Hemp Growing

A major unanswered question is whether expansion began with hunter-gatherer groups, perhaps as a result of the invention of particular technologies or behavioral innovations, or much more recently with the advent of agriculture. Early mtDNA studies suggested that humans experienced a burst of population growth between 30 and 130 thousand years ago —well before the start of agriculture. More recent results have extended the time frame for sub-Saharan African growth to 213–12 kya, depending in part on mtDNA haplogroup. However, it is populations—not haplogroups— that are subject to growth, and many present-day hunter-gatherer groups, including those in Africa, do not exhibit any mtDNA signal of demographic expansion at all. On the other hand, Y chromosome sequence data are compatible with a model of constant size for both hunter-gatherer and farming populations in Africa. Autosomal microsatellites tend to indicate an early start to population growth, but there is disagreement among studies on the time of expansion and whether or not the expansions involved African populations. Zhivotovsky et al. examined a large autosomal microsatellite dataset in 52 worldwide populations and concluded that African farmers, but not hunter-gatherers, exhibit the signal of population growth. Unfortunately, inferences of demographic parameters based on the above mentioned loci may be unreliable due to the possible confounding effects of natural selection or evolutionary stochasticity , best way to dry bud or uncertainty in our understanding of mutation rates or the underlying mutation process. A more reliable source of information regarding past population size change comes from multilocus nuclear sequence studies.

Once polymorphism data from multiple X-linked and autosomal loci began to appear, clear discrepancies with inferences based on both mtDNA and microsatellites emerged. For example, most non-African populations tend to have positive Tajima’s D values— reflecting possible contractions in Ne—while most African populations tend to have only slightly negative values. Indeed, the largest re-sequencing study to date that targets unlinked autosomal non-coding regions finds that patterns of neutral polymorphism in non-African populations reject the standard constant size model, and are most compatible with a range of bottleneck models invoking a large reduction in effective population size some time after the appearance of modern humans in Africa. In contrast, data from the sole African population examined, the Hausa of Cameroon, were compatible with demographic equilibrium, as well as with a set of recent population expansion models. In this paper, we expand upon the work of Voight et al. by analyzing a re-sequencing dataset comprised of 20 independentlyevolving autosomal non-coding regions in 7 human populations. Our sub-Saharan African populations include the San from Namibia, Biaka from the Central African Republic, Mandenka from Senegal, and Yorubans from Nigeria. Our multilocus analysis, which focuses on two summary statistics with power to detect population growth , follows a two-step approach. We employ a simulation-based method to test the hypothesis that populations experienced exponential growth after a period of constant size. When the hypothesis cannot be rejected, we then fit parameters of this two-phase growth model to our data using approximate Bayesian computation. As in previous studies, we find that the non-African data are not consistent with a simple growth model. On the other hand all four sub-Saharan African samples fit the two-phase growth model, and we are able to infer a range of onset times and growth rates for each population.

We sample sub-Saharan African populations that practice different subsistence strategies and then ask whether the inferred signals of population growth are shared between, or specific to, food-gathering or food-producing groups.Our understanding of population size changes in human prehistory has improved as our genetic datasets and analysis methods have become more sophisticated. Early studies of the pairwise mismatch distribution in mitochondrial DNA suggested dramatic increases in population size between 110 and 70 kya in sub-Saharan Africa. More recent coalescent studies have also favored 50- to 100-fold growth occurring between 213 and 12 kya. Conversely, modern surveys of nuclear sequence variation at unlinked loci have not provided clear evidence for rapid population growth from small ancestral size. For example, African populations usually exhibit slightly negative Tajima’s D values, while non-African populations tend to have positive Tajima’s D values. Different patterns of polymorphism in African and non-African populations have been interpreted as reflecting a history of bottleneck in the ancestry of non-Africans. Therefore, the question of when anatomically modern human populations began to expand in size is better addressed in sub-Saharan African populations because more recent demographic events likely obscure signals of population growth in the ancestors of nonAfrican groups. Bottlenecks, in particular, can mask the effects of earlier, as well as later, population growth.However, thus far, very few surveys of nuclear DNA sequence variation have been performed in sub-Saharan African populations, and interpretations drawn by existing studies have been complicated by the different populations and loci analyzed, the kinds of analyses performed, and the different growth models assumed.

The earliest studies considered only the few existing nuclear sequence data available in the literature at the time, and explored only a small set of growth model parameters. Later studies adopted a more explicit hypothesis-testing framework, but focused on only a single African population. For instance, Pluzhnikov et al. analyzed a large resequence dataset of noncoding autosomal regions for the Hausa of Cameroon . They determined that while observed summaries of the site frequency spectrum did not statistically reject a null model of constant size, they were consistent with a range of alternative growth models. Consequently, Voight et al. turned to a goodness-of-fit approach to determine better estimates of the time of onset of growth and the growth rate in the Hausa. By generating approximate likelihoods for the mean of observed summary statistics over a grid of parameter values, they determined that the Hausa best fit a growth model beginning ,1,000 generations ago with a per-generation growth rate a of 0.7561023 . Assuming a generation time of 25 years, this corresponds to an overall ,2-fold growth rate from ancestral to modern size beginning ,25 kya. Here, we extend these sorts of analyses to a greater range of African populations: two hunter-gathers, the San of Namibia and the Biaka of the Central African Republic; and two food producers, the Mandenka of Senegal and the Yorubans of Nigeria. All four groups show depressed values of Tajima’s D and Rozas’ R2 coupled with a high proportion of singleton mutations . These patterns of sequence polymorphism are suggestive of population growth. We therefore tested our multilocus African dataset to determine whether we could reject models of population growth, and adopted the best aspects of previous hypothesis-testing and inference approaches. We first employed hypothesis-testing to determine, by coalescent simulation, whether a range of growth models could be rejected in favor of constant size using the method pioneered by Pluzhnikov et al.. When growth could not be rejected, we fitted parameters of the two phase growth model to our data using approximate Bayesian computation . Thus, we conditioned simulations on each locus individually , cannabis grow setup and explored a continuous range of parameter values rather than restricting our search to a set of predetermined grid coordinates. All of our African populations best fit models with relatively low population growth beginning in the late Pleistocene . Even with ,112-kb of sequence data per individual, a large range of growth models are consistent with our 95% credible regions for t and a. We cannot, for instance, statistically distinguish different rates and times of growth among our four sub-Saharan African samples. However, our hunter-gather populations show a tendency towards slightly older and stronger growth than our food-producing populations . Furthermore, we detect a strongly negative, non-linear association between t and a . This effect, which has been identified previously, implies that sequence data from our four African populations are consistent either with weaker growth beginning earlier in the Late Pleistocene, or with stronger growth commencing more recently. Interestingly, we can reject an onset of population growth for the San during the Holocene , and therefore, growth in this population is not linked to the development of agriculture. Although we cannot reject an onset of growth associated with agriculture for the Biaka, Mandenka and Yorubans, our best fitting models do not favor this interpretation. Indeed, the limited size of our dataset gives us more power to infer older rather than more recent growth. We see little effect from the increased size of the dataset obtained for Yorubans. Even though we increased both the number of samples and the number of loci , estimates of the rate and timing of growth are comparable to those inferred for the Mandenka, and our 95% credible region is not appreciably smaller. This is interesting given that, under a model of population growth, expected values of Tajima’s D depend to some extent on sample size. With regard to the small increase in the number of loci in our Yoruban dataset, recent power analyses by Adams and Hudson suggest that orders of magnitude more data may be necessary to obtain growth model parameters with substantially greater accuracy, especially in models involving recent growth. Furthermore, the modern effective sizes we infer – on the order of 105 – are much smaller than regional census sizes.

This discrepancy partly reflects the fact that effective size is not a simple proxy for census size. However, another explanation also seems likely: under a model of exponential growth, the bulk of the population increase is weighted towards the present, and for the aforementioned reasons [28], we are not likely to capture the effects of substantial increases population size in modern times. Although population growth seems like a reasonable demographic model for human groups on non-genetic grounds [1,2,34], humans have likely experienced both population growth and population structure at some time in the past. The question is whether and to what extent either or both of these aspects of population history left a signature on patterns of variation. To explore the effects of alternate models of population structure on patterns of genetic variation, we use a coalescent simulation approach. In particular, we examine how Tajima’s D and Rozas’ R2 respond under models incorporating low-frequency gene flow in a structured population, recent admixture, and cryptic population structure . We assume a two-deme splitting model with i) a constant low level of gene flow, ii) a single admixture event occurring ,3 kya , and iii) population structure collapsing ,150 years ago . All of these processes produce very slight reductions in Tajima’s D and Rozas’ R2, but the mean deviations never exceed 0.27 and 0.011, respectively. To put these values in perspective, such deviations represent no more than 10% and 12% of the variance naturally observed for Tajima’s D and Rozas’ R2 under the corresponding standard neutral models with no gene flow, admixture, or cryptic population structure. Although these confounding factors may have caused our growth estimates to appear slightly older or stronger than they actually are, their effects are minor. Similarly, biases in our estimates of per-locus mutation and recombination rates are unlikely to have major effects on our inferences. For instance, elevated recombination would lead to a lower variance of Tajima’s D and Rozas’ R2, which would return growth estimates with less uncertainty, while elevated mutation rates would shorten our time frames, and hence return younger growth estimates. Estimates of growth rates under the isolation-with-migration model, which simultaneously accounts for population structure and gene flow, are consistent with our inference of an increase in the effective size of sub-Saharan African populations. Although growth rates are lower than suggested by ABC, we still infer that African populations experienced ,5-fold growth from ancestral sizes. While a simple two-phase growth model is too simplistic to fully describe African population history, it is interesting to note that a more complex model incorporating an ancient bottleneck does not fit African resequencing data. This is in marked contrast to the large reduction in population size that the same studies inferred for non-Africans. We therefore suggest that our growth estimates genuinely reflect a substantial increase in effective size among sub-Saharan African populations beginning in the Late Pleistocene. However, we note that these inferences could be complicated by other forms of population structure not accounted for in our models. While some authors have speculated that human populations underwent sudden expansions in population size in response to dramatic climatic events, technological inventions, or behavioral changes that took place earlier than 50 kya, our data are more consistent with a model of exponential growth beginning after 50 kya, but certainly before the Holocene.

Our results emphasize that lytic phages are likely to be an important component of the microbiome and are capable of influencing both bacterial abundance and diversity over short timescales.In our first of multiple experiments , we conducted a proof of concept experiment. We used ddPCR to measure quantities of known phage and bacterial host in size fractions of our mock community , and we determined that our fractionation method effectively concentrates phages from the leaf wash, allowing us to deplete them from both the “bacteria only” and 100K MWCO filtrate fractions of the leaf microbiome . FRS and SHL bacteriophages were effectively depleted, although the ddPCR signal was not entirely eliminated in the 0.22- µm filter bacterial recovery fraction . Phage levels were concentrated from the 0.22- µm flow-through fractions in the 100K MWCO centrifugation unit, representing bacteria plus phage treatment. Lastly, we also measured decreased levels of phage in the 100K MWCO flow-through fractions, representing the additional phage-depleted inoculum: bacteria plus filtrate. FRS and SHL phages are approximately 60 and 80 nm in size, respectively, and we thus presume that most phages in the environmental samples that are that size or larger should be retained in the upper portion of the 100K MWCO centrifugation unit. Membrane pore size for the unit we used is 10 nm; therefore, curing and drying weed smaller phage particles should have been retained in the upper fraction as well. Overall, we therefore consider both the bacterial/fungal fraction and the 100K MWCO flow-through fraction phage-depleted, but not necessarily absent of all phage.

Lastly, levels of P. syringae pv. tomato abundance was measured in all fractions , and signal was also detected in the non-bacterial fractions. However, this is likely due to the detection of DNA and not the presence of live cells, as bacteria could not be cultured from those filtered fractions . As seen in Figure 4-1d,infectious phage particles were present in the initial leaf wash, and they were also sufficiently high in concentration to completely lyse the bacterial lawn in the 0.22 µm flow through and 100K MWCO concentrate fractions, as little to no bacterial growth is observed. By comparison, a solid bacterial lawn is seen in the 0.22- µm filter recovery sample, where most phages appear to be depleted. As evidenced by a small number of plaques, a few bacteriophages are present in the 100K MWCO filtrate. This further supports the possibility that the third treatment, bacteria plus filtrate, was phage-depleted, but not completely free of phages, in our subsequent field experiments.After rarefaction and filtering, there were a total of 200 OTUs present in the spray inoculum from field experiment 2 representing taxa from the four top phyla commonly found in the phyllosphere: Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes. As expected, the bacterial composition of inoculum from the three different treatments, sampled after resuspension with/without phage but before growth, has similar rank order of relative abundance for the top OTUs . Observed differences in relative abundance of specific taxa may be due in part to concentrated free bacterial DNA in the 100K MWCO fraction. Given the way in which inocula was prepared , it is unlikely that the bacterial communities differed substantially between treatments at inoculation.Using a community-level phage depletion approach, we found that the phage fraction of the phyllosphere microbiome from field-grown tomato plants impacted bacterial abundance and composition during microbiome establishment on a new host.

When microbial communities were sprayed onto juvenile tomato plants after either phage depletion or resuspension with the depleted phage-fraction, we observed decreased abundance in the latter treatment after 24 hours across three different experiments : first with six independent leaf wash sources , then with one leaf wash source and six plant replicates per treatment , and finally with a constructed bacterial community and natural phage fraction . Using 16S rRNA Illumina MiSeq data from field experiment 2, we were able to further show that the phage-fraction of the phyllosphere affects microbiome composition, including relative abundance of specific OTUs . We observed an effect of phage depletion treatment on community dissimilarity between treatments after 24 hours, but not after 7 days . We also found some evidence for differences in both alpha and beta diversity between phage depleted and phage re-suspended communities after 7 days . Overall, these results support the idea that lytic phages can mediate bacterial dynamics within host-associated bacterial communities, as they have been found to do in free-living communities. Across these experiments we observed a decrease in overall bacterial abundance 24 hours after inoculation, suggesting that phages affected growth of the most common and/or fastest growing bacterial strains during colonization of a new plant host. However, it is important to note that decreased overall bacterial abundance is not necessarily an expected outcome of lytic phage action within a microbiome. This is both because phage-mediated lysis has been shown in some cases to increase population growth due to release of nutrients but also because other strains that are not being targeted by phages should be able to offset any decreased growth of susceptible bacteria.

That the impact of phages on abundance in our experiments was short-lived suggests either that phages are particularly impactful during initial colonization, as bacterial population are rapidly growing, or that resistant bacterial strains/species increased in density over time to utilize existing resources. Indeed, the Kill the Winner hypothesis predicts that phages should most commonly prey upon highly competitive bacterial species. Results of our sequencing efforts supports this model, as we found different relative abundances of the two dominant families when the phage fraction was versus was not present in the initial inoculum. After 24 hours, the bacteria plus phage treatment plants were observed to have lower abundances of Pseudomonads, but when the phage-fraction was depleted there was an overabundance of an OTU within the family Enterobacteriaceae. However, after seven days the differences in relative abundance of these two OTUs were no longer observed to differ among treatments. Although only marginally significant, the presence of phage in the inoculum also led to an increase in alpha diversity at seven days post-inoculation. Again, this result may have been driven by a decrease of Pseudomonads after the first 24 hours, perhaps allowing a richer community to develop after the first week. Interestingly, when comparing beta diversity among treatments using averaged Bray-Curtis distances between samples within a treatment, we found an interaction effect between day sampled and inoculum treatment. This suggests that the phage fraction of the microbiome may also be having an effect on among-host microbiome diversity, initially driving divergence among communities as the empty niches are filled, , but eventually leading to more synchronous community structure. It is important to note that the patterns we observed were based on the depletion of lytic phages from the microbiome at the point of inoculation, but there were almost certainly many temperate phages remaining and possibly some lytic phages contained within bacterial cells at the time of collection/filtration. As such, it is possible that differences in treatment effect observed between 24 hours and 7 days were due to the resurgence of phages in the phage-depleted communities rather than loss of phages in the bacteria plus phage treatment. The observed transience of phage-mediated impacts on abundance and diversity is intriguing, and longer-term studies with more time points are needed to better understand temporal effects of phages on bacterial communities. One question we were not able to directly address in this series of experiments is the constituents of the leaf wash filtrate . The molecules and small proteins found in this filtrate had a surprisingly large and variable impact on the phyllosphere microbiome, impacting both abundance and community composition and causing high variation among biological replicates. In future experiments, additional size fractionation of the leaf wash filtrate and/or mass spectrometry analysis of these fractions may help address this question. As observed in our proof of concept experiment, cannabis drying system it is also possible that some bacteriophages made it through the filtration step and were present in this treatment. We decided to eliminate this treatment from many of our analyses due not to the effect of the treatment itself but rather due to the high variances observed across replicate plants. In most cases, plants within this treatment spanned the variation observed in both the bacteria alone treatment and the bacteria plus phage treatment. It was therefore unclear to us how to interpret this treatment and what biological significance it might have, but further study is certainly warranted. Another limitation of this work is that we have not identified the specific phages in the phage-fraction of the experiment. We have taken measures to ensure that the method used for separation of microbiome fractions is effective at separating phage from bacteria, but in order to fully describe the diversity of phage, as we have done for the bacterial community, one would need to take a metagenomics approach.

Furthermore, there may be other entities that are phage-sized in thatfraction of the microbiome, such as extracellular vesicles or spores of bacteria such as Bacillus that impact upon microbiome colonization. However, given that the current estimates of phages largely outnumber bacteria in the environment, we expect non-phage particles to be far less abundant than phages in this size fraction. This was recently shown for outer-membrane vesicles, where they were estimated to represent less than 0.01-1% of SYRB DNA-stained phage-sized particles quantified in seawater. Furthermore, we cannot rule out the possibility that the presence of phage, but not their predation on specific taxa, is causing the effects we are observing. However, by recapitulating the results of decreased abundance in bacteria after 24 hours when a phage fraction was present in our constructed community, we were able to lend some insight to this question. In this case, our detection of a phage capable of lysing a member of the constructed community suggested that the phage fraction was most likely driving the observed decrease in abundance. This is further supported by the fact that the phage was found to lyse Pantoea agglomerans, a member of the family Enterobacteriaceae, which we have found to be in high relative abundance in 16S rRNA community data in both this experiment and other unpublished work. Another important note is that the ddPCR protocol used here relies on lysis of bacteria cells through a hot-start step in the PCR. Because of this, it is possible that our abundance measures do not take into account hard-to-lyse bacteria. Finally, we did not include any analyses of the fungal communities in these microbiomes, as it was outside the scope of the current work. However, it is possible that our filtration methods also impacted any fungal viruses that might have been present in this study. How fungal communities are influenced by viruses within the microbiome is certainly an open question in the field that warrants further study. Given the building evidence that the phyllsophere microbiome is a key component of plant fitness, influencing key functional traits and likely protecting host plants against disease, the idea that lytic phages impact these communities is of direct relevance to plant health. A better understanding of bacteria-phage dynamics within these systems may present opportunities for manipulating the plant microbiome and ultimately increasing plant health. These ideas can be extended to the human microbiome, where the role of phages is proving to be appreciable. With regard to using phages in therapeutics, their role in controlling bacterial community dynamics and local adaptation is an important consideration for both phage-therapy to target specific pathogens and full-microbiome perturbations or replacements via fecal transplants. Overall, our results make a significant contribution towards the empirically demonstration of the role that phages play in shaping bacterial community structure in natural systems. This may be through, but is not limited to, impacts on bacterial abundance, composition, competitive-dynamics, and/or diversity. These effects are ultimately likely to affect the overall stability and function of the microbiome, and consequently, host fitness. In conclusion, it is becoming increasingly clear that phages should be considered when seeking to understand the diversity, evolution, and ecology of any microbiome.With the goal of using a ‘natural’ microbiome for subsequent studies, we sampled tomato leaves from the UC Davis Student Farm between the months of August and October. For field experiment 1, inoculum was generated from each of six different sites from across three different fields . For the subsequent experiment with sequencing data, field experiment 2, leaves were pooled across fields into a single diverse inoculum source .

The first experiment was performed less than one month after collection, and the last experiment was done six months after collection.For each experimental replicate within an experiment , 6 seeds from each field-collected tomato type were placed into sterile 1.5 mL Eppendorf tubes and submerged in 400µl of sterile 10 mM MgCl2 solution and sonicated for 15 mins in a Branson M5800 sonicator. This sonicating water bath is different from laboratory sonicators used to disrupt cells; instead, these baths will dislodge bacteria with minimal disruption of their cell integrity. The liquid was then transferred into new sterile tubes and used as seed microbiome inocula. Prior to inoculation, seeds were surface sterilized using the following procedure: Seeds were first soaked in 2.7% bleach solution for 20 minutes, then washed with sterile ddH2O three times to remove any excess bleach. The last washes were plated on KB agar plates and incubated at 28 °C for 24 hours. After sterilization, 40µl of the original seed wash was pipetted directly on top of each individual seed. We did this so that each seed would receive roughly the same number of microbes that was removed during the sonication step. The removal and re-addition process was done, in general, so that every seed used in the experiment would undergo the exact same procedure, and the only difference would be receiving microbiota or not. Negative control seeds were each inoculated with 40µl of 10 mM MgCl2.In order to culture bacteria from the seeds used in this experiment, seeds were sonicated into sterile buffer, as above. Next, cannabis storage the seed wash was diluted 1:10 in sterile 10 mM MgCl2 solution and plated onto KB agar and Lysogeny Broth agar.

They were incubated for 48 hours at 28 °C. We were only able to culture bacteria from tomato types 4 and 2. On average, we cultured 40 colony- forming units from each TT4 seed. To isolate individual strains from the microbial community, we picked morphologically distinct colonies, based on color and surface, and streaked them on new nutrient agar where they were grown for 24 hours at 28 °C. Liquid cultures were attained by inoculation into liquid KB and grown on an orbital shaker at 28 °C overnight.For consistency amongst tomato plant hosts, Money Maker seeds were used for all further experiments measuring the impact of particular seed-associated microbiota. Seeds were sterilized as described above. In addition to testing our own bacterial isolates ZM1, ZM2, and ZM3, we also included Biological Control strains, kindly provided by Dr. V. Stockwell, Oregon State. These two strains are Pantoea agglomerans strain E325A and Pantoea vagans strain C9-1. Bacterial inoculum was prepared as follows: isolates were grown overnight on an orbital shaker in LB at 28 °C. We measured the optical density at 600 nm of the overnight culture and plated the culture on LB agar, incubated overnight at 28 °C to obtain their CFU counts. The remainder of the liquid culture was stored at 4 °C overnight. The next day, we calculated a CFU to OD ratio, and re-measured the OD to account for any growth that occurred of the liquid culture overnight. We pelleted the bacteria at 4000 X G for 5 minutes, re-suspended in sterile 10 mM MgCl2 solution, and diluted to the appropriate concentration. Each seed was inoculated by pipetting the bacterial culture directly on top of each individual seed. In Figure 2-6, we inoculated seeds with pure cultures at a final inoculum density of 40 CFU/seed to approximately match the observed natural densities.

Each experimental replicate held four seedlings, and we had three experimental replicates per isolate per treatment. Disease severity was monitored for 10 days after plate flooding . For dose response curves, the density of bacteria applied to the seeds ranged from 4×10-1 to 4×106 CFU/seed with control replicates not receiving any, and disease was monitored for nine days. We did not replicate at the plate level for dose response curves.Pseudomonas syringae density was quantified from each experimental replicate using droplet digital PCR using a fluorescent probe targeting the Pseudomonas 16S gene as fully described elsewhere. Briefly, seedling homogenates were diluted 1:10, and 2µl of homogenate was used as template in the BioRad QX200 ddPCR reaction. In analyzing positive droplets, all thresholds were set using negative, no template controls and positive pure Pst DNA controls. As with analysis of AUDPC, Pst densities are a measure of each plate experimental replicate, as described above. Bacterial abundances are normalized to total seedling weight within a plate and reported as copy of 16S rRNA gene per gram of plant material. For negative ddPCR controls, we always attempted to measure Pst in the MgCl2 inoculated plant controls for all experiments as well as Pantoea DNA. Although the Pst probe was designed to be specific to Pseudomonads, we did this to ensure our probe was only amplifying Pst and not Pantoea nor any plant material. The signal amplitudes for sterile plant-only and Pantoea isolates-only controls were the same as those of no-template, sterile ddH20 controls, indicating that indeed, there was no detectable background amplification of Pantoea species when using the Pseudomonas probe, and no Pseudomonas was present in the negative controls.

Using pure DNA of individual bacterial isolates, we amplified and sequenced 16S genes using 27R and 1492R primers . In addition to 16S, we sought to further discriminate against our potential strains, and so we amplified the gyrB gene and rpoB genes with previously published primers and PCR protocols. We performed a BLASTn search of all isolate sequences and recorded the top hits with the highest identity . Phylogenetic tree of isolates and neighbors were built using gyrB sequences. We placed our isolates within a subset of samples previously mapped in a Pantoea phylogenetic tree by Rezzonico et al.. Dr. T. Smits kindly provided the E325 gyrB sequence. The evolutionary history of our isolates and other strains was inferred by using the Maximum Likelihood method based on the Tamura-Nei model. The tree with the highest log likelihood is shown. Initial trees for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood approach, and then selecting the topology with superior log likelihood value. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 13 nucleotide sequences. All positions containing gaps and missing data were eliminated. There were a total of 316 positions in the final dataset. Evolutionary analyses were conducted in MEGA7.MiSeq sequencing files were demultiplexed by QB3 sequencing facility. Reads were combined into contigs using VSearch, and the remainder of the analysis was carried out in Mothur version 1.41.3 following their MiSeq SOP. Data were quality-filtered by length, ambiguous bases, and homopolymer length using the recommended Mothur parameters. Chimeras were removed using UChime. We used a 99% similarity cut-off for defining OTUs. The Silva reference database was used for sequence alignment and taxonomic assignment. Archaeal, chloroplast, mitochondrial and unknown domain DNA sequences were removed. Once an OTU table was generated in Mothur, the remainder of the analysis was performed in R using the Phyloseq package version 1.19.1 and Vegan package version 2.4-5. To account for reagent contaminants, we also sequenced DNA extraction kit controls and PCR controls along with our samples. Contaminant OTUs from control samples that were at a similar or higher relative abundance in control samples compared to experimental samples were removed from the full OTU table. Data were rarified to 50,000 reads per sample and singletons were removed.The field of microbiome science spans both basic and applied research in human health, agriculture, and environmental change. As our understanding of the ability of the microbiome to influence host health and shape host traits deepens, curing bud there is increasing interest in selecting and/or designing microbiomes for specific traits or functions. Such trait-based selection of microbiomes has the potential to shape the future of agriculture and medicine. In agriculture, below ground microbiota have already proven capable of shifting the flowering time of plant hosts, enhancing drought resistance, and even altering above-ground herbivory. However, long-term, repeatable success of future efforts will rely on a fundamental understanding of the assembly of, selection within, and co-evolution among microbiota within these communities. One of the challenges facing successful, rational microbiome manipulation and assembly is disentangling the forces naturally shaping the communities, including both host characteristics and constant microbial immigration on community stability. For example, in both humans and plants, there is contrasting evidence for the relative importance of the environment versus host genotype in shaping the microbiome, and dispersal has been shown to override host genetics in an experimental zebra fish system. One powerful but under-utilized approach to understand and experimentally control for the factors shaping microbiome composition and diversity is experimental evolution.

Measuring changes of populations or communities over time under controlled settings in response to a known selection pressure has proved a powerful force in gaining fundamental understanding of both host-pathogen evolution and microbial evolution. Here, we harness an experimental evolution approach in order to study how an entire microbial community can be selected upon in a plant host environment that varies across disease resistance-associated genotypes. We employ a microbiome passaging approach using the phyllosphere microbiome of tomato as a model system to determine if the microbial community could become adapted to the plant host environment. The phyllosphere, defined as the aerial surfaces of the plant, is a globally important microbial habitat. Microbial communities in this habitat can shape important plant traits such as protection against foliar disease and growth. Successful traitbased selection on the phyllosphere could therefore allow for enhancement of plant health, but this critically depends on the ability to select for a well-adapted microbial community that is relatively stable against invasion. We collected a diverse phyllosphere microbiome from tomatoes grown in an agricultural setting and transplanted it onto green-house grown plants using a transplantation method previously shown to be effective for lettuce [118]. We serially passaged this diverse microbiome on each of four cohorts of tomato plants of five different genotypes for a total of 30 weeks. We then measured adaptation of the community both computationally by fitting community structure to neutral models, and empirically using community coalescence experiments in which communities from different passaged lines are combined together and re-inoculated onto host plants in a common garden experiment. Overall, we were able to measure and characterize the response of the phyllosphere microbiome to selection in the plant host environment under greenhouse conditions, and select for a stable and well-adapted plant-associated microbiome.A diverse starting inoculum was collected from field grown, mature tomato plants. This field-microbiome was spray inoculated onto 30 tomato plants of 5 different genotypes, with six replicates each. Two-week old tomato plants were spray-inoculated once per week for five weeks, and then sampled in their entirety ten days after the final inoculation . The phyllosphere microbiome of each plant was then individually passaged on these genetically distinct hosts over the course of four eight-week long passages; P1, P2, P3, and P4 . Microbiomes were not pooled across plants within a given plant genotype, resulting in 30 independent selection lines. Control plants were inoculated with an equal volume of either heat killed inoculum or sterile buffer every week. At the end of each passage, bacterial density was measured and normalized to the weight of each plant , and communities were sequenced using 16S rRNA amplicon sequencing. We first measured the impact of host genotype on bacterial community structure . Using Bray-Curtis dissimilarity measures, we performed an ANOSIM test and found that plant genotype could explain 29% of dissimilarity between microbiomes in P1 . In P2, plant genotype similarly explains 28% of the variation in bacterial community dissimilarity . However, genotype becomes an insignificant driver of community composition in both P3 and P4 . The genotype effect observed in P1 was robust to removal of the primary outlying line , and that same line had too low read depth to be analyzed at P2, and thus was excluded from this analysis at the rarefaction step. By P3, this line was included, as it did not fall outside of the 95% confidence intervals for P3 clustering. We also sought to determine if there were more subtle influences of host genotype on the community that were not uncovered through analyzing Bray-Curtis distances alone.

This upstream production facility uses the same method of expression and follows the same schedule as the base case upstream facility.Transient expression in plants is a method of recombinantly producing proteins without stable integration of genes in the nuclear or chloroplast genome. The main advantages of using this method are reducing the extensive amount of time needed to develop a stable transgenic line and overcoming biosafety concerns with growing transgenic food crops in the field expressing heterologous proteins. Transient expression is attainable through several systems including biolistic delivery of naked DNA, agrobacteria, and infection with viral vectors. Notably, the use of viral vectors has been marked suitable for application on a field-scale due to the flexibility of production, and the quick accumulation of target proteins while achieving high yields. A new report has shown efficacy in delivering RNA viral particles using a 1–3 bar pressure, 1–4 mm atomizer nozzles spray devices in the presence of an abrasive to cause mechanical wounding of plant cell wall. GRAS notices GRN 738 and GRN 910 describe production of thaumatin in edible plant species and N. benthamiana, respectively. The expression of thaumatin in leaf tissue of the food crops Beta vulgaris , Spinacia oleracea , or Lactuca sativa  is generally lower than in N. benthamiana. However, despite having lower expression levels, the absence of pyridine alkaloids that are present in Nicotiana species is a major advantage for production in food crops because of the significant downstream resources needed to remove alkaloids in Nicotiana-based products. The ultimate solution may be a high-expressing engineered Nicotiana host devoid of alkaloid biosynthesis, but that option was not modeled in this study.

The transient production facility is designed to produce 50 MT of purified thaumatin in spinach, annually, air racking over 153 batches due to longer turnaround time required for S. oleracea compared to N. tabacum crops. Each batch has a duration of 67.8 days and a recipe cycle time of 1.94 days.The proposed base case upstream field production facility, displayed in Figure 1, consists of a 540 acre block of land divided into 22 plots, each of which is suitable for growing 318,000 kg FW of N. tabacum, carrying 477 kg of thaumatin, accounting for downstream recovery of 66.8%. It is assumed that the facility is located in a suitable climate where the growth of N. tabacum is attainable throughout the year, ignoring variations in production between batches . Each batch starts with direct seeding of transgenic N. tabacum plants in the field . The seeds are left to germinate for two weeks followed by vegetative growth for 3 more weeks post germination . After a total of 35 days post seeding, a tractor sprayer applies 4900 L of a 4% ethanol solution to the plot’s crop, triggering the synthesis and accumulation of thaumatin in plant biomass. The plants are incubated for 7 more days, during which time they continue to uptake nutrients and express thaumatin. After 42 days from seeding, the batch is harvested through two mechanical harvesters andfour hopper trucks at a rate of 17,000 kg/h and transported to downstream processing facility using a conveyer belt . The plot undergoes a turnaround period of three days for which the labor and equipment cost is included. No pesticides, fungicides, or herbicides costs are added due to the assumption that not enough growing degree days are accumulated during the batch cycle duration , for disease-causing organisms to be a concern.

The base case downstream processing facility is designed to purify and formulate 318.5 kg/batch of thaumatin with 98% purity. A DSP batch starts with shredding plant biomass using two industrial shredders , each processing 40,000 kg of plant biomass/h. This step is designed to homogenize the leaves and stems to facilitate the extraction process. Shredded plant material is then mixed with an acetate buffer in a 0.8 L of buffer to 1 kg of biomass ratio. This step leverages stability of thaumatin at low pH to precipitate host plant proteins that aren’t stable under acidic conditions. The extraction buffer consists of 50 mM acetic acid and 150 mM sodium chloride mixture at a pH of 4.0. The resulting plant slurry is then fed into a screw press to separate most of the dry plant material. A screw press is recommended for this step because it minimizes the amount of extraction buffer needed by forcing out more plant sap with the increasing pressure inside the chamber. The crude extract stream obtained from the screw press unit is sent to three parallel P&F filtration units for initial clarification, each having a membrane area of 190 m2 . Furthermore, the model assumes the use of food-grade filter membranes designed to include 10 filter sheets with decreasing particle retention size from 25 to 0.1 µm. The acetate buffer is applied once again as cake wash with a 0.2 L buffer to 1 L extract ratio. Diatomaceous earth is added to this step as a filter aid in a 6:100. The stability of thaumatin at low pH and high temperatures facilitates the precipitation of more host cell proteins as well as other undesired plant-derived compounds. Using seven heating tanks , the plant extract is then heated to 60 ◦C for 60 min. Following heat incubation, the stream is sent to a P&F filtration unit to capture the heat-precipitated proteins. It is assumed that a 90% reduction of N. tabacum total soluble proteins is attainable following the heat incubation and precipitation steps. Concentrating the thaumatin stream prior to the ultrafiltration/diafiltration step is necessary to avoid processing large liquid volumes ~573,000 L further downstream.

It has been reported that thaumatin experiences a loss in sweetness when heated above 70 ◦C at a pH of 7.0; therefore, the product stream undergoes concentration by evaporation prior to neutralizing the solution since the protein can sustain higher temperatures at a low pH.The triple effect evaporation unit is designed to evaporate 90% of the water content in the stream at 109 ◦C, 77 ◦C, and 40 ◦C in the first, second, and third effect, respectively, over 4 h.The exiting stream is then neutralized with 1:1 molar ratio and mixed in V-101 for 30 min and sent to the P&F filtration unit to remove any precipitated materials. An additional 1.5% loss of thaumatin during this step is assumed. Because soluble impurities such as nicotine and other pyridine alkaloids are abundant in N. tabacum plants, a UF/DF step is necessary to eliminate small molecules. The UF/DF unit consists of 4 stacked cassette holders, each containing twenty 3.5 m2 cassettes. Since thaumatin is a 22 kDa protein, a membrane with MWCO of 5 kDa is used per working process knowledge. Assuming a conservative flux of 30 L/, the inlet stream is concentrated using a concentration factor of 5, diafiltered 10 times against reverse osmosis water, then re-concentrated using a CF of 5 over 20.6 h, resulting in a 75% pure thaumatin and nicotine content of 1.08 mg/kg thaumatin. A retention coefficient of 0.9993 was assumed for thaumatin, resulting in 5.8% thaumatin loss in UF/DF . The retentate is then sent to five CEX chromatography columns operating in parallel which was modeled based on unpublished data from Nomad Bioscience GmbH . GE Healthcare Capto S resin with an assumed binding capacity of 150 g/L was used in this analysis. Table S2 shows the downstream losses breakdown per unit operation. Spray drying is used as a final formulation step over other means of industrial drying due to the heat sensitivity of thaumatin.The simulated facility consists of three sections—Virion production laboratory , curing cannabis spinach field growth, and DSP. A list of base case design parameters and assumptions is shown in Table S3. The VPL process is adopted from a recent article entailing the production of RNA viral particles from agrobacteria carrying a PVX construct. The laboratory is sized to produce 7900 L of spray solution per batch for application in the field. Nicotiana benthamiana plants are used as the host to produce the viral particles to inoculate spinach. N. benthamiana seeds are germinated in soilless plant substrate at a density of 94 plants per tray. Seedlings are grown hydroponically , under LEDs, until reaching manufacturing maturity at day 35. Agrobacterium tumefaciens is grown for 24 h, before being left in a 4 L flask overnight, and the A. tumefaciens suspension is added to MES buffer in V-101. N. benthamiana infiltration takes place in a vacuum agroinfiltration chamber for 24 h followed by incubation for 7 days in . N. benthamiana biomass production, agrobacterium growth, agroinfiltration, and incubation parameters are adapted from. After the incubation period, 41.5 kg of N. benthamiana fresh weight are ground and mixed with PBS buffer in a 5:1 buffer:biomass ratio.

The extract is then sent to a decanter centrifuge to separate plant dry matter from the liquid phase which is clarified by dead-end filtration , followed by mixing the permeate with 35.9 kg of diatomaceous earth and 7780 L of water to reach a final concentration of 1014 viral particles/L and 4.55 g diatomaceous earth/L. Diatomaceous earth is used as an abrasive to mechanically wound plant cell walls allowing the virions to enter the cytoplasm of the cell. The final spray is stored in for 13 h before field application. Field operation starts at the beginning of each batch with the direct seeding of 28.3 million Spinacia oleracea seeds over 22.6 acres. Spinach is planted over 80-inch beds with an assumed 3 ft spacing between beds, resulting in 14,520 linear bed feet per acre. Seeds are germinated and grown in the field for 44.5 days, during which time a drip irrigation system delivers irrigation water and soluble fertilizer to the soil. It is assumed that 200 acre-inches of irrigation water and 64 tons of fertilizer are needed per batch. A tractor on which multiple high-pressure spray devices are mounted is used to deliver the viral particle solution at a rate of 2 acres/h. This method of delivery has shown high effectiveness. Spinach plants are incubated in the field for 15 days post-infection. During that period, thaumatin starts to accumulate in the crop at an average expressionlevel of 1 g/kg FW after 15 days post-spraying. At day 60, two mechanical harvests collect a total of 344 MT spinach biomass, carrying 344 kg thaumatin, with the aid of four hopper trucks, which is transferred to a 500-m-long conveyor belt that extends from the field collection site to the DSP section of the facility. Harvesting occurs at an average rate of 17,000 kg FW/h, which is estimated based on a harvester speed of 5 km/h and 14,520 linear bed feet per acre. A more simplified downstream processing, enabled by the use of spinach as a host, starts with mixing plant material with 65 ◦C water before extracting the green juice through a screw press . The resulting GJ is heated for 1 h at 65 ◦C in ten jacketed tanks , then concentrated by evaporation to reduce product stream volume for further purification steps. Since thaumatin is not stable at temperatures above 70 ◦C at neutral pH, evaporation is performed at a low temperature of 40 ◦C and 0.074 bar vacuum pressure. Thermally degraded host cell proteins and impurities are eliminated in a P&F filtration unit designed to include 10 filter sheets with decreasing particle retention size from 25 to 0.1 µm. Smaller impurities are removed using a diafiltration unit with 5 kDa molecular weight cut off cassettes in a similar process as described in Section 3.3, the retentate is spray dried in to obtain a final product which has 5% water content, and 348 kg of solid material containing 94% pure thaumatin and 6% spinach impurities. These impurities are expected to be water soluble, heat stable molecules in the range of 5–100 kDa, according to the theoretical design of the filtration scheme.As shown in Figure 3a, field labor is the highest contributor to the upstream field facility followed by consumables. Detailed labor requirement and cost estimation calculations can be found in Tables S7 and S8. Consumables include mechanical harvester and tractor’s fuel, lubrication, and repair costs and other field equipment repair costs. Upstream indoor facility AOC breakdown elucidates a high cost of consumables due to the cost of soilless plant substrate, followed by high energy consumption from the LED lighting system used for plant growth.

Bio-fuel production is driven by mandates for renewable transport fuels, weak land use regulation, production subsidies, and speculation by energy and commodity companies in both developing and industrial countries . Although global estimates of the scale of industrial bio-fuel production are difficult to make, the World Bank calculates that 36 million ha were dedicated to bio-fuel production globally in 2008, doubling the 2004 level. Oil palm production in Indonesia and Malaysia indicates the emerging trajectory: aided by government policies and subsidies, oil palm plantations grew in Indonesia from 3.6 million ha in 1961 to 8.1 million ha by 2009 . The consequences of the expansion of oil palm include ongoing displacement of smallholders, increasing monoculture, and abandonment of food cropping, though the extent to which these effects are occurring remains uncertain . Across the Global South, oil palm and sugarcane plantations may provide only a tenth of the jobs when compared to the livelihoods generated through smallholder farming .Despite expansion of large-scale commercial agriculture, smallholders still make up 85% of circa 525 million farms worldwide . Such farmers span a spectrum from traditional, pipp drying racks indigenous growers using no external inputs to those with heavy dependency on modern seed varieties, fertilizers, and pesticides, but up to 50% of smallholders are thought to utilize resource conserving farming methods .

While they represent the bulk of the agricultural population, estimated at circa 2.6 billion people , due to land inequalities they often do not control the bulk of the arable land . These disparities are largest in South America, and least pronounced in Africa . Another sign of intensifying inequalities is that mean farm size has decreased in many parts of Africa and Asia , increasing the vulnerability of small farmers and exacerbating the poverty in these regions, while large landholdings are increasingly controlled by a small number of people . Despite poverty, the current contribution of small farms to global food production is significant. Herrero et al. estimate that mixed crop and livestock systems supply 50% of the worlds’ cereal, 60% of the world’s meat and 75% of the world’s dairy production. Much of this production is locally produced and consumed, and provides the main source of food for the world’s 1 billion poor . Altieri considers that traditional indigenous agriculture supplies 30 – 50% of the world’s food. suggests that the contribution of smallholders to food production is increasing in some countries because of changing national socioeconomic and political situations and government policies favoring domestic food self-sufficiency . As indicated previously, not all smallholder agriculture would be considered DFS. Perhaps 50% of smallholder farmers use agro-industrial inputs or have not adopted agroecological methods .

Qualitative research suggests that through implementation of “sustainable intensification”, a set of resource conserving practices also used in DFS , such farms could become 60-100% more productive, potentially contributing far more to local and global food security , although rigorous, quantitative comparisons are both lacking and needed . Overall, small-scale diversified farmers face continuous, intensifying pressures from the encroachment of industrial supply chains . However, in parts of the developing world, diversified farming systems are actually expanding, in response to food sovereignty movements, smallholder desires for healthier and more economically independent lives, and some level of civil society and government support. Agroecological techniques are site specific and tend to be transferred from location to location through horizontal communication and social networks, with much adaptation by local communities . Evidence of the rising adoption of agroecological principles in many Latin and Central American countries exist through the many cases of campesino-tocampesino training reported, as well as the increasingly global spread of the La Via Campesina movement . Cuba is a case where the transition to agroecological practices has been particularly rapid ; in this case the expansion was a response to a severe food security crisis and lack of fossil fuel inputs following collapse of the former USSR and associated subsidies to industrialized agriculture . To some degree, DFS are also expanding in industrial countries despite the vastly more inhospitable political and economic conditions that may prevail, particularly in the U.S.

There, as in Australia and many European countries, there is growing demand for organic and locally produced fruits, vegetables, fish, and meat, which is spawning an increase in the number of small-scale, highly diverse farms, often supplying urban markets . In the U.S., certified organic agriculture has grown markedly, rising from less than 1 million acres in 1990 to 4.8 million acres in 2008 and comprises 0.7% of agricultural production with 20,000 producers . Worldwide, organic agriculture has tripled from 11 million ha in 1999 to 37.2 million ha in 160 countries as of 2009 and currently makes up 0.9% of agricultural production , with 1.8 million producers in 2009, predominantly from Asia and Africa. Nonetheless, while organic agriculture tends to support greater biodiversity than conventional farms , not all organic farms are DFS . Much organic agriculture has become increasingly large-scale and homogeneous as producers and food companies strive to maximize profits and meet growing market demand .The series begins by examining what is known about how DFS maintain a range of ecosystem services that provide critical inputs to farming, including soil quality, water use efficiency, control of weeds, diseases and pests, pollination services, carbon sequestration, energy efficiency/greenhouse warming potential, resistance and resilience to climate change, food production, and biodiversity. By comparing DFS to conventional industrial systems, Kremen and Miles find that DFS significantly enhance all the ecosystem services measured with the exception of crop production, although not necessarily to the level required to control pests and diseases or provide sufficient pollination. The authors note that relatively few research dollars have yet been applied to the improvement of DFS compared to conventional systems; redressing this substantial inequality in public and private investment is necessary to close yield gaps while maintaining environmental benefits. The authors recommend that new research should be holistic and integrated across many components of the farming system to identify management systems that can take advantage of potential synergies. Next, Bacon et al. seek to simultaneously deepen our understanding of the social consequences of DFS vs. industrial production and to unpack several key influences affecting continuity, change, and possibilities for transformation of these systems. Case studies from California’s Central Valley, Mesoamerican coffee agroforestry systems, and agricultural parks in the European Union, identify the critical role of government policy in an agricultural system’s emergence and the combination of market demand and multiactor governance that provide continuity. They find that the spread of DFS will generate social benefits, including decreased pesticide exposures, improved food security, longer agricultural working seasons, and healthier diets, but may also generate new costs, such as increased muscular skeletal injuries associated with higher manual labor demands. Social movements can alter governance arrangements and influence both the spread of DFS and the creation of policies that increase environmental benefits and reduce social costs. However, broader changes to the market and political structures and economic policies of agriculture are needed to enable a socially sustainable expansion of DFS. Iles and Marsh consider several examples of obstacles to the adoption and spread of DFS in industrialized agricultural systems. These include the broader political economic context of industrialized agriculture, the erosion of farmer knowledge, and supply chain and marketing conditions that limit farmers’ ability to adopt diversified practices.

To overcome these obstacles and nurture DFS, policy makers can transform agricultural research, develop peer-to-peer learning processes, support recruitment of new farmers, invest in improved agricultural conservation programs, compensate for provision of ecosystem services in working landscapes, pipp horticulture and develop direct links to consumers and institutional markets. In contrast to analyzing a market-led expansion of DFS, Rosset and Martinez-Torrez propose a theoretical framework focused on disputed rural territories and repeasantization to understand how and why rural social movements have increasingly adopted agroecology and diversified farming systems as part of their discourse and practice. Rural spaces are increasingly disputed as agribusiness seeks to “grab land”, control production systems, and remove many rural inhabitants from the land, while small-scale farmers, rural workers, indigenous communities and women are increasingly organized into social movements, such as Via Campesina, that seek to repopulate or maintain these landscapes through the defense of their food, seed, and land sovereignty. For peasants, family farmers and their social movements, agroecology helps both to build autonomy from unfavorable markets and to restore degraded soils. The social process of sharing these practices and values from farmer to farmer , coupled with broader global social movements, help bring alternatives such as DFS to scale. We finish the series with an in-depth analyses of specific farming or social systems. Sayre et al. examine how ranching is the most ecologically sustainable segment of the U.S. meat industry and exemplifies many of the defining characteristics of DFS. Rangelands also provide other ecosystem services, including watershed functioning, wildlife habitat, recreation, and tourism. Innovations in marketing, incentives and easement programs that augment ranch income, creative land tenure arrangements, and collaborations among ranchers can support greater diversification. Taking advantage of rancher knowledge and stewardship can support the sustainability of ranching and its associated public benefits. We have attempted to launch the concept of DFS by encouraging broad based interdisciplinary collaboration and practice from the outset, through combining our analysis of the ecology of food production with complementary questions of food access, distribution, and structure of the agri-food systems. This special feature thus incorporates insights from ecology, economics, political economy, and related social science fields to create a more inclusive analysis of the challenges and opportunities that influence efforts to achieve food security and the multiple dimensions of sustainable agriculture.In 1519, at the time of the arrival of the Spanish invaders to the Basin of Mexico, the people in the region ran a sophisticated system of agriculture that was able to feed its large human population, estimated by different studies between 1 and 3 million . Successful farming in central and western Mesoamerica depended critically on the ability to keep an accurate calendar to predict the seasons. Apart from the wet tropics of the coastal plains of the Gulf of Mexico and the Caribbean, all other regions of Mesoamerica, namely the Mexican Altiplano, the Balsas Basin, and the seasonally dry ecosystems of the Pacific slopes of Mexico, share a highly cyclical precipitation pattern with a dry spring followed by a monsoon-type rainy season in summer and early fall. Precipitation-wise, the most unpredictable time of the year is mid-, and in some parts late, spring; the “arid fore-summer” that precedes the arrival of the Mexican monsoon . Planting too early, following the cue of a first haphazard early rain, can be disastrous if the true rainy season does not continue. Waiting to plant late, after the monsoon season has clearly started, can expose the corn field, or milpa, to an overly short growing season and will also put the crop under competition from weeds that have already germinated. Wild plants in these highly seasonal ecosystems often have traits that allow them to hedge the risk of a false moisture cue. Annual plants often have heteromorphic seeds, some of which germinate with a single rain pulse while others remain dormant and germinate after successive rainfall events . Other plants have lignified seed-retention structures that release seeds gradually into the environment as the dry spring progresses . Finally, woody perennials often flower in the dry early spring in response to photoperiod, independently of moisture availability, and shed their seeds in late spring or early summer when the monsoon season is starting . In this latter group, the physiological ability to detect the season independently of precipitation cues is critically important to avoid premature germination. Accurate timekeeping must have also been strategically critical for pre-Hispanic farmers, who, in order to be successful, had to prepare the milpa fields before the onset of the monsoon rains and plant as early as possible while, at the same time, being able to disregard false early rainfall signals. In the 16th century, Diego Durán noted the importance that the native calendar had for these communities “to know the days in which they had to sow and harvest, till, and cultivate the corn field.” He also noted as “a very remarkable fact” that the Mexica farmers followed strictly the calendrically based instruction of the elders to plant and harvest their fields and would not start their farming activities without their approval.

Water depths, as measured in the middle of the fields, were maintained between 0.3 m to 0.5 m for all years. Inlet structures were fitted with 3-mm mesh screens to permit water inflow and prevent egress of stocked salmon. Outlet structures were fitted with 3-mm mesh screens in the 2013 and 2016 experiments. However, in 2014 and 2015, outlet structures were left open with a 5-cm diameter hole drilled in the middle of a 3.8-cm × 14-cm board and placed near the top of the water level in the rice box to investigate volitional out migration patterns of the stocked salmon. Each outlet structure was fitted with a live car trap placed in the outlet canal, which allowed for collection of all exiting fish. In 2014 and 2015, live cars were checked daily for the duration of the experiments to enumerate the number of emigrating salmon. In past experiments we observed a tendency for a portion of hatchery fish to “scatter” upon initial release into floodplain fields. This behavior reliably abated after several days as fish acclimated to new conditions. For this reason, downstream exiting fish were restocked back to the inlet side of the fields for the first week of 2014. In 2015, fish were similarly restocked for two weeks.Substrate type– 2013. After harvest, vertical grow system rice farmers typically treat the residual rice straw remaining in the fields using one of several methods; thus an important question was whether differences in treatment of rice straw created different outcomes for rearing fish.

Nine fields were randomly assigned to one of three post-harvest substrate treatments: rice stubble, disced, or fallow. The rice stubble substrate treatment consisted of standing stalks that remained after rice plants were cut for harvest using a rice harvesting combine tractor. The disced treatment consisted of plowing rice straw into the soil, a practice farmers use to promote stubble decomposition. The fallow habitat had not been planted with rice during the previous growing season but instead consisted of weedy herbaceous vegetation that voluntarily colonized the fields during the growing season and was left standing during the experiment. More details on the 2013 experimental design can be found in publications by our colleagues. Depth refugia– 2014. Avian predation on fish in aquaculture fields is a well-known problem. Avian predation has the potential to be a significant source of fish mortality in winter-flooded rice fields as California’s Central Valley is positioned directly within the winter habitat of diverse bird populations in the Pacific flyway. We evaluated whether trenching could provide depth refuge as a potential method for reducing avian predation on fish in winter-flooded rice fields. In 2014, nine fields all with a disced substrate, were randomly assigned to one of three treatments: three fields were assigned no perimeter trench, three were assigned a 0.5 m deep perimeter trench, and three were assigned a 1.0 m deep perimeter trench. All trenches were constructed on the north and east sides of the fields running continuously from the inlet structure in the northwest corner to the drain structure in the southeast corner. All trenches were approximately 1.0 m wide with the outermost edges of the trench spaced approximately 1.0m from the exterior levee surrounding the field.

We created this spacing specifically so depth refuges were outside the striking distance of wading birds such as herons and egrets which frequent the shallow water of the perimeter levees. Survival data for three fields was excluded from the analysis due to loss of containment on the inlet side of three fields during the last week of the experiment allowing fish to escape upstream into the inlet canal. Ancillary effects of the trench treatments on field drainage efficiency and volitional migration of fish were also investigated. Drainage practices– 2015. We wanted to know if we could create artificial hydrologic cues to trigger fish out-migration from fields. To investigate drainage practice effects on fish survival, the nine fields were randomly assigned one of three draining treatments: 1) fast drain, where inlet water was cut off and outlet boards were removed rapidly, resulting in the water draining off the fields in a single day; 2) slow drain with inflow, where water levels were lowered by 5 cm per day at the outlet while inflow was maintained through a mesh screen; and 3) slow drain without inflow, where water levels were lowered 5 cm per day at the outlet and inflow was cut off by boarding up the inlet structure. The drainage duration for both slow drain procedures lasted for 10 days with daily outmigration of salmon measured in the outlet traps. All nine experimental fields had a rice stubble substrate following the rice harvest in fall 2014, and a 0.5 m deep perimeter trench was constructed in all fields connecting the inlet and outlet structures running along the north and east sides of the fields. The trenches were approximately 1.0 m wide and spaced 1.0 m infield from perimeter levees. Survival through time– 2016.

To examine in-field survivorship of juvenile salmon through time, fish were stocked in six of the nine flooded experimental fields. During each of following six weeks, one randomly selected field was drained using the fast drain procedure, detailed in the 2015 experiment. All fields had fallow substrate as described in the 2013 experiment and 0.5 m deep trenches as described in the 2015 experiment. An impending bypass flood event near the end of the study forced the drainage of the last field 4 days earlier than scheduled. In-field water quality. Across all years and fields, we recorded continuous water temperatures in 15-min intervals using HOBO U22 temperature loggers anchored in a fixed vertical position on a metal t-post approximately 10 cm above the substrate in the middle of each field as well as trench substrate for a representative set of treatments when applicable. Localized temperature refugia in the trenches was evaluated in its capacity to create thermal buffering by comparing daily maximum water temperatures in the bottom of a trench to those in the middle of the field. Analysis of other physical water quality parameters, nutrient loading, and primary productivity in these experimental rice fields can be found in publications by our colleagues. Zooplankton abundance. Throughout all years, a randomly stratified subset of three fields was sampled for zooplankton weekly except in 2013 where all nine fields were sampled weekly. A 30-cm diameter 150-μm mesh zooplankton net was thrown 5 m and retrieved through the water column four times, once in each cardinal direction. In 2013, benthic macroinvertebrates were sampled separately using benthic sweeps, but due to high sedimentation, high spatial and temporal sample replication, and low overall contribution to the invertebrate community, the additional processing was deemed unnecessary in subsequent years. Furthermore, the zooplankton tow method is effective for assessing pelagic zooplankton and macroinvertebrate community assemblages while improving sample processing efficiency since it avoids the heavy sedimentation associated with benthic sweeps on wetland substrates. Additionally, we also relied on the stomach contents of in-field salmon to better inform the assemblage of macroinvertebrates present in the floodplain food web and their contribution to the diet of in-field salmon . Sampling location in each test plot was determined randomly via a selection of random x and y distances from a random number table. All samples were preserved in a solution of 95% ethanol. Organisms were identified with the aid of a dissecting microscope at four times magnification to the lowest taxonomic level possible using several widely recognized keys. Abundance estimates were calculated from homogenized subsamples of known volume and extrapolated to the volume sampled during the initial net throws. Salmon stomach contents. A random sub-sample of in-field salmon captured during weekly sampling with 4.8-mm mesh seine and sequential field draining were sacrificed, pipp racking transported on ice, and stored in a freezer at -22˚C. A total of 532 salmon stomachs were dissected using a dissecting microscope at four times magnification. Prey items were enumerated, but due to variable decomposition, prey item identification in the stomachs was limited to taxonomic order. Overall salmon survival and growth. Estimates of initially stocked salmon in each field were calculated by establishing a fish per kilogram ratio and multiplying by the total weight applied to each field, except in 2016 where the overall number of stocked fish was sufficiently low to count individually.

Stocking density was calculated by dividing the estimate of initially stocked salmon by the field area . Fish lethally sampled for stomach content analysis during weekly sampling were subtracted from the initial stocking estimate. Total salmon survival in each field was cumulatively enumerated in the outlet live car traps except during the restocking phase of 2014 and 2015 when volitionally emigrating fish were restocked to the inlet side of the fields. During field drainage, seines were used to collect stranded fish out of standing water and these fish were added to the cumulative survival count from the outlet live cars with the recovery method recorded. Survival in 2015 was calculated from only the fast drain treatment fields since the drawn out drainage methods were not comparable to drainage methods in other years. Prior to stocking in each year, mean initial fork length and wet weight were calculated from a random sample of 30 live fish measured to the nearest millimeter and weighed to the nearest hundredth of a gram with an Ohaus Scout Pro SP202 scale . For 2013–2015, we conducted weekly in-field fish sampling with a 4.8 mm mesh seine to capture a target of 30 fish per treatment, with the fork length and wet weight measured. In 2016, fish size data were collected from a random sample of 30 fish in out-migrant traps as individual fields were drained weekly.Percent survival for each field was calculated as the total number of recovered fish divided by number of initially stocked fish, times 100. Analysis of covariance was used to test for interaction effects between field substrate treatment and time which would indicate treatment effects on salmon growth rates. In this model, fork length was the dependent variable with field substrate, day of the experiment and an interaction term as the independent variables. When the assumptions of normality and homogeneity of variance were satisfied, as tested by the Shapiro-Wilk and Levene tests respectively, a one-way analysis of variance was used to test for significant differences in survival due to field drainage treatments. A post hoc Tukey honestly significant differences test was used to test all pairwise comparisons of field drainage practices. When the assumptions of normality and/or homogeneity of variance were not satisfied, non-parametric Kruskal-Wallis analysis was used to test for significant differences in survival and daily volitional outmigration due to field trench depth treatments and to test for differences in overall mean total zooplankton densities between years and substrate types. A post hoc Dunn’s test was used to test all pairwise comparisons of daily volitional outmigration due to field trench depth treatments. Linear regression was used to estimate apparent growth rates and to examine the relationship between salmon survival and day of the experiment . Linear regression was also used to evaluate the degree of within-field thermal refugia via the relationship between daily maximum water temperature differences in the trenches and daily maximum water temperature in the middle of the field . Statistical significance was declared at an α = 0.05 level. All analyses were conducted in R v3.6.1.Apparent fork length growth rate for juvenile salmon did not differ significantly between treatments . The slopes from individual linear regressions of fork length predicted by day for each treatment resulted in estimated apparent growth rates of 1.01 mm d-1 for the stubble treatment, 0.99 mm d-1 for the disced treatment, and 0.95 mm d-1 for the fallow treatment. As previously published, found no statistical difference between total abundance of zooplankton between treatments, but did find high overall abundance and a trend of increasing zooplankton over experiment duration. Across all samples, cladocera were the most abundant group of zooplankton, making up over 50% of the total zooplankton assemblage. Cladoceran zooplankton was the most common prey item found in juvenile salmon stomach contents as this taxon comprised on average 94.0% ± 1.0% SE of the diet composition across all treatments. Chironomid midges were the second most common prey item and comprised an average of 4.8% ± 1.0% SE of the diets.

The age of the operator is likely to influence the size of the dairy operation because it is likely that as an operator gets older and remains in the dairy industry as a dairy operator, they expand their business. Since most dairy farm operators enter the industry when they are young, age is likely to be highly correlated dairy farm experience and often with specific experience at a specific farm in a particular location. Therefore, it is reasonable to suggest that age is heavily correlated with on-farm experiences which is a form of human capital. High level of human capital at the farm level could be hypothesized to be attributed to a farm’s success and growth. The trend of increasing farm size as the age of the operator increases is likely to occur until they reach the age of retirement, maybe decreasing slightly as they get closer to retirement age. Table 4.3 shows the share of dairy operators by age range, state, and year. We can see that the average age of dairy farm operators is increasing for both female and male operators. Based on the information available, I include the following variables in my analysis: the average age of operators and maximum age of any one operator . There are no COA questions directly asking about the farm’s level of sales diversification . However, I created a variable intended to capture sales diversification by taking the share of milk or dairy sales divided by total sales revenue.

This gives an idea of the level of sale diversification on the dairy farm with dairies with little to no sale diversification being near one and those with significant sales diversification with lower values. I also included the share of operators that have off farm employment . These are not clear independent variables, vertical grow rack system as there appears to simultaneity bias between sales diversification and other variables. For the farm size variables, of the individual farm at time , are the dependent variables including Cowsit number of milk cows , TMDit total sales revenue from dairy or milk, and TVPit total value of production.Table 4.4 shows the regression results for Equation 1 with the maximum age selected as the age variable. First starting with the farm size variable, number of milk cows, the sales diversification is significant and with a 1% increase in share of sale diversification relates to about 124% increase in the number of milk cows. Whereas a 1% increase in the share of operators with off farm employment would suggest a decrease by 31.1% of the number of milk cows. Finally, age has relatively little relationship with the number of milk cows on the farm but does show that a year increase in the max age does correspond with an increase by about 0.7%. Next, using the milk sales or dairy sales as the farm size variable, there are very similar results to those for the number of milk cows. The relationship of the maximum age of the operator remains the same. I find that a 1% increase in the share of operators with off farm employment relates to a decrease in the total milk or dairy sales of about 32.4%.

Interestingly, a 1% increase in sales diversification suggests an increase of 215% in total milk or dairy sales. Finally, when we consider the farm size variable total value of production, the relationship of the maximum age of the operator remains similar to the results of the other farm size variables with a year increase in the maximum age there is a decrease of 0.6% in the total value of production. I also find that a 1% increase in the share of operators with off farm income corresponds to a decrease by 32.2% of the total value of production. In contrast with the other two farm size variable specifications, a 1% increase in sales diversification relates to a decrease in the total value of production by 34.1%. Table 4.5 shows the regression results for Equation 1 with the mean age selected as the age variable. First starting with the farm size number of milk cows, I find that the coefficient on the mean age variable is not significant. A 1% increase in the share of operators with off farm employment suggests a decrease in the number of milk cows by 30.8%. Whereas a 1% increase in sales diversification corresponds with an increase of 107% in the number of milk cows. Now looking at the farm size variable total milk or dairy sales, the mean age variable is now significant. A year increase in the mean age of dairy operators relates to a decrease of 0.1% in the total milk or dairy sales. Sales diversification level has a relatively strong relationship with a 189% increase in the total milk or dairy sales given a 1% increase in the level of sales diversification. Finally, when we consider the total value of production as the farm size variable, a year increase in the mean age of dairy operators corresponds to a decrease in the total value of production by 0.1%. Also, a 1% increase in the share of operators with off farm employment relates to a decrease in the total value of production by 32% and a 1% increase in sales diversification suggests a decrease the total value of production by 39.3%.Dairy farms have long been run by men, with relatively few women acknowledged as farm operators. Women have played a substantial role on farms, even when their contribution was often not classified as contributing to the farm operation or management. The role of women on farms has likely changed along with changes in agriculture itself. With the rapidly changing dairy industry, it is important to document the validity of assumptions we have about the demographics of farm operators. Successful farms have high quality management, and women have become a crucial part of the supply of farm management expertise. Based on recent U.S. Department of Agriculture Census of Agriculture data, there appears to be both an increase in the share of female dairy farmer operators and an increase in the share of dairies with at least one female operator. There are two confounding factors that influence these statistics, but fundamentally it implies that farms that have been successful have tended to include female operators. Furthermore, the current data support the previously held assumption that there are a significant number of dairies that are run by spouses with a large share of female farm operators married to a principal operator. Understanding the correlation between the presence and the share of female operators, as well as operations run by spouses on farm size provides insight to a previously limited section of agricultural economics literature. Furthermore, by providing evidence and understanding of dairy farm management demographics this research is able to add to discussions about the future of the dairy industry and a better understanding past patterns.Very little agricultural economics literature has addressed the intersection of gender and agricultural industry in developed countries, but there has been some work on this topic for developing countries .

Historically, being a farm operator has been thought of as a male profession with the work done by women on farms tending not to be labeled as farm management. Interest in the role of women on farms is prevalent across several disciplines with some sociology and anthropology research on women in agriculture claiming that women farmers tend to run smaller farms and adopt more sustainable practices than their male counterparts . There has been no agricultural economics research on the role and impact of female operators in agriculture for the dairy industry, specifically. An increase in the share of commercial dairy farms with a female operator suggests that farms that have not exited, during a trend of consolidation, are likely to have a female operator as compared those with only male operators. However, the increase in shares of women may also reflect a change in the practice of reporting to data collectors in addition to a change in actual farm practices. This chapter explores the hypotheses that the presence of a female operator on the dairy farm may indicate that the dairy farm is more adaptable or more open to change in management practices. Listing a female farm operator among all the farm operators may be at least correlated with a willingness to adopt new technology, diversify sales, grow rack with lights or increase vertical integration on the dairy farm. This is a feasible hypothesis because the presence of a female operator may indicate that the farm is more open to change than many peers in the industry. Part-time farming is common in crop and beef cow-calf operations, whereas commercial dairy farm operators tend to be full-time operators. Also, in the dairy industry, a female operator of dairy farms is likely to be married to a principal operator. Having both spouses as farm operators likely implies less off-farm income and, therefore, higher financial reliance on the dairy farm’s success than for families with more diversified income sources. Moreover, dairy farms tend to have more concentrated farm incomes with crop and dairy enterprises vertically integrated rather than the diversification common among crop farms. This changes the incentives of the spousal operators to remain economically viable because it likely increases risk aversion leading to diversification of sales and mitigation of feed price volatility risk by increasing economies of scope. The COA finding of an increase in the share of women dairy operators and farms with women operators reflects three things: an actual increase in women operators playing a more prominent role, their male associates being more likely to recognize and report female operators, and changes in COA questions that better collect previously unmeasured management activity by women. It is important but difficult to disentangle how these factors affect the data. The increase in the share of female dairy farms must be considered against the broader pattern of dairy farm consolidation, changes in dairy farm size distribution, farm characteristics, and geographic shifts . This research seeks to provide statistical evidence of differences in farm size of dairies operated by dairies with at least one female operator relative to all male operators, the share of female operators, and those operated by spouses. By considering farms with at least one female operator and/or married operators as a “treatment” group, I compare the herd size, milk or dairy sales, and total value of production, between the two treatment groups, while holding location and year constant. This chapter is structured as follows: a brief overview of previous literature on the intersection of women and agriculture, a description of COA data related to women and farm operators, a discussion of statistics, empirical method, and results, and then a brief conclusion.Research on the intersection of women and agriculture has tended to be limited in scope and by academic discipline. Previous research on the topic from an agricultural economic perspective has focused on the intersection of women and agriculture in developing countries or limited its analysis to some demographic statistics on female farm operators without much commodity distinction within the agricultural industry. Industry distinction is important because of generally held assumptions about particular commodity farms, including that dairy farms are run by spouses. Moreover, although there have been many anthropology and sociology research studies that have been done on the intersection of women and agriculture in both developing and developed countries, these have tended to be on a case study basis that are limited in geographic scope. I found little empirical agricultural economics research on the patterns over time and across states of female farmers, and I found no prior research on the economics of patterns of female operators in the dairy industry, specifically. A recent article by Schmidt et al. summarizes the current literature on the intersection of women and agriculture, specifying that most economic literature on this subject focuses on developing nations. The article calls for further research on this topic to further characterize the change in gender demographics and collect information on influences in the economy that may have impacted or continue to impact the number of female farm operators in agriculture. Schmidt et al. outline three possible influences on the share of female farmers, including push-pull factors, characteristics of local agriculture, and the type of farming practiced.

Previous studies emphasized that microbiota change depending on whether it is associated with solid particles or liquid fractions. As a consequence, the mode of manure application will likely to influence the microbial load in the cropland receiving manure as fertilizer. Both liquid and solid manure are applied as fertilizer in developing as well as developed countries; however, a detailed research in terms of microbial communities of compost manure and irrigation manure is rare—if not unavailable. Increasing public concern with regards to the microbial load in manure fertilizers and associated health risks necessitates the scope of such studies. Moreover, the trend in dairy industry shows that larger dairies, which confine the relatively large animal population in limited acreages, are more efficient than smaller dairies, and their number is increasing consistently. This means that the manure production in future dairy farms will increase as a result of higher animal density in a relatively limited area. Eventually, the increased manure production will be applied in cropland with or without treatment. There are many treatment options for manure, including anaerobic digestion, composting, lagoon,and drying, and the impacts of these methods on microbial population are relatively unknown at a large scale. In addition to manure treatment methods, both environmental and dairy farm specific factors influence the microbial communities in manure. Previous studies, rolling grow tables which have explored the microbial community in anaerobic digestion treatment of various waste including sludge, dairy manure, and slaughterhouse waste, indicated the presence of microbial communities of Bacteroidetes, Proteobacteria, Firmicutes, Chloroflexi, Spirochetes, Clostridia, and Synergistia.

Other studies reporting the microbial population of cow gut indicated the presence of various microbial communities including Spirochaetes, Flavobacteria, Sphingobacteria, Actinobacteria, Chloroflexi, Firmicutes, and Proteobacteria species. Of these, all studies dealing with animal waste-borne microbial pathogens indicated that animal waste may act as a reservoir of human pathogens, and it has a potential to contaminate ambient water resources and pose risk to public and animal health.The risk of microbial pollution caused by the application of manure fertilizer can be minimized by improving the existing understanding of microbial population in manure, and the effects of available treatment methods, which are in general used or recommended. This reconnaissance research based on our hypothesis proved that a relatively large microbial population persists in manure even after treatment. Regardless of composting, drying, solid-liquid separation, and lagoon, a diverse microbial population that includes pathogenic bacteria resides in manure, and the elimination of these microbial pathogens in manure requires further research. The ranking of top 15 species in FP and CP is shown in Table 1. In general, the abundance of bacteria for FP and CP was different than the abundance in FM, PL, and SL . As an example, the top right corner showed the high abundance of microbial communities mostly in CP and FP, and these microbial communities were less abundant in top left corner of heat map mostly showing FM, PL, and SL . Similarly, species such as Desulfobulbus, Bacteroidetes, Clostridiales, Clostridium, and Ruminococcaceae were more abundant in FM, PL, and SL than in CP and FP . A heat map displaying the bacterial community in liquid samples and solid samples and corresponding PCA plots are shown in supplementary figures . Considering that manure is abundantly used as fertilizer, we hypothesized that the methods of manure handling may have different impacts on microbial population in liquid manure.

We examined the top 15 microbial communities in liquid manure samples obtained from lagoons. Table 2 indicates the rankings of top 15 microbial communities in FM, PL, and SL samples. In PL, Bacteroidetes, Ruminococcaceae, and Cloacibacillus accounted 10.6%, 6.7%, and 4.5%, respectively. The unclassified bacteria in PL accounted 12.4%. Compared to PL, the three most abundant species in FM were Ruminococcaceae, Clostridium, and Flavobacteriaceae accounting for 8.9%, 5.1%, and 2.8%, respectively. The unaccounted bacteria in FM were 18.1%. The abundance of the top three species in SL samples was 9.8%, 7.6% and 2.6%, respectively. Moreover, the pathogenic bacteria of genus Clostridium persist in all three types of liquid samples . Compared to liquid manure samples, this population was not as dominant in solid manure samples. Solid manure, which was collected in this study, had been passed through either a compost or piling system. One plausible reason could be ascribed to the elevated temperature of manure piles. In general, the temperature profile of compost piles reaches to 55–60˚C, while the temperature of lagoon manure remains low . Considering our sampling strategy, which involves collecting samples from multiple dairies, certain differences in microbiota among solid and liquid samples are expected, and results are tabulated in Tables 1 and 2. The ranking of top 15 species in overall solid and liquid samples was developed, and results are shown in Table 3. The common species in solid and liquid samples include Flavobacteriaceae, Ruminococcaceae, and Pseudomonas. As asserted in our hypothesis, the level of microbial population in manure fertilizer changes with the mode of samples , which indicates that the treatment methods such as composting may have different impacts on manure in terms of microbial population compared to lagoon system. The results listed in Table 3 and Fig 5 prove our hypothesis to be true. Overall results showed that manure pile samples cluster together, while the flushed manure and lagoon samples cluster together.

Additionally, fresh solid samples cluster with the flush manure samples, indicating a certain degree of microbial commonality in untreated fresh liquid and solid samples. The distinct microbial communities in solid and liquid samples might be attributed to the varying effects of the anaerobic process in lagoon environment and composting process in the pile system. Interestingly, fresh piles and old piles did not show considerable differences in microbial communities, which suggest a need for further investigation to understand the effect of manure drying and composting on the change in microbial communities. In general, primary lagoon samples showed relatively high clustering. Secondary lagoon samples were less varied, which suggest that over time, microbial communities in lagoon environment develop similar profiles. Future studies focused on understanding the effect of manure retention time in lagoon microbial community and functional profile can provide additional insights needed for evaluating the microbiota of manure fertilizers. The results of this study suggest that the microbial diversity can potentially change during manure handling, and adapting suitable methods may influence cropland soil microbiota positively.Excessive application of dairy manure as fertilizer is considered to be a cause microbial pollution in ambient water. To understand the potential impact of dairy manure application as fertilizer in terms of microbial pollution and diversity, here we studied the microbiome of dairy manure under various treatment conditions. Analysis was performed on the flushed manure, solid manure, and manure of lagoon systems. The 16S rRNA-based microbial analysis demonstrated that a large, diverse bacterial population inhabits the manure and changes with manure treatments. Results showed a considerable difference in population among microbiomes of liquid and solid manure. The microbiomes of primary and secondary lagoon manure were comparable. The microbial populations of fresh manure piles and old manure piles were similar, which might be attributable to a lesser impact of composting and drying under the studied conditions. The considerable differences among microbiomes of liquid and solid samples indicate that the application of solid manure as fertilizer may have different impacts on cropland in terms of microbial population compared to when liquid manure is applied as fertilizer.This thesis deals with two important trends in the U.S. dairy industry: 1) increases in farm size, and 2) the increases in prevalence of female dairy farm operators. This research explores detailed data on farm size changes in major U.S. dairy states and document consolidation and other trends in the patterns of dairy farm size distributions. The dairy industry is of interest, not only because it is an important industry measured by production value, flood drain table but also because of its environmental and social importance. Declines in the number of dairies have raised concerns based on their impact on rural communities, particularly movement of dairies out of local regions and, the potential fall in local employment opportunities. New data on farm operator characteristics allow us to better analyze the trends of gender demographics and the influence of operators’ ages relative to farm size. There has been very little economic research related to the increasing role of female operators in the dairy industry. Trends toward more women operators and fewer dairy farms suggests correlations between the role of women in the dairy industry and herd size per farm and other farm characteristics. Looking overall at U.S. trend in operations with milk cows, Figure 1.1 shows that since 1982, the number of operations with milk cows has decreased rapidly and the average number of milk cows per farm has increased. This graph describes a trend of consolidation in the dairy industry, as defined as operations with milk cows. Despite the slight decrease in number of milk cows there has been an increase in the U.S. milk production . These changes characterize the consolidation within the dairy industry.These national trends mask large differences by state. Some states, such as California, has seen growth of herd sizes into the range of 2,000 or more milk cows per farm. Other states, such as Wisconsin have experienced equally rapid increases in herd size per farm in percentage terms, but herd sizes of larger farms in Wisconsin are in the range of 500 cows per farm. Consolidation is common in other farm industries. An important contribution of this thesis is to document and characterizes this trend over time for an important industry, which is of significance to agricultural economic research.

Consolidation may have allowed dairies to capture improved productivity and efficiency on the farm. How dairy farm size changes in response to these and other factors are important in considering future trends in farm size and their impact on milk production in the United States. My research seeks to help explain recent patterns of farm size change in the dairy industry, considering trends in operator characteristics and management, while accounting for regional differences. The share of women dairy farmers has increased. Historically, farming has been a stereotypically male occupation. Despite contributing to farm production and farm management, surveys, and censuses, have been limited in their collection of data on the contributions of women as farm operators. I hypothesize that some of growth in female contribution to farm operation is due to changes in social and gender norms in reporting. One contribution of my research is to attempt to separate, to the extent possible, changes in management and operations on dairy farms from how such activities are reported. Demographic trends in farm operation and management are important because they help researchers and policy makers get a better sense of who runs the operations in an industry by age, gender, and other characteristics. The dairy industry remains predominately male. However, since 2002, there has been a substantial increase in the share of women dairy farm operators and an increase in the absolute number of dairy farms with at least one female operator in many places. The share of commercial dairies with at least one female core operator has increased across all states, except New Mexico. New York saw the largest increase in the share of commercial dairies with at least one female core operator from 36% to 55%. California saw a 40% increase in the share of commercial dairies with at least one female core operator. This trend, which has occurred while dairy farm consolidation has proceeded at a similar pace suggests that the participation of female dairy farm operators may positively affect dairy farm herd size and economic viability.As noted in the previous chapter, for the statistical estimation in the thesis I will utilize data for the USDA COA. Under “Census of Agriculture Act of 1997”, The COA is a federally mandated Census of all U.S. farms and ranches every five years, and it captures individual farm-level data on production costs, operators’ characteristics, land use, number of milk cows, revenue, etc. The data and statistics resulting from this Census are reported at the county or state level and research using the individual level data is restricted to USDA research or special request for non-USDA entities. I was given special permission to have access to individual farm-level data for census years of 2002, 2007, 2012, and 2017 from the following specified states: California, Idaho, New Mexico, New York, Texas, and Wisconsin.

As shown in this gives a good combination of accuracy and efficiency for this problem class. Having a well-designed boundary-layer mesh in wind-turbine simulations is critical for achieving engineering accuracy with a reasonable number of degrees of freedom. During operation, wind turbine blades undergo large global rotational motions, as well as local flap wise and edgewise bending, and axial torsion deformations. As a result, in order to account for the blade motion and to simultaneously maintain good-quality boundary-layer discretization, a moving-mesh technique should be employed where the boundary-layer mesh follows the blades as they moves through space. In the case of standalone wind-turbine-rotor FSI computations this may be accomplished by applying a global rotation to the entire aerodynamics mesh, and handling the remaining blade deflection using elastic mesh moving as in [18]. A jacobian-based stiffening technique in elastic mesh moving is essential for maintaining the integrity of the elements in the blade boundary layers. In the case a full machine is considered, the spinning rotor interacts with the tower. This interaction is strong and needs to be modeled explicitly. In the recent wind-turbine FSI computations presented in the wind-turbine hub was assumed to spin with a fixed, prescribed angular velocity, and the tower was assumed to be stationary. The aerodynamics of rotor-tower interaction was handled using a sliding-interface technique. In this technique, 2×4 flood tray rather than rotating the entire computational domain, only the inner cylindrical subdomain that encloses the rotor undergoes a spinning motion inside the cylindrical cut-out of the outer stationary domain. The two domains do not overlap, and, as a result, create a sliding cylindrical interface with a priori non-matching discretizations on each side.

The continuity of the kinematic and traction variables across the non-matching sliding interface is enforced weakly.In order to simulate more complicated FSI scenarios, such as rotor yawing for HAWTs, or even basic operation for VAWTs, additional computational technology is required. In the case of HAWT rotor yawing motion, the entire gearbox undergoes rotation parallel to the tower axis, and this rotation must be transferred to the rotor and hub without interfering with the rotor spinning motion. In the case of basic VAWT operation, the air flow spins the rotor, which is connected to a flexible tower with struts. Furthermore, the moving-mesh aerodynamics formulation for this expanded problem class can no longer have a fixed sliding interface. For example, in the case of the rotor yawing motion, in order to keep the good quality of the aerodynamics mesh and prevent the rotor blades from crossing the boundary of the rotor cylindrical domain, it is preferred that the sliding interface follows the motion of the gearbox, while accommodating the spinning rotor. This results in two cylindrical surfaces moving together while one spins inside the other. Another challenge in FSI simulations is to model the geometrically complex structures with its nonlinear material distribution, which undergoes large deformation. A combination of a rotation-free multilayer composite Kirchhoff–Love shell and beam allows for the rotor to spin freely and for the tower and blades to undergo elastic deformations. An isogeometric analysis with NURBS based elements representation is used to construct analysis-suitable geometry. The NURBS-based IGA may be seen as a combination of CAD basis functions and the isoparametric concept and may be extended to T-splines and subdivision surfaces. Because of the rational nature of the basis functions the circular shapes can be represented exactly which reduce the geometrical-approximation error when modeling complex-shaped wind turbine blades.

Furthermore, the higher order continuity is achieved with NURBS basis functions and the geometry is preserved unchanged under the mesh refinement process, which is not the case in FEM. The dissertation is outlined as follows. In Chapter 2 we state the ALE-VMS formulation of aerodynamics in combination with our sliding interface approach for the simulation of mechanical components in relative motion. To validate our aerodynamic formulation we show the computations of a small-scale Darrieus-type wind turbines. One is a 3.5 kW wind turbine tested in NRC wind tunnel. For this turbine two cases were simulated: A single turbine, and two counter-rotating turbines placed side-by-side in close proximity to one another. For a single turbine a mesh refinement study was performed, and results were compared to experimental data. Another turbine is designed by Windspire with rated power of 1.2 kW. For this case the computational results were compared to a field test experiments conducted by the National Renewable Energy Lab and Caltech Field Laboratory for Optimized Wind Energy. In Chapter 3 we present the coupled Kirchhoff–Love shell for an arbitrary composite layup of wind turbine blades. To verify the model we perform the eigen frequency analysis of recently designed offshore wind turbine blade and CX-100 blade, which compare favorably to the experimental data. In Chapter 4 we introduce the coupled FSI formulation employed in this work with non matching discretization of the aerodynamic and structural domains. Later in the chapter we present FSI computations of the Micon 65/13M wind turbine. Both the aerodynamics and FSI torque results fall within the range predicted by the field tests for this wind turbine. The FSI case shows high-frequency fluctuations in the aerodynamic torque, which are due to the high-frequency vibration of the blades. Next, the FSI computations of offshore HAWT under yawing motion is presented and the discretization techniques employed and the aforementioned enhancement of the sliding-interface formulation are described.

We conclude with the FSI computations of the Windspire VAWT and discuss start-up issues. In Chapter 5 we draw conclusions and discuss possible future research directions.The aerodynamics simulations are performed for a three-blade, high-solidity VAWT with the rated power of 3.5 kW. The prototype is a Darrieus H-type turbine designed by Cleanfield Energy Corporation. Full-scale tests for this turbine were conducted in the National Research Council low-speed wind tunnel at McMaster University . Experimental studies for this turbine focused on the application of VAWTs in urban areas. The turbine has a tower height of 7 m. The blades, 3 m in height, are connected to the tower by the struts of length 1.25 m. This value is taken as the rotor radius. A symmetric NACA0015 airfoil profile with chord length of 0.4 m is employed along the entire length of the blades. See Figure 2.1 for an illustration. The computations were carried out for constant inflow wind speed of 10 m/s, and constant, fixed rotor speed of 115 rpm. This set up corresponds to the tip speed ration of 1.5, which gave maximum rotor power as reported in [32,58]. However, it was also reported for the wind tunnel tests that the control mechanism employed was able to maintain an average rotor speed of 115 rpm with the deviation of ±2.5 rpm. This means the actual rotor speed was never constant. The air density and viscosity are set to 1.23 kg/m3 and 1.78 × 10−5 kg/, respectively. On the inflow, flood and drain table the wind speed of 10 m/s is prescribed. On the top, bottom and side surfaces of the stationary domain no-penetration boundary conditions are prescribed, while zero traction boundary condition is set on the outflow. No-slip boundary conditions are imposed weakly on the rotor blades and tower. The struts are not modeled in this work to reduce computational cost. The struts are not expected to significantly influence the results for this VAWT design. The computations were carried out in a parallel computing environment. The meshes, which consist of linear triangular prisms in the boundary layers and linear tetrahedra elsewhere, are partitioned into subdomains using METIS, and each subdomain is assigned to a compute core. The parallel implementation of the methodology may be found in [80]. The time step is set to 1.0 × 10−5 s for all cases.We first compute a single VAWT and assess the resolution demands for this class of problems. The stationary domain has the outer dimensions of 50 m, 20 m, and 30 m in the stream-wise, vertical, and span-wise directions, respectively. The VAWT centerline is located 15 m from the inflow and side boundaries. The radius and height of the spinning cylinder are both 4 m. Three meshes are used with increasing levels of refinement. The overall mesh statistics are summarized in Table 2.1. The finest mesh has over 17M elements. The details of the boundary-layer discretization are as follows. For Mesh 1, the size of the first element in the wall-normal direction is 0.000667 m, and 15 layers of prismatic elements were generated with a growth ratio of 1.15.

For Mesh 2, the size of the first element in the wall-normal direction is 0.000470 m, and 21 layers of prismatic elements were generated with a growth ratio of 1.1. For Mesh 3, the size of the first element in the wall-normal direction is 0.000333 m, and 30 layers of prismatic elements were generated with a growth ratio of 1.05. Figure 2.14 shows a 2D slice of Mesh 2, focusing on the boundary-layer discretization of the blade.Time history of the computed aerodynamic torque is plotted in Figure 2.5 together with the experimental value reported for these operating conditions. Only the mean value of the torque was reported in [32, 58]. Note that after a couple of cycles a nearly periodic solution is attained. Mesh 1 predicts the average torque of about 52 Nm, Mesh 2 gives the average torque of about 70 Nm, and Mesh 3 predicts the average torque of about 80 Nm, while the targeted experimental value is about 90 Nm. Looking further at the curves we observe that the largest differences between the predicted values of the torque between the meshes occur at the maxima and minima of the curves. Also note that the torque fluctuation during the cycle is nearly 200 Nm, which is over twice the average. One way to mitigate such high torque variations is to allow variable rotor speed.Figure 2.6 shows a snapshot of vorticity colored by flow speed. The upstream blade generates tip vortices near its top and bottom sections. Note that no large vortices are present in the middle section of the blade. There, as the flow separates on the airfoil surface, larger vortices immediately break up into fine-grained trailing-edge turbulence. The tip vortex and trailing-edge turbulence are then convected with the ambient windvelocity, and impact the tower, as well as the blade that happens to be in the downwind position in the spin cycle. However, as it is evident from the torque time histories shown in Figure 2.5, these do not produce a major impact on the rotor loads, at least for a chosen set of wind and rotor speeds. The situation may, of course, change for a different set of operating conditions.Here we investigate two counter-rotating turbines placed side-by-side in close proximity to one another. The wind and rotor speeds are the same as before, however, the turbines rotate out of phase, with the difference of 60◦ . The distance between the towers of the two turbines is 2.64R, where R =1.25 m is the rotor radius. This distance between the turbines falls in the range investigated in the experimental work of [1].The stationary domain has the outer dimensions of 50 m, 20 m, and 33.3 m in the stream-wise, vertical, and span-wise directions, respectively. The centerline of each VAWT is located 15 m from the inflow and 15 m from its closest side boundary. The radius and height of the spinning cylinders are 1.45 m and 4 m, respectively. A 2D slice of the computational-domain mesh focusing on the two rotors is shown in Figure 2.7. The boundary layer discretization employed for this computation is the same as that of Mesh 2 in the previous section.Figure 2.8 shows the time history of the aerodynamic torque for the two-turbine case. The time history of the torque for a single VAWT simulation is shown for comparison. Note that while the maxima of all curves are virtually coincident, the minima are lower for the case of multiple turbines. Also note that the multiple-turbine torque curves exhibit some fluctuation near their minima, while the single-turbine torque curve is smooth near its minima.

SAMMIE CAD provides a 3-D environment and full control of human mockups, which makes it possible to evaluate those complex interactions. The simulations performed in SAMMIECAD consisted of: creating 3-D human mockups; creating 3-D ATV mockups; and integrating and in the virtual environment to simulate their interaction. For each simulation, the correct reach posture was achieved by positioning the human limbs according to the specific task’s requirement. For example, a seated position was adopted when evaluating fit criterion 10 , as shown in Fig. 3a. On the other hand, a standing straddling posture was selected when evaluating fit criterion 4 , as shown in Fig. 3b. Some criteria involve the youth reaching a specific control . The feature ‘‘Reach” under the ‘‘Human” menu on SAMMIE CAD was used to evaluate the ability of the youth mockups to reach the selected controls. The ‘‘Reach” was set as ‘‘Absolute,” and ‘‘Object Point” was set as ‘‘Control.” When the selected control could be successfully reached, the software would display an animation of the human limb reaching the desired object . On the other hand, if the control was out of reach, SAMMIE CAD would show an error window and display the required distance for the human limb to reach the desired control . Simulations involving buttons and levers were performed with the fingertip of the index finger or the thumb, accordingly. All controls on the right side of the ATV were simulated with the right hand/foot, and all controls on the left side of the ATV were simulated with the left hand/foot.

Specific controls that required using both hands, such as the handlebars, seedling starter trays were simulated with both hands. Criteria 1, 2, 3, and 11 were evaluated through Matlab because their assessment required the computation of simpler calculations, such as the distance between the rider’s knee and the ATV’s handlebars. Matlab also provided the ability to automate the calculations for a more efficient data analysis. A code was generated based on conditional statements to assess whether riders’ anthropometric measures conformed to the constraints imposed by the ATV design. For instance, when evaluating criterion 1, the distance between the ATV footrests and the handlebars minus the rider’s knee height must be greater than 200 mm . For each reach criterion, riders received a binary score . Riders with a total score of 11 were classified as ‘‘capable of riding the ATV.” On the other hand, riders with a total score below 11 were classified as ‘‘not capable of riding the ATV.”In order to validate the results of the virtual simulations, an experiment including three adults and one study ATV was carried out. Each subject had completed an ATV safety riding course prior to the experiment and was awarded a certificate from the ATV Safety Institute . The capability of the subjects to fulfill each fit criterion was evaluated and recorded. For the field tests, a measuring tape graduated in mm was used to measure distances and a digital angle finder to measure angles. To assist in some of the angle measurements, a straight edge 4800 ruler and a mag-netic level were used. The anthropometric measures of the subjects were taken with a body-measuring tape and then used as input in SAMMIE CAD to create 3-D mockups.

The results observed in the experimental setting were then compared to those observed in the virtual simulations through the Cohen’s Kappa coefficient , which is a statistic widely used to measure inter-rater reliability for qualitative items . A Z-test was performed to evaluate whether the value of K was statistically different than zero, which would imply that the virtual simulations are reasonable.Seventeen ATV models were evaluated from eight different manufacturers. Engine capacity ranged from 174-686 cc, with most vehicles in 100–400 cc . Moreover, 58 % of the ATVs evaluated included electric power steering , 4 wheel-drive , solid suspension , and manual transmission . Findings of individual reach criteria for the ATV models are presented in Tables 2 and 3, for males and females, respectively. The last column of those tables represents the percent of observations for which riders scored 11 points . Criterion 1 seemed difficult for 16-year-old-males of the 95th body-size percentile. This result may be attributed to the height of these subjects, which decreases the gap between their knee and the handlebars .Unlike criterion 1, criterion 2 did not present any difficulty for the virtual youth . Indeed, virtual subjects of all ages, body-size percentiles, and genders succeeded in this criterion for all evaluated vehicles. Criteria 3, 4, 6, 7, 8, 9, 10, and 11 all presented a similar trend where young riders do not conform well to these criteria, but older riders do . The contrast in success rate among subjects of different ages and height percentiles are likely also attributed to the variations in height among the subjects. For example, virtual 8-year-old-female riders of the 95th percentile did not pass criterion 5 for any of the evaluated ATVs. In contrast, their 16-yearold-counterpart passed the same criterion for 75 % of the evaluated ATVs , a surprising difference of 75 %. The results from Tables 2 and 3 indicate that 8-year-old youth would probably not be able to control utility vehicles when traversing rough or uneven terrains . This finding likely explains the fact that youth are more subject to loss of control events than adults .

The results of the simulations related to Criterion 7 indicated that youth 9 years old and younger are more likely to lean forward over 30 when raised off the seat to reach the handlebars of agricultural ATVs. As a result, the center of gravity of the ATV can shift forward, thus increasing the chances of a tip over. Lastly, some results of the simulations related to Criterion 5 were concerning. Males up to 11 years old and females up to 13 of the 50th percentile passed this criterion for less than 50 % of the evaluated ATVs.The percent of ATVs in which riders passed all criteria is presented in Fig. 4. The main finding is that certain youth should not ride most utility ATVs. For instance, the average male operator aged 16 passed all 11 safety criteria for less than 60 % of the evaluated vehicles. That number decreases sharply for younger youth or youth of the same age but smaller height percentile. A similar trend was also observed for female operators.The results of the validation tests are presented in Table 4 and summarized in a confusion matrix . In the confusion matrix, the outcome of the test is labeled in both horizontal and vertical axes. The horizontal axis represents the number of outcomes predicted by the virtual simulations, and the vertical axis represents the ground truth data . The results of the virtual simulations were very close to those of the field tests, with a total accuracy of 88 %. The Z-test determined that the Cohen’s Kappa coefficient was significantly greater than zero , botanicare trays indicating that the virtual simulations are reasonable. This approach to evaluate ergonomic inconsistencies between youth’s anthropometry and the operational requirements of ATVs proved to be an effective and accurate technique. Not all results of the virtual simulations matched those of the field tests. One unexpected result is related to criterion 6 . It was observed that the mean angle between the riders’ upper leg and the horizontal plane was 16.7 , slightly above the recommended threshold . Similarly, two subjects failed to pass criterion 5 in the actual field tests but passed it in the virtual simulation. During the field tests, riders were asked to sit comfortably as if they were just about to start riding the ATV. We argue that it would be possible for riders to adjust their way of sitting so they would pass both fit criteria; however, it would not result in the most ergonomic posture from the rider’s standpoint. On the other hand, in the virtual simulations, our ultimate goal was to place the 3-D subjects’ mockups to physically conform to the proposed fit criteria. Thus, it was impossible to predict whether the final adopted postures in the simulations would match those selected by the riders in the validation tests. Therefore, we argue that despite some outcomes of the virtual simulations did not match those of the field tests, the results of the virtual simulations are still reasonable. One just has to be cognizant that the outcomes of the virtual simulations represent a hypothetical scenario where the rider is able to attain a posture based on their anthropometric measures relative to the ATV, not on their preferences.

This study evaluated limitations in youth’s anthropometric dimensions when riding commonly used ATVs. Using a combination of actual field measurements and a novel digital simulation approach, the present study evaluated 11 ATV fit criteria for youth. The major finding was that youth are not recommended to ride adult-sized ATV models, which is a common practice in the United States , 2010; Jennissen et al., 2014. This finding raises serious concern regarding youth’s ability to ride ATVs, especially when unsupervised.The present findings outlined that some youth are too small, which makes them incapable of properly reaching the vehicle’s hand/foot brakes, resting their feet on the footrests, or having to lean forward beyond 30 to reach the handlebars when rising off the seat. Failing to activate the ATV brakes limits the youth’s ability to reduce the speed or to stop the vehicle, which likely prevents them from avoiding unexpected hazards, such as obstacles or bystanders . In fact, previous research has shown that a significant number of ATV incidents include hitting a stationary object . In addition, the inability to place the feet on the footrests when not breaking the ATV entails a functional loss of control of the vehicle. ATV LCEs occur frequently and are a significant cause of injury and death in agriculture . This finding indicates an opportunity for manufacturers to consider changing the design of their machines, allowing riders to adjust the ATV’s seat height, which would likely reduce longitudinal torso impact while traversing rough and uneven terrains. Furthermore, leaning beyond 30 can cause the ATV to tip forward, resulting in a rollover. Most ATV-related crashes on farms and ranches, especially those resulting in deaths, involve rollovers . On the other hand, some youth are too tall, which decreases the clearance zone between their legs and the handlebars. A clearance zone smaller than 200 mm makes it difficult for the rider to properly reach and steer the handlebars . Consequently, riders may lose control of the vehicle or have difficulty keeping it at a safe speed. As mentioned before, these series of events can lead to injuries and deaths.Furthermore, despite some results showing that youth are capable of riding many of the ATVs evaluated in this study, other risk factors such as experience, psychological, and cognitive development cannot be overlooked . Youth who are high in thrill-seeking are more likely to engage in risky ATV riding behaviors, regardless of their safety awareness . Those cases require external interventions, such as changes in legislation, improved ATV design, and use of crush protection devices .The results of this validation experiment showed that some riders failed criteria 5 and 6 even though they seemed able to operate the study vehicle comfortably and safely according to our ATV safety research team. Particularly, subjects 1, 2 and 3 presented elbow angles of 129 , 170 and 172.5 , respectively. While fit criterion 5 recommends an elbow angle between 90 and 135 , it is not uncommon to see motorcycle riders reporting comfortable elbow angle values up to 168 . Moreover, subjects 1, 2, and 3 presented upper leg angles of 14 , 14.7 , and 21.4 , respectively . A previous survey regarding motorcycle riders’ perceived comfortable posture reported optimum upper leg angles as high as 23 . It is our understanding that fit guidelines 5 and 6 are rather conservative, and their proposed thresholds may rule out riders that are perfectly able to ride utility ATVs safely and comfortably. As such, we propose some modifications to those fit guidelines. First, we recommend that the rider’s elbow angle should be between 90 and 170 as long as the rider feels comfortable steering the handlebars and is able to pass fit criteria 8 and 11 .