Monthly Archives: October 2023

Farms rely heavily on donated land and volunteer and citizen labor

While there are limitations in our ability to generalize findings to the East Bay urban farming landscape as a whole due to the relatively small sample size, we obtained a fairly representative sample of the diversity of farm types in the East Bay based on our typology of the original 120 farm types . Survey questions fell into nine categories: 1) Background Info, 2) Farm Description, 3) Operating Expenses and Revenues, 4) Land Access and Tenure, 5) Production and Soil Health, 6) Distribution, 7) “Waste” and Compost, 8) Food Access, and 9) Training, Communications, and Follow Up. There were a few open-ended questions allowing farmers to express what they saw as the three largest challenges facing urban agriculture operations in the area, and policy-relevant suggestions for securing spaces for urban farms and increasing community food security. In addition, we interviewed five urban farmers to deepen our understanding of the social, political, economic, and ecological constraints under which their farms operate. These farmers are particularly involved in networking efforts to strengthen urban farm viability in the East Bay. Four out of five represent locally prominent non-profit farms and one subject represents an alternative cooperatively-run urban farm; three interview subjects are women and two are men. Our study complied with UC Berkeley’s Institutional Review Board protocol for the protection of human subjects and all participants gave consent for participation.Farmers reported diversified distribution methods including volunteers harvesting and taking food home , on-site consumption , on-site farm stand distribution, CSA boxes at pick up sites,vertical cannabis and volunteers delivering produce directly to distribution sites . Some gleaning and second harvesting occurs at urban farms and gardens with potential for growth given reported “unharvested” and “wasted” food percentages.

Backyard produce is also exchanged through crop swaps and neighborhood food boxes . Eight operations reported having access to a refrigerated truck for food deliveries, and two are willing to share their truck with other farmers. There is no universally used or city-organized process for distributing produce off of urban farms and into the community, yet there exists great interest in aggregating produce or distribution channels , an unrealized goal of urban farmers in the East Bay. All of the food system stakeholders involved in our study are working towards transformative food system change, focused on increasing equity, food security, and access to healthy, locally sourced food. See Box 1 for a description of one of the non-farmer stakeholders engaged in the food recovery and distribution system, who has recently established an aggregation hub to serve as a network for reducing food waste and channeling excess food in the urban community to those who are food insecure.Farmers in our study stressed the importance of producing non-food related values on their farms, including education and community building. One farmer in particular emphasized their organization’s mission of “growing urban farmers growing food,” or teaching other people how to grow a portion of their food basket, thus unlocking food sovereignty and food literacy while increasing healthy food access. Another respondent reported that their farm is “highly desirable for adults with special needs that need a safe place to be outside,” echoing respondents who point out the intimate connection between food and health . Farms frequently reported hosting educational and community-building workshops, cooking and food processing demonstrations, harvest festivals, and other open-to-the-public community events enhancing the resilience and connectivity of people, communities and ecosystems. Social networks emerged as an important theme for enabling the establishment of urban farms , and sustaining operations through social connections between urban farmers and other food justice and health advocates. Farmers identified three primary challenges: revenue, land, and labor inputs.

Half of all respondents reported farm earnings of $1,500 annually or less, and all four operations receiving over $250,000 in annual revenue are well-funded non-profit operations . Regardless of for-profit or non-profit status, most farms reported multiple sources of revenue as important to their continued operation , with an average of 3 revenue streams per farm. All non-profit farms reported multiple revenue streams except for three, who were sustained entirely by either board donations, membership fees , and grants. The most important revenue sources for non-profits include grants, grassroots fundraising, and unsolicited donations rather than sales. In addition to these monetary sources, all farms reported receiving substantial non-monetary support , which adds to the precarity of operations when these informal support channels disappear.Land tenure arrangements range from land accessed without payment through contracts with City or School District officials, to arrangements where a token fee is paid , to more formal leasing arrangements at the utility-owned Sunol Ag Park, where land tenants pay $1000/acre/year for their plots, ranging from 1-3 acres. Only five of the respondents owned their land , representing a mix of for-profit and nonprofit operations . Challenges around land access, security, and tenure were the most frequently occurring theme in the survey long response and interview analysis process, including consensus that land access is the largest barrier to scaling UA in the East Bay. The cost of labor, and relatedly, access to capital and grant funding to pay living wage salaries, were also extremely significant challenges identified by survey respondents. The majority of respondents stated that most of their labor is volunteer rather than paid, with nonprofit respondents reporting this more frequently than for profit enterprises . The maximum number of paid staff at any operation is 20 , while the average is 4. Many farms reported the desire to be able to hire and pay workers more, but not having sufficient revenue to accomplish that goal. Annual volunteer labor participants on farms ranged from 0 to 1542 with an average of 97 volunteers, representing a significant public interest in participating in local food production. Not surprisingly, amount of paid labor and total farm income are positively correlated . However, volunteer labor is also positively but more moderately correlated with total farm income .The farmers in our study acknowledged many challenges facing urban agriculture, stemming both from the high economic costs of production and land rents, and insufficient monetary returns from produce sales.

They also framed these challenges through a food justice lens, arguing that the current political economy does not fully compensate farmers for the social-ecological services provided from their farms. Farmers articulated many solutions that could improve the viability of their farm operations including: conversion of city parks into food producing gardens with paid staff, government and institutional procurement goals for urban produced foods, municipal investment in cooperatives or other community based food production , and establishment of aggregation hubs and distribution infrastructure.Our survey results describe a highly diversified East Bay Agroecosystem comprising urban farmers and other food system stakeholders that are growing food as well as food literacy, civic engagement, connectivity, and community. Applying an agroecological lens to interpret our findings of East Bay urban agriculture operations reveals the many agoecological practices farms have long been engaged in, as well as the important distinctions of UAE that still need to be explored, and specific threats to agroecology in urban areas. Pimbert suggests that “agroecology’s focus on whole food systems invites urban producers to think beyond their garden plots and consider broader issues such as citizens’ access to food within urban municipalities and the governance of food systems.” We argue that applying an agroecological lens to the urban context also invites researchers and urban planners and policymakers to think beyond garden plots and singular benefits of food production, to consider these sites as part of a larger agro-ecosystem with synergistic social,vertical farming pros and cons cultural and ecological dimensions. We reference the 10 elements of agroecology to illustrate the dynamics of how these elements manifest in practice in this urban context.All of the farms in our survey follow agroecological production practices which include a focus on building soil health through, most commonly, cover cropping, compost application, and no-till practices. These practices produce synergistic effects of adding fertility to the soil through organic matter amendments and boosting water holding capacity. Soil building practices are a response to the impetus to remediate toxins present in urban soils , a prerequisite to intensive cultivation and unique consideration of the urban farm environment. Overall, production practices on our urban farms seek to conserve, protect and enhance natural resources.

Our survey respondents described numerous strategies for enabling diversified, intensive production of fruits, vegetables, and other agricultural products. These strategies span both short and long-term, from planting in raised beds with imported soil, to building soil health in situ via heavy applications of compost, manure, and cover crops for several years leading up to vegetable crop production. There is a growing interest in using no-till practices, which are among the suite of practices associated with “carbon farming” for enhancing soil carbon sequestration . This illustrates a synergistic opportunity for urban food policy and urban climate policy, showing where urban food production and city Climate Action Plans 4 can converge and generate mutual support . Farmers are also engaged in innovative resource recycling and resource use efficiency and other strategies to enhance resilience such as installing rainwater catchment systems in concert with swales and soil health practices to optimize use of this scarce resource. Farms are planting native flowers and shrubs to attract beneficial insects, rather than purchasing chemical inputs for pest management. From a city planning perspective, the impetus to remediate storm water overflows and maintain corridors for essential pollinators are two priorities that can be met through incentivizing and planning spaces for UAE.East Bay urban farms reflect multiple scales and forms of diversity including agrobiodiversity, organizational and participant diversity, diversified sources of capital, labor and land arrangements, as well as diversified modes of exchange. Diversity among operations technically doing the same thing- growing food in cities- signals the fluid, flexible, peripheral, and at times revolutionary nature of urban food production spaces, which may conflict with orresist the institutional, political-economic status quo . Urban farms rely on diverse revenue streams from their diversity of activities beyond sale of produce. These activities, including educational services and community events, are important to elevate in policy conversations. Valuing and therefore protecting urban food production spaces requires thinking differently about them in a context like the San Francisco Bay Area. One stakeholder suggested considering urban farms as museums, providing essential cultural and educational offerings to city residents . The quality of the food and the value of the education, health, and community building, are strong arguments for including urban farms in an urban-agroecological framework for city planning and efforts to improve CFS. The diversity of land access agreements and labor sources used by urban farmers in the East Bay underscores equity considerations in urban agroecological transitions. Even 50% of the for-profit enterprises reported relying on volunteer labor, speaking to both the precarious economics of running an economically viable for-profit food production business in the city, and the interest among young people and aspiring farmers in gaining agroecological cultivation skills through arrangements where they donate their labor free of charge. Volunteer labor substitutes for revenue to a certain degree, allowing farms to exist and distribute food informally without needing to generate much revenue or provide many jobs. In the UA literature, reliance on volunteer labor comes under criticism for being a product of the “neoliberal city,” where responsibility for action falls to the individual rather than the state, and the equity concerns around who is able to volunteer their time are problematized . By reporting the common use of volunteers on East Bay urban farms, we do not seek to promote or valorize this practice, but rather recognize it as a necessary interim step occurring in our study context in the absence of dramatic local government intervention or radical reforms to address community food insecurity: those who are willing and able are participating through civic engagement in urban farms to produce, harvest and distribute healthy food to those in need. Many volunteers are retired or recent graduates, seeking opportunities to contribute meaningfully to their communities. The volunteers we have communicated with generally report positive experiences and enjoyment from their time digging in the soil.

Covariate imputation was based on the distribution of non-missing covariates only

The estimated outdoor PAH concentration represented 7 of the 9 individual PAHs measured in the residential dust. Since both outdoor PAH estimates and traffic density were approximately log-normally distributed, their logged values were used for statistical analyses. The urban indicator variable was coded as either 1, for residences in census blocks classified as “urban” ; or 0, for those classified as “rural” or “other” by the 2000 U.S. Census . A multiple-imputation procedure was used to borrow information from available measurements to impute values for missing data. In simulation studies, multiple imputation has been shown to produce unbiased effect estimates and appropriate confidence intervals . The data had three types of missing data: missing residential-dust PAH values, residential-dust PAH values below the limit of detection, and missing covariate data. Overall, 70 residential-dust PAH measurements were missing for 56 subjects. These PAH measurements were missing as a result of interference from co-eluting compounds during GC-MS analysis, which made detection of some individual PAHs impossible. In addition, there were 63 residential-dust PAH measurements below the limit of detection in 44 participant households. Finally, 246 of the subjects had at least one missing covariate, because respondents were either unable or unwilling to complete all of the survey questions . Because the 9 individual PAHs were correlated in the data, the multiple imputation strategy was particularly useful. Specifically, using Proc MI the joint multivariate normal distribution for the 9 correlated PAHs was estimated. Then,dry cannabis for each missing value, a probability distribution was created conditional upon the values for the non-missing PAHs . Next, five possible imputations for the missing value were randomly drawn from the conditional probability distribution bounded so that each of the randomly drawn values was greater than the limit of detection. The random sampling addressed uncertainty due to missing values and resulted in more valid statistical inferences than single imputation.

Additionally, the relative magnitude of missing PAH estimates reflected the profile of the corresponding non-missing PAHs for the same subjects. A similar procedure was used to estimate five possible values for each PAH measurement below the limit of detection and each missing covariate of interest.Again, logical bounds were set on the randomly selected values so that the estimates were reasonable . Ultimately, five complete data sets were created with five imputed values for each of the three types of missing data. Regression analyses were performed separately on each data set and the results were combined to produce inferential results. The goal of the regression analysis was to build a model that would be useful in predicting concentrations of PAH in residential dust given the questionnaire- and GIS based variables. As such, the deletion-substitution-addition algorithm, a tool for model selection written in R , was used to choose an optimal model from the list of candidate variables. All households and all imputed values were included in the DSA procedure. For each model considered, the DSA algorithm performed a 10-fold cross validation procedure with 10 repeated rounds. Each round of cross-validation involved randomly partitioning the data into 10 complementary subsets, fitting a regression model based on 9/10 of the data, and validating the model by comparing predicted and measured values in the remaining data . This process was repeated 10 times each round so that each partition was used as the validation set once. Finally, to reduce variability, 10 rounds of cross-validation were performed using different partitions, and the regression coefficients were averaged over the rounds. The ‘best’ model was the one that minimized the mean error between the predicted and observed values in 100 validation sets. The parameters in this ‘best’ model should be the most useful in predicting residential dust PAH concentrations in other households from the NCCLS population.

The search for the ‘best’ model began with the intercept-only model and proceeded iteratively by comparing the best model at each step with: 1) a deletion step which removed a term from the model, 2) a substitution step which replaced one term with another, and 3) an addition step which added a term to the model. Initially, the DSA algorithm was restricted so that it produced a model with only linear effects and no interaction terms. However, after narrowing the model selection to the most informative variables, the DSA procedure was repeated and 2nd order non-linear terms and two-way interactions that improved the model fit were added. Statistical analyses in this chapter included 277 cases and 306 controls with PAH residential-dust measurements. As shown in Table 21, individual PAH detection rates ranged from 94-100% and individual PAH concentrations ranged from below detection to a maximum of 2,450 ng/g. The sum of the 9 residential-dust PAH concentrations for the 583 residences ranged from 54-11,170 ng/g, with a median value of 479 ng/g. Table 22 shows the Pearson correlation coefficients between individual log-transformed residential-dust PAH concentrations. In general, levels of the 9 PAHs were moderately to highly correlated. Table 23 shows the Pearson correlation coefficients between total logtransformed residential-dust PAH concentrations and covariates of interest for the multiple imputation analysis and for the participants with complete covariate and PAH data. In general, the correlation coefficients were similar regardless of how missing data were treated. In the bivariate analysis, residence age, traffic density, and outdoor PAH concentrations were the covariates most strongly correlated with total PAH concentrations in residential dust. Table 23 also shows the number of subjects with missing values for the variables of interest. Table 24 shows the sum of the 9 PAH concentrations by covariates of interest. Based on the DSA algorithm that used all homes and included imputed values, six main effects were selected for the optimal model of logged total PAH concentrations in residential dust and subsequently two non-linear terms were added. Table 25 shows the parameter estimates and 95% confidence intervals for the optimal logged residential-dust PAH concentration model given the uncertainty introduced by the multiple imputation analysis .

Restricting the analysis to only HVS3- sampled homes , yielded a model with similar parameter estimates, but with slightly larger confidence intervals . The variable size of sampling area was marginally significant in the model with only HVS3-sampled homes. Similarly, restricting the analysis to only subjects with complete data yielded a model with parameter estimates similar to those in Model 1, but with slightly larger confidence intervals . The overall fit of Model 1 was R 2 = 0.15. During cross validation of Model 1, the average difference between the predicted total PAH concentration in residential dust and the measured total PAH concentration in residential dust was 0.67 . For comparison, the average difference between any measured total PAH concentration in residential dust and the average total PAH concentration in residential dust was 0.72. Figure 10 compares the measured and predicted total PAH concentrations in residential dust . Table 26 shows predicted total PAH concentrations in residential dust for various combinations of the six variables using parameter estimates from Model 1. Table 26, demonstrates the added effect of each term in the model on total residential-dust PAH concentration. For example, while holding all other variables constant,cannabis drying the added effect of indoor gas heating increased the predicted total PAH concentration in residential dust from 510 to 600 ng/g. Two suspected sources of indoor PAHs, i.e., indoor gas heating and estimated outdoor PAH levels, were significant predictors of total residential-dust PAH concentrations in the models. Interestingly, the age of the residence had the most significant effect on total residential-dust PAH concentrations, with older residences having higher PAH concentrations. The age of residence had a similar effect in the previous analysis of nicotine concentrations in residential dust . Previous researchers have shown that only about 5% of the total dust loading present in a 10 year-old carpet is available as surface dust, whereas the larger portion resides deep within the carpet and is not removed by typical cleaning . Taken together, these findings suggest that environmental contaminants can accumulate in household carpets over years or decades . The child’s age at enrollment was also a significant predictor of PAH concentrations in residential dust. Older children appeared to have higher concentrations of PAHs in their residential dust. In bivariate analyses, a child’s age at enrollment was positively correlated with the amount of time his or her family had lived in the current residence and with the age of the carpet sampled . While duration at residence and carpet age were not significant predictors of PAH levels, child’s age may be a more reliably reported surrogate for the age of the dust collected. If so, the positive regression coefficient for the child’s age variable is further evidence that PAHs accumulate in residential dust over time. Residence in an apartment/condominium, duplex/townhouse, or mobile home compared to a single family home, was also a significant predictor of the PAH concentrations in residential dust, with higher concentrations seen for multiple family dwellings. In Model 1, if the residence was not a single family home, the predicted total PAH concentration increased . Because apartments, mobile homes, and townhouses are typically smaller than single family homes, this result is consistent with a previous finding that concentrations of environmental contaminants in residential dust increased with decreasing square footage of the residence .

Presumably, given a constant number of PAH sources ; a smaller residence would have a greater PAH concentration. The mother’s ethnicity was also a significant predictor of PAH concentrations in residential dust. Hispanic mothers appeared to have lower PAH concentrations in their residential dust than non-Hispanic mothers. Notably, Hispanic mothers were also more likely to report that their carpets were vacuumed more than once a week and were less likely to live in an urban census tract . Although these other factors were not selected as variables in the optimal residential-dust PAH model, in bivariate analyses, vacuum frequency was negatively correlated with PAH concentrations and urban location was positively correlated with PAH concentrations. While the DSA algorithm identified several significant determinants of total PAH concentrations in residential dust, even the optimal model only explained a small portion of the total variability of the data . Moreover, during cross validation, the optimal model was only marginally better at predicting PAH concentrations in residential dust than the intercept model . Ultimately, it seems that even the most relevant self-reported and GIS-based data provided only limited information about residential PAH levels; this underscores the importance of making environmental or biological measurements. As discussed, dust samples were collected using both the HVS3 and household vacuum cleaners. Restricting the regression analysis to only those homes with dust collected by the HVS3 did little to change the estimates of the parameters used in Model 1 . This reinforces previous findings from the NCCLS and suggests that collecting residential dust from household vacuum cleaners is a useful alternative to the more expensive and labor-intensive HVS3 sampling method. An implicit assumption of the multiple imputation procedure is that the distribution of the missing data depends only on the observed data. This assumption is plausible given the large size and correlation of the set of predictors used for imputation . Moreover, restricting the regression to participants with complete data had little impact on the estimates of the parameters used in Model 1 . Indeed, whereas the parameter estimates were similar, the standard errors and confidence intervals were smaller for Model 1 than for Model 3. Thus, it appears that the multiple imputation of missing data was useful. The one variable that was substantially different in Model 3 was the variable identifying the residence as an apartment. However, because this variable had only one missing observation, the discrepancy probably points to data censoring in Model 3 rather than to failure of the imputation process. The PAH concentrations measured in residential dust in this chapter were generally lower than those previously reported for residences in Durham, NC , in the Rio Grande Valley, TX , in Cape Cod, MA , in Long Island, NY , and in Ottawa, Canada . However, a recent study of dust from residences in Kuwait found PAH concentrations similar to those reported in this chapter.

More details regarding the adaptation of the parent curriculum are described elsewhere

Adults that were interested in the study were screened and enrolled in the study if all inclusion criteria were met. Baseline data collection was then scheduled for adults and children. After baseline data collection, study parents were informed which treatment group they were in. This intervention component sought to increase vegetable intake among children in the ECE programs. We collaborated with the Leadership Committee and Osage Nation ECE program site managers, teachers, cooks, and staff at the tribal farm to determine which produce was of interest and available for the study. Since the climate in Oklahoma is unpredictable during the intervention months, ranging from cold, icy winters to a warm, wet spring, we utilized the farm and supplemented any produce they were unable to grow from a local supermarket. More details regarding the tribal farm are described elsewhere. The farm-to-school nutrition and gardening curriculum was adapted for NA children from two curricula: Early Sprouts; and Watch Me Grow. More details regarding how we adapted the curriculum are described elsewhere. The FRESH farm to-school nutrition and gardening curriculum included knowledge, reading, gardening, and indoor and outdoor sensory activities, comprised of three themes that were taught for five weeks each: Harvest ; Explore ; and Sprout . The focus of the curriculum was on six target vegetables: tomatoes, bell peppers, spinach, squash, butter beans, and carrots. The weekly curriculum for each theme included a reading activity , indoor and outdoor sensory activity, and cooking activity, which included a take-home recipe kit. The intended duration for each activity varied: 5–30 min for reading activities,cannabis curing up to 60 min for indoor and outdoor sensory activities, and up to 75 min for cooking activities. All weekly lessons were assembled in a handbook and distributed to intervention teachers for implementation.

Garden beds for the outdoor sensory activities and cooking activities were built and maintained by the Harvest Land farm staff at Osage Nation. All intervention children also took home a family recipe kit, including ingredients and a recipe to repeat the cooking activity with their family to increase exposure to the vegetables. Although the FRESH study did not directly intervene upon dietary intake of parents, we did include a passive online and in-person hybrid parent curriculum, adapted from the Choose Health LA’s Healthy Parenting Workshops , with components from the First Nations Development Institute’s Food Sovereignty Assessment Tool and the Grassroots International’s Food for Thought and Action curriculum. The online curriculum comprised of 12 short video modules focused on providing parents with strategies to support their children in eating healthier foods and included healthy lifestyle education and healthy parenting practices. The in-person component included three in-person family night workshops that focused on food sovereignty in the community and community capacity building for health. The last component included menu modifications at the ECE programs. Further description of the menu modifications from the FRESH study can be found elsewhere. In short, fresh vegetables from Osage Nation’s Harvest Land farm were harvested and delivered to the ECEs to be incorporated into the ECE menus. The menus were modified to achieve best practices established by the Child and Adult Care Food Program , which included more vegetables and fruits as snacks, replacing whole grains for refined grains, reducing fried foods, and removing sugar-sweetened beverages. The menus included the six target vegetables from the farm-to-school curriculum provided from the Osage Nation farm two times weekly and provided to the children in meals or offered as snacks within each menu cycle.

Dietary intake for children was assessed by measuring the consumption of the six target vegetables in the FRESH farm-to-school curriculum using the weighed plate waste method to assess objective levels of vegetable consumption. During the plate waste administration, we also assessed preference, or willingness to try, target vegetables. Trained researchers rated each child’s interaction with each target vegetable using a five-point checklist to measure observed willingness to try. The rating options were: Did not remove vegetable from box, removed food, but did not bring to nose/mouth, removed food and brought to nose/mouth, but did not put food in mouth, put food in mouth, but did not swallow food , put food in mouth and swallowed. More information regarding the child food consumption methods is provided elsewhere . Dietary intake for adults was evaluated using the National Cancer Institute’s Automated Self-Administered 24 h Recall. Recalls were obtained either in-person or via phone by trained university staff. Recall data were used to estimate mean intake of total energy , total sugar , total fats , total fruits , and total vegetables between intervention and control groups. We also used the 7-item Fruit and Vegetables Behavior Checklist to assess combined fruit and vegetable intake in cups per day.The aims of the FRESH study were to improve dietary intake , BMI, systolic blood pressure , health status, and food insecurity among NA families. FRESH is one of the first comprehensive multi-component, multi-level CBPR studies to use a farm-to-school and parent curricula to build community capacity and reduce obesity risk among NA families attending ECE programs. Although the FRESH study did not improve BMI or other secondary outcomes among children, there were significant increases in vegetable intake. Previous studies looking at vegetable intake and BMI improvement among children showed varied results. Some randomized controlled trials found that nutrition interventions that resulted in significant increase of vegetable intake also found a decrease in BMI, whereas others found no change among BMI; the latter finding is consistent with our study.

One nutrition and gardening intervention that implemented a randomized controlled trial at school found that BMI significantly improved in the intervention group compared to the controls; however, this study involved older children and a longer intervention period. At follow-up, regarding the willingness to try scale, we found significant increases in scores for tomatoes for both treatment groups and increases in scores for beans in the intervention group. Our findings are similar to the Nutrition Matters! curriculum, which found a significant increase in willingness to try scores in three fruits and vegetables among the nutrition and gardening group,curing cannabis and is consistent with a previous study that found that repeated exposures to vegetables led to an increase in children’s willingness to try target vegetables. Among the adults, the FRESH study did not improve vegetable intake, BMI, blood pressure, or food security. However, in the intervention group, there was a trend toward increased fruit and vegetable intake from baseline to post-intervention. At follow-up, total sugar intake and total energy significantly improved among the intervention group compared to the controls. Our results differ from a previous online nutritional intervention among NA participants that found an increase in vegetable intake. However, as the FRESH intervention focused primarily on children with a secondary component including the parents, significant results were not expected. Our study had several strengths. This study used a randomized controlled design, able to compare intervention and control groups. Although the study was under powered, we still found a trend towards increased vegetable intake in intervention adults. In addition, we used objective measures of vegetable consumption in children rather than a dietary recall, providing a more comprehensive dietary intake. This was noted as a suggestion among authors in a systematic review on garden-based interventions among preschoolers. Another strength of our study is providing children with repeat taste exposure of vegetables, which past research has shown to be effective in increasing intake. Furthermore, our study focused on providing the ECE menus with local fresh vegetables, addressing the need for studies that intervene in the social determinants of health. The Osage Nation is a reservation that has limited access to healthy and fresh foods, and this study built upon and strengthened local resources by facilitating the process for supplying the ECEs with the local produce. The limitations of this study include the challenge of implementing some of the dietary measures among children.

For example, there were negative values on plate waste vegetables, which were determined to likely be the result of water condensation. In addition, we did not directly intervene with parents, which contributed to low participation rates in the study’s online component of the parent curriculum. Only 56% of parents attended the first week of the online curriculum and 12% attended the final week. However, in contrast, participation in the in-person component of the parent/family intervention was nearly twice as high as the online participation, although it also decreased as the intervention continued. Since the original implementation of the FRESH study, the Osage Nation Harvest Land farm built upon the lessons from the study process and findings to expand its produce to a greater number of tribal programs and services. The Harvest Land farm now features commercial-grade aquaponics systems and eight new state-of-the-art greenhouses, which are able to grow various vegetables year-round. The farm is now in the process of developing a tribally specific community-supported agriculture program, which aims to increase access and intake of fresh produce. The principal goal of environmental epidemiology is to characterize how environmental factors affect human health. Specifically, environmental epidemiologists seek to quantify a dose-response relationship between the level of a hazardous agent in the environment and the severity of its health impact on a population. To this end, epidemiologists have generally classified potential exposures to environmental agents on the basis of self-reported information. However, self-reported exposure surrogates are generally qualitative and they may not accurately reflect true environmental exposures. When the discrepancy between estimated and true exposure levels is substantial, the true relationship between exposure and disease will be obscured. As such, the development of quantitative and objective measures of exposure is a critical aspect of environmental epidemiology. Recently, investigators have considered estimating exposures to indoor contaminants using toxicant levels in residential-dust samples, because dust measurements are quantitative and objective. Although, in practice, few epidemiological studies have employed estimates of exposure using dust samples, this dissertation will show that concentrations of chemical contaminants in residential dust can be useful surrogates for indoor chemical exposures This dissertation will focus on three chemical classes which have been associated with either childhood leukemia in the Northern California Childhood Leukemia Study or with developmental effects in other studies , namely, polybrominated diphenyl ethers , polychlorinated biphenyls , and polycyclic aromatic hydrocarbons , as well as nicotine . All of these chemicals are ubiquitous contaminants in residential dust due to their many indoor sources. Nicotine is a specific marker of cigarette smoke; PBDEs have been used as chemical flame retardants in consumer goods ; PCBs have been used in a host of consumer products, including fluorescent lights, televisions, and refrigerators ; and PAHs are produced by indoor combustion sources, including cigarette smoke, wood-burning fireplaces, and gas appliances . PBDE, PCB, PAH, and nicotine molecules on dust can enter the body via inhalation, via inadvertent ingestion after hand-to-mouth contact, or via direct absorption through the skin . For some individuals, notably children, dust likely contributes a substantial portion of the overall intake of PBDEs , PCBs , and PAHs and nicotine levels in dust offer a useful quantitative measure of cigarette smoking in the home .There have been several reviews of the use of dust as a medium for measuring chemical contamination in the home . Investigators have generally sampled residential dust by obtaining dust from subjects’ household vacuum cleaners or by collecting dust from floors, carpets or other surfaces using a standardized vacuum cleaner, such as a high volume surface sampler . Use of household vacuum cleaner bags eliminates the need for an in-home visit, which reduces study costs and minimizes the invasiveness of home sampling. In contrast, collecting dust with a standardized protocol allows investigators to know the location and time of dust collection. Some investigators have collected dust from household surfaces using a brush or broom . For example, Tan et al. collected dust from the upper surface of a fan blade to measure room-wide contamination. Researchers have made direct comparisons between chemicals measured in dust taken from household vacuum cleaners and dust collected using a standardized protocol in the same residence .

This further explains the presence of heterogeneity across individuals along with time effects

About 88.7% of the pastoralists significantly felt the effects of climate change in the form of recurring droughts, whereas the other 7.5% expressed their moderate feelings towards the effects. The remaining 3.8% of pastorals said they were unaware of the effects or did not know at all. Similarly, semi-pastoral , agro-pastoral and mixed-farming communities significantly perceived that prolonged drought was a major challenge that mainly damaged their natural resource base and was followed by lack of feed and water for people and animals . Similarly, Masih et al. showed that drought severely harms the ecosystem and worsens human crises. More than 94% of pastoral, semi-pastoral, agropastoral and mixed-farming communities perceived that a lack of animal feed was their critical challenge during drought periods. Crop failure that directly causes scarcity of animal feed is also associated with lack of rain and drying of water sources . As a result, semi-pastoral , agro-pastoral and mixed-farming communities perceived that climate change, manifesting as drought, had significant destroyed crops. Households described that they experienced crop failure twice or more times within five years . These show that the Afar people are vulnerable to the vagaries of nature. Because existing streams and rivers are drying up, pastoral , semi-pastoral , agro-pastoral and mixed-farming communities significantly perceived scarcity of water across many villages in Aba’ala. These community members further perceived the effects of climate change in terms of rainfall variability, increased temperature, untimely raining and flooding, prevalence of animal and human diseases, shortage of food for human and drying of streams and other water sources . During drought, many livestock owners used the same water sources for their animals to drink from. As expressed by key informants, cattle herds that compete for similar water sources and grazing land areas were likely to be exposed to several diseases. This shows the need for introducing better cattle management mechanisms such as zero grazing, provision of clean water and improved veterinary services,indoor grow trays which may address problems related to disease prevalence when livestock herds compete for scarce sources .This study attempted to explore correlations between households’ perception of the changing climate and their adaptation actions applied in the last five consecutive years.

For the analysis, household’s perception runs from 0 = I don’t feel, 1 = I don’t know and 2 = I significantly felt. Table 3 presents percentage of households who perceived the adverse influences of climate change and their corresponding adaptation strategies. About 99.8% of pastoral communities perceived the ill effects of climate change. To cope with the effects, households pursue livestock mobility as their prime strategy. In the case of pastoral communities, we found that livestock farming was dominantly supporting their livelihood bases. Pastoral communities supplemented their living through livestock mobility to other potential areas where they could find water and natural grazing. On the contrary, about 94.1% of mixed-farming communities, 88.3% of agro-pastorals and 67.5% of semi-pastorals perceived livestock mobility as inferior strategy to other adaptation options. A statistical test shows households’ significant differences in pursuing livestock mobility associated with their perception differences. As shown in Table 3, perception of mixed-farming communities for practising zero grazing recorded the highest level . However, a small proportion of pastorals , semi-pastorals and agro-pastorals perceived zero grazing as an appropriate adaptation strategy. The study also showed that perception of households towards the use of pasturing and breeding was statistically insignificant differences among pastoral, semipastoral, agro-pastoral and mixed-farming communities. While 91% of mixed-farming strongly perceived livestock restocking as their means to respond to climate-related risks, pastorals , semi-pastorals and agropastorals showed only a negligible interest in this strategy. As reported by key respondents, most households perceived the importance of restocking, especially to improve their herds by purchasing drought resistant cattle and small ruminants. Perception of pastoral, semi-pastoral, agro-pastoral and mixed-farming communities towards the importance of livestock destocking accounted for 42.5, 36, 33 and 41%, respectively. Cropping was also extensively perceived as a pathway to cope with a multitude threats of climate change, upon which semi-pastoral , agro-pastoral and mixed-farming communities were highly dependent on to supplement their subsistence living.

Considering the strategy to reverse unexpected future climate shocks and uncertainties, agro-pastoral and mixed-farming communities used animal feed storage. This might be because livestock feed storage applied by the mixed-farming communities is associated with their long-term experience in cropping. Their involvement in cropping during good seasons allows them to harvest a sufficient amount of straw and hay, which would also enable them to keep stocking for unprecedented future feed crises. Key informants and group discussions further confirmed that feed stocking is possible, either through their own production, purchasing or both. Irrigation was also perceived as a strategy to adapt to climate change by pastoral , semi-pastoral , agropastoral and mixed-farming communities.Despite water scarcity being a major challenge in the Aba’ala district, mixed-farming communities used to grow cereal crops with spate irrigation. As reported by key informants, this practice started in the 1960s when cropping started in Aba’ala. Experienced farmers who were involved in spate irrigation described that the use of flood diversion into farmlands provided several benefits. For example, farmers were able to grow cereal crops, enrich water wells and ponds for humans and animals, alleviate moisture stress and keep ecological balance. With respect to pastorals and semi-pastorals, livestock mobility to other areas in search of water and natural grazing is crucial for sustenance .In addition to the aforementioned adaptation strategies, communities in the Afar region attempted to be involved in various income-generating activities. Income from livestock is among multiple sources that support the livelihoods of rural communities. As presented in Table 4, this sector serves as the main source of living for 105 pastoral, 40 semi-pastoral, 70 agro-pastoral and 98 mixed-farming communities. The contribution of livestock accounted for about 65.7% of the total income. In order to estimate the net income generated from livestock, all expenses made for purchasing fodder, payments for hired labour and fees for veterinary services were deducted from total gross income. All components of livestock income sources such as sales of live animals, milk, butter oil, hides and skins were accounted for. In the Ab’ala district, community members generated income from non-agricultural wages. For instance, 54 pastoral, 39 semi-pastoral, 54 agro-pastoral and 80 mixed-farming communities took employment from several organizations. Self-employment was also found as the key income strategy to the rural communities.

There were 15 pastoral, 6 semi-pastoral, 7 agro-pastoral and 23 mixed-farming communities who engaged in generating several income-generating enterprises. Households who engaged in crop production accounted for nearly 191 people across the district. Among these, 39 were semipastoral, 54 agro-pastoral and 98 mixed-farming communities. About 14% of the income of those household heads was supplemented from cropping. As shown in Table 4, both relief and remittance also contributed to households’ total income share by 14.1 and 0.23%, respectively. A total of 92 pastoral, 33 semi-pastoral, 59 agropastoral and 69 mixed-farming communities generate income from selling firewood and charcoal, accounting for about 4.1% of the total income and implying reliance on exploiting the natural forests for energy and commercial purposes . This may be taken as an indication of how income constraints can pressure rural people to keep on selling firewood and charcoal to meet their short-term needs without considering the longterm burdens on the natural resource base. Hence,2 tier grow tray continual damage of the natural forest can accentuate the negative effects of climatic change in the area. In this context, the key informants further recommended urgent measures to enable the fuel-wood sellers to shift to compatible income diversification alternatives like honey, salt mining, commercial tree plantation, livestock rearing and trading, which are eco-friendly livelihood alternatives. Similar conclusions made by Habibah et al. indicate that locals can be active participants in protecting the natural resources if they find that they could maintain their long-term benefits in sustainable ways.In the face of the changing climate and realizing the consequent adverse effects, rural communities in Afar continue applying a number of adaptation methods provided that they expect benefits out of their adaptation actions . In this section, we examine the linkages between various types of adaptation measures and their corresponding effects on the income of pastoral, semi-pastoral, agropastoral and mixed-farming communities in the Afar region. Prior to estimations, we conducted several tests to verify which model was appropriately needed for doing the analysis. As individual sampled households were proportionally drawn from the four types of communities, estimation of adaptation effects on income of households necessitates considering two key potential sources of variation, which means at both individual and group levels. While estimation using ordinary least square is useful to capture individual variations, random effects model incorporates non-systematic variances across groups and entities. Thus, mixed OLSrandom effects test allows distinguishing what proportion of the variance in income can be attributed by individual differences compared to group differences arising from the four communities . The estimated result of the Intraclass Correlation Coefficient shows that nearly 35% of the variance among household income is due to adaptation differences attributed to the four community groups, whereas the remaining 65% is due to adaptation differences across individual households.

As estimated results using ordinary least square regression only account for individual variations and ignore average variances across community groups, the use of OLS provides biased coefficients. Moreover, the null hypothesis of no differences among the parameters of the four community groups is rejected using the likelihood ratio test. Evidence of the likelihood ratio test versus linear regression yields Prob = 0.0000, which clearly shows the need to deploy other estimation methods. Considering these results, we further checked whether random effects or fixed effects model can appropriately capture both individual and group variations. Following Wooldridge and Baltagi , the statistical justification for the use of the fixed effects model over the random effects model was checked using the Hausman test. The test revealed that the null hypothesis that assumed random differences in coefficients is found statistically and significantly different from zero at 1% level . This justifies the rejection of the null hypothesis and the need to employ consistent estimation using the fixed effects model. Beyond this, we checked whether serial correlation had spiral effects on the dependent variable by applying dynamic fixed effects regression over four consecutive lagged years . The output showed that variations observed in the dependent variable were not statistically associated with unprecedented effects that might arise from previous years. The correlation coefficient between residuals within groups and the overall error term shows the effect of time differences across the panel years. As shown in Table 6, the attribution of such differences over the course of the five years was found to hold nearly 53.6% of the total variation in the dependent variable .The use of fixed effects model in this situation, therefore, distinctively differentiates the effects of adaptation measures applied by individual households from the effects attributed to panel years. As shown in Table 6, the results of the fixed effects regression show that the major adaptation actions that are found to have statistically significant effects on the income of households are water harvesting, livestock diversification, migration and production of hay and straw.The estimated coefficient on age of household heads may help associate both labour force age group and various adaptation measures applied at village level. As illustrated in Table 6, age of household head was negatively and significantly related to the annual mean income. The negative result of the household head’s age may indicate that older people who could not fit physically to accomplish adaptation activities cannot improve their income level. As uptake of improved adaptation practices is difficult for older households, they remain to stick on applying traditional practices that already known to them . This might be because older people usually tend to devote their time and resources on religious affairs. In the Afar culture, it is believed that old age is the time for doing charity and sanity. Considering the social and economic dynamic, key informants reported that older people usually do not commit in meeting long-term planning towards improving their income benefits.

Experimental treatments varied by the number of people in the room and their activity

We therefore conclude that CO2 baits are beneficial when targeting Anopheles spp., as their use may lead to increased capture rates in comparison to non-baited CDC-LT. These findings are consistent with previous studies, which have shown that dry ice baited CDC-LT are a good alternative choice to collect malaria vectors including An. minimus s.l., and An. maculatus s.l. and An. sawadwongporni Rattanarithikul and Green, respectively.In contrast, previous studies on African and Brazilian malaria vectors, specifically An. arabiensis Patton, An. funestus s.l. Giles , An. darlingi Root , and An. aquasalis Curry have shown that CO2 was insufficiently attractive as a standalone bait and that traps using CO2 in mixed odor baits or together with body odors may provide better results. Most of the collected Anopheles mosquitoes were in the unfed state and feeding status did not seem to impact capture efficiency when comparing indoor and outdoor trap locations. This stands in contrast to a previous study that indicated a preferential capture of blood fed mosquitoes and An. funestus by CDC-LT in indoor locations in Zambia, however this may be attributable to the low numbers of blood fed mosquitoes observed in this study and that the captured Anopheles species commonly exhibit a zoophilic host preference. Culex spp. were the most abundant species collected in this study. Overall, there was no significant difference in the capture efficiency of baited or unbaited traps and/ or trap locations . Similar to Anopheles spp. there was a tendency that CO2 baited traps were more efficient than unbaited traps in outdoor locations. When Cx. vishnui was considered separately, capture efficiency was significantly higher in CO2 baited traps. More detailed analysis revealed that this effect was restricted to traps placed outdoors and in the hot season . Cx. vishnui is a main vector of Japanese Encephalitis Virus. It is most commonly found in fragmented forest, rural, and suburban habitats and is exophagic in nature,dutch buckets preferentially feeding on pigs. This may explain why it is more frequently trapped in outdoor locations.

Previous studies have shown improved collected mosquito numbers in CO2 baited traps for Cx. quinquefasciatus in French Polynesia, and Cx. quinquefasciatus and Cx. annulioris Theobald in Kenya. In addition, the use of CDC-LT with dry ice was most effective for trapping of Cx. quinquefasciatus when compared with UV light traps and gravid traps in China. This effect was not observed in the present study but this may be attributable to the low numbers ofCx. quinquefasciatus captured. Traps were mostly placed in villages surrounded by mountains and forests whereas Cx. quinquefasciatus is a mostly urban mosquito species and known to breed in open drains polluted with organic matter. Therefore, the trap setting strategy applied in this study may not have been suitable to capture large numbers of Cx. quinquifasciatus. Armigeres mosquitoes were captured consistently better outdoors in the CO2 baited traps and this effect was consistent across seasons. Ar. subalbatus primarily occurs in plantation areas and forests, and is mainly active during the day particularly in the crepuscular period. This may explain its preferential capture in outdoor locations. Ar. subalbatus is known to transmit Wuchereria bancrofti and several zoonotic filarial worms such as Brugia pahangi. While some previous studies have compared captured Ar. subalbatus numbers using different types of traps, we are not aware of a direct comparison of CO2 vs. nonCO2 and indoor vs. outdoor trap placements for this mosquito species. Overall, the number of Aedes species mosquitoes captured in this study was low and most captured Aedes mosquitoes were Ae. albopictus. Although previous studies have indicated that CDC-LT are amongst the most efficient traps for the capture of some Aedes species these differences were not apparent for Ae. albopictus. CO2 baiting slightly increased Ae. aegypti capture in a comparative trapping study in Manaus. In the present study, there were no statistically significant differences in trapping efficacy with or without CO2 and the placement of the traps. Ae. albopictus seemed to have a tendency of preferential indoor capture . Extended trapping studies would need to be conducted in order to determine whether capture efficiency is improved by CO2 and/or whether indoor/outdoor trap placement is important. Aedes trapping studies commonly use BG traps and it has been shown that these are more effective in capturing Aedes than CDC-LT. This study is limited by several factors.

Trap placement was irregular and the number of trap nights differed considerably between villages and months of year . While most previous studies distinguish between traps by counting absolute mosquito numbers, due to the complex and irregular placement of the traps in this study we compared the rate of mosquito capture per unit time, rather than absolute numbers. Although CDC-LT baited with CO2 were shown to increase capture rate for several mosquito species including several important disease vectors , it should be noted that the traps require daily dry ice and battery changes limiting the scope of trapping studies, as each trap needs to be maintained every day. Over 94 % of female mosquitoes in the trapped population were not blood-fed. It is unclear whether these individuals are newly emerged or parous females that have not yet taken a blood meal. The ratio of nulliparous to parous female mosquitoes may represent an important entomological parameter to be determined in future studies. Normally, An. minimus s.l. and An. maculatus s.l. are regarded as exophilic. A surprisingly small percentage of occupants in the study houses reported using ITNs . We cannot exclude the possibility that concurrent usage of ITN decreased indoor biting, but our analyses did not show such an effect modification, possibly because our sample numbers are too small. Other factors, such as house structures and the presence of domestic animals around houses might further affect mosquito behavior. Further studies should be conducted to comparatively evaluate whether the species composition, and the blood-fed and physiological age distribution of captured mosquitoes is similar for CDC-LT and human landing catches and thus, if CDC-LT are truly capable of capturing representative samples of those mosquitoes relevant for human disease transmission. This study highlights differences in trapping efficiency of CDC-LT for different mosquito species. Our study thus provides important orientation for more targeted future vector trapping studies on the Thai-Myanmar border, an important cross-border malaria transmission region.Human occupants are an important source of microbes in indoor environments. On indoor surfaces, direct contact leads to a rapidly generated signature of the occupants , one that is predictable based on the nature of the human contact. Airborne microbial levels increase when rooms are occupied compared to unoccupied conditions, and humans have been reported to be a source of bacteria and fungi in settled dust samples.

A full understanding of the role that occupancy plays in airborne microbial quantity and composition, particularly in comparison with other sources such as ventilation supply from outdoor air, is just beginning to emerge. While a recent study showed the influence of human body-associated bacteria in office buildings, investigations into the source strength of humans on indoor bioaerosols have predominantly focused on occupied classrooms. Hospodsky et al. showed that human occupancy in a university classroom setting leads to a nearly 10× increase in bacterial genomes and that emissions from human skin make a significant direct contribution to the bacteria in indoor air. More recently, Hospodsky et al. determined in a sample of children’s classrooms that the emission rates attributable to occupants ranged from 0.8 million to 35 million bacteria cells per person-hour and from 3 million to 57 million fungal cells per person-hour. Two broad routes have been identified through which human occupants emit bioaerosol particles. One route is through direct shedding, which includes particles directly coming off bodies and clothing. The second route is through resuspension from inanimate room surfaces,grow bucket whereby occupants’ movements disturb microbial materials that had previously settled onto or colonized indoor materials. The goal of the present study is to explore the relative contribution of occupancy compared to other sources in shaping indoor bioaerosol composition using replicated experiments conducted under controlled conditions. For one component of the study, we sought to identify how the number and activity of occupants influences the biological components of the indoor air; we report those results here. Elsewhere, we report the emission rates of fluorescent biological aerosol particles from human occupants. We combine information from filter samples subjected to molecular-based analysis of DNA with the real-time size-resolved optical monitoring of total particle number concentrations. Outdoor samples were collected simultaneously and analyzed, which allows us to put the effect of occupants in the context of the contribution of outdoor air, as introduced via the ventilation system. We hypothesized that the signature of human-emitted microbes would increase with the number and activity of occupants, would be greater when the flooring was carpet that was exposed rather than covered with plastic sheeting, and would be stronger for bacteria than fungi. We expected that airborne microbial composition during low occupancy periods would be compositionally similar to that of outdoor air. We also expected that composition during high human occupancy periods would be similar across samples because of the shared signature of the human microbiome and because of the anticipated strong contribution of occupants to total airborne levels.Experiments were conducted in a controlled environmental chamber designed to simulate an office room.

The chamber has a floor area of 30 m2 and the ceiling height is 2.5 m. Ventilation in the chamber is controlled by an independent heating, ventilation, and air-conditioning system. The HVAC system supplies thermally and humidity controlled outdoor air that has passed through filters with a MERV 7 rating. There is no air recirculation with the single-pass ventilation system. The duct length from the outside air inlet to the room is approximately 10 m, and the ventilation air passes through a filter, supply fan, cooling coil, and heating coil in sequence. Outdoor temperatures during the test period ranged from 10 to 20°C. The outdoor humidity conditions were such that the cooling coil temperature was always above the dewpoint and therefore dry. The room was pressurized so that infiltration from adjacent building spaces was negligible. The ventilation fan speed was set to maintain a constant air-exchange rate, which was calculated by the exponential decay of the net CO2 level during the post-experimental period when the room quickly went from being occupied to unoccupied. The air-exchange rate was so determined to be 2.8 ± 0.2 h-1.The chamber floor consisted of closed-loop nylon carpet tiles, and we executed experiments both with the chamber carpet exposed and with it covered in plastic sheeting. To minimize static electricity effects during the walking treatment of the covered floor, we placed rectangular strips of a conductive material on top of the plastic sheeting. Subjects walked only on these strips. The duration of each experiment was two hours, a period of time allowing for sufficient collection of bioaerosol material for community analysis while still a time-resolved sampling period aligned with the particle-based instrumentation The five experimental combinations of occupancy and activity were each replicated three times on two different floor types, for a total of 30 experiments . Participants were members of the university community , including students, researchers, and faculty, and each of the high-occupancy sampling periods included both men and women. Participants provided written informed consent for the research, which was approved by the University of California Committee for the Protection of Human Subjects Protocol ID 2013-01-4927.Bioaerosol samples were collected by drawing air through open-face filters. Analytic filter cups of 47 mm diameter, 0.2 μm pore size cellulose nitrate membrane were suspended upside down at a height of 1.5 m . The attached vacuum pump was set to a flow rate of 25 liters per minute, so that a total volume of 3 m3 of air was sampled per experiment. We note that in sampling the entire 2-hour experimental period, the procedure captures both times when conditions are in transition and when steady-state conditions should prevail. For outdoor samples, the filter cup was suspended upside down, outside a window on the same building face at ~ 5 m from the outdoor air intake for the HVAC system. This sampling approach should only collect particles with diameters smaller than ~ 50 μm .

The Employment Development Department initiated a weekly farm labor report

Time-based pay is now the norm , but use 01 both piece rates and houdy rates for different jobs on the same larm is very common. As one survey respondent writes, “It all depends on what we’re doing rat the time,” Three 01 four respondents overall pay mostly by time, typically by the hour but a sizable minority by the week or month. Time-based pay is particularly dominant in the animal products, nuts, and nonedibles sectors, where businesses tend to be more capital-intensive and have smaller payrolls, Weekly or monthly salaries are paid by a substantial majority of animal producers. Output incentive pay that either constitutes workers’ total earnings or supplements their hourly wages is most common in production of grapes and other fruit. Many larm busmesses , in all crop and size classifications, offer supplementary incentive pay based on valued results other than output quantity. Time-based pay can be designed to encourage continued employment, high level perlormance, or both. An explicit structure of wages on a farm reveals to workers the opportunities that exist to increase income by moving up in a pay range for a given job or advancing to a higher-paying position. Hourly wage differences among employecs on a larm may reflect both “job factors” and “individual lac tors” . The lairness 01 paying more lor work in jobs that entail more skill, responsibility, or unpleasantness, and lor better or longer service within a business, is generally accepted. Problems in applying this concept usually stem Irom difficulty in measuring all except the last 01 these laclors. Do farmers pay different hourly rales to production employees in the same type 01 job? Almost three in live do. They base differences most commonly on length of employment ,botanicare rolling benches which can be measured objectively, and secondarily on evaluation 01 worker performance.

Far lewer vary wages as a lunction 01 time 01 work shilt , season of year, current financial status of the farm, and such worker characteristics as versatility, previous experience, judgment, and reliability. A majority of farmers who use hourly rates adjust them yearly, nearly one-quarter do so at irregular intervals, and a fifth scasonally . Piece rates arc as commonly set each season as each year, and some farmers change them with every entry of a crew into a new field. In determining pay rates, farmers give most attention to comparative information obtained through their own systematic surveys and information conversations with other local operators. A large majority indicates giving some consideration to what their employees say and a bare majority to published survey results. Farm operators offer various fringe benefits in addition to monetary wages. While employers are generally required by Jaw to provide a few “mandatory” benefits, most farmers give one or more additional fringe benefits at their own option. Survey respondents provide all optional benefits much more frequently to year-round than to seasonal workers . They most commonly report offering to “some” or “most” year-round employees vacation pay , health insurance , and housing . Farms with larger payrolls and those organized as corporations tend significantly more to provide all benefits except housing and transportation. Farmers fluent in their workers’ language arc more likely to include farm products in the tota; compensation package. Other benefits that respondents mention providing for employees include pension plans, holiday pay, paid utilities, free lunches, gasoline and car repairs, interest-free loaos, term life insurance, and use of a farmer’s own vacation home in the mountains, About three times as many farmers say that they offer such benefits to year-round employees as to seasonal employees.Labor management is no longer only about dealing with workers, if it ever was. It is no secret that farmers have felt their operations increasingly constrained by government requirements as well as by market competition. Relations between farmers and the people they hire are subject to a large set of public policies that apply to the many but are comprehended by the few. Agricultural employers and workers are challenged yearly to keep up with new developments that alter an already bewildering array of legal obligations and constraints?

The laws affecting farm labor management are formidable in their variety, fluidity, and sheer volume. One kind sets standards for specific terms of employment , a second regulates interaction between employer and employee , and a third affects overall supply of labor and workforce development outside the employment relationship .Several law5 require farmers to report to the government about their operations. Agricultural employers, like all others. arc obliged to regularly submit information on their payrolls and employees, and to respond to “anous agency requests for other information. During a typical month of active production, farmers and their office staffs spend a median seven person-hours completing the employment-related reports that are required by federal, state, and local agencies . The larger the farm payroll, the more ad’ministrative time is devoted to these reports, as many as 29 hours median for farms with $] million payrolls. Almost two in five of these largest employers, and some of even the smallest employers, spend 40 or more hours per month on reporting. While a plurality of firms in the smallest-payroll group devotes less time than 2 hours per month to employment reports, ]5 percent in this class and 39 percent of respondents overall spend ten hours or more.Government forms arc infamous for their design and instructions. Survey respondents overall cite understanding report requirements second only to filling out the forms as the most consuming task in preparing reports to agencies. Perhaps because they have more specialized office staff, however, larger firms tend to find that comprehending instructions takes less time than gathering records and obtaining information needed from workers. Certainly not all communication with government agencies is via the dreaded paper form. Farm operators make phone or personal cont~ct with agency staff members to obtain technical advice, clarification of rules and legal standards, and other practical information; and agencies get in touch with farmers for inspections and audits. Only one in ten respondents, disproportionately those with small payrolls, report having had no communication during 1991 and 1992 with any of eight listed agencies . UC Cooperative Extension and the county Agricultural Commissioner’s office are the two agencies with which farm operators most commonly made contact, the U.s. Department of Labor and the state Division of Occupational Safety and Health least. The Employment Development Department and US Internal Revenue Service were the agencies initiating contact with the greatest shares of farmers.

While farms of every size approached Cooperative Extension, an educational and research institution, at roughly the same rate, the larger businesses were significantly more in touch with each of the other agencies, which have regulatory as well as informational functions. Reported rates of contact initiated by Cal-OSHA, the Labor Commissioner, the DOL, and the INS–presumably for law enforcement–are extremely sensitive to payroll size, those by the EDD and the IRS considerably less so. It is quite possible that EDD, like most of the enforcement agencies, actually has a proclivity to inspect larger operations. If respondents took this very survey to be a contact by EDD, numerous non-inspectees from all size groups would have indicated having EDD-initiated communication, thus obscuring in our results a true relationship between regulatory contact and farm size.Of all the laws affecting the agricultural community in California since 1986, the Immigration Reform and Control Act has been most pervasive in the farm labor market. Requiring all employers to conform to new hiring standards and offering generous opportunities for alien legalization, it raised issues for employers, aliens, and government agencies. Its impact in agriculture was to be shaped through individual responses to the inducements and penalties it created. Farm operators faced choices about not only the new recruitment, selection, and record-keeping obligations, but also their non-regulated management practices and labor relations more generally.Underlying the special treatment of agriculture by !RCA were assumptions about buyers and sellers of agricultural labor. Responses to some provisions were rather immediate and far reaching, but the effects of others and the law as a whole would take form gradually. Most employer and alien decisions that the law was designed to influence were in the future, and the very context of these decisions was fluid. Provisions did not all take effect at once,commercial plant racks and many key implementing regulations and administrative polieies took months, some even years, to establish.. The accuracy of predictions about agriculture after immigration reform could not be assessed until well after December 1, 19BB, when the SAW application period ended and employer sanctions became fully applicable in agriculture. Nevertheless, the watch was on early for indications of what lRCA would bring. Long-term effects might be reflected as changes in: the composition of the farm workforce, the mobility and occupational choices of newly legalized former farm workers, workers’ exercise of legal protections for employees, union organizing activity, pay and other terms of employment in agriculture, reliance on farm labor contractors. use of production technologies that substitute machinery for labor, and ultimately, the viability and structure of labor-intensive agriculture in the United Stat~s. California farm employers were understandably concerned about the impact of the new law. In spring 1987, fears of widespread summer harvest disruptions were fed by general confusion about the new law, by IRCA regulations that restricted farm workers in Mexico from entering the United States to file SAW applications and obtain temporary work authorization, and by spot shortages of labor to perform early season tasks. Agriculture took a regular place on the nightly news, and government agencies prepared to cope with crisis. The INS convened a public meeting in Irvine to promote an exchange of informed views and suggestions among representative of grower, labor, and federal organizations. The most pessimistic scenarios were not nearly realized. Transitional niles and offices were set up to facilitate the entrance of pending SAW applicants from Mexico. The temporary relaxation of documentation standards for proving work eligibility eased the employment of SAW applicants from either side of the border.

Harvests progressed through the summer and fall with little abnormality. In our 1987 survey, only thirty respondents specified major business adjustments to IRCA that they had already made or contemplated. Most common were reducing the labor intensity of operations by using more machines or changing the mix of crops produced, and reducing the size of the business or leaving agriculture entirely. Six years later, it is widely reported that more people arc looking for agricultural jobs than are needed to fill them in most regions most of the time. The overall supply of labor available to farms incalifornia has been expanded by the !RCA legalization of more than a half-million agricultural workers, continued legal as well as illegal immigration, and the loss of employment opportunities in other parts of the state economy. Real earnings of hired farm workers have eroded, and employment by farm labor contractors has increased. What has IRCA wrought, from the farm operator’s perspective? Above all, much more employment paperwork. Fully three-quarters of respondents agree strongly that the law has had this effect, and another 21 percent agree somewhat . More than four of five say that there seems to be less hiring of undocumented farm workers, and a similar proportion that their labor costs have increased because of the immigration reform law. The meaning of these responses, however, is uncertain. Not all “documented” workers have legitimate papers, and higher costs may be less attributable to workers raising their asking prices than to various non-wage expenses, such as for compliance reporting and workers’ compensation insurance. A large majority of respondents sees a reduction in questioning of workers by Border Patrol officers, presumably because resources have been shifted to auditing employers. Smaller but nevertheless substantial shares of the survey sample report that lRCA has made it more difficult to find high quality workers or sufficient numbers of workers, and almost half that they have had to make some adjustment in their recruiting efforts. These views on the impact of the 1986 law are quite comparable across different business size classes.While not specifically attributing change in the labor market to immigration reform, more than a quarter of farm operators regard it generally harder now than it was in 1986 to recruit as many capable production workers as they need, far more than see it as easier <table G-4L Though this tendency to find recruitment more difficult now exists in every commodity group, it is most pronounced among producers of animal commodities, grapes, non-edibles, and “other” crops, and it is rather weak among vegetable producers.

The questions arose during instrument design as well as when responses were arriving

In addition to evaluating pig behaviors through RGB videos, automation can be further facilitated by other sources. For example, videos containing depth information are useful to estimate pig body weights. Body weight is a critical trait associated with growing rate, feed efficiency, and meat biomass. Conventionally, pigs are weighed on the electronic scale in the pen, but it can be either inaccurate when more than one pig is standing on the scale or expensive if the scale is integrated into the feeding system. A past study has presented a video-based pipeline that can successfully estimate pig body weight with an RGB-depth camera by segmenting pig contours . By combining the existing work and VTag, which can continuously track pig positions, the fully automatic system of pig weighing is feasible for farms with limited resources. The implemented trackers have successfully shown their great performance of tracking objects in their published papers . However, the trackers failed to track pigs without any human supervision in our presented results. The potential reasons may be explained by the difference in the monitoring context. In our study, the tracked objects share similar morphological features. Even for the feature-rich areas, such as pig heads and tails where unique spatial patterns are observed, they were hard for trackers to distinguish the difference between different pig individuals. The trackers easily lost tracking the correct individual when two pigs frequently interact with each other in a short period. Another reason is the video quality. In the papers where the trackers were published, the demonstrated videos recorded at least 20 FPS. Whereas the studied datasets only have 6 FPS,vertical indoor growing system which is a common setting in practical farming to reduce power dissipation and save data storage. Such low FPS videos reduce the similarity between consecutive frames and increase the chance of mismatching tracked features over time .

Additionally, when the tracked object moves rapidly, the track features are more likely to become blurry in a low-FPS video. These limitations make the tracking task in livestock farming more difficult than the regular tracking task, where videos have 30 FPS, and the video frames are assumed to be similar in adjacent frames and the tracked features are unique compared to other objects. We also included pre-trained models, YOLOv5 and Mask R-CNN, in our benchmark study. The low-FPS results indicate that it is difficult to fulfill real-time long-term monitoring in livestock farming without accessing GPU resources. Although we did not show their precision in the current work, the detection results are not comparable with the presented trackers. It is because the models were trained by COCO datasets, in which top-view pig images are not included. In some video frames, pigs are either not detected, or 2 adjacent pigs are identified as the same object. Besides, without further modification of the models, it cannot force tracking the certain number of objects. These limitations make the evaluation difficult when we want to compare the precision of tracking the same number of pigs. In conclusion, the results suggest that the object detection models are not as suitable as object tracking algorithms in the pig monitoring tasks. Further improvement can be made in the current version of VTag. For example, VTag is found to mis-identified pig identities when individuals frequently contact each other in a short time as described earlier. Although the wrong labels can be corrected manually, it still requires time and effort from humans’ supervision. One way to reduce such error is to utilize a strategy called template learning, which was discussed in the literature . The general idea of this strategy is to first select the video frames in which pigs are not in close range of their neighbors. Then, the pig morphology observed in the selected frames is learned as “templates”.

Finally, the model can use the templates to update the predictions in the frames where pig positions are mis-identified due to the closed distance between pigs. In addition to improving the algorithm, adding information by wearable devices is also helpful to increase the monitoring precision. The devices, including motion sensors, magnetometers, gyroscopes, and GPS receivers, have been widely used to monitor behavioral patterns in large farming environments . Specifically, with the use of tracking collar wore by pastured livestock, grazing behaviors were successfully identified for cattle and sheep . The spatial resolution for outdoor studies was further improved to centimeter-level by coupling sensor collars with signal receivers deployed around the farm . In group-housed scenarios, Smartbow , a commercialized ear-tag sensor system, also demonstrated promising results in monitoring complex interactions on reproduction traits in swine cohorts and feeding behaviors of cows . In conclusion, by coupling the VTag algorithm and the described improvement, the automation of the assessment system is expected to monitor more complex farm settings.Much consultation went into developing the survey instrument, including several preliminary discussions with farmers. Cognizant of how besieged many feel by requests for information, we wanted to make ours as welcome or at least as tolerable as possible. Most farmers advised that, although their time was limited, they were not strongly predisposed to either participate in or refuse any survey that might come along. Rather, their decisions to respond would depend on how a given survey was presented, how relevant its content appeared. and how easily it could be completed. A notable guideline that was offered by one Fresno area iarmer and coniirmed by others was, “Don’t make me go to my file cabine!.” The survey requested information from farm operators about labor procurement, compensation, other personnel management practices, administration, legal compliance, and iuture outlook.

Within these broad areas the questionnaire contained specific items on employee recruitment channels, engagement offann labor contractors and custom harvesters, use of the public Employment Service, pre-employment screening and hiring procedures, pay basis and wage structure, fringe benefits, supervision and communications with workers, use of personnel management professionals, record keeping, compliance reporting, and contact with government agencies. The questionnaire is in Appendix 1. Content, wording, organization, and fonnat were refined over a two-month period. The draft instrument was reviewed in detail by two University of California Cooperative Extension fann advisors and pretested with three fanners who operate businesses quite different in size and other basic respects. The main objective of the pre-tests was to identify problems with meanings of tenns, clarity of questions and appropriateness of multiple choices. This phase yielded valuable guidance for refinements incorporated in the final version of the questionnaire. A shortened version, sent in the third mailing to two-thirds of non-respondents , is in Appendix 2.The California Employment Development Department provided identification and employment data on a specified study population1 from its file of employer unemployment insurance reports. The data record on each farm employer who paid wages during any quarter in 1991 included: name, mailing address, county code, and SIC code; wages paid in each quarter of 1991; and number of employees in each month of 1991. The population consisted of approximately 22,537 fann businesses. It excluded a total of 13,691 agricultural employers in the following categories during 1991: farm labor contractors; nurseries; veterinary services; other animal services; landscape and horticultural services; and grape growers in Fresno, Tulare, and Kern Counties. Each business in the VI file is typed by a 4-digit standard industrial classification code in the 01, 02, and 07 series . Of the 1991 monthly average number of job-holders in all agriculture , about 54 percent were employed by the target population of fann operators. Vse of the VI data base to identify California fann owners and operators suffers from two broad problems: incomplete coverage or entries , and the imprecise basis for employer groupmg. The imprecision problem stems from both the requirement that employers declare a single SIC when setting up accounts with EDD and the ambiguity built into the very structure of the SIC classification system. Because the SIC structure mixesclasses defined by commodity and by function ,rolling tables the full complement of employers cannot be sorted on either basis. The crops actually produced by even those farm businesses properly classified by a commodity code may be difficult to identify, if the farms are diversified operations or if the classes they fall into are broadly defined . Moreover, reliance on SIC codes to define bounds of the population could have caused errors of false inclusion or exclusion, most likely of businesses with both farm and nonfarm operations. Reports from farmers who also run retail outlets or catalogue sales, for example, may r may not be under a crop code. Some farmers who had been initially classified under nonfarm SICs were excluded from the population tape provided. On the other hand, some businesses that no longer operate farms but have UI records that are still tagged with commodity SICs, were included in the population.

The latter type of case is less problematic than the former, as questionnaire recipients who should not have been in the population could easily exclude themselves from consideration. But misclassified non-recipients who should have been but were not included in the population had no chance of getting selected to the sample. Even after screening businesses in the UI file by SIC code, we faced many questions about who should or should not be included.A first step toward minimizing invalid selections to the sample was to remove from the population file all entities which reported not a single employee or dollar of payroll in 1991, and to which, if still in business, questions on labor management were not likely to be relevant. Family-run farms that procure all their paid labor through service contracts might have thus, however, been eliminated in error. A second step was to give an explicit option on the instrument for recipients who do not Own or operate farms to select themselves out. Nevertheless, establishing a clear definition of “farm owner and operator” proved more important and difficult than anticipated.The survey was designed to include farm operators from all size groups, geographic regions, and commodities represented in the population. A less extensive survey that we conducted in 1987 had a 25 percent rate of response. The initial plan for this study was to obtain 1,000 responses by sampling approximately 5,000 farmers, conservatively assuming 20 percent participation. Ultimately we altered the strategy to pursue a like number of responses by eliciting higher participation from a smaller sample. Because the proportion of larger employers in the farmer population is much smaller than the shares of production and labor they manage in California, we stratified the population by size and oversampled from the larger-size strata. Sampling was random within each of the seven size strata, thus selecting farm businesses for the survey from all regions and commodity groups. The size measure used in stratifying the population was total wages paid in 1991, computed for each business in the UI file as the sum of its four quarterly wage figures. Businesses with both zero wages in every quarter and zero employment in every month were eliminated from consideration. The smallest 25 percent of the remaining population consisted of business with reported annual wages up to $9,617, the next 25 percent <those below the 50th percentile had wages up to $32,135, the next 25 percent up to $104,728, the next 15 percent up to $294,806, the next 5 percent up to $571,435, the next 4 percent up to $1,837,310, and the top 1 percent had wages exceeding $1,837,310. The total survey sample of 2,500 was drawn such that 1,375 were from the smallest three size groups combined , 375 05 percent from the next group , 625 from the next two groups combined , and 125 from the top-size group . Businesses selected to the sample that had incomplete addresses on file were replaced through random drawings from their respective size strata. Using employment or pavroll data from the UI file as indicators of farm business size may have led to mis-classification by size, because the amount and cost of labor procured through contract is not represented in these UI records.

Mechanical design and compliance have also been used to reduce the effects of variability and uncertainty

The large majority of robotic sensing applications involve proximal remote sensing, i.e., non-contact measurements – from distances that range from millimeters to a few tens of meters away from the target – of electromagnetic energy reflected, transmitted or emitted from plant or soil material; sonic energy reflected from plants; or chemical composition of volatile molecules in gases emitted from plants. Proximal remote sensing can be performed from unmanned ground vehicles or low-altitude flying unmanned aerial vehicles ; sensor networks can also be used . Current technology offers a plethora of sensors and methods that can be used to assess crop and environmental biophysical and biochemical properties, at increasing spatial and temporal resolutions. Imaging sensors that cover the visible, near-infrared , and shortwave infrared spectral regions are very common. A comprehensive review of non-proximal and proximal electromagnetic remote sensing for precision agriculture was given in . Proximal remote sensing technologies for crop production are reviewed in ; plant disease sensing is reviewed in detail in ; weed sensing is covered in , and pest/invertebrates sensing in . One type of sensing involves acquiring an image of the crop, removing background and non-crop pixels , and estimating the per-pixel biophysical variables of interest, or performing species classification for weeding applications. Estimation is commonly done through various types of regression . For example, during a training phase,grow vertical images of leaf samples from differently irrigated plants would be recorded, and appropriate spectral features or indices would be regressed against the known leaf water contents. The trained model would be evaluated and later used to estimate leaf water content from spectral images of the same crop.

Pixel-level plant species classification is done by extracting spectral features or appropriate spectral indices and training classifiers . In other cases, estimation of some properties – in particular those related to shape – is possible directly from images at appropriate spectra, using established image processing and computer vision techniques, or from 3D point clouds acquired by laser scanners or 3D cameras. Examples of such properties include the number of fruits in parts of a tree canopy , tree traits related to trunk and branch geometries and structure , phenotyping , shape-based weed detection and classification , and plant disease symptom identification from leaf and stem images in the visible range . Crop sensing is essential for plant phenotyping during breeding, and for precision farming applications in crop production. Next, the main challenges that are common to crop sensing tasks in different applications are presented, and potential contributions of robotic technologies are discussed.A major challenge is to estimate crop and environment properties – including plant detection and species classification – with accuracy and precision that are adequate for confident crop management actions. Wide variations in environmental conditions affect the quality of measurements taken in the field. For example, leaf spectral reflectance is affected by ambient light and relative angle of measurement. Additionally, the biological variability of plant responses to the environment can result in the same cause producing a wide range of measured responses on different plants. This makes it difficult to estimate consistently and reliably crop and biotic environment properties from sensor data. The responses are also often nonlinear and may change with time/plant growth stage. Finally, multiple causes/stresses can contribute toward a certain response , making it impossible for an ‘inverse’ model to map sensor data to a single stress source. Agricultural robots offer the possibility of automated data collection with a suite of complementary sensing modalities, concurrently, from large numbers of plants, at many different locations, under widely ranging environmental conditions.

Large amounts of such data can enhance our ability to calibrate regression models or train classification algorithms, in particular deep learning networks, which are increasingly being used in the agricultural domain and require large training data sets . Examples of this capability is the use of deep networks for flower and fruit detection in tree canopies, and the “See and Spray” system that uses deep learning to identify and kill weeds . Data from robots from different growers could be shared and aggregated too, although issues of data ownership and transmission over limited bandwidth need to be resolved. The creation of large, open-access benchmark data sets can accelerate progress in this area. Furthermore, sensors on robots can be calibrated regularly, something which is important for high-quality, reliable data. Other ways to reduce uncertainty is for robots to use complementary sensors to measure the same crop property of interest, and fuse measurements , or to measure from different viewpoints. For example, theoretical work shows that if a fruit can be detected in n independent images, the uncertainty in its position in the canopy decreases with n. Multiple sensing modalities can also help disambiguate between alternative interpretations of the data or discover multiple causes for them. New sensor technologies, such as Multi-spectral terrestrial laser scanning which measures target geometry and reflectance simultaneously at several wavelengths can also be utilized in the future by robots to assess crop health and structure simultaneously.Another major challenge is to sense all plant parts necessary for the application at hand, given limitations in crop visibility. Complicated plant structures with mutually visually occluding parts make it difficult to acquire enough data to reliably and accurately assess crop properties , recover 3D canopy structure for plant phenotyping or detect and count flowers and fruits for yield prediction and harvesting, respectively. This is compounded by our desire/need for high-throughput sensing which restricts the amount of time available to ‘scan’ plants with sensors moving to multiple viewpoints. Robot teams can be used to distribute the sensing load and provide multiple independent views of the crops. For example, fruit visibility for citrus trees has been reported to lie in the range between 40% and 70% depending on the tree and viewpoint , but rose to 91% when combining visible fruit from multiple perspective images .

A complementary approach is to utilize biology and horticultural practices such as tree training or leaf thinning, to simplify canopy structures and improve visibility. For example, when V-trellised apple trees were meticulously pruned and thinned to eliminate any occlusions for the remaining fruits, 100% visibility was achieved for a total of 193 apples in 54 images, and 78% at the tree bottom with an average of 92% was reported in . Another practical challenge relates to the large volume of data generated by sensors, and especially high-resolution imaging sensors. Fast and cheap storage of these data onboard their robotic carriers is challenging, as is wireless data transmission, when it is required. Application-specific data reduction can help ease this problem. The necessary compute power to process the data can also be very significant, especially if real-time sensor based operation is desired. It is often possible to collect field data in a first step, process the data off-line to create maps of the properties of interest , and apply appropriate inputs in a second step. However, inaccuracies in vehicle positioning during steps one and two, combined with increased fuel and other operation costs and limited operational time windows often necessitate an “on-the-go” approach,vertical grow systems where the robot measures crop properties and takes appropriate action on-line, in a single step. Examples include variable rate precision spraying, selective weeding, and fertilizer spreading. Again, teams of robots could be used to implement on-the go applications, where slower moving speeds are compensated by team size and operation over extended time windows.Interaction via mass delivery is performed primarily through deposition of chemical sprays and precision application of liquid or solid nutrients . Delivered energy can be radiative or mechanical, through actions such as impacting, shearing, cutting, pushing/pulling. In some cases the delivered energy results in removal of mass . Example applications include mechanical destruction of weeds, tree pruning, cane tying, flower/leaf/fruit removal for thinning or sampling, fruit and vegetable picking. Some applications involve delivery of both material and energy. Examples include blowing air to remove flowers for thinning, or bugs for pest management ; killing weeds with steam or sand blown in air streams or flame ; and robotic pollination, where a soft brush is used to apply pollen on flowers . Physical interaction with the crop environment includes tillage and soil sampling operations , and for some horticultural crops it may include using robotic actuation to carry plant or crop containers , manipulate canopy support structures or irrigation infrastructure . In general, applications that require physical contact/manipulation with sensitive plant components and tissue that must not be damaged have not advanced as much as applications that rely on mass or energy delivery without contact. The main reasons are that robotic manipulation which is already hard in other domains can be even harder in agricultural applications, because it must be performed fast and carefully, because living tissues can be easily damaged. Manipulation for fruit picking have received a lot of attention because of the economic importance of the operation .

Fruits can be picked by cutting their stems with a cutting device; pulling; rotation/twisting; or combined pulling and twisting. Clearly, the more complicated the detachment motion is, the more time-consuming it will be, but in many cases a higher picking efficiency can be achieved because of fruit damage reduction during detachment. Fruit damage from bruises, scratches, cuts, or punctures results in decreased quality and shelf life. Thus, fruit harvesting manipulators must avoid excessive forces or pressure, inappropriate stem separation or accidental contact with other objects .Contact-based crop manipulation systems typically involve one or more robot arms, each equipped with an end-effector. Fruit harvesting is the biggest application domain , although manipulation systems have been used for operations such as de-leafing , taking leaf samples , stomping weeds , and measuring stalk strength . Arms are often custom designed and fabricated to match the task; commercial, off-the-shelf robot arms are also used, especially when emphasis is given on prototyping. Various arm types have been used, including cartesian, SCARA, articulated, cylindrical, spherical and parallel/delta designs. Most reported applications use open-loop control to bring the end-effector to its target . That is, the position of the target is estimated in the robot frame using sensors and the actuator/arm moves to that position using position control. Closed-loop visual servoing has also been used to guide a weeding robot’s or fruit-picking robot’s end effector. End-effectors for fruit picking have received a lot of attention and all the main fruit detachment mechanisms have been tried .For example, properly-sized vacuum grippers can pick/suck fruits of various sizes without having to center exactly the end-effector in front of the targeted fruit . Also, a large variety of grippers for soft, irregular objects like fruits and vegetables have been developed using approaches that include from air , contact and rheological change . Once a fruit is picked, it must be transported to a bin. Two main approaches have been developed for fruit conveyance. One is applicable only to suction grippers and spherical fruits, and uses a vacuum tube connected to the end-effector to transport the picked fruit to the bin . In this case there is no delay because of conveyance, as the arm can move to the next fruit without waiting. However, the vacuum tube system must be carefully designed so that fruits don’t get bruised during transport. The other approach is to move the grasped fruit to some “home” location where it can be released to a conveyance system or directly to the bin. This increases transport time, which may hurt throughput. Clearly, there are several design and engineering challenges involved with this step.Combining high throughput with very high efficiency is a major challenge for physical interaction with crops in a selective, targeted manner; examples of such selective interactions are killing weeds or picking fruits or vegetables. For example, reported fruit picking efficiency in literature for single-arm robots harvesting apple or citrus trees ranges between 50% to 84%; pick cycle time ranges from 3 to 14.3s . However, one worker on an orchard platform can easily maintain a picking speed of approximately 1 apple per 1.5 seconds with efficiency greater than 95% . Hence, replacing ten pickers with one machine would require building a 10-40 faster robotic harvester that picks gently enough to harvest 95% of the fruit successfully, without damage, and do so at a reasonable cost!

The diverse effects discussed above have potentially very different time horizons

It is worth noting that the dose-response function estimated by Pope III et al. is non-linear and it has support in exposures of from 0.18 to 0.90 mg/day and then above 18 mg/day so we need to rely on the linear interpolation of the values up to 18mg/day. This caveat does not seem a strong weakness since the linearization is highly accurate in this neighborhood, with R-squared values of 0.99 , 0.96 , 0.86 , and 0.80 . The changes in exposure are associated with a decrease in relative risk of lung cancer from 4.0 to 3.1 for the female head, nearly a 25% reduction. The relative risk for the male head falls by 33%, while the reduction for female child is 25% and for the male child is almost 30%. The estimated reductions in the relative risk of ischemic heart disease, cardiovascular disease, and cardiopulmonary disease are between 3 to 4%. Consistent with the results in lung cancer, these changes are higher for adult males, but the differences at these levels of exposure are relatively small. As we have argued earlier, access to electricity should unleash a series of changes. In fact, some recent evidence suggests that electricity may increase female labor supply or improvements in educational outcomes, consumption, and income . However, not all studies find impacts beyond lighting . Besides these mixed results, there is almost no evidence on the mechanisms that drive the changes pointed above. Some changes, like improvements in indoor air quality, are expected to be present in most settings, but in most cases impact will depend heavily on household and context characteristics. This is because before electricity can impact income or expenditure, households need resources to invest in new tools and complementary inputs, knowledge on how to operate these technologies, and demand for the goods and services produced with this new method. Constraints in access to credit or inputs, insufficient demand, or lack of know-how can prevent these changes from being reflected in economic outcomes like labor supply,cannabis drying rack income or expenditure. In addition, the effects on human capital could be negative if electrification increases child labor.

A major challenge in the literature of the effects of electrification is the identification of causal effects because electrification potentially unleashes a number of changes through a complex chain of causality. The identification of causal effects is further complicated because the changes interact with each other, sometimes increasing the exacerbating the effects and others attenuating them. Since the electric grid cannot be expanded randomly, recent studies use time variation and instrumental variables , mainly geographic variables, to deal with the endogeneity of connection. Studies have used land gradient , distance to hydroelectric dams , distance to the electricity line , and distance to power generating plants and baseline electrification rate in the locality . The first-stage relationship in this type of studies is usually clear: since land gradient affects the cost of grid expansion, it is correlated with the probability of grid connection. The exclusion restriction is usually more difficult to justify. Land gradient, for instance, plausibly affects the cost of building and maintaining other types of infrastructure, like roads, schools or hospitals, thus potentially affecting transportation costs and access to markets as well as education and health outcomes. Land gradient also may affect the crop varieties that can be grown in a region , thus influencing directly economic activity and income flows.2 Thus, the exclusion restriction requires the observed variation in land gradient to be in a range that does not affect other types of infrastructure, crops, or other economic activities; or, alternatively, it requires the variation in said variables generated by variation in land gradient to have no effect on the outcomes of interest. This may perhaps be not too far from reality in some settings, but it is ultimately an assumption that cannot be directly confronted with the data. Randomized Encouragement Designs offer an appealing alternative. This approach, originated by Imbens and Angrist , consists in randomly allocating incentives to connect to the grid, and using them as instruments in an IV estimation. It has been used extensively in other contexts , but Bernard and Torero is the first study to implement a RED approach to study electrification in developing countries. We implemented an approach similar to theirs. In our study setting, households were required to pay a $100 fee for a security inspection in order to get an electric connection.

We randomly allocated discount vouchers for 20% and 50% off the inspection fee, thus generating exogenousvariation in the connection cost. The problem with the RED approach is empirical: the complier sub-population is small , which is associated with low predictive power in the first stage, which in turn results in noisy IV estimates. To avoid relying on noisy estimates, we report are the “reduced form” effect of voucher allocation on the outcome of interest, also known as intent to treat estimates. ITT estimates are of inherent interest, since they correspond to what would be observed if our experiment were to be replicated. We rely on the “first stage” relationship between voucher allocation and grid connection to argue that vouchers influenced outcomes through their effect on electrification . Having investigated in chapter 2 the effect of electrification on indoor air quality and the implied health effects, we turn to investigate other mechanisms through which electrification can unleash the changes: time use, and electronic appliance ownership, and we show that through these channels, electricity can unleash effects on human capital and income. Next, we study the changes in time use by household members, uncovering increases on investment in education among children and increased non-farm work among adults. First, school-age children from voucher recipient households increase time studying at home, both in the intensive and extensive margin. Since this time is being spent in an environment with lower pollutant concentration, in a smokeless, better illuminated room, each minute of studying is more productive as well. Accordingly, we find evidence of improvement in a math skills index among voucher recipients. As a placebo test we show that there are no effects on years of schooling. We do not find systematic changes in time allocation among adult females, but adult males adjust their work activities, reducing time in independent farm work and increasing time in other work. Similarly, the probability of having engaged in non-agricultural independent work in the four weeks leading to the survey increased by 13 percentage points among voucher recipients. This change seems to be concentrated among 30-40 years old, although the standard errors are too wide to arrive to conclusions.

These change towards independent non-farm labor activities is arguably responsible for income gains: annual per capita income increased by $186 among voucher recipients . These income changes had some distributional effects, with voucher recipients being 10 percentage points less likely to have income below the median, but not more likely to reach the highest income quartile. Finally, we examine changes in appliance ownership. We find increases in ownership of television sets, stereos, refrigerators and blenders. Access to refrigeration haspotentially important effects on food storage, food safety, and nutrition. If materialized, this would reinforce the health gains from reduced indoor air pollution. The time savings generated by blenders and other kitchen appliances could have further welfare effects. Television and stereos primarily improve leisure, but television has also been shown to increase access to information, political participation,grow trays 4×4 and even contraceptive awareness. The remainder of this document is organized as follows. Section 2 describes the conceptual framework that guides our study. Section 3 presents the study setting and discusses the data. Section 4 presents the econometric approach. We discuss the main results in section 5, and section 6 concludes. Down the chain of causality, electricity may impact income. However, the channels through which this would happen are unclear. It may be that electrification opens the door to new, more profitable activities, but it is also possible that agents work longer hours in the same activities to afford new appliances. Access to electricity affects the marginal value of labor and leisure among adults. As we argued in the previous section, electricity facilitates access to television, increasing the marginal value of leisure. Banerjee and Duflo point out the critical importance of leisure time among in poor households’ utility, and the role that television plays in it. Television has other benefits, since it also increases awareness about news, politics, birth control, aspirations, etc. An increase in the marginal value of leisure should pull down labor supply. On the other hand, these new appliances need to be paid for. TV sets, refrigerators, and other appliances are not free to buy or to operate. Banerjee and Duflo argue that poor households leave profitable opportunities untouched because the extra income would not make a salient impact in their lives. In the face of electrification, household members may decide to work more in order to afford new appliances. However, electricity can also increase the marginal productivity of labor. Independent workers may now use power tools, farmers may use water pumps, shopkeepers may offer refrigerated goods. But all these changes require access to the tools, pumps, and refrigerators, which require the use of savings, access to credit or other sources of income. At baseline 17% of households had access to credit , but 70% reportedly believed to have access. By round 4, 87% believed to have access to credit, but only 20% had take up some.

This increase is far from sufficient to explain the increase in asset ownership we find below. If they can’t be paid for with credit, households would need transfers from family or to work more to increase income. The literature suggests that, after lighting, the first appliance bought by households are television sets . As discussed above, television increases the marginal value of leisure but also access to information. Refrigerators save time by reducing the frequency of trips to the market, but they also increase the variety of foods that can be consumed by the household and provide better food storage. Thus, access to refrigeration is another channel through which electrification can impact health. Appliances like blenders and microwaves probably have little impact on the final product, but can save time in household chores. For a given set of household chores, this is equivalent to relaxing the time constraint, which is unequivocally welfare increasing. The additional time may be allocated to leisure or work, for instance sewing, or cooking for sale. It may also replace the male in some farm tasks, while the male works in other things. Additional work, at least in principle, should lead to higher income which should lead to higher welfare. Electricity also facilitates access to cellphones, since they can be now charged at home, more cheaply. Cellphones have been shown to increase access to information and generate income gains . Other devices, like DVD players or stereos are primarily to increase the marginal utility of leisure, which can also enhance welfare.The improvements in indoor air pollution studied in chapter 1, for instance, are almost immediate, given that PM2.5 concentration will decrease a few minutes after putting out a kerosene lamp. In addition, the effect should be permanent. As long as other PM2.5-generating activities are not altered by electrification and households do not revert to traditional lighting, overnight PM2.5 concentration will remain at the new low level. Both conditions seem to hold in this case. In chapter 1 we saw that cooking practices – the main source of PM2.5 – are unaffected by electrification. Reversion to traditional lighting seems highly unlikely, since at least in the first four years of our study we don’t see households dropping their electric connections. Other effects take time, for a variety of reasons. Before an individual starts new income generating activities some conditions must be met. First, they need to realize there is a new opportunity, which is not always obvious. They also need to overcome any potential liquidity or credit constraint in order to gain access to human and physical capital, secure access to a steady stream of inputs, and find a demand that is not only steady, but also large enough to make the new enterprise profitable, net of all costs and risks.

RNA-seq approaches could ultimately lead to the identification of candidate genes

Uncovering phenotypic traits and the molecular basis for PUE are important early steps in breeding for phosphate use efficiency. No commercial breeding programs exist for watercress worldwide, but germplasm collections are emerging. Development of new watercress varieties is also no doubt limited by the lack of genomic information for this crop. Payne screened a germplasm collection under indoor and outdoor conditions and identified significant variation in several traits including stem length, stem diameter and antioxidant concentrations. Voutsina used RNA-seq to analyse the first watercress transcriptome and identified differences in antioxidant capacity and glucosinolate biosynthesis across a germplasm collection of watercress, with 71% of the watercress transcripts annotated based on orthology to Arabidopsis. Jeon et al. independently used RNA-seq approaches to assemble the watercress transcriptome de novo. They identified 33 candidate genes related to glucosinolate biosynthetic pathways using Arabidopsis glucosinolate genes to search for homologous sequences in the watercress transcriptome. Additionally, a watercress mapping population comprising 259 F2 individuals was established by Voutsina using parents with contrasting nutrient and growth phenotypes. Genotype-by-sequencing of this mapping population enabled the construction of first genetic linkage map for watercress and identified 17 QTL for morphological traits of interest, antioxidant capacity and cytotoxicity against human cancer cells. However, no root traits were assessed in this work. Screening this mapping population and wider germplasm for root traits may reveal individuals with extreme PUE phenotypes,indoor farming equipment which could allow the development of markers and QTL associated with this complex trait.Together these findings will assist in developing commercial cultivars with a reduced need for phosphate, and a reduced negative environmental impact.

To assess whether homologs of the candidate PUE genes identified in this study exist in watercress, available watercress transcriptome data was mined for 13 key PUE genes selected from Table 2. This included genes whose expression was induced more than tenfold in at least 2 independent studies , plus PHR1, the global regulator of P starvation responses. Annotated transcripts were obtained from transcriptomic studies of watercress by Voutsina et al., 2016; Jeon et al., 2017; Müller et al., 2021 and matches for these candidate genes were assessed by searching for genes using AGI [47, 133, 221, 222]. Across all studies, strong matches were found for PHT1;4, SPX1, PHR1, MDG3, PEPC1, PLDζ1/2, PSR2 and SQD2, with all corresponding e-values ranging from 0 to 3.00E-32. Additionally, Müller et al. identified homologous transcripts for MDG2. Voutsina et al. had transcripts corresponding to PHT1;3, and MDG2, and Jeon et al. had hits for PHT1;2 and PHT1;3. No matching transcripts were found for PHT1;1 in any of the three studies. Where FDR values were < 0.05, changes to expression patterns were noted. Interestingly, Müller et al. also observed varying levels of upregulation of PHT1;4 and PHR1 following submergence, which may suggest a link between phosphate starvation and submergence responses.A number of pyrethroids, such as permethrin, cypermethrin, deltamethrin, and esfenvalerate, have been reported to be present in house dust with detection frequency ranges from various studies of 45–100%, 5–64%, 5–17% and 5–29%, respectively . Much of this data was collected before or in the same year as the federally mandated phase-out of residential uses of the organophosphate pesticides chlorpyrifos and diazinon in 2001, which subsequently caused household pyrethroid use to increase . This can be seen in the above mentioned studies, with the highest %Ds of pyrethroids occurring in studies whose samples were collected during or after 2001. Although pyrethroids have low toxicity, particularly compared to other insecticides, studies have shown that high levels of exposure to pyrethroids may cause significant toxicity and health effects, including acute neurotoxic effects , immunotoxic effects and negative effects on mammalian reproduction .

Pyrethroids are also possible human carcinogens . Families living in close proximity to farms may have higher than average pyrethroid exposure due to household pesticide use, drift from agricultural application and take-home exposure pathways from occupational use by another family member . High levels of pesticides in carpet dust are a particular concern for young children who, due to their continual exploration of their environments, spend a large amount of time on the floor and have increased hand to mouth activity, resulting in increased exposure to pollutants through dermal and non-dietary ingestion routes . These two factors combined make children living in agricultural communities especially susceptible to pesticide exposure . Data on pyrethroid concentrations in the house dust of rural farm worker homes is limited. This study was conducted in order to address participant concerns about pesticide exposure in the community-based Mexican Immigration to California: Agricultural Safety and Acculturation study. Our objectives were to characterize the levels of pyrethroid pesticides in the house dust of farm worker families and characterize their residential pesticide application practices in order to evaluate possible associations between the dust levels and pesticide use practices. We report the pesticide use data and levels of pyrethroid pesticides in indoor dust collected in 2009 as measured by questionnaires and dust concentrations of the pyrethroids cis– and trans-permethrin, cypermethrin, deltamethrin, esfenvalerate and resmethrin among 55 households of farm worker families living in Mendota, CA. Single dust samples were collected from 105 homes of families participating in the MICASA study. Of the 105 available samples, 70 had sufficient quantities of dust after sieving for instrumental analysis of pyrethroids. Of those, there were 55 samples selected, with relatively higher selection probabilities assigned to those households with elevated levels of the common pyrethroid urinary metabolite 3-phenoxybenzoic acid in urine samples collected from the children in order to increase the probability of having detectable levels of pyrethroids in the dust. Data on the 55 dust samples that were analyzed are presented here. Data on urine concentrations will be reported in a future publication.

The MICASA study is a prospective cohort sample of 467 hired farm worker family households from Mendota, CA, designed to evaluate occupational and environmental exposures of significance for a farm worker population. Households were sampled from randomly selected census blocks and, following door-to-door enumeration, those households containing at least one hired farm worker were contacted for recruitment. Eligible participants in the MICASA study were men and women, residing in Mendota, CA, ages 18–55 years, self-identified as Mexican or Central American, and with at least one household member who worked in agriculture 45 days or more in the previous year, with both members of the household completing the interview . MICASA recruitment was conducted between January 2006 and May 2007. Recruitment for the home pyrethroid exposure study began in February of 2009, and sample collection took place between June and December of 2009. The analysis highlighted in this paper was designed to look at levels of pyrethroid pesticides in the homes of the MICASA study population. Because children typically have higher levels of exposure to pesticides , we restricted eligibility to those MICASA families with at least one child aged 7 or under at the time of recruitment in order to better understand pyrethroid sources in this potentially highly exposed population. Among the MICASA households completing baseline interviews, 175 were eligible for participation in the home pyrethroid exposure study. Eligible households were listed in random order for contact. One hundred twenty seven households were contacted for recruitment before reaching our goal of 105 households who agreed to participate and were enrolled in the study. The remaining 22 households either could not be contacted or declined to participate. If a family had multiple eligible children, one child was randomly selected and enrolled. At the time of sample collection, children ranged from 2 to 8 years of age. Written informed consent was obtained from each participant. Each study component was described verbally and in writing to the participant prior to obtaining written informed consent. Spanish was the primary language of the participants,farm shelving thus the study description and written informed consent were provided in Spanish. All study procedures were approved by the University of California, Davis, Institutional Review Board. Dust samples were collected and questionnaires were conducted between June and December of 2009. Dust samples were collected in the main living area of the home, which was defined as the most frequently used room in the house that was not a bedroom or kitchen. Dust samples were collected using a Eureka Mighty-Mite vacuum cleaner and standard crevice tool attachment modified to collect dust into a 19 × 90 mm cellulose extraction thimble that was secured to the crevice tool using a rubber O-ring. More detailed information on collection methods using the Eureka Mighty Mite have been described elsewhere . The square footage of the main living area was measured and recorded as well as the temperature and humidity. Dust was collected over the equivalent of the entire measured floor area.

Once sampling was complete, the thimble was removed from the Mighty Mite, wrapped in cleaned foil, weighed, placed in a polyethylene zip-top bag and labeled with the household ID number. Dust samples were then refrigerated at the MICASA field office for generally less than one day and delivered on ice to UC Davis, where they were stored in a −20 °C freezer until sample extraction and analysis. All Mighty-Mite equipment was cleaned using a 1% solution of detergent and hot water and allowed to air-dry between home visits in order to prevent cross-contamination. At the time of sample collection, a questionnaire was administered to the mothers. We obtained the frequency of pesticide use in both the hot and cold season of the previous year, including sprays, foggers, sticky traps, bait traps, gels, and any application by professional exterminators. Participants were asked if anyone living in the home had seen rodents, rodent feces, live or dead roaches, roach feces or ants inside the home at any time in the last year, with answer options including: large amounts, moderate amounts, none or don’t know. On the day of dust collection a staff member conducted a pesticide inventory in which detailed information on all pesticide products in the home was recorded, this included the name of each product, the size of the product container, the EPA registration number and all active ingredients. Summary statistics for the pyrethroid data were calculated. For concentrations below the limit of detection , an imputed value was assigned equal to the LOD divided by the square root of 2 . A Spearman rank-order correlation procedure was used to determine the intra-household correlations between particular pyrethroid concentrations, with significance set at p < 0.05. A Spearman rank-order correlation procedure and 95% confidence intervals were used to evaluate associations between interview questionnaire variables and the presence of pyrethroid pesticides in the house dust, with significance set at p < 0.05. As part of the main MICASA study questions on pesticide use were asked of the full cohort of 436 households in both an interview conducted from January 2006 to May 2007 and an interview conducted from February 2009 to June 2010. These questions were asked of both the male and female heads of household. Responses to these questions allowed us to look at the consistency of reporting pesticide use among family members as well as the consistency of reporting pesticide use over time. In both interviews the male and female heads of household were asked separately if either they or anyone in the household uses indoor and/or outdoor pesticide sprays. The consistency of responses to these pesticide use questions between the men and women from the same household was assessed using Cohen’s kappa, a measure of chance-corrected agreement . Temporal comparisons from the same participant between the two interviews conducted approximately 3 years apart were also made using Cohen’s kappa. All statistical analyses were performed using SAS version 9.2 . We assessed the levels of pyrethroid pesticides in 55 homes in a farm worker population by laboratory measurements of permethrin, cypermethrin, resmethrin, esfenvalerate and deltamethrin in house dust samples and by questionnaire data. This population had a relatively low educational level, with less than half of the participants reporting a 6th grade education or higher, in contrast to the 85% of U.S. adults who have a high school diploma . Detectable levels of the common pyrethroids permethrin, cypermethrin, deltamethrin, esfenvalerate and resmethrin were found in the dust samples collected in this study.

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