Hemp Growing

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 .

All Cannabis germplasm is completely interfertile, suggesting that the genus consists of a single species, with two subspecies and subsp. indica . Through DNA analysis, evidence pointed towards C. sativa, C. indica, C. ruderalis populations of Cannabis as being distinct subspecies of Cannabis sativa . Common vernacular terms, sativa and indica, have been used to describe different types of marijuana, causing much confusion for consumers who believe that these different forms of Cannabis happen to function in different ways. Generally, sativa types are plants with tall and slender morphology, narrow leaflets, and late maturation while indica types have shortened stature, broad leaflets, and early maturation . Whatever true population differences there may have been between indica and sativa types of C. sativa subsp. indica have become lost over generations of repeated hybridization events , although clearer population differentiation between hemp and marijuana still exist . Thus, industrial hemp is simply classified taxonomically as Cannabis sativa L. with low amounts of THC . One way to classify genotypes or populations of C. sativa is through their chemotypes, or chemical phenotypes, based on the predominant cannabinoids in the plant, in particular, cannabidiol , cannabigerol , and tetrahydrocannabinol . C. sativa is divided into five different chemotypes: THC dominant, referred to as the drug-type, CBD dominant, referred to as fiber-type, and one which THC and CBD are present in equal proportion. Two other chemotypes, CBG dominant and those which contain low concentrations of cannabinoids, are less frequently used in scientific literature . The classification of C. sativa germplasm with chemotypes enables scientists to easily classify individuals for breeding for certain uses like pharmacology, fiber, or seed . Cannabis sativa L. is a dioecious plant with individuals from the male and female sexes having distinct morphological differences .

Male individuals are described as slenderer in stature than their female counterparts and have less reproductive biomass compared to a females’ dense inflorescence . Differences in morphology, how to cure cannabis specifically between the sexes of hemp plants, only become apparent after the seedling stage . The genetic basis for sex in Cannabis is determined by the inheritance of either an X or Y chromosome from the male parent. Female plants have a sticky inflorescence that captures wind-dispersed pollen from male plants . Male flowers, within their hanging panicles, have a perianth of five sepals that surround the androecium; the anthers at maturity split lengthwise, releasing the pollen grains . Female flowers develop as thick clusters called racemes, and receive the pollen grains through insect, wind, or mechanical dispersion onto the pistils. In production settings, formation of seeds is undesirable if the use of hemp is the extraction of essential oils or the sale of the flowers themselves; consequently, most hemp producers prefer to grow only female individuals and avoid fertilization from male plants . Grandular trichomes, a form of sessile trichomes, cover the surface of female flowering tissues and produce cannabinoid oils . The secreted oils which burst from these trichome sacs coat the surface with a sticky resin, which results in flowers that are waxy in texture . Cannabinoids likely serve multiple purposes within the Cannabis plant, such as a defense response against herbivory from insects and the dissipation of heat stress in the environment. Cannabinoids are produced in substantially higher quantities when exposed to UV-B radiation , suggesting they act as a barrier against the damaging effects of UV-B radiation .

There are over 180 different cannabinoids present in C. sativa, with the primary cannabinoids being THC, also called Δ9-THC, CBD, and CBC . These cannabinoids coexist along with their acid-precursors: tetrahydrocannabinolic acid , cannabidiolic acid , and cannabichromenic acid . The acid forms of these cannabinoids change into their decarboxylated forms primarily from the application of lightand/or heat onto the harvested crop, but the decarboxylated forms still exist at certain levels within the flowering tissue before harvesting . All acid forms of cannabinoids come from a primary precursor phytocannabinoid called cannabigerolic acid . CBGA is a product of two metabolites, olivetolic acid and geranyl diphosphate, which are formed from the polyketide pathway and plastidial deoxyxylulose phosphate/methyl-erythritol phosphate pathway , respectively . Cannabinoids have been used as a component of human therapeutic medicine for thousands of years . Specific cannabinoids have been used to minimize chronic pain, improve sleep quality, and treat a wide variety of other ailments. C. sativa has obtained more attention over the last 30 years as a source of medicine in America after California passed the Compassionate Use Act of 1996, a bill which allowed the state to provide patients with access to medical marijuana “in the treatment of cancer, anorexia, AIDS, chronic pain, spasticity, glaucoma, arthritis, migraine, or any other illness for which marijuana provides relief” . Since then, there has been increasing interest in the use of cannabinoids as a way of minimizing pain, a malady affecting 1 in 5 Americans on a daily basis . CBD is of particular interest because it can provide pain relief without the psychoactive effects that come with other cannabinoids like THC .

The global value of CBD products is 2.8 billion dollars as of 2020 and is expected to increase on the order of 20-23% year-to-year over the next five years . In America, Cannabis was a widely grown crop for many years during the 18th and 19th centuries before being replaced by cotton as the predominant crop used for textiles . With increased interest in the crop medically for its oils, it has also renewed interest in its fiber , oil, and seed . Hemp bast, the long fibers from the outer stem of hemp, can be used to make carpets, shoes, diapers, insulation, yarn, composite materials, and plastics . The hurd, or inner core fibers of the hemp plant, can be used to create hempcrete, animal bedding, potting mix, and soil amendments . With a wide variety of uses, industrial hemp fibers, as of 2019, have a current market value of $4.46 billion with an expected compound annual growth rate of 33% through 2027 to total $43.8 billion . With the value of industrial hemp between its fiber and CBD products currently being valued at over $7.2 billion, there is increasing interest in agronomic improvements for the crop. Hemp is typically grown in field settings but can be grown in greenhouses when growers are focusing on greater pest control, year-round growth, and control of lighting regiment. In field settings, hemp generally is grown on well-drained soil which is high in fertility . Planting density varies among varieties and the grower’s intended use for the crop. Hemp grown for oil extraction requires wider spacing to promote branching and flower development; planting densities for the purpose of harvesting its fiber is typically double that of oil seed varieties, however, in general the architecture of the plant itself is strongly associated with the density of planting, nutritionally availability within the soil, and the length of day that the plant is exposed too during its life cycle . Hemp is either planted by direct seeding or through the transplantation of seedlings or clones . When hemp is grown for medicinal oil extraction, the field typically consists of only female plants. Usually this is accomplished by using “feminized” seed, which is produced from female plants that have been induced to produce male gametes and seed using either chemical or environmental stress which results in seeds which will produce seeds which result mostly in female plants . This process of masculizing female hemp plants on an industrial scale is done through foliar applications of silver thiosulfate . The rationale for producing feminized seeds, outside of maintaining genetically identical inbred lines for commercial sale, is to minimize the production of seeds within the flowering tissue of female plants due to consumer, grower, and processor preferences. Hemp improvement can be done through the use of phenotypic recurrent selection, aka mass-selection, by selecting the best individual plants based on field performance and using their seed for the next evaluation cycle . Once an elite variety has been developed, back crossing can be used to incorporate new traits from undesirable germplasm . For traits such as fiber quantity and quality, with high trait heritability, trimming cannabis mass-selection can work well . Other breeding practices have been employed for the purpose of either increasing variation, specifically when crossbreeding individuals, or for fixing a trait through inbreeding to produce inbred lines and/or to capitalize on heterosis of F1 hybrid cultivars .Advances in biotechnology can accelerate the incorporation of a trait into an existing population with high accuracy and speed using next generation sequencing and marker assisted selection .

Marker assisted selection works to track the phenotype of an individual plant by associating genetic polymorphisms with trait variation, enabling selection on seedling plants without having to grow the individual to maturity . Recent advances in sequencing technology, specifically with the development of next-generation sequencing , has reduced the price of sequencing whole genomes of individual plants , making marker identification and use more tractable. Day neutrality is a trait which is present in many agricultural crops such as soybeans, wheat, barley, rice, tomatoes, strawberries, and alfalfa . Several genes involved light sensing contribute to differences among genotypes in time of flower and in day neutrality; the major genes appear to be conserved across species. For instance, day-neutrality is controlled by CONSTANS , a gene which encodes a transcription factor involved in the transduction of light signals, promoting the expression of other genes downstream . The genetic basis for the change from vegetative growth to flowering within hemp cultivars is mostly unknown. Petit et al. , identified six QTLs related to genes which control the perception and transduction of light and their associated transcription factors. However, hemp germplasm has a wide range of flowering times, affected by both genetics and the environment . Hemp, Cannabis sativa L. is a valuable medicinal, fiber, seed, and oil crop. Understanding and manipulating the flowering time of hemp could facilitate cultivar development for diverse environments and cropping systems. The time at which hemp transitions from vegetative growth to flowering is critical for the development and quality of the final harvested product. Most hemp germplasm requires short days to begin flowering and producing seed, oil, and cannabinoid products . Before the transition to flowering can occur, a period of vegetative growth stage controlled by the accumulation of thermal time in the environment is necessary . Vegetative growth for hemp is optimal around 30o C and continues to a maximum temperature about 42o C. Around 300 to 600 units of cumulated thermal time over 1o C must occur for the plant to be able to initiate flowering if the critical daylength is reached . While typically a short-day plant, hemp germplasm has considerable variation in TOF . Some hemp germplasm has day-neutral flowering, which means that regardless of photoperiod, they will flower after a certain amount of thermal time has accumulated . Day-neutral Cannabis varieties can go from seed to flowering in a soon as four weeks, and some varieties can be harvested within 100 days after seeding. The trait for day-neutrality originated from the ruderalis variety of Cannabis sativa in parts of Southern Russia. These plants are described as being short and stalkyin nature, while producing small amounts of flower with low concentrations of THC . Despite its undesirable attributes, ruderalis has been used to introgress day neutrality into high yielding flower and oil varieties. The day neutral trait can provide significant agronomic benefits by standardizing harvest time and potentially enabling growers to have more harvest cycles in one year compared to those growing day-sensitive varieties of hemp which takes much longer to flower in most environments. The genetic basis for day neutrality, specifically in hemp, is currently unknown. We hypothesized that at least one major locus controls day neutral flowering with multiple minor loci affecting the variation in time to flower within day neutral or daylength sensitive germplasm. To test this hypothesis, we hybridized day neutral and daylength sensitive accessions of hemp and evaluated the progeny for flowering time across three field trials over two years in order and genotyped them using next generation sequencing technology to identify genetic loci involved in flowering time. Hemp seeds were placed into 72-cell flats containing potting soil mix, composed of mostly perlite, and were then covered in vermiculite and watered daily.

The remote and rugged terrain that once attracted illicit cultivation attempting to avoid detection may now hinder the ability of farms in these areas to comply with environmental regulatory standards and ultimately become licensed. Multiple environmental permits may be required in order to obtain a CDFA cultivation license if growing outdoors or in greenhouses, and the fees and information requirements may represent significant hurdles to some farmers . Initial evidence indicates the first wave of regulated, industrial scale cannabis has largely begun not in the historical epicenter of Northern California, but in the hills and isolated valleys of the Central Coast. Although select counties in the Central Valley have allowed a token number of large cannabis farms , licenses for farms of equivalent size have been issued en masse in counties such as Santa Barbara and Monterey . In these areas, it is not yet clear if large-scale cannabis farms will more commonly repurpose existing infrastructure or stake out new territory further from existing agriculture and closer to natural spaces. Understanding the potential environmental impacts of industrialized cannabis requires knowing where and why extensification or intensification occurs, patterns of natural resource use, and how these dynamics relate to regulatory mandates. For example, regarding the issue of water, the legacy of illegal cannabis cultivation sites and their tendency to divert from streams and springs has led to these sources of water becoming tightly regulated within the legal cannabis industry. In particular, air racking extraction from these sources is prohibited during the growing season and farms that rely on them must either instead collect and store this water in the off season or use an alternative source.

For the majority of regulated cannabis farms, the choice has been to use well water and this is already the most common source for irrigation . Given the hurdle of developing sufficient storage, especially at increasingly larger farm sizes, the use of wells will likely increase in frequency . While the story of groundwater depletion in California is common knowledge , the use of wells by commercial cannabis farms is fundamentally different, given their tendency to occur outside of large aquifer basins . Such wells that are located instead adjacent to watercourses, in small alluvial aquifers, or fractured bedrock have potential to reduce crucial stream flow during the summer months , although this topic is currently understudied and generally beyond the purview of current California groundwater policy . This is a concern not only for large-scale farms in the new frontier of the Central Coast, but also for farms on the North Coast that must compete with these operations. The prospect of farms on the North Coast increasing in size to compete and shifting to groundwater extraction to comply has latent environmental implications, deserving further research. Characterized by geographically isolated, small farms, the informal cannabis cultivation sector represented a form of agriculture distinct from California’s consolidated, credit-supported industrial model. The historic legacy of the cannabis industry in Northern California has differentiated this region and led to worldwide recognition of its products. However, remoteness has now isolated traditional cultivation regions from an emerging legal supply chain and new markets, while obligating farms to navigate high regulatory costs associated with operating in environmentally sensitive locations.

How farmers in these remote watersheds respond holds importance for the future of the cannabis industry and its socio-environmental dynamics. Formation of grower cooperatives , appellation systems , caps on farm size and license consolidation , or a return to medical provisioning collectives may provide tools to overcome steep start-up and licensing costs for small farmers. Due to ongoing federal prohibition, farmers do not have access to traditional supports such as bank loans or crop insurance and instead must rely on private capital, limiting engagement in legal markets by many smaller farmers. Paradoxically, if barriers to capital are reduced, it may invite institutional investment and accelerate further industrial consolidation and up-scaling. More research is needed to understand the socio-ecological dynamics that underpin changes in cannabis cultivation in California and beyond. California’s statewide legalization opens some research pathways through regulatory databases, harmonization among institutions and jurisdictions, and new funding mechanisms. Yet, federal prohibition still creates a “quasi-legal challenge” for robust research , with consequences for effective policy-making and environmental health . Federal prohibition can inhibit institutional funding , discourage participation of informants in studies , lead to suspicion of researchers by potential study participants , create difficulty for Institutional Review Boards and researchers to estimate and mitigate risks for human subjects, and create asymmetries in data collection and knowledge types. High numbers of producers operating outside of regulatory systems impede the ability for comprehensive and representative studies and projects limited to only “compliant” producers cannot account for the total socio-ecological dynamics of cultivation.

With a lack of robust research, resulting policies cannot be driven by direct research on cannabis, may impair social equity in the transition of informal producers to formal markets , and complicate implementation of strategies to govern common environmental resources . Further, policies that incentivize industrial consolidation and eliminate small producers may have environmental consequences . Among cannabis producers, decisions about production can alter ecological and hydrological conditions, requiring attention not just to stream flows, pest management, indicator species, and wildlife movement patterns, but also to the social calculus of decision-making. Analyses of production decisions necessitate the development of qualitative research methods to understand how policy formations, social norms, knowledge access, and enforcement practices, among other variables, shape production practices across different kinds of ecologies and regions. In particular, many farmers are electing to avoid compliance . Factors influencing non-compliance may derive from prohibition or may only emerge post-legalization . Understanding the perspectives of non-compliant farmers is crucial to researching the social and ecological dynamics of cannabis production. Equally important is a qualitative, political economic understanding of how and why policies take particular forms in certain jurisdictions at certain times. Which interests and groups are most and least influential in forming polIcy? Who bears the consequences and rewards of the resulting regulatory regimes? Rapid transformations in cannabis policy are corresponding to the emergence of new scales, practices, and ecological consequences of cannabis cultivation. Lessons from other legal forms of agriculture suggest that increased market pressures may lead to industrialization, extensification and/or intensification, and increased reliance on credit fueled by debt. Siting patterns described here indicate that all three may already be trending upward in California since legalization of production for recreational use. As changes rapidly occur, research is urgently needed to understand the relations between regulatory change, farm size, location, environmental outcomes, and the geographical distributions of benefits and impacts. Such analyses will aid policy maker’s ability to govern and farmer’s capacities to participate in this newly regulated industry. If done with an eye toward equitable and just outcomes, drying weed it may also point the way toward a cannabis agriculture that incorporates and learns from the lessons and failures of industrialized agricultural production. Assessing the environmental impacts of the cannabis industry in Northern California has been notoriously difficult . The federally illegal status of cannabis has prevented researchers from obtaining funding and authorization to study cultivation practices . Fear of federal enforcement has also driven the industry into one of the most sparsely populated and rugged regions of the state , further limiting opportunities for research. An improved understanding of cannabis cultivators’ water use practices is a particularly pressing need.

Given the propensity of cannabis growers to establish farms in small, upper watersheds, where streams that support salmonids and other sensitive species are vulnerable to dewatering , significant concerns have been raised over the potential impacts of diverting surface water for cannabis cultivation. The environmental impacts of stream diversions are likely to be greatest during the dry summer months , which coincide with the peak of the growing season for cannabis. Further, because cannabis cultivation operations often exhibit spatial clustering , some areas with higher densities of cultivation sites may contain multiple, small diversions that collectively exert significant effects on streams . An important assumption underlying these concerns, however, is that cultivators rely primarily on surface water diversions for irrigation during the growing season. Assessments of water use impacts on the environment may be inaccurate if cultivators in fact use water from other sources. For instance, withdrawals from wells may affect surface flows immediately, after a lag or not at all, depending on the well’s location and its degree of hydrologic connectivity with surface water sources . Documenting the degree to which cannabis cultivators extract their water from above ground and below ground sources is therefore a high priority. In 2015, the North Coast Regional Water Quality Control Board , one of nine regional boards of the State Water Resources Control Board, developed a Cannabis Waste Discharge Regulatory Program to address cannabis cultivation’s impacts on water, including stream flow depletion and water quality degradation. A key feature of the cannabis program is an annual reporting system that requires enrollees to report the water source they use and the amount of water they use each month of the year. Enrollees are further required to document their compliance status with several standard conditions of operation established by the cannabis program. These include a Water Storage and Use Condition, which requires cultivators to develop off-stream storage facilities to minimize surface water diversions during low flow periods, among other water conservation measures. Reports that demonstrate noncompliance with the Water Storage and Use Standard Condition indicate that enrollees have not yet implemented operational changes necessary for achieving regulatory compliance. In this research, we analyzed data gathered from annual reports covering 2017 to gain a greater understanding of how water is extracted from the environment for cannabis cultivation. The data used in this study was collected from cannabis sites enrolled for regulatory coverage under the cannabis program. The program was adopted in August 2015, with the majority of enrollees entering the program in late 2016 and early 2017. The data presented in this article was collected from annual reports submitted in 2018 , which reflected site conditions during the 2017 cultivation year. The data therefore represents, for the majority of enrollees in the cannabis program, the first full season of cultivation regulated by the water quality control board. Because the data was self-reported, we screened reports for quality and restricted the dataset to reports prepared by professional consultants. Most such reports were prepared by approved third-party programs that partnered with the board to provide efficient administration of, and verification of conformity with, the cannabis program. Additional criteria for excluding reports included claims of applying water from storage without any corresponding input to storage, substantial water input from rain during dry summer months and failure to list a proper water source. Reports containing outliers of monthly water extraction amounts were also identified and excluded due to the likelihood of erroneous reporting or the difficulty of estimating water use at very large operations. Extreme outliers were defined as those values outside 1.5 times the bounds of the interquartile range . Farms were not required to use water meters, and those without meters often estimated usage based on how frequently they filled and emptied small, temporary storage tanks otherwise used for gravity feed systems or nutrient mixing. The final dataset included 901 reports. Parcels of land where cannabis was cultivated — including multiple contiguous parcels under single ownership — constituted a site, and this is the scale on which reporting was conducted. The spatial extent of the cannabis program included all of California’s North Coast region ; however, only a subset of the counties in this region allow cannabis cultivation and therefore reports were only received from the following counties: Humboldt , Trinity , Mendocino and Sonoma . Because Sonoma County contributed relatively little data, we combined Sonoma County’s enrollments with those from Mendocino County when making county-level comparisons. The data used for this analysis included the source and amount of water that cultivators added to storage each month as well as the source and amount of water applied to plants each month. We did not analyze absolute water extraction rates. Rather, we used the amount of water extracted each month — whether water was added to storage or applied to plants directly from the source — to analyze seasonal variation in each water source’s share of total water extraction. Water sources included: surface , spring , rain , well , delivery and municipal .