Hemp Growing

Distance-based redundancy analysis is an extension of PCoA to model multivariate data and it was used to assess the amount the main food groups and confounding factors together explain of the compositional variation of gut microbiomes between individuals and visualizing the direction of the associations. PERMANOVA, factorfit, and dbRDA were run with 999 permutations. In per-taxa analyses we used a multivariate analysis by linear models to analyze associations of each main food group and their subgroups with all taxa at species level. Benjamini-Hochberg false discovery rate corrected P vlues were used to adjust for multiple comparisons. Prior to analysis the relative abundances of the taxa were centered log-ratio –transformed. For the cluster analyses, the taxa with significant associations with the main food groupsfrom per-taxa analysis were clustered based on proportionality using Ward minimum variance method and the optimal number of clusters was determined using KelleyGardner-Sutcliffe penalty function. The results were visualized with a heatmap. A pathway analysis was conducted between Kyoto Encyclopedia of Genes and Genomes orthology groups and the food groups using linear regression analysis. The relative abundances of KO-groups for each sample were obtained from the strain-level outputs of SHOGUN and the data on KO-groups were log10-transformed prior to analysis. Benjamini-Hochberg FDR corrected P values were used to adjust for multiple comparisons. Statistically significant associations of main food groups were further visualized using FuncTree 2. Analyses were controlled for potential confounding factors based on prior literature. These included age, sex, BMI, smoking status, 4×4 grow tray usage of possible microbiome-altering medications and total energy intake. The level of statistical significance for analyses was set at a P value<0.05 . Statistical analyses were performed using R version 3.6.3 and the following packages phyloseq, microbiome, vegan, maptree and ComplexHeatmap. 

The method of engaging deeper spatio-historical understandings is not just to explain the multiple processes/forces driving China’s agrarian change, but more importantly to suggest a different analytical and political frame that “shed[s] light on the slippages, openings, and contradictions where pressure might be applied, as well as connections and alliances from which new possibilities might emerge”. In other words, my research marks a distinct approach by ‘denaturalizing’ social science bounded fields that operate in a bounded concept of space, place, and identity. The major difference between my theoretical and methodological approach and that of the authors just mentioned involves my open, non-teleological understanding of rural transformation in China, and the spatial dimension of possibilities within a globalizing world.Let me begin with the first form—the introduction of machinery to rice production. Just a few years ago, in Xialongkou and the surrounding hill counties, oxen constituted an indispensable animal power in small-scale household agricultural production. Its’ significance was not only in the ancient past, but also during the Maoist collectivization period, and continued after agricultural de-collectivization in 1978. This use of animal traction was of course not confined to western Jiangxi province. In Shanxi Province in the northwest, oxen were important in both traditional times and under the socialist era, as made clear in Liu Qing’s novel Epic of Creating Enterprise . Published at the height of the socialist collectivization movement, Liu describes how, for the first time, privately-owned oxen and other draft animals were leashed and placed under the roofs of collectivized pens, a key step towards collective agriculture. Liu Qing illustrates how dramatic and revolutionary an event it was. When the collective agriculture system was dismantled in the late 1970s, the important role of oxen was unaffected. In the case of Xialongkou and surrounding counties, oxen continued to be used in agricultural work until 2011 . What has been the impact of applying agricultural machines in Xialongkou? In the past, each family took from seven to fourteen days to plough rice paddies by oxen. Plouging was mostly men’s work. Now, villagers can hire a tractor to plough the entire village’s land in two to three days. 

The mechanizing process was also noted in rural Taiwan in the 1990s . In Xialongkou, villagers generally agree that there has been no impact on output per unit of rice paddy. Experienced rice cultivators estimate that about one mu yields around five hundred kilograms of unhusked rice, whether using oxen or machines. However, for the same output per unit of land, each household, and especially its men, clearly now invests fewer workdays in the task of ploughing. In terms of harvesting, machines have also replaced hand-held sickles, by means of which it normally took seven to ten days for all family/household members working together to deal with this “agricultural busy work” . With a hired harvesting machine , the entire village’s “agricultural busy work” can now be finished in one or two days. Of course, hiring machines cost money. As of 2019, one mu cost 80 RMB for ploughing and 100 RMB for harvesting, though prices for hiring machines have been increasing over the past few years and will presumably continue to do so in the near future. The application of agricultural machinery is commonly associated with increasing economies of scale and efficiency, a sign of modern progress and science. The phenomenon of agribusiness companies, a new trend in China’s agrarian capitalism, has attracted considerable attention in scholarship . But large-scale agribusiness is rare in the hill country. What one encounters most commonly are small household farms that remain small and fragmented due to the socialist legacy and topographical conditions. On the surface, agricultural machines seem to accelerate agricultural capitalism in China. Ever since China’s economic reforms of the 1980s, concerns have grown about the rise of capitalist relations and its destruction of agriculture and land commodification in rural China . In Xialongkou, however, changes brought about by machinery like datian ji and gedao ji are integrated into small household agriculture. In the hill country, machines have merely replaced two tasks of rice production: ploughing and harvesting. Rather than expand cultivation, the net effect has been agricultural de-intensification—a decrease in annual agricultural work per person, especially men’s work. As a result, men now have the time to engage in diverse odd jobs in villages and small towns since the late 2000s.

The next section discusses the second form of agricultural de-intensification, land fallowing.Unlike the effect of agricultural machinery, de-intensification through land fallowing requires taking into consideration both temporal and spatial dimensions. One thing to note is that in Xialongkou, all rice fields are irrigated by hill streams flowing down from higher ground via natural gravity, not by the river that runs in front of the village yearlong. As will become clearer later, this water source is crucial to understanding both temporal and spatial aspects of land fallowing. Seen from a temporal perspective, de-intensification comes in three types: 1) shifting from multi-cropping to single cropping ; 2) shifting from multi-cropping to short fallowing; and 3) shifting from single cropping to short fallowing, bush fallowing, or forest fallowing. The first form is really a seasonal fallowing within an annual cycle, whereas the latter two involve a much longer fallow time extending beyond the yearly cycle. All three of these forms involve, fundamentally, greenhouse racking the reduction in agricultural intensity per unit of land. As Table 3 shows, in Xialongkou and its surrounding villages, at least from the 1980s to 2008, there were four major crops cultivated in paddy fields in a traditional lunar calendar year cycle. They were “early rice” , “big rice” , “second rice” , and “rape” . The “early rice” was transplanted from a seedling bed in early March and harvested in June. The “big rice” was transplanted from a seedling bed in early May and harvested around the time of the MidAutumn Festival . The “second rice” was transplanted from a seedling bed in June and harvested in October. It is called “second rice” because the rice is grown on a paddy where “early rice” has just been reaped; the paddy is immediately re-tilled and covered with a layer of animal manure and night soil . Thus, rice crops were formerly transplanted and harvested three times per year in this hill country, though not all of the three harvests made use of the same rice plots. From the perspective of rice cultivation, land use involved a mixture of single-cropping and double-cropping. It was labor intensive agricultural production that is not unusual in rural China. After the third harvest, some of the rice paddies were tilled again and fertilized once more with animal manure, with rapeseed then broadcasted on top as a winter crop. The agricultural calendar cycle came to an end when the rape crop was harvested in late February. As shown in Table 3,“early rice” and “second rice” have been largely eliminated since 2008. The three distinct rice crops have been reduced to just one—“big rice”—which is now grown after a winter rape crop.The daughter-in-law works at the Township post office, which was sub-contacted to her father one year ago. Regarding the family ox, the young couple believes using machinery is the way to go. It is the modernization process. The Daniu couple kept the family ox to save on the unnecessary expense of hiring “smashing field” machines. Many older villagers who praise the Daniu family for their industriousness and thrifty ethic share their views. At the heart of the generational divide lies the difference between waged labor and agricultural work as life styles. Although the Daniu couple lives at a level of semi-subsistence, making few purchases from the market, they are by no means resisting the market and consumption. The couple seeks market opportunities to sell their labor by doing varied odd jobs, just like the rest of Xialongkou residents. The difference is that the Daniucouple keeps agricultural work as their core way of life and other odd jobs as sidelines, while the younger generation like the son and daughter-in-law reverses the relationship between the family farm and outside waged labor. It is not uncommon to observe young adults remaining idle at their village home while their older parents work hard in the rice fields. One common reason is that they are uninterested in non-monetary returns as an alternative to waged jobs.5 But the generational tension is more complicated than simply the divide between different values and attitudes towards agricultural work. An interesting twist involves how both young and old integrate the traditional value of filial piety into agricultural work. Let me illustrate this point with the story of Shaogang. In brief, not all young villagers could avoid the “soil” —a pun in both vernacular and official discourse that refers both to agriculture and to family responsibility. Shaogang, an ex-migrant, in his late 30s, has had a middle school education. For many years, he worked and lived in Zhejiang province. He came back to Xialongkou Hamlet to till the family land with a small tractor in 2012. He is the youngest son and now has the duty to look after his elderly parents, the result of a collective decision made with his two older brothers. Although his peers have a low opinion of agricultural work, tilling the family land has become a moral duty linked to filial piety and family responsibility. Shaogang believes that village elders should “enjoy life” by not working in the rice fields. He came back to farm with the help of machines. Van de Ploge et al. have rightly pointed out the importance of family land that glued three generations of rural family/households. Yet their analysis falls short on rural population’s attachment to the land, especially the elderly generation that tirelessly reinvests and works on the family land for their adult children. They overlook the impact of the existing generational divide on work and lifestyles. The generational divide is real. Unlike Shaogang, most young people do not choose to return to till the land, even if they have the option now to use tractors. Instead, migrants can fulfill their filial duties and family responsibility through remittances, and by persuading their elderly parents to take a rest from hard physical work . One often overhears elderly villagers speaking highly of elderly who do not need to work under the sun: “Look at yourself.

The most harmful diseases are mastitis and foot rot , which reduce the quality of dairy products, inhibit the development of the dairy industry, and have a negative impact on food security and human health. Bovine mastitis is a major disease affecting the dairy industry worldwide with huge economic losses and decreased animal health. Furthermore, it is a common but complicated disease in high-yielding dairy farms. During the course of dairy cow breeding, it is quickly spread and difficult to cure, resulting in serious economic losses. Farm management, particularly proper treatment of manure, has become an issue of concern in many farms. China has the largest population of livestock animals of any country in the world; however, many livestock farms have poor animal manure management facilities for the treatment and disposal of manure. It is therefore suggested that manure management needs to be improved in China. An increasing number of studies have found that poor farm management can lead to severe environmental problems such as the pollution of air, water, and land. The milk industry is one of the five leading strategic industries in the Ningxia region of China. However, few studies have examined the effect of poor dairy farm management on microbial community compositions and diversity among the dairy farm matrix in this region. By using pyrosequencing of metagenomic 16S rDNA, grow trays the objective of this study was to characterize bacterial diversity in feces, manure, and soils in dairy farms in the Ningxia region of China.Three dairy farms representative of typical dairy farm operations in the Ningxia region were enrolled in this study. Farms 1, 2, and 3 were located in the suburbs of Xingqing , Jinfeng , and Xixia districts of Yinchuan City, respectively. Between April and July of 2016, fresh feces, manure, and soil samples were collected from these farms.

Fresh fecal samples were collected within 30 seconds of excrement from lactating cows. Manure samples were collected from piles of accumulated manure without further treatment. Soil samples were collected from around the farms at different depths . To eliminate error caused by individual differences or unrelated factors, fresh fecal samples were a blend of at least six cow feces and manure and soil samples were a blend of at least six sampling sites or depths. Each sample from each farm was mixed separately, with a total of 27 samples being generated after mixing. All samples were refrigerated immediately after collection and during transportation to the laboratory. Upon arrival at the laboratory, samples were stored at −80° C until processing.Metagenomic DNA was extracted from all types of samples using a QIAampR DNA Stool Mini Kit according to the manufacturer’s instructions. Sequencing by synthesis was performed on an Illumina HiSeq 250 platform .Sequence analysis was performed using the Sparse software . Sequences with ≥97% similarity were assigned to the same operational taxonomic unit . The representative sequence for each OTU was screened for further annotation. Sample reads were assembled using mothur v1.3213. Moreover, high-quality sequences were aligned against the SILVA database. Sequences were further qualitatively trimmed using a 2% cluster error, and chimeras were removed using UCHIME. Assignment of OTUs was performed at 97% identity using the furthest neighbor algorithm. Taxonomic assignments were made against the Ribosomal Database Project database. For comparisons, groups were normalized to include 27 samples, each randomly subsampled to 25,000 sequence reads . For determination of the percentages of sequence reads and OTUs unique to each group, no normalization was performed.

The UniFrac distances were calculated using QIIME software , and these data were used to build the UPGMA sample cluster tree. Jackknifed beta diversity included both unweighted and weighted UniFrac distances calculated with 10 times subsampling, and these distances were visualized by principal coordinate analysis. Principal component analysis , PCoA, and non-metric multidimensional scaling analysis graphs were drawn using the R software . Taxonomy assignment of OTUs was performed by comparing sequences to the Greengenes database . The Mann–Whitney U test was used to test for the significance of alpha diversity. Two-sided Student’s t-test was conducted to determine the significance of beta diversity between sample groups. Linear discriminant analysis coupled with effect size was performed to identify the bacterial taxa represented between groups at the genus or higher taxonomy levels. The functional profiles of microbial communities were predicted using PICRUSt. The bootstrap Mann–Whitney U test with 1000 permutations wasalso used to identify gene pathways or OTUs with significantly different abundances between groups. The R packages “phyloseq” and “heat map” were used for data analysis and plotting .In total, 2.2 million strands of 16S rDNA amplicon data were generated from the 27 samples using pyrosequencing. After trimming and cleaning, this number was reduced to 69,065 high-quality reads with a median length of 253 bp. Only 1.5% of the sequences were identified as chimeras and were excluded from further analysis. The number of sequences in the 27 filtered samples was in the range of 47,924 to 83,624 sequences, and after homogenizing these sequences, the sequences were concentrated to around 45,000. An OTU table was generated by clustering all of the sequences into OTUs with a 97% similarity level. The species observed among the samples showed that the same samples from different farms possessed the same patterns, with the number of microbial notes in soil being higher than that in other samples.

The samples were grouped by category, and the main annotations are shown in Table 1. A bacterial community bar chart of all samples was constructed at the phylum level, from which 47 units were annotated from fecal samples, 44 units were annotated from manure samples, and 50 units were annotated from soil samples . The percentages of each of the top 10 phyla in all of the samples are shown in Table 1. These phyla were abundant and accounted for >94.63% of the entire bacterial communities in all of the samples. Therefore, these 10 phyla of bacteria were chosen for further analysis. In the class-level analysis, 75 classes of bacteria were detected among the 10 phyla . At the order level, 63 bacterial orders were noted from the Proteobacteria.The distribution of the relative abundances of bacteria at the phylum level varied among fresh feces, manure, and soil samples. The dominating phylum of bacteria in fresh feces was Firmicutes , followed by Bacteroidetes , Proteobacteria , and Spirochaetes . In manure, the dominating phylum was Proteobacteria , followed by Firmicutes , Bacteroidetes , and Actinobacteria . In soil, the dominating phylum was Proteobacteria , followed by Bacteroidetes , Actinobacteria , and Firmicutes . Data show that Proteobacteria, Bacteroidetes, and Firmicutes were the main phyla in all types of samples, although the percentage of Firmicutes was slightly lower than that of Actinobacteria in the soil samples. The three phyla were distributed at approximately similar ratios in the two manure samples , while Proteobacteria dominated in one manure sample . In contrast to the fecal and manure samples, Proteobacteria dominated the bacterial communities in all of the soil samples . In fresh feces, the phylum Firmicutes was predominantly composed of the three classes Clostridia, Bacilli, and Erysipelotrichi, the phylum Proteobacteria was comprised mainly of the classes Gammaproteobacteria, Betaproteobacteria, Alphaproteobacteria, Deltaproteobacteria, and Epsilonproteobacteria, the phylum Bacteroidetes was mainly composed of the classes Bacteroidia, Cytophagia, Sphingobacteriia, Saprospirae, Rhodothermi, and Flavobacteria, and the phylum Spirochaetes was mainly composed of the classes Spirochaetes, MVP-15, Brevinematae, and Leptospirae . In manure, the compositions of the classes in the phyla Proteobacteria, Firmicutes, pruning cannabis and Bacteroidetes were similar to those in fresh feces. However, the phylum Firmicutes in manure consisted of an additional class called AHT28. In addition, the phylum Actinobacteria in manure was mainly composed of the classes Actinobacteria, Acidimicrobiia, Thermoleophilia, Rubrobacteria, Nitriliruptoria, and Coriobacteriia . The data within the OTU table was compared between 5,000 randomly selected samples each at a 97% nucleotide identity level. To investigate variations in the distributions within microbial communities, all OTUs were assigned taxonomically using the RDP classifier. Among a total of 8819 OTUs, most bacteria were concentrated into five phyla, namely, Firmicutes, Bacteroidetes, Proteobacteria, Spirochaetes, and Actinobacteria. The heat map results showed that the compositions of the microbes in the same types of samples appeared to be similar .PCA profiles indicated that microbial communities varied depending on the type of sample. Principal components 1 and 2 demonstrated 14.11% and 11.91% of the total variance, respectively . PCA profiles showed significant separations between F1, F2, and F3 and M1, M2, and M3 treatments at three sites, especially for S1, S2, and S3. Treatments M1–M3 had higher scores than the corresponding treatments F1–F3 along the PC1 axis. According to the PC2 axis, PCA profile scores for treatment S3 were higher than those for both of the corresponding treatments S1 and S2.

To determine the similarity between different samples, a clustering tree was constructed using UPGMA , which is a commonly used method for cluster analysis. Interestingly, the same type of samples from different farms clustered within the same branches . Further, beta analysis of microbial diversity showed that the diversity of species within the fecal, manure, and soil groups was rather small, in contrast to the large differences among the fecal and soil groups . In the analysis of human pathogenic bacteria, this trend was also detected. The boxplot showing the phylum level classification in terms of both bacterial diversity and the diversity of zoonotic pathogens revealed that the fresh feces contained a great abundance of Firmicutes, but a low diversity of zoonotic pathogens. By contrast, little change in diversity was observed in the accumulated manure or soil samples .The diversity and relative abundance of zoonotic pathogens in the manure and soil samples are shown in Figure 8. In total, 32 species of pathogenic bacteria were found in feces, manure, and soils. Acinetobacter calcoaceticus and Bacillus cereus were the dominant zoonotic species in feces, followed by Enterococcus faecalis, Streptococcus uberis 0140J, Escherichia coli O26: H11, Corynebacterium diphtheria, Staphylococcus aureus C0673, and Pseudomonas aeruginosa. The relative abundance of B. cereus in feces was 3 to 17 times higher, respectively, compared with that in manure and soil. Among the 32 pathogens, the relative abundance of 20 pathogens varied significantly in feces and manure, with the relative abundance in manure 2 to 19 times higher than that in feces. The number of Actinomycetes in manure was much higher than that in fresh feces. Further, at the genus level, although the number of genera in manure decreased, the number of bacteria and Pseudomonas increased significantly. This phenomenon was closely correlated with the abundances in feces and manure. In a more detailed classification order, the first dairy farm was detected having 27 zoonotic species, of which five species were increased in abundance in manure,including S. aureus M0406, Clostridium perfringens B str. ATCC 3626, A. calcoaceticus, Bacteroides fragilis NCTC 9343, and Bacteroides vulgatus CL09T03C04. In the second dairy farm, 34 zoonotic species were found, of which 14 species were increased in abundance in manure, namely, B. cereus, Listeria monocytogenes FSL R2-503, S. aureus M0406 and C0673, E. faecalis, S. uberis 0140J, Streptococcus dysgalactiae, Clostridium botulinum, A. calcoaceticus, Acinetobacter baumanii BIDMC 57, Proteus mirabilis BB2000, and Vibrio cholera VCC19. Finally, in the third dairy farm, 29 zoonotic species were found, of which 15 species showed increased abundance in manure, namely, B. cereus, L. monocytogenes FSL R2-503, S. aureus M0406 and C0673, B. cereus, S. uberis, S. dysgalactiae, E. faecalis, P. aeruginosa, Klebsiella pneumoniae, C. diphtheria, Yersinia pestis biovar Antiqua B42003004, V. cholera VCC19, and B. fragilis 3725-D9-ii. Statistical analysis of the OTU at the order level shows that the number of zoonotic bacteria in dairy farm 1 and dairy farm 2 was significantly higher in the manure than in the fresh fecal samples .In the Ningxia region, manure from most of the dairy farms is used as an organic fertilizer by local farmers without proper treatment. However, to our knowledge, the composition, diversity, and abundance of bacterial communities in the manure that has not been properly treated in this region are poorly understood, but this manure is being used as a fertilizer. Livestock and poultry manure contains feces, urine, litter, nose stains, blood stains, shed skin, hair, and placental material.

The sample types included skin-on/bone-in chicken, ground beef, ground turkey, and pork chops . Meat samples in different packages , vacuum packing, and paper wrapping were placed on ice immediately after purchasing, transported to the lab in refrigerated conditions, and processed within 72 hours after collection. Samples were processed according to the NARMS Retail Meat Surveillance protocol . Briefly, 50 g of each sample was placed into 250 ml buffered peptone water in whirl-pak bags and massaged by hand for 3 minutes. After massaging, the homogenates were incubated at 35°C for 18–24 h. Then, 0.1 ml overnight enrichment was transferred to 10 ml RVR10 and incubated at 42°C for 20-24 hours. The RVR10 enrichments were streaked onto XLT-4 . Two colonies of presumptive Salmonella based on colony morphology were then streaked onto blood agar plates  and incubated at 35°C for 20-24 hours. Presumptive Salmonella isolates were banked in Brucella broth with 15% glycerol tubes and shipped to the FDA’s Center for Veterinary Medicine for antimicrobial susceptibility test, whole genome sequencing , and other analysis.R statistical software Statistical Computing, Vienna, Austria was used to do all the analysis. The statistical significance is defined at an alpha level of 0.05. Descriptive statistics for the prevalence of Salmonella and distribution of antimicrobial susceptibility test results were conducted in R. Data analysis was conducted on a total of 132 Salmonella isolates from 130 different meat samples due to two Salmonella isolates of different serotypes recovered from 2 pork samples. The reduced antibiotic use category is composed of samples with label claims of no antibiotic ever and/or organic in packages. The association between Salmonella prevalence in retail meat samples and sampling region, season, meat type, pots for cannabis plants package type, label claim, and store types based on their size were assessed using Fisher’s exact test.

Post-hoc analysis was performed using the cldList function of the R package companion . The correlation between phenotypical and genotypical AMR was calculated by dividing the number of phenotypical antimicrobial-resistant Salmonella isolates by the number of Salmonella isolates with corresponding resistant genes. A heatmap of hierarchical clustering was generated utilizing the heatmap3. package in R .The occurrence of Salmonella in retail meat in California in this study was higher than both the national average  and that from the previous year in California . This overall higher prevalence was likely due to higher recovery rate by 1) changes in the NARMS protocol the high number of whole chicken carcasses samples collected in Southern California than Northern California in 2019 . Previous research has also reported a higher prevalence of Salmonella in whole chicken carcasses compared to cut samples of chicken . In addition to the higher number and higher prevalence of Salmonella in whole chicken carcasses from Southern California, other chicken parts such as breasts, wings, and legs from Southern California also had a higher prevalence of Salmonella compared to those from Northern California, which collectively contributed to the significant higher Salmonella prevalence in chicken in Southern California in contrast to that in Northern California . Meat types in this study were not collected in identical proportions: chicken samples – 479 , ground turkey samples – 240 , ground beef samples – 65 , and pork samples – 65 . The disproportionate distribution of samples among the meat types might affect the Salmonella recovery rate from different types of meat. For instance, a larger variety of chicken samples were purchased as compared to turkey and beef, where only ground samples were collected. Our data indicated the highest recovery of Salmonella was in chicken samples , followed by ground turkey and pork samples , and zero recovery from ground beef samples. The cause of the higher prevalence of Salmonella in chicken was aforementioned.

With respect to prevalence in other types of meat, ground turkey samples were lower compared to the national average , pork and ground beef samples were close to the national averages of 4.00% in pork and ~1% in ground beef . The high prevalence of Salmonella in chicken compared to other types of retail meat has been well-documented in previous studies . In general, chicken is a significant source of Salmonella, as contamination can potentially occur throughout the entire production chain, from farm to transportation, during processing in slaughterhouses, and on retail shelves . The prevalence of Salmonella in chicken also varied by U.S. states . For example, Zhao et al. reported that higher prevalence in chicken might be caused by sample type , while Nyirabahizi et al. found that regional factors may impact the prevalence of Salmonella. The current study revealed a notably greater prevalence of Salmonella in samples with reduced antibiotics claim compared to those from conventional production . These findings differ from previous NARMS data, which reported a slightly higher prevalence of Salmonella in samples with conventional production compared to those with reduced antibiotics claims. . One possible explanation might be attributed, at least in part, to the survival and propagation of Salmonella on these farms, which may be favored by conditions associated with reduced antibiotic use. For example, reduced antimicrobial use might result in fewer interventions to control bacterial infections on farms, and lack of routine antimicrobial treatments might increase the prevalence of Salmonella. Moreover, lack of antimicrobial use can affect the competitive balance between beneficial and harmful bacteria in the gut of animals, leading to an increased Salmonella prevalence .

It is also worth noting that larger numbers of pork, ground beef, and ground turkey samples from conventional production had low or zero recoveries of Salmonella, which contributed to the overall low prevalence of Salmonella in meat samples from conventional raise in our study. More than 2,600 Salmonella serotypes have been identified, with specific serotypes frequently associated with severe illnesses . The present study classified the 132 Salmonella isolates into 25 serotypes. Among these serotypes, those frequently implicated in foodborne illness are S. Typhimurium and S. Enteritidis. In the present study, both S. Typhimurium and S. Enteritidis were found in chicken samples. Salmonella Infantis accounted for 64.29% of MDR isolates and was the most prevalent MDR serotype in retail meat in California in 2019, which was different than the national NARMS data. In 2019, the most common MDR Salmonella serotype was I 4,[5],12:i: which comprised 26% of nationwide MDR isolates . However, the rise of MDR S. Infantis caused the national average of MDR Salmonella strains in retail chicken to increase from 20% in2018 to 32% in 2019 . In our study, all the S. Infantis isolates were from chicken samples, which was consistent with the national trend. Finally, the prevalence of MDR Salmonella isolates in our study in California was at the same level as the national average from the NARMS 2019 surveillance data . In the present study, a high prevalence of resistance to tetracycline and streptomycin in Salmonella isolates from poultry samples was observed. This is consistent with the results of the NARMS retail meat surveillance in California in 2018 and the NARMS national AMR data of Salmonella from retail poultry in 2008-2017 . Tetracycline has been commonly used in poultry farming to prevent and cure different poultry illnesses, such as respiratory problems, gut inflammation, and joint infection . Streptomycin, as one of the earliest aminoglycosides developed for combating bacterial infections in the poultry industry, has been utilized against various pathogens including E. coli, Salmonella, Mycoplasma, and Staphylococcus . A notable observation in the current study is that the majority of S. Infantis isolates were resistant to ciprofloxacin and nalidixic acid despite restrictions in fluoroquinolone use in food animal production in the U.S. We attempted to determine the relationships between the occurrence of Salmonella resistance and the claims of antibiotics use. However, indoor cannabis grow system no significant difference was found between isolates with reduced antibiotic claims and conventional in single and multidrug resistance despite the fact that resistance to two drugs was higher in isolates with claims of reduced antibiotics than in isolates with claims of conventional production. Whole genome sequencing has been an essential tool for the characterization and confirmation of AMR in bacteria, especially in the identification of resistance mechanisms where AST has limitations . Our results showed that genotypic resistance was highly corellated with phenotypic resistance, with a sensitivity of 96.85%. Only one of the three Salmonella isolates that exhibited resistance to gentamicin by AST lacked the corresponding resistance gene by WGS analysis. This discordance might be due to the presence of undetected AMR genetic determinants or misclassification of the isolate from AST . On the other hand, the ability of WGS to detect only known AMR genetic determinants highlights the importance of continuous traditional AST for comprehensive AMR assessment .

Consequently, it remains valuable to incorporate both WGS and AST to assess AMR patterns in pathogens, particularly given the potential of new resistance genes continuing to emerge. Plasmid replicons are essential genetic elements that play a crucial role in the dissemination of antimicrobial resistant genes within and between bacterial species . Therefore, the identification and characterization of plasmids can provide insight into the transmission potential of AMR genes between or within bacteria species . In the present study, we discovered various plasmid replicons among various Salmonella serovars. Many of these plasmid replicons have previously been associated with AMR genes. Seventy-six of these isolates were from chicken samples, and one isolate was from the ground turkey sample. Previous studies reported plasmid replicon IncX1 being associated with beta-lactam, quinolones, and tetracycline resistance genes . Additionally, in the present study, plasmid replicon IncFIB was found in 7 MDR, ESBLproducing S. Infantis isolates, and all these isolates came from poultry samples. This is worrisome because previously, plasmid replicon IncFIB has been linked to S. Infantis clone with large megaplasmid, which has been disseminating quickly in the U.S. and worldwide during the last nine years . In the present research, all the MDR S. Infantis genes harbored a gyrA mutation that confers resistance to fluoroquinolone, and four MDR S.Infantis had the extended-spectrum beta-lactamase gene blaCTX-M-65. The fast spread of this MDR S. Infantis clone is concerning as it might undermine the existing treatment options to treat infections.Antimicrobial resistance has become a serious issue worldwide, challenging existing treatment options in human and veterinary medicine. Identifying the transmission routes of antimicrobial-resistant bacteria and the characterization of resistance patterns in antimicrobial resistant bacteria is crucial in combatting the antimicrobial resistance problem. The first study tried to identify probable transmission routes of bacteria from poultry farm environments to farm workers. According to previous studies, farm workers were exposed to antimicrobial resistance via direct contact with farm animals or indirectly through farm environments such as urine or feces, water, and soil. We isolated E. coli from environmental samples and worker’s outwear and footwear samples. Then, antimicrobial patterns in E. coli isolates were characterized. The results showed that E. coli isolates from environmental and worker’s samples shared similar resistant patterns, implying that antimicrobial-resistant bacteria might be transmitted to workers. The results also indicated that the door handles of the facilities pose a serious risk to worker’s health, and worker’s outwear and footwear an important defense to limit the transmission of ARB or ARG. Occupational exposure of farm workers to antimicrobial resistance has been long neglected, and further studies are needed to raise awareness among policymakers and farm workers. The objective of the second study was to characterize antimicrobial resistance patterns in Salmonella from the collected retail meat samples in California. The study found that whole chicken samples had a higher prevalence of Salmonella compared to other chicken parts. Overall, Salmonella isolates from chicken samples were resistant to most of the tested antimicrobial drugs. Resistance to streptomycin and tetracycline was very high in Salmonella isolates. The multi-resistance pattern was most prevalent in S. Infantis isolates.Phenotypical resistance in Salmonella was confirmed by using Whole Genome Sequencing , and WGS accurately found resistance in bacteria with a sensitivity of 96.85 %. Additionally, WGS allowed us to find plasmid replicons that play a crucial role in the transmission of antimicrobial resistance. The study identified resistance genes mutation and plasmid replicons which were associated with the previous outbreaks in North America. The study characterized antimicrobial resistance patterns in Salmonella and identified resistant genes and plasmid replicons that play crucial roles in the dissemination of AMR. Additionally, this comprehensive study helped to identify regional patterns of antimicrobial resistance in Salmonella and established a baseline understanding of the current resistance patterns.

The drinking water standard has since been changed to 50 mg/L, but the STLC was not adjusted. Since wildlife must be protected at concentrations that are orders of magnitude less than the STLC, raising the STLC would have no impact on hazards to wildlife. Indeed, increasing the legally defined STLC would have an insignificant impact on environmental hazards but would facilitate opportunities to maintain an economically sustainable agriculture production system in the western San Joaquin Valley.Microorganisms, including bacteria, have developed mechanisms to protect themselves from the effects of antibacterial substances for several billion years . Antimicrobial resistance is the capability of microorganisms to survive the effects of antimicrobial agents via various mechanisms, such as a mutation in an existing gene or obtaining resistant genes through horizontal gene transfer . Four main mechanisms of AMR in bacteria are limiting the uptake of a drug, modifying a drug target, inactivating a drug and active drug efflux . Additionally, there are two main biological pathways that enable the evolution and dissemination of resistance: vertical gene transfer and horizontal gene transfer . Although the widespread usage of antibiotics began in the middle of the last century after the discovery of penicillin by Alexander Fleming, antimicrobial resistance has been recognized as a global threat and one of the leading public health problems of the 21st century only over the last two decades . The World Health Organization’s first report on worldwide surveillance of AMR, cannabis equipment which was released in April 2014, showed the seriousness of the AMR problem Many factors are attributed to the emergence and spread of AMR, including overuse and misuse of antimicrobial drugs, poor sanitation, poor practice of disease prevention and control, lack of knowledge, public awareness, legislation and lack of innovation and development of drug resources .

However, overuse or misuse of antimicrobial drugs is considered the leading cause of the emergence and spread of AMR or accelerated development of AMR . Especially, the usage of antimicrobial drugs in food animal production has been considered as one of the contributors to the AMR problem . In the U.S., approximately 2.8 million clinical infections and 35,000 human deaths occur annually . According to an estimate, by 2050, AMR could be the leading cause of mortality, with 10 million deaths, surpassing cancer in the mortality rate . Besides taking polls on people’s lives, AMR causes an economic burden to the patients. It makes diseases and hospital stays longer, medical costs higher, and treatments ineffective and sometimes impossible to treat . Making exact estimates of the burden of AMR is challenging. According to the recent review by Naylor et al. , the economic burden from AMR per case is around $21832 and over $ 3 trillion in GDP loss. The economic burden from AMR in the U.S.,as reported by the Centers for Disease Control and Prevention , is around 4-5 billion dollars every year. It is expected that by 2050, a total global economic cost of US$100 trillion will be attributed to AMR . Long-term exposure to antibiotics might weaken the immune system in humans, cause digestive problems, and have carcinogenic effects . Some infectious diseases easily treatable with penicillin in the past now require second and third-line antibiotics due to AMR. Other medical areas, such as chemotherapy for cancer treatment, organ transplantation, hip replacement surgery, intensive care for pre-term newborns, and many others, depend on the availability of efficient antibiotic drugs. Infections triggered by MDR bacteria are the main contributors to morbidity and mortality in people undergoing the above mentioned procedures. For example, studies have shown high AMR rates in infections in patients with cancer and liver transplantation .

Antimicrobial agents are a semi-synthetic or synthetic substance that kills or inhibits microorganisms . Since the 1940s, antibiotic agents have been used in the livestock industry for disease prevention and treatment and growth promotion. Antimicrobial agents in livestock have yielded healthier and more productive animals with lower disease incidence and reduced morbidity while lowering the cost of animal food production . However, this honeymoon period was short-lived, and these benefits have yielded adverse outcomes: overuse and misuse of antimicrobials in food animal production resulted in an accelerated increase in antimicrobial resistance, and food animals have become reservoirs of antibiotic-resistant bacteria . Antibiotic-resistant bacteria and their genetic determinants can be transferred from food animals to humans via direct or indirect contact or food chains . Increased demand for protein led to the global spread of intensive farming , and antimicrobial usage has become an essential part of intensive farming . Global food animal production increased 4-5-fold since 1961. In turn, intensive farming expansion has led to increased consumption of antimicrobials worldwide . Two-thirds of the total medically important antimicrobials in the U.S. are associated with food animal production . In 2019 alone 11,000 tons of antibiotics were used in animal production . It is projected that the consumption of antimicrobials will rise by 67% by 2030 worldwide, and the rise is likely caused by expected consumer demand for livestock products as the global population and affluent people are increasing in developed countries . According to the Bayesian regression framework conducted by , antimicrobial consumption is expected to increase in pigs and chickens compared to cattle. Antibiotics in food animals can be divided into three categories: therapeutic use, disease prevention, and growth promotion .

Usage of antibiotics for disease prevention and growth promotion in food animals are distinct practices, and they are differentiated based on the purpose of usage, stage of lifecycle, timing of antibiotic administration, and dosage levels. For example, in disease prevention, antibiotics are administered at therapeutic levels , while in growth promotion, antibiotics are administered at subtherapeutic levels . Another example antibiotics for disease prevention are administered for a short period of time before outbreaks, while antibiotics for growth promotion are administered continuously during the animal’s growth phase or over a long period . However, there might still be a blurred line between these two practices. There are still possibilities to use antibiotics as growth promoters despite the U.S. banned growth promoters in 2017. For example, dosing food animals continuously with antibiotics for disease prevention has been used in large-scale farming . Antibiotics are administered via feed, water, or intramuscular injection . Antimicrobials are administered to the entire flock or group in intensive farming via feed or water. The purpose of this practice is to prevent the spread of the disease. However, this practice sometimes results in overuse or misuse of antimicrobials, increasing ARB in animals. Additionally, the occurrence of infectious diseases and usage of antimicrobials depends on endogenous risk factors and farmers’ decision-making, which can be influenced by cost-benefit analysis, farmer’s expertise, and behavior .WHO has recognized the necessity of coordinated global efforts to mitigate the AMR spread and recommended avoiding using antimicrobials in food animal production . New European regulations on veterinary medicine and medicated feed are expected to substantially reduce antimicrobial usage in food animal production throughout Europe in the future . In the U.S., the FDA has been regulating antimicrobial drug prescriptions by implementing stricter policies on using antimicrobials over the years. For example, in 2017, the FDA banned the usage of growth promoters in the production of food animals, vertical grow shelf and in the same year, it also required veterinary prescriptions for “important antimicrobials” defined as Veterinary Feed Directive drugs. Therefore, banning antimicrobials as growth promoters in Europe in 2003, followed by the U.S. in 2017, helped to reduce antimicrobial consumption . For example, in the Netherlands, consumption of antimicrobials decreased by 70 % between 2009 and 2019, and the resistance of some species of bacteria decreased compared to the previous years . FDA has been developing strategies to reduce the usage of antimicrobials in food animal production . For example, in 2015, the FDA updated the new animal drug regulations to put into practice the veterinary feed directive , and according to this update, VFD drugs have been allowed only under the professional oversight of a licensed veterinarian. Recent studies have shown the effectiveness of reducing the usage of antimicrobials in food animal production in taming AMR. For example, the literature review conducted by Tang et al. has shown that restricting antimicrobial usage in food animal production might reduce animal ARB by up to 39 %. However, with an increase in global population and wealthy people in developing countries, demand for animal protein is expected to increase, further challenging the combat to reduce AMR.

Critically important antimicrobials play an essential role in treating life-threatening infectious diseases in humans and animals, and they are considered the last line of defense against some serious infectious illnesses . The List of Critically Important Antimicrobials of WHO for Human Medicine was developed in 2005 and has been updated since, the latest being updated in 2018 . Since its development, the CIA List has been the benchmark for food animal producers worldwide by providing essential guidance . WHO classified antimicrobials into three groups: important, highly important, and critically important. . Most antimicrobial classes on the WHO CIA list belong to the “Critically Important” category, and fewer belong to other groups. Apart from the WHO CIA list, the World Organization for Animal Health created a CIA list, which is a list of essential antimicrobials for veterinary medicine. Additionally, the WHO encourages countries to have their own CIA list, and the U.S. The CIAs have been further classified into high-priority and highest-priority CIAs based on the number of people with infections for which limited antimicrobials are available and the rate of usage among high-risk groups in human medicine . The highest priority CIAs are the quinolones , 3rd and higher-generation cephalosporins, macrolides and ketolides, glycopeptides, and polymyxins . High-priority CIAs are aminoglycosides, penicillins, ansamycins, penems, glycylcyclines, lipopeptides, monobactams, oxazolidinones, and mycobacterial drugs. Highly important antimicrobials are tetracyclines, amphenicols, cephalosporins , lincosamides, pseudomonic acids,riminofenazines, steroid antibacterials, streptogramins, sulfonamides, sulfones. Important antimicrobials are aminocyclitols, cyclic polypeptides, nitrofurantoin, nitroimidazoles, and pleuromutilins . Most antimicrobial classes are common in both veterinary and human medicine; nevertheless, the importance of some antimicrobials might differ based on species and application . According to the World Health Organization’s categorization, critically important antibiotics for human medicine are fluoroquinolones, thirdand fourth-generation cephalosporins, macrolides, glycopeptides, and polymyxins . Penicillin, macrolides, and fluoroquinolones are mainly used to treat human infections, while tetracyclines, penicillin, and sulfonamides are frequently used to treat food animal infections . Not long after its invention, penicillin was used to treat bovine mastitis to sustain the sustain milk supplies during World War II. In 1948 sulfaquinoxaline was used in poultry for the prevention of coccidiosis. In the U.S., antibiotics in livestock were first approved in 1951, giving birth to antibiotic-reliant large-scale food animal production operations . In recent years, in the U.S., antibiotic consumption in chicken production is decreased dramatically. For example, in 2018, ninety-two percent of broilers were produced without using medically important antimicrobials . Penicillin and tetracycline are mostly used in pigs worldwide . In 2012, tetracycline accounted for 41 % of total sold antimicrobials in the U.S. . Extended-spectrum cephalosporins, macrolides, fluoroquinolones, and polymyxins are classes of CIA usedin pigs and cattle worldwide . In the U.S., 43% of all medically essential antimicrobials were consumed in cattle in 2016 In 2017, WHO released recommendations on the utilization of medically important antimicrobials in animal agriculture to keep the effectiveness of medically important antimicrobials. In 2018, the WHO issued guidelines on using HP-CIAs in food-producing animals; the guidelines recommended not to use HP-CIAs for human medicine in treating food animals with infectious disease diagnoses . These measures have been taken to fight antimicrobial resistance and preserve the effectiveness of CIAs.Occupational exposure of animal farm workers to AMR has been largely neglected and unrecognized due to different reasons such as scarce knowledge about AMR burden, lack of adequate regulations, lack or insufficiency of surveillance and monitoring, and economic factors . The chance of transmission of ARB or antibiotic resistance genes from food animals to farm workers is very high based on previous research, which reported that occupational exposure poses a risk to farm workers . Specifically, ARB can be transmitted from animals to farm works through various ways, including direct contact with animals or animal feces or products, inhalation of dust or aerosols containing ARB or ARG, contact with ARB or ARG contaminated surfaces, equipment, tools, water, or food .

These concerns have played out in many places. Globally, intensive farming practices have resulted in over application of nutrients that cause environmental damage, such as soil toxicity from nutrient pooling and seepage, and water quality issues . There are also human health impacts from pesticide application. Rice farming in particular accounts for 48 percent of cropland greenhouse gas emissions, but only 15 percent of crop kilocalories . Double- and triple-mono-cropping of rice in Asia has also resulted in soil nutrient mining, increased pest problems, soil toxicity, and salinity issues . Impacts on the Mekong River Delta are extensive. Environmental and economic viability of triple rice cropping is highly questionable. Water quality in the Mekong River Delta has declined rapidly, with heavy metal, phosphorus potassium, and nitrate pollution above Vietnamese acceptable limits for surface waters . Simultaneously, available nitrogen, phosphorus, and potassium in the soil diminish as irrigation water is flushed through the system . In addition to and because of these environmental impacts, the triple crop burden is decreasingly economically viable in the Mekong River Delta compared to a double crop rotation . A “balanced cropping” system of two rice crops and one fallow period outperforms the triple crop environmentally and economically. Further, conventional farming practices create concentrated pockets of wealth in fewer farmers’ hands, creating a larger gap between rich and poor farmers in the MRD .Sustainable intensification is a philosophy conceived in the 1990s based on reducing environmental damage of agricultural production, while increasing yield without increasing the total land under production . SI was conceived as a middle ground to take advantage of areas already in production, 4×8 grow table with wheels while looking to fulfill future food production demands. SI increases food security and minimizes environmental damage, with the goal of “environmentalizing agricultural development” .

SI is based on the logical progression that food production must increase in the coming decades, and that this increase must be met through intensification rather than extensification of agriculture . Thus, SI has two defining characteristics. First, it focuses on production increases, rather than holistic farm level balance . And second, it is a goal, rather than a method of achieving sustainability, with an infinite number of production regimes and avenues available to achieve its end . For these reasons, it has been criticized for excluding social, political, and economic factors in production systems while privileging production . It has also been highly criticized for ignoring the distributive issues that are often the origin to food security problems, repackaging genetically modified crops as a silver bullet for SI outcomes, and failing to fund or privilege agroecological methods to achieve SI . In other words, it has been coopted by the very same entrenched powers of the Green Revolution that pushed intensification practices in the first place, such as the World Bank and United Nations branches. This case study takes a critical look at the SI policies in the MRD of Vietnam to understand the mixture of social, environmental, and economic considerations of current rice cropping guidance from the Ministry of Agriculture and Rural Development . New efforts aim to improve the social equity and justice aspects of SI as a resilience strategy . This includes understanding the cultural and biophysical limitations facing smallholders with varying degrees of intensification and diversification on their farms. This study aims to understand the barriers to SI adoption in the MRD. Sustainably intense and diverse cropping systems, such as crop-livestock and agroforestry, create emergent properties that support soil fertility, sustained and increased yields, and pest regulation .

Case studies in Vietnam have shown outstanding results for combined rice-fish culture systems , fruit tree intercropping, and vegetable strips on bunded fields . However, the majority of the time, powerful non-government organizations as well as MARD push “diversification” practices that include purchasing more drought- or flood-resistant cultivars of rice. MARD extended the Agricultural Competitiveness Program , a World Bank program, to the Mekong River Delta in November 2012. The program promotes the slogan “1 Must Do, 5 Reductions” a catchy and easy way for farmers to adopt more sustainable practices. The “1 Must” promotes use of improved seeds, certified by the distributor; while the “5 Reductions” means reducing water, fertilizer, pesticides, post harvest loss, and seed inputs. The ACP was rolled out in 2013 and adoption data indicates that 70 percent of farmers in the MRD are users . However, adoption is uneven across the delta and between genders. This case study looks at adoption of 1M5R through a variety of specific CI and SI practices.Decisions to adopt SI practices are based on access to resources and information, which is unequal between men and women. The Food and Agriculture Organization estimates that if women had equal access to productive resources, agricultural yields would rise and there would be 100 to 150 million fewer hungry people . Women are more likely to live in poverty and less likely to own land or resources, have control over production, obtain secondary school education, have institutional support, access information, maintain freedom of association, or gain positions in decision-making bodies . Women’s “triple burden” of child bearing, domestic care, and on-farm duties limit their ability to attend educational and trainings. 1M5R is the primary avenue through which men are gaining access to information and training.

Trainings on integrated pest management , alternative wetting and drying , certified seeds, and post harvest loss have been ongoing since 1992, when the “3 Reductions, 3 Gains” campaign was under way . The 3R3G campaign, 1M5R’s predecessor, pushed for reductions in seeds, pesticides, and fertilizers; and gains in yield, farmers’ health, and the environment. However, the majority of trainings consisted of male farmers . Additionally, even with the proper training, it has been shown that women often do not have the necessary time or capital to correctly implement sustainable practices . This study looks at issues of unequal access between the sexes, and relates it to SI and CI adoption rates. Men and women tend to use different strategies to deal with stresses on the farm, such as soil quality issues and increasing demands for productivity. Growers may intensify production, diversify cropping systems, or abandon their farming operations to seek wage labor . Case studies in South America demonstrate that women are more likely to intensify farming practices, such as mechanization and investment in tree crops, to reduce labor demands . Men, on the other hand, tend to perform more labor-intensive activities on the farm, or simply abandon their own land to sell their labor. However, the influence of gender on SI adoption is poorly understood in general, let alone in Vietnam. A case study in Kenya demonstrated a promising approach to evaluating gendered differences in SI adoption using a livelihood survey approach . A subsequent case study in Burkina Faso demonstrated a similar approach . This study contributes to the SI adoption literature by using this approach in Southeast Asia, as well as including “capabilities” in addition to other standard capitals included in livelihood analyses.Methods for sampling, data collection, and data analysis for this project were adopted from similar case studies of intensification and gender . The study uses a household survey designed around the five “capitals,” or the livelihoods approach. Surveys were chosen for this research project in order to gather quantitatively meaningful information to deduce statistical relationships between household capitals and farming practice adoption. The survey included gender-disaggregated plot-level management questions to understand production practice adoption . The survey consisted of a structured questionnaire designed to understand how men and women manage plot level decisions differently, grow tray stand including how remittances from household members that have migrated to urban areas impact agricultural practices. It also includes planned crop diversity, or number and abundance of species, gathered through observation and structured questionnaire, as is common in similar studies . Each survey was given to the adult head of household, if available, or their spouse if not. The preliminary survey was pilot-tested on October 25 and November 9, 2015. After both test interviews, it was heavily edited and altered according to feedback from households and extension agents.

The final surveys were eight pages, with over 350 questions, including a detailed map of the farm to illustrate resources and access to transportation infrastructure. Extension agents conducted 160 household surveys between November 10 and 13, 2015, covering 187 total plots . The majority of the questions included fixed responses, while a few open-ended questions were included to build information about types of pesticides and fertilizers, crop choices, and other such items not well known to the research team in this particular district. Again, this approach fits squarely into the livelihoods tradition of research, designed to understand how the mix of capitals in a household influence natural resource management choices. Staff at Nong Lam University in Ho Chi Minh City translated the open-ended responses.As other similar farm practice adoption studies have done, we employed a multi-stage sampling protocol for choosing study farms, using purposive sampling at the Province-, District-, and hamlet-level; and proportionate random sampling at the hamlet and sub-hamlet-level . Tien Giang was chosen for the household survey because it is within the Mekong River floodplain with a high degree of irrigation infrastructure and ample water availability, making the lands most suitable for the triple cropping system . Historically, they have a high proportion of triple rice cropping and a pattern of seasonal and permanent out-migration , making it an ideal case study illustrating the confluence of SI, gender, and mobile families.A snapshot of SI practice adoption illustrates that some are much more popular than others, regardless of gender . First, it is clear that in the hamlets sampled, reduced tillage and water-saving practices are widely adopted by the majority of farmers, with average adoption rates of 75 percent and 87 percent, respectively. IPM and composting or applying organic fertilizer are less popular, with adoption rates of 60 percent and 36 percent, respectively. Finally, intercropping and using mulch are not widely adopted, with an average of 10 percent adoption, and 7, respectively. CI practices are shown to be consistently popular, with adoption rates at 60 percent or higher in the sample population. However, pesticides, herbicides, and non-plough machinery are particularly popular amongst farmers, with average adoption rates of 86 and 88 percent, respectively; while using a plough is the least popular practice at 60 percent adoption. These popularity numbers support the conclusion that CI practices, overall, are much more popular with farmers in Tien Giang Province.Positive correlations indicate that the practices are complementary, while a negative correlation indicates that one practice may replace the other , summarized in Figure 9. The less popular practices illustrated in Figure 9 include intercropping , using compost or organic fertilizer , and using mulch . Intercropping is significantly positively correlated with composting and organic fertilizer use . Compost and organic fertilizer is significantly positively correlated with using mulch . Composting and mulch use are primarily focused on soil quality and nutrient management. Intercropping aims to diversify a farmer’s crop while also efficiently managing nutrient cycling. All three practices are linked through their common goal of recycling, reducing, and efficiently distributing nutrients. The more frequently adopted practices include reduced tillage , water-saving efforts , and integrated pest management . Reduced tillage adoption is significantly positively correlated with water-saving efforts and integrated pest management . Similarly, these more popular practices are linked in theory of water, soil, and pesticide management. CI practice correlations tell a similarly logical story, but are not correlated along popularity lines. Machinery use and plough use are negatively correlated with intercropping . This indication of non-complementarity is because of the difficulty of using machinery with varying types of crops in close proximity to one another. Machinery and plough use are highly correlated with each other, as two sides of the technology adoption coin. Improved seed use is highly correlated with reduced tillage due to the necessity to use herbicides to control weeds that crop up when using reduced tillage. Improved seeds are necessary to withstand herbicide use on these fields. Reduced tillage is highly correlated with machinery use , as is using more chemical fertilizer . This result is not intuitive to agroecology principles, as reduced tillage is usually linked with reduced machinery use as well as reduced need for fertility inputs due to increased soil quality.

Cao and Wang showed that root exudates from strawberries inhibited the growth of strawberries due to autotoxicity. In hydroponics, Kitazawa et al. identified the causal compound of strawberry autotoxicity as benzoic acid. Further, Asao et al. demonstrated that destroying benzoic acid by electrodegradation in hydroponic strawberries increased the plant growth and fruit yield. In soil culture, however, the accumulation and fate of autotoxic compounds in the rhizosphere is not well understood . For example, a toxic level of benzoic acid was absorbed to soil particles, which may explain the reason for the limited allelopathic effect of the compound at concentrations often recorded in natural soil . Overall, the causes of yield decline are complex in general and the exact cause in this study is unknown. However, we demonstrated that the use of the integrated approach can reduce the amount of yield loss, the goal of the study. For example, although each pathogen alone did not cause significant damage, combination of Cylindrocarpon spp., and Pythium spp. caused significant root disease in apple seedlings . More information is needed on how this cropping system would be influenced by rotations with different crops and by emerging pathogens such as Fusarium oxysporum and Macrophomina phaseolina .Actual crop rotations of organic strawberries and vegetables in the central coast of California are complex. Specialized berry growers in California usually lease land and grow strawberries on different fields every year , whereas small-scale organic growers tend to grow both strawberries and vegetables on the same fields. For a specialized organic strawberry grower to rotate fields with other organic vegetable growers, vertical grow choosing which vegetable crops will be used in rotation as done in the present study may not be an option.

Further, if a host crop susceptible to V. dahliae was planted during the break period between strawberries, the level of yield decline can be greater than we found in this study . Finally, the present study demonstrated the positive effect of a longer rotation on strawberry production. In Europe , the Northeast and Midwest United States, and in eastern Canada , a minimum of a three-year rotation is recommended for strawberries that do not use chemical fumigants. Some diversified small-scale organic growers in central coastal California have been maintaining a fiveyear rotation for their strawberries. By diversifying their cropping rotations, they benefit both from improved strawberry yields and access to alternative markets such as farmers’ markets or Community Supported Agriculture systems where greater diversity provides economic advantage . The results of this study justify such practice when it is feasible. ACKNOWLEDGMENTS We express our deep appreciation to Daniel Schmida of Sandpiper Farm who collaborated in designing and managing the field trial, and collecting fruit yield data of the trial. This project was initiated by Robert Stephens, the landowner of the Elkhorn Ranch. He generously loaned the 1acre site for this trial. We thank technical support by Patty Ayala and Katherine E. Kammeijer of UC Cooperative Extension at Salinas, Bree Eagle, David Mendoza, Osvaldo Gomez, Jonah Landor-Yamagata, Alisa LaRue, Mary Sweeters, Susan Lee, Stephanie Garcia, Natalie Lopez, Adam Romero, Loren Mueller, Elizabeth Geisler, Whitney Grover, Amy Hwang, Cameron Joseph Kaplan, Jennifer Leah Smith, Evan Dorroh Watson, Alexa Christine Jones, Jacob Anthony Edmonds, Nebiyu Oluma Demissie, Paul Tho Tran, Jeana Lee, Sara Emery, Balyn Rose, David Griese, Lisa Evans, Nikola Korte, and Pedro Alfonso Garcia Galavís at UCSC.

The project was partially funded by the North American Strawberry Growers Association, the California Strawberry Commission, the Ruth and AlfredHeller Chair in Agroecology at UCSC, the Center for Agroecology and Sustainable Food Systems at UCSC, the Halliday Foundation, the Organic Farming Research Foundation, USDA Integrated Organic Program 2004- 51300-02232, USDA Special Grant 2004-34424-14408, and the Central Coast Regional Water Quality Control Board Non-Point Source Pollution Fund of the Community Foundation for Monterey County. Weather data were kindly provided by the Elkhorn Slough National Estuarine Research Reserve.As fossil fuels supplies continue to dwindle and global warming becomes more of a pressing issue, countries around the world are looking to renewable energy to supply their citizens with clean, reliable, electrical power. Many political leaders, including those from the EU, US, China and Japan have mandated that their countries or states must produce a certain amount of electricity from these sources in the next few decades. Although solar and nuclear energy have their merits, they also exhibit certain drawbacks. Nuclear energy has recently been spurned by governments in Germany and Japan over fears of its safety following the 2011 Fukushima Daiichi disaster. However, nuclear plants can be built on a scale unrivaled by other renewable sources, on the order of gigawatts of power production. As Chinese manufacturers have entered the market, the efficiency of solar panels have been increasing while their unit costs have dropped. However, industrial-scale solar plants, using either photo-voltaic or solar concentrating technology, have not found public acceptance as much as residential rooftop installations. Meanwhile, the installed global capacity of wind energy has been increasing exponentially since the mid-1990s .

Many industry experts believe that most of the ideal onshore installation sites in industrialized countries, such as the EU and US, have been exploited. Offshore wind has emerged as a relatively untapped sustainable resource, especially in deep water. We discuss the plentiful offshore wind resource specifically off of California in Section 1.1.1. Thus far, the cost of deep water offshore wind has prohibited any commercial-scale installations to take place. The technology introduced in this thesis is an attempt to lower the cost of this technology per unit of installed capacity.The oil embargo and ensuing crisis in the 1970s led many governments to increase funding for renewable sources, such as wind energy. At this time, the wind community was testing many different types of wind turbines, including vertical and horizontal axis, with 2-4 blades in upwind and downwind configurations. Considering the main design constraints at the time, which were to maximize sub-MW, land-based turbine efficiency, vertical-axis wind turbines were proven to be inferior. Figure 1.1 compares the power coefficient of a BONUS 37-m horizontal-axis wind turbine with the Sandia 34-m two-speed vertical-axis wind turbine. A turbine’s power coefficient will be defined explicitly in Chapter 3. For now, it can be considered a measure of a turbine’s efficiency. The two-speed, vertical-axis wind turbine exhibits lower performance over the lower wind speeds, although the authors of claim that a true variable speed turbine may reach the performance of a horizontalaxis turbine.This thesis will investigate a novel concept for floating vertical-axis turbines. Specifically, a triangular, semi-submersible floating platform supporting counter-rotating vertical-axis turbines will be investigated. In Chapter 2, we introduce the hydrodynamics of a single, truncated floating cylinder in finite-depth waters undergoing slow-drift motion. The author’s code for this application is bench marked against the results from a well-developed in-house software for a fixed cylinder. The velocity potential, describing the motion of the inviscid fluid particles is expanded in a double perturbation series with respect to the wave amplitude as well as the slow-drift velocities. The slow-drift motion introduces terms, such as the double-body added-mass as well as terms proportional to the amplitude of the response of the fast-scale motions. Furthermore, second-order, steady wave-exciting forces and moments, which can be represented by first-order terms are examined. We introduce how the hydrodynamic force can be transformed from the frequency domain into the time domain so that it can be useful for time-domain simulations. The method is then extended to multiple, interacting, truncated cylinders for the wave-exciting force, which results from the scattering potential. Results are shown in the frequency domain for a three-column, rolling grow table semi-submersible platform that was used for model testing. In Chapter 3 the aerodynamics of vertical-axis wind turbines are discussed. First, a well established blade-element momentum theory called the Double-Multiple Streamtube Method is introduced. The drawbacks of the model, including the need for experimental airfoil lift and drag data and dynamic stall modules motivate the discussion of higher-fidelity simulations of airfoils and turbines. Another in-house fluid dynamics software, utilizing a method known as Implicit Large Eddy Simulations , is briefly presented next. The method is used for three different applications, all of which are in the low-Reynolds regime: first, a single static airfoil over a wide range of angles of attack; second, a simple, two-bladed turbine that has detailed experimental data for validation; third, a pair of counter-rotating turbines with a variable distance between the rotors. These results are used to inform the types of forces to be applied to the model platform.In Chapter 4 we introduce the Multiple Integrated Synchronized Turbine platform. This semi-submersible, three-column platform has two, counter-rotating vertical axis wind turbines on two of the columns.

The turbines are connected in such a way that constrains the turbines to rotate in equal and opposite directions. We discuss the concept of hybrid simulation and its application to the MIST platform. The experimental model platform and the related components, including the drive train, generators, circuit and control implementation are detailed. We briefly discuss the Wind-Input Generator that was used to simulate the effect of the wind turbines on the platform. The communication system, which can send data between the microprocessors on the platform as well as to the datalogger, has redundant wired and wireless technologies. Data from the model tests concerning the open-loop control of the actuation system are presented. In Chapter 5, we numerically recreate the model experiments described in Chapter 4, implementing the theory developed in Chapter 2. We detail how the constraints imposed by the drive train reduce the number of degrees of freedom in the system. In the numerical model, a non-linear control system can be used to control both the yaw position of the platform and the rotational velocity of the turbines. A type of non-linear control, called feedback linearization, is described and used to optimize the power from the turbines. We present results from simulations in the time-domain using the control software SIMULINK when the platform is subject to regular waves. The aerodynamics and hydrodynamics are decoupled, which is shown to be a reasonable assumption due to the minimal pitch and roll motion of the platform in operational sea states. Chapter 6 includes some concluding remarks, including parallels with modern aerospace industry. We describe many of the ways the research could be extended and improved upon in the theoretical, numerical and experimental fields. Finally, we show a commercial development that has been inspired by this research.Data provided in this article includes two simulations using the Variable-Resolution CESM model. CESM version 1.5.5, a fully coupled atmospheric, land, ocean, and sea ice model, was utilized. Both simulations used the F-component set , which prescribes sea-surface temperatures and sea ice but dynamically evolves the atmosphere and land surface component models. The atmospheric component model is the Community Atmosphere Model, version 5.3 with the spectral-element dynamical core in the variable-resolution con- figuration. The VR model grid used for this study, depicted in Fig. 2 from the reference article, was generated for use in CAM and CLM with the open-source software package SQuadGen. On this grid the finest horizontal resolution is 0.125° , with a quasi-uniform 1° mesh over the remainder of the globe. Two simulations were conducted using this grid structure: First, the historical run covers the period from October 1st, 1979 to December 31st, 2000, with first three months discarded as the spin-up period, for a total of 21-years. This historical time period was chosen to provide an adequate sampling of inter-annual variability, to coincide with the time period from the rest of the modeling and reanalysis datasets, and because observed sea surface temperatures were only available through 2005. For projecting future wind energy change, our mid-century simulation ran with the “business as usual” Representative Concentration Pathway 8.5 from October 1st, 2029 to December 31st, 2050, again discarding the first three months for a total of 21-years. Greenhouse gas and aerosol forcing are prescribed based on historical or RCP8.5 concentrations for each simulation. More details on VR-CESM can be found in, and the model has been applied to previous studies.The Det Norske Veritas Germanischer Lloyd Virtual Met product is derived from a hybrid dynamical-statistical downscaling system based upon the Weather Research and Forecasting model and an analog-based ensemble downscaling method.

Farmers also shared that in some circumstances, such as in early spring, they are not able to realize the full potential of a winter cover crop if they are forced to mow the cover crop early to plant cash crops and ensure the harvest timeline of a high-value summer vegetable crop. The cover crop approach to soil fertility takes “persistence,” as one farmer emphasized; another farmer similarly pointed out that the benefits of cover cropping “are not always realized in the crop year. You’re in it [organic agriculture] for the long haul, there is no quick fix.” Indeed, farmers who choose to regularly plant cover crops to build soil fertility, rather than just add N-based fertilizers, reported that they came up against issues of land tenure and access to land, market pressures, and long-term economic sustainability.To build on conversations about soil fertility, farmers also provided responses to interview questions that asked them to elaborate on the usefulness of available soil tests to gauge soil fertility more broadly—and then more specifically, the usefulness of soil tests in informing their soil fertility program and/or management approaches on-farm. Overall, only three of 13 farmers reported regularly using and relying on soil tests to inform their soil fertility program or aspects of their farm operation. These farmers offered very short responses and did not elaborate. For example, one farmer shared that they “test twice a year in general,” and that they “rely on the results of the soil tests to tweak [their] fertility program.” Another farmer said briefly, “We use soil tests… we utilize them to decide what to do to try to improve the soil.” A third farmer admitted that though he “used to do a soil test every year, indoor plant table literally used to spend hundreds of dollars per year on soil tests,” he found that the results of soil tests did not change year-to-year and were, as he put it, very “stable.” This particular farmer no longer regularly uses or relies on soil testing for their farm operation.

The remaining ten farmers confirmed that they had previously submitted a soil test, usually once and most often to a local commercial lab in the region. These farmers expressed a range of sentiments when asked about the usefulness of soil tests, including disappointment, distrust, or both, particularly in the capacity of soil tests to inform soil fertility on their farm. Some farmers said directly, “I just don’t trust soil tests,” or “frankly, I don’t believe a lot in soil testing because it’s too standardized,” while other farmers initially stated they had used “limited” or “infrequent” soil tests, and then later admitted that they did not use or rely on soil tests on their farm operation. These farmers tended to focus on the limitations of soil tests that they encountered for their particular farm application. Limitations of soil tests discussed by farmers varied. Farmers stated that soil tests often confirmed what they already knew about their soil and did not add new information. For this reason, some farmers used results from a soil test as a guide, while other farmers found results to be redundant and therefore less useful to their farm operation. Because issues with soil fertility were sometimes linked to inherent soil characteristics within a particular field, such as poor drainage or heavily sandy soil, farmers found that soil tests were not able to provide new insight to overcome these environmental limitations. “I’m not able to correct that environmental limitation [ie, poor drainage] by adding more nitrogen,” one farmer emphasized. A different farmer echoed this sentiment, saying that “I’m not going to magically get rid of issues that soil tests show… I can only slightly move the needle, no matter what I do.” Most farmers recognized that soil tests produced inconsistent results because of differences in timing and location of sampling.

As one farmer noted, “You can take the same sample a couple months apart from the same field and get very different results.” Likewise, another farmer shared that, “I still struggle with the fact that I can send in two different soil tests and get two very different results. To me that seems like the science is not there.” Farmers also emphasized that each of their “fields are all so different” with “a lot of irregularity in [their] soil.” According to several farmers, soil tests did not account for variations in soil texture and soil structure, despite their observations of the influence of both edaphic characteristics on soil test results. For example, one farmer pointed out that fields that were plowed or were previously furrow irrigated created marked differences in soil test results. Similarly, another farmer shared that if a sample for soil testing was taken from an irregular patch in a field with heavier clay, differences in soil texture across samples skewed soil test results. If a systematic sampling approach was not considered, several farmers emphasized that results of soil tests might be “misleading.” Another source of inconsistency that farmers voiced stemmed from variation in protocols used across different labs that processed soil samples. One farmer stated that in their experience, “soil tests are not really accurate, because if I use a different lab, a different person [ie, consultant] doing the soil test, it’s all different.” For example, one farmer pointed out that they do not use soluble forms of nitrogen, and instead relied on their animal rotations and cover crops to supply nutrients as part of their fertility program; this farmer emphasized that, “I think we need to get to a place with soil testing where it would be more applicable or be more accurately useful for a farm like mine. Farmer explanations of their selection of Field A or Field B were remarkably consistent across respondents. Selection of Field A was primarily based on crop productivity across all farms.

Farmers also selected a field for this category because a particular field maintained good soil moisture or because a particular field did not need as much N-based fertilizer added each season compared to all other fields. Farmers also cited several reasons for selecting their low fertility fields. These fields tended to have patchy growth, low crop productivity, or in some cases, required additional N-based fertilizer to be added each season to meet production goals. Table 3 shows a comparison of soil indicators for fertility for Field A and Field B across all farms. Ammonium concentrations were low across all farms, and ranged from 0.10 – 2.79 µg-N g-soil-1 for Fields A and 0.16 – 2.09 µg-N g-soil-1 for Fields B. Net mineralization rates were also low, and ranged from 0.08 – 1.51 µg-N g-soil-1 day-1 for Fields A and 0.05 – 1.08 µg-N g-soil-1 day-1 for Fields B. Net nitrification rates were markedly higher, and ranged widely from 1.53 – 21.45 µg-N g-soil-1 day-1 for Fields A and 2.71 – 25.18 µg-N g-soil-1 day-1 for Fields B. Nitrate concentrations were similar to values commonly found in organic agricultural systems in the region, and ranged from 2.56 – 18.12 µg-N g-soil-1 for Fields A and 4.46 – 23.24 µg-N g-soil-1 for Fields B . No differences were detected between Field A and Field B among these four soil indicators. Across all farms, total soil nitrogen ranged from 0.07 – 0.21 mg-N kg-soil-1 for Fields A and 0.11 – 0.23 mg-N kg-soil-1 for Fields B . Total N values were significantly different between Fields A and Fields B , hydroponics flood table with a mean value of 0.12 mg-N kg-soil-1 for Field A and a mean value of 0.15 mg-N kg-soil-1 for Field B. Total organic carbon was not significantly different between Fields A and B, and ranged from 0.77 – 2.40 mg-C kg-soil-1 for Fields A and 0.87 – 2.43 mg-C kg-soil-1 for Fields B. POXC values were in the typical range for organic agricultural systems in the region, and ranged from 225 – 707 mg-C kg-soil-1 for Fields A and 276 – 899 mg-C kg-soil-1 for Fields B . Soil protein values ranged from 2.21 – 7.51 g g-soil-1 for Fields A and 1.86 – 8.91 g g-soil-1 for Fields B. PCA indicated strong relationships among several key management variables; the results of the PCA also provided strong differentiation among farms along the first two principal components, which together accounted for 77.4% of the variability across farms . The first principal component explained 55.1% of the variation, and the second component explained 22.3% of the variation observed across all farms. Both components had eigenvalues greater than 1.0. Additional N-based fertilizer represented the management variable most associated with PC 1—followed by tillage, and inversely ICLS. While crop diversity, cover crop frequency, and crop rotation patterns also contributed to the overall variation explained by PC 1, these management variables were weaker in comparison to N-based fertilizer additions, ICLS, and tillage. On the other hand, variables with the strongest contribution to PC 2 were crop diversity, cover crop frequency, and crop rotation patterns.

Figure 1 summarizes the spatial distribution of all farms based on PCA results with PC 1 as the x-axis and PC 2 as the y-axis. As shown in Figure 3, the results of the nearest neighbor analysis order each farm from 1 to 13, and provide a basis for visualization of the gradient in management. Therefore, this gradient in management, strongly driven by the amount of external N-based fertilizer applied on-farm, served as the basis for further visual comparison of Fields A and FieldsB across all farms . As shown in Figure 2a, the difference in soil ammonium concentration between fields was low among farms on the low end of the gradient. At the middle and high end of the gradient, farms showed greater soil ammonium concentrations in Field B compared to Field A—with the exception of two farms. Farm by farm, net N mineralization rates followed trends identical to soil ammonium concentrations. Soil nitrate concentrations varied widely among farms and did not produce any consistent trends ; however, a majority of farms showed greater soil nitrate concentrations in Field B compared to Field A regardless of the management gradient. Like net N mineralization rates, net N nitrification rates followed trends analogous to nitrate concentrations farm by farm. For both mineralization and nitrification rates, a majority of farms showed greater rates in Field B compared to Field A, regardless of the gradient in management. Differences between Field A and Field B for total N, total C, and POXC followed identical trends farm by farm . Among farms on the high end of the gradient, the difference in total C between fields was consistently low . Similarly, the difference between fields in soil protein values were also consistently low at the high end of the gradient . Radar plots provided further comparison of Field A and Field B across all eight indicators for soil fertility along the gradient in management developed above . As mentioned, because the level of N-based fertilizer input was a strong driver of the management gradient, radar plots were divided to reflect low, medium, and high N-based fertilizer inputs. Shown in Figure 3L is the high overlap in soil indicators, with the exception of net N mineralization and nitrification rates, between Field A and B. However, among farms with medium N-based fertilizer input , the overlap of soil indicators between fields is minimal; Field B tended to show higher concentrations of soil ammonium and soil nitrate than Field A, while Field A tends to show higher values for total N, total C, POXC, and soil protein among these farms. Among high input farms , differences between fields were less evident in terms of soil ammonium concentration, total N, total C, POXC, and soil protein, though soil nitrate concentrations and net N mineralization and nitrification rates did show noticeable differences in values between the two fields.The results presented above are reflective of the perspectives, observations, and experiences of a sample of organic farmers in Yolo County, California, USA, and offer an enhanced understanding of soil health and fertility from this particular node of the organic movement . Here, we focus less, as prior studies have commonly done, on a comparative analysis that quantitatively compares farmers perception of soil health to results of soil laboratory analyses ; instead, we lead the discussion with farmer knowledge of soil health and fertility, and explore emergent synergies with ongoing soil health research and soil indicator results.

To build on the results of the LDA, we performed a variation partitioning analysis to determine the level of variation in soil organic matter indicators explained by the soil texture variables, soil management variables, and their interactions . VPA was performed using the vegan package in R .Using indicator variables for soil organic matter levels, we performed a k-means cluster analysis to develop a meaningful classification of farms. Scree plot results indicated that three clusters produced the most consistent separation of field sites. As shown in Figure 1, the two dimensional cluster analysis produced a strong first dimension , which explained 86.7% of the separation among the 27 field sites. Total N, total C, POXC, and soil protein variables strongly explained this separation of farm types, as shown by the lack of overlap among the clusters along the Dimension 1 axis. Histogram results provide a visual summary of linear difference among the three clusters and further confirms minimal overlap among clusters; however, Cluster I and Cluster II fields showed low dissimilarity between values 0 and -2 . Results from the average distance-based linkages of the dendrogram analysis similarly further established the accuracy of field site groupings determined by the cluster analysis. These results indicated that Cluster II sites were more closely related to Cluster III sites compared to Cluster I sites . ANOSIM showed strongly significant global differences among the three clusters , rolling flood tables where a value of 1 delineates 0% overlap between clusters. Overall, ANOSIM verified the farm types obtained from the cluster analysis. In addition, ANOSIM pairwise t-tests that compared each individual cluster in pairs confirmed strongly significant dissimilarities between Cluster I and Cluster III sites .

ANOSIM pairwise t-tests also indicated that Cluster I sites were significantly divergent from Cluster II sites; however, Cluster I and Cluster II showed less dissimilarities than Cluster II and Cluster III sites . Classification of farm sites using k-means clustering closely matched differences in on-farm management approaches . It is important to note that while general trends between clusters and management emerged, the management practices analyzed here do not fully encompass the management regimes of each farm field site, and are intended to be exploratory rather than definitive. Several general trends emerged across the three farm types . For instance, Farm Type I, comprised of six field sites, consisted of fields with higher crop abundance values and fields that more frequently planted cover crops compared to Farm Type III. These sites used lower impact machines and applied a lower number of tillage passes compared to Farm Type II and III. In contrast, Farm Type II, also comprised of six field sites, and Farm Type III, comprised of fifteen field sites, represented fields on the lower end of crop abundance values and sites that applied cover crop plantings at a lower frequency than Farm Type I. Farm Type III on average applied a higher number of tillage passes and on average were on the lower end of ICLS index compared to both Farm Type I and Farm Type II. In general, Farm Type II used management approaches that frequently overlapped with Farm Type III, and less frequently overlapped with Farm Type I. Overall, farm types significantly differentiated based on indicators for soil organic matter levels . For all four indicators displayed in Figure 2, differences among the three farm types were highly significant .

As visualized in the side-by-side box plot comparisons for all four indicators for soil organic matter levels, Farm Type I consistently showed the highest mean values across all four indicators, while Farm Type III consistently showed the lowest mean values across all four indicators. Farm Type I had mean values of 0.21 mg-N kg-soil-1 for total soil N, 2.3 mg-C kg-soil-1 for total organic C, 787 mg-C kg-soil-1 for POXC, and 7.4 g g-soil-1 for soil protein; compared to Farm Type I, Farm Type III had means values 43% lower for total soil N, 48% lower for total organic C, 58% for POXC, and 66% lower for soil protein. Compared to Farm Type I, Farm Type II had mean values 38% lower for total soil N, 26% lower for total organic C, 28% lower for POXC, and 30% lower for soil protein than Farm Type I. Standard errors for all four indicators are shown in Figure 2.We found across all 27 farm sites sampled that gross N mineralization rates ranged from 0.05 – 4.82 µg-NH4+ -N g-soil-1 day-1 and gross N nitrification rates ranged from 0.55 – 5.90 µg-NO3- -N gsoil-1 day-1 . We determined net N mineralization rates ranged from 0.07 – 1.51 µg-NH4+ -N g-soil-1 day-1 , while net N nitrification rates had a wider range from 1.53 – 25.18 µg-NO3- -N g-soil-1 day-1 . We visually compare the six key N cycling variables—pools of inorganic N , and net and gross N rates—across the three farm types . Despite the variation in net and gross N mineralization and nitrification rates, using the farm types developed above, we found that N cycling variables were not significantly different across the three farm types for all six variables examined—based on ANOVA results . Given the variation in gross N rates reported above, we further explored the drivers of this variation in gross N rates using mixed modelling approaches.

Table 10 shows results provide for the linear mixed models used for the prediction of potential gross ammonification rates . Soil ammonium concentration and % sand were significant predictors of gross mineralization rates. While not significant, indicators for SOM were selected and also included in the model, based on AIC results. We also provide results from the selected linear mixed model used for prediction of potential gross nitrification rates in Table 11. As shown, indicators for SOM emerged as the sole significant covariate . While not significant, crop abundance was also selected and included in the model, as determined by AIC results.This on-farm study found significant differentiation among the organic farm field sites sampled based on soil organic matter levels—and created a gradient in soil quality among the three farm types. While we found that differences in soil quality were generally aligned with trends in management among sites, soil texture—rather than management—emerged as the stronger driver of soil quality. Though initially, we found that net and gross N cycling rates were not significantly different across farm types, gross N cycling rates showed considerable variation among farm types. To determine drivers of this variation, we explored key predictors for soil N cycling and found that SOM indicators influenced gross N mineralization and nitrification rates, in particular gross nitrification rates.Each of the four indicators for soil organic matter used in this study—total soil N, total organic C, POXC, and soil protein—showed a strong correlation with farm type, and collectively, flood and drain tray created a gradient in soil quality . Farm Type I consistently showed the highest values for total soil N, total organic C, POXC, and soil protein, which suggests sites in this farm type had higher soil quality compared to Farm Type II and III; similarly, Farm Type II consistently showed intermediate values for all four indicators for soil organic matter. Lastly, Farm Type III consistently showed the lowest values across all four indicators, which suggests sites in this latter farm type had lower soil quality compared to the other two farm types. These initial results highlight the usefulness of establishing farm typologies based on indicators for soil organic matter as a novel approach to study gradients in soil quality on organic farms. The three farm types generated based on soil organic matter levels served as a key starting point for further analysis of the role of management in relation to soil quality. Accordingly, not only were the three farm types identified in this study significantly different based on indicators for soil organic matter levels, but the farm types also aligned with general trends in management among sites, which indicated a link between soil organic matter levels and management. In particular, as the four indicators for soil organic matter collectively serve as a proxy for soil quality, our results suggest that soil quality indicators may show responsiveness to the impacts of short-term management. In our study, crop diversity, crop rotational complexity, and tillage emerged as the strongest drivers of farm type differences, as shown by LDA coefficients . These results also coincided with average values for management variables compared across all three farm types , though variables for ICLS and cover crop application overlapped considerably across all three farms.

These cursory findings extend results from ongoing work from others , including a recent 4-year study by Sprunger et al. —which focused on organic corn systems in the Midwest. Sprunger et al. likewise reported strong links between soil metrics such as total N, total C, soil protein, and POXC—and on-farm management practices, such as crop rotation patterns, manure and cover crop application, and tillage. While extensive work has been done on organic corn and grain systems in the midwestern region of the US, our study provides new insight on the applicability of these common soil metrics in entirely different organic farming systems and climate regions—specifically on high-value vegetable farms operating in the dry, hot Mediterranean climates of northern California. Our results also underscore the usefulness of on-farm interviews in developing management variables that are potentially linked to soil indicators . Whereas most previous studies have frequently utilized mail-in surveys that rely on binary responses from farmers to understand management , our study, following Guthman and others, highlights the uneven gradient in management practices that exists among organic farms and the importance of in-depth interviews . For example, rather than simply noting the presence or absence of tillage at a field site, our study accounted for the number of tillage passes per season that a farmer implemented on a particular field site, which required soliciting a range of responses from each farmer to create a congruent metric across all field sites. As displayed in Table 6, the mean values for frequency of tillage and crop abundance differed across the three farm types in our study; these management variables strongly separated Farm Type I from the other two farm types and weakly correlated with soil quality. On the other hand, crop rotational complexity generally separated all three farm types, but did not correlate with increasing soil quality. These results suggest that while certain management practices may increase soil organic matter pools as frequency decreases, some management practices may require finding a “sweet spot” to achieve higher soil organic matter levels. Relatedly, the implementation of ICLS did not appear to be as strong of a source of differentiation among the three farm types. One reason for this weak link between soil organic matter levels and ICLS may be due to the lack of a temporal component in the development of this soil metric. For example, some farms may have recently rotated livestock on their fields, while other farms may not have rotated livestock for several years on that particular field; our metric does not capture such spatial and temporal differences. Though limited studies on organic systems in California currently exist, previous studies in the midwestern US have found that the integration of livestock does increase organic matter levels on-farm ; however, based on our results, crop diversity, crop rotational complexity, and frequency of tillage present stronger influences than cover crop application and ICLS in differentiating working organic farms—at least in this particular context.While management is undoubtedly an important driver of soil organic matter levels, our findings also suggest that soil texture may play a more significant role than management in determining levels of SOM than originally considered. Though management explained 18% of the variance among the three farm types, further analysis showed that soil textural class was the more dominant factor as shown in Figure 5; in fact, soil texture class was 44% greater than management in explaining the three farm types. This important result from our study complements parallel findings from Sprunger et al. , who also determined that soil textural class, rather than management, explained the largest amount of variation among the soil indicators they measured on their midwestern US-based organic corn systems . Our combined findings provide an initial indication that regardless of the organic system— ie, crop, climate, and/or geography—soil texture is the more dominant determinant of soil indicators for soil quality rather than the diverse management practices applied to these systems .

It is also possible that gender norms themselves slowly respond to female employment, in which case the longer term impact on violence could differ from the deleterious effect observed here. Rather than suggesting that female employment should not be encouraged, the evidence presented here indicates that economic theory, domestic violence policy and female employment programs should take the costs to men of violations of traditional gender roles seriously – insofar as such violations prove costly for women.Farming is inherently knowledge intensive. This knowledge base is multi-faceted and context specific, and often informed by scientists, researchers, policymakers, government, extension agents as well as by farmers. While farmer knowledge is a critical component of this knowledge base, in the United States farmer knowledge has been widely underappreciated . Long considered “informal” knowledge, farmer knowledge is generally not regarded as scientifically valid and therefore infrequently recorded, whether formally or informally . Since the 1950s, due to an increase in knowledge standardization within production agriculture combined with widespread deskilling among farmers and farm workers, farmer knowledge has become increasingly undervalued . However, farmers who practice alternative agriculture often amass an incredible wealth and depth of knowledge that integrates multiple ways of knowing and reflects diverse knowledge systems for thinking about evidence; perhaps most importantly, farmer knowledge is based in practice . If current trends in consolidation of land ownership, dry rack cannabis chemical-based intensification of agriculture, and standardization of farmer knowledge continue, local farmer knowledge may be endangered or permanently lost .

Before this occurs, it is essential that we elevate the critical role of farmer knowledge and: 1) understand the key features of farmer knowledge; 2) understand the substance of farmer knowledge; and 3) systematically document farmer knowledge in specific local contexts. Understanding the substance of farmer knowledge serves as a first step to conserve this essential knowledge base in practice; however, it is equally critical to document the particularities of farmer expertise in local contexts to provide essential knowledge for other contemporaneous and future generations of farmers, scientists, and policymakers alike. Moving forward, there is therefore a need to elevate the importance and value of farmer knowledge across multiple disciplines such that farmer knowledge is considered “expert” knowledge throughout alternative agriculture . While other studies attempt to integrate the artificial binary between “formal” and “informal,” or “expert” and “non-expert” knowledge and view the two forms of knowledge as complementary , in this paper we maintain that farmer knowledge is scientifically valid, expert knowledge and therefore warrants formal, standalone documentation within the scientific literature .While it is true that the terms “traditional,” “folk,” and/or “indigenous” knowledge are applied in certain contexts, in this paper, the term “local knowledge” is most appropriate , as farmer participants were all white and all either first- or second generation settlers on unceded Patwin-speaking Wintun Nation tribal lands in Yolo County, CA. To frame this paper, we apply Agrawal’s definition of local knowledge as knowledge that is “integrally linked with the lives of people, always produced in dynamic interactions among humans and between humans and nature, and constantly changing.” This definition of local knowledge recognizes the key elements of local knowledge: 1) It is produced by people and among people; 2) It is always produced in relationship with nature; and 3) It is a dynamic process.

More broadly defined, local knowledge involves dynamic processes and complex systems of experiences, practices, and skills developed and sustained by people in their environmental and socioeconomic realties . Further, local knowledge may develop even within one or two generations of place-based experience . In the US, there exists a handful of studies documenting rural local knowledge and rancher local knowledge . Very few studies explicitly examine local knowledge in the context of alternative agricultural or organic systems, referred to as “farmer knowledge” in the literature. This type of knowledge is a subset of local knowledge that enables knowledge holders to farm alternatively in their specifical local contexts. To date, most formal studies on farmer knowledge tend to focus on farmer decision making as it relates to the adoption of new practices . Few studies exist at the intersection of local knowledge, alternative agriculture, and soil management.To consider this gap, we focus this study on a significant epicenter for alternative agriculture in the United States: Yolo County, California, which represents unceded Patwin-speaking Wintun Nation tribal lands. This region in northern California is unique in that it is among the handful of places in the country that emerged as a catalyst and knowledge hub for the organic agriculture movement and where a large concentration of high value, innovative organic production farms continue to thrive today. Due to a unique set of historical and ecological circumstances, the region experienced an influx of organic farmers beginning in the 1970s . During this decade, Yolo County—in combination with Santa Cruz, CA—became a significant node in the organic movement. Its emergence as a significant node was in part due to Yolo County’s proximity to the San Francisco Bay Area and the University of California, Davis—which provided key institutional support—and also partially due to the existence of largely prime agricultural lands combined with a temperate climate ideal for growing year-round.

As a result, Yolo County became one of a few of places where regulations for organic production first evolved and experimentation with organic farming first emerged . Following the farm financial crisis of the 1980s, land prices in the County sharply dropped ; this economic window provided an opportunity for a new generation of farmers to insert a more ecologically-minded approach to farming. Many of these farmers arrived to Yolo County relatively new to farming —often young, educated white urbanites with a desire to farm alternatively to the industrial agribusinesses that had historically dominated the landscape of Yolo County since the early 1900s . When these so-called “back-to-the-land” farmers arrived, many were particularly interested in soil fertility—a conscious effort to avoid “mining the soil” and address ongoing issues with soil degradation in agriculture . While initially these back-to-the-landers lacked historically- and ecologically specific knowledge of the lands they cultivated , over the last three decades or more, it is highly probable that they have individually amassed a wealth of local, place-based knowledge of their specific management contexts and soil landscapes . In this sense, farmer knowledge of soil management presents a particularly salient entry point for further examination in the context of Yolo County specifically. How did these particular farmers address the challenge of soil management in their region? What have they individually and collectively learned about soil management, in theory and in practice? Such questions are particularly important to consider given that—from a pedological and agricultural perspective—soils are heterogenous across landscapes. For example, even at the scale of a single field, roll bench differences in microenvironments, management histories, inherent soil characteristics, and time of year can all dramatically influence how a particular field can be most effectively managed. Addressing this challenge in soil management and understanding the nuances of soil management are fundamental to organic systems—where deep place-based knowledge of soil landscapes is the basis for building and sustaining healthy soils on-farm—and more broadly, resilient agriculture. Yet, farmer knowledge of soil management is still generally under-researched, particularly in the United States and particularly among organic farmers. Though documentation of farmer knowledge of soil management in alternative agriculture exists, most studies focus within the “development” context . Similarly, research on indigenous knowledge of soil is frequently approached from an ethnopedological or traditional ecological knowledge perspective , and lacks focus on production and/or organic agriculture. To date, farmer knowledge of local soil landscapes and related soil management practices remains entirely undocumented in Yolo County. Yet, the unique historical and ecological context makes farmer knowledge of soil health and soil management in this region especially important to document; this knowledge is potentially foundational as organic farmers adapt their farming approaches and management in the face of increasing social, economic, and environmental uncertainties.Though many organic farmers in Yolo County are informed by principles of alternative agriculture when managing their soils, it is less clear how these farmers have translated their ethos into practice and the substance of the soil management practices applied. To address this gap, we examined local farmer ethos and practical knowledge of soil management in this region. Our objectives were to: 1) understand how farmers acquire local knowledge of their soils; 2) document what organic farmers know about their soils; and 3) determine how these farmers translate this local knowledge into specific management practices related to soil health and on farm resilience.This research is informed by a Farmer First approach, which recognizes farmers as experts and crucial partners in researching and innovating solutions for resilient, alternative agriculture . The Farmer First approach recognizes multiple knowledge forms and challenges the standard “information transfer” pipeline model that is often applied in research and extension contexts . We used an open-ended, qualitative approach that relied on in-depth and in-person interviews to study farmer knowledge. Such methods are complementary to surveys that use quantitative methods for capturing a large sample of responses . Because they are more open-ended, qualitative approaches allow for more unanticipated directions ; however, as Scoones and Thompson point out, removing local knowledge from its local context and attempting to fit it into the constrictive framework of Western scientific rationality is likely to lead to significant errors in interpretation, assimilation, and application.

While interviews are not able to capture the quantity of farmer input that surveys do, in-depth interviews allow researchers to access a deeper knowledge base that has inherent value—despite limitations in scalability and/or transferability—as participants respond in their own words, using their own categorization, and perceived associations . Such in-depth interviews are therefore essential to research on farmer knowledge and local knowledge .In-person interviews were conducted in the winter, between December 2019 – February 2020; three interviews were conducted in December 2020. We used a two-tiered interview process, where we scheduled an initial field visit and then returned for an in-depth, semi-structured interview. The purpose of the preliminary field visit was to help establish rapport and increase the amount and depth of knowledge farmers shared during the semi-structured interviews. The initial field visit typically lasted one hour and was completed with all thirteen participants. Farmers were asked to walk through their farm and talk more generally about their fields, their management practices, and their understanding of the term “soil health.” The field interview also provided an opportunity for open dialogue with farmers regarding management practices and local knowledge . Because local knowledge is often tacit, the field component was beneficial to connect knowledge shared to specific fields and specific practices. After the initial field visits, all 13 farmers were contacted to participate in a follow up visit to their farm that consisted of a semi-structured interview followed by a brief survey. These in-depth interviews allowed us to ask the same questions of each farmer so that comparisons between interviews could be made. To develop interview questions for the semistructured interviews , we established initial topics such as the farmer’s background, farm history, general farm management and soil management approaches. We consulted with two organic farmers to develop final interview questions. The final format of the semi-structured interviews was designed to encourage deep knowledge sharing. For example, the interview questions were structured such that questions revisited topics to allow interviewees to expand on and deepen their answer with each subsequent version of the question. Certain questions attempted to understand farmer perspectives from multiple angles and avoided scientific jargon or frameworks whenever possible. Most questions promoted open-ended responses to elicit the full range of possible responses from farmers. In the interviews, we posed questions about the history and background of the participant and their farm operation, how participants learned to farm, and to describe this process of learning in their own words, as well as details about their general management approaches. Farmers were encouraged to share specific stories and observations that related to specific questions. Next, we asked a detailed set of questions about their soil management practices, including specific questions about soil quality and soil fertility on their farm. In this context, soil quality focused on ecological aspects of their soil’s ability to perform key functions for their farm operation ; while soil fertility centered on agronomic aspects of their soils’ ability to sustain nutrients necessary for production agriculture .

Under team pay, biased upstream workers are unable to increase the relative pay of favored downstream workers by distorting relative supply. As a result, horizontal misallocation of flowers was eliminated. Total output in teams in which the two processors were of different ethnic groups therefore increased, the introduction of team pay returning the difference in output between such teams and homogeneous teams to pre-conflict levels. Overall output also increased, even though the results indicate that team pay led processors to freeride on each others’ effort. This paper’s results indicate that, if taste for discrimination is high enough, firms are forced to adopt “second best” policies to limit the distortions caused by such discrimination. But entirely removing workers’ incentives for discrimination is difficult. At the plant, team pay had little effect on the degree of discrimination in teams that were ethnically differentiated vertically rather than horizontally, as also predicted by the model. The obvious “solution” to discrimination – segregating workers – may be undesirable for reasons unrelated to productivity in the short term. The extent and multiplier effects of taste-based misallocation also depend on a number of other factors, such as pay systems, the structure of production, and the “geographical” distribution of ethnic groups in the productive system, however. More speculatively, cannabis grow equipment it is possible that such factors respond endogenously to ethnic diversity. Social segregation is commonly observed in diverse societies but likely becomes harder to achieve as urbanization brings larger groups of workers together. The linkages and specialization required in industrialized production are rarely observed in the most ethnically diverse countries.

My findings also suggest that the economic costs of ethnic diversity vary with the political environment. Relatively brief episodes of ethnic conflict can have a long-lasting impact on economically distortionary attitudes: I find no decay in discrimination in the nine months after conflict ended. Multiple equilibria may thus exist if the occurence of conflict itself depends on attitudes towards non-coethnics, some diverse societies being characterized by tolerance and little conflict and others by ethnic biases and frequent conflict.The quotes above illustrate a prevailing view among policymakers which sees the creation of job opportunities for parents – especially mothers – as a quintessential tool for improving the lives of children in poor countries. The view appears to be based in large part on extrapolation of findings from studies of the effects in the household of increases in unearned income. Relying on such extrapolation may be adequate if the dominant household models – in which children typically appear only as an expenditure category for the decision-making parents 1 – provide an accurate picture of a poor country household. If instead there is substitution between parents’ and childrens’ time use, then employment may be a fundamentally different “treatment” than pure income transfers due to its implications for the employed parent’s time use. In that case the lack of causal evidence on the consequences for children of parent’s employment is a problematic gap in the literature on poor countries. Taking advantage of a field experiment that randomized long-term job offers this paper presents direct evidence on the impact of a parent’s employment on children’s lives. Five Ethiopian flower farms agreed to allocate fall 2008 job offers through a lottery system.

The experiment was “natural” in the sense that parents sought employment in the exact same way they would have done in the absence of the research team. Because households thus themselves determined if the mother or the father applied, I analyze the two sub-samples separately. The farms were willing to randomize job offers because open positions attracted large numbers of mostly inexperienced applicants and screening was difficult. Before the lottery took place, enumerators surveyed acceptable applicants. Winners and losers were re-surveyed five to seven months after employment commenced. The randomization was effectively stratified on gender. The main results are as follows. As daughters take over house-work left undone when a mother gets employed, their school-time falls by 24 percent per week. Daughters’ time use is unaffected by father’s employment. An increase in sons’ school time of about ten percent when a mother or a father gets employed appears to be due to higher household income; sons’ house-work time is unaffected by parents’ employment. After documenting the impact of parents’ employment on childrens’ time use, I present a simple collective framework in which each parent attaches weight to daughters’ well-being and daughters derive utility from going to school, but only females can do house-work . The framework highlights the variables upon which heterogeneity in the response to mother’s employment is likely to depend if the primary underlying force is time use substitution between mothers and daughters.

Testing the framework’s predictions, I find that, the higher the proportion of daughters – a variable that is shown to be exogenous in the sample studied – the less negative the impact of mother’s employment on a given daughter’s school-time, the greater the weight attached to daughters’ well-being, the less negative the impact of mother’s employment on a daughter’s school-time, and the greater the initial bargaining power of the mother, the greater the reduction in daughters’ school-time when mothers get employed. Daughters themselves appear to have little influence over the change in their time use when mothers get employed. Interestingly, selection into mother’s versus father’s employment appears to depend on the same covariates, providing further evidence of the importance of female house-work substitution. These results have important implications for the design of employment programs and for how selection into parent’s employment and its effects in the household should be modeled. If full-time school enrollment is not universal, explicitly accounting for children’s time use is important. In situations where the house-work necessary to run a household is time consuming, the substitutability between parents’ and children’s effort introduces a potential trade-off between parents’ and children’s preferences. If house-work is effectively gender specific, then the conventional wisdom – that economically empowering mothers is of greater benefit to daughters than empowering fathers – is not necessarily the full story when it comes to parent’s employment, even if mothers weigh daughters’ well-being more than fathers do. The reason is that mothers may face a trade-off between own and daughters’ time use that fathers do not. If female participation in the market economy over time influences the norms governing the division of labor in the household, then the longer-term effects of mother’s employment may differ from those observed here, but such norms are likely slow to change. This paper builds on and extends the overlapping literatures on adult employment, vertical grow rack child labor and schooling, and intra-household decision-making in poor countries. Causal evidence on the effects in the household of long-term parental employment in poor countries is to my knowledge largely absent, credible exogenous variation in employment rarely being available. Indirect inference – for example on the basis of findings from studies of unearned income – has been attempted, but there are good reasons to study parent’s employment directly. Beyond the implied time use reconfiguration, employment may for example affect the two parents’ relative bargaining power differently than government transfers or income from other sources do. This paper presents the first experimental evidence on the effects in the household of a parent’s long-term employment. Children’s time use is one of the primary determinants of human capital accumulation and child well-being. The degree of substitutability between parents’ and children’s time use is therefore important. Several existing studies find correlations between a mother’s employment status and children’s time use in poor countries . Doran convincingly shows that adults in Mexico work more when children work less due to an exogenous increase in time spent in school.

But his focus is on paid child labor; though understudied in the literature due to a lack of data child house-work is much more common than paid work in most of the developing world, and the effect of parents’ time use on children’s time use is typically of greater relevance for policy than the converse. Gender specificity of house-work in combination with the typically greater time requirements of “female” responsibilities may be a particularly important though often overlooked form of son favoritism, especially because child labor and schooling are negatively related . I take advantage of an exogenous increase in mother’s and father’s work hours to provide causal evidence on time use substitution between mothers, fathers, daughters and sons. Bar and Basu argue that an inverted-U relationship can arise because of missing labor markets for children: the results in this paper suggest that missing labor markets for adults can also lead to a range in which child labor may appear to be increasing in parents’ income. As formal employment opportunities arise for mothers, daughters may be forced to take over house-work. The preferences of children and parents are not perfectly aligned, even if parents are partially altruistic. An important question is how much influence children have over their own lives: the review in Edmonds argues that our almost complete lack of knowledge about parent-child agency and who makes child time use decisions is the most pressing issue in the literature on child labor. This paper’s results indicate that the reconfiguration of a daughter’s time that occurs when a mother gets employed in rural Ethiopia is decided by parents, primarily mothers, while daughters themselves have little influence over the change in their time use. The paper is organized as follows. In section 2, I present the setting and the experiment. The reduced form time use estimates are in section 3. In section 4, I present a simple theoretical framework of household work and schooling decisions that illustrates the forces that underlie the results in section 3, and derive auxiliary predictions. The predictions are tested in sections 5. Section 6 provides further evidence on how time use decisions are made and section 7 analyzes selection into employment.Growth in the commercial floriculture sector in Ethiopia has been explosive in recent years, fueled in part by government incentives and in part by the abundant availability of cheap land and labor in rural areas. In 2008, 81 flower farms employed around 50,000 unskilled workers. Most flower farm workers work in greenhouses, growing and harvesting flowers, or in “pack houses”, packaging flowers and preparing them for shipping. Over 70 percent of flower farm workers are women . Hiring on Ethiopian flower farms typically takes place in October and November, before the main growing and harvesting season. The supervisors on five flower farms agreed to randomize job offers during fall 2008 because of an unusual situation in the labor market for flower farm workers at the time. Because comparable jobs were seldom available in the areas suitable for flower growing, applicants almost always outnumbered the positions to be filled by large margins. Ethiopian flower farms – still getting to grips with cost components significantly larger than labor and with little ability to predict the productivity of the mostly uneducated, illiterate and inexperienced applicants – did not prioritize optimization of the unskilled workforce . Because supervisors were already allocating job offers relatively arbitrarily when approached by the research team, explicit randomization was a modest procedural change. When Ethiopian flower farms hire, word is typically spread in nearby villages. Job-seekers arrive at the farm on announced “hiring days”. At the participating farms, supervisors first excluded any unacceptable applicants. A team of enumerators then carried out the baseline survey with the remaining applicants. Finally, the names of the number of female and male workers to be hired were drawn randomly from a hat. The full sample thus consists of 527 households in which at least one spouse applied to a flower farm job and was deemed acceptable for hiring. There are 346 women in the sample and 188 men: in almost all cases one of two spouses applied. We attempted to re-interview everyone in the treatment and control groups 5 – 7 months after employment commenced. Because few farms were hiring workers in the season that followed the randomization, only 6 re-interviewed individuals in the control group had managed to obtain employment. Careful tracking procedures led to a re-interview rate of 88 percent and no statistically significant differential attrition. Almost all the job-seekers are parents: the focus here is on the effects of a parent’s employment for children in the household.