumptions for how the grid will meet marginal demands in the near, intermediate and long terms. This transition period from a few million EVs to 100 million EVs will take some time, and environmental impacts will need to be more fully understood. As EV adoption increases and the grid is expanded to meet new demands for electrification, such transition tools and methods can be increasingly valuable to researchers, planners, policymakers, drying cannabis and infrastructure decision-makers. As such, our present work provides much needed additional insight and may be useful to inform 2nd order factors and more complex and integrated guidance. Going a step further by exploring limitations and pursuing additional rough orders of magnitude could have tremendous value for the transportation research community. It would also facilitate a more direct and apples-apples comparison of EVs to other technologies there are substantial shortcomings as penetration rates grow. We conclude with a brief recap. It is clear that at certain very low very modest levels of EV deployment, something like an average assessment of the weighted mix of resources may not be illogical or even inaccurate. It is beyond the scope of this study to determine exactly at what penetration rates things change, but it can be stated that at significant increases in EV charging, in particular at certain hours of the day and months and seasons of the year, the assumption of weighted mixes breaks down. Some preliminary findings from the use cases investigated in the present study reveal that conditions most at risk of yielding higher than desired CO2 emissions rates include afternoon charging at the workplace and early evening charging at the residence. Conversely, it seems that residential overnight charging may, at present, be one of the lowest impact scenarios for EV charging.On the behavior side, it’s clear that managing charging events throughout the 24 hours of the day should merit greater attention. It appears that residential charging may be environmentally preferred compared to workplace charging under certain conditions. This may be an important near-term way to mitigate the unintended effects of higher marginal emissions impacts. This, however, is a simplified observation, since it assumes adequate all-electric range and adequate access to residential EV charging. Neither of these assumptions is necessarily sound at higher penetration rates or given certain economic barriers and social inequities. Furthermore, charging management and behavior alone will likely be inadequate as EV shares grow to levels that push up against current resource capacities and are not yet envisioned fully and accommodated for utility resource plans in the 3-to-10-year horizon. Future study is anticipated to further inform decision-making around such scenarios, including the ability to convert the predominantly historical approach dispatch to a predictive forecasting approach where a 2030 scenario is developed that can better simulate future resources, both fossil, and non-fossil, and how they will be deployed to meet a growing load to support electric transportation. While the use case results are of interest in their own right, it should be noted they are sensitive to the source data for the selected region and approaches taken to integrate electric charging behaviors. While the particular insights may not apply to other regions, it is important to note that the research term also intended to conduct a regional use case as a validation for the methodology. The methodology has been constructed and described in sufficient detail that it can be combined with other data sets and regional attributes for the purpose of adapting it as a decision support tools, beyond the particular region selected herein. Thus, the methodology is shown to be scalable and more broadly applied to other time horizons and regions, and leverage other data sets.This research study focuses on a few specific vehicle types around light-duty vehicle uses in order to develop a comparative framework with a manageable technical scope. While additional use cases and extensions are suggested as areas of future work, the frameworks and simulation-based comparisons are extremely generalizable and extendable because of the organizational approach. A lot of attention to detail has been paid to the development of physics-based vehicle models, including consideration of architectures, power train, overall accessory loads, and sensitivity to drive cycle and external ambient temperatures. Similar attention to detail has been paid to developing practical and representative EV charging profiles, reasonable mapping of standard drive cycles to real-world trips and travel behavior, and high-fidelity analyses of existing grid dispatch methods based on real-world data. A primary contribution of this effort is therefore the integration of each of the individual subsystems and independent data sources, including hourly grid characteristics, toward a novel understanding of the complex impacts of vehicle electrification. In short, the team has successfully achieved its chief aim of laying the groundwork for a more complete understanding of these results at scale. Regarding EV charging behavior, we have considered data from multiple concurrent sources which provides insight into when people are most likely to be charging their EVs today.

The diverse mix resulted in more consistent ground cover in this study, and ground cover led to greater weed suppression. However, cover crop incidence, not the specific cover crop treatment, was the primary driver for suppression and other effects on weeds in this study. These results are consistent with previous experimental results of cover crop and weed competition that highlight the importance of cover crop abundance, not diversity, in weed competition . It is important to note that these studies primarily measured cover crop and weed biomass, while the present study came to a similar conclusion by measuring incidence. Additionally, these studies are in annual cropping systems. Agricultural systems, whether annual or perennial, are designed to support ample plant growth, and this resource-rich environment favors the asymmetric competition that is associated with abundant, fast-growing, cultivated plants. Maintaining biodiversity is a major challenge for agroecosystems, but this study continues to challenge the importance of functional diversity for achieving agronomic management goals like weedy vegetation management. Agricultural plant communities are not diverse compared to plant communities in non-agricultural systems. The cover crop mixes in this study represented a significant increase in orchard plant diversity, essentially doubling species richness in the mature orchards . Among treatments, weed species richness was highest in the young orchard , where the orchard floor was relatively unshaded and still populated with many weed species carried over from the previous pasture system. Weed community assembly may be affected by cover crops during the early stages of orchard development, commercial hydroponic systems but more research is needed to understand the effects of cover crop competition on filtering weed communities over timescales relevant for orchard production.

Cover crop species in this study were primarily selected for their relevance to almond management goals other than weed suppression. When considering multifunctionality, there are significant tradeoffs between agroecosystem services associated with various cover crop mixes. Managing cover crops for maximum weed suppression, and therefore maximum abundance, may detract from other orchard or cover crop management goals. For example, rye was included in the multifunctional mix in this study and is known to be an important component species for weed suppression , but persistent residues from high-biomass species like rye could negatively affect on-ground almond harvest several months after cover crop termination. However, weed-suppressing cover crops are also likely to contribute to other ecosystem services. Large and abundant cover crops are more effective in exploitative competition due to asymmetric resource acquisition, such as root competition for soil nutrients . The same mechanism that facilitates competition in this example also facilitates improved soil structure and increased soil organic matter. In another example, cover crop functional diversity could enhance competition through niche differentiation, as well as enhance pollination services by increasing floral resource diversity. Abundant single-species cover crops may be the best for outcompeting weeds, but a multifunctional cover crop mix may be designed to enhance other orchard management goals and protect against environmental uncertainty. Weed suppression may be essentially aprerequisite towards achieving an abundant, competitive, and multifunctional cover crop, but cover crop species and management practices should be selected with consideration for other ecosystem services that may complement orchard production. Balancing multifunctionality against singular management goals like weed management presents opportunities for integration of cover crops into conventional cropping systems.

Conventional weed management is chiefly a tool for reducing biodiversity, but cover crops can reduce weed infestation while promoting functional biodiversity. Uncertainty of outcomes remains a challenge for the practical adoption of cover crops by orchard growers; specific cover crop management practices should be planned alongside specific management goals . Particularly important is uncertainty related to the timing of winter rainfall. In the almond study system, cover crop planting is timed to coincide with the beginning of winter rains, which would reduce demand for supplemental irrigation of the cover crop. Weedy plants also depend on this rainfall for germination, and timely planting of the cover crop can align cover crop emergence with weed emergence. Cover crop mix diversity could be one way to hedge against increasingly uncertain winter rains. In this study, the diverse mix had a level of diversity that led to more stability, even if that stability did not consistently lead to enhanced weed suppression. Adjustments in cover crop phenology could be an important line of future research. Perennial cropping systems have significant temporal flexibility compared to annual cropping systems, where relatively few options exist for growing a cover crop during the cash crop growing season. Optimization of when a cover crop is planted and terminated in the orchard could improve weed suppression or other ecosystem services. Furthermore, the timing of these management actions could differ across orchard cropping systems, depending on climate or various needs of the main crop. Examples that could facilitate different cover crop management timings compared to those used in the present study could include cropping systems such as citrus, which is harvested during winter months, pistachio, which utilizes off-ground harvest during the fall, or apples, which are frequently grown in climates with colder winters. While this study implemented cover crops on a time scale relevant for adoption in contemporary orchards, further understanding of the cumulative impacts of cover cropping on the decades-long scale of orchard lifespans could further improve temporal arrangements of cover crops.

Sustainable orchard cropping systems require vegetation management programs that produce accessible orchard floors while minimizing management intensity. Orchard systems in California require significant upfront investment that expose orchard growers to heightened risks related to climate change, water scarcity, and land use change compared to more flexible annual cropping systems. Sustainable management systems could reduce risk for orchard growers who manage over 1 million ha of almonds D.A. Webb, walnuts , stone fruit , and similar orchard crops . Weed management is an important area for orchard sustainability improvements, given that vegetation and vegetation management practices affect many environmental quality parameters across the orchard agroecosystem, including factors such as herbicide use intensity, soil health, water quality, and air contaminants . Rather than seeking vegetation-free orchard floors, growers could potentially cultivate orchard floor vegetation that contributes to ancillary management goals and provides additional ecosystem services . Cover crops offer flexible management options for creating a functional orchard floor . As one cultural management practice within a suite of integrated pest management practices, cover crops can provide a framework for understanding the seasonality and phenology of weed life cycles while also promoting grower acceptance for some level of orchard vegetation . Typically, commercial orchards in California will have a zone of high intensity weed management in a strip centered on the tree row, often 25-50% of the orchard floor, with less intensive weed management in the remainder of the alley between rows . The high intensity tree strip is maintained to keep weeds from interfering with irrigation infrastructure and minimize non-crop water use in the irrigated area. In crops that are harvested from the orchard floor, which includes many tree nut crops, the alley is generally managed to be weed free ahead of crop harvest in the late summer, where heavy plant residues could impede sweepers and other harvest equipment. Alley management in the winter can vary with grower preferences, but cover crops could easily be implemented in this zone so long as cover crop residues do not affect crop harvest operations. California has mild, rainy winters, which are conducive to cover crop growth. Furthermore, tree nut and stone fruit crops are deciduous, and dormant tree canopies allow ample light to reach the orchard floor throughout the winter, until trees leaf out in mid-February for almonds and later in the spring for other species. Therefore, cover crops in California orchards could have minimal negative impacts on the cash crop if they consist of winter annual plants, since these species have a predictable life cycle that could usually begin with winter rains without supplemental irrigation and ends during hot, dry summers common in the Mediterranean climate of central California. With this context, cannabis racking systems winter annual cover crop species could be used to displace winter weeds in orchard alleys. Literature focusing on cover crops and weed management often centers on annual cropping systems, with cover crops growing in the off season between annual cash crops resulting in temporal separation . Heavy cover crop residues drive weed control by limiting the emergence of weed seedlings before and during cash crop emergence . In contrast, cover crops in orchard systems have spatial separation between cover crops and cash crops, which increases the importance of interference with concurrently growing weeds .

Spatial separation also creates flexibility by reducing restrictions on the cover crop growing season imposed by annual cash crop planting and harvest, and information about the phenology of plant competition could help optimize the management of an abundant, competitive cover crop . Finally, California orchards undergo dormant-season management like pruning and orchard sanitation, which could create tradeoffs between these management practices and a winter cover crop. For these reasons, weed-suppressing cover crops require additional research that informs practical management guidelines relevant to orchard systems in this California. Specific cover crop management recommendations could help growers balance the many functions of cover crops and support various ecosystem services and management goals. Specific management recommendations also support adoption by reducing knowledge barriers of this complex cultural management practice. Would-be adopters of orchard cover crops need to develop a plan that addresses many aspects cover crop establishment and management and acknowledges potential tradeoffs. To address this need, we developed specific questions about cover crop planting date, the phenology of crop-weed competition, and intensified cover crop practices. Research on intensified cover crop management could help us understand how agronomic practices including planting rate, fertilizer or herbicide inputs, cover crop species mixtures, and cover crop termination practices interact with many aspects of agroecosystem function . Likewise, varied cover crop planting date information helps us understand how cover crop establishment affects cover crop development and when weed competition occur relative to cover crop establishment and the onset of winter rains. Our objectives were to assess how different aspects of orchard cover crop management affect winter weed management. We evaluated how cover crop management system and planting date impacted cover crop and weed biomass. We also evaluated how cover crop planting date affects cover crop and weed emergence rates. Finally, we evaluated how cover crop management systems differentially affect summer weed emergence through different levels of cover crop residue. Together, these research questions can provide information about to what extent covercrops contribute to overall orchard floor vegetation management and which cover crop management practices have the largest effect on weed suppression.We initiated two different experiments to separately examine the effects of intensified cover crop management systems and cover crop planting date in nut orchards. These were small plot experiments in research orchards with commercially relevant cultural practices, including tree spacing, tree strip management, and irrigation. The ‘intensification experiment’ involved a range of cereal rye cover crop management intensities, from minimal management to an intensively-managed forage intercrop, planted in a walnut orchard. The ‘planting date experiment’ involved two different multi-species cover crop mixes each planted at early and late planting dates in an almond orchard. These experiments focused on plant population and community characteristics of orchard floor vegetation in the orchard alleys only. We used the different orchards as a study system but did not intensively monitor orchard crop performance or yield. Namely, almonds maintain a leaf canopy for a greater portion of each year, but the almond orchard in this study was younger with a smaller tree canopy compared to our older walnut orchard. However, the orchard floor environment is generally similar in almond and walnut cropping systems, and each has similar cultural factors including irrigation, alley and strip management, and winter pruning and pest management operations. For cover crops to be a feasible management strategy, they should work in a variety of orchard systems, conditions, and life cycle stages. Therefore, understanding how cover crops influence vegetation management across different orchards is a key aspect of this study. The intensification and planting date experiments were managed independently of one another, but there is a shared treatment to facilitate comparisons between the experiments. Intensification experiment.

The cultivation will effectively hold soil moisture for 30 days or longer, allowing for successive plantings over time with minimal effort. To ensure harvest continuity for staggered plantings, plant the next round of beans when the prior planting is in the “crook” stage , approximately every 10 days.When produced on a commercial scale, beans should always be planted to moisture rather than irrigated up. If you plant beans into dry soils and then irrigate them to initiate germination, weed seeds will germinate at the same time as the newly planted beans. In most cases weeds will quickly outcompete the beans and compromise effective weed management. It is not possible to cover labor costs, through sale of the crop, for hand weeding when weed pressure is high. See the publication Tillage, Bed Formation, and Planting to Moisture in this Grower Guide series for additional details. When planting to moisture, use a wide “Alabama” shovel mounted on the planter to run ahead and push off the dry dirt on top of the bed. Set the shovel so that it goes deep enough to get into the sub-surface moisture. When set correctly, the shovel leaves a flat “V” pattern down the center of the bed. On most soil types, as long as you can see some slight darkening of the soil that is exposed when the bed top is knocked off compared to the drier surface soil, there should be enough residual pre-irrigation moisture in the soil at the bottom of the “V” to initiate germination of the newly planted bean seed. Plant beans at the low point in the middle of the bed to conserve moisture deeper in the soil . In most cases 1–1.5” is a good planting depth for strong germination, growth rack although beans can emerge when planted 2” deep as long as the soil is “loose” and not compacted above the seed. Loose soil above the seed line also limits evaporative loss.

When beans seeds all emerge at roughly the same time, you know you have done a good job of planting them. Note that most bean planters are designed to drop seed into a small trench that is then covered with soil and firmed up with the planter press wheel. Avoid planting when the soil is too wet as the press wheel can create a compacted layer over the seed that dries into a firm crust, which can significantly impede successful bean seed emergence.When choosing a tool-bar-mounted planter for larger-scale bean plantings , select a planter that singulates the seed, has double disc openers that cut deep into the soil , and has a press/tamp wheel. Options include the Clean Seeder TP , the John Deere 71 “flexi” planter , and the International 185 planter planter. Note that on smaller farms, the cost of this type of planter can be a significant capital expense. Although no longer available new, the John Deere 71 “flexi” planter is still one of the more common planters used on the Central Coast for planting beans . It is called a “flexi” planter because it is designed to “flex” or “float” over heavy residue. This design feature makes it an ideal planter for planting beans following cover crop incorporation in the spring.The Clean Seeder TP and John Deere 71 planters have double disc openers that create a very narrow opening in the soi lFigure 2. The seed falls into the opening while the sides of the opening are being held open by the discs. The small trench created by the openers easily collapses once the disc openers pass. The press wheel on the planter firms the soil over the seed and helps to reestablish capillarity, which improves soil moisture movement from lower in the soil horizon. If you question whether there is adequate soil moisture, assess moisture early the next day after planting. Soil moisture typically improves overnight as a result of the light compaction created by the press wheel. The advantage of double disc openers is that they can easily cut through or roll over residual cover crop or crop residue.

In comparison, Planet Jr. planters use a fixed opener shoe, which gathers field trash when used to plant at depths greater than 1 inch. When residual crop residue wraps on a fixed shoe, it pushes soil away from the seed line, causing skips in planting and an uneven surface. Plate planters such as the Clean Seeder TP-TB, John Deere 71, and International 185 do an excellent job of “singulation” of the bean seed. Driven by the press wheel, the seed plate rotates in the bottom of the seed hopper. The holes in the plate allow single seeds to drop into the hopper cells. The cells rotate over an opening in the bottom of the hopper, and with the help of a “knocker,” drop the seed at a selected spacing as the planter moves through the field. Planting depth can be adjusted with a rotating cam on the side of the planter, which changes the angle of the press wheel in relation to the disc openers. Seed spacing is set based on the number of holes in the seed plate, as well as gearing, which is easily changed. Select seed plates carefully to match varieties, since bean varieties vary significantly in size and shape.When beans germinate, they lift the two seed halves above the soil surface. This is referred to as “epigeal” germination. The emerging beans will first push through the soil with the stem in a “crook” position and then the cotyledons will emerge, followed by the first true leaves as the hypocotyl straightens out following emergence. The early stage of germination when the stem first appears above ground is referred to as “in the crook” . Emerging beans are very susceptible to heat damage at the soil surface as they push upward. When planting in late summer , farmers in warmer inland valleys of California commonly put a “soil cap” on the bean seed line with small disc hillers . The hillers are attached to, clone rack and run directly behind the planter to form a small mound of loose soil directly over the seed line. During times of high daytime temperatures, growers dig up the embibed seeds daily until they see uniform radical emergence. They then mechanically knock off the cap. If timing is good, the beans will emerge through the soil during the cool of the evening, thus avoiding the issue of stand loss due to high soil temperatures.

In cooler coastal production areas you do not need to cap the seed lines. However, a very light soil cap helps keep the soil loose and moist. This can improve ease of emergence and stand uniformity in both extremes—when the soil is either slightly too wet or too dry.When soil moisture is optimal, planting to moisture allows the beans to germinate but limits weed germination, as most weed seeds require more moisture to trigger germination compared to the large bean seed. The beans emerge in a small trench in the middle of the bed, where vigorously growing beans will easily outgrow most weeds . Let the beans grow as long as possible without irrigation to allow them to root deeply and to minimize weed competition. In most climate zones on soils with decent water holding capacity, the beans can grow to full bloom before you need to irrigate . Once beans are 5–6” tall, do a first cultivation. Use a standard 3-bar cultivator with a set of reverse disc hillers running along each side of the plant line, side knives along the sides of the beds, and sweeps in the furrows. Run small chisels behind the tractor tires to a depth of about 4” to break tractor tire compaction and facilitate subsequent cultivations. This first cultivation will effectively terminate most newly germinated weeds from seed line to seed line. Then lay drip tape along the seed line. As the beans continue to elongate, use a rolling cultivator for the second—and most effective—cultivation. Properly set, the cultivator will gently return the dirt that was pushed off the bed top at time of planting to the middle of the bed . The bed should end up looking just like it did prior to planting. If bean stems are long enough, this “dirting” cultivation will effectively cover both the drip line and smother any weeds that have emerged in the seed line while covering only the lower stem of the bean plant. This last cultivation, or “dirting,” leading up to harvest is commonly practiced on large acreages of many agronomic crops that are planted to moisture in situations where herbicides are not used. To perform this cultivation successfully requires specific and tightly adjusted implements, and a significant level of tractor skill. As small farms scale-up to mid-sized farms and larger acreages, planting to moisture and dirting can significantly reduce the labor required for weed control.On small plots , you can easily plant beans to moisture by hand. Pre-irrigate your beds, and use hand tools to take out the newly germinated weeds. Push the bean seed into the deeper moisture by hand. Any subsequent weed growth can be handled with a wheel hoe or hula hoe. On larger plots , plant beans to moisture with simple tools. Use a rototiller following preirrigation to terminate weeds and form a soil or “dust” mulch. Form a planting trench with a small furrowing shovel mounted on a wheel hoe to access deeper soil moisture. Plant with a Planet Junior push seeder using the general purpose “deep” shoe, or the Jang large seed push seeder . On this scale, weeds can be managed using hand tools or wheel hoes.Time the first post-emergence irrigation based on subtle signs of water stress in the bean plants during warmer days— especially later in the day. Pay attention to slight changes in the color of the bean plants: a plant with adequate soil moisture appears dark green; when stressed, the green color shows hints of gray. Following emergence of the first true leaves, water stress will be very evident as a stressed plant tends to push the first true leaves together and upright. These stress symptoms typically show up on field edges where pre-irrigation coverage may not have been adequate, or where soil is more compacted.Monitor stress daily and wait as long as possible before the first irrigation to allow for deep rooting, promote early and uniform flowering, and inhibit weed competition. Apply about an inch of water with the first drip irrigation. Subsequent irrigations should be scheduled using evapotranspiration data from your local CIMIS station or another sources , and based on the percent canopy of the bean plants at time of irrigation.Fresh market beans are at their best when the seeds inside the pod are still very small and the bean is still tender. Bean pods form quickly; the harvest must be timed well to avoid harvesting any beans that are “over-mature.” On many fresh market “round pod” bean varieties, you shouldn’t be able to see bumps in the pod indicating seed development. Open random beans to check for over-maturation. Another good field test to determine market quality is to break the beans in half—they should break easily and not bend. Harvest in the morning when the temperature is cooler. Harvest efficiency is related to the picker’s ability to grab as many beans as possible in a single handful and pull them off the plant with enough care not to break any beans at the stem end. Efficient bean harvest is a fine art that takes a strong back, practice, and fast hands. A good bean picker is able to pick 50 lbs., or roughly two 5-gallon buckets per hour. This rate is only possible when the beans are heavily laden with evenly mature green beans. Sorting beans when harvesting is simply not economically viable. Depending on variety and uniformity of maturation, a stand of beans can be harvested one, two, or three times. The over-mature pods from extended harvest need to be sorted out, and this task is simply not economical. When the next succession is ready to go it is time to walk away from the last planting.Field bindweed is a perennial weed that can persist in many cultivated and unmanaged landscapes .

Model fitting is less sensitive to smaller n, allows for unbalanced designs , and is easier to calculate variances . Our experimental design for many performance measures requires subsampling multiple individuals within plots, and model fitting with random effects allowed for the incorporation of the variance among subsamples, which would be lost if we took the average to calculate an FEV. The performance measures of biomass, cover, and height were log transformed and fit with linear mixed effect models . We performed model selection using backwards stepwise Akaike Information Criterion and the ran ANOVAs followed by post-hoc multiple comparison tests. Percent cover was modeled at a) the community level to capture net community feedbacks that may be hidden when comparing smaller differences among species and b) the individual species level to see if a particular species was driving the feedback. Biomass was assessed only on the community level. Plant height was modelled individually for each species, as height inherently differs due to natural history, and we are primarily interested in how soil conditioning effects within-species height variation. Seed production measures were fitted with generalized linear mixed-effects model for the negative binomial family with the “glmmTMB” package . Likelihood ratio tests were used to determine significance of fixed effects. Native flowering individual was modeled on both the individual species level and community level, as done previously for percent cover. Kruskal-Wallis tests were used on A. triuncialis and S. pulchra seed production, vertical growing racks as the data violated model assumptions due to few surviving individuals. Germination of all seeded species was analyzed with a time-to-event model using the “drc” and “drmSeedGerm” packages .

This method models the cumulative germination curve with the interaction of soil conditioning and community treatment, assuming a log-logistic distribution of germination time and accounting for ungerminated seeds. The model parameters that correspond to time to reach 50% germination and maximum germination were compared via the compParm function to evaluate the drivers of differences in germination patterns between conditioning and community treatments.This long-term field study demonstrated that native and exotic communities both experienced negative feedbacks– performing better in the other community’s soil. The eleven year conditioning phase provided a unique opportunity to assess feedbacks in response to soil changes that may take years to develop, such as build-up of soil organic matter. Setting both experimental phases in the field was necessary to understand the overall strength and role of feedbacks in structuring plant communities in a more realistic setting, as both phases experienced high environmental variability. Any long-term soil changes due to vegetation composition were strong enough to be detected over the observed variability in soil texture, ground squirrel disturbance, and annual precipitation . The two-year feedback phase also experienced strong variation in annual precipitation, as it included an extremely wet year followed by an extremely dry year . The detection of feedbacks required measuring multiple plant responses, as the natives and the exotics did not exhibit feedbacks within the same trait, nor did either show a feedback in above ground biomass, the most commonly measured indicator of plant fitness. Overall, our results suggest the need to measure multiple traits and life stages to capture potential responses to soil conditioning . We detected negative feedbacks on both native and exotic grasses, suggesting California grasslands are structured by negative feedbacks, similar to many other systems , and that positive feedbacks are not a main factor in exotic grass dominance .

The feedbacks were stronger for the native community, which had improved performance in exotic soil, as measured by cover, height, and reproductive productivity. These results are similar to a number of other studies, indicating that negative plant-soil feedbacks are stronger for native than exotic species . Previous greenhouse studies of California grassland species showed that in the short term , native species experienced positive feedbacks and performed worse in exotic conditioned soil, the opposite of our results . This may be due to different feedback mechanisms at play in a shorter conditioning phase or the difference in life stages tested. The exotic grass community had greater below ground biomass in native-conditioned soils. Another feedback experiment with similar species linked greater deep-root exotic biomass to the increased soil organic matter and water holding capacity in deep soils cultured for 10 years by the native perennial Stipa pulchra . Our study did not detect these physical and chemical soil differences but that may be because our site was drier and had higher clay content. It is also possible that these soil changes did occur and were ecologically but not statistically significant in our study. It is also possible that other driving mechanisms increased exotic deep root-growth in native soils at our site, such as potentially enhanced soil aggregation from the deeper perennial roots with more fine root hairs which would increase plant suitability due to more pore space and water flow .The native vs. exotic communities did not differ in their effects on soil water holding capacity, percent carbon or nitrogen, nor soil organic matter. Differences did arise in their effects on net nitrogen mineralization and nitrification, and fungal and bacterial community composition. The exotic plant communities cultivated significantly different fungal and bacterial community compositions from those cultivated by natives, similar to many other studies .

The fungal communities were distinct across soil conditioning treatments at all soil depths, but the bacterial communities differed by conditioning treatment only in the shallowest and deepest soil zones, supporting our hypothesis that differences in rooting depths are a factor in soil conditioning. Changes in microbial composition may directly impact plant performance , or may indirectly alter plant performance by changing resource availability . The native vs exotic communities also differed in effects on the abundances of nitrifying bacteria, AMF, fungal pathogens and saprotrophs, and soil nitrogen cycling, and below we’ll discuss how similar changes have been linked to plant performance in other feedback studies. However, determining the causal mechanisms behind the observed feedbacks is beyond the scope of this experiment, as the observed net feedbacks might be due to these differences in soil properties, properties not measured in this study, or from their interactions.Fungi in the plant pathogen and plant pathogen-saprotrophs guilds were greater in abundance in native-conditioned soil compared to exotic soil. This difference may be due to the life histories of the two grass groups, as the perennial native roots provide a constant food source for pathogens whereas the annual exotic roots die off every spring. Alternatively, fungal pathogenic richness is positively correlated with specific root tip number, which is likely higher in native roots as they are deeper with more fine root hairs . Greater pathogen abundance in native-conditioned soil plots could also be attributed to being largely dominated by S. pulchra in the conditioning phase, and evidence suggests that greater soil pathogen accumulation occurs under monocultures . Negative plant-soil feedbacks are common and thought to maintain plant diversity, and are often attributed to the buildup of localized pathogens . Thus, the strong negative feedback observed in the native grass community may be influenced by lower exposure to pathogenic attack in exoticconditioned soils. In a study with similar species, native grasses also experienced a strong negative feedback, but doubled in growth when native soils were sterilized , supporting the suggestion that pathogens are driving the feedback. Further, pathogens accumulate over time, and the longer conditioning phase of our experiment may explain the difference in feedback direction compared to shorter-term studies . The lower fungal abundance in exotic-cultured soils did not benefit exotic grasses, however, as they performed worse in their own cultivated soils. They may culture their own specific pathogens and also be less susceptible to generalist pathogens than native grasses . Saprotrophs were also greater in native-conditioned soils and could potentially have become parasitic which can occur when soils are dominated by a single species, growing racks and the native soils were largely conditioned by S. pulchra . However, while these could potentially contribute to the natives’ negative PSF, these effects might be short-lived, as saprotroph composition can change quickly in response to the current plant community .We did not find any difference in total N between the two conditioned soils, but net mineralization and nitrification rates were lower in soils conditioned by exotic grasses compared to the native perennials. These results are similar to those found by Parker et al. 2012 and Carey et al. 2017. Lower net rates can occur because decomposition and N release are slower , and/or when there is higher microbial immobilization, resulting in high competition between plants and microbes for N .

If the difference in rates of N cycling influenced native performance, we would expect greater native above ground biomass on native-conditioned soil, which we did not see. Corbin and D’Antonio suggest that changes in mineralization and nitrification rates are easily reversed under a new plant community, and so would not lead to feedbacks. We also detected changes in the nitrifying bacteria; ammonia oxidizing and nitrite oxidizing bacteria were both greater in the deeper exotic-conditioned soils than the native-conditioned soils. As there are fewer deep roots in exotic plots, bacteria in those soils have greater access to ammonia and nitrite. Similarly, Hawkes et al. looked at soils under the native S. pulchra and exotics B. hordeaceus and A. barbata, species used in our study, and also found greater AOB abundance in exotic soils.The presence of a competitor can eclipse, neutralize, or change the strength of a feedback in a species . Assessing plants grown in a community with both intra and inter-specific competition can thus help us understand the proportional role of feedbacks in community structure, particularly if we are interested in communities comprised of species commonly found together in nature, such as our native mix and our exotic mix. Our community treatments do not allow us to tease apart individual species’ contributions to soil conditioning nor touch on whether the direction and magnitude of a feedback is dependent on the specific identity of the conditioning plant and the feedback plant , but did show feedbacks were still observed overall in our two communities. In California grasslands, certain groups can remain dominant over time, but the stability of the group is due to variations in which species dominate within the groups, as environmental conditions change . Our results thus are relevant to the diverse and shifting communities found in California grasslands, especially as feedbacks can be non-additive in a community compared to monoculture . Community type played a major role in native grass abundance, and only slightly affected the exotic grasses. Native establishment was so poor in the mixed native and exotic community that we were not able to analyze most performance measures. This is not surprising, as exotic annual grasses in this system both germinate and grow much faster than native perennials, which allows them to outcompete the native seedlings for light and soil moisture . The few native individuals growing in competition with the exotics, however, still experienced a negative feedback in percent cover. Thus, feedbacks influence natives more subtly in full competition with exotics but can be a stronger control in restoration settings where competition by exotics is actively minimized. As the exotic grasses are the better competitor at the seedling stage, competition with natives did not alter exotic feedback overall. Our results show that competition among native and exotic grasses clearly outweighs the role of plant-soil feedbacks in community structure, with exotic dominance resulting regardless of soil provenance.Our results do not support our hypothesis that exotic annual grass invasion negatively impacts native restoration through the soil, suggesting that soil amelioration may not be necessary to improve restoration success. Studies comparing remnant, restored, and invaded grasslands found that soil biotic communities take years to recover , although another found mycorrhizal communities specific to S. pulchra returned quickly after restoration . Fortunately, it appears that exotic soil conditioning at our site does not majorly hinder native establishment and restoration success but rather benefits natives, even though exotic soils still have been found to decrease native performance when compared to sterilized soil , highlighting native grass susceptibility to pathogens. When establishing native cover is the main goal of a restoration project, reaching 30% native cover is considered a success. Higher cover is very important for the long-term success of a project, as once established, native perennial grasses become more competitive against future exotic annual seed pressure . Thus, the 15% difference in percent cover observed in our study is substantial.