Tag: stacking pots

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Credible and prediction intervals in the shoot at harvest were similar for both models

However, Southern California, a region that suffers from a similar degree of water shortage, currently uses less than ~3% of municipal wastewater in agriculture, while discharging ~1.5 million acre-feet effluent per year into the Pacific Ocean . Secondary municipal wastewater effluent for ocean discharge is often sufficient to support both the nutrient and water needs for food production. Water reuse in agriculture can bring municipal water reclamation effluent to nearby farms within the city limit, thus promoting local agriculture and also reducing the rate of farmland loss to urban development. While the use of reclaimed water in agriculture offers a multitude of societal and agronomical benefits, broader adoption faces great challenges. One of the important challenges is ensuring the safety of food products in light of a plethora of human pathogens that may be present in recycled wastewater. Past studies have identified risks associated with irrigating food with recycled wastewater through the retention of the irrigation water on edible plant surfaces during overhead irrigation . With the emphasis on water conservation and reduction of evapotranspiration, subsurface drip irrigation is gaining popularity . Since there is lesser contact between water and the plant surface, the chance of surface contamination of pathogens is reduced. However, this new practice presents risk of uptake of microbial pathogens into plants. Such internalized pathogens are of greater concerns as washing, even with disinfectants, may not affect pathogens sheltered in the vasculature. Although pathogen transport through root uptake and subsequent internalization into the plant has been a growing research area, results vary due to differences in experimental design, systems tested, and pathogens and crops examined . Among the array of pathogens causing food borne illness that may be carried by treated wastewater, viruses are of the greatest concern but least studied. According to the CDC, 60% of U.S. food borne outbreaks associated with eating leafy greens were caused by noroviruses ,stacking pots while Salmonella and E. coli only accounted for 10% of the outbreaks . Estimates of global food borne illness prevalence associated with NoV surpass all other pathogens considered.

Viruses are also of concern because they persist in secondary wastewater effluents in high concentrations . They do not settle well in sedimentation basins and are also more resistant to degradation than bacteria . Therefore, in the absence of solid scientific understanding of the risks involved, the public are likely less receptive to adopting treated wastewater for agricultural irrigation. NoV internalization in hydroponic systems has been quantified by DiCaprio et al. . Internalization in crops grown in soil is considered lesser but nevertheless occurs. However, the only risk assessment that considered the possibility of NoV internalization in plants assumed a simple ratio of viruses in the feed water over viruses in produce at harvest to account for internalization. The time dependence of viral loads in lettuce was not explored and such an approach did not permit insights into the key factors influencing viral uptake in plants. In this study, we introduce a viral transport model to predict the viral load in crisp head lettuce at harvest given the viral load in the feed water. It is parameterized for both hydroponic and soil systems. We demonstrate its utility by performing a quantitative microbial risk assessment . Strategies to reduce risk enabled by such a model are explored, and a sensitivity analysis highlights possible factors affecting risk.The plant transpiration rate was adopted as the viral transport rate ) based on: 1) previous reports of passive bacterial transport in plants , 2) the significantly smaller size of viruses compared to bacteria, and 3) the lack of known specific interactions between human viruses and plant hosts . Accordingly, viral transport rate in hydroponically grown lettuce was determined from the previously reported transpiration model , in which the transpiration rate is proportional to the lettuce growth rate and is influenced by cultivar specific factors . These cultivar specific factors used in our model were predicted using the hydroponic crisp head lettuce growth experiment carried out by DiCaprio et al. described in Section 2.3 . Since the transpiration rate in soil grown lettuce is significantly higher than that in the hydroponic system, viral transport rate in soil grown lettuce was obtained directly from the graphs published by Gallardo et al. using WebPlotDigitizer . The shoot growth rate for soil grown lettuce was determined using Eq. 9 . In the absence of a published root growth model for lettuce in soil, a fixed root volume of 100 cm3 was used. In the viral transport model, viral transfer efficiency was used to account for the potential “barrier” between each compartment .

The existence of such a “barrier” is evident from field experiments where some microbial pathogens were internalized in the root but not in the shoot of plants . In addition, viral transfer efficiencies also account for differing observations in pathogen internalization due to the type of pathogen or lettuce. For example, DiCaprio et al. reported the internalization of NoV into lettuce, while Urbanucci et al. did not detect any NoV in another type of lettuce grown in feed water seeded with viruses. The values of ηgr and ηrs were determined by fitting the model to experimental data reported by DiCaprio et al. and is detailed in Section 2.3. The viral removal in the growth medium includes both die-off and AD, while only natural die-off was considered in the lettuce root and shoot. AD kinetic constants as well as the growth medium viral decay constant in the hydroponic case were obtained by fitting the model to the data from DiCaprio et al. . Viral AD in soil has been investigated in both lab scale soil columns and field studies . In our model, viral AD constants in soil were obtained from the experiments of Schijven et al. , who investigated MS2 phage kinetics in sandy soil in field experiments. As the MS2 phage was transported with the water in soil, the AD rates changed with the distance from the source of viruses. To capture the range of AD rates, two scenarios of viral behavior in soils were investigated. Scenario 1 used the AD rates estimated at the site closest to the viral source , while scenario 2 used data from the farthest site . In contrast to lab scale soil column studies, field studies provided more realistic viral removal rates . Using surrogate MS2 phage for NoV provided conservative risk estimates since MS2 attached to a lesser extent than NoV in several soil types . The viral decay rate in the soil determined by Roberts et al. was adopted because the experimental temperature and soil type are more relevant to lettuce growing conditions compared to the other decay study . Decay rates in the root and shoot were used from the hydroponic system predictions.The transport model was fitted to log10 viral concentration data from DiCaprio et al. , extracted from graphs therein using WebPlotDigitizer . In these experiments, NoV of a known concentration was spiked in the feed water of hydroponic lettuce and was monitored in the feed water, the root and shoot over time.

While fitting the model, an initial feed volume of 800 mL was adopted and parameters producing final volumes of b200 mL were rejected. To fit the model while accounting for uncertainty in the data, a Bayesian approach was used to maximize the likelihood of the data given the parameters. A posterior distribution of the parameters was obtained by the differential evolution Markov chain  algorithm,strawberry gutter system which can be parallelized and can handle multi-modality of the posteriors distribution without fine tuning the jumping distribution. Computation was carried out on MATLAB R2016a and its ParCompTool running on the High Performance Computing facility at UC Irvine.Table 3 lists the parameters estimated by model fitting and their search bounds. Fitting data from DiCaprio et al. without including viral AD to the tank walls was attempted but the results were not used in the risk estimates due to the poor fit of model to the data. The rationale behind the model fitting procedure and diagnostics are discussed in Supplementary section S1H.A summary of the model fitting exercise for viral transport in hydroponic grown lettuce is presented in Fig. 2. Under the assumption of first order viral decay, NoV loads in water at two time points did not fall in the credible region of model predictions, indicating that mere first order decay was unsuitable to capture the observed viral concentration data. The addition of the AD factor into the model addressed this inadequacy and importantly supported the curvature observed in the experimental data. This result indicates the AD of viruses to hydroponic tank wall is an important factor to include in predicting viral concentration in all three compartments .The adequacy of model fit was also revealed by the credible intervals of the predicted parameters for the model with AD . Four of the predicted parameters: at, bt, kdec, s and kp, were restricted to a smaller subset of the search bounds, indicating that they were identifiable. In contrast, the viral transfer efficiency η and the kinetic parameters spanned the entirety of their search space and were poorly identifiable. However, this does not suggest that each parameter can independently take any value in its range because the joint distributions of the parameters indicate how fixing one parameter influences the likelihood of another parameter . Hence, despite the large range of an individual parameter, the coordination between the parameters constrained the model predictions to produce reliable outcomes . Therefore, the performance of the model with AD was considered adequate for estimating parameters used for risk prediction.Risk estimates for lettuce grown in the hydroponic tank or soil are presented in Fig. 4. Across these systems, the FP model predicted the highest risk while the 1F1 model predicted the lowest risk. For a given risk model, higher risk was predicted in the hydroponic system than in the soil. This is a consequence of the very low detachment rates in soil compared to the attachment rates. Comparison of results from Sc1 and Sc2 of soil grown lettuce indicated lower risks and disease burdens under Sc1 . Comparing with the safety guidelines, the lowest risk predicted in the hydroponic system is higher than the U.S. EPA defined acceptable annual drinking water risk of 10−4 for each risk model. The annual burdens are also above the 10−6 benchmark recommended by the WHO . In the case of soil grown lettuce, neither Sc1 nor Sc2 met the U.S. EPA safety benchmark. Two risk models predicted borderline disease burden according to the WHO benchmark, for soil grown lettuce in Sc1, but under Sc2 the risk still did not meet the safety guideline. Neither increasing holding time of the lettuce to two days after harvesting nor using bigger tanks significantly altered the predicted risk . In comparison, the risk estimates of Sales-Ortells et al. are higher than range of soil grown lettuce outcomes presented here for 2 of 3 models. The SCSA sensitivity indices are presented in Fig. 5. For hydroponically grown lettuce, the top 3 factors influencing daily risk are amount of lettuce consumed, time since last irrigation and the term involving consumption and ρshoot. Also, the risk estimates are robust to the fitted parameters despite low identifiability of some model parameters . For soil grown lettuce, kp appears to be the major influential parameter, followed by the input viral concentration in irrigation water and the lettuce harvest time. Scorr is near zero, suggesting lesser influence of correlation in the input parameters.In this study, we modeled the internalization and transport of NoV from irrigation water to the lettuce using ordinary differential equations to capture the dynamic processes of viral transport in lettuce. This first attempt is aimed at underscoring the importance of the effect of time in determining the final risk outcome. The modeling approach from this study may be customized for other scenarios for the management of water reuse practices and for developing new guidelines for food safety. Moreover, this study identifies critical gaps in the current knowledge of pathogen transport in plants and calls for further lab and field studies to better understand risk of water reuse.

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Institutional barriers also constrain producers from moving into individual farming

The overall objectives of our proposed paper is to: systematically document the post-reform trends in agricultural performance in Asia, Europe, and the Former Soviet Union; identify the main reform strategies and institutional innovations that have contributed to the successes and failures of the sector; analyze the mechanisms by which reform policies and initial conditions have affected the transition process in agriculture; and draw lessons and policy implications from the experiment and identify the gaps in our understanding of the role and performance of agriculture in transition. As part of this effort, we attempt to address a number of intriguing and important questions on the performance of individual countries or regions during transition. Why has China been so successful in its reforms, while Russia has not? Why is it that some CEECs have rebounded and showing robust productivity growth, while others have not? Why has agriculture in so many FSU nations continued to perform so poorly? In addition, we will address questions about the process of reform. Why has land restitution predominated in Europe but not in Russia or China? Why did institutions of exchange collapse in the non-Asian economies in the early stages of reform but continued to function in Vietnam and China? What explains the apparent divergence in the performance effects after the first year of reform in China and Vietnam, on the one hand, and much of the rest of the transitional world on the other? In particular, how have land reform and rural input-supply/ procurement enterprise restructuring affected productivity? Which institutions of exchange and contracting have or have not emerged, and why? How has the structure of the economy at the outset of transition, and other initial conditions, affected the transition process? To meet our objectives and answer some of the questions,stacking pots we will begin by laying out the record on performance — examining the main bodies of data that demonstrate the changes in agricultural output, income, and productivity in the years after transition.

In doing so, we will show how some of the countries have recorded similar performances, while others have developed quite differently. We will identify several “patterns of transition” based on these performance indicators and much of our subsequent discussion will analyze the success of transition according to these classifications. Next, as the first step in our search for answers as to what explains these different patterns, we examine differences in the points of departure of the transition countries as well as the nature of the policy reforms that have affected agriculture. The initial conditions that we hypothesize may explain part of the transition period’s performance include the nature of agricultural technology at the beginning of the reforms , the structure of the economy , the extent of collectivization, and the magnitude of trade distortions. The key policy interventions that we should expect to affect agriculture’s performance during transition include land right reforms and farm restructuring; price and subsidization policies; the approach to the liberalization of agricultural commodity and input markets; general macro-economic and general institutional reforms; and the attention of sectoral leaders to the level of new and maintenance-oriented public goods investment . After documenting the dramatic differences in initial conditions and in reform policies among the transitional countries, we seek to demonstrate which of the differences determine the path a country’s agriculture takes. In other words, we offer answers to the question why transition in agriculture in some countries has been successful and not in others. Here, we seek to generalize about the main causes for differences between the countries and the mechanisms that have affected performance. In particular, we argue that the debate on the optimality of Big-Bang versus gradualism oversimplifies the reform problem. The empirical evidence suggests that the road to a successful transition is more subtle and successful transitions in Asia and Europe have elements of both gradual and radical reforms.

To explain the reform successes and failures we emphasize the role of the political environment in the early reform years and the potential for agricultural growth that exists at the start of reforms. We find that both have not only influenced the choice of the reform policies, but also the effect of the reform policies. We also conclude that the initial level of price distortions and the pace of market liberalization were especially influential in explaining differences in the early stages of transition but that the influence of the factors has diminished over time. Investment, land rights, and farm restructuring policies, in contrast, are assuming a more important role as the agricultural reforms have matured.In the last section we draw policy implications and lessons from the agricultural transition experiences. We argue that one should be careful about which indicator to use for measuring success and failure of transition. We conclude that all reform strategies in order to be successful need to include some certain policy ingredients . However, a powerful lesson is that although all the pieces are ultimately needed, there is a lot of room for variation in the form of institutions that can be successful, and optimal policies and institutions may vary according to initial conditions. In other words, there is no single optimal transition path. Whatever the reason—either initial conditions, reform policies, or both—remarkable differences can be observed when examining the performance of agriculture in the transitional countries during the first decade of reform . From the start of the reforms, output increased rapidly in China. After 10 years output had increased by 60 percent. In Vietnam, output also rose sharply, increasing by nearly 40 percent during the first decade of reform.Output trends followed a different set of contours outside of Asia. Production fell sharply in the first 5 years of transition in both the CEECs and in the FSU countries. Since the mid-1990s, output stabilized in most of the CEECs. In Russia and Ukraine, however, the fall continued declining to nearly 50 percent of pre-reform output. Productivity trends, while similar to those of output in certain countries, diverged in others . For example, for the entire reform period, labor productivity in the agricultural sectors of China and Vietnam, measured as output per farm worker, rose steadily like output. The productivity trends for Russia and Ukraine also mirror those of the nation’s output: labor productivity fell over 30 percent between 1990 and 1999. Productivity trends for some CEECs, however, differ from those of output. For example, output per worker almost doubled over the first decade after transition in Hungary.

Labor productivity also rose strongly in the Czech Republic and Slovakia in the 1990s, even as output was falling. While reliable estimates on total factor productivity are scarcer, the general picture is similar as the one described by the labor productivity trends. In China and Vietnam, TFP rose during the reform era . In several CEECs, TFP in crop production started increasing early on in transition . What has been behind the observed trends? To the extent that we can better understand the sources of growth, decline, and recovery, we may be able to more precisely predict what is in store for the future and derive more accurate policy implications. We start by examining initial conditions,grow lights since they may affect how a country proceeds after a change. Next, we examine the impact of policy actions taken by reforms: the record on property rights, price and subsidy policies, and a large number of measures that can be labeled as actions taken to promote the emergence of institutions of exchange, including markets. The final subsection briefly examines the record of countries in the management of agricultural investment. Although comparisons of economies in transition are reasonable, given their common reliance on central planning and shared transition era goals of liberalization and faster growth, differences in initial conditions at the outset of reform may temper comparisons. In general, the Asian economies had a much lower levels of development than the transition countries in Europe. For example, the share of agriculture in employment was more than 70% in China and Vietnam. In contrast, less than 20 percent of the working population in Russia and most of the CEECs is employed in agriculture. The demographic structure of the countries also affects the way output is produced. Farms in China and Vietnam are much more labor-intensive. The man/land ratio was more than five times higher in Asia than in Central Europe or Russia . The length of time under collectivized agriculture also may affect transition. Although pre-transition agriculture was characterized by the dominance of large-scale farms in almost all the countries,the collectivization of agriculture occurred early this century in Russia, while only after the second World War in the CEECs and East Asia. Experience with private farming and any understanding of markets was more likely completely lost during several generations under Communism in most of the FSU nations. In contrast, private farming survived in rural households in many other countries.Land ownership prior to reform also differed among the countries. In China, the collective retained legal and effective property rights both before and after the implementation of HRS.

In Russia and other FSU countries, however, land was nationalized during Communism. In many CEECs much of the collective farm land was still legally owned by individuals, although effective property rights were controlled by the state or the collective farms . Paradoxically, while these legal differences probably had little impact on the operation of the land in the various countries in the pre-reform era, they had a much stronger effect on land reforms afterward liberalization. In particular, pre-reform ownership can be quite closely linked to the demand for land restitution in the CEECs . Finally, pre-reform tax, subsidy and trade policies differed significantly among the countries. In China and Vietnam, authorities heavily taxed agriculture . In contrast, leaders in most of the CEECs and the FSU nations supported agriculture with heavy subsidies . Moreover, while some of the taxes and subsidies were direct, some differences in rates of taxation and subsidy were related to trade policies. Trade policies also affect the degree of access that consumers and producers have to world markets and how much producers are subject to global competition. For example, FSU countries were strongly integrated into the CMEA system, and traded mainly with other communist countries. The share of CMEA exports as a percent of GDP amounted to around 30 percent in Russia and Ukraine. The CEECs also traded with other countries, but CMEA exports still made up around 10 percent of GDP in countries like Hungary and the Czech Republic. In contrast, China and Vietnam mainly traded with nonCMEA countries.The reforms in China and Vietnam started with radical decollectivization and reshuffling of property rights. Reformers in China re-allocated land rights from the communes, brigades and teams to rural households and completely broke up the larger collective farms into small-scale household farms. The resulting changes in incentives triggered both strong growth of output and a dramatic increase in productivity . Doi Moi, Vietnam’s reform program in the 1980s closely followed China’s strategy and land reform also positively affected the nation’s agricultural output . In contrast, many large-scale farm organizations survived the transition in the FSU and the CEECs. Large-scale farms, under a variety of legal organizations, still cultivated more than 75% of the land in Russia, Ukraine, most of the FSU nations, and a number of CEECs five years after the start of the reforms. The break-up of the former collective and state farms into individual farms has been strongest in countries in which the collective and state farms were least efficient and most labor intensive . Importantly, the shift also was higher in regions where at least some private farming survived during Communist rule. Although the share farmed by large corporate farms has fallen gradually over the past decade in most transition countries, it is a slow process and it is not obvious that they will disappear in the near future. In some countries, such as Russia and Slovakia, policies still heavily favor large corporate farms.The corporate farms also may be providing services that provide up- and downstream activities substituting for missing markets . In many countries, such as Hungary and Bulgaria, a dual farm structure is emerging with some large-scale farms and many small-scale individual farms .

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Each enclosure contained an array of six rectangular PIT antennas arranged in the same orientation

Given that the proposed California threshold is 0%, a scenario in which both GM and non-GM products are offered side-by-side in the market seems unlikely. Some non-GM products may remain unlabeled if food companies are able to find substituting ingredients that are not at any risk of containing GM. But certified non-GM products will mostly disappear. As U.S. corn, canola, and soybean production uses primarily GM varieties, Prop 37 labeling standards will force change in the composition of retail products offered. As the initiative applies only to California, it may not be profitable to undergo a reduction of GM inputs for one state. If this is the case, then the vast majority of food products that are not completely GM-free will bear the new label. As a consequence, a fraction of consumers now wary of the label may shift their consumption towards organic. Such a transition implies potential gains for organic growers but potential losses for conventional growers. Today, a move towards “non-GM” or “naturally grown” labels is underway, especially with natural grocers. Some organic corn and soybean growers in the U.S. have converted back to conventional with non-GM seeds, thereby saving labor and other costs, while still getting similar price premia. The “non-GM” or “natural” products are the closest competition for organic products now; but they will be reduced or eliminated with Prop 37 due to forced relabeling and the prohibition of terms such as “naturally grown” on food labels . Table 4 outlines the likely impacts of Prop 37 on various categories of food and beverages.conducted a series of field studies during 2012-2017. To test fish and food web responses within different land-management scenarios, we conducted our project on standard rice and winter wheat fields, adjacent fallow lands,stackable planters and rice fields with different harvest practices or other experimental modifications. This work yielded several publications that provided insight into habitat conditions in flooded rice fields for fish and invertebrates . The focus of our effort was on rearing habitat for young Chinook Salmon, but this work may also be relevant to other native fishes.

The goal of this paper is to summarize the key lessons learned from 6 years of research on the feasibility of using farm fields as rearing habitat for juvenile Chinook Salmon in the Yolo Bypass and other Central Valley locations. Our hope is that our summary will provide guidance to future researchers, as well as inform managers as they evaluate potential management approaches. An important caveat is that our studies were not intended as a proof of concept for any specific management actions. Rather, our research was intended to examine some of the attributes that could reduce limitations to rearing conditions identified in early research, and gain insight into some of the key considerations for potential future agricultural floodplain management. A second major caveat is that we had to rely on juvenile hatchery Chinook Salmon as a surrogate for wild Chinook Salmon, our ultimate target for habitat restoration. We recognize that there are several potential differences in the behavior of hatchery and wild Chinook Salmon . However, hatchery salmon were the only feasible alternative in this case since downstream migrating wild juvenile Chinook Salmon were mostly cut off from the Yolo Bypass because of extreme drought conditions. Nonetheless, hatchery salmon have been used successfully as a research tool in many types of ecological studies, so many of the lessons learned here should have at least some relevance to wild Sacramento River Chinook Salmon. Finally, our project was separate from a number of other fish management research projects in agricultural parcels, such as current efforts to investigate whether invertebrates grown on flooded rice fields can be used as a food subsidy for adjacent river channels . The Yolo Bypass is a 24,000-ha, partially leveed flood basin that is used to safely convey floodwaters away from Sacramento Valley communities . The Yolo Bypass contains a suite of habitats including agricultural lands, managed wetlands, upland habitat, and perennial ponds and channels, with broad open-water tidal wetlands at its downstream end where it joins the Sacramento-San Joaquin Delta .

The basin receives seasonal inflow from the Sacramento River, Colusa Basin , Cache Creek, and Putah Creek, as well as substantial perennial tidal flow from the San Francisco Estuary via the lower Sacramento River at the downstream end of the floodplain . The Yolo Bypass floods to various degrees in approximately 80% of water years, but inundation events are often relatively short and sometimes driven entirely by inflow from the west-side tributaries. The most substantial flow events come from the Sacramento River, which enters the Yolo Bypass via Fremont Weir and Sacramento Weir. However, in drought periods, such as during 2012-2015, there is little or no flooding.For each year, we evaluated water quality , food web responses , and fish growth and condition . Water temperature in fields was recorded continuously at 10- to 15-minute intervals with Onset HOBO® loggers, and a suite of other water-quality parameters was measured and recorded using handheld and continuously installed multi-parameter sondes. We included plankton sampling with the broad goal of characterizing the communities and densities of phytoplankton and zooplankton in the study fields. Because long-term monitoring of the Yolo Bypass includes weekly plankton sampling in both the perennial Yolo Bypass channel of the Toe Drain and the Sacramento River, we could compare our experimental fields to productivity across habitats. Because the study fields were shallow compared to canal and riverine channel environments, sampling methods had to be slightly modified compared to the Toe Drain and Sacramento River. As a result, we used hand-tosses of a smaller 30-cm zooplankton net , recording the length of the toss, and the relative percent of the net mouth that was submerged during net retrieval. Detailed methods for zooplankton sampling are described in Corline et al. . Fish used in the experiments were primarily fall-run Chinook Salmon parr obtained from Feather River Fish Hatchery; however, small numbers of wild Sacramento River Chinook Salmon were also studied in 2013 and 2013, 2015, and 2016 . The majority of the study fish were free swimming throughout the flooded fields, but mesh cages were also used as a tool to compare hatchery salmon growth and survival across substrates in 2012 or habitats in 2016 and 2017. The initial study year was a pilot effort to evaluate whether managed flooding of a rice field could provide suitable habitat for juvenile salmon rearing, and to assess associated growth and survival. A single 2-ha field contained a patchwork of four agricultural substrate types, including disced , short rice stubble , high rice stubble , and fallow vegetation. Approximately 10,200 juvenile salmon were released in the field, with a subset implanted with passive integrated transponder tags, so individuals could be identified, and individual growth rates could be measured. Twenty PIT-tagged fish were also released in each of eight enclosures placed over patches of the different substrate types, to determine if growth rates differed .Substrates in flooded rice fields differ from those that juvenile salmon may encounter in natural floodplains or riverine systems. Thus, the goal of the second study year was to investigate whether juvenile salmon had differential growth and survival rates across agricultural substrates,stacking pots and whether they would preferentially use a specific substrate type when given a choice. Our logic was that understanding these responses could provide insight into whether some agricultural practices provide more suitable salmon rearing conditions than others.

To compare growth and survival rates across rice stubble, disced, and fallow substrates, we created a series of nine 0.8-ha experimental fields with individual inlets and outlets, with three replicates of each substrate . We placed approximately 4,600 hatchery origin juvenile Chinook Salmon in each field for 40 days and measured weekly during the study period to estimate average growth rates. To examine substrate preference, we used PIT-tag technology to track individual fish in two large circular enclosures . In addition to examining the potential for preference among agricultural substrates, this study also investigated whether newer and smaller PIT tags were viable for detecting juvenile salmon movements in these habitats. One enclosure included three habitat treatments , and the other served as a comparison with only the disced treatment.Fish remained in the enclosures for 14 days, during which occupancy data were collected. Detailed methods can be found in Conrad et al. .As an engineered floodplain, the Yolo Bypass is designed to drain efficiently. During moderate inundation events, availability of floodplain habitat can be brief—persisting for a week or less. In 2016, our focus was to test the feasibility of using agricultural infrastructure to extend the duration of a small to moderate flood event, increasing the length of time flooded habitat was available to fish. We called this idea “flood extension.” We planned similar studies in other study years, but extreme weather events prevented implementation . Landowner partners in the Yolo Bypass at Knaggs Ranch, Conaway Ranch, and Swanston Ranch agreed to maintain shallow inundation for 3 to 4 weeks in a designated experimental field after a natural flood event. At Knaggs Ranch, the landowner made modest to extensive modifications to the drainage infrastructure to allow more control over the drainage rate from the inundated field to the Toe Drain. At Swanston and Conaway ranches, inundation was maintained with flash boards, which could be removed once it was time to drain the field. During the first week of flood extension, we held stocked hatchery salmon and any entrained natural-origin salmon, allowing us to estimate growth and survival rates upon drainage. Thereafter, we allowed salmon to leave fields if they chose to do so. We outfitted field drains with a plastic mesh live-car trap, where we captured and measured emigrating individuals before they proceeded downstream. In 2016, the attempt to test a “flood extension” concept was unsuccessful because inundation occurred late in the season, resulting in unsuitably warm water temperatures for juvenile salmon in our experimental fields. We therefore made a second attempt to conduct a flood extension pilot in 2017 at Knaggs Ranch, Conaway Ranch, and Swanston Ranch, and at a new site in the Yolo Bypass Wildlife Area located south of Interstate 80 between the cities of Davis and Sacramento . Field infrastructure was identical to 2016, with the YBWA utilizing flash boards to hold water in similar fashion to Conaway and Swanston ranches. As we describe below, high flows made it infeasible to complete the flood extension work, although we were still able to conduct water-quality and food-web sampling, along with the use of experimental cages to evaluate salmon growth comparatively across experimental sites.Previous research has shown that inundated Yolo Bypass floodplain habitat typically has substantially higher densities of phytoplankton, zooplankton, and drift invertebrates than the adjacent Sacramento River across a suite of water year types . Our studies consistently showed that managed inundation of agricultural fields supported statistically higher levels of phytoplankton and invertebrates than the Sacramento River . Also notable was that phytoplankton and zooplankton densities in our flooded experimental fields in Yolo Bypass were higher than those measured during river inundated flood events and in the Toe Drain, a perennial tidal channel . In addition, the invertebrate community in flooded rice fields was completely dominated by zooplankton , particularly Cladocera, whereas drift invertebrates such as Diptera were found in higher concentrations in study sites at Conaway Ranch and Dos Rios. Drift invertebrates are often a more substantial part of the food web in natural flood events in Yolo Bypass . Nonetheless, zooplankton densities can be relatively high in Yolo Bypass during dry seasons and drought years . The specific reasons for these differences include longer residence time and shallower depths in the Yolo Bypass than in adjacent perennial river channels . Water source also may have been important for quantity and composition of invertebrates, including zooplankton, since all the managed flooding work was conducted using water from Knights Landing Ridge Cut, not the Sacramento River.Given the high densities of prey in the flooded fields, along with the low metabolic costs of maintaining position in a relatively low-velocity environment, it is not surprising that growth rates of juvenile salmon were comparatively high . This result was consistent across approaches used: cages, enclosures open to the substrate, and free-swimming fish.