Households widely used livestock mobility to adapt to the effects of climate change

Posted on October 26, 2023 by hemp2022

In most cases, older people in rural villages persistently rely on practising their own old experiences, which might not fit the varying nature of climatic changes. Realizing that they could not actively manage to boost their income sources, they rather transfer their wealth to their children in the form of gift or bequest. Even if they have long-term prospects to re-invest their financial holdings, older people commonly rely on supports from young family members . In the Afar drylands areas where households are sensitized to the effects of drought occurrences, access to water harvesting actions could have important implications on their income improvement. In this study, access to water sources was found to have a positive and statistically significant effect on household income . The positive result suggests that most agro-pastoralists and mixed-farming communities sought to cope with several drought events by engaging in various small scale irrigation schemes such as flood diversion from the Tigray highlands, which had positively contributed to their income. Most mixed-farming communities and agropastoralists that relied on perennial water sources produce vegetables and crops whereas semipastoralists and pastoralists that live distantly from perennial water sources depend on ponds that might be used only for few months. Moreover, community members who are near to perennial water sources are able to enhance sedentary way of life due to the opportunity it offers them for better access to animal feed and improved income. In tackling drought related challenges owing to climate change, farm communities who are aware of the importance of water sources for the improvement of their income sources have engaged in water harvesting activities. Particularly, the Afar pastorals, semi-pastorals and agro-pastorals moved to potential areas where they could find natural grazing and water sources for their livestock. As shown in Table 6,ebb and flow rolling benches the estimation results of the fixed effects model indicate that migration is positively and significantly related to the household income.

The results further reveal that households who keep moving their cattle herds to better pasture had achieved higher income than those who never moved. In congruence with this finding, Moritz et al. indicated that livestock mobility is the innovative means of sustaining rural livelihoods by which pastoralists are able to fully utilize untapped rangeland resources in distant areas. Household heads further explained their past experiences that livestock mobility has been used to escape away during a disease breakout in a specific area. In the Afar culture, people widely share information and new events by way of traditional communication called Dagu . With the help of getting information via Dagu, pastorals and agro-pastorals used to move their livestock to safer areas. Overall, migration for the Afar pastoralists serves as a means to search livestock feed and water, as a strategy to rescue their livestock from unexpected events, as a channel to reach new market opportunities and as a pathway to build social capital with newly contacted people in their destination areas . More importantly, pastoral mobility serves a source of income in areas where crop cultivation has not yet been applied. Recently, reported research findings indicated that pastoralists in west and east African countries have continued to respond to climate-related challenges by moving their livestock to better areas . In contrary, other research reports suggest that the pastoral mode of life is an outdated system, which is currently in crisis owing to ‘too many people and few livestock’, which has created imbalances among humans, livestock and the environment . Besides cattle, livestock diversification that includes small ruminants is found to have a significant relationship with income . The pursuance of households on diversified livestock strategies might allow them to build locally fitting adaptive capacity, which would enable them to address problems related to climatic risks and uncertainties. In the study area, households that diversify their livestock had easy access to liquid money via sales of livestock products and live animals. Likewise, Degefa reported that people that pursue diversified income sources through production of improved livestock varieties such as cattle, camel, goats and sheep are more likely to achieve sustainable livelihoods. The implication is that diversified income may serve as a means to heighten the purchasing power of pastoralists and agro-pastoralists, by which they can easily access staple foods and veterinary services .

It was also found that getting access to animal feed through production of hay and straw had a positive and statistically significant effect on income . The positive effect of animal feed on the mean income shows that households would like to gather hay and straw to reduce unexpected losses of animals due to risks associated to lack of fodder. On one side, households that collected animal feed using own family labour were able to save some portion of money that might have spent for hiring labour. This suggests that households having easy access to collect hay and straw are more likely to earn a higher income. Therefore, communities in the Afar region put their efforts into collecting hay and straw to feed their animals. Particularly, feed collection in Aba’ala is usually practised by mixed-farming and agro-pastoral community members during THE wet season. Informants further expressed that livestock owners who collected excess hay and straw can earn extra income by selling some of it during dry season.The exposome comprises the totality of environmental exposures experienced from conception onwards during a human life and has been associated with human health outcomes. Both allergens and microbes are often associated with inhalable airborne particles; these particles have a substantial impact on human immune response and health outcomes. The indoor air microbiome, or aerobiome, represents an exchange nexus between a number of different sources of bacteria, fungi, and viruses, including humans , pets, and outside air. In the developed world, the average person spends upwards of 87% of their time indoors, including one third of their lives sleeping, during which they inhale significant quantities of indoor air. The human microbiome can dramatically shape the indoor environment through the dispersal of skin and respiratory-associated microbes, with approximately 37 million bacterial genomic units and 7million fungal genomic units released from the average person per hour. Allergens are defined as antigens, including microbial cells and metabolic products, that can lead to a type I immune reaction in people with atopy , mainly through the immunoglobulin E response pathway. Indoor air quality and antigenic burden are of special concern for human health, with indoor mold exposure shown to correlate with allergic diseases. Conversely, children who are exposed to a greater degree of dust associated microbial diversity often have lower rates of asthma.

Household aerosols can modulate immune response in a protective manner depending on the constituents.Indoor and outdoor air have a large overlap in bacterial composition, with indoor air closely resembling outdoor air. Outdoor air has been shown to be a significant contributor to the indoor aerobiome, with 50% or more of the community composition attributable to outdoor sources However, there is a significant enrichment of human-associated bacteria in indoors relative to outdoor air, and this can vary based on building design. Outdoor air is often significantly more microbiologically diverse than indoor air. Sources of bacteria associated with airborne dust indoors can have originated from soil and plant leaf surfaces, and the types and sources can vary by season due to changing ecological conditions, as well as by geographic location. Seasonal variation in the microbial constituents of outdoor air has been demonstrated in Chicago , where a large proportion of the summer aerobiome comprises soil- and leaf-associated bacteria. How this outdoor air variability influences indoor air is highly dependent upon building design. At the same time, the indoor aerobiome is often less diverse than the outdoors and can maintain a greater proportion of bacteria closely related to known pathogens. In outdoor air, these bacteria are often significantly less abundant or below the level of detection. Therefore, indoor airborne allergen exposure comprises both endogenous and exogenous sources. Prior studies have examined the dust and constituent particles in the air of many built environments , which have been documented in recent publications from the NHANES 2005– 2006 program. Airborne allergen quantification and characterization have, in the past,rolling benches hydro been more technically challenging than collection of settled dust with a vacuum cleaner. Settled dust is not necessarily representative of inhaled air, and here, we leveraged the Inspirotec electrokinetic air sampling device that allows for the collection of airborne allergens. This device is sufficiently simple to operate that samples can be collected by the patients themselves, in their own homes. In this study, we deployed the Inspirotec sampling device in 65 Chicago area homes, which were occupied by patients with clinically diagnosed allergy and asthma, as part of a larger study incorporating measurement of common household allergen profiles. The microbiota and airborne allergens were analyzed from the same samples, and differences in allergen and aerobiome profiles between bedrooms were assessed along with survey data from participants, providing a Microbiome Wide Association Study between the different environments.In this study, we characterize the various factors that influence the diversity and composition of the aerobiome of bedrooms in the Chicago area. A small number of prevalent bacteria form a “core microbiome,” suggesting either a common source of these bacteria or common factors that promote a similar community. If there is a significant outdoor component, the core microbiome would represent a group of ubiquitously distributed organisms. By contrast, if homes independently assemble, the presence of common core organisms could represent similar conditions across homes selecting for similar communities. There is some evidence that indoor air communities assemble in a manner independent of the specific occupants of the respective built environment, suggesting a large outdoor component. Further, it is unclear if these core organisms represent a set of unique strain-level organisms or an ensemble of closely related organisms. As our sequences are clustered into operational taxonomic units at 97% nucleotide identity, our data is not sufficient to distinguish between closely related organisms. Further, this may not be improved by sub-OTU methods, as 16S data is often not capable of distinguishing strain-level organisms. Finally, geographic proximity is significantly positively correlated with bacterial community similarity, which supports an outdoor contribution, as homes that are close together would have more similar input from outdoor air.

Despite the large number of allergens sampled, few appear to have a relationship to the aerobiome, which may be due to insufficient sample size or lack of microbial association. This may be addressed by a more focused study with a large sample size for many of the allergens. At the same time, a number of allergens appear to have links to changes in community composition. Correlation of dog ownership with diversity and changes in the composition of the microbial community is well known, and thus, the observation that both dog allergen and dog ownership appear to have this effect is unsurprising. The difference between microbial communities that associate with dog ownership and dog allergen load may be explained by the fact that these are not identical populations. At one end of the distribution, homes with dog ownership and no dog allergen detected could indicate a tight home where dogs are excluded from the bedroom and the air supply to the bedroom is well controlled. At the other end, homes with dog allergen and no dog ownership could represent a sub-population in which dog allergen is introduced by some outside traffic entering homes from unknown external sources, but not with an attendant associated microbial community. This study demonstrates that dog allergen load as well as geographical location can influence the aerobiome captured in homes and that distinct microbial sub-communities arise in relation to these factors. Interestingly, this relationship was not observed for cats, which could be because cats are commonly indoor only pets and likely would not contribute to the dispersal of exogenous microbiota into residential homes. Mold allergens also had a significant correlation with bacterial diversity. Their presence may not represent a causal relationship, but may be the result of a possible unidentified common causal factor. Alternaria is a genus of saprophytic fungi found in soil and decaying plant matter and is a common allergen in humid regions. It has also been found to be a constituent of indoor air, especially in households with indoor plants.

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There was one fungus that stood out as an indicator of the influence of human occupancy

Posted on October 25, 2023 by hemp2022

The settling velocities of larger particles would exceed the upward speed of air entering the filter cups. Two vacuumed samples of floor dust from the chamber carpet were collected at the beginning and end of the study period, December 2013. The Dustream Collector was used to isolate the vacuumed material in the head of the sampling wand, and the vacuum cleaner was run for a 1-min duration while moving across the full extent of the chamber floor. Total particle number concentrations were measured at a frequency of once per minute with the Met One GT 526 optical particle counters , which measures particle number concentrations in six bins according to optical diameter: 0.3–0.5, 0.5– 0.7, 0.7–1.0, 1.0–2.0, 2.0–5, and >5 μm, respectively.DNA extraction protocols followed those used previously for indoor bioaerosols and are detailed in S1 File. Starting material was half of the filter from the filter cup or 200 mg of unprocessed dust from the floor dust sample. To determine the composition of the microbial communities, we used the approach of sequencing a universal “barcode,” i.e., a region of DNA targeted to a specific group of organisms that can be used for identification in samples containing a mixture of many taxa. Specifically, we targeted the V4-V5 region of the bacterial 16S rRNA gene and the ITS1 region of the fungal rRNA gene. Bacterial primers were those adopted for the Earth Microbiome Project while fungal primers were those recently described by Smith and Peay . Samples were split across two Illumina MiSeq lanes for 250 base pair paired-end sequencing at the Stanford Functional Genomics Facility. The raw sequence data were deposited into NCBI’s Sequence Read Archive under study accession SRP049464.For fungi both the forward and reverse reads for each sequence could be paired before downstream analysis. For bacteria the quality of the R2 sequencing reads was low; consequently we only proceeded with the R1 reads. The general processing approach involved quality filtering, pairing reads ,dutch bucket for tomatoes clustering reads into operational taxonomic units at 97% similarity, checking for chimeric sequences, and identifying taxonomy against a reference database.

To implement these steps, we utilized cutadapt, Trimmomatic, UPARSE scripts, homerTools, and the UNITE and Greengenes  databases for fungi and bacteria, respectively. Specific program settings are detailed in S1 File.We adopted several OTU quality filtering and bench marking steps. First, we removed the OTUs that were unclassified after taxonomic identification. Second, we analyzed a fungal mock community of 18 fungal taxa whose abundance of extracted DNA was skewed to mimic a natural community and then pooled. Examining the sequences of the two mock community samples, we found that only taxa with 10 or more sequences should be included in further analyses. That is, the mock community was recovered only when OTUs with greater than 10 reads were considered; otherwise, the mock community had much higher richness than initially pooled. The specific threshold value is likely to be run-specific, as a similar approach using a mock community in another study informed a lower threshold value of 3 reads. As a comparison, other studies have taken the approach of excluding sequences that do not surpass a certain percentage of all reads . Third, we processed negative extraction controls with our samples. Even though visualization of the amplicons on an agarose gel showed no amplification, sequencing yielded reads in these negative samples. We subtracted the number of sequence reads in the negative samples from the environmental samples. We note that doing analysis without these quality-filtering steps produced qualitatively similar results as those reported here. After bio-informatics processing, 3.6 million and 4.2 million fungal and bacterial sequencing reads, respectively, were retained for analysis. Analyses were executed in R, utilizing the vegan and labdsv packages as needed. Phylogenetic analysis of the bacteria samples was conducted in QIIME. To ensure even representation of sequences per samples, samples were rarified to 5,000 sequences per sample for both fungi and bacteria.Initial exploration of the results suggested a higher than expected contribution of the ventilation supply air to the indoor bioaerosol composition. To assess whether the ventilation system itself might be contributing, we conducted two additional experiments in June, 2014, one with 2 people walking and one with the chamber unoccupied .

In addition to two samples of vacuumed floor dust collected at the start and the end of the day and the outdoor and indoor air samples, we included a third air sampler deploying an analytical filter cup within the sub-floor plenum, from which the supply air enters the chamber. These eight samples were processed as detailed above.Looking broadly at the composition of the identified microbes in aerosol particles, many of the common fungal taxa were familiar from culture and microscopy-based work, including species of Cladosporium, Aureobasidium, Phoma, Alternaria, Rhodotorula, and Penicillium. Other abundant taxa included yeasts , plant pathogens , and wood rot fungi . The dominant bacterial phylum was Proteobacteria, followed by Firmicutes and Actinobacteria. In addition to the common inhabitants of human skin, gut, and oral cavities such as Actinomycetales, Lactobacillales , and Enterobacteriales, we also observed high abundance of the outdoor-associated taxa such as Burkholderiales, Pseudomonadales, Flavobacteriales, and Streptophyta . Like other environmental surveys, most taxa appeared sporadically: over 50% of the taxa were present in only one sampling period, and over 80% of the taxa appeared in less than 10% of the samples. Considering both frequency and abundance, there was large overlap in microbial taxa between indoor and outdoor air samples. Table 1 shows the most frequently encountered taxa in the chamber air , and their corresponding frequency in outdoor samples. There were no frequently observed taxa in indoor air that were entirely absent from outdoor air. Two of the 40 taxa in this table had a greater than 2× frequency of occurrence in indoor air compared to outdoor air: the fungus Sordaria sp. and the bacterium Streptococcus sp. Fig 1 shows the abundant taxa in chamber air, outdoor air, and floor dust. Only five of the 15 fungal taxa in this set were shared across the abundant indoor and outdoor air samples, whereas 12 bacterial taxa were shared. The most abundant fungal taxon indoors was Battarea steveni, a puffball that is discussed later in the context of human-mediated transport. To determine which measured factors predict indoor microbial composition we applied an analysis of variance statistical model based on distance matrices. As shown using the Canberra community distance, fungal and bacterial communities shared similar patterns with some notable differences .

Indoor and outdoor air samples from the main experiments were more similar to each other and distinct from samples collected during the secondary experiments, which included supply-air sampling. Within the main study period, the indoor and outdoor fungal and bacterial aerosols were significantly different from each other, both when the carpet was exposed and when it was covered , although the percentage of the variation in composition explained by location was marginal . Bacterial community relationships across samples based on UniFrac, a distance index that considers phylogenetic relationships,blueberry grow pot yielded similar results to those based on taxonomic relationships . Due to the highly variable nature of the ITS marker, phylogenetic analysis was not applied to the fungal communities.We applied a statistical model to determine which measured factors predict indoor microbial composition. The sampling date and occupancy level were statistically significant predictors, explaining approximately 36% and 13% of the variation in composition, respectively . The influence of sampling date and occupancy level can be interpreted as follows: bioaerosols tend to be more similar in composition if they were collected on the same day or during the same occupancy level. After explaining variation by date and occupancy level, the effect of flooring and time of day were not significant predictors. Overall, 50% of the variation in microbial composition was unexplained by the measured variables. Higher occupancy periods were associated with ~ 2× greater taxon richness for both fungi and bacteria, and, as would be expected, this trend was unmatched in the outdoor samples . This increase in richness did not appear to be due to the addition of human-associated taxa, as those taxa did not dominate the occupied periods when looking at the entire microbial community. Considering sequence read abundance , the sum of five human skin taxa—Propionibacterineae, Staphylococcus, Enterobacteriaceae, Corynebacterineae, and Streptomycetaceae—comprise 4.3% of the indoor air sequences and 3.7% of the outdoor air samples, indicating only a modest enrichment of these species as contributors to indoor air microbial composition. Considering the frequency of taxon occurrence , there were 26 fungal taxa found in at least five of the six 8-person experiments, and only two showed increased frequency with increasing occupancy.

Rhodotorula mucilaginosa is a likely human commensal, and Aureobasidium pullulans was abundant in the floor dust samples. Likewise, of the 134 bacterial taxa found in most of the 8 person experiments, eight showed increased frequency with increased occupancy: Streptophyta, Solibacterales, Corynebacterium, Arcobacter cryaerophilus, Actinomyces, Chroococcidiopsi, Oxalobacteraceae, and Chlorophyta. Given that most of the detected bacterial sequences are environmental rather than skin-associated, this evidence suggests that resuspension of outdoor-derived microbes from indoor surfaces and/or from occupants’ clothing was a stronger source than direct shedding from human bodies. We explored patterns of ecological distance between indoor and outdoor pairs; however, few patterns emerged. The absolute values of the distances between indoor and outdoor pairs were significantly less for bacteria than fungi . Contrary to expectations, indoor air was not observed to be more compositionally similar to outdoor air in low occupancy periods than during higher occupancy periods . Moreover, high occupancy periods were not found to be more compositionally similar to each other than low occupancy periods were to each other .The most abundant fungus detected in the aggregate indoor air samples was Battarrea stevenii. This puffball appeared in high read abundance in two 8-person experiments and was not found in the paired outdoor air samples. The likely explanation is that a member of our research group acted as an inadvertent vector for the transport of these spores into the chamber. This research group member, who was also one of our study subjects, had previously handled specimens of Battarrea wearing the same sweater later worn in the chamber. In those two sampling periods, Battarea comprised 5% and 2% of all fungal sequences in indoor air.Floor dust can serve as a source of bioaerosols, so we collected vacuumed floor samples of dust from the carpet. The floor dust samples, despite being collected months apart, were similar in composition to each other, and were more similar to the composition of outdoor air than of indoor air . Considering the fifteen most abundant taxa in the respective samples, floor dust shared only one fungus and three bacteria with the indoor and outdoor air samples. The mean richness of microbes in the floor dust samples was 2× or 3× higher than that of indoor air samples for fungi and bacteria, respectively, and approximately 20% of the taxa detected were specific to the floor dust samples. The fungi in the air of walking experiments were slightly more similar to the floor dust when the carpet was exposed than when it was covered , while there was no difference for bacteria. The ventilation system itself could be a source of microbes, and we included a secondary set of experiments that included sampling the supply air. This effort yielded only a few data points, so we simply note the patterns. The air samples from the secondary study were similar to each other and distinct from the main study samples to varying degrees: for fungi, the secondary samples were quite different compositionally from the main study samples but for bacteria less so . For neither bacteria nor fungi was the ventilation system itself an obvious source of indoor microbes. That is, the taxa that were abundant in the supply air were also abundant outdoors. Those taxa that are present in the supply and indoor air but absent from outdoor air have relatively low read abundance .

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Vagaries of climate and the marketplace affect both how much and when labor is to be deployed

Posted on October 24, 2023 by hemp2022

The reported recruitment difficulties are not necessarily inconsistent with observations of oversupply in the farm labor market. These survey findings may be seen as the result of respondent gamesmanship, but they alternatively may be taken as signs of legally authorized workers leaving agriculture, of farmers more carefully screening prospective hires for job-related knowledge and abilities, or of production technologies and job requirements changing. Tasks for which respondents had most difficulty finding capable and reliable production workers in 1991-92 cover a broad spectrum. The lesser-skilled work mentioned includes picking, packing, hoeing,and general labor. More commonly specified were tasks that require higher technical and cognitive skills, such as: girdling vines; operating and maintaining almond hullers, tree shakers, hay balers, forklifts, computers, or other equipment; managing and caring for animals; accounting for financial transactions; setting up and running irrigation systems; driving tractors with various rigs; and supervising other employees. A respondent who seems to have experienced the consequences of indiscriminate hiring writes that it was particularly hard to find “tractor drivers with some brains.” Survey respondents register strong concern about recruitment five years hence . If there is a surplus of capable workers today, few farm operators expect it to get any larger or even to last on its current scale. Nearly half think recruitment will be more difficult in 1997, a quarter see no change, and another fourth do not even venture to guess. Some may anticipate a recovery from the economic recession in California, which would certainly alter the balance of total supply and demand in the labor market. Respondents foresee a future collection of hard-to-fill jobs even more extensive than the 1991-92 set. Many comments specifically name or refer to jobs that require mechanical, mathematical, language, and managerial skills, suggesting anticipation of a more technologically sophisticated,ebb and flow trays capitalintensive agriculture. Nevertheless, tasks that demand mainly the application of physical strength and stamina under uncomfortable conditions arc also well represented on the list.

Farm operators would entertain multiple strategies for coping with labor procurement problems that may develop in the future. Their strongest inclinations are to adopt technological changes that substitute for labor input, and to step up their recruitment efforts . Smaller majorities of respondents say that they would also consider offering better terms of employment, lowering selection standards, and contracting more with FLCs or custom harvesters. One-third would look to shift their enterprise mix toward less labor-intensive’ crops, and more than a quarter to leave farm business altogether.Until the wave of studies on workers sparked by the 1986 immigration reform law, it was often lamented that too little was known about the hired farm workforce. There was then, and there has continued to be, even less known about how that workforce is managed. Influenced by legal, technological, market, and other contextual factors, farm labor management includes several types of decisions that in tum have direct consequences for agricultural businesses and workers. This study has attempted to analytically describe the different means by which people are brought to and dealt with on farms, to map management practices as they currently are, not to speculate on their adequacy from economic or public policy perspectives. Information from our survey provides for beller understanding of labor management across the range of California fam1s–and of the farms themselves. Though data from any self-administered questionnaire are to be interpreted with caution, these findings clearly tell of a complex industry comprised of diverse production firms and relationships among them. The structure of production agriculture embodies not only vertically integrated producer-marketers but also networks of more specialized, interdependent entities. These entities join efforts through temporary contracts, accomplishing a functional coordination that others pursue through relatively fixed roles and rules in a single organization. California farms exhibit as much variety in their organizational and management characteristics as in their products. Common to all is reliance on the work of people–more than a million different individuals who perform agricultural work some time during the year. While many farm operators intimately link their businesses with family and life style, most depend on people outside the family circle.

Only six percent of all year-round workers on farms, and two percent of the yearly peak workforce, are members of an operator’s family. The labor of non-family workers, who are responsible for the bulk of commodity production in this state, is one of the essential inputs that farmers may procure from external suppliers and contractors. Farmers obtain a large amount of non-employee labor through farm labor contractors , and pest control operators. Two-thirds of all workers on farms are direct farm employees, more or less in particular crop and business size sectors. But three of five farm businesses in the survey also used at least one FLC or CH In 1992. The shares of total labor obtamed from outside providers have increased since 1986. when directly employed workers were 72 percent of all at peak. Farm operators do not sort neatly into groups of either direct employers or labor service customers. Because engaging workers through both employment and contract is the norm, attempts to distinguish farms that hire directly from those that obtain labor in other ways are not likely to be illuminating. There are functions served in procuring labor by either means. The widespread use of contractors notwithstanding, most workers on farms are in fact employees on the payroll, and they are managed in styles that run from the very casual to the systematic. Structure in the personnel function–the extent to which labor management policies, responsibilities, and processes are clearly rationalized–is usually greater in large farm businesses. A bigger scale of operation makes more economical as well as necessary the employment of personnel staff specialists to facilitate hiring, developing, and keeping productive workers. Larger operators in the survey more commonly utilize in-house or external professionals to assist in managing human resources. They tend more to obtain information on prospective employees through written applications and medical exams; to communicate through employee handbooks. written job descriptions and work rules, staff meetings, and regular performance evaluations; to directly employ bilingual supervisors; and to offer non-mandatory fringe benefits. They also verify more carefully the legitimacy of FLCs with whom they do business. And they get audited more by regulatory agencies. Larger farms have assisted more of their formerly undocumented workers through !RCA legalization processes, and they have retained these employees from 1986 to 1992 at higher rates than smaller firms. More generally within a gi\’en commodity sector, employment stability and structured personnel management appear to better reinforce one other in larger farm businesses, particularly those which have geographic or crop diversity that softens net seasonal swings in the need for labor.

Employees are more likely to work year-round in large operations, and, on the whole, year-round farm employees are better compensated, receive more fringe benefits, and have more job security than their seasonal counterparts. Many farms preserve job stability for a core of employees by keeping their organizations lean and contracting for FLC or CH crews to meet additional necds during periods of high activity. This stabilization strategy. however, may effectively define or perpetuate the division between two tiers in the farm workforce. Sometimes juxtaposed in adjoining fields are crews from both tiers harvesting for the very same company label but under quite different terms of employment. Where FLCs are able to hold their operating expenses below those that farmers would incur for their own hires, they can offer customers a current cost advantage over direct employment. Even where they cannot, contractors are appealing to farmers who want to reduce their employment transactions, communication problems, legal liabilities, and technical difficulties in managing personnel. The total need for labor in California agriculture fluctuates over the course of a year, and in most crop sectors the work activity at a given farm swings with the seasons more sharply than in the statewide aggregate. Peak employment in an average farm business is more than three times the year-round level,rolling greenhouse benches and the number of different people employed some time during a year is half again the number present at peak activity. Administrative costs accompany every addition to and deletion from the farm payroll, and personnel transactions would be more numerous yet if not for outside service providers. It is not surprising that FLC employees make up almost twice as much of the peak as of the year-round workforce. The unpredictability of staffing for tasks that depend on weather and biological phenomena magnifies the value of “just-in-time” delivery of labor.Even a most disciplined farmer cannot be confident about seasonal employment plans far in advance, Employing a larger workforce than needed in off-peak periods, to avoid cyclical layoffs and recalls, makes labor expense more of a fixed overhead than a variable operating cost. Arranging for contractors to mobilize people and equipment when needed, in contrast, can help tic labor expense more closely to actual task accomplishment while keeping direct employment lean and stable. Contractual arrangements for labor thus may also enhance longer run flexibility to alter future production, technology, staffing, and terms of employment within the farm business. Cultural and language differences between farm operators and workers compound the challenges of direct recruitment, selection, supervision, instruction, and other job-related communication. More than one-third of farmers cannot communicate directly in the language understood by most of their production employees, usually Spanish, and another third have limited fluency. Finally, it is most difficult for agricultural managers to procure labor from capable workers and stay within all legal guidelines without getting overwhelmed by mandates, prohibitions, and reports.

Although growers and contractors may be deemed jointly liable for violations of some employee protections, farmers reduce or eliminate exposure to claims of wrongdoing by using contract labor. Alter two decades of legislation narrowing gaps between employee protections in the agricultural and non-farm sectors, farmers arc subject to pretty much the same liabilities and constraints as employers in other industries. Judicial decisions giving employees more legal rights within their jobs have also raised the costs and risks of maintaining a directly hired workforce. Increased regulatory complexity and the paperwork associated with agricultural employment in particular have added to reasons for contracting out tasks. The eligibility verification and nondiscrimination provisions of !RCA are only two of many bases for charges that agencies or workers may level against farm employers. Thus, there are practical business considerations behind the use of labor contractors and outside service providers that may include but do not hinge on the 1986 immigration reform. In broad terms, growers patronize contractors to get work done when needed by people who can do it without presenting undue complications. Finding and dealing with contractors, however, can involve other complications that farmers weigh against the burdens of hiring and managing their own employees.Streams of immigrants have been boosting the supply of labor available to California farms for more than one hundred years, but labor procurement is not and will not be merely a matter of numbers. Neither farm jobs nor farm workers are an undifferentiated mass. Even as the post-!RCA labor glut was developing, farmers had trouble filling jobs, and most now arc at least somewhat concerned about finding enough workers with the right qualifications to meet their operiltional needs in the future. Regardless of how many people Me looking for employment, farm operators may have trouble engaging workers with skills that arc suited to emerging and future technologies. Patterns of demand for agricultural labor will undoubtedly be different by the end of this decade. Technological innovations that change farm jobs will hilve effects on who porforn1s them and how these workers are managed. While production systems may retain their basic characters, the context if not the content of virtually every agricultural job will be altered somewhat before the 21st century. Mechanization in the past has been designed to achieve a variety of private and social benefits, such as improved crop quality, more efficient use of fertilizers and pesticides, reduced worker exposure to hazards, preservation of environmental quality, and conservation of water and energy. Whether or not explicitly intended, an increase in labor productivity–or a decrease in the number of people needed to produce a given output–usually has accompanied the other benefits of such change.

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Response files were then imported to mainframe and desktop computers for analysis

Posted on October 23, 2023 by hemp2022

An extremely large business with large acreage and production value, for example, may have been classed in the small or medium groups if it made extensive use of fles. We acknowledge this problem in stratifying the population for sampling. and we avoid it in the presentation of findings by using other measures of size–total labor expense, production value, acres–as appropriate.The first mailing to businesses in our survey sample was made during late October 1992. An envelope containing a cover letter, final version of the questionnaire, administrative control sheet, summary request postcard, and return envelope was sent to each of the 2,500 selected farm owners or operators. The Postal Service promptly returned more than one-hundred of the envelopes with invalid or nonexistent addresses, thus reducing the effective sample size. Usable returns from farmers began to arrive within a few days and continued to accumulate at a substantial rate for one month. The second mailing, a simple reminder postcard, went out to all non-respondents during the first week of December. By the end of January 1993, a lotol of 586 completed and usable questionnaires had been returned.For every questionnaire recipient who went to the trouble of telling us that he or she was not or might not be part of the population, there were likely others in the same situation who did not bother to communicate. The response rate after the first two mailings did not measure up to expectations based on long experience of the Survey Research Center in similar studies. The sheer length of the survey instrument was considered partly but not solely responsible. We therefore conducted an interim study to assess the extent to which “non-response” was from such people who should not have been in the sample to begin with, and to identify other factors discouraging response that could be modified to improve the third mailing.

The most currently available EDD employment and payroll data on 200 randomly selected non-respondents were acquired and examined,growers solutions and they indicated that 22 of these farms were not currently in business. A sub-sample’ of 20 was then chosen for brief phone interviews, which yielded no single’, clear explanation but did bear out that instrument length and personal time constraints were important factors to recipients. For the third mailing, in March 1993, we devised a short version of the questionnaire one-half the size of the original and excluding items most burdensome to complete. The short form was thus a subset of the long; it contained no new items. The full original questionnaire was·sent to one-third of non-respondents remaining in the sample and the short form to two-thirds. By early May the Postal Service had returned as undeliverable a total of 151 of the envelopes initially mailed to thc 2,500 businesses selected to the sample. Another 89 <3.6 percent of the selected businesses informed us by mail or phone call that they wcre no longer operating, had never been in business, or were not in a farm business. The valid population list thus numbered 2,260, and we heard formally from 955 . There were 3D outright refusals , one farmer regretfully and regrettably “unable to participate” due to major illness, and a total of 924 complete and usable responses before a May 7, 1993, cut-off date. An additional 19completed questionnaires–received after May 7, and one received earlier but not representing what we construe to be a “farm business,” are not included in the data file analyzed for this report. Written or called-in comments from questionnaire recipients who did not participate in the survey were recorded verbatim. These remarks, several quite candid, are grouped by reason for non-participation and presented in Appendix3. The completion rate varies little across payroll size based on VI file data, from a low of 38 percent in the 50-75th percentile group to a high of 46 percent in the 95-99th percentile group. Smaller operators up to the 50th percentile, while close to the sample average in questionnaire completion rate, were markedly more likely to report that they had left farm business.

Survey participation varied more strongly as a function of SIC code. Dairy operators had the lowest response rate among commodity-based SIC groups, and producers of nuts , citrus fruit , and other fruit the highest. Amazingly, response rates on the third mailing were Virtually the same for farmers receiving the original full-length questionnaire as for those who were asked to complete the shortened version . It is difficult to figure.Data from usable responses were entered and verified In a specialized database program into which logical sequences and consistency checks had been built.Findings have been aggregated and are presented in this report mostly as cross-tabulations for the sample as a whole and for farm size, region,l, and commodity groupings. More sophisticated multivariate techniques were used to test for connections between respective farm characteristics and employment practices. Significant relationships are noted in the text of sections D·G. The measure of size used in most tables, particularly those referring to management of the farm’s own employees, is direct payroll as reported on the questionnaire. Total labor expense, including direct payroll plus payments to contractors and other labor service providers, is used in presentation of findings more logically related to size of the entire farm operation. Geographical classification is based on the county in which the respondent farm reports prodcuing its greatest revenue, not on its address of record in the VI file that defined the survey population. The addresses listed in the EDD record for many operators do not correspond to the locations of their farm business activity. Several in the survey sampler in fact, received their questionnaires in other states: Arizona, Colorado, Florid” Indiana. Kentuckv, Nevada, and New York.California farms are most commonly organized as &Ole proprietorships. Other forms of organization are, in order of frequency, corporations, family partnerships, and non-family partnerships. In the survey sample, corporations and family partnerships together make up more than half of all farm businesses. All four of these organizational types exist in every farm size group, but farms with larger payrolls are much more likely to be corporations and those with smaller payrolls to be sole proprietorships . Geographically ,the Central Coast region has a larger than average share of corporate farms and non-family partnerships , and a smaller share of sole proprietorships . These farms have been operating uncler their present owners or family predecessors an average of nearly 30 years, one since 1855 and shortest in the coastal regions . Although one respondent operates in more than ten counties, more than four-fifths do all their farming in one county, and more than half of the rest in two . They perform an average of 3.8 farming functions , most frequently harvesting , cultivation and plant care , planting , and land preparation .

Only a third of the businesses market farm commodities. Less than one-fifth limit their operations to a single function, animal products firms by far most likely to be among them. Respondents specified quite a collection of “other” functions as part of their businesses, including manure spreading, catering, beekeeping, selling retail, equestrian training, trucking, financing, holding in cold storage, irrigating, labor contracting, aerial spraying, and almond hulling. Most of these could be — but have not been — interpreted as one of the functions listed on the survey questionnaire .All major crop types are well represented among respondents . Farms that primarily produce tree fruits or other fruits constitute a quarter of those participating. Makmg up about an eighth each are producers of animals, nuts, grapes , vegetables, and “other crops.” Most in the “non-edibles” group indicated that they grow cotton; producers of hay, flowers, silage, and seeds are also in this class. Because their numbers arc quite small, we lumped into the “other crops” group farms that report mainly producing grains, other edible field crops, ornamentals, and other nursery products. Other crops in this group that were specifically indicated by one or more respondents include bees, oysters, herbs, sugar beets, flowers, olives, avocados, horseradish, storks, trout, and meal worms. As with “other” specified business functions,dry racks for weed farms have not been reassigned in the data base from the respondent-indicated crop category to other plausibly appropriate crop types listed on the questionnaire. Relationships between crop type and region are evident. Although most crop types are found in most or all regions, some are concentrated in one or two. Most nu t production is in the central valley regions, grapes in the North Coast and San Joaquin Valley regions , vegetables in the Central and South Coasts, and non-edibles in the San Joaquin Valley and Desert. Agriculture in counties designated as “other” is heavily based on animals, and “other crops” arc an unusually large share of Sacramento Valley farm products.Farms with greatest acreage arc In the Sacramento Valley and counties iksignated “other”, but the Central Coast and Desert tend to have the largest operations with respect to sales value, total labor expense, annual payroll, and peak employment level . Revenues, labor costs, and employment levels arc smallest, on average, in the “other”, North Coast, and Sacramento Valley regions. The ratios of median labor expense to sales value, and median payroll to sales value are rough measures of aggregate labor-intensity of farm production. The ratio of payroll to total labor expense is an inverse indicator of the extent to which labor is obtained from non-employees. On this basis, the findings presented in the table suggest that reliance on labor contractors and custom harvesters is greatest in the South Coast and San Joaquin Valley regions.The work of fann businesses in the survey is performed by an average of 89 workers at peak, 27 year round . Vegetable fanns have by far the largest numbers of workers , and grape and other fruit operations also have much larger work forces than other crop groups. The size difference between seasonal peak and year-round work forces is proportionately greatest in grapes and smallest in animal commodities producticid, There are many roads to farm work, and several types of relationship between fann operator and labor provider.

Two-thirds of all workers in farm businesses, at peak activity as well as year around, are employees on the farm payroll. Farmer family members are only six percent of the overall year-round workforce, two percent at peak, They arc somewhat more likely than not to be on the books as employees, rather than as “unpaid family members.” Larger operators provide the longest periods of employment during a year, over the total survey sample as well as within crop classifications. Significant regression results show that the greater the payroll, the higher the ratio of year-round employees to peak employees, indicating that larger farms tend less to hire and layoff workers around their seasonal variations in need for labor, Employees of farm labor contractors are nearly a quarter of all workers at peak, on average, and one-seventh of workers year-round , FLC employees thus make up almost twice as much of the peak as of the year-round workforce. Although the activity of FLCs is popularly associated with fruit and vegetable production, their employees constitute large segments of the peak work forre in all crop groups except animals. Custom harvester . Only in the tree and other fruit group, where the ratio of employees to all workers on farms was lowest among major commodity classes in 1986, has there been even a slight relative increase in direct farm employment since then. Both the share of farmers in the survey gettmg labor from FLCs and CHs, and the average number of these service providers doing business with each farm reportedly increased from 1986 to 1992. Three in five farm busmcsses purchased services from at least one FLC or CH in 1992. Producers of vegetables were most inclined to use labor contractors, and producers of non-edibles to use custom harvesters . Farmers’ use of licensed pest control operators similarly grew over this period, with two-thirds of vegetable firms and three-quarters of non-edibles firms obtaining service from one or more PCOs.Unwanted turnover raises “arious administrative and supervisory costs, and workforce stability is generally valued. Turnover is both expensive in itself and often a symptom of other problems. Farms experience employee turno’er both during and between production years.

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Innovative end-effector design and control can also increase throughput and efficiency

Posted on October 20, 2023 by hemp2022

Several factors render this combination challenging to achieve. Living tissues can be easily damaged and handling them typically requires slow, careful manipulation that avoids excessive forces or pressures. Biological variation introduces large variability in physical properties such as shape, size, mass, firmness of the targeted plants or plant components. This variability, coupled with uncertainty in the sensing system and limitations in the performance of control systems can affect negatively the accuracy, speed, success rate and effectiveness of the operation. Reduced accuracy can cause damage to the targeted part of the plant or nearby plant parts , or the entire plant . It may also cause reduced throughput due to misses and repeats, or reduced efficiency if no repeats are attempted. Visual servoing/guidance of robot actuators can reduce uncertainty and increase efficiency, but uneven illumination, shadows cast by branches and leaves, partial occlusions, and branches acting as obstacles present significant challenges in real-world conditions . Guiding the end-effector by combining inputs from multiple cameras is an approach that could be adapted to agricultural settings . Another possible direction is using deep reinforcement learning to learn visual servoing that is robust to visual variation, changes in viewing angle and appearance, and occlusions .If stem-cutting is used, challenges include detecting and cutting quickly and robustly from a large range of approaches, in the presence of touching fruits and twigs. If pulling is used, the force required to detach fruits depends on the type and maturity of the fruits, the approach angle of the end-effector, and on whether rotation is also used. Some fruits require concurrent, controlled, vertical grow rack synchronized rotation and pulling to reduce skin/peel damage at the stem-fruit interface , a task that is complex and not easily modeled. Deep reinforcement learning for grasping is a possible approach to build sophisticated controllers for such tasks.

Innovations in materials, design and control for soft robots could also be adapted to fruit picking and crop handling in general . Another important factor is limited accessibility of the targeted plants or their parts by robot end-effectors. Accessibility can be limited by plant structure, positioning, interference with neighboring plants or structures, and robot design. For example, in robotic weeding, weeds that are very close to a crop-plant’s stem and hidden under its canopy are not easily accessible by the end-effector without damaging the crop . In fruit harvesting, fruits in tree canopies that are positioned behind other fruits, branches or trellis wires also have limited accessibility by robotic harvesting arms. Accessibility can be improved by introducing dexterous, multi-d of actuation systems. However, control complexity can reduce throughput; the overall system cost will also be higher. Breeding and horticultural practices can also be utilized to improve accessibility. For example, tree cultivars with smaller and simpler canopies, training systems that impose simpler – planar – canopy geometrical structures along fruit thinning operations can contribute to higher fruit accessibility/reach ability. To some extent, it is the availability of trellised planar architectures and precision fruit thinning which result in very high fruit visibility and reach ability that have enabled robotic harvesting to emerge recently as a potentially cost effective approach to mechanical fruit harvesting at commercial scale. However, the cost and required labor demand for maintaining meticulously thinned and pruned trellised trees can be very high. Moreover, not all fruit trees can be trained in such narrow, planar systems. A promising approach that can be used to guide “breeding for manipulation” is the use of plant and robot geometric models to co-design tree structures and machines to optimize manipulation reach ability and throughput . Also, the use of large numbers of simpler, cheaper actuators that approach plants from different positions has shown promise in terms of reach ability , and could be adopted to increase overall throughput.Endotoxins are lipopoly saccharides in the outer membranes of Gram-negative bacteria that are distributed widely on plants, in soil, water, and the intestines of humans and animals [reviewed by Myatt and Milton ; Spaan et al. ].

Endotoxins are found in indoor dust generated by human activity and pets and are also found adsorbed onto the surfaces of combustion particles Inhaled endotoxins are bound by an LPSbinding protein that, in turn, binds to specific cell receptor [CD14 , a Toll-like receptor ], and initiates signaling pathways that lead to expression of proinflammatory cytokines that result in lung inflammation, increases in epithelial permeability, and activation of systemic inflammation . Although high concentrations of aerosolized endotoxin have been recognized as a cause of lung disease in cotton workers and swine handlers , recent interest has focused on the complex role of nonoccupational indoor and outdoor endotoxin concentrations in the occurrence of immunoglobulin E –mediated allergy and asthma . Biological responses to endotoxin, in theory, could lead both to suppression of IgE-mediated responses through the stimulation of interleukin 12 and to the worsening of airway inflammation, a hallmark of asthma . These effects have been reported at endotoxin concentrations lower than those found in high-risk occupational settings. Several studies have associated elevated levels of house dust endotoxins with a) increased respiratory symptoms in infants ; b) worsening of existing asthma that is independent of the levels of other common indoor allergens ; c) decreased frequency of positive IgEmediated skin test reactions in infants ; and d) decreased occurrence of hay fever and positive prick skin test in children . Rural residence, particularly on farms with animal exposure, has been reported to reduce risk of asthma . Despite the known high levels of endotoxin in these settings , definitive evidence that endotoxin, and not some other component of the microbial flora, is associated with this decreased risk has not been established . Most studies of the association between human exposure to endotoxins and allergic and respiratory disease have focused on concentrations of endotoxin in samples of house dust . Few studies have evaluated the correlation between endotoxin concentrations in dust and air , which appears to be low— correlation < 0.3 . Several recent studies have described ambient concentrations of endotoxin. Endotoxin concentrations in New Orleans, Louisiana, after flooding from Hurricane Katrina were high in flooded [3.9 EU /m3] and non-flooded areas and did not differ between indoor and outdoor environments . Ambient endotoxin concentrations in a large area of Southern California were below a 5.5-EU/m3 limit for adverse health effects in occupational settings quoted by the authors . The highest endotoxin content per milligram of PM10 was found in the mountain and desert areas. No seasonal patterns were detected. A 5.5-month study at the University of North Carolina found that ambient endotoxin concentrations were greater in coarse particles [aerodynamic diameters between 2.5 and 10 µm ] than in particles with aerodynamic diameters < 2.5 µm . An extensive study of size-fractionated bioaerosol was performed in 20 homes in and around Palo Alto, California . During the daytime, the highest concentrations of endotoxin were in particles with aerodynamic diameters > 10 µm , followed by the PMc size fraction. At night, the highest concentrations occurred in the PMc size fraction. Of the above studies, only the study in Southern California provides some data on spatial distributions of endotoxin based on where subjects resided; however,grow light racks potential ambient sources were not investigated . As part of a study of the effects of ambient air pollution on the natural history of children with asthma, we characterized the temporal and spatial distributions of ambient endotoxin over several years in Fresno and Clovis, California , a city surrounded by large tracts of land devoted to agriculture and animal husbandry. As part of a study to evaluate the role of ambient air pollution and bioaerosols on the natural history of childhood asthma, in this article, we focus on ambient endotoxin, its spatial distribution in relation to these sources, and the influence of meteorologic factors on daily concentrations. Fresno is located in the San Joaquin Valley near the southern end of the Central Valley of California. In 2006, the population was 466,700. The study area was confined to a circle with a radius of 20 km, with its center at the ambient air monitoring station operated by the California Air Resources Board .

The city is bound on three sides by land used primarily for agriculture and in the northeast by native vegetation. Two major interstate highways cross the study area: California State Highway 99 from northwest to southeast and Interstate 41 from north to south. The wind patterns are variable [see Supplemental Material ]. For data on collection of ambient concentration, see Supplemental Material, Figure S1 .Daily ambient endotoxin was collected year-round at the California ARB central ambient monitoring site at 3425 First Street in Fresno as part of the exposure assessment for the Fresno Asthmatic Children’s Environment Study . FACES is a cohort of 315 children 6–11 years of age at enrollment with clinically active asthma. All subjects lived within a 20-km radius of a U.S. Environmental Protection Agency Super Site located in Fresno. Subjects were followed with biannual evaluations of respiratory health, pre- and post bronchodilator spirometry, skin prick testing and household surveys. Subjects also completed three 14-day panel studies over three seasons based on ambient pollution concentrations in the study area. Initially, the samples were collected at the First Street site from midnight to midnight. In early 2002, the collection times were changed to 2000 to 2000 hours to coincide with the times that data were collected during panel studies and the times of the intensive sampling of 83 homes selected to cover the full range of indoor and outdoor exposures in the study community. Daily samples reported here cover 13 May 2001 through 31 October 2004. Additional samples were collected from June 2002 to August 2003 at 10 school locations , with two mobile trailers outfitted by the ARB to include the instrumentation identical to that located at the First Street site. In parallel, ambient endotoxin samples were collected inside and outside 83 homes between 6 February 2002 and 22 February 2003 over 5 days during the 2-week panel studies of the children. Twenty eight homes were sampled twice during two separate panels in two seasons . Concentrations were also measured at each location for elemental carbon , PM2.5, and PM10. Concentrations of PMc were determined by the difference . On the residential sampling days, 24-hr samples were collected at up to eight locations: First Street, Fremont School, one other school, and up to five residences . At First Street and the schools, airborne endotoxin was collected on 47-mm Teflon filters in a Partisol-Plus Model 2025 Sequential Air Sampler with a PM10 inlet . Samples were collected at a nominal flow rate of 8.33 L/min for 24 hr. At residences, 24-hr integrated samples were collected with Harvard type PM10 impactors at 10 L/min flow rate in a multileg sampler. One sampling leg used 37-mm Teflon filters for determination of PM10 mass and endotoxins. The other sampling legs employed inlets and filter media for determination of PM2.5 mass, sulfate and nitrate, organochlorines and EC, nicotine, metals, and polycyclic aromatic hydrocarbons. Filters were loaded and unloaded in 24-hr periods before and after the sampling period and sent to the laboratory for analysis. Collocated endotoxin data collected with the two different samplers differed by < 0.1 EU/m3, on average .We confined our analysis to 45 of 107 residential sampling days that also had First Street endotoxin measurements and occurred during the dry season for the reasons stated previously. However, for spatial mapping of concentration patterns, we further restricted our analysis to days when six or more locations from all sites had data, which reduced the data set to 22 dry-season sampling days. The sparseness of the data at most sampling locations limits the applications of conventional spatial analysis methods; nonetheless, the data are sufficient to describe a) the relations between concentrations at schools and the central air monitoring station using regression equations and coefficients of divergence [see Supplemental Material, Equation 1 ]; b) the range of daily spatial variability across the urban area using coefficients of spatial variations ; and c) the average spatial patterns.

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A recent review of coverage approaches can be found in Galceran and Carreras

Posted on October 19, 2023 by hemp2022

Each of these steps takes time, and economic models say little about how much time. Other activities, like changes in study habits, could be quicker but, again, economic models say nothing about the length of time. Will children change their study habits as soon as they get electricity, or will they need an adjustment period before they reach a new equilibrium? Habit formation is not immediate, but in addition study time is jointly determined with time allocated to other activities. Time allocated to those other activities may be fluctuating in response to changes in their parents’ time allocation and, as we have just discussed, these changes take time. The time-resilience of the effects is another important aspect of the electrification effects, and regrettably no solid conclusions can be derived from our study. The problem is that the size and significance of an effect may change between rounds because the effect is temporary, or because the electrification rate in the non-encouraged group is catching up with that in the encouraged group, so the non-encouraged group is experiencing the same effects as the encouraged group. Think of a regression of labor supply on voucher allocation, estimated round by round. Assume we find a positive and significant coefficient in the first follow-up survey, but non-significant coefficients in the regressions for the second and third follow-ups. The increase in labor supply may have been a transitory effect of electrification. For instance, think of households increasing their labor supply to raise the money for the connection fee and some appliances, after which labor supply reverts to the preelectrification level. However, it may also be the case that the effect is permanent, but since with the pass of time the electrification rate in the non-encourage group catches up with the encouraged group, non-encouraged households are starting to experience the same changes than their counterparts4 . As the electrification rate increases in the non-encouraged group, these households will experience similar changes as the treatment group: labor supply will increase among the non-encouraged group, catching up with the encouraged group, and the coefficient on voucher allocation would become non-significant. To distinguish empirically between “fading out” and “catching up” effects,horticulture products one could think of looking at the evolution of the mean outcome per group. If the difference in labor supply disappears because the mean in the treatment group reverted to the pre-electrification level, one may be tempted to label the effect as “fading out”.

If the difference disappears because the mean in the non-encouraged group jumped closer to the encouraged group, one may be tempted to label the effect as “catching up”, but this issue is not as simple. Imagine that electrification increases labor supply 5 percentage points in the first year and then labor supply reverts to its baseline level. By the first follow-up survey we would find this difference between encouraged and non-encouraged households. Imagine that between the first and second follow up there is an economy wide shock that increases labor supply by 5 percentage points. The effect of electrification fades out in the encouraged group, decreasing labor supply by 5 percentage points, but the shock counteracts this effect, so the encouraged group ends up with the same level as in the first follow-up, 5 percentage points above their baseline value. Due to the economy-wide shock, labor supply also increases by 5 percentage points among the non-encouraged group. Since labor supply is higher for both groups, it would appear that the non-encouraged group “caught up” with the encouraged group, while in reality the effect in labor supply just faded out. It is similarly easy to come up with examples showing effects that seem to fade out when in reality groups are catching up. Therefore, to differentiate catching up from fading out we would need an experimental group that remains off the grid during the whole study period.We must significantly increase the production of consumer-safe, high-quality food, feed, fiber and bio-fuel products to cover the needs of an increasing world population that has more purchasing power and affluence, and ensuing per capita consumption. This must be accomplished in an economically and environmentally sustainable fashion that conserves the resource base, including biodiversity, water, and soil, despite limitations in arable land and fresh water resources. On the cultivation side, agricultural robotics technologies are essential in achieving this goal by providing mobile sensing, computation and actuation that enable precision farming at ever – increasing spatial and temporal resolutions, even at individual plant level. Such selective, individual plant care systems have been called “phytotechnology” and hold great potential for maximizing production while minimizing water, chemical and energy inputs. On the breeding side, fast development of radically improved crop varieties will rely on our ability to functionally link – to model and predict – the plant phenotype as the result of the interactions of genotype, field environment, and crop management.

This is the challenging task of field phenomics or phenotyping, i.e., the automated, high–throughput, proximal, non–destructive measurement of plants’ phenotypes in fields. “Breeding is essentially a numbers game: the more crosses and environments used for selection, the greater the probability of identifying superior variation” . Agricultural robots can offer the mobility, advanced sensing and physical sampling required for high-throughput field phenotyping.In the past decades, farmers, and in particular fruit, vegetable and horticultural farmers have relied on hired, low-wage workers, especially during the harvest periods. Recent studies indicate that as a result of socioeconomic, structural and political factors, local and migrant farm labor supply cannot keep up with demand in many parts of the world . Also, due to increasing industrialization and urbanization large countries like China are already moving towards the Lewis turning point, where surplus rural labor reaches a financial zero ; China is expected to reach it between 2020 and 2025 . Agricultural robots hold the potential to remedy existing and imminent farm labor shortages by increasing worker efficiency and safety acting as co-bots interacting with workers , or by replacing workers in low skill, labor-intensive tasks, like manual weeding or fruit and vegetable harvesting.Many agricultural robots have been developed to perform precision farming operations and replace or augment humans in certain tasks. These robots come in two main types: I) self-propelled mobile robots, and II) robotic “smart” implements that are carried by a vehicle. Type-I robots span wide ranges of sizes and designs. Conventional agricultural self propelled machines such as tractors, sprayers, and combine harvesters have been “robotized” over the last decade through the introduction of GPS/GNSS auto-guidance systems. These machines are commercially available today and constitute the large majority of “agricultural robots”. They can drive autonomously in parallel rows inside fields while a human operator supervises and performs cultivation-related tasks; turn autonomously at field headlands to enter the next row; and coordinate their operations . Autonomous cabinless general purpose ‘tractor robots’ were recently introduced by several companies that are compatible with standard cultivation implements . These larger robots are designed primarily for arable farming related operations that require higher power and throughput, such as ploughing, multi-row seeding, fertilizing, and spraying, harvesting and transporting.

A large number of smaller type-I special purpose mobile robots have also been introduced for lower-power applications such as scouting and weeding of a smaller number of rows at a time. Most of these robots are research prototypes introduced by various research groups. A few commercial or near-commercial mobile robots have emerged in applications like container handling in nurseries and seeding , respectively. Small robots like Xaver are envisioned to operate in teams and are an example of a proposed paradigm shift in the agricultural machinery industry,plant grow trays which is to utilize teams of small lightweight robots to replace large and heavy machines, primarily to reduce soil compaction.Type-II robots have been developed for various applications, and some are already commercially available, in applications like transplanting, lettuce thinning and mechanical weeding . Robotic implements at pre-commercial stage are also developed for applications like fruit harvesting and vine pruning in orchards and vineyards, respectively. Other orchard operations such as flower and green fruit thinning to control crop load have also been targeted for automation.Recent review articles have discussed some of the opportunities and challenges for agricultural robots and analyzed their functional sub-systems ; summarized reported research grouped by application type and suggested performance measures for evaluation ; and presented a large number of examples of applications of robotics in the agricultural and forestry domains and highlighted existing challenges . The goals of this article are to: 1) highlight the distinctive issues, requirements and challenges that operating in agricultural production environments imposes on the navigation, sensing and actuation functions of agricultural robots; 2) present existing approaches for implementing these functions on agricultural robots and their relationships with methods from other areas such as field or service robotics; 3) identify limitations of these approaches and discuss possible future directions for overcoming them. The rest of the article is organized as follows. The next section discusses autonomous navigation , as it is the cornerstone capability for many agricultural robotics tasks. Afterwards, sensing relating to crop and growing environment is discussed, where the focus is on assessing information about the crop and its environment in order to act upon it. Finally, interaction with the crop and its environment is discussed, followed by summary and conclusions. Clearly, the first three operations are not independent. For example, the spatial arrangement of field rows and row-traversal sequence that minimize working time depend not only on field geometry and row spacing, but also on vehicle mobility and maneuvering during turning at headlands to switch rows. The prevailing approach has been to assume obstacle-free headlands and use geometric approximations of headland maneuvering costs derived analytically – rather than numerically – to solve problems #1 or #2 independently, or combined. In the general robotics literature the combined problem is referred to as coverage path planning . An emerging idea in agricultural robotics is the utilization of teams of small autonomous machines to replace large machines . In such scenarios, routing, motion planning and auto-guidance approaches must be extended to multiple robots.

When these machines operate in parallel but independently the extensions deal mostly with splitting the field and avoiding collisions. However, when machines collaborate, as for example combine harvesters and unloading service trucks do during harvesting, issues of coordination, scheduling and dispatching need to be addressed. This scenario is also known as field logistics and will be covered as part of vehicle routing.The operation computes a complete spatial coverage of the field with geometric primitives that are compatible with and sufficient for the task, and optimal in some sense. Headland space for maneuvering must also be generated. Agricultural fields can have complex, non-convex shapes, with non-cultivated pieces of land inside them. Fields of complex geometry should not be traversed with a single orientation; the efficiency would be too low because of excessive turning. Also, fields are not necessarily polygonal, they may have curved boundaries and may not be flat. Additionally, most agricultural machines are nonholonomic and may carry a trailer/implement, which makes computing turning cost between swaths non trivial . Finally, agricultural fields are not always flat and field traversal must take into account slope and vehicle stability and constraints such as soil erosion and compaction.Computing a complete spatial coverage of a field with geometric primitives is in principle equivalent to solving an exact cellular decomposition problem .Choset and Pignon, developed the Boustrophedon cellular decomposition . This approach splits the area into polygonal cells that can be covered exactly by linear back-and-forth motions. Since crops are planted in rows, this approach has been adopted by most researchers. A common approach is to split complex fields into simpler convex sub-fields via a line sweeping method, and compute the optimal driving direction and headland arrangement for each sub-field using an appropriate cost function that encodes vehicle maneuvering in obstacle-free headland space . This approach has been extended for 3D terrain .Existing approaches assume that headland space is free of obstacles and block rows are traversed consecutively, i.e., there is no row-skipping.

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It is very likely that products used contained other pyrethroids’ active ingredients as well

Posted on October 17, 2023 by hemp2022

Most of these pyrethroids have been detected in house dust from several different studies . The majority of these studies were conducted with the general population and two were conducted with farm working communities; however there was little difference between pyrethroid concentrations in the house dust from the two types of populations. We observed lower detection frequencies and/or lower median concentrations of cis– and trans-permethrin than many of these studies . We also observed lower or comparable detection frequencies and median concentrations of cypermethrin in our study as compared to others . Deltamethrin and esfenvalerate were detected more frequently in the house dust from our study than in any other . Only two other studies looked at resmethrin in house dust, and neither was able to find detectable levels compared to the 29% detection in our study. The differences in detection frequencies in our study as compared to these other studies may be the result of different LODs. Additionally, our study population was restricted to only those families with young children, potentially causing differences in pesticide use practices when compared to a more diverse population containing people of differing ages, marital statuses, and living arrangements. Also, because we weighted our sample selection to those households whose participants already showed exposure to pyrethroids, a true random sampling from our study population may have exhibited lower detection frequencies than what has been reported here. We also did not observe the seemingly extreme outliers or maximum concentrations several orders of magnitude over the median concentration that some of the other studies reported. This may be due to our study population being better trained in pesticide use practices and precautions from work in agriculture than urban dwellers. We wanted to examine the potential reasons for the lack of correlations with the questionnaire data.

We used data from the main MICASA study questions on pesticide use, which were asked of the full cohort of 436 households in two interviews,hemp drying racks the first conducted from January 2006 to May 2007 and the second from February 2009 to June 2010. The consistency of responses to these pesticide use questions between the men and women from the same household was assessed and within-household levels of agreement were moderately high. Use was reported by both the man and the woman in 44% of the households in which either the man or the woman reported using outdoor pesticide sprays during the first interview. Assuming pesticides were actually applied if reported by either the man or the woman, asking only the man or the woman would misclassify many of the households that used pesticides as non-users, which may be partially responsible for the lack of correlation. Temporal comparisons from the same participant between the two interviews conducted approximately 3 years apart were also made. A larger fraction of the population reported using pesticides at the second interview than at the first interview, with only between 5.6 and 6.7% of individuals reporting use for both time periods. The low levels of agreement could be due either to actual changes in use patterns or due to differences in reporting and may also be partially responsible for the lack of correlation between questionnaire responses and house dust concentrations. Many previous studies have reported that residential pesticide use questions were ineffective at identifying exposure levels . We also saw a lack of consistency in the relationships between questionnaire data and measured levels of pyrethroids in the house dust . There was a positive correlation with reported outdoor pesticide use and pyrethroid levels in the house dust. However there was no relationship with indoor pesticide use. We found a slightly negative correlation with outdoor traps and levels of indoor pyrethroids, suggesting that families that use traps to reduce their pest problems use less pesticide in their homes.

A possible reason for the lack of correlations between reported pesticide use and pyrethroid levels found in the home is that the questionnaire asked about any pesticide products used for insect control, while we only measured five specific pyrethroid compounds. There are also likely to be large discrepancies in the amount of pesticide applied, as well as cleaning practices between participants. This information was not accounted for in our questionnaire. The most promising predictor of exposure was the pesticide inventory. There was a significant correlation between the pesticide inventory and the sum of pyrethroid concentrations found in the house dust. With traditional questionnaires, it is often difficult for participants to accurately recall pesticide use. The pesticide inventory on the other hand is relatively easy data to collect, requiring only a few minutes time for the interviewer to note the pesticide products present in the participant’s homes. Although neither method gives information on what, or the concentrations of, specific pesticides that may be found in the physical samples from the home, the pesticide inventory may be a more useful tool to predict possible pesticide exposure than the traditional participant recall. This study has many limitations. Data from households with higher levels of dust or whose children had higher pyrethroid metabolite levels in the urine were more likely to be analyzed, which can be expected to lead to a positive bias in our estimates of household pyrethroid levels. Our small sample size limited the statistical power and may have prevented us from observing statistically significant correlations in our data. Additionally, as mentioned above, there was a lack of consistent reporting of pesticide applications between husband and wife. In 2009, 1.3 billion people lacked access to electricity at home . At night, households with no access to electricity make do mostly with candles or kerosene lamps to satisfy their illumination needs. These sources of light provide poor illumination and, more importantly, emit high amounts of pollutants harmful for human health. In fact, indoor air pollution is the third leading risk factor for global disease burden, after high blood pressure and smoking .2 Given the stylized fact that lighting is one of the first uses of electricity in newly electrified areas , electrification is expected to decrease IAP levels by replacing traditional source of lighting, like kerosene, candles, and wood sticks.

These reductions and their potential health effects are often argued to be one of the main benefits of electrification, but there is no solid empirical evidence to date. Our paper contributes to filling this gap by providing the first experimental estimates of the relationship between household electrification and indoor air pollution. To answer this key question we collected,industrial rolling racks within the frame of a clean experimental design, a uniquely rich dataset that pairs minute-by-minute fine particulate matter concentration with detailed data on household members’ time allocation.The reductions in overnight PM2.5 concentration result in large and significant falls in acute respiratory infections among children under 6. Depending on the exact specification voucher recipients report 37 to 44% lower incidence of ARI in the four weeks preceding the survey than non-recipients. To assess further health implications of the observed reductions in PM2.5 concentration among the population over 6, in section 6.1 we use data from the time allocation module to estimate the change in daily exposure to PM2.5. The resulting reductions in exposure to PM2.5 are large but unequally distributed among household members. Adult males benefit the most, with 59% lower exposure. Since adult females are still exposed to high PM2.5 concentrations while cooking, they benefit the least, with reductions in exposure of 33%. The figures for children are 46% and 39% . The dose response function recently developed by Pope III et al. based on first and second-hand tobacco smoking associates the figures we find with large reductions in the relative risk of lung cancer, 25% for adult females, 33% adult males, with the respective figures for children falling 25% and from 30% . Although the composition of PM2.5 generated by kerosene combustion is not the same as the one generated by cigarette smoking, the current scientific evidence cannot reject that their health effects are similar. The mechanism behind the PM2.5 reductions in our study setting is a substitution away from kerosene lighting. Electrification caused large reductions in kerosene expenditures, while changes in other traditional lighting sources like candles are small in magnitude and not statistically significant. We find no evidence of changes in cooking practices either. The reduction in kerosene use has important health implications, because although kerosene is usually considered a cleaner alternative to biomass, emissions from kerosene-burning devices are considered extremely harmful for human health. Aside from PM2.5, kerosene emissions include carbon monoxide , nitric oxides , and sulfur dioxide . These pollutants can impair lung function and increase infectious illness, asthma, and cancer risks Lam et al. . The reduction in kerosene use also has important environmental consequences, since kerosene lighting is responsible for 7 percent of annual black carbon emissions globally . The reductions in PM2.5 concentration found in this study are not necessarily obvious ex-ante for four reasons. First, since households continue to use fuelwood forcooking, and woodsmoke produces higher IAP concentration than lighting fuels, the resulting reductions in IAP may have been small and thus irrelevant for policy. In fact, given that there is still scarce evidence in the specialized literature, the evidence of a strong, positive relationship between kerosene use and PM2.5 concentration in a sample in which 70% of households rely on fuel wood for cooking is a contribution to the environmental health literature.Second, it may be the case that only the heaviest kerosene users experience significant reductions, and thus the average reduction may just be an illusion.

We explore this issue and find even though the highest polluters indeed get the highest gains, 80% of households experienced significant reductions in overnight PM2.5 concentration. Third, voucher recipients may have not adopted electricity at a high enough rate, thus the relationship between receiving a voucher and connecting to the grid may be too weak to reflect in IAP measurements. Fourth, households may be effective in dealing with IAP from lighting sources.The study closest to ours is Bernard and Torero , in which the authors implemented a RED to study the effects of electrification in Ethiopia. We build on their work with four main differences. First, our main outcome of interest is PM2.5, which is not measured in their study. Second, our design allows to control for externalities in adoption of electric connections, which due to characteristics of the Ethiopian electrification program is not possible in their study. Third, our study allows to analyze dynamic effects of household electrification, since it includes a baseline and three follow-up follow-up survey.Our findings differ qualitatively from the typical findings in the literature on improved cook stoves in two dimensions. First, the effects found in field studies on improved cook stoves are null or small, especially when compared to the effects expected from laboratory or controlled field studies.The second dimension is that unlike the cook stove literature , the reductions in PM2.5 observed in our sample are steady over time. The time resilience of the effects we find strengthens the link between household electrification and human health discussed in the preceding paragraphs. The remainder of this document is organized as follows. Section 2 presents the study setting and discusses the data. Section 3 describes the conceptual framework that guides our study. Section 4 presents the econometric approach. We discuss the main results in section 5. In section 6 we combine the findings on PM2.5 concentration with time-use data to infer PM2.5 exposure and health implications. Section 7 presents a note on the profitability of replicating our by a private agent, and section 8 concludes. The study takes place during a recent grid extension and intensification program in northern El Salvador, designed to be rolled-out in three phases according to construction costs and accessibility. In this program, the El Salvadorian government covered all the installation costs up to the electric meter, and households had to pay for their internal wiring and a connection fee . The fee for the safety certification is of around US$ 100. It is non-trivial for a household, amounting to roughly 20% of annual per capita income in our sample.

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The interpretation of the technique is based on a ratio of classification error to that of baseline error

Posted on October 16, 2023 by hemp2022

Across studies, different passive samplers have been used—samplers that vary in the nature of the material, size, and subsequent laboratory handling—and it has been questioned whether the specific sampler chosen could influence comparisons of different environments. In this study, we compare the microbial composition and quantity of settled dust that emerged when using different types of passive sampling approaches.Several lines of evidence indicate that, within each experimental setting, bacterial composition was similar within a sampling environment regardless of the sampler type used to characterize that environment. That is, bacterial composition of the passively collected dust correlated most strongly with the particular environment in which the sample was collected rather than with the particular method of dust collection, and this was true both for in situ building samples and for experimental conditions . Statistical analysis confirmed that the sampling environment was the single largest predictor of microbial community composition within a study and that sampler type was found to have much less predictive power, even if differences between sampler types reached statistical significance . Moreover, we utilized supervised learning to determine if unlabeled communities could be classified as belonging to a particular sampler type based on a set of labeled training communities.For each of the USA homes, Finland buildings, and experimental chamber, this ratio was ~1, indicating that the classifier performed no better than random guessing at which sampler types from which experimentally unlabeled microbial communities were derived . On the other hand, the ratio of classification error to baseline error for classifying sampling environment was ≥2.3,vertical cannabis grow indicating that the classifier performs at least twice as well as random guessing for determining the particular dust environment. Lastly, we examined the diversity of taxa detected in the different sampler types within a given study component , as this study was not focused on how diversity compared across the environments.

Using a mixed effect model, Shannon diversity was not found to vary across the sampler types , and observed richness significantly varied only in the chamber component , where it was lower in the EDCs compared to other sampling approaches. In addition, our data speak to two aspects of sampling repeatability. In the USA homes, samplers were placed at two heights, and in the Finland buildings, duplicate samplers were placed side by side at the same location. In each of these trials, duplicate samples were statistically indistinguishable with regard to bacterial composition . The taxonomic composition observed was largely consistent with other recent studies of indoor bacterial microbiomes . Ten groups—the Staphylococcaceae, Micrococcaceae, Moraxellaceae, Corynebacteriaceae, Streptococcaceae, Sphingomonadaceae, Bartonellaceae, Enterobacteriaceae, Rhodobacteraceae, and Streptophyta—combined to ~50 % of sequence reads . Within the chamber trials, for which the microbial community composition of the input dust is known through direct sequencing, there are modest differences in the compositional proportions between the vacuum dust and passive samplers. However, the passive samplers are all skewed in the same direction, such that Pseudomonadales, Enterobacteriales, and Streptophyta are underrepresented in the passive collectors, relative to their abundance in the vacuum dust that was aerosolized into the chamber . Figure 2 highlights the top-most abundant taxa by sequence reads, and the full dataset is available as Additional file 2. Within the building-based observations, taxa tended to vary in their relative abundances rather than in their detection. For example, within Finland buildings, 21 of the 25 most abundant taxa found in the petri dishes were common to the top taxa detected in the EDC and 15 were common to the top taxa in the TefTex. It was only the more rare taxa that were detected in one sampler and missed entirely in others. For instance, a bacterial operational taxonomic unit belonging to the family Dermatophilaceae represented 0.08 % of the sequences in the Petri dish sequences and 0.004 % of the sequences in the EDC but was not detected in the TefTex samples. Within USA homes, Streptophyta comprised a much larger percentage of the reads in petri dishes than the other sampler types. Fungal data were available for only one component of the study, that from USA homes.

Using an approach similar to that used for bacteria, the sampling environment of the USA homes explained over half the variation in fungal composition while sampler type was not a significant predictor .Quantitative PCR was used to estimate the microbial quantity collected in each of the samplers. Tables 3 and 4 report the bacterial and fungal counts, respectively, and additional quantitative PCR markers and more detailed information on analyses of the Finland building samples are included . Because experimental protocols were different in the USA and Finland , absolute values of microbial quantities across study components are difficult to compare. This was particularly the case for the extraction protocol of EDC and TefTex samplers, where the Finnish protocol included a rigorous and more efficient dust extraction procedure. In the USA homes, the highest yields of microbial biomass were found in the petri dish, followed by TefTex and the two EDCs, which had similar yields. The relative differences across locations matched predictions based on occupancy, although we acknowledge low sample numbers. For example, within the USA, quantities were lowest for house 1, which was occupied by a single occupant, and highest for house 3 occupied by a family of five with three dogs. In Finland, houses showed higher microbial biomass than work settings . In contrast to the home settings, yields from the chamber did not show such clear trends. In the chamber, which had much higher particle loading onto the samplers compared to the buildings, TefTex samplers most often showed the highest yields, followed by the petri dish samplers. For bacteria, the mean ratios of biomass detected relative the highest yield in TefTex were 0.7 for petri dish, 0.5 for EDC1, and 0.2 for EDC2; for fungi, the mean ratios were 0.7 for petri dish, 0.5 for EDC1, and 0.2 for EDC2. Side-by-side samplers in the Finland component of the study allows for examination of the correlation between duplicate samplers. Table 5 summarizes Pearson’s correlations of duplicate sampler qPCR determinations. Overall, strong and highly significant correlations were observed for the duplicate determinations in most cases, except in some cases for the TefTex material. The highest correlations were found for EDC3, followed by petri dish, and then TefTex. Although limited by a small number of different sampling environments and duplicate samples, analyses of the intraclass correlation and coefficient of variation of duplicates showed similar trends, with highest correlation/ lowest variation observed for EDC3, followed by petri dish sampling, then the TefTex material. Lastly, correlations of biomass determinations between different sampler types were strong .

Further information is detailed in Additional file 4.Passive collection of dust settled over a defined period represents a valuable tool for assessing microbial exposures in indoor environments, and this study sought to examine how the choice of passive sampler could affect estimates of the community composition and microbial biomass from the settled dust of different environments. We found that, for a given dust environment, estimates of bacterial community composition and diversity in passively collected airborne dust were similar regardless of the sampler type, as were estimates from our smaller study of fungal community composition. In the experimental chamber study, we did note an underestimate of some groups of bacteria, Pseudomonadales, Enterobacteriales, and Streptophyta, relative to the vacuum dust used in the dispersion, but the underestimation was similar for all collection methods. In contrast,vertical farming system estimation of the quantity of microbes was more sensitive to differences in both the dust loading of the environment and the experimental procedures used to collect, extract, and process the dust from the samplers. We discuss three areas of the experimental pipeline in which the different sampler types could vary in their efficiencies: collection, retention, and extraction.For collection efficiency, we refer to the properties of the sampler itself for collecting settling dust. For instance, the electrostatic properties of some surfaces could potentially bias the kind of settling particles that deposit. Many microbial spores carry a small net electrical charge, either positive or negative, although it is generally thought that most are slightly negative. A similarly negatively charged sampler surface could repel particles. All sampler types used here are electronegative to varying degrees, but it is unclear how much charge the samplers retain after heat treatment, if used, or after time employed in the field. Another property of the sampler that could affect collection is whether the material is likely to become saturated, thereby preventing further dust collection. It remains to be tested whether the small bias observed in the collection of some bacteria taxa in passive samplers relative to the source dust is a consequence of disproportional aerosolization of the source dust, size dependence of particle settling, surface charge of the sampler relative to the surface charge of the bioaerosols, or some other process. Another component of sampling efficiency is related to the retention of particles once collected or whether the forces generated by air speeds indoors are sufficient to overcome the adhesion forces between particles and passive collection surfaces. There are observations that the release of dust collected on “smooth” surfaces, such as petri dishes, are greater than from fibrous materials such as TefTex and EDCs. However, the microbial compositions in cow stables were similar between a plastic passive sampler and an electrostatic wipe. Under experimental conditions, resuspension of particles has been studied at air speeds that are orders of magnitude higher than the typical range of speeds in indoor air. In a typical household, the likelihood for a passive sampler to encounter air speeds sufficient to resuspend particles likely depends on the location of the sampler with regard to occupant movements and ventilation strategies. Lastly, the release of biological material from the sampling matrix and subsequent collection is the dominant factor affecting the extraction efficiency of dust and associated microbial material. In all samplers, the dust must first be isolated from the sampler, and in this study, the quantity of airborne dust in the experimental system affected the quantitative estimates that resulted. Within the building-based trials, under levels of particle loading typically encountered in the built environment, the petri dishes almost always yielded higher cell abundance than TefTex or EDCs , likely due to the simple process of using a swab to recover microbes from the sampler.

The step of pre-extraction of the dust from the fabric-based samplers requires specialized equipment and suspension in buffers. A more rigorous microbial recovery process that was employed in Finland, as compared to the USA , narrowed the gap in recovery between plain petri dishes and EDCs. In the chamber system, particle loading was much higher than representative conditions. For instance, with 1.77 g of dust fed, the surface dust loading at the bottom of the chamber was approximately 2.3 g/m2 . With a typical dust fall rate in residences of ~0.005 g/, it would take approximately 460 days to reach this level of dust in the sampler. Under this high particle loading such that a thick layer of dust was left in the samplers , one swab was insufficient to remove all the dust from one petri dish, resulting in an underestimation of microbial biomass per petri dish. As microbial differences across different environments were detectable with each of the passive sampling methods tested here , another consideration is the practical implications of employing the different samplers in field studies. Each sampler had limitations in particular aspects . For instance, sampling materials will vary in their ease of acquiring, preparing, and shipping the material. More importantly, however, are the different protocols—and accompanying equipment—required for isolating the dust from the samplers. The pre-extraction steps of the dust from the fabric-based samplers increase the time and expense of the protocol compared to the petri dish protocol. Considering the economics of implementing and processing the samplers in light of the composition and quantitative results here, petri dish samplers represent a robust method for passive dust collection, although the extraction process may require some additional labor in high particle loading environments compared to more typical building environments.

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Soil moisture monitoring practices help ensure precise frequency and duration of irrigations

Posted on October 13, 2023 by hemp2022

The significant vgsc mutations observed could be a result of selection pressure build-up that is due to more contact with insecticides in indoor-based interventions. From Kisian, the G119S mutation was present at low frequencies even though it was higher in the progeny of mosquitoes resting indoors compared to those resting outdoors. This was more in Kisian, where the vgsc mutations were at lower frequencies than in Kimaeti. These findings suggest that these mutations could be arising from different pressures that could be present in the lowland and absent in the highland.The metabolic enzymes, associated with insecticide resistance activities were found to be elevated, more in indoor resting malaria mosquitoes compared to the outdoor counterparts from both sites. From the phenotypic assays, pre-exposure to PBO synergist restored the susceptibility of the malaria vectors to the pyrethroids commonly used in LLINs by public health. Phenotypic exposures with prior PBO contact demonstrated more activity of monooxygenases in aiding metabolic resistance. The involvement of monooxygenases in pyrethroid resistance has been reported in Western Kenya. In Kimaeti, there was increased levels β-esterases, higher indoors than outdoors. Kisian, on the other hand, did not show involvement of β-esterases in contributing to resistance as shown by similar levels in indoor and outdoor resting mosquitoes. The glutathione-S-transferase possibly played a part in the resistance levels as a previous study reported since it was higher in mosquitoes resting indoors than those resting outdoors from both Kisian and Kimaeti. These levels, therefore, suggest that monooxygenases were the main mechanism of insecticide resistance in Kisian, especially with the low frequency of resistant alleles, whereas in Kimaeti, the case pointed be a combination of genotypic and metabolic mechanisms. The expression of phenotypic,grow solutions greenhouse genotypic and metabolic resistance appears to be higher in indoor than outdoor resting malaria mosquitoes in these regions.

The widespread use of LLINs in attempts to controlling these vectors and the extensive agrochemical use could be strengthening the increase of insecticide resistance in the sites. The higher levels indoors suggest that these mosquitoes could be resting indoors because they are adequately resistant to the insecticides used in LLINs, posing a threat to the wide coverage LLINs. On the other hand, outdoors, the resistance mechanisms were present as well pointing to exposure to these insecticide-based interventions in just enough pressure to elicit expression of the resistance traits. The levels of resistance could be enough to elicit an increase in malaria incidence due to the reduced mortality of resistant malaria vectors that could hinder current vector control interventions.Increasing temperatures and higher variability in precipitation in California are part of a larger regional trend in the Western United States . This is consistent with global trends that indicate that 2000-2010 has been warmer at the Earth’s surface than any preceding decade since 1850 . Observed increases in temperature and precipitation extremes in semi-arid regions, such as Southern California, clearly translate into more severe future impacts than analogous trends in temperate regions, such as projections of increased frequency and duration of heat waves and droughts over the remainder of the current century . Previous studies suggest that agriculture in the largely irrigated Western United States may not be as susceptible to precipitation trends as agriculture in the more temperate East . This holds for long-run mean precipitation conditions . However, this conclusion minimizes the severity of the recent drought experienced in California with historically low precipitation and soil moisture levels . The recurrence and longer duration of droughts in California over the past two decades has greatly affected the agricultural industry, which, on average, uses about 80% of freshwater resources . Figure 1.1 illustrates the percentage of California’s area in drought from 2000-2016. Not only does this reveal the large spatial and temporal extent of the most recent drought, but the colors reveal the large area under extreme and exceptional drought from mid-2013 to 2017. The most immediate economic impacts are lost agricultural revenue emanating from fallowed acres and yield declines, and farm job losses for one of the most vulnerable socioeconomic groups. For example, the 2009 drought resulted in revenue losses of $370 million with fallowing of 285 thousand acres in the San Joaquin Valley, and almost 10 thousand farm jobs losses .

Arguably the most important variables explaining how agriculture will be affected by climatic changes are those of human ingenuity at the farm level. Human ingenuity is simply another word for adaptation to climate change in order to minimize welfare losses. Thus, the overarching theme of our three subsequent analyses is quantifying grower responsiveness to farm-level microclimate in Southern California, our study area. Using original survey data, we study differential impacts of short-run weather and long-run climate—based on farm size, type, and water source—on productivity per acre and likelihood of adopting water management practices, which have not been studied in previous county-level analyses. Further, we are able to decompose water sources into price, pricing structure, frequency of rate increases, senior water rights, quality, and type of source . In addition to studying farm-level productivity, we study short-run fluctuations in weather on likelihood of adoption of water management technologies and practices, and on parcel-level land sales. Our contribution to the literature is based upon an original survey instrument we developed and disseminated to growers in the region . The contact information was taken from the respective county Agricultural Commissioner Offices. This survey is comprised of 28 multiple choice and fill-in questions on grower, farm, and water source characteristics. This was disseminated via mail by a team of 3 undergraduate students, to growers in the study region, with a 14.6% response rate. We focus on Southern California agriculture, specifically Imperial, Riverside, San Diego, and Ventura counties. The region is often overlooked as analyses tend to focus on the Central Valley, California’s most productive agricultural region. Yet, there are several crops for which 50% or more of California’s production originates in these four counties, including raspberries, lemons, flowers and foliage, avocado, and sudan hay. All of the state’s date and sugar beet production originates in these four counties . Imperial, Riverside, San Diego, and Ventura counties are amongst the top 15 agricultural counties in the state, representing approximately 16% of statewide agricultural revenue . They also represent the diverse climate of the region with two coastal , and two desert counties.

The 4 counties also vary in farm size with San Diego County having the largest share of farms under 10 acres, and, at the other extreme, Imperial County having the largest share of farms with 1000 or more acres . There is also a wide distribution in gross revenue across these counties . An immediate concern with aggregation at the county level is the omission of data on decision-maker/grower , farm ,marijuana drying rack and detailed water source attributes . Excluding such information assumes a priori a limited role of the economic agent to influence farmland productivity. It also simplifies the inherent complexity in representing farm and water source characteristics. It is not for lack of explanatory power that these variables are excluded. It is more likely that they would have been studied had they been available in existing data sources. The USDA Farm and Ranch Irrigation Survey , a major source of US agricultural data for economic analyses, does not provide these variables at the farm level to researchers. There is, however, little reason to assume that the climate, soil, and water variables in county-level studies are correlated with any of these microlevel variables, thus ruling out the potential bias in climate, soil, and water estimators. Aggregation at the county level also leaves the model susceptible to measurement error on certain explanatory variables . Measurement error is defined as an imprecise measure of an economic variable, dependent or explanatory, which has a well-defined quantitative meaning . 1 Following the classical errors-in variable assumption, this could lead to estimators that are asymptotically inconsistent and biased downward in their respective probability limits . 2 The remaining sections in this chapter present the theoretical framework behind each of the 3 empirical analyses in this dissertation: the Farm-Level Ricardian, the Discrete Choice of Adoption, and the Parcel-Level Models. Each subsection also includes hypotheses on the impact of climate and other key variables on the respective dependent variables . In addition to studying the impact of climate and other relevant variables on farmland productivity, we study the factors influencing the adoption of technologies to monitor soil moisture and salinity.5 Adoption of climate-effective monitoring practices is particularly important as projections of prolonged drought continue throughout the current century. Most growers in our sample have already adopted micro-irrigation practices for vegetables, orchards, and vineyards, and extension experts suggest that consistent and/or sophisticated monitoring of growing conditions represents the next stage of irrigation efficiency adaptations .

Salinity monitoring affects water availability in both the short and long run. Too much leaching leads to water waste and, ultimately poor irrigation and economic efficiency. Too little leaching affects soil salinity and water quality at both the farm and basin level, and ultimately water availability at the farm-level in the long run. We implement logistic regression, consistent with previous studies on technology adoption , to study the factors influencing adoption of at least one soil moisture monitoring practice , or at least one water salinity monitoring practice . Prior to implementing the pilot survey, we received approval from the UCR Institutional Review Board.There were two primary objectives to the pilot survey: field-test survey questions, and gauge response rate. Rather than rely on focus groups to field-test the survey questions, we chose to disseminate a pilot survey. The major benefit of sending a pilot survey is that we could potentially receive valuable input from respondents who could not participate in focus groups due to financial, time, or physical constraints. A second benefit was time savings in survey implementation. Focus groups require managing multiple schedules to find a convenient meeting time and place, and possibly funding travel and accommodation. Although we planned to disseminate an online survey, we had not yet at that stage secured assistance from either Agricultural Extension or Farm Bureaus in each county to host our survey. In order to save time, we sent the pilot survey via postal mail using contact information from the Agricultural Commissioner Pesticide Permit Database . An informal team of fellow graduate students and family/friends helped prepare the pilot phase mailings. Each mailing package included invitation letters , consent documents , first version of questionnaire , and a self-addressed return envelope. Using a random-number function in Microsoft Excel, we randomly selected 300 respondents in total from Riverside and San Diego counties. We selected these counties as they are representative of the type of agriculture found in the region . Based on our discussions with extension experts,8 we were sensitive to the potential apprehension with which Imperial County growers, in particular, would react to our survey. Growers in Imperial County have held senior water rights for over a century due to the Seven-Party Agreement.They are aware that they have been criticized for using less efficient irrigation practices , and many fear that they will be mandated to change these practices . Thus, they may be hesitant to providing any information on irrigation and other practices. In order to minimize Imperial growers’ time burden, we chose to field test the survey on a potentially more receptive audience, and send only the final survey to Imperial. Since Ventura County has a relatively similar distribution of farm types as San Diego County , we also decided to exclude Ventura from the pilot. The pilot survey consists of 20 questions, including grower characteristics , farm characteristics , water source characteristics , water management practices , perceptions of water scarcity , and an open-ended comment space at the end of the survey . The majority of these questions are multiple choice often with an “other” choice that included an option to write in a response that was not pre-determined. Eight questions are fill-in style. We received a roughly 10% response rate from the pilot phase , and learned valuable lessons on question structure for preparing the final survey. First, there were far too many questions on water scarcity perceptions, which could be consolidated into fewer questions. Second, income questions were better placed at the end of the survey to minimize participant suspicion.

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Dogs have been identified as a major identifiable source of endotoxin

Posted on October 12, 2023 by hemp2022

Analyses were conducted across both regions and separately by region given the difference noted above. We also used mixed models to analyze the prediction of personal and indoor endotoxin exposure by the following household characteristics: dog and cat ownership, including the number of dogs or cats and whether dogs or cats were allowed in the house ; number of people living in the home, carpet , whether it was it customary to remove shoes before entering the home, observed cockroaches, observed rodents, flooding damage, surface mold or mildew, livestock, central air conditioning, and region . Personal and family characteristics were also used in the prediction models and included age group , sex, race-ethnicity, mother’s education, and family income. For predictor variables in the indoor endotoxin models, we found insufficient variability across the 12 homes for the more refined categories used in the personal models . Therefore, we dropped carpet cleaning, cockroach and rodent presence, shoe removal, livestock, and air-conditioning. We also dichotomized cat and dog ownership and family income. We began with crude prediction models adjusted for personal temperature, personal relative humidity and study region for personal endotoxin, and study region for indoor endotoxin . We then selected the best multivariate model based on stepwise backward elimination of predictors with the largest p-value over 0.05, and on model fit by AIC. Removed variables were added back singly to the final model to test the appropriateness of the final model.We found detectable endotoxin concentrations in 376 daily personal PM2.5 filters analyzed [median 0.57, range 0.002 – 25.3 EU/m3 ]. All 52 personal field blank filters showed low or non-detectable endotoxin . Within-subject coefficients of variation for personal endotoxin ranged from 69% to 224% . We also successfully extracted and found detectable endotoxin concentrations in all 317 daily Harvard Impactor filters from the stationary site active samplers. As described in Table 1, these included 97 ambient, 109 indoor and 101 outdoor home filters,greenhouse racking and 10 filters from a site in Whittier that served as both an outdoor home and ambient site during one 10-day run, and served as the central ambient site for remaining 10-day runs.

The 42 blank filters at the stationary sites showed low or non-detectable endotoxin . For the comparisons with available indoor and outdoor measurements there were 116 and 113 personal endotoxin measurements, respectively, among the 14 subjects living in those 12 homes. For the comparisons with available ambient endotoxin measurements there were 339 personal endotoxin measurements among the 45 subjects. For the analysis of personal vs. fixed site endotoxin in regression models, one subject for just one day lacked personal temperature and humidity as covariates leaving 338 ambient, 115 indoor, and 112 outdoor observations for analysis. There were all or nearly all 376 personal endotoxin measurements for the comparisons with available ambient air pollution. Ambient air pollutant measurements were nearly complete with at least 407 days for each variable available for comparison with the 423 days of ambient endotoxin measurements. Descriptive statistics regarding all of the exposures by region are shown in Table 2. Arithmetic mean and median personal endotoxin exposures were higher in Riverside than in Whittier. Consistent with this, outdoor home and ambient endotoxin were higher in Riverside than in Whittier. However, indoor endotoxin exposures were higher in Whittier than in Riverside. Although arithmetic mean personal endotoxin was higher than indoor, outdoor or ambient levels across both regions, the median personal endotoxin was only higher in Riverside. This is a reflection of the typical skewed distribution of endotoxin exposures. Indoor to outdoor endotoxin ratios of medians were clearly opposite between the two sites with a ratio < 1.0 at Riverside and a ratio > 1 at Whittier .Actual indoor concentrations reflected this difference with a much lower indoor concentration in Riverside than in Whittier. We show correlation matrixes separately for Riverside and Whittier relating personal endotoxin and stationary site endotoxin to personal and stationary site endotoxin and air pollutants . We found personal endotoxin in both Riverside and Whittier was not significantly correlated with indoor endotoxin or any of the indoor air pollutants. Personal endotoxin was not significantly correlated with outdoor home endotoxin in either Riverside or Whittier. We observed small positive correlations between personal and ambient endotoxin in Riverside but not Whittier. Outdoor home and ambient endotoxin measurements were strongly correlated. In both Riverside and Whittier, personal endotoxin showed a small inverse correlations with personal PM2.5, and small positive correlations with personal PM2.5 EC and OC, which were larger in Whittier.

Personal endotoxin positively correlated with personal temperature in Riverside but negatively correlated with personal temperature in Whittier.Personal endotoxin in both Riverside and Whittier were not significantly correlated with any of the indoor air pollutants. Indoor endotoxin in Riverside, on the other hand, was strongly positively correlated with indoor PM2.5 EC and moderately correlated with indoor PM2.5 mass and OC, whereas in Whittier these correlations were positive but much smaller. Both personal and outdoor home endotoxin in Riverside were not significantly correlated with any outdoor home air pollutant measurement. We observed a small inverse correlation between personal endotoxin and outdoor home PM2.5 in Whittier. Outdoor home endotoxin showed small positive correlations with outdoor home PM2.5, EC and OC in Whittier. In Whittier, ambient temperature and O3 were negatively correlated with personal endotoxin. In Whittier, but not Riverside, ambient endotoxin showed small positive correlations with ambient traffic-related air pollutants and temperature and small inverse correlations with relative humidity.The prediction of personal endotoxin in mixed regression models by the various stationary site measurements of endotoxin are shown in Table 4 including both sites together and separately by region. Ambient endotoxin for the 14 subjects in monitored homes, and their exposure to indoor and outdoor home endotoxin were not significant predictors of personal endotoxin. However, ambient endotoxin for all 45 subjects was a significant positive predictor of personal endotoxin. The regional models show that the overall association was attributable to measurements at both sites, although the regression coefficient for Riverside was twice as large as Whittier. However, the regression coefficient for Whittier was more significant than Riverside . Figures 1-2 show scatter plots and results of linear regression models for the relation between log transformed indoor and outdoor home endotoxin across the 10-day monitoring sessions in 4 homes in Riverside and 8 homes in Whittier. In both regions, the relation was positive, with outdoor endotoxin explaining 25-28% of the variability in indoor endotoxin. The analysis of the relation between personal endotoxin and household or subject characteristics shows a clear positive association with dog ownership in crude models adjusted for personal temperature, personal relative humidity and region .

For each dog owned, personal endotoxin exposure approximately doubles. Interestingly, compared with having no dogs, the strongest and only significant association with personal endotoxin in crude models was for dogs that were only occasionally indoors. This contrasts the finding for cats since the only significant association was for having cats that were often indoors compared with having no cats. Other variables were significantly positively associated with personal endotoxin in the crude models,equipment for growing weed and they included reports of flooding damage and sex . Nominal associations included increasing personal endotoxin by the number of household residents and lower personal endotoxin among Hispanics. The final selected multivariate model included only cat and dog numbers adjusted for personal temperature, personal relative humidity and region. We found a relative increase in endotoxin for each dog of 1.76 , and for each cat of 1.39 . Residence of dogs and cats were not included due to expected dependent relations with the number of animals Chi-Square p-value < 0.0001. Adding back single excluded variables to this final model did not improve the fit of the model and showed that each of those variables were non-significant including flooding damage and male sex . Flood and cat were positively associated with each other . As a result, cat number confounded the association with flood and the association with cats also decreased by 37.5% as well . The analysis of the relation between indoor endotoxin and household or subject characteristics shows that unlike the personal exposure models, dog and cat ownership was not associated with indoor endotoxin . Only three variables were significant in the crude models,reports of flood damage, which was unexpectedly associated with lower endotoxin, Hispanic subjects associated with higher endotoxin , and high school or lower education level in mothers that was associated with lower endotoxin. The final selected multivariate model included only flooding damage and lower education levels in mothers.Our results suggest that fixed site measurements of endotoxin in the home environment may not adequately represent daily personal exposures. The finding of a positive association between ambient and personal endotoxin exposure is not particularly relevant to research used to investigate relations of respiratory health to endotoxin , but it does have some relevance regarding potential impacts of regional sources on personal exposure. It is possible that the limited sample size was insufficient to detect an association of personal with home endotoxin. Evidence in support of that view is that when we limited the analysis of prediction of personal endotoxin by ambient endotoxin to the monitored homes , associations were non-significant but point estimates were similar to those for the 45 subjects . Nevertheless, although we had a limited sample size in the 14 subjects, the findings for the relation of personal endotoxin exposure with indoor home endotoxin exposure , suggest that other micro-environments and personal activities are important to assess.

Given that our analysis was based on daily exposures using measurements all conducted with active 24-hour samplers, our conclusion that any one fixed site measurement may not adequately represent personal exposure applies to short-term exposures that may be involved in the acute exacerbation of asthma. We assessed the potential importance of other locations and physical activity by using previously reported data on quarter-hourly time-activity reports from an electronic diary that each subject filled out throughout follow-up. We found that on average, around 73% of time was spent at home indoor, 1.7% at home outdoor, 12.6% at school indoor, 1.8% at school outdoor, 4% in-transit, 4.3% indoor elsewhere, and 2.6% outdoor elsewhere. Out of an estimated average of 40 min per day of diary reported moderate to strenuous activity , 82% occurred while away from home. Such higher levels of activity may be important in promoting personal endotoxin exposure as a result of the so-called “personal dust cloud.” This is a phenomenon where localized personal activities lead to increased PM exposure by re-suspension of settled PM, which brings the breathing zone of subjects into closer contact with PM from various sources. The highly skewed distribution of personal endotoxin we observed may be partly due to the generation of personal clouds that results from subject activity, including activity around sources of resuspended dust. Our findings of a general lack of correlation between personal and home micro-environmental endotoxin are consistent with the findings of Rabinovitch et al.. In a panel of school children with asthma, they found geometric mean personal endotoxin was higher than indoor or outdoor school endotoxin levels, and personal endotoxin was not correlated with these stationary site measurements.The present results show a positive association between personal endotoxin and the number of dogs and cats owned, as expected, and this substantiates the utility of the personal exposure measurements. This finding is consistent with a sub-study of 10 children by Rabinovitch et al. who found personal endotoxin exposures were significantly higher in 3 households with dogs and one with cats compared with 6 households with no furry pets. We found the association of personal endotoxin was strongest among subjects with dogs that were only occasionally indoors. This could be attributed to entrainment of debris from the outdoor environment into the indoor environment, including fecal matter. However, we found no association between indoor endotoxin and dog or cat ownership. This may be due to either the smaller sample size or that personal exposure is more dynamic as would be expected from the generation of personal dust clouds.

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